Typhoon wind field intelligent forecasting method and device based on multi-source data fusion

Abstract

The invention relates to a typhoon wind field intelligent forecasting method and device based on multi-source data fusion, relates to the technical field of typhoon wind field forecasting, and provides a typhoon wind field intelligent forecasting method based on multi-priority queues and dynamic time alignment through combination of multi-source data fusion and an intelligent computing technology. The problem of time scale difference of different typhoon life cycles is effectively solved, the precision and efficiency of typhoon wind field forecasting are further remarkably improved, and more reliable technical support is provided for typhoon disaster prevention decision making.

Description

Technical Field

[0001] The present application relates to the technical field of typhoon wind field prediction, and in particular to a typhoon wind field intelligent forecasting method and device based on multi-source data fusion. Background Art

[0002] In the field of typhoon forecasting, traditional wind field prediction methods mainly rely on numerical weather prediction (NWP) models and single observation data. Their accuracy is limited by the initial field error of the model, the uncertainty of the parameterization scheme, and the limitations of data coverage. In existing technologies, although data sources such as satellite remote sensing, ground observation stations, buoys and radars can provide multi-dimensional information, they lack efficient data fusion mechanisms, resulting in low information utilization and difficulty in capturing the fine structure of typhoons and their dynamic evolution. In addition, traditional statistical methods or simple machine learning models have difficulty in handling the nonlinear correlation of multi-source heterogeneous data, especially under extreme weather conditions, where forecast deviations are significant. Therefore, there is an urgent need for an intelligent forecasting method that can deeply integrate multi-source observation data with numerical model outputs and use artificial intelligence technology to explore the inherent laws of the data, so as to improve the spatiotemporal accuracy and robustness of typhoon wind field forecasts and provide more reliable technical support for disaster prevention and mitigation decision-making. Summary of the Invention

[0003] Based on this, it is necessary to provide a typhoon wind field intelligent forecasting method and device based on multi-source data fusion to address the above technical problems, which can improve the accuracy and efficiency of typhoon wind field forecasting.

[0004] In a first aspect, a typhoon wind field intelligent forecasting method based on multi-source data fusion is provided, the method comprising: Based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; Determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; The typhoon wind field at the next moment is constructed according to the typhoon data used to construct the typhoon wind field at the next moment, and the typhoon wind field at the next moment is displayed.

[0005] Optionally, based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to concurrently acquire multi-source data related to historical typhoons in the target area, including: Determine dynamic typhoon data priority indicators; Determining the priorities of the different types of typhoon data based on the dynamic classification index of typhoon data priority; Determining the number of parallel threads according to the priority classification results of the different types of typhoon data; Based on the number of parallel threads, multiple threads are started, and the multiple threads are used to obtain multi-source data related to historical typhoons in the target area in parallel.

[0006] Optionally, based on multi-source data related to historical typhoons in the target area, determining a typhoon data set for multiple development stages in the entire life cycle of the target typhoon includes: Determining that the target typhoon's entire life cycle includes multiple development stages, including the generation stage, the intensification stage, the maturity stage, and the decay stage; Identify the key influencing factors and typhoon wind field characteristics at each development stage; Based on the mapping relationship between the key influencing factors of each development stage and the typhoon wind field characteristics, a typhoon data set of multiple development stages in the entire life cycle of the target typhoon is generated.

[0007] Optionally, aligning the time nodes of multiple target typhoons of the same type includes: Determining a third time length for each development stage based on first time lengths of the full life cycles of multiple target typhoons of the same type and second time lengths corresponding to each same development stage within the multiple full life cycles, wherein the third time length is used to describe the alignment time length, includes: in, Indicates the first time length, Indicates the second time length, Indicates the target typhoon number, Indicates the The original length of a typhoon's entire life cycle, Indicates the first The original length of time for the same development stage, Indicates the third time length; In response to Within a first preset range, defining the second time length as the third time length; In response to If the time is not within the first preset range, the second time length is corrected, and the corrected second time length is defined as the third time length; According to the third time length of each development stage, the time nodes of the multiple target typhoons of the same type are aligned.

[0008] Optionally, determining target development stage standard parameters corresponding to multiple identical development stages includes: Determine the key influencing factors for generating typhoon wind field characteristics during the target development stage, and the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics; Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined.

[0009] Optionally, based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, determining the target development stage standard parameters includes: Obtaining a probability value of each of the key influencing factors leading to the generation of typhoon wind field characteristics; Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined to include: in, represents the standard parameter of the target development stage, represents the correction parameter, represents the correlation coefficient, Indicates the number of time nodes, represents the probability value of the jth key influencing factor, Indicates the number of key influencing factors, Indicates the total sea temperature value corresponding to the xth time node, Indicates the total power assignment corresponding to the xth time node, Indicates the total ocean heat content value corresponding to the xth time node, Indicates the total friction force value corresponding to the xth time node, Indicates the total value of the d-th bias item at the x-th time node, represents the number of bias terms, 、 、 、 and Both represent weight coefficients.

[0010] Optionally, based on the comparison results of the real-time parameters of the target development stage and the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage, determining the typhoon data used to construct the typhoon wind field at the next moment includes: Acquire real-time parameters of the target development stage and quantify the time nodes to which the real-time parameters belong; According to the quantification results, the standard parameters of the target development stage corresponding to the same time node are compared with the real-time parameters of the target development stage to obtain a comparison result; In response to the comparison result being less than a preset threshold, obtaining parameter offset coefficients of other development stages before the target development stage, the parameter offset coefficients being used to describe the absolute value of the difference between the real-time parameter and the standard parameter; determining an average coefficient of a plurality of parameter offset coefficients; determining an adjustment value of the typhoon data of the typhoon wind field according to the average coefficient and the preset ratio; The typhoon data of the typhoon wind field at the current moment is adjusted based on the adjustment value of the typhoon data of the typhoon wind field to obtain typhoon data for constructing the typhoon wind field at the next moment.

[0011] In a second aspect, a typhoon wind field intelligent forecasting device based on multi-source data fusion is provided, the device comprising: The data acquisition module is used to determine the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to use multiple threads to obtain multi-source data related to historical typhoons in the target area in parallel; a data set determination module, configured to determine, based on multi-source data related to historical typhoons in the target area, typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; An alignment module is used to align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; a typhoon data determination module, configured to determine the real-time parameters of the target area at the target development stage, and determine the typhoon data used to construct the typhoon wind field at the next moment based on the comparison results of the real-time parameters of the target development stage with the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage; The typhoon wind field construction module is used to construct the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and to display the typhoon wind field at the next moment.

[0012] Optionally, based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to concurrently acquire multi-source data related to historical typhoons in the target area, including: Determine dynamic typhoon data priority indicators; Determining the priorities of the different types of typhoon data based on the dynamic classification index of typhoon data priority; Determining the number of parallel threads according to the priority classification results of the different types of typhoon data; Based on the number of parallel threads, multiple threads are started, and the multiple threads are used to obtain multi-source data related to historical typhoons in the target area in parallel.

[0013] Optionally, based on multi-source data related to historical typhoons in the target area, determining a typhoon data set for multiple development stages in the entire life cycle of the target typhoon includes: Determining that the target typhoon's entire life cycle includes multiple development stages, including the generation stage, the intensification stage, the maturity stage, and the decay stage; Identify the key influencing factors and typhoon wind field characteristics at each development stage; Based on the mapping relationship between the key influencing factors of each development stage and the typhoon wind field characteristics, a typhoon data set of multiple development stages in the entire life cycle of the target typhoon is generated.

[0014] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed: Based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; Determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; The typhoon wind field at the next moment is constructed according to the typhoon data used to construct the typhoon wind field at the next moment, and the typhoon wind field at the next moment is displayed.

[0015] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; Determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; The typhoon wind field at the next moment is constructed according to the typhoon data used to construct the typhoon wind field at the next moment, and the typhoon wind field at the next moment is displayed.

[0016] In a fifth aspect, a computer program product is provided, the computer program product comprising a computer program, wherein when the computer program is executed by a processor, the following steps are implemented: Based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; Determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; The typhoon wind field at the next moment is constructed according to the typhoon data used to construct the typhoon wind field at the next moment, and the typhoon wind field at the next moment is displayed.

[0017] The above-mentioned typhoon wind field intelligent forecasting method and device based on multi-source data fusion, the method includes: based on a multi-priority queue mechanism, determining the priority of different types of typhoon data, so as to use multiple threads to parallelly obtain multi-source data related to historical typhoons in the target area; based on the multi-source data related to historical typhoons in the target area, determining the typhoon data sets of multiple development stages in the entire life cycle of the target typhoon, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; aligning the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple same development stages; determining the real-time parameters of the target area in the target development stage, and determining the typhoon data used to construct the typhoon wind field at the next moment based on the comparison results of the real-time parameters of the target development stage and the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage; constructing the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment. The typhoon wind field intelligent forecasting method based on multi-priority queues and dynamic time alignment proposed in this application effectively solves the problem of time scale differences in different typhoon life cycles by combining multi-source data fusion with intelligent computing technology, thereby significantly improving the accuracy and efficiency of typhoon wind field forecasts and providing more reliable technical support for typhoon disaster prevention decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a diagram of an application environment of a typhoon wind field intelligent forecasting method based on multi-source data fusion in one embodiment; Figure 2 1 is a flow chart of a method for intelligent typhoon wind field forecasting based on multi-source data fusion in one embodiment; Figure 3 1 is another flow chart of a typhoon wind field intelligent forecasting method based on multi-source data fusion in one embodiment; Figure 4 1 is a structural block diagram of a typhoon wind field intelligent forecasting device based on multi-source data fusion in one embodiment; Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] It should be understood that in the description of this application, unless the context clearly requires otherwise, words such as "include", "comprises", and the like throughout the specification should be interpreted as inclusive rather than exclusive or exhaustive; that is, as "including but not limited to".

[0021] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" is two or more.

[0022] It should be noted that the terms "S1", "S2", etc. are used only for the purpose of describing the steps and do not specifically refer to the order or sequence, nor are they used to limit this application. They are merely for the convenience of describing the method of this application and should not be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0023] The typhoon wind field intelligent forecasting method based on multi-source data fusion provided in this application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with a data processing platform provided on the server 104 via a network. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0024] In one embodiment, Figure 2 As shown in the figure, a typhoon wind field intelligent forecasting method based on multi-source data fusion is provided. Figure 1 The following steps are used as an example to illustrate the terminal in the figure: S1: Based on the multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; S2: Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; S3: Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; S4: determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; S5: constructing the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment.

[0025] In some specific implementations, based on a multi-priority queue mechanism, determining the priorities of different types of typhoon data, and using multiple threads to concurrently acquire multi-source data related to historical typhoons in a target area includes: Determine dynamic typhoon data priority indicators; Determining the priorities of the different types of typhoon data based on the dynamic classification index of typhoon data priority; Determining the number of parallel threads according to the priority classification results of the different types of typhoon data; Based on the number of parallel threads, multiple threads are started, and the multiple threads are used to obtain multi-source data related to historical typhoons in the target area in parallel.

[0026] Specifically, the multi-threading mechanism refers to a method of dividing a large task into several parallel subtasks, and each subtask is executed relatively independently in parallel to improve execution efficiency. In addition, since there are many categories of data collected and processed, the collection schedule, product and message generation requirements of each type of data have different priorities and timeliness requirements, and need to be scheduled accordingly according to the priority and timeliness requirements of the task. Therefore, the system needs to provide an effective priority queue scheduling mechanism to ensure the timeliness of high-priority data while ensuring that other data can also be collected within the specified time limit; among them, the dynamic division index of typhoon data priority generally refers to similarity priority (such as path similarity, intensity similarity, etc.), timeliness priority (such as recent typhoon data, etc.), data quality priority (such as high-precision data, data integrity, etc.) and business demand priority. If the current typhoon is likely to strengthen rapidly, the priority of historical rapidly intensified typhoon cases will be increased. If If the former typhoon may affect densely populated areas, the priority of typhoon data that has caused major disasters in history will be increased. This indicator can be selected according to actual needs. This application gives priority to the priority obtained by combining region, path and intensity. For example, the target area can include the North Indian Ocean, etc. The method of combining the three is the weight assignment method, which is a commonly used method. The specific process will not be repeated here. The higher the priority, the higher the importance, and the corresponding data has priority acquisition rights. The number of parallel threads is generally determined by the amount of data corresponding to the high priority and the time required by the user, so that all high-priority data can be acquired in parallel and efficiently at one time. Other data can be acquired asynchronously or synchronously after all high-priority data are acquired. Multi-source data can include historical typhoon wind field related data, such as wind speed, wind direction, wind field radius, etc., as well as key factors that lead to the generation of typhoon wind fields, such as ocean thermal conditions (sea temperature, etc.), atmosphere (low-level vorticity), Coriolis force, initial disturbance, humidity and land friction, etc. Further, as Figure 3 As shown, the data can be obtained from the Typhoon Operational Database.

[0027] In some specific embodiments, determining a typhoon data set for multiple development stages in the life cycle of a target typhoon based on multi-source data related to historical typhoons in the target area includes: Determining that the target typhoon's entire life cycle includes multiple development stages, including a generation stage, an intensification stage, a maturity stage, and a decay stage. That is, a typhoon generally includes a generation period, an intensification period, a maturity period, and a decay period; Determine the key influencing factors and typhoon wind field characteristics for each development stage. For example, key influencing factors during the formation period include warm seawater (usually ≥26.5°C) providing latent heat of evaporation, ocean heat content, low-level moist air inflow, and the Coriolis force. Typhoon wind field characteristics include small-scale wind circles, localized severe convection, and weak and chaotic circulation. These factors are deduced and will not be elaborated on here. Based on the mapping relationship between the key influencing factors of each development stage and the typhoon wind field characteristics, a typhoon data set of multiple development stages in the entire life cycle of the target typhoon is generated.

[0028] In some specific implementations, aligning the time nodes of multiple target typhoons of the same type includes: Determining a third time length for each development stage based on first time lengths of the full life cycles of multiple target typhoons of the same type and second time lengths corresponding to each same development stage within the multiple full life cycles, wherein the third time length is used to describe the alignment time length, includes: in, Indicates the first time length, Indicates the second time length, Indicates the target typhoon number, Indicates the The original length of a typhoon's entire life cycle, Indicates the first The original length of time for the same development stage, Indicates the third time length; In response to Within a first preset range, the second time length is defined as the third time length, wherein the first preset range can be set according to actual needs; In response to If the time is not within the first preset range, the second time length is corrected, and the corrected second time length is defined as the third time length; According to the third time length of each development stage, the time nodes of the multiple target typhoons of the same type are aligned. For example, if the third time length of a certain development stage is 12 hours, then the time length of the same development stage of the same type of typhoon is normalized to 12 hours, and so on. No further details will be given.

[0029] In some specific embodiments, determining target development stage standard parameters corresponding to multiple identical development stages includes: Determine the key influencing factors for the generation of typhoon wind field characteristics during the target development stage, as well as the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics. For example, the key influencing factors during the generation period include warm seawater (usually ≥26.5℃) providing latent heat of evaporation, ocean heat content, low-level moist air inflow, and the Coriolis force. When the duration of warm seawater reaches a certain value, it will combine with other influencing factors to lead to the formation of a typhoon. At this time, warm seawater is a strongly correlated factor. The probability value can be determined through multiple experiments, such as 50%, and so on, and will not be repeated here. Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined.

[0030] In some specific embodiments, based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, determining the target development stage standard parameter includes: Obtaining a probability value of each of the key influencing factors leading to the generation of typhoon wind field characteristics; Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined to include: in, represents the standard parameter of the target development stage, Represents a correction parameter, wherein the correction parameter is an adjustable parameter and can represent the degree of influence, etc. represents the correlation coefficient, Indicates the number of time nodes, represents the probability value of the jth key influencing factor, Indicates the number of key influencing factors, Indicates the total sea temperature value corresponding to the xth time node, Indicates the total power assignment corresponding to the xth time node, Indicates the total ocean heat content value corresponding to the xth time node, Indicates the total friction force corresponding to the xth time node, that is, the ground friction force, Indicates the total value of the d-th bias item at the x-th time node, represents the number of bias terms, 、 、 、 and Both represent weight coefficients, where the above values ​​are determined through experiments or domain experts, and the bias term refers to other fluctuation factors.

[0031] In some specific embodiments, determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameter of the target development stage with the standard parameter of the target development stage, and a parameter offset coefficient of other development stages before the target development stage, includes: Acquire real-time parameters of the target development stage and quantify the time nodes to which the real-time parameters belong. Generally, typhoons of the same type are used for comparison and have similar development stages. Therefore, the time nodes can be quantified based on the most similar typhoon node data in the historical stage. The same type can refer to the same structure (such as the radius of the wind circle), the same path, and / or the same intensity. According to the quantification results, the target development stage standard parameter corresponding to the same time node is compared with the real-time parameter of the target development stage to obtain a comparison result, wherein the comparison result refers to the difference between the target development stage standard parameter and the real-time parameter of the target development stage, and the absolute value of the difference is taken as the comparison result, which is the parameter offset coefficient described below; In response to the comparison result being less than a preset threshold, obtaining parameter offset coefficients of other development stages before the target development stage, wherein the parameter offset coefficients are used to describe the absolute value of the difference between the real-time parameter and the standard parameter, wherein the preset threshold can be set according to actual needs. When the comparison result is less than the preset threshold, it indicates that the quantization result of the time node is relatively accurate; Determine the average coefficient of multiple parameter offset coefficients, that is, add the multiple parameter offset coefficients and take the average value, which is the average coefficient; Determine the adjustment value of the typhoon data of the typhoon wind field according to the average coefficient and the preset ratio, wherein the preset ratio can be set according to actual needs, such as 100 times, etc. When the average coefficient is 0.4, the adjustment value is 40; Based on the adjustment value of the typhoon data of the typhoon wind field, the typhoon data of the typhoon wind field at the current moment is adjusted to obtain the typhoon data used to construct the typhoon wind field at the next moment. For example, when it is in the enhancement period, its wind speed increases by 40, and so on, which will not be repeated.

[0032] The system used in the above scheme is as follows Figure 3As shown, the above-mentioned step algorithm is set in the system. Specifically, the typhoon wind field and high wind pattern data are collected and stored through the numerical simulation database. After the data collection program collects the relevant meteorological data, the data processing and conversion program processes, converts, and processes the data and stores it in the data source. The data source mainly provides data services to the outside world through sharing and database access interfaces. The application component reads the data and parses and processes the data into refined high wind forecast products and stores them in the product library; the business application part provides data retrieval functions to retrieve various types of data for graphical display and interaction, and displays and publishes the generated text products. Specifically, combined with the predicted data, cloud map data, and radar data, the typhoon center is tracked using the wind field and pressure field for the typhoon circulation in the high-resolution numerical model to obtain the current numerical model forecast of the typhoon path. The typhoon path and typhoon intensity at the corresponding moment are obtained, and the model path input is provided to the typhoon refined gale correction module. The typhoon path forecast for the target area is downscaled, and a subjective typhoon forecast path with a resolution of 3 hours is made, which maintains consistency in time resolution with the typhoon path predicted by the numerical model. The typhoon in the grid wind field predicted by the numerical model is filtered to separate the typhoon wind field and the background wind field. The typhoon wind field is corrected according to the path and intensity obtained in the typhoon subjective path downscaling module, and then superimposed on the background wind field to form a new gridded forecast wind field. When the typhoon approaches land or islands, the wind field circulation is corrected according to the terrain friction system, and the typhoon at each stage is displayed on the display interface. Among them, the typhoon path display component adopts the interface display style of MICAPS4, is drawn with the default font provided by MICAPS4, and uses the graphic drawing method provided by MICAPS.

[0033] In the above-mentioned typhoon wind field intelligent forecasting method based on multi-source data fusion, the method includes: determining the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to use multiple threads to parallelly obtain multi-source data related to historical typhoons in the target area; determining typhoon data sets for multiple development stages in the entire life cycle of the target typhoon based on the multi-source data related to historical typhoons in the target area, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; aligning the time nodes of multiple target typhoons of the same type to determine target development stage standard parameters corresponding to multiple same development stages; determining the real-time parameters of the target area in the target development stage, and determining the typhoon data used to construct the typhoon wind field at the next moment based on the comparison results of the real-time parameters of the target development stage and the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage; constructing the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment. The typhoon wind field intelligent forecasting method based on multi-priority queues and dynamic time alignment proposed in this application effectively solves the problem of time scale differences in different typhoon life cycles by combining multi-source data fusion with intelligent computing technology, thereby significantly improving the accuracy and efficiency of typhoon wind field forecasts and providing more reliable technical support for typhoon disaster prevention decisions.

[0034] It should be understood that although Figure 2-Figure 3 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2-Figure 3 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0035] In one embodiment, Figure 4 As shown, a typhoon wind field intelligent forecasting device based on multi-source data fusion is provided, comprising: a data acquisition module, a data set determination module, an alignment module, a typhoon data determination module and a typhoon wind field construction module, wherein: The data acquisition module is used to determine the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to use multiple threads to obtain multi-source data related to historical typhoons in the target area in parallel; a data set determination module, configured to determine, based on multi-source data related to historical typhoons in the target area, typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; An alignment module is used to align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; a typhoon data determination module, configured to determine the real-time parameters of the target area at the target development stage, and determine the typhoon data used to construct the typhoon wind field at the next moment based on the comparison results of the real-time parameters of the target development stage with the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage; The typhoon wind field construction module is used to construct the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and to display the typhoon wind field at the next moment.

[0036] As a preferred implementation, in an embodiment of the present invention, the data acquisition module is specifically configured to: Determine dynamic typhoon data priority indicators; Determining the priorities of the different types of typhoon data based on the dynamic classification index of typhoon data priority; Determining the number of parallel threads according to the priority classification results of the different types of typhoon data; Based on the number of parallel threads, multiple threads are started, and the multiple threads are used to obtain multi-source data related to historical typhoons in the target area in parallel.

[0037] As a preferred implementation, in an embodiment of the present invention, the data set determination module is specifically configured to: Determining that the target typhoon's entire life cycle includes multiple development stages, including the generation stage, the intensification stage, the maturity stage, and the decay stage; Identify the key influencing factors and typhoon wind field characteristics at each development stage; Based on the mapping relationship between the key influencing factors of each development stage and the typhoon wind field characteristics, a typhoon data set of multiple development stages in the entire life cycle of the target typhoon is generated.

[0038] Regarding the specific limitations of the typhoon wind farm intelligent forecasting device based on multi-source data fusion, please refer to the limitations of the typhoon wind farm intelligent forecasting method based on multi-source data fusion above, which will not be repeated here. The various modules in the above-mentioned typhoon wind farm intelligent forecasting device based on multi-source data fusion can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0039] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a typhoon wind field intelligent forecasting method based on multi-source data fusion is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0040] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0041] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: S1: Based on the multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; S2: Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; S3: Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; S4: determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; S5: constructing the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment.

[0042] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Based on the multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; S2: Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; S3: Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; S4: determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; S5: constructing the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment.

[0043] In one embodiment, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the following steps: S1: Based on the multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; S2: Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; S3: Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; S4: determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; S5: constructing the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment.

[0044] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0045] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0046] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the scope of the present application, and such modifications and improvements are all within the scope of protection of the present application.

Claims

1. A typhoon wind field intelligent forecasting method based on multi-source data fusion, characterized in that: The method comprises: Based on a multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to obtain multi-source data related to historical typhoons in the target area in parallel; Based on multi-source data related to historical typhoons in the target area, determine typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; Align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; Determining real-time parameters of the target area at a target development stage, and determining typhoon data for constructing a typhoon wind field at a next moment based on a comparison result of the real-time parameters of the target development stage with standard parameters of the target development stage, as well as parameter offset coefficients of other development stages before the target development stage; The typhoon wind field at the next moment is constructed according to the typhoon data used to construct the typhoon wind field at the next moment, and the typhoon wind field at the next moment is displayed.

2. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 1 is characterized in that: Based on the multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to concurrently obtain multi-source data related to historical typhoons in the target area, including: Determine dynamic typhoon data priority indicators; Determining the priorities of the different types of typhoon data based on the dynamic classification index of typhoon data priority; Determining the number of parallel threads according to the priority classification results of the different types of typhoon data; Based on the number of parallel threads, multiple threads are started, and the multiple threads are used to obtain multi-source data related to historical typhoons in the target area in parallel.

3. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 2 is characterized in that: Based on multi-source data related to historical typhoons in the target area, a typhoon data set for multiple development stages in the target typhoon's life cycle is determined, including: Determining that the target typhoon's entire life cycle includes multiple development stages, including the generation stage, the intensification stage, the maturity stage, and the decay stage; Identify the key influencing factors and typhoon wind field characteristics at each development stage; Based on the mapping relationship between the key influencing factors of each development stage and the typhoon wind field characteristics, a typhoon data set of multiple development stages in the entire life cycle of the target typhoon is generated.

4. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 3 is characterized in that: Aligning the time nodes of multiple target typhoons of the same type includes: Determining a third time length for each development stage based on first time lengths of the full life cycles of multiple target typhoons of the same type and second time lengths corresponding to each same development stage within the multiple full life cycles, wherein the third time length is used to describe the alignment time length, includes: in, Indicates the first time length, Indicates the second time length, Indicates the target typhoon number, Indicates the The original length of a typhoon's entire life cycle, Indicates the first The original length of time for the same development stage, Indicates the third time length; In response to Within a first preset range, defining the second time length as the third time length; In response to If the time is not within the first preset range, the second time length is corrected, and the corrected second time length is defined as the third time length; According to the third time length of each development stage, the time nodes of the multiple target typhoons of the same type are aligned.

5. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 4 is characterized in that: Determine the target development stage standard parameters corresponding to multiple identical development stages include: Determine the key influencing factors for generating typhoon wind field characteristics during the target development stage, as well as the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics; Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined.

6. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 5 is characterized in that: Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined to include: Obtaining a probability value of each of the key influencing factors leading to the generation of typhoon wind field characteristics; Based on the probability value of each key influencing factor leading to the generation of typhoon wind field characteristics, the standard parameters of the target development stage are determined to include: in, Indicates the standard parameter of the target development stage, represents the correction parameter, represents the correlation coefficient, Indicates the number of time nodes, represents the probability value of the jth key influencing factor, Indicates the number of key influencing factors, Indicates the total sea temperature value corresponding to the xth time node, Indicates the total power assignment corresponding to the xth time node, Indicates the total ocean heat content value corresponding to the xth time node, Indicates the total friction force value corresponding to the xth time node, Indicates the total value of the d-th bias item at the x-th time node, represents the number of bias terms, 、 、 、 and Both represent weight coefficients.

7. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 6 is characterized in that: Based on the comparison results of the real-time parameters of the target development stage and the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage, typhoon data for constructing the typhoon wind field at the next moment are determined, including: Acquire real-time parameters of the target development stage and quantify the time nodes to which the real-time parameters belong; According to the quantification results, the standard parameters of the target development stage corresponding to the same time node are compared with the real-time parameters of the target development stage to obtain a comparison result; In response to the comparison result being less than a preset threshold, obtaining parameter offset coefficients of other development stages before the target development stage, the parameter offset coefficients being used to describe the absolute value of the difference between the real-time parameter and the standard parameter; determining an average coefficient of a plurality of parameter offset coefficients; determining an adjustment value of the typhoon data of the typhoon wind field according to the average coefficient and the preset ratio; The typhoon data of the typhoon wind field at the current moment is adjusted based on the adjustment value of the typhoon data of the typhoon wind field to obtain typhoon data for constructing the typhoon wind field at the next moment.

8. A typhoon wind field intelligent forecasting device based on multi-source data fusion, characterized in that: The device comprises: The data acquisition module is used to determine the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to use multiple threads to parallelly acquire multi-source data related to historical typhoons in the target area; a data set determination module, configured to determine, based on multi-source data related to historical typhoons in the target area, typhoon data sets for multiple development stages in the target typhoon's entire life cycle, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes; An alignment module is used to align the time nodes of multiple target typhoons of the same type to determine the target development stage standard parameters corresponding to multiple identical development stages; a typhoon data determination module, configured to determine the real-time parameters of the target area at the target development stage, and determine the typhoon data used to construct the typhoon wind field at the next moment based on the comparison results of the real-time parameters of the target development stage with the standard parameters of the target development stage, as well as the parameter offset coefficients of other development stages before the target development stage; The typhoon wind field construction module is used to construct the typhoon wind field at the next moment according to the typhoon data used to construct the typhoon wind field at the next moment, and to display the typhoon wind field at the next moment.

9. The typhoon wind field intelligent forecasting device based on multi-source data fusion according to claim 8, characterized in that: Based on the multi-priority queue mechanism, the priorities of different types of typhoon data are determined, so that multiple threads can be used to concurrently obtain multi-source data related to historical typhoons in the target area, including: Determine the dynamic priority classification indicators for typhoon data; Determining the priorities of the different types of typhoon data based on the dynamic classification index of typhoon data priority; Determining the number of parallel threads according to the priority classification results of the different types of typhoon data; Based on the number of parallel threads, multiple threads are started, and the multiple threads are used to obtain multi-source data related to historical typhoons in the target area in parallel.

10. The typhoon wind field intelligent forecasting device based on multi-source data fusion according to claim 9, characterized in that: Based on multi-source data related to historical typhoons in the target area, a typhoon data set for multiple development stages in the target typhoon's life cycle is determined, including: Determining that the target typhoon's entire life cycle includes multiple development stages, including the generation stage, the intensification stage, the maturity stage, and the decay stage; Identify the key influencing factors and typhoon wind field characteristics at each development stage; Based on the mapping relationship between the key influencing factors of each development stage and the typhoon wind field characteristics, a typhoon data set of multiple development stages in the entire life cycle of the target typhoon is generated.

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