An integrated prediction method for typhoon tracks based on marine meteorological big data

Abstract

The present invention discloses a typhoon path integrated prediction method based on marine meteorological big data, comprising: collecting multi-source marine meteorological data, aligning and fusing the multi-source marine meteorological data in time and space resolution to obtain a marine meteorological model; extracting typhoon meteorological characteristics from the marine meteorological model, acquiring a historical typhoon data set, and predicting the typhoon's initial action path according to the typhoon meteorological characteristics and the historical typhoon data set; detecting and acquiring the ocean temperature and salinity values on the initial action path, and correcting the initial action path according to the ocean temperature and salinity anomaly values to obtain a corrected action path; based on a preset movement prediction model, combining the typhoon's passing path and the marine meteorological model, calculating the typhoon's mutation point and its turning probability; correcting the corrected action path according to the mutation point and its turning probability to obtain a final typhoon path, and displaying the final typhoon path on a geographic information platform.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological forecasting, and in particular to a typhoon path integrated prediction method based on marine meteorological big data. Background Art

[0002] Tropical cyclones (typhoons) are one of the most destructive natural disasters in the world. Typhoons can be generated throughout the year in the northwest Pacific Ocean where China is located, resulting in frequent typhoon attacks on coastal and some inland areas. Therefore, typhoon research plays an important role in the meteorological community. Typhoon activities have significant statistical laws and historical similarity characteristics. Although modern satellite remote sensing and numerical forecasting technologies have been highly developed, similar case analysis is still an important basis for typhoon forecasting and disaster prevention and mitigation decision-making. How to effectively use historical observation data, conduct typhoon similarity analysis, and provide forecasters with more reliable similar reference information, thereby improving the accuracy of typhoon forecasts, has important scientific and practical significance for disaster prevention and mitigation work.

[0003] Traditional typhoon path prediction mainly relies on numerical weather forecast models, which predict typhoon paths by solving physical equations of atmospheric motion. However, these models have certain limitations in resolution and description of complex physical processes, making it difficult to further improve prediction accuracy. Summary of the invention

[0004] The present invention provides a typhoon path integrated prediction method based on marine meteorological big data to solve the technical problems in the prior art that the typhoon path prediction ability is weak, the resolution of the existing model is low, resulting in high limitations in the description of the typhoon process and the inability to achieve high prediction accuracy.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a typhoon path integrated prediction method based on marine meteorological big data, comprising:

[0006] Collecting multi-source marine meteorological data, and aligning and fusing the multi-source marine meteorological data in time and space resolution to obtain a marine meteorological model;

[0007] Extracting typhoon meteorological characteristics from the marine meteorological model and acquiring a historical typhoon data set, and predicting the initial movement path of the typhoon based on the typhoon meteorological characteristics and the historical typhoon data set;

[0008] Detecting and acquiring the ocean temperature and salinity values on the initial action path, and determining the ocean temperature and salinity anomaly values, and correcting the initial action path according to the ocean temperature and salinity anomaly values to obtain a corrected action path;

[0009] Based on the preset movement prediction model, combined with the typhoon's path and the marine meteorological model, the typhoon's mutation point and its turning probability are calculated;

[0010] According to the mutation points and their turning probabilities, correct the corrected action path to obtain the final typhoon path, and display the final typhoon path on a geographic information platform.

[0011] As a preferred solution, collecting multi-source marine meteorological data, aligning and fusing the multi-source marine meteorological data in terms of time and space resolution to obtain a marine meteorological model, specifically including:

[0012] Collect multi-source marine meteorological data; the multi-source marine meteorological data includes: satellite remote sensing data, ground observation data, ocean current data, and radar data;

[0013] Clean the collected satellite remote sensing data, ground observation data, ocean current data, and radar data respectively to remove the noise data and outliers in the satellite remote sensing data, ground observation data, ocean current data, and radar data;

[0014] Based on a preset time resolution and a preset space resolution, uniformly align the satellite remote sensing data, ground observation data, ocean current data, and radar data to a spatio-temporal grid;

[0015] Through the Kalman filtering algorithm, fuse the spatio-temporal grid and the satellite remote sensing data, ground observation data, ocean current data, and radar data therein to construct a marine meteorological model.

[0016] As a preferred solution, extract typhoon meteorological characteristics from the marine meteorological model, obtain a historical typhoon data set, and predict the initial action path of the typhoon according to the typhoon meteorological characteristics and the historical typhoon data set, specifically including:

[0017] In the marine meteorological model, extract the atmospheric spatial distribution and the ocean circulation distribution, and extract the time series data corresponding to the atmospheric spatial distribution and the ocean circulation distribution in the marine meteorological model; wherein, the typhoon meteorological characteristics include: the atmospheric spatial distribution, the ocean circulation distribution, and the time characteristics corresponding to the atmospheric spatial distribution and the ocean circulation distribution respectively;

[0018] Input the atmospheric spatial distribution, the ocean circulation distribution, and the corresponding time series data into a preset path prediction model, and output the future action path of the typhoon;

[0019] Obtain a historical typhoon data set; wherein, the historical typhoon data set includes a number of historical typhoons and their corresponding historical paths;

[0020] Based on the current position of the typhoon, determine the similarity between the path passed by the current typhoon and the historical path of the historical typhoon in the previous section before the current typhoon position, and extract the historical path corresponding to the historical typhoon with the highest similarity as the historical correction path;

[0021] According to the historical correction path, preliminarily correct the future action path to obtain the initial action path.

[0022] As a preferred solution, the construction method of the preset path prediction model includes:

[0023] Obtain historical typhoon sample data; the historical typhoon sample data includes: historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and historical path samples;

[0024] Construct a deep learning model, and use the historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and time series data as inputs, and the historical path samples as outputs, and perform iterative training on the deep learning model until the preset number of iterations is reached to obtain the trained preset path prediction model.

[0025] As a preferred solution, detect and obtain the ocean temperature and salinity values on the initial action path, determine the ocean temperature and salinity anomaly values, and correct the initial action path according to the ocean temperature and salinity anomaly values to obtain the corrected action path, specifically including:

[0026] Based on ocean buoy devices, detect the ocean temperature and salinity values at each position point on the initial action path;

[0027] According to the current seasonal time, determine the preset ocean temperature and salinity range, and based on the ocean temperature and salinity range, determine the ocean temperature and salinity values outside the ocean temperature and salinity range as ocean temperature and salinity anomaly values, and use the position points belonging to the ocean temperature and salinity anomaly values as path correction points; where the number of the path correction points is at least 1;

[0028] According to the path correction points and their corresponding ocean temperature and salinity anomaly values, correct the initial action path to obtain the corrected action path.

[0029] As a preferred solution, according to the path correction points and their corresponding ocean temperature and salinity anomaly values, correct the initial action path to obtain the corrected action path, specifically including:

[0030] In the process of correcting the initial action path at each path correction point, the first path correction point on the initial action path is determined, and the correction direction and degree are determined according to the ocean temperature and salinity anomaly corresponding to the first path correction point, so that the initial action path is corrected according to the correction direction and degree corresponding to the first path correction point to obtain a corrected first action path, and then starting from the corrected position point, the first path correction point is re-determined on the corrected first action path, so that the corrected first action path is re-corrected based on the re-determined first path correction point; until there are no ocean temperature and salinity anomalies at each point on the corrected typhoon action path, the final corrected action path is obtained.

[0031] As a preferred solution, the preset movement prediction model is combined with the typhoon's path and the marine meteorological model to calculate the typhoon's mutation point and its turning probability, which specifically includes:

[0032] Constructing a mathematical model based on a long short-term memory network as a preset movement prediction model; wherein the preset movement prediction model is trained by using historical typhoon paths and historical time series data in a historical typhoon data set;

[0033] Inputting the path of the typhoon and the time series data of the corresponding path in the marine meteorological model into the preset movement prediction model to obtain the movement action path;

[0034] The corrected action path is compared with the moving action path to determine the mutation point of the typhoon, and based on the marine meteorological model, the direction in which the typhoon turns at the mutation point and the probability of turning are calculated.

[0035] Accordingly, the present invention also provides a typhoon path integrated prediction system based on marine meteorological big data, comprising:

[0036] An alignment and fusion module is used to collect multi-source marine meteorological data, and align and fuse the multi-source marine meteorological data in time and space resolution to obtain a marine meteorological model;

[0037] A prediction module, used to extract typhoon meteorological characteristics from the marine meteorological model and obtain a historical typhoon data set, and predict the initial movement path of the typhoon based on the typhoon meteorological characteristics and the historical typhoon data set;

[0038] A correction module, used for detecting and obtaining the ocean temperature and salinity values on the initial action path, and determining the ocean temperature and salinity anomaly values, and correcting the initial action path according to the ocean temperature and salinity anomaly values to obtain a corrected action path;

[0039] A mutation module, configured to calculate the mutation points and their turning probabilities of a typhoon based on a preset movement prediction model, in combination with the passing path of the typhoon and the ocean meteorological model;

[0040] A deviation correction module, configured to correct the corrected action path according to the mutation points and their turning probabilities to obtain the final typhoon path, and display the final typhoon path on a geographic information platform.

[0041] Correspondingly, the present invention further provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for integrated prediction of typhoon paths based on ocean meteorological big data as described in any one of the above is implemented.

[0042] Correspondingly, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for integrated prediction of typhoon paths based on ocean meteorological big data as described in any one of the above.

[0043] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0044] By collecting multi-source ocean meteorological data, aligning and fusing the multi-source ocean meteorological data in terms of time and space resolution to construct an ocean meteorological model, thereby improving the resolution of the mathematical model in time and space, avoiding the limitations of typhoon process description, and by extracting the typhoon meteorological characteristics therein and combining with historical typhoon data sets to predict the initial action path of the typhoon, so as to be able to predict the action path of the typhoon at the level of atmospheric distribution and ocean circulation. At the same time, the ocean temperature and salinity values on the initial action path are obtained to correct the initial action path, realizing the addition of the influence of ocean temperature and salinity on the basis of the predicted path, improving the accuracy and dimension of typhoon path prediction. Finally, in combination with a preset movement prediction model, the mutation points of the corrected action path are determined and the turning probabilities are calculated, so as to correct the corrected action path to obtain the final typhoon path, improving the prediction ability for new and abnormal typhoon paths to achieve more accurate prediction of typhoon paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 : is a flowchart of the steps of a method for integrated prediction of typhoon paths based on ocean meteorological big data provided by an embodiment of the present invention;

[0046] Figure 2 : is a structural diagram of a system for integrated prediction of typhoon paths based on ocean meteorological big data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1

[0049] Please refer to Figure 1 , a typhoon path integrated prediction method based on marine meteorological big data provided by an embodiment of the present invention, including the following steps S101-S105:

[0050] Step S101: Collect multi-source marine meteorological data, and align and fuse the multi-source marine meteorological data in terms of time and space resolution to obtain a marine meteorological model.

[0051] As a preferred solution of this embodiment, the step of collecting multi-source marine meteorological data, aligning and fusing the multi-source marine meteorological data in terms of time and space resolution to obtain a marine meteorological model specifically includes:

[0052] Collect multi-source marine meteorological data; the multi-source marine meteorological data includes: satellite remote sensing data, ground observation data, ocean current data, and radar data;

[0053] Clean the collected satellite remote sensing data, ground observation data, ocean current data, and radar data respectively to remove the noise data and outliers in the satellite remote sensing data, ground observation data, ocean current data, and radar data;

[0054] Based on a preset time resolution and a preset space resolution, uniformly align the satellite remote sensing data, ground observation data, ocean current data, and radar data to a spatio-temporal grid;

[0055] Through the Kalman filtering algorithm, fuse the spatio-temporal grid and the satellite remote sensing data, ground observation data, ocean current data, and radar data therein to construct a marine meteorological model.

[0056] In this embodiment, satellite remote sensing data includes information such as the position, intensity, cloud system structure, wind circle range, precipitation distribution, and atmospheric temperature and humidity profiles of typhoons. Meteorological satellites equipped with instruments such as microwave scatterometers, microwave radiometers, infrared and visible imagers can be used to collect data such as typhoon cloud images, wind fields, temperature, and humidity. Ground observation data covers meteorological elements such as wind speed, wind direction, air pressure, precipitation, temperature, and humidity during typhoon passage, as well as oceanographic elements such as tide levels and waves. Ocean current data includes ocean current speed and direction at different depths, sea surface current field distribution, etc. Ocean current data can help analyze the impact of the ocean on typhoon intensity and path and can be obtained through drifting buoys. Radar data is obtained through shipborne navigation radars installed on ships and offshore facilities and can be used to monitor wave / swell height, period and direction, sea surface current speed and direction, etc.

[0057] In this embodiment, various types of collected data are cleaned separately to remove noise data and outliers, ensuring data quality and avoiding interference from abnormal data in subsequent analysis, providing a reliable data basis for the construction of the ocean meteorological model. The satellite remote sensing data, ground observation data, ocean current data, and radar data are all aligned to the spatio-temporal grid based on a preset time resolution and spatial resolution, making the data from different sources consistent in the time and space dimensions, facilitating subsequent fusion processing, and at the same time ensuring that the resolution of the model in time and space matches the actual requirements, improving the model's ability to describe and predict ocean meteorological phenomena.

[0058] In this embodiment, the Kalman filter algorithm is used to fuse the spatio-temporal grid and various types of data therein. The Kalman filter is a recursive algorithm based on the state space model. It can effectively fuse multi-source data and construct an ocean meteorological model through dynamic estimation and update of the data. The Kalman filter is based on the state space model, which includes a state equation and an observation equation. The state equation describes the dynamic changes of multi-source ocean meteorological data, and the observation equation links the actual observed data (taking the satellite remote sensing data, ground observation data, ocean current data, and radar data on the spatio-temporal grid as the input of the observed data) with the state variables. Then, using recursive estimation, that is, the Kalman filter, through a recursive method, combines model prediction and actual observed data to perform an optimal estimation of the state variables, thereby performing state prediction, covariance prediction, Kalman gain calculation, state update, and covariance update. Finally, according to the reliability and correlation of the observed data, the weights of each data source are dynamically adjusted, giving priority to high-precision and high-reliability data, and thus realizing the fusion of multi-source ocean meteorological data. The fused ocean meteorological model can more comprehensively reflect the state and change trend of the ocean meteorological system, providing more accurate data support for ocean meteorological forecasts such as typhoon path prediction.

[0059] Step S102: Extract typhoon meteorological features from the ocean meteorological model, obtain a historical typhoon dataset, and predict the initial movement path of the typhoon based on the typhoon meteorological features and the historical typhoon dataset.

[0060] As a preferred solution of this embodiment, extracting typhoon meteorological features from the ocean meteorological model, obtaining a historical typhoon dataset, and predicting the initial movement path of the typhoon based on the typhoon meteorological features and the historical typhoon dataset specifically include:

[0061] In the ocean meteorological model, extract the atmospheric spatial distribution and ocean circulation distribution, and extract the time series data corresponding to the atmospheric spatial distribution and ocean circulation distribution in the ocean meteorological model; wherein, the typhoon meteorological features include: atmospheric spatial distribution, ocean circulation distribution, and time features corresponding to the atmospheric spatial distribution and ocean circulation distribution respectively;

[0062] Input the atmospheric spatial distribution, ocean circulation distribution, and their respective time series data into a preset path prediction model, and output the future movement path of the typhoon;

[0063] Obtain a historical typhoon dataset; wherein, the historical typhoon dataset includes a number of historical typhoons and their corresponding historical paths;

[0064] Based on the position of the current typhoon, determine the similarity between the path that the current typhoon has passed through and the historical path of the historical typhoon in the section before the current typhoon position, and extract the historical path corresponding to the historical typhoon with the highest similarity as the historical correction path;

[0065] Preliminarily correct the future movement path according to the historical correction path to obtain the initial movement path.

[0066] In this embodiment, extract the atmospheric spatial distribution and ocean circulation distribution data from the constructed ocean meteorological model. The atmospheric spatial distribution and ocean circulation distribution data can reflect the environmental conditions required for the occurrence and development of typhoons, such as atmospheric elements such as temperature, humidity, and wind field, and ocean elements such as ocean current speed and direction. At the same time, extract the time series data corresponding to the atmospheric spatial distribution and ocean circulation distribution. Among them, the time series data can show the changes of each atmospheric spatial distribution and ocean circulation distribution over time during the development of the typhoon, so as to construct a comprehensive typhoon meteorological feature set including atmospheric spatial distribution, ocean circulation distribution, and corresponding time features.

[0067] In this embodiment, input the above-extracted data as input features into a preset path prediction model. Among them, the preset path prediction model is constructed based on historical typhoon data and ocean meteorological knowledge, and can predict the possible future movement path of the typhoon according to the input meteorological feature data.

[0068] In this embodiment, a historical typhoon dataset containing a number of historical typhoons and their corresponding historical paths is collected. Based on the position of the current typhoon, the path passed by the current typhoon is compared with the historical paths of historical typhoons in the section before the current position. By calculating the similarity, the historical typhoon path that is closest to the path of the current typhoon, i.e., the historical correction path, is found. Taking the historical correction path as a reference, the future action path output by the prediction model is preliminarily corrected to obtain the initial action path, thereby combining historical observation data with the model prediction results and improving the accuracy and credibility of the predicted path.

[0069] In this embodiment, the historical correction path can preliminarily correct the future action path by calculating the prediction deviation between the historical correction path and the future action path obtained through a preset path prediction model under similar historical conditions, and applying this deviation to the current predicted path for correction. Further, the correction of the future action path can also be achieved by performing a weighted average of the future action path output by the prediction model and the historical correction path based on the similarity.

[0070] In this embodiment, by integrating various key meteorological elements such as the atmospheric spatial distribution and ocean circulation distribution and their time series data, more comprehensive and detailed information is provided for typhoon path prediction, making the prediction results closer to the actual typhoon behavior. At the same time, by introducing the historical typhoon dataset and extracting the historical correction path based on similarity calculation, the empirical value of historical data can be fully utilized to effectively correct the model prediction results, reduce prediction errors, and improve the reliability of the initial action path.

[0071] As a preferred solution of this embodiment, the construction method of the preset path prediction model includes:

[0072] Obtain historical typhoon sample data; the historical typhoon sample data includes: historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and historical path samples;

[0073] Construct a deep learning model, and use the historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and time series data as inputs, and the historical path samples as outputs. Iteratively train the deep learning model until a preset number of iterations is reached to obtain the trained preset path prediction model.

[0074] In this embodiment, historical typhoon sample data is collected, including historical atmospheric spatial distribution samples (such as temperature, humidity, wind field, etc.), historical ocean circulation distribution samples (such as ocean current velocity, direction, etc.), and historical path samples (such as typhoon longitude, latitude, time, etc.). Among them, the historical typhoon sample data can be obtained from multiple sources such as meteorological satellites, ground observation stations, and ocean buoys. Furthermore, a deep learning model suitable for time series prediction is selected, such as long short-term memory network (LSTM), convolutional neural network (CNN) combined with attention mechanism, etc. Among them, LSTM is suitable for processing sequence data and can capture time-dependent relationships; CNN combined with attention mechanism can extract spatial features and focus on key information. Taking the historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and time series data as model inputs and the historical path samples as outputs, a neural network including an input layer, a hidden layer, and an output layer is constructed. The hidden layer can adopt an LSTM layer or a CNN layer combined with an attention mechanism to extract time series and spatial features. The processed data set is divided into a training set and a validation set. The training set is used to iteratively train the model. The predicted value is calculated through forward propagation. The loss function (such as mean squared error MSE) is used to evaluate the difference between the predicted value and the true value. Then, the model parameters are updated through backpropagation and optimization algorithms (such as Adam) to reduce the loss. At the same time, hyperparameters such as the learning rate, batch size, and number of iterations are adjusted to optimize the model performance. Finally, a trained preset path prediction model is obtained.

[0075] Step S103: Detect and obtain the ocean temperature and salinity values on the initial action path, determine the ocean temperature and salinity outliers, and correct the initial action path according to the ocean temperature and salinity outliers to obtain a corrected action path.

[0076] As a preferred solution of this embodiment, the detecting and obtaining the ocean temperature and salinity values on the initial action path, determining the ocean temperature and salinity outliers, and correcting the initial action path according to the ocean temperature and salinity outliers to obtain a corrected action path specifically includes:

[0077] Based on ocean buoy equipment, detect the ocean temperature and salinity values at each position point on the initial action path;

[0078] According to the current seasonal time, determine a preset ocean temperature and salinity range, and based on the ocean temperature and salinity range, determine the ocean temperature and salinity values outside the ocean temperature and salinity range as ocean temperature and salinity outliers, and use the position points belonging to the ocean temperature and salinity outliers as path correction points; where the number of the path correction points is at least 1;

[0079] According to the path correction points and their corresponding ocean temperature and salinity outliers, correct the initial action path to obtain a corrected action path.

[0080] In this embodiment, first, based on the ocean buoy device, the ocean temperature and salinity values at each position point on the first action path are detected. Then, according to the current season, a preset ocean temperature and salinity range is determined, and the values exceeding this range are defined as ocean temperature and salinity anomaly values, and the corresponding position points are the path correction points. Finally, based on these correction points and their temperature and salinity anomaly values, the initial action path is adjusted to obtain the corrected action path.

[0081] In this embodiment, the ocean temperature and salinity detection device can be an ocean buoy device, which is equipped with a temperature sensor (such as a platinum resistance) and a conductivity sensor. The platinum resistance temperature measurement is based on the principle that the resistance changes linearly with temperature, and the conductivity sensor detects the conductivity of seawater to calculate the salinity. Some buoys are also equipped with a salinometer to directly measure the salinity. After the buoy enters the water, the sensors start to measure the temperature and conductivity of the seawater. The measured data can be transmitted to the onshore data center through satellite communication and other means after being sorted out by the data acquisition system. Some buoys have the ability to process data in real time and can perform simple smoothing and correction processing.

[0082] In this embodiment, according to the current season time, referring to the historical ocean temperature and salinity data and oceanographic research, a reasonable preset ocean temperature and salinity range is determined. Among them, this range will vary in different sea areas and seasons and can be set according to the actual situation. The detected ocean temperature and salinity values are compared with the preset range, and the values exceeding the range are the ocean temperature and salinity anomaly values. Among them, the path points corresponding to the anomaly values are the path correction points, and the number is at least one. Exemplarily, based on the probability statistical characteristics of ocean observation data in the region, it is checked whether the temperature and salinity elements follow the seasonal change statistical law. The annual / month average value a of the statistical station position element data can be passed. mn and the corresponding mean square deviation sd are selected, and a reasonable multiple m is selected (preferably, m = 3 can be selected). If the observed value X does not satisfy the formula: a mn - m×sd ≤ X ≤ a mn + m×sd, then it is determined that it is an anomaly value and needs further analysis.

[0083] In this embodiment, a correction model or algorithm, such as interpolation method, statistical regression model or physical ocean model, is established according to the path correction points and their ocean temperature and salinity anomalies. Considering the influence mechanism of ocean temperature and salinity anomalies on the typhoon path, such as changing the heat and water vapor exchange between the ocean and the atmosphere, ocean circulation, etc., the initial action path is adjusted. Applying the anomalies of ocean temperature and salinity to the initial action path, the corrected action path is obtained, which is more in line with the actual ocean environmental conditions. At the same time, buoy equipment is used to directly detect the ocean temperature and salinity values, providing rich measured data, avoiding data missing or indirect calculation relying on other data sources, and improving the scientificity and reliability of path correction. Since the change of ocean temperature and salinity structure can affect the heat transfer from the ocean to the atmosphere. For example, the abnormal change of ocean surface temperature may affect the intensity and moving speed of the typhoon. A warmer ocean surface temperature can provide more heat and water vapor for the typhoon, thus enhancing the intensity of the typhoon. And ocean temperature and salinity anomalies may lead to changes in ocean circulation, which in turn affect the typhoon path. Therefore, this embodiment can also avoid the situation in the prior art that only considers the influence of atmospheric distribution and ocean circulation, while ignoring the actual influence of ocean temperature and salinity on the typhoon.

[0084] As a preferred solution of this embodiment, the initial action path is corrected according to the path correction points and their corresponding ocean temperature and salinity anomalies to obtain the corrected action path, which specifically includes:

[0085] In the process of correcting the initial action path at each path correction point, the first path correction point on the initial action path is determined, and according to the ocean temperature and salinity anomaly corresponding to the first path correction point, its correction direction and correction degree are determined. Thus, according to the correction direction and correction degree corresponding to the first path correction point, the initial action path is corrected to obtain the corrected first action path. Then, starting from the corrected position point, the first path correction point is re-determined on the corrected first action path, so that based on the re-determined first path correction point, the corrected first action path is corrected again; until there are no ocean temperature and salinity anomalies at all points on the corrected typhoon action path, the final corrected action path is obtained.

[0086] In this embodiment, on the initial action path, starting from the current position point of the typhoon as the starting point, each position point is checked in turn to find the first point where the ocean temperature and salinity value exceeds the preset range, which is the first path correction point. Furthermore, the correction direction and degree are determined, that is, according to the ocean temperature and salinity anomaly value of the first path correction point, combined with oceanographic principles and the typhoon path prediction model, the correction direction (such as shifting to the left or right) and the correction degree (the distance or angle of the shift) are determined. For example, if the ocean temperature and salinity anomaly value causes the typhoon to possibly shift in a certain direction. Then, through the determined correction direction and degree, the initial action path is adjusted to obtain the first corrected action path. Then, the path correction point is re-determined. Starting from the path point after the last correction, the subsequent position points are continuously checked on the first corrected action path, the first path correction point on this path is re-determined, the correction direction and degree are determined again according to the ocean temperature and salinity anomaly value corresponding to the new path correction point, and the path is corrected. This process is repeated until there are no ocean temperature and salinity anomalies at each point on the corrected typhoon action path.

[0087] In this embodiment, when the ocean temperature and salinity values of all position points on the corrected typhoon action path are within the preset range, the correction process is terminated to obtain the final corrected action path, so as to be able to monitor the ocean temperature and salinity anomalies in real time and dynamically adjust the typhoon path prediction according to the anomaly situation, thereby more accurately reflecting the actual path change of the typhoon. At the same time, by analyzing the ocean temperature and salinity anomaly values of each path correction point in turn and re-determining the specific correction direction and degree, the path correction is made more targeted and effective. Finally, through the iterative correction process, the influence of ocean temperature and salinity anomalies on the path is gradually eliminated, and finally a relatively accurate typhoon action path is obtained.

[0088] Step S104: Based on the preset movement prediction model, combined with the passing path of the typhoon and the ocean meteorological model, calculate the mutation point of the typhoon and its turning probability.

[0089] As a preferred solution of this embodiment, the calculating the mutation point of the typhoon and its turning probability based on the preset movement prediction model, combined with the passing path of the typhoon and the ocean meteorological model, specifically includes:

[0090] Construct a mathematical model based on the long short-term memory network as the preset movement prediction model; wherein, the preset movement prediction model is trained through the historical typhoon paths and their historical time series data in the historical typhoon dataset;

[0091] Input the passing path of the typhoon and the time series data of the corresponding passing path in the ocean meteorological model into the preset movement prediction model to obtain the movement action path;

[0092] Compare the corrected action path and the movement action path to determine the mutation point of the typhoon, and based on the ocean meteorological model, calculate the turning direction and the turning probability of the typhoon at this mutation point.

[0093] In this embodiment, an LSTM neural network model including an input layer, a hidden layer (LSTM layer), and an output layer is constructed. The input layer receives the processed historical typhoon path and its time series data. The hidden layer uses the LSTM structure to extract time-dependent features, and the output layer predicts the future typhoon path. A historical typhoon data set including the historical typhoon path and the corresponding time series data (such as atmospheric and ocean data) is collected. After data preprocessing (cleaning, standardization) and feature engineering (extracting time steps, constructing supervised learning problems), it is divided into a training set and a validation set. Then, the training set is used to iteratively train the LSTM model. The model parameters are updated through optimization algorithms (such as Adam) and loss functions (such as mean squared error) to minimize the difference between the predicted value and the true value and improve the prediction accuracy. Among them, the training continues until a preset number of iterations is reached or the convergence condition is met.

[0094] In this embodiment, the passing path of the typhoon and the corresponding time series data are extracted from the ocean meteorological model and preprocessed. The format needs to be the same as the input during model training. The processed data is input into the trained LSTM model to obtain the predicted movement action path. Furthermore, the corrected action path and the movement action path are compared point by point, the differences at each point are calculated and the mutation points are identified, and a threshold is set. When the path difference exceeds the limit, this point is determined as a mutation point. Among them, the threshold can be set according to the actual situation.

[0095] In this embodiment, based on the ocean meteorological model, the ocean environmental characteristics (such as temperature-salinity distribution, ocean current) and atmospheric conditions (such as wind field, air pressure) at the mutation point are analyzed to predict the turning direction of the typhoon, and according to the historical typhoon data and model analysis, the probability of the typhoon turning at the mutation point is calculated to provide a quantitative assessment.

[0096] In this embodiment, to calculate the direction of the typhoon's turning and its turning probability, the longitude and latitude of two position points before and after the mutation point can be converted into radian values, and the moving direction of the typhoon at the mutation point can be calculated using the longitude and latitude radian values before and after the mutation point. That is, by calculating the change in longitude and latitude of two position points before and after the mutation point, the moving direction of the typhoon can be obtained using the spherical trigonometry formula. This method takes into account the influence of the earth's curvature and can more accurately reflect the moving direction of the typhoon in the spherical coordinate system. The turning probability can be calculated by collecting historical typhoon data, including the turning situations of typhoons under different conditions, especially the turning directions and frequencies when the environment is similar to the current mutation point (such as similar ocean temperature and salinity conditions, atmospheric circulation patterns, etc.). The number of times the typhoon turns in each direction under similar conditions is statistically counted to calculate the turning probability. For example, if among 100 historical cases with an environment similar to the current mutation point, the typhoon turns left 70 times and turns right 30 times, then the probability of turning left is 70% and the probability of turning right is 30%.

[0097] It can be understood that the method based on historical data statistics utilizes the similarity and historical repeatability principles of typhoon paths and assumes that under similar environmental conditions, the turning behavior of typhoons has certain statistical regularities.

[0098] In this embodiment, the typhoon path prediction can also be corrected by how to use the Dynamic Time Warping (DTW) algorithm. Among them, the Dynamic Time Warping (DTW) algorithm is a method for measuring the similarity of two time series, especially suitable for time series with inconsistent lengths. In typhoon path prediction, the DTW algorithm can correct the prediction results by comparing the predicted path with historical similar paths. The core idea of the DTW algorithm is to find the optimal matching typhoon path between two time series through dynamic programming, so that the cumulative distance on the path is minimized.

[0099] First, construct a distance matrix: Assume that the two time series are Q=(q1,q2,…,qn) and C=(c1,c2,…,cm), with lengths n and m respectively. Construct an n×m matrix D, where D[i,j] represents the distance between qi and cj (usually using the Euclidean distance).

[0100] Cumulative distance calculation: Define the cumulative distance matrix D. Starting from D[1,1], calculate the cumulative distance of each point step by step. The calculation formula for the cumulative distance is:

[0101] D[i,j]=d(qi,cj)+min{D[i - 1,j],D[i,j - 1],D[i - 1,j - 1]}

[0102] Among them, d(qi, cj) is the distance between qi and cj.

[0103] Finding the optimal typhoon path: Starting from the lower right corner D[n, m] of the matrix, trace back to the upper left corner D[1, 1] to find the typhoon path with the minimum cumulative distance, and this typhoon path is the optimal matching path.

[0104] Furthermore, set the constraint conditions: The path must start from the lower left corner (1, 1) and end at the upper right corner (n, m). At the same time, ensure continuity and monotonicity. Among them, continuity means that the next point of the path can only be the point on the right, below, or lower right of the current point; monotonicity means that the path must increase monotonically with time and there cannot be any backtracking.

[0105] In this embodiment, the DTW algorithm can be used to compare the predicted path with the historical similar paths for typhoon path prediction and correct the prediction results: Collect historical typhoon path data and current predicted path data, and convert them into time series format. Use the DTW algorithm to calculate the similarity between the current predicted path and the historical paths, and find the most similar historical path. Adjust the current predicted path according to the most similar historical path. For example, if there is an obvious deviation in a certain area of the historical path, the predicted path can be adjusted in that direction.

[0106] Furthermore, adjust the predicted path according to the optimal matching path to make it closer to the historical path. For example, if there is a large deviation between the predicted path and the historical path at a certain point, it can be corrected through interpolation or smoothing processing.

[0107] Step S105: Correct the corrected action path according to the mutation point and its turning probability to obtain the final typhoon path, and display the final typhoon path on the geographic information platform.

[0108] In this embodiment, by using historical typhoon data and the characteristics of the current typhoon, after calculating the turning probability of the typhoon at each mutation point, analyze the change in the moving direction of the typhoon at the mutation point to determine the possible turning directions. Then, according to the turning probability and turning directions, correct each mutation point on the initial action path. For the direction with a higher turning probability, appropriately adjust the offset of the path to make the path closer to the possible actual trend. Repeat the above steps to continuously update the current position and path prediction of the typhoon until the typhoon ends or reaches the predetermined prediction duration.

[0109] In this embodiment, the turning probability reflects the possibility of the typhoon turning in different directions at the mutation point. By considering these probabilities in the path correction, the corrected path can be made more consistent with the actual behavior pattern of the typhoon, so that this embodiment can combine multiple factors in the marine meteorological model (such as temperature and salinity distribution, ocean currents, atmospheric circulation, etc.) to more comprehensively evaluate the possibility of the typhoon turning and improve the accuracy of the path correction. As the typhoon moves and new data is obtained, the input and prediction results of the model are continuously updated, and the path correction strategy is adjusted in time to ensure the accuracy and timeliness of the final path.

[0110] Furthermore, finally, the final typhoon path is displayed on the geographic information platform, so that users can intuitively understand the typhoon direction and the manifestation of future predicted results, thereby improving the user experience.

[0111] Implementing the above embodiments has the following effects:

[0112] By collecting multi-source marine meteorological data and aligning and fusing the multi-source marine meteorological data in time and space resolution, a marine meteorological model is constructed, thereby improving the temporal and spatial resolution of the mathematical model and avoiding the limitations of the typhoon process description. By extracting the typhoon meteorological characteristics and combining them with historical typhoon data sets, the typhoon's initial action path is predicted, so that the typhoon's action path can be predicted at the atmospheric distribution and ocean circulation levels. At the same time, the ocean temperature and salinity values on the initial action path are obtained to correct the initial action path, increase the influence of ocean temperature and salinity on the predicted path, and improve the accuracy and dimension of the typhoon path prediction. Finally, the preset movement prediction model is combined to determine the mutation point of the corrected action path and calculate the turning probability, so as to correct the corrected action path and obtain the final typhoon path, thereby improving the ability to predict new and abnormal typhoon paths, so as to achieve a more accurate prediction of the typhoon path.

[0113] Embodiment 2

[0114] See also Figure 2 , which is a typhoon path integrated prediction system based on marine meteorological big data provided by the present invention, comprising:

[0115] The alignment and fusion module 201 is used to collect multi-source marine meteorological data, and align and fuse the multi-source marine meteorological data in time and space resolution to obtain a marine meteorological model;

[0116] A prediction module 202 is used to extract typhoon meteorological characteristics from the marine meteorological model and obtain a historical typhoon data set, and predict the initial movement path of the typhoon based on the typhoon meteorological characteristics and the historical typhoon data set;

[0117] The correction module 203 is used to detect and obtain the ocean temperature and salinity values on the initial action path, determine the ocean temperature and salinity anomaly values, and correct the initial action path according to the ocean temperature and salinity anomaly values to obtain a corrected action path;

[0118] The mutation module 204 is used to calculate the mutation points and their turning probabilities of the typhoon based on a preset movement prediction model, combined with the passing path of the typhoon and the ocean meteorological model;

[0119] The deviation correction module 205 is used to correct the corrected action path according to the mutation points and their turning probabilities to obtain the final typhoon path, and display the final typhoon path on the geographic information platform.

[0120] As a preferred solution, collecting multi-source ocean meteorological data, aligning and fusing the multi-source ocean meteorological data in terms of time and space resolution to obtain an ocean meteorological model specifically includes:

[0121] Collecting multi-source ocean meteorological data; the multi-source ocean meteorological data includes: satellite remote sensing data, ground observation data, ocean current data, and radar data;

[0122] Cleaning the collected satellite remote sensing data, ground observation data, ocean current data, and radar data respectively to remove the noise data and anomaly values in the satellite remote sensing data, ground observation data, ocean current data, and radar data;

[0123] Based on a preset time resolution and a preset space resolution, uniformly aligning the satellite remote sensing data, ground observation data, ocean current data, and radar data to the spatio-temporal grid;

[0124] Through the Kalman filtering algorithm, fusing the spatio-temporal grid and the satellite remote sensing data, ground observation data, ocean current data, and radar data therein to construct an ocean meteorological model.

[0125] As a preferred solution, extracting typhoon meteorological characteristics from the ocean meteorological model, obtaining a historical typhoon data set, and predicting the initial action path of the typhoon according to the typhoon meteorological characteristics and the historical typhoon data set specifically includes:

[0126] In the ocean meteorological model, extracting the atmospheric spatial distribution and the ocean circulation distribution, and extracting the time series data corresponding to the atmospheric spatial distribution and the ocean circulation distribution in the ocean meteorological model; wherein, the typhoon meteorological characteristics include: the atmospheric spatial distribution, the ocean circulation distribution, and the time characteristics corresponding to the atmospheric spatial distribution and the ocean circulation distribution respectively;

[0127] Input the atmospheric spatial distribution, ocean circulation distribution, and their corresponding time series data into a preset path prediction model to output the future movement path of the typhoon.

[0128] Obtain a historical typhoon dataset; wherein, the historical typhoon dataset includes a number of historical typhoons and their corresponding historical paths.

[0129] Based on the position of the current typhoon, determine the similarity between the path passed by the current typhoon and the historical paths of historical typhoons before the current typhoon's position, and extract the historical path corresponding to the historical typhoon with the highest similarity as the historical correction path.

[0130] Preliminarily correct the future movement path according to the historical correction path to obtain an initial movement path.

[0131] As a preferred solution, the construction method of the preset path prediction model includes:

[0132] Obtain historical typhoon sample data; the historical typhoon sample data includes: historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and historical path samples.

[0133] Construct a deep learning model, and use the historical atmospheric spatial distribution samples, historical ocean circulation distribution samples, and time series data as inputs, and the historical path samples as outputs. Iteratively train the deep learning model until a preset number of iterations is reached to obtain a trained preset path prediction model.

[0134] As a preferred solution, detect and obtain the ocean temperature and salinity values on the initial movement path, determine the ocean temperature and salinity anomaly values, and correct the initial movement path according to the ocean temperature and salinity anomaly values to obtain a corrected movement path, which specifically includes:

[0135] Based on ocean buoy devices, detect the ocean temperature and salinity values at each position point on the initial movement path.

[0136] According to the current seasonal time, determine a preset ocean temperature and salinity range, and based on the ocean temperature and salinity range, determine the ocean temperature and salinity values outside the ocean temperature and salinity range as ocean temperature and salinity anomaly values, and use the position points belonging to the ocean temperature and salinity anomaly values as path correction points; wherein, the number of the path correction points is at least 1.

[0137] Correct the initial movement path according to the path correction points and their corresponding ocean temperature and salinity anomaly values to obtain a corrected movement path.

[0138] As a preferred solution, correct the initial movement path according to the path correction points and their corresponding ocean temperature and salinity anomaly values to obtain a corrected movement path, which specifically includes:

[0139] During the process of correcting the initial action path at each path correction point, determine the first path correction point on the initial action path, and based on the ocean temperature and salinity anomaly value corresponding to the first path correction point, determine its correction direction and correction degree. Then, according to the correction direction and correction degree corresponding to the first path correction point, correct the initial action path to obtain the corrected first action path. Furthermore, starting from the corrected position point, re-determine the first path correction point on the corrected first action path, so as to re-correct the corrected first action path based on the re-determined first path correction point; until there is no ocean temperature and salinity anomaly value at each point on the corrected typhoon action path, the final corrected action path is obtained.

[0140] As a preferred solution, calculating the mutation point and its turning probability of the typhoon based on the preset movement prediction model, in combination with the passing path of the typhoon and the ocean meteorological model, specifically includes:

[0141] Construct a mathematical model based on a long short-term memory network as the preset movement prediction model; wherein, the preset movement prediction model is trained through the historical typhoon paths and their historical time series data in the historical typhoon dataset.

[0142] Input the passing path of the typhoon and the time series data of the corresponding passing path in the ocean meteorological model into the preset movement prediction model to obtain the movement action path.

[0143] Compare the corrected action path with the movement action path to determine the mutation point of the typhoon, and based on the ocean meteorological model, calculate the turning direction and its turning probability of the typhoon at this mutation point.

[0144] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described device can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0145] Implementing the above embodiments has the following effects:

[0146] By collecting multi-source marine meteorological data and aligning and fusing the multi-source marine meteorological data in time and space resolution, a marine meteorological model is constructed, thereby improving the temporal and spatial resolution of the mathematical model and avoiding the limitations of the typhoon process description. By extracting the typhoon meteorological characteristics and combining them with historical typhoon data sets, the typhoon's initial action path is predicted, so that the typhoon's action path can be predicted at the atmospheric distribution and ocean circulation levels. At the same time, the ocean temperature and salinity values on the initial action path are obtained to correct the initial action path, increase the influence of ocean temperature and salinity on the predicted path, and improve the accuracy and dimension of the typhoon path prediction. Finally, the preset movement prediction model is combined to determine the mutation point of the corrected action path and calculate the turning probability, so as to correct the corrected action path and obtain the final typhoon path, thereby improving the ability to predict new and abnormal typhoon paths, so as to achieve a more accurate prediction of the typhoon path.

[0147] Embodiment 3

[0148] Correspondingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the typhoon path integrated prediction method based on marine meteorological big data as described in any one of the above embodiments.

[0149] The terminal device of this embodiment includes: a processor, a memory, and a computer program and a computer instruction stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned embodiment 1 is implemented, for example: Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment, such as the correction module 205, are implemented.

[0150] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device. For example, the correction module 205 is used to correct the correction action path according to the mutation point and its turning probability, obtain the final typhoon path, and display the final typhoon path on the geographic information platform.

[0151] The terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the schematic diagram is only an example of the terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the terminal device may also include input / output devices, network access devices, a bus, etc.

[0152] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device and connects various parts of the entire terminal device through various interfaces and lines.

[0153] The memory may be used to store the computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function, etc.; the data storage area may store data created according to the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0154] Among them, if the modules / units integrated in the terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0155] Embodiment 4

[0156] Correspondingly, the present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the typhoon path integrated prediction method based on ocean meteorological big data as described in any one of the above embodiments.

[0157] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An integrated typhoon path prediction method based on marine meteorological big data, characterized in that, Including: Collect multi-source marine meteorological data, align and fuse the multi-source marine meteorological data in terms of time and space resolution to obtain a marine meteorological model; Extract typhoon meteorological characteristics from the marine meteorological model, obtain a historical typhoon dataset, and predict the initial action path of the typhoon based on the typhoon meteorological characteristics and the historical typhoon dataset; Detect and obtain the ocean temperature and salinity values on the initial action path, determine the ocean temperature and salinity anomaly values, and correct the initial action path according to the ocean temperature and salinity anomaly values to obtain a corrected action path; wherein, based on ocean buoy equipment, detect the ocean temperature and salinity values at each position point on the initial action path; determine a preset ocean temperature and salinity range according to the current seasonal time, and based on the ocean temperature and salinity range, determine the ocean temperature and salinity values outside the ocean temperature and salinity range as ocean temperature and salinity anomaly values, and use the position points belonging to the ocean temperature and salinity anomaly values as path correction points; wherein, the number of the path correction points is at least 1; correct the initial action path according to the path correction points and their corresponding ocean temperature and salinity anomaly values to obtain a corrected action path; Based on a preset movement prediction model, combine the passing path of the typhoon and the marine meteorological model to calculate the mutation points of the typhoon and their turning probabilities; wherein, construct a mathematical model based on a long short-term memory network as the preset movement prediction model; wherein, the preset movement prediction model is trained by the historical typhoon paths and their historical time series data in the historical typhoon dataset; input the passing path of the typhoon and the time series data of the corresponding passing path in the marine meteorological model into the preset movement prediction model to obtain a movement action path; compare the corrected action path with the movement action path to determine the mutation points of the typhoon, and based on the marine meteorological model, calculate the turning direction and its turning probability of the typhoon at the mutation points; Correct the corrected action path according to the mutation points and their turning probabilities to obtain the final typhoon path, and display the final typhoon path on a geographic information platform.

2. The typhoon path integrated prediction method based on marine meteorological big data according to claim 1, wherein The collecting of the multi-source marine meteorological data and the aligning and fusing of the multi-source marine meteorological data in terms of time and space resolution to obtain a marine meteorological model specifically include: Collect multi-source marine meteorological data; the multi-source marine meteorological data includes: satellite remote sensing data, ground observation data, ocean current data, and radar data; Clean the collected satellite remote sensing data, ground observation data, ocean current data, and radar data respectively to remove the noise data and anomaly values in the satellite remote sensing data, ground observation data, ocean current data, and radar data; Based on a preset time resolution and a preset space resolution, uniformly align the satellite remote sensing data, ground observation data, ocean current data, and radar data to a spatio-temporal grid; Through a Kalman filtering algorithm, fuse the spatio-temporal grid and the satellite remote sensing data, ground observation data, ocean current data, and radar data therein to construct a marine meteorological model.

3. The typhoon path integrated prediction method based on marine meteorological big data according to claim 2, characterized in that, Extract typhoon meteorological features from the ocean meteorological model, obtain a historical typhoon data set, and predict the initial action path of the typhoon according to the typhoon meteorological features and the historical typhoon data set, specifically including: In the ocean meteorological model, extract the atmospheric spatial distribution and ocean circulation distribution, and extract the time series data corresponding to the atmospheric spatial distribution and ocean circulation distribution in the ocean meteorological model; wherein, the typhoon meteorological features include: atmospheric spatial distribution, ocean circulation distribution, and time features corresponding to the atmospheric spatial distribution and ocean circulation distribution respectively; Input the atmospheric spatial distribution, ocean circulation distribution and their corresponding time series data into a preset path prediction model, and output the future action path of the typhoon; Obtain a historical typhoon data set; wherein, the historical typhoon data set includes a number of historical typhoons and their corresponding historical paths; Based on the position of the current typhoon, determine the similarity between the path passed by the current typhoon and the historical path of the historical typhoon before the current typhoon position, and extract the historical path corresponding to the historical typhoon with the highest similarity as the historical correction path; Preliminarily correct the future action path according to the historical correction path to obtain the initial action path.

4. The typhoon path integrated prediction method based on marine meteorological big data according to claim 3, wherein, The construction method of the preset path prediction model includes: Obtain historical typhoon sample data; the historical typhoon sample data includes: historical atmospheric spatial distribution samples, historical ocean circulation distribution samples and historical path samples; Construct a deep learning model, and use the historical atmospheric spatial distribution samples, historical ocean circulation distribution samples and time series data as inputs, and the historical path samples as outputs, and perform iterative training on the deep learning model until a preset number of iterations is reached to obtain the trained preset path prediction model.

5. The typhoon path integrated prediction method based on marine meteorological big data according to claim 4, characterized in that, Correct the initial action path according to the path correction points and their corresponding ocean temperature and salinity anomalies to obtain the corrected action path, specifically including: During the process of correcting the initial action path at each path correction point, determine the first path correction point on the initial action path, and determine its correction direction and correction degree according to the ocean temperature and salinity anomaly corresponding to the first path correction point, so as to correct the initial action path according to the correction direction and correction degree corresponding to the first path correction point to obtain the corrected first action path, and then start from the corrected position point, re-determine the first path correction point on the corrected first action path, so that based on the re-determined first path correction point, the corrected first action path is corrected again; until there are no ocean temperature and salinity anomalies at each point on the corrected typhoon action path, the final corrected action path is obtained.

6. A typhoon track integrated prediction system based on ocean meteorological big data, characterized in that, Including: An alignment and fusion module, used to collect multi-source ocean meteorological data, and perform time and space resolution alignment and fusion on the multi-source ocean meteorological data to obtain an ocean meteorological model; A prediction module, used to extract typhoon meteorological features from the ocean meteorological model, obtain a historical typhoon data set, and predict the initial action path of the typhoon according to the typhoon meteorological features and the historical typhoon data set; A correction module, for detecting and obtaining the ocean temperature and salinity values on the initial action path, and determining the ocean temperature and salinity anomaly values, and correcting the initial action path according to the ocean temperature and salinity anomaly values to obtain a corrected action path; wherein, based on the ocean buoy equipment, the ocean temperature and salinity values of each position point on the initial action path are detected; according to the current season time, a preset ocean temperature and salinity range is determined, and based on the ocean temperature and salinity range, the ocean temperature and salinity values outside the ocean temperature and salinity range are determined as ocean temperature and salinity anomaly values, and the position points belonging to the ocean temperature and salinity anomaly values are used as path correction points; wherein the number of the path correction points is at least 1; according to the path correction points and their corresponding ocean temperature and salinity anomaly values, the initial action path is corrected to obtain a corrected action path; A mutation module is used to calculate the mutation point of the typhoon and its turning probability based on a preset movement prediction model, in combination with the typhoon's path and the marine meteorological model; wherein a mathematical model based on a long short-term memory network is constructed as a preset movement prediction model; wherein the preset movement prediction model is trained by using the historical typhoon paths and their historical time series data in the historical typhoon data set; the typhoon's path and the time series data of the corresponding path in the marine meteorological model are input into the preset movement prediction model to obtain a movement action path; the corrected action path is compared with the movement action path to determine the mutation point of the typhoon, and based on the marine meteorological model, the direction in which the typhoon turns at the mutation point and its turning probability are calculated; The correction module is used to correct the corrected action path according to the mutation point and its turning probability, obtain the final typhoon path, and display the final typhoon path on the geographic information platform.

7. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the typhoon path integrated prediction method based on marine meteorological big data as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the typhoon path integrated prediction method based on marine meteorological big data as described in any one of claims 1 to 5.

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