CN117388951A - Plum rain prediction method, apparatus and equipment - Google Patents

Plum rain prediction method, apparatus and equipment Download PDF

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Publication number
CN117388951A
CN117388951A CN202311181166.3A CN202311181166A CN117388951A CN 117388951 A CN117388951 A CN 117388951A CN 202311181166 A CN202311181166 A CN 202311181166A CN 117388951 A CN117388951 A CN 117388951A
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China
Prior art keywords
historical
year
plum
target
rain
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Inventor
李飒
逯智科
闫姝
刘鑫
王绍民
魏守成
胡世铭
张俊伟
黄婷婷
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Huaneng Fujian Energy Development Co ltd
Huaneng Clean Energy Research Institute
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Huaneng Fujian Energy Development Co ltd
Huaneng Clean Energy Research Institute
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Priority to CN202311181166.3A priority Critical patent/CN117388951A/en
Publication of CN117388951A publication Critical patent/CN117388951A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services

Abstract

The application provides a method, a device and equipment for predicting plum rain. The method comprises the steps of obtaining determinable early Nino index in the history and obtaining a historical plum rain characteristic data set; the correlation between the determinable el Nino index in each history and each plum-rain characteristic in the historical plum-rain characteristic data set corresponding to the next year of the year in which the el Nino index is positioned is analyzed one by one to obtain el Nino index with strong correlation and plum-rain characteristics; establishing an el Nino index and meretrix characteristic model according to the el Nino index and meretrix characteristic with strong correlation; the el Nino index is input to the el Nino index and the merry characteristic model to predict the merry characteristic data set of the next year of the year in which the input el Nino index is located. Therefore, a mode for predicting the characteristic of the plum rain by using the relation model of the el Nino index and the characteristic of the plum rain is provided, and the efficiency and the accuracy for predicting the characteristic of the plum rain are improved.

Description

Plum rain prediction method, apparatus and equipment
Technical Field
The application relates to the technical field of big data analysis, in particular to a method, a device and equipment for predicting plum rain.
Background
Plum rain is a product in the seasonal adjustment process of atmospheric flows, and has important influence on river basin precipitation. Plum rain characteristics, particularly abnormal plum rain, are important in reducing drought and flood disasters. Sea temperature changes, particularly the early Nino and southern billows phenomenon (ENSO) events, as background fields for changes in atmospheric circulation, have a significant impact on the activity of plum rain.
In the related art, the plum rain feature is determined mainly according to the rainfall, the rainy day and the high-pressure position of the subtropical zone of the meteorological ground observation station, the prediction of the plum rain feature depends on subjective analysis of a weather table predictor, however, the method for predicting the plum rain feature based on the weather table predictor depends on judgment of the weather table predictor seriously, the prediction result of the plum rain feature may not be objective, and the prediction of the plum rain feature depends on manpower, so that the efficiency of the prediction of the plum rain feature is low.
Disclosure of Invention
Therefore, an object of the present application is to provide a method, a device and equipment for predicting plum rain.
According to an embodiment of the first aspect of the present application, there is provided a method for predicting plum rain, including: acquiring a historical plum rain feature data set of a target area in a plurality of historical years, wherein the historical plum rain feature data set comprises a plurality of plum rain features; acquiring the el nino index of a target ocean area corresponding to the plum rain fall of the target area in a plurality of historical years; for each historical year, analyzing the correlation between the el nino index corresponding to the historical year and each of the plum-rain features in the historical plum-rain feature dataset corresponding to the next year of the historical year one by one to acquire a target plum-rain feature with strong correlation with the el nino index corresponding to the historical year from the historical plum-rain feature dataset corresponding to the next year; establishing an el Nino index and plum rain characteristic relation model according to the el Nino index corresponding to each historical year and the corresponding target plum rain characteristic; and inputting the el Nino index corresponding to the target ocean area in the target year into the el Nino index and plum rain characteristic relation model so as to predict the plum rain characteristic data set of the target area in the next year of the target year.
Optionally, the acquiring the el nino index corresponding to the historical years for the target ocean area related to the precipitation of plum at the target area includes: acquiring a target ocean area related to the plum rain fall of the target area; acquiring sea surface temperature data corresponding to the target ocean area in a plurality of historical years; for each historical year, determining the el nino index corresponding to the historical year of the target ocean area according to the sea surface temperature data corresponding to the historical year of the target ocean area.
Optionally, for each historical year, determining, according to sea surface temperature data corresponding to the historical year for the target ocean area, an el nino index corresponding to the historical year for the target ocean area includes: for each historical year, determining the sea temperature level of the target ocean area corresponding to the historical year according to the sea surface temperature data of the target ocean area corresponding to the historical year; and determining the el Nino index corresponding to the historical year of the target ocean area according to the sea temperature level corresponding to the historical year of the target ocean area.
Optionally, the acquiring the historical plum rain feature data set of the target area in a plurality of historical years includes: acquiring ground precipitation observation data corresponding to the target area in a plurality of historical years; for each historical year, determining a plurality of plum rain features corresponding to the historical year of the target area according to ground precipitation observation data corresponding to the historical year of the target area; and according to a plurality of plum rain characteristics corresponding to the target area in the historical year, establishing a historical plum rain characteristic set corresponding to the target area in the historical year.
Optionally, for each historical year, the analyzing the correlation between the el nino index corresponding to the historical year and each of the plum features in the historical plum feature dataset corresponding to the next year of the historical year one by one to obtain, from the historical plum feature dataset corresponding to the next year, a target plum feature having a strong correlation with the el nino index corresponding to the historical year includes: for each historical year, acquiring a historical plum blossom rain characteristic data set corresponding to the next year of the historical year; determining a rank correlation coefficient between the el nino index corresponding to the historical year and each of the plum rain features in the historical plum rain feature dataset corresponding to the next year, respectively; and taking the plum rain characteristic with the rank correlation coefficient larger than a preset correlation coefficient threshold value in the historical plum rain characteristic data set corresponding to the next year as a target plum rain characteristic with strong correlation of the el Nino index corresponding to the historical year.
Optionally, after the target merany feature is multiple, the method further includes, after establishing a model of a relationship between the el nino index and the merany feature according to the el nino index corresponding to each historical year and the corresponding target merany feature: for each target plum-rain feature, determining a statistical relationship between the el-Ninuo index and the target plum-rain feature according to the el-Ninuo index corresponding to each historical year and the corresponding target plum-rain feature; selecting an optimal target regression method from a plurality of preset regression methods according to the statistical relationship between the el Nino index and the target plum rain characteristic; according to the target regression method, carrying out prediction verification on the statistical relationship between the el Nino index and the target plum rain feature to obtain a prediction verification result; and perfecting the el Nino index and the plum rain feature model according to the prediction verification result.
Optionally, the establishing the relationship model between the el nino index and the merry characteristic according to the el nino index corresponding to each historical year and the corresponding target merry characteristic includes: inputting the corresponding el Nino index of each historical year into an initial el Nino index and merry characteristic model, obtaining a predicted plum rain feature data set corresponding to the next year corresponding to the historical year; and training the initial el Nino index and the plum rain feature model according to the plum rain feature data set corresponding to the next year and the predicted plum rain feature data set to obtain the el Nino index and the plum rain feature model.
According to the plum-rain prediction method of the embodiment of the application, in the process of predicting the plum-rain characteristics corresponding to the next year of the target year, the correlation analysis is performed on the basis of the el-Nino index corresponding to each historical year in the target area and the obtained historical plum-rain characteristic data set corresponding to the historical year and the historical plum-rain characteristic data set corresponding to the next year, the method comprises the steps of obtaining the early Ninuo index and the plum rain feature with strong correlation, establishing an early Ninuo index and the plum rain feature model according to the early Ninuo index and the plum rain feature with strong correlation, and inputting the early Ninuo index corresponding to the target ocean area in the target year into the early Ninuo index and the plum rain feature relation model so as to predict the plum rain feature data set of the target area in the next year of the target year. Therefore, a mode for predicting the plum-rain characteristics by using the relation model of the el Nino index and the plum-rain characteristics is provided, objective prediction of the plum-rain characteristics of the next year of the corresponding year is facilitated, and efficiency and accuracy of predicting the plum-rain characteristics are improved.
According to a second aspect of the present application, there is provided an apparatus for predicting plum rain, comprising: the first acquisition module is used for acquiring a historical plum rain feature data set of a target area in a plurality of historical years, wherein the historical plum rain feature data set comprises a plurality of plum rain features; the second acquisition module is used for acquiring the el Nino indexes of the target ocean area corresponding to the plum rain drop in the target area in a plurality of historical years; the analysis module is used for analyzing the correlation between the early Ninuo index corresponding to each historical year and each plum-rain characteristic in the historical plum-rain characteristic data set corresponding to the next year of the historical year one by one so as to acquire a target plum-rain characteristic with strong correlation with the early Ninuo index corresponding to the historical year from the historical plum-rain characteristic data set corresponding to the next year; the building module is used for building a relation model of the el Nino index and the plum rain feature according to the el Nino index corresponding to each historical year and the corresponding target plum rain feature; and the prediction module is used for inputting the el Nino index corresponding to the target ocean area in the target year into the el Nino index and plum rain characteristic relation model so as to predict the plum rain characteristic data set of the target area in the next year of the target year.
According to an embodiment of a third aspect of the present application, there is provided an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor is used for realizing the plum rain prediction method disclosed by the embodiment of the application when executing the program.
In the plum-rain prediction method and device provided by the embodiment of the application, in the process of predicting the plum-rain characteristics corresponding to the next year of the target year, the correlation analysis is performed on the el nino index corresponding to each historical year and the historical plum-rain characteristic data set corresponding to the next year based on the el nino index corresponding to each historical year and the obtained historical plum-rain characteristic data set of the target area, the method comprises the steps of obtaining the early Ninuo index and the plum rain feature with strong correlation, establishing an early Ninuo index and the plum rain feature model according to the early Ninuo index and the plum rain feature with strong correlation, and inputting the early Ninuo index corresponding to the target ocean area in the target year into the early Ninuo index and the plum rain feature relation model so as to predict the plum rain feature data set of the target area in the next year of the target year. Therefore, a mode for predicting the plum-rain characteristics by using the relation model of the el Nino index and the plum-rain characteristics is provided, objective prediction of the plum-rain characteristics of the next year of the corresponding year is facilitated, and efficiency and accuracy of predicting the plum-rain characteristics are improved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
Fig. 1 is a schematic flow chart of a method for predicting merry-go-round according to an embodiment of the present disclosure;
fig. 2 is a second flow chart of a method for predicting merry-go-round according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a method for predicting merry-go-round according to an embodiment of the present disclosure;
fig. 4 is a flow chart diagram of a method for predicting merry-go-round according to an embodiment of the present disclosure;
FIG. 5 is a flow chart of training the model of the relationship between the el Nino index and the plum rain characteristics according to the embodiment of the present application;
fig. 6 is a schematic structural diagram of a merry-go-round prediction apparatus according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of another apparatus for predicting merry-rain according to an embodiment of the present application
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The technical scheme of the application is to acquire, store, use, process and the like data, which all meet the relevant regulations of national laws and regulations.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for predicting merry-go-round according to an embodiment of the present application.
As shown in fig. 1, the method comprises the steps of:
step 101, acquiring a historical plum rain feature data set of a target area in a plurality of historical years, wherein the historical plum rain feature data set comprises a plurality of plum rain features 。
It should be noted that, the plum rain prediction method provided in the embodiments of the present application may be performed by a plum rain prediction device, where the plum rain prediction device may be implemented by software and/or hardware. The plum blossom predicting device may be an electronic device or may be configured in an electronic device. The embodiment of the application will be described by taking an example in which the plum rain prediction method is configured in an electronic device.
The electronic device may be any device with computing capability, for example, may be a personal computer, a mobile terminal, a server, etc., and the mobile terminal may be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, etc., which have various operating systems, touch screens, and/or display screens.
It is to be understood that the target area may be determined according to the actual requirement of the staff, and the embodiment is not particularly limited.
The target region may be any region where the occurrence of the plum rain phenomenon occurs, for example, the target region may be a region where the occurrence of the plum rain phenomenon occurs in a certain country.
In this example, the historical year may be a year prior to the target year, e.g., the target year is the current year, and correspondingly, the historical year may be a year prior to the current year.
Plum rain, a phenomenon of continuous overcast and overcast occurring every year, in which the date of entering and exiting plum, the length of the plum rain period, the precipitation amount, etc. are important characteristics of plum rain.
In this example, a historical plum rain feature dataset is obtained from a plurality of plum rain features corresponding to the occurrence of plum rain phenomena in the target region over the historical year.
Among other things, it is understood that the historical plum rain feature dataset may include, but is not limited to, a historical plum rain precipitation feature, a historical plum in/out time interval flat feature, and a historical plum rain duration feature.
Step 102, acquiring the el nino index corresponding to the historical years in the target ocean area related to the plum rain drop in the target area.
In some examples, sea surface temperature data corresponding to a target ocean area in a plurality of historical years may be obtained, the years in which the ENSO event occurs in the plurality of historical years may be determined according to the corresponding sea surface temperature data, and analysis and calculation may be performed on the ENSO signal in the years in which the ENSO event occurs to obtain the corresponding early Nino index of the target ocean area in the plurality of historical years.
Among other things, it is understood that the precipitation of plum rain in a target area may be related to the occurrence of an ENSO event in a target marine area.
Step 103, for each historical year, analyzing the correlation between the el nino index corresponding to the historical year and each of the plum-rain features in the historical plum-rain feature dataset corresponding to the next year of the historical year one by one, so as to obtain the target plum-rain feature with strong correlation with the el nino index corresponding to the historical year from the historical plum-rain feature dataset corresponding to the next year.
Among other things, it is understood that the el nino index is related to a number of merjerushing characteristics in the next year of the historical year in which it resides.
In this example, for each historical year, the correlation between the el nino index corresponding to the historical year and the historical plum-rain characteristic dataset corresponding to the next year of the historical year, respectively, is analyzed, the correlation between the corresponding historical plum-in/out interval flat characteristics and the correlation between the corresponding historical plum-rain duration characteristics are analyzed, to obtain the target plum-rain characteristics with a strong correlation between the el nino index corresponding to the historical year and the historical plum-rain characteristic dataset corresponding to the next year, the historical plum-in/out interval flat, and the historical plum-rain duration characteristics.
Correlation coefficients are used to study the amount of correlation between variables.
In this example, the correlation coefficient between the corresponding el nino index for the historical year and the corresponding historical mermaid precipitation characteristics, the corresponding historical mermaid in/out interval flat characteristics and the corresponding historical mermaid duration characteristics may be used to determine the target mermaid characteristics for which there is a strong correlation with the el nino index for each historical year.
For example, each of the historical merjer characteristics in the historical merjer characteristics dataset corresponding to the historical year and corresponding to the next year of the historical year is analytically calculated by a Spearman (Spearman) model to obtain a rank correlation coefficient between the historical merjer precipitation characteristics in the historical year corresponding to the el nino index and the historical merjer characteristics dataset corresponding to the next year, the corresponding historical merjer in/out time interval flat characteristics, and the corresponding historical merjer duration characteristics.
In this example, for each historical year, a historical merry feature dataset corresponding to the next year of the historical year is obtained, a correlation coefficient between the el nino index corresponding to the historical year and each merry feature in the historical merry feature dataset corresponding to the next year is determined, and a merry feature in the historical merry feature dataset corresponding to the next year, the merry feature having a correlation coefficient greater than a preset correlation coefficient threshold, is taken as a target merry feature having a strong correlation with the el nino index corresponding to the historical year.
It can be understood that the preset correlation coefficient threshold is preset by a worker according to actual requirements, and is not limited herein.
And 104, establishing a relation model of the el Nino index and the plum rain characteristic according to the el Nino index corresponding to each historical year and the corresponding target plum rain characteristic.
In some examples, a statistical analysis of the corresponding el nino index and the corresponding target merry characteristics for each historical year is performed to build an el nino index and merry characteristic relationship model.
Step 105, inputting the el nino index corresponding to the target ocean area in the target year into the el nino index and the merry characteristic relation model to predict the merry characteristic data set of the target area in the next year of the target year.
It will be appreciated that the el nino index and the merry characteristic relationship model are established prior to inputting the el nino index corresponding to the target ocean area at the target year into the el nino index and merry characteristic relationship model.
Among these, it is understood that the predicted target region includes a plum rain precipitation amount feature, a plum in/out time interval level feature, and a plum rain duration feature in the plum rain feature dataset of the next year of the target year.
The target year may be any one of the predetermined years, for example, the target year may be the currently corresponding year, that is, the target year may be the year corresponding to the present year.
For example, the year of interest is the year of interest, and the el nino index of the ocean area of interest in the year of interest is input to the el nino index and the merry characteristic relation model to predict the merry characteristic data set of the ocean area of interest in the next year.
In this embodiment, after determining the characteristic data set of the plum rain in the target area in the next year based on the el nino index and the characteristic relation model of the plum rain, the service forecast guiding advice corresponding to the determined non-characteristic data set may be obtained, so as to provide guiding advice for crop planting in the target area, urban disaster prevention, disaster reduction, and the like.
In the plum-rain prediction method provided by the embodiment of the application, in the process of predicting the plum-rain characteristics corresponding to the next year of the target year, the correlation analysis is performed on the el-Nino index corresponding to each historical year and the historical plum-rain characteristic data set corresponding to the next year based on the el-Nino index corresponding to each historical year and the obtained historical plum-rain characteristic data set of the target area, the method comprises the steps of obtaining the early Ninuo index and the plum rain feature with strong correlation, establishing an early Ninuo index and the plum rain feature model according to the early Ninuo index and the plum rain feature with strong correlation, and inputting the early Ninuo index corresponding to the target ocean area in the target year into the early Ninuo index and the plum rain feature relation model so as to predict the plum rain feature data set of the target area in the next year of the target year. Therefore, a mode for predicting the plum-rain characteristics by using the relation model of the el Nino index and the plum-rain characteristics is provided, objective prediction of the plum-rain characteristics of the next year of the corresponding year is facilitated, and efficiency and accuracy of predicting the plum-rain characteristics are improved.
Based on the above-described embodiments, in order to determine whether there is a strong correlation between the el nino index and each of the plum-rain features in the historical plum-rain feature dataset corresponding to the next year of the year in which it is located, a description will first be given of how each of the plum-rain features in the historical plum-rain feature dataset is acquired, and a method of this embodiment is further exemplarily described below with reference to fig. 2.
Fig. 2 is a second flowchart of a method for predicting merry-go-round according to an embodiment of the present application.
As shown in fig. 2, the method may include:
step 201, obtaining ground precipitation observation data corresponding to a plurality of historical years in a target area.
It will be appreciated that ground precipitation observation data may be obtained directly from a weather observation station of a worker in a target area, and is not limited herein.
The ground precipitation observation data may include, but is not limited to, precipitation amount data, precipitation time data, and precipitation duration data of the target area over a plurality of historical years.
Step 202, determining a plurality of plum rain features corresponding to the historical year of the target area according to ground precipitation observation data corresponding to the historical year of the target area for each historical year.
In some examples, the ground precipitation observation data corresponding to the historical year is characterized according to the ground precipitation observation data of the target area, and a plurality of plum rain features corresponding to the historical year can be determined, wherein the plurality of plum rain features comprise a plum rain precipitation amount feature, a plum in/out time interval flat feature and a plum rain duration feature.
It should be noted that, for each historical year, average calculation may be performed on the plum-in/out time features corresponding to the historical year to obtain average plum-in/out time, and difference calculation may be performed on each plum-in/out time feature corresponding to the historical year and the average plum-in/out time to obtain a plum-in/out time interval flat feature.
It is understood that, according to the plum-entering/exiting time interval flat characteristic, the difference between the plum-entering/exiting time of the falling plum rain in the target area and the average plum-entering/exiting time in the target area can be known in detail.
Step 203, according to the multiple plum rain features corresponding to the target area in the historical year, a historical plum rain feature set corresponding to the target area in the historical year is established.
In this example, the historical plum rain feature set corresponding to the target address in the historical year may be established based on the historical plum rain precipitation amount feature corresponding to the target region in the historical year, the historical plum in/out time interval flat feature, and the historical plum rain duration feature.
Wherein it is understood that each of the historical years has a corresponding one of the historical merry-go-round feature sets.
In this example, in the process of acquiring the historical plum blossom rain feature data set, ground precipitation observation data corresponding to a plurality of historical years in a target region is acquired first, and for each historical year, feature analysis is performed on the ground precipitation observation data corresponding to the historical years in the target region to determine a plurality of plum blossom rain features corresponding to the historical years in the target region, and according to the plurality of plum blossom rain features corresponding to the historical years in the target region, a historical plum blossom rain feature set corresponding to the historical years in the target region is further established. Therefore, in the process of acquiring the historical plum-rain characteristic data set, the detailed characteristic of the falling plum-rain in the target area can be acquired, so that the accuracy of the el Nino index and the prediction of the plum-rain characteristic model is improved.
In one embodiment of the present application, for a clear understanding of the process of obtaining the el nino index for the target ocean area associated with the precipitation of plum rain in the target area over a plurality of historical years, one possible implementation of obtaining the el nino index for the target ocean area associated with the precipitation of plum rain in the target area over a plurality of historical years is described below with reference to fig. 3.
Fig. 3 is a flowchart III of a method for predicting the plum rain according to an embodiment of the present application.
As shown in fig. 3, the method comprises the steps of:
step 301, a target marine area associated with precipitation of plum in a target area is acquired.
Wherein it is understood that the target ocean area refers to an ocean area associated with rainfall in the target area.
Step 302, sea surface temperature data corresponding to a target ocean area in a plurality of historical years is obtained.
Wherein the sea surface temperature data comprises sea surface temperature.
Step 303, determining the sea temperature level of the target ocean area corresponding to the historical year according to the sea temperature data of the target ocean area corresponding to the historical year for each historical year.
The sea temperature is flat, and the degree of deviation between the sea temperature at a certain place or a certain period and the average sea temperature at the place or the period.
In this example, average value calculation is performed on sea surface temperature data corresponding to the historical year of the target ocean area to obtain average sea surface temperature of the target ocean area, and difference value calculation is performed on sea surface temperature data corresponding to the historical year of the target ocean area and average sea surface temperature of the target ocean area to obtain sea temperature range corresponding to the historical year of the target ocean area.
And step 304, determining the el Nino index corresponding to the historical year of the target ocean area according to the sea temperature level corresponding to the historical year of the target ocean area.
In some examples, after determining that there is a strong correlation of the target mermaid feature with the el nino index corresponding to the historical year, in the case where there are a plurality of strong correlation of the target mermaid features, in order to improve the accuracy of the prediction of the mermaid feature of the established el nino index and mermaid feature relationship model, the correlation between the el nino index corresponding to the historical year and the target mermaid feature may be verified multiple times, thereby perfecting the el nino index and the mermaid feature relationship model.
In one embodiment of the present application, the model of the el nino index versus merrain characteristics is refined in order to make clear what is known. The plum rain prediction method of this embodiment is further exemplarily described below with reference to fig. 4.
Fig. 4 is a flowchart of a method for predicting merry-go-round according to an embodiment of the present application.
As shown in fig. 4, the method comprises the steps of:
step 401, determining a statistical relationship between the el nino index and the target merjer characteristic according to the el nino index corresponding to each historical year and the corresponding target merjer characteristic for each target merjer characteristic.
The statistical relationship refers to a non-one-to-one correspondence between the el nino index and the target merry-go-round, but will vary within a certain range according to a certain rule.
In this example, the statistical relationship between the el Nino index and the target merjer signature may be determined from the correlation coefficients between the el Nino index corresponding to each historical year and the corresponding target merjer signature.
It is to be understood that, for a specific description of determining the correlation coefficient between the corresponding el nino index and the corresponding target merrain characteristic for each historical year, reference may be made to the description of the embodiment of the present application, which is not repeated herein.
Step 402, selecting an optimal target regression method from a plurality of preset regression methods according to the statistical relationship between the el nino index and the target merry rain characteristic.
It is understood that the preset multiple regression methods are preset in advance by a worker according to the relevant experience, and are not limited herein.
For example, the preset multiple regression methods may be a polynomial regression method, a logistic regression method, and a regression regularization method, and according to the polynomial regression method, the logistic regression method, and the regression regularization method, the statistical relationship between the el nino index and the target meretrix characteristic is verified for multiple times, so that the optimal target regression method is selected from the preset multiple regression methods.
And step 403, according to a target regression method, carrying out prediction verification on the statistical relationship between the el Nino index and the target plum rain feature so as to obtain a prediction verification result.
As a possible implementation manner, after selecting an optimal target regression method from a plurality of preset regression methods, inputting the el nino index into an established relationship model between el nino index and the sleet feature according to the target regression method to obtain the sleet feature corresponding to the next year, and verifying the sleet feature corresponding to the next year with the target sleet feature, thereby obtaining a prediction verification result.
And step 404, perfecting the el Nino index and the plum rain characteristic model according to the prediction verification result.
In this example, for each target mermaid feature, according to the corresponding el nino index and the corresponding target mermaid feature of each historical year, determining the statistical relationship between the el nino index and the target mermaid feature, according to the statistical relationship between the el nino index and the target mermaid feature, selecting an optimal target regression method from a plurality of preset regression methods, and according to the target regression method, performing prediction verification on the statistical relationship between the el nino index and the target mermaid feature to obtain a prediction verification result, thereby completing the improvement of the el nino index and the mermaid feature model and improving the accuracy of the el nino index and the mermaid feature model prediction.
In some exemplary embodiments, in order to clearly understand how the el-nito index and the merry-go characterization model are trained, an example of the training el-nito index and the merry-go characterization model is further illustratively described below in conjunction with fig. 5.
FIG. 5 is a flow chart of training an el Nino index and a plum rain feature model according to an embodiment of the present application;
as shown in fig. 5, the process of training the el nino index and merrain feature model may include:
step 501, inputting the el nino index corresponding to the historical year into the initial el nino index and the merry characteristic model for each historical year to obtain the predicted merry characteristic data set corresponding to the next year corresponding to the historical year.
It will be appreciated that an initial model of the el Nino index and the merjer signature may be established based on the el Nino index corresponding to each historical year and the corresponding target merjer signature.
In some examples, after the initial el-nito index and merany feature model is established, the el-nito index corresponding to the historical year and the target merany feature corresponding to the next year may be used as the training set of the initial el-nito index and merany feature model in order to improve the accuracy of the prediction.
Wherein, the corresponding el Nino index of the historical year can be used as the input of the initial el Nino index and the characteristic model of the plum rain, the target plum-blossom characteristic corresponding to the next year may be used as the output of the initial el nino index and plum-blossom characteristic model.
Step 502, training the initial el nino index and the mermaid feature model according to the mermaid feature data set and the predicted mermaid feature data set corresponding to the next year to obtain the el nino index and the mermaid feature model.
It is understood that the plum blossom characteristic data set corresponding to the next year and the predicted plum blossom characteristic data set may not be identical.
It will be appreciated that training the initial el nino index and the merrill feature model may reduce the difference between the merrill feature dataset corresponding to the next year and the predicted merrill feature dataset, and further obtain the el nino index and the merrill feature model.
In this example, the corresponding el nino index and the corresponding target merany feature of each historical year are used as the training set of the initial el nino index and merany feature model to obtain the trained el nino index and merany feature model, so that the following merany feature data set of the next year of the target year can be accurately predicted based on the trained el nino index and merany feature model.
Fig. 6 is a schematic structural diagram of a merry-go-round prediction apparatus according to an embodiment of the present application.
As shown, the plum blossom prediction apparatus 600 includes: a first acquisition module 601, a second acquisition module 602, an analysis module 603, a building module 604, and a prediction module 605.
A first obtaining module 601, configured to obtain a historical plum blossom feature dataset of a target region in a plurality of historical years, where the historical plum blossom feature dataset includes a plurality of plum blossom features;
a second obtaining module 602, configured to obtain el nino indexes corresponding to a plurality of historical years in a target ocean area related to precipitation of plum rain in a target area;
an analysis module 603, configured to analyze, for each historical year, a correlation between the el nino index corresponding to the historical year and each of the merjejuno features in the historical merjejuno feature dataset corresponding to a next year of the historical year one by one, so as to obtain, from the historical merjejuno feature dataset corresponding to the next year, a target merjejuno feature having a strong correlation with the el nino index corresponding to the historical year;
the establishing module 604 is configured to establish a relationship model between the el nino index and the meretrix characteristic according to the el nino index corresponding to each historical year and the corresponding target meretrix characteristic;
The prediction module 605 is configured to input the el nino index corresponding to the target ocean area in the target year into the el nino index and merry characteristic relation model, so as to predict the merry characteristic data set of the target ocean area in the next year of the target year.
In one embodiment of the present application, the second obtaining module 602 is specifically configured to:
acquiring a target ocean area related to the precipitation of plum rain in a target area;
acquiring sea surface temperature data corresponding to a target sea area in a plurality of historical years;
for each historical year, determining the el nino index corresponding to the historical year of the target ocean area according to the sea surface temperature data corresponding to the historical year of the target ocean area.
In one embodiment of the present application, the specific process of determining the el nino index corresponding to the historical year for the target ocean area according to the sea surface temperature data corresponding to the historical year for each historical year by the second acquisition module 602 is:
for each historical year, determining the sea temperature level of the target ocean area corresponding to the historical year according to the sea surface temperature data of the target ocean area corresponding to the historical year;
and determining the el Nino index corresponding to the historical year of the target ocean area according to the sea temperature level corresponding to the historical year of the target ocean area.
In one embodiment of the present application, the first obtaining module 601 is specifically configured to:
acquiring ground precipitation observation data corresponding to a plurality of historical years in a target area;
for each historical year, determining a plurality of plum rain features corresponding to the historical year of the target area according to ground precipitation observation data corresponding to the historical year of the target area;
and according to a plurality of plum rain characteristics corresponding to the target area in the historical year, establishing a historical plum rain characteristic set corresponding to the target area in the historical year.
In one embodiment of the present application, the analysis module 603 is specifically configured to:
for each historical year, acquiring a historical plum blossom rain characteristic data set corresponding to the next year of the historical year;
determining a correlation coefficient between the el nino index corresponding to the historical year and each of the plum rain features in the historical plum rain feature dataset corresponding to the next year;
and taking the plum rain characteristics with the correlation coefficient larger than a preset correlation coefficient threshold value in the historical plum rain characteristic data set corresponding to the next year as target plum rain characteristics with strong correlation in the early Nino index corresponding to the historical year.
Based on the foregoing embodiments, another possible implementation manner of the apparatus for predicting the plum rain is provided in the embodiments of the present application, and fig. 7 is a schematic structural diagram of another apparatus for predicting the plum rain provided in the embodiments of the present application, where on the basis of the foregoing embodiments, the apparatus for predicting the plum rain 700 further includes: perfecting module 706 and training module 707.
In one embodiment of the present application, the perfecting module 706 is specifically configured to, when the target plum rain feature is multiple:
for each target plum-rain feature, determining a statistical relationship between the el-Nino index and the target plum-rain feature according to the el-Nino index corresponding to each historical year and the corresponding target plum-rain feature;
selecting an optimal target regression method from a plurality of preset regression methods according to the statistical relationship between the el Nino index and the target plum rain characteristic;
according to a target regression method, carrying out prediction verification on the statistical relationship between the el Nino index and the target plum rain characteristic to obtain a prediction verification result;
and (3) perfecting the el Nino index and the plum rain characteristic model according to the prediction verification result.
In one embodiment of the present application, the training module 707 is specifically configured to:
inputting the early late stage index and the early late stage characteristic model of the early late stage index corresponding to each historical year to obtain a predicted early late stage characteristic data set corresponding to the next year corresponding to the historical year;
training the initial el Nino index and the plum rain feature model according to the plum rain feature data set and the predicted plum rain feature data set corresponding to the next year to obtain the el Nino index and the plum rain feature model.
In the process of predicting the plum-rain characteristics corresponding to the next year of the target year, the correlation analysis is performed on the el-Ninuo index corresponding to each historical year and the historical plum-rain characteristic data set corresponding to the next year based on the el-Ninuo index corresponding to each historical year and the acquired historical plum-rain characteristic data set of the target area, so as to obtain the el-Ninuo index and the plum-rain characteristics with strong correlation, an el-Ninuo index and a plum-rain characteristic model is built according to the el-Ninuo index and the plum-rain characteristics with strong correlation, and the el-Ninuo index corresponding to the target ocean area in the target year is input to the el-Ninuo index and the plum-rain characteristic relation model, so that the plum-rain characteristic data set of the target area in the next year of the target year is predicted. Therefore, a mode for predicting the plum-rain characteristics by using the relation model of the el Nino index and the plum-rain characteristics is provided, objective prediction of the plum-rain characteristics of the next year of the corresponding year is facilitated, and efficiency and accuracy of predicting the plum-rain characteristics are improved.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 8, the electronic device 800 may include: a transceiver 801, a processor 802, and a memory 803.
Processor 802 executes the computer-executable instructions stored in the memory, causing processor 802 to perform the aspects of the embodiments described above. The processor 802 may be a general-purpose processor including a central processing unit CPU, a network processor (network processor, NP), etc.; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.
The memory 803 is coupled to the processor 802 via a system bus and communicates with each other, and the memory 803 is adapted to store computer program instructions.
The system bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The transceiver is used to enable communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). The memory may include random access memory (random access memory, RAM) and may also include non-volatile memory (non-volatile memory).
The electronic device provided in the embodiment of the present application may be a terminal device in the above embodiment.
The embodiment of the application also provides a chip for running the instruction, which is used for executing the technical scheme of the plum rain prediction method in the embodiment.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and when the computer instructions run on a computer, the computer is caused to execute the technical scheme of the plum rain prediction method in the embodiment.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (10)

1. A method for predicting characteristics of plum rain, the method comprising:
acquiring a historical plum rain feature data set of a target area in a plurality of historical years, wherein the historical plum rain feature data set comprises a plurality of plum rain features;
acquiring the el nino index of a target ocean area corresponding to the plum rain fall of the target area in a plurality of historical years;
for each historical year, analyzing the correlation between the el nino index corresponding to the historical year and each of the plum-rain features in the historical plum-rain feature dataset corresponding to the next year of the historical year one by one to acquire a target plum-rain feature with strong correlation with the el nino index corresponding to the historical year from the historical plum-rain feature dataset corresponding to the next year;
establishing an el Nino index and plum rain characteristic relation model according to the el Nino index corresponding to each historical year and the corresponding target plum rain characteristic;
And inputting the el Nino index corresponding to the target ocean area in the target year into the el Nino index and plum rain characteristic relation model so as to predict the plum rain characteristic data set of the target area in the next year of the target year.
2. The method of claim 1, wherein said obtaining the el nino index for a plurality of historical years for a target marine area associated with precipitation of plum rain in the target area comprises:
acquiring a target ocean area related to the plum rain fall of the target area;
acquiring sea surface temperature data corresponding to the target ocean area in a plurality of historical years;
for each historical year, determining the el nino index corresponding to the historical year of the target ocean area according to the sea surface temperature data corresponding to the historical year of the target ocean area.
3. The method of claim 2, wherein for each historical year, determining an el nino index for the target marine region at the historical year from the sea surface temperature data for the target marine region at the historical year comprises:
for each historical year, determining the sea temperature level of the target ocean area corresponding to the historical year according to the sea surface temperature data of the target ocean area corresponding to the historical year;
And determining the el Nino index corresponding to the historical year of the target ocean area according to the sea temperature level corresponding to the historical year of the target ocean area.
4. The method of claim 1, wherein the acquiring the historical merry-go-round feature dataset for the target region over a plurality of historical years comprises:
acquiring ground precipitation observation data corresponding to the target area in a plurality of historical years;
for each historical year, determining a plurality of plum rain features corresponding to the historical year of the target area according to ground precipitation observation data corresponding to the historical year of the target area;
and according to a plurality of plum rain characteristics corresponding to the target area in the historical year, establishing a historical plum rain characteristic set corresponding to the target area in the historical year.
5. The method of claim 1 wherein for each historical year, the step of analyzing the correlation between the el nino index corresponding to the historical year and each of the merje features in the historical merje feature dataset corresponding to the next year of the historical year one by one to obtain a target merje feature having a strong correlation with the el nino index corresponding to the historical year from the historical merje feature dataset corresponding to the next year comprises:
For each historical year, acquiring a historical plum blossom rain characteristic data set corresponding to the next year of the historical year;
determining a correlation coefficient between the el nino index corresponding to the historical year and each of the plum-rain features in the historical plum-rain feature dataset corresponding to the next year, respectively;
and taking the plum rain features with the correlation coefficient larger than a preset correlation coefficient threshold value in the historical plum rain feature data set corresponding to the next year as target plum rain features with strong correlation of the el Nino index corresponding to the historical year.
6. The method of claim 1 wherein after said target mermaid feature is a plurality of, said establishing an model of the relationship between the el nino index and the mermaid feature based on the el nino index corresponding to each historical year and the corresponding target mermaid feature, said method further comprises:
for each target plum-rain feature, determining a statistical relationship between the el-Ninuo index and the target plum-rain feature according to the el-Ninuo index corresponding to each historical year and the corresponding target plum-rain feature;
selecting an optimal target regression method from a plurality of preset regression methods according to the statistical relationship between the el Nino index and the target plum rain characteristic;
According to the target regression method, carrying out prediction verification on the statistical relationship between the el Nino index and the target plum rain feature to obtain a prediction verification result;
and perfecting the el Nino index and the plum rain feature model according to the prediction verification result.
7. The method of any of claims 1-6 wherein said establishing an el nino index versus merany feature model based on the el nino index corresponding to each historical year and the corresponding target merany feature comprises:
inputting the corresponding el Nino index of each historical year into an initial el Nino index and merry characteristic model, obtaining a predicted plum rain feature data set corresponding to the next year corresponding to the historical year;
and training the initial el Nino index and the plum rain feature model according to the plum rain feature data set corresponding to the next year and the predicted plum rain feature data set to obtain the el Nino index and the plum rain feature model.
8. A plum blossom prediction device, the device comprising:
the first acquisition module is used for acquiring a historical plum rain feature data set of a target area in a plurality of historical years, wherein the historical plum rain feature data set comprises a plurality of plum rain features;
The second acquisition module is used for acquiring the el Nino indexes of the target ocean area corresponding to the plum rain drop in the target area in a plurality of historical years;
the analysis module is used for analyzing the correlation between the early Ninuo index corresponding to each historical year and each plum-rain characteristic in the historical plum-rain characteristic data set corresponding to the next year of the historical year one by one so as to acquire a target plum-rain characteristic with strong correlation with the early Ninuo index corresponding to the historical year from the historical plum-rain characteristic data set corresponding to the next year;
the building module is used for building a relation model of the el Nino index and the plum rain feature according to the el Nino index corresponding to each historical year and the corresponding target plum rain feature;
and the prediction module is used for inputting the el Nino index corresponding to the target ocean area in the target year into the el Nino index and plum rain characteristic relation model so as to predict the plum rain characteristic data set of the target area in the next year of the target year.
9. An electronic device, comprising:
a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of plum blossom prediction as claimed in any one of claims 1 to 7 when the program is executed.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the method of plum blossom prediction as claimed in any one of claims 1 to 7.
CN202311181166.3A 2023-09-13 2023-09-13 Plum rain prediction method, apparatus and equipment Pending CN117388951A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117849908A (en) * 2024-03-08 2024-04-09 江苏省气候中心 Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field
CN117849908B (en) * 2024-03-08 2024-05-10 江苏省气候中心 Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117849908A (en) * 2024-03-08 2024-04-09 江苏省气候中心 Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field
CN117849908B (en) * 2024-03-08 2024-05-10 江苏省气候中心 Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field

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