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

Abstract

The application relates to a typhoon wind field intelligent prediction method and device based on multi-source data fusion, and relates to the technical field of typhoon wind field prediction. The typhoon wind field intelligent prediction method based on a multi-priority queue and dynamic time alignment is combined with multi-source data fusion and intelligent calculation technology, effectively solves the time scale difference problem of different typhoon life cycles, and significantly improves the accuracy and efficiency of typhoon wind field prediction, thereby providing more reliable technical support for typhoon disaster prevention decision-making.

Description

TECHNICAL FIELD

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

[0002] In the field of typhoon prediction, the traditional wind field prediction method mainly relies on numerical weather prediction (NWP) model and single observation data, and its accuracy is limited by the initial field error of the model, the uncertainty of the parameterization scheme and the limitation of the data coverage range. In the prior art, although satellite remote sensing, ground observation stations, buoys and radar data sources can provide multi-dimensional information, they lack an efficient data fusion mechanism, resulting in low information utilization rate and difficulty in capturing the fine structure and dynamic evolution of typhoons. In addition, traditional statistical methods or simple machine learning models are difficult to handle the nonlinear correlation of multi-source heterogeneous data, especially the significant prediction deviation under extreme weather conditions. Therefore, there is an urgent need for an intelligent prediction method that can deeply fuse multi-source observation data and numerical model output and use artificial intelligence technology to mine the internal rules of the data, in order to improve the spatiotemporal accuracy and robustness of typhoon wind field prediction and provide more reliable technical support for disaster prevention and mitigation decision-making. SUMMARY

[0003] Therefore, it is necessary to provide a typhoon wind field intelligent prediction method and device based on multi-source data fusion, which can improve the accuracy and efficiency of typhoon wind field prediction.

[0004] In a first aspect, a typhoon wind field intelligent prediction method based on multi-source data fusion is provided, which comprises:

[0005] Based on the multi-priority queue mechanism, the priority of different kinds of typhoon data is determined to obtain the multi-source data related to the historical typhoon of the target area in parallel by using multiple threads;

[0006] Based on the multi-source data related to the historical typhoon of the target area, the typhoon data set of multiple development stages in the whole life cycle of the target typhoon is determined, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes the typhoon data corresponding to multiple time nodes;

[0007] 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;

[0008] Determining the real-time parameters of the target area at the target development stage, determining the typhoon data used to construct the typhoon wind field at the next moment based on the comparison result of the real-time parameters of the target development stage and the target development stage standard parameters, and the parameter offset coefficient of other development stages before the target development stage;

[0009] According to the typhoon data used for constructing the typhoon wind field of the next moment, the typhoon wind field of the next moment is constructed, and the typhoon wind field of the next moment is displayed.

[0010] Optionally, based on the multi-priority queue mechanism, the priority of different kinds of typhoon data is determined to utilize multiple threads to acquire the multi-source data related to the historical typhoon of the target area in parallel, which comprises:

[0011] The typhoon data priority dynamic division index is determined.

[0012] Based on the typhoon data priority dynamic division index, the priority of different kinds of typhoon data is determined.

[0013] According to the priority division result of the different kinds of typhoon data, the number of parallel threads is determined.

[0014] Based on the number of parallel threads, multiple threads are started, and the multi-source data related to the historical typhoon of the target area is acquired in parallel by using the multiple threads.

[0015] Optionally, based on the multi-source data related to the historical typhoon of the target area, the typhoon data set of multiple development stages in the whole life cycle of the target typhoon is determined, which comprises:

[0016] It is determined that the whole life cycle of the target typhoon includes multiple development stages, and the development stages include the generation stage, the enhancement stage, the mature stage and the decay stage.

[0017] The key influencing factors and typhoon wind field characteristics of each development stage are determined.

[0018] Based on the mapping relationship between the key influencing factors and typhoon wind field characteristics of each development stage, the typhoon data set of multiple development stages in the whole life cycle of the target typhoon is generated.

[0019] Optionally, the time nodes of multiple target typhoons of the same type are aligned, which comprises:

[0020] According to the first time length of the whole life cycle of multiple target typhoons of the same type and the second time length corresponding to each same development stage in the whole life cycle, the third time length of each development stage is determined, which is used to describe the alignment time length, comprising:

[0021]

[0022]

[0023] wherein, represents the first time length, represents the second time length, representing a number of target typhoons, representing an original time length of a whole life cycle of a first typhoon, representing an original time length of a whole life cycle of a second typhoon, representing an original time length of a same development stage of a first typhoon in a plurality of whole life cycles, representing an original time length of a same development stage of a second typhoon in the plurality of whole life cycles, representing a third time length;

[0024] in response to the second time length being within a first preset range, defining the second time length as the third time length;

[0025] in response to the second time length not being within the first preset range, correcting the second time length, and defining a corrected second time length as the third time length;

[0026] aligning time nodes of a plurality of target typhoons of a same type according to the third time length of each development stage.

[0027] Optionally, determining a target development stage standard parameter corresponding to a plurality of same development stages comprises:

[0028] determining a key influence factor of a typhoon wind field feature generated by a target development stage, and a probability value of each key influence factor leading to the typhoon wind field feature generated by the target development stage;

[0029] determining the target development stage standard parameter based on the probability value of each key influence factor leading to the typhoon wind field feature generated by the target development stage.

[0030] Optionally, determining the target development stage standard parameter based on the probability value of each key influence factor leading to the typhoon wind field feature generated by the target development stage comprises:

[0031] obtaining the probability value of each key influence factor leading to the typhoon wind field feature generated by the target development stage;

[0032] determining the target development stage standard parameter based on the probability value of each key influence factor leading to the typhoon wind field feature generated by the target development stage comprises:

[0033]

[0034]

[0035] wherein, the target development stage standard parameter, a correction parameter, a correlation coefficient, a number of time nodes, a probability value of a jth key influence factor, represents the number of key influencing factors, represents the total sea temperature assignment corresponding to the xth time node, represents the total dynamic assignment corresponding to the xth time node, represents the total ocean heat content assignment corresponding to the xth time node, represents the total friction assignment corresponding to the xth time node, represents the total assignment of the dth bias term at the xth time node, represents the number of bias terms, 、 、 、 and all represent weight coefficients.

[0036] Optionally, based on the comparison result of the real-time parameter of the target development stage and the standard parameter of the target development stage, and the parameter offset coefficient of other development stages before the target development stage, the typhoon data used to construct the typhoon wind field of the next moment is determined, comprising:

[0037] Obtaining the real-time parameter of the target development stage, and quantifying the time node to which the real-time parameter belongs;

[0038] According to the quantification result, comparing the standard parameter of the target development stage corresponding to the same time node with the real-time parameter of the target development stage to obtain a comparison result;

[0039] In response to the comparison result being less than a preset threshold, obtaining the parameter offset coefficient of other development stages before the target development stage, the parameter offset coefficient being used to describe the absolute value of the difference between the real-time parameter and the standard parameter;

[0040] Determining an average coefficient of a plurality of parameter offset coefficients;

[0041] According to the average coefficient and a preset proportion, determining an adjustment value of the typhoon data of the typhoon wind field;

[0042] Based on the adjustment value of the typhoon data of the typhoon wind field, adjusting the typhoon data of the current moment typhoon wind field to obtain the typhoon data used to construct the typhoon wind field of the next moment.

[0043] In a second aspect, a typhoon wind field intelligent forecasting device based on multi-source data fusion is provided, and the device comprises:

[0044] A data acquisition module is configured to determine the priority of different kinds of typhoon data based on a multi-priority queue mechanism, so as to acquire multi-source data related to historical typhoons in a target area in parallel by using multiple threads.

[0045] a data set determination module configured to determine a plurality of typhoon data sets corresponding to a plurality of development stages in a whole life cycle of a target typhoon based on historical typhoon-related multi-source data of the target area, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to a plurality of time nodes;

[0046] an alignment module configured to align time nodes of a plurality of target typhoons of the same type to determine target development stage standard parameters corresponding to a plurality of same development stages;

[0047] a typhoon data determination module configured to determine real-time parameters of the target area in a target development stage, determine typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameters of the target development stage and the target development stage standard parameters and a parameter offset coefficient of other development stages before the target development stage;

[0048] a typhoon wind field construction module configured to construct a typhoon wind field at the next time according to the typhoon data for constructing the typhoon wind field at the next time, and display the typhoon wind field at the next time.

[0049] Optionally, based on a multi-priority queue mechanism, priorities of different kinds of typhoon data are determined to utilize a plurality of threads to acquire the historical typhoon-related multi-source data of the target area in parallel, which includes:

[0050] determining a dynamic division index of typhoon data priority;

[0051] determining the priorities of the different kinds of typhoon data based on the dynamic division index of typhoon data priority;

[0052] determining a number of parallel threads according to the priority division result of the different kinds of typhoon data;

[0053] starting a plurality of threads based on the number of parallel threads, and utilizing the plurality of threads to acquire the historical typhoon-related multi-source data of the target area in parallel.

[0054] Optionally, determining a plurality of typhoon data sets corresponding to a plurality of development stages in a whole life cycle of a target typhoon based on historical typhoon-related multi-source data of the target area includes:

[0055] determining that the whole life cycle of the target typhoon includes a plurality of development stages, and the development stages include a generation stage, an enhancement stage, a mature stage and a decay stage;

[0056] determining key influence factors and typhoon wind field characteristics of each development stage;

[0057] 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 whole life cycle of the target typhoon is generated.

[0058] In a third aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0059] Based on the multi-priority queue mechanism, the priority of different kinds of typhoon data is determined to acquire the multi-source data related to the historical typhoons of the target area in parallel by using multiple threads.

[0060] Based on the multi-source data related to the historical typhoons of the target area, a typhoon data set of multiple development stages in the whole life cycle of the target typhoon is determined, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes.

[0061] The time nodes of multiple target typhoons of the same type are aligned to determine target development stage standard parameters corresponding to multiple same development stages.

[0062] The real-time parameters of the target area at the target development stage are determined, and based on the comparison result of the real-time parameters at the target development stage and the target development stage standard parameters and the parameter offset coefficient of other development stages before the target development stage, typhoon data for constructing a typhoon wind field at the next moment is determined.

[0063] According to the typhoon data for constructing a typhoon wind field at the next moment, a typhoon wind field at the next moment is constructed, and the typhoon wind field at the next moment is displayed.

[0064] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0065] Based on the multi-priority queue mechanism, the priority of different kinds of typhoon data is determined to acquire the multi-source data related to the historical typhoons of the target area in parallel by using multiple threads.

[0066] Based on the multi-source data related to the historical typhoons of the target area, a typhoon data set of multiple development stages in the whole life cycle of the target typhoon is determined, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to multiple time nodes.

[0067] The time nodes of multiple target typhoons of the same type are aligned to determine target development stage standard parameters corresponding to multiple same development stages.

[0068] determining real-time parameters of the target region at a target development stage, determining typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameters of the target development stage and standard parameters of the target development stage, and a parameter offset coefficient of other development stages before the target development stage;

[0069] constructing the typhoon wind field at the next time according to the typhoon data for constructing the typhoon wind field at the next time, and displaying the typhoon wind field at the next time.

[0070] In a fifth aspect, a computer program product is provided, which includes a computer program, and the computer program, when executed by a processor, implements the following steps:

[0071] determining priorities of different kinds of typhoon data based on a multi-priority queue mechanism, to acquire, in parallel by using multiple threads, multi-source data related to historical typhoons of a target region;

[0072] determining a set of typhoon data of multiple development stages in a whole life cycle of a target typhoon based on the multi-source data related to historical typhoons of the target region, wherein each development stage corresponds to a set of typhoon data, and each set of typhoon data includes typhoon data corresponding to multiple time nodes;

[0073] aligning time nodes of multiple target typhoons of the same type to determine target standard parameters of multiple same development stages;

[0074] determining real-time parameters of the target region at a target development stage, determining typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameters of the target development stage and standard parameters of the target development stage, and a parameter offset coefficient of other development stages before the target development stage;

[0075] constructing the typhoon wind field at the next time according to the typhoon data for constructing the typhoon wind field at the next time, and displaying the typhoon wind field at the next time.

[0076] The typhoon wind field intelligent prediction method and device based on multi-source data fusion, the method comprises the following steps: determining the priority of different kinds of typhoon data based on a multi-priority queue mechanism, so as to acquire the multi-source data related to the historical typhoon of the target area in parallel by using multiple threads; determining the typhoon data set of multiple development stages in the whole life cycle of the target typhoon based on the multi-source data related to the historical typhoon of the target area, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes the 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 typhoon data used for constructing the typhoon wind field at the next moment based on the comparison result of the real-time parameters of the target development stage and the target development stage standard parameters, and the parameter offset coefficient of other development stages before the target development stage; constructing the typhoon wind field at the next moment according to the typhoon data used for constructing the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment. The typhoon wind field intelligent prediction method based on multi-priority queue and dynamic time alignment provided in the application effectively solves the problem of time scale difference of different typhoon life cycles by combining multi-source data fusion and intelligent computing technology, thereby significantly improving the accuracy and efficiency of typhoon wind field prediction, and providing more reliable technical support for typhoon disaster prevention decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 An application environment diagram of the typhoon wind field intelligent prediction method based on multi-source data fusion in an embodiment;

[0078] Figure 2 A flowchart of the typhoon wind field intelligent prediction method based on multi-source data fusion in an embodiment;

[0079] Figure 3 Another flowchart of the typhoon wind field intelligent prediction method based on multi-source data fusion in an embodiment;

[0080] Figure 4 A structural block diagram of the typhoon wind field intelligent prediction device based on multi-source data fusion in an embodiment;

[0081] Figure 5 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0082] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0083] It should be understood that, in the description of the present application, unless the context clearly requires otherwise, the terms "comprise", "comprising", and the like in the whole specification should be interpreted as inclusive rather than exclusive or exhaustive; that is, as "including but not limited to".

[0084] It should also be understood that the terms "first", "second", and the like are used only for the purpose of description, and should not be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise stated, the meaning of "multiple" is two or more.

[0085] It should be noted that the terms "S1", "S2", and the like are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor limit the present application. They are only used to facilitate the description of the method of the present application, and should not be understood as indicating the order of the steps. In addition, the technical solutions of various embodiments can be combined with each other, but must be based on the realization of a person of ordinary skill in the art. When the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist, nor is it within the scope of protection claimed by the present application.

[0086] The typhoon wind field intelligent prediction method based on multi-source data fusion provided by the present application can be applied to the application environment as shown in Figure 1 . Among them, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but is not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server 104 can be realized by an independent server or a server cluster composed of multiple servers.

[0087] In one embodiment, as shown in Figure 2 , a typhoon wind field intelligent prediction method based on multi-source data fusion is provided. Taking the terminal in Figure 1 as an example, the method includes the following steps:

[0088] S1: Based on a multi-priority queue mechanism, the priority of different kinds of typhoon data is determined to obtain multi-source data related to historical typhoons in a target area in parallel by using multiple threads;

[0089] S2: determining a set of typhoon data of a plurality of development stages in a whole life cycle of a target typhoon based on historical typhoon-related multi-source data of the target region, wherein each development stage corresponds to a set of typhoon data, and each set of typhoon data includes typhoon data corresponding to a plurality of time nodes;

[0090] S3: aligning time nodes of a plurality of target typhoons of the same type to determine target standard parameters of a plurality of same development stages;

[0091] S4: determining real-time parameters of the target region at a target development stage, determining typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameters of the target development stage and the target standard parameters of the target development stage, and a parameter offset coefficient of other development stages before the target development stage;

[0092] S5: constructing a typhoon wind field at a next time according to the typhoon data for constructing the typhoon wind field at the next time, and displaying the typhoon wind field at the next time.

[0093] In some embodiments, based on a multi-priority queue mechanism, priorities of different kinds of typhoon data are determined to utilize a plurality of threads to acquire historical typhoon-related multi-source data of a target region in parallel, which includes:

[0094] determining a dynamic division index of typhoon data priority;

[0095] determining the priorities of the different kinds of typhoon data based on the dynamic division index of typhoon data priority;

[0096] determining the number of parallel threads according to the priority division result of the different kinds of typhoon data;

[0097] starting a plurality of threads based on the number of parallel threads, and utilizing the plurality of threads to acquire historical typhoon-related multi-source data of a target region in parallel.

[0098] Specifically, the multi-thread mechanism refers to a method of dividing a large task into several parallel sub-tasks, and each sub-task is executed independently in parallel to improve execution efficiency. In addition, since the data collected and processed through data collection and processing has various categories, the collection time arrangement, product and message generation requirements of each type of data have different priority and timeliness requirements, and need to be scheduled 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 allowing other data to be collected within the specified time limit. The typhoon data priority dynamic division index generally refers to similarity priority (such as path similarity, intensity similarity, etc.), timeliness priority (such as recent typhoon data), data quality priority (such as high-precision data, data integrity, etc.), and business demand priority. For example, if the current typhoon may rapidly intensify, the priority of historical typhoon cases that rapidly intensified is increased, and if the current typhoon may affect densely populated areas, the priority of historical typhoon data that caused major disasters is increased. This index can be selected according to actual needs. The application prioritizes the priority obtained by combining the three of region, path and intensity. For example, the target region can include the North Indian Ocean, and the method of combining the three is the weight assignment method, which is a commonly used method. The specific process is not repeated here. The higher the priority, the higher the importance, and the corresponding data has the right to acquire priority. The number of parallel threads is generally determined by the amount of data corresponding to high priority and the time required by the user to be able to efficiently acquire all high-priority data at one time. Other data can be acquired asynchronously or synchronously after all high-priority data has been acquired. Multi-source data can include historical typhoon wind field related data such as wind speed, wind direction, wind field radius, and key factors that lead to the generation of typhoon wind field such as ocean thermal conditions (sea temperature), atmosphere (low-level vorticity), Coriolis force, initial disturbance, humidity and land friction. Further, as shown in Figure 3 The data can be obtained from the typhoon business database.

[0099] In some embodiments, based on the historical typhoon related multi-source data of the target region, the typhoon data set of each development stage in the full life cycle of the target typhoon is determined.

[0100] The full life cycle of the target typhoon includes multiple development stages, including the generation stage, the intensification stage, the mature stage and the decay stage, i.e. the typhoon generally includes the generation period, the intensification period, the mature period and the decay period.

[0101] 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.

[0102] 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.

[0103] In some specific implementations, aligning the time nodes of multiple target typhoons of the same type includes:

[0104] 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:

[0105]

[0106]

[0107] 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;

[0108] 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;

[0109] 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;

[0110] 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.

[0111] In some embodiments, determining the target development stage standard parameter corresponding to the plurality of same development stages comprises:

[0112] determining the key influence factors of the target development stage generating typhoon wind field characteristics, and the probability value of each key influence factor leading to the generation of typhoon wind field characteristics, wherein the key influence factors of the generation period include warm sea water (usually ≥ 26.5℃) providing latent heat of evaporation, ocean heat content, low-level wet air inflow, and geostrophic deflection force, etc., and when the duration of the warm sea water reaches a certain value, it will combine with other influence factors to lead to the generation of typhoon, at this time, the warm sea water is a strong correlation factor, and the probability value can be determined through multiple tests, such as 50%, etc., and the like will not be repeated here;

[0113] Based on the probability value of each key influence factor leading to the generation of typhoon wind field characteristics, the target development stage standard parameter is determined.

[0114] In some embodiments, determining the target development stage standard parameter based on the probability value of each key influence factor leading to the generation of typhoon wind field characteristics comprises:

[0115] obtaining the probability value of each key influence factor leading to the generation of typhoon wind field characteristics;

[0116] Based on the probability value of each key influence factor leading to the generation of typhoon wind field characteristics, the target development stage standard parameter is determined.

[0117]

[0118]

[0119] wherein, the target development stage standard parameter is represented by P, the correction parameter is represented by a, which is an adjustable parameter and can represent the degree of influence, etc., the correlation coefficient is represented by r, the number of time nodes is represented by n, the probability value of the jth key influence factor is represented by p j, the number of key influence factors is represented by m, the total sea temperature value corresponding to the xth time node is represented by T x, the total dynamic value corresponding to the xth time node is represented by D x, the total ocean heat content value corresponding to the xth time node is represented by H x, the total friction value corresponding to the xth time node is represented by F x, the total value of the dth bias term at the xth time node is represented by B x d, represents the number of bias terms, , , , and all represent weight coefficients, wherein the above assignments are determined by experiments or field experts, and the bias term refers to other fluctuation factors.

[0120] In some embodiments, based on the comparison result of the real-time parameter of the target development stage and the standard parameter of the target development stage, and the parameter offset coefficient of other development stages before the target development stage, the typhoon data for constructing the typhoon wind field at the next moment is determined, comprising:

[0121] The real-time parameter of the target development stage is obtained, and the time node to which the real-time parameter belongs is quantized, wherein generally, typhoons of the same type and similar development stages are compared, and therefore, the time node can be quantized according to the most similar typhoon node data in the historical stage, and the same type can refer to the same structure (such as wind circle radius), the same path and / or the same intensity, etc.

[0122] According to the quantization result, the standard parameter of the target development stage 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 standard parameter of the target development stage and the real-time parameter of the target development stage, and the absolute value of the difference is taken as the comparison result, that is, the parameter offset coefficient described below;

[0123] In response to the comparison result being less than a preset threshold, the parameter offset coefficient of other development stages before the target development stage is obtained, and the parameter offset coefficient is 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, and when the comparison result is less than the preset threshold, it indicates that the time node quantization result is relatively accurate;

[0124] An average coefficient of a plurality of parameter offset coefficients is determined, that is, the average value of the plurality of parameter offset coefficients is added, that is, the average coefficient;

[0125] According to the average coefficient and a preset proportion, an adjustment value of the typhoon data of the typhoon wind field is determined, wherein the preset proportion can be set according to actual needs, such as 100 times, etc., and when the average coefficient is 0.4, the adjustment value is 40;

[0126] Based on the adjustment value of the typhoon data of the typhoon wind field, the typhoon data of the current moment typhoon wind field is adjusted to obtain the typhoon data for constructing the typhoon wind field at the next moment, such as when it is in the strengthening period, the wind speed is increased by 40, and the like is not repeated.

[0127] The system to which the above scheme is applied is as followsFigure 3 As shown in the above, the step algorithm is set in the system, specifically, the typhoon wind field and gale mode data are collected and stored through the numerical simulation database, after the relevant meteorological data are collected by the data collection program, the data are processed, converted, handled and stored into the data source through the data processing conversion program, the data source provides data services to the outside through the sharing and database access interface, the application component part reads the data and processes the data to produce fine gale forecast products and stores them into the product library; the business application part provides data retrieval function to retrieve various data for graphical display and interaction, and displays and publishes the generated text products, specifically, combined with the predicted data and cloud chart data, radar data, the typhoon circulation in the high-resolution numerical mode is tracked by using the wind field and pressure field, the current numerical mode forecast typhoon path and the corresponding typhoon intensity are obtained, and the mode path input is provided for the typhoon fine gale correction module, the target area typhoon path forecast is reduced in scale, the 3-hour resolution typhoon subjective forecast path is obtained, which is consistent with the time resolution of the numerical mode forecast typhoon path; the typhoon in the numerical mode forecast grid wind field 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 is superimposed into the background wind field to form a new grid forecast wind field, when the typhoon approaches the land or island, the wind field circulation is corrected according to the terrain friction system, and the typhoon in each stage is displayed on the display interface, wherein the typhoon path display component adopts the interface display style of MICAPS4, uses the default font provided by MICAPS4 to draw, and uses the graphic drawing method provided by MICAPS.

[0128] In the typhoon wind field intelligent forecasting method based on multi-source data fusion, the method comprises the following steps: determining the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to acquire historical typhoon related multi-source data of a target region in parallel by using multiple threads; determining a typhoon data set of multiple development stages in a whole life cycle of a target typhoon based on the historical typhoon related multi-source data of the target region, wherein each development stage corresponds to a typhoon data set, and each typhoon data set comprises typhoon data corresponding to multiple time nodes; aligning time nodes of multiple target typhoons of the same type to determine target development stage standard parameters corresponding to multiple same development stages; determining typhoon data used for constructing a typhoon wind field at a next moment based on a comparison result of real-time parameters of the target region at a target development stage and the target development stage standard parameters and a parameter offset coefficient of other development stages before the target development stage; constructing a typhoon wind field at the next moment according to the typhoon data used for constructing 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 queue and dynamic time alignment provided in the application effectively solves the time scale difference problem of different typhoon life cycles by combining multi-source data fusion and intelligent computing technology, thereby significantly improving the accuracy and efficiency of typhoon wind field forecasting and providing more reliable technical support for typhoon disaster prevention decision-making.

[0129] It should be understood that, although Figures 2-3 the steps in the flowchart of the method are shown in sequence according to the arrows, these steps are not necessarily executed in sequence according to the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, Figures 2-3 at least part of the steps in the method can comprise multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0130] In one embodiment, as shown in Figure 4 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:

[0131] The data acquisition module is configured to determine the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to acquire historical typhoon related multi-source data of a target region in parallel by using multiple threads.

[0132] a data set determination module configured to determine a typhoon data set of each development stage in a whole life cycle of a target typhoon based on historical typhoon-related multi-source data of the target area, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to a plurality of time nodes;

[0133] an alignment module configured to align time nodes of a plurality of target typhoons of the same type to determine a target development stage standard parameter corresponding to a plurality of same development stages;

[0134] a typhoon data determination module configured to determine a real-time parameter of the target area in a target development stage, determine typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameter of the target development stage and the target development stage standard parameter and a parameter offset coefficient of other development stages before the target development stage;

[0135] a typhoon wind field construction module configured to construct a typhoon wind field at a next time according to the typhoon data for constructing the typhoon wind field at the next time, and display the typhoon wind field at the next time.

[0136] As a preferred embodiment, in the embodiment of the present application, the data acquisition module is specifically configured to:

[0137] determine a typhoon data priority dynamic division index;

[0138] determine the priority of the different kinds of typhoon data based on the typhoon data priority dynamic division index;

[0139] determine the number of parallel threads according to the priority division result of the different kinds of typhoon data;

[0140] start a plurality of threads based on the number of parallel threads, and acquire the historical typhoon-related multi-source data of the target area in parallel by using the plurality of threads.

[0141] As a preferred embodiment, in the embodiment of the present application, the data set determination module is specifically configured to:

[0142] determine that the whole life cycle of the target typhoon includes a plurality of development stages, and the development stages include a generation stage, an enhancement stage, a mature stage and a decay stage;

[0143] determine key influence factors and typhoon wind field characteristics of each development stage;

[0144] generate the typhoon data set of each development stage in the whole life cycle of the target typhoon based on a mapping relationship between the key influence factors and the typhoon wind field characteristics of each development stage.

[0145] The specific limitations of the typhoon wind field intelligent forecasting device based on multi-source data fusion can refer to the limitations of the typhoon wind field intelligent forecasting method based on multi-source data fusion described above, and will not be repeated here. Each module in the typhoon wind field intelligent forecasting device based on multi-source data fusion described above can be realized by software, hardware and their combination in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to each module by the processor.

[0146] In one embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 5 The computer device includes a processor, a memory, a network interface, a display screen and an input device connected by 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 operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a typhoon wind field intelligent forecasting method based on multi-source data fusion. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0147] Those skilled in the art can understand that Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0148] In one embodiment, a computer device is provided, which includes a memory, a processor and a computer program stored in the memory and executable on the processor. The processor implements the following steps when executing the computer program:

[0149] S1: Based on a multi-priority queue mechanism, the priority of different kinds of typhoon data is determined to acquire multi-source data related to historical typhoons in a target area in parallel by using multiple threads;

[0150] S2: determining a typhoon data set of a plurality of development stages in a whole life cycle of a 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 a plurality of time nodes;

[0151] S3: aligning time nodes of a plurality of target typhoons of the same type to determine a target development stage standard parameter corresponding to a plurality of same development stages;

[0152] S4: determining a real-time parameter of the target area at a target development stage, determining typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameter of the target development stage and the target development stage standard parameter and a parameter offset coefficient of other development stages before the target development stage;

[0153] S5: constructing a typhoon wind field at a next time according to the typhoon data for constructing the typhoon wind field at the next time, and displaying the typhoon wind field at the next time.

[0154] In one embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the following steps:

[0155] S1: determining priorities of different kinds of typhoon data based on a multi-priority queue mechanism to acquire multi-source data related to historical typhoons in a target area in parallel by using a plurality of threads;

[0156] S2: determining a typhoon data set of a plurality of development stages in a whole life cycle of a 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 a plurality of time nodes;

[0157] S3: aligning time nodes of a plurality of target typhoons of the same type to determine a target development stage standard parameter corresponding to a plurality of same development stages;

[0158] S4: determining a real-time parameter of the target area at a target development stage, determining typhoon data for constructing a typhoon wind field at a next time based on a comparison result of the real-time parameter of the target development stage and the target development stage standard parameter and a parameter offset coefficient of other development stages before the target development stage;

[0159] S5: constructing a typhoon wind field at a next time according to the typhoon data for constructing the typhoon wind field at the next time, and displaying the typhoon wind field at the next time.

[0160] In one embodiment, a computer program product is provided, which comprises a computer program, which, when executed by a processor, implements the following steps:

[0161] S1: determining the priority of different kinds of typhoon data based on a multi-priority queue mechanism, to acquire the historical typhoon related multi-source data of a target area in parallel by using multiple threads;

[0162] S2: determining a typhoon data set of multiple development stages in the whole life cycle of a target typhoon based on the historical typhoon related multi-source data of the target area, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes the typhoon data corresponding to multiple time nodes;

[0163] S3: 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;

[0164] S4: determining the real-time parameters of the target area at the target development stage, determining the typhoon data for constructing the typhoon wind field at the next moment based on the comparison result of the real-time parameters of the target development stage and the target development stage standard parameters, and the parameter offset coefficient of other development stages before the target development stage;

[0165] S5: constructing the typhoon wind field at the next moment according to the typhoon data for constructing the typhoon wind field at the next moment, and displaying the typhoon wind field at the next moment.

[0166] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0167] The technical features of the above embodiments can be combined in any way. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0168] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope 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: determining the priority of different kinds of typhoon data based on a multi-priority queue mechanism to acquire historical typhoon related multi-source data of a target area in parallel by using multiple threads; based on the historical typhoon related multi-source data of the target area, determining a typhoon data set of multiple development stages in the whole life cycle of a 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 target development stage standard parameters corresponding to multiple same development stages; determining the real-time parameters of the target area at the target development stage, determining the typhoon data for constructing the typhoon wind field of the next moment based on the comparison result of the real-time parameters of the target development stage and the target development stage standard parameters, and the parameter offset coefficient of other development stages before the target development stage; constructing the typhoon wind field of the next moment according to the typhoon data for constructing the typhoon wind field of the next moment, and displaying the typhoon wind field of the next moment; determining the priority of different kinds of typhoon data based on a multi-priority queue mechanism to acquire historical typhoon related multi-source data of a target area in parallel by using multiple threads comprises: determining a typhoon data priority dynamic division index; determining the priority of different kinds of typhoon data based on the typhoon data priority dynamic division index; determining the number of parallel threads according to the priority division result of the different kinds of typhoon data; starting multiple threads based on the number of parallel threads, and acquiring historical typhoon related multi-source data of a target area in parallel by using the multiple threads; determining a typhoon data set of multiple development stages in the whole life cycle of a target typhoon based on the historical typhoon related multi-source data of the target area comprises: determining that the whole life cycle of the target typhoon includes multiple development stages, and the development stages include a generation stage, an enhancement stage, a mature stage and a decay stage; determining the key influencing factors and typhoon wind field characteristics of each development stage; generating a typhoon data set of multiple development stages in the whole life cycle of the target typhoon based on the mapping relationship between the key influencing factors and typhoon wind field characteristics of each development stage; aligning the time nodes of multiple target typhoons of the same type comprises: determining the third time length of each development stage according to the first time length of the whole life cycle of multiple target typhoons of the same type and the second time length corresponding to each same development stage within the whole life cycle, wherein the third time length is used to describe the alignment time length, comprising: wherein, represents a first time length, represents a second time length, represents a target number of typhoons, represents an original time length of a full life cycle of a typhoon, represents an original time length of a same development stage in a plurality of full life cycles, represents a third time length;​ in response to within a first preset range, the second time length is defined as the third time length; in response to If the second time length 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. aligning the time nodes of the multiple target typhoons of the same type according to the third time length of each development stage. 2.The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 1, characterized in that, determining target development stage standard parameters corresponding to multiple same development stages comprises: determining the key influencing factors of the target development stage for generating typhoon wind field characteristics, and the probability value of each key influencing factor for causing the generation of typhoon wind field characteristics; determining the target development stage standard parameters based on the probability value of each key influencing factor for causing the generation of typhoon wind field characteristics. 3.The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 2, characterized in that, The target development stage standard parameter is determined based on the probability value of each key influence factor leading to generation of a typhoon wind field feature, and the target development stage standard parameter includes: The probability value of each key influence factor leading to generation of a typhoon wind field feature is obtained; The target development stage standard parameter is determined based on the probability value of each key influence factor leading to generation of a typhoon wind field feature, and the target development stage standard parameter includes: wherein, denotes a target development stage standard parameter, denotes a correction parameter, denotes a correlation coefficient, denotes a number of time nodes, denotes a probability value of the jth key influencing factor, denotes a number of key influencing factors, denotes a total sea surface temperature value corresponding to the xth time node, denotes a total dynamic value corresponding to the xth time node, denotes a total ocean heat content value corresponding to the xth time node, denotes a total friction value corresponding to the xth time node, denotes a total value of the dth bias term at the xth time node, denotes a number of bias terms, , , , and all denote weight coefficients.

4. The typhoon wind field intelligent forecasting method based on multi-source data fusion according to claim 3, characterized in that, Based on the comparison result of the real-time parameter of the target development stage and the target development stage standard parameter, and the parameter offset coefficient of other development stages before the target development stage, the typhoon data for constructing a typhoon wind field at the next moment is determined, including: The real-time parameter of the target development stage is obtained, and the time node to which the real-time parameter belongs is quantized; According to the quantization result, 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; In response to the comparison result being less than a preset threshold, the parameter offset coefficient of other development stages before the target development stage is obtained, and the parameter offset coefficient is used to describe the absolute value of the difference between the real-time parameter and the standard parameter; An average coefficient of a plurality of parameter offset coefficients is determined; According to the average coefficient and a preset proportion, an adjustment value of the typhoon data of the typhoon wind field is determined; Based on the adjustment value of the typhoon data of the typhoon wind field, the typhoon data of the current moment typhoon wind field is adjusted to obtain the typhoon data for constructing a typhoon wind field at the next moment.

5. A typhoon wind field intelligent forecasting device based on multi-source data fusion, characterized in that, The device includes: A data acquisition module is configured to determine the priority of different types of typhoon data based on a multi-priority queue mechanism, so as to acquire historical typhoon-related multi-source data of a target area in parallel by using multiple threads. A data set determination module is configured to determine a typhoon data set of a plurality of development stages in a full life cycle of a target typhoon based on the historical typhoon-related multi-source data of the target area, wherein each development stage corresponds to a typhoon data set, and each typhoon data set includes typhoon data corresponding to a plurality of time nodes. An alignment module is configured to align time nodes of a plurality of target typhoons of the same type to determine target development stage standard parameters corresponding to a plurality of same development stages. A typhoon data determination module is configured to determine a real-time parameter of the target area at a target development stage, and determine typhoon data for constructing a typhoon wind field at the next moment based on a comparison result of the real-time parameter of the target development stage and the target development stage standard parameter, and a parameter offset coefficient of other development stages before the target development stage. A typhoon wind field construction module is configured to construct a typhoon wind field at the next moment based on the typhoon data for constructing the typhoon wind field at the next moment, and display the typhoon wind field at the next moment. Determination of the priority of different types of typhoon data based on a multi-priority queue mechanism to acquire historical typhoon-related multi-source data of a target area in parallel by using multiple threads includes: A typhoon data priority dynamic division index is determined. The priority of the different types of typhoon data is determined based on the typhoon data priority dynamic division index. The number of parallel threads is determined according to the priority division result of the different types of typhoon data. Based on the number of parallel threads, a plurality of threads are started, and the plurality of threads are used to acquire historical typhoon-related multi-source data of the target area in parallel; Based on the historical typhoon-related multi-source data of the target area, the target typhoon data set of a plurality of development stages in the whole life cycle of the target typhoon is determined, including: It is determined that the target typhoon whole life cycle includes a plurality of development stages, and the development stages include a generation stage, an enhancement stage, a mature stage and a decay stage; Determine the key influencing factors and typhoon wind field characteristics of each development stage; Based on the mapping relationship between the key influencing factors and the typhoon wind field characteristics of each development stage, the target typhoon data set of a plurality of development stages in the whole life cycle of the target typhoon is generated; Aligning the time nodes of a plurality of target typhoons of the same type includes: According to the first time length of the whole life cycle of a plurality of target typhoons of the same type and the second time length corresponding to each same development stage in a plurality of whole life cycles, the third time length of each development stage is determined, which is used to describe the alignment time length, including: wherein, represents a first time length, represents a second time length, represents a target number of typhoons, represents an original time length of a full life cycle of a typhoon, represents an original time length of a same development stage in a plurality of full life cycles, represents a third time length;​ in response to within a first preset range, the second time length is defined as the third time length; in response to If the second time length 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 plurality of target typhoons of the same type are aligned.

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