CN111638564A - Rainfall forecasting method, device, equipment and storage medium - Google Patents
Rainfall forecasting method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a rainfall forecasting method, device, equipment and storage medium, and belongs to the technical field of artificial intelligence for forecasting rainfall. The method comprises the following steps: acquiring the information of a predicted circulating current field of a region to be predicted, wherein the predicted circulating current field information records the characteristics of the circulating current field at a plurality of time points in the future; performing dimensionality reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimensionality reduction array corresponding to the predicted circulation field information; searching out a similar historical feature dimension reduction array with the highest similarity to a future feature dimension reduction array in a pre-established historical circulation field sample library; and predicting the future rainfall condition of the region to be predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array. By the adoption of the method and the device, the rainfall conditions in the medium-long term can be accurately predicted.
Description
Technical Field
The application belongs to the technical field of artificial intelligence for forecasting rainfall, and particularly relates to a rainfall forecasting method, device, equipment and storage medium.
Background
In the context of global warming, storm disasters have become a non-negligible problem worldwide, and therefore, improving the accuracy of forecasts of stormy weather has become especially important.
In the prior art, rainfall is generally forecasted by analyzing a radar echo diagram. Or forecasting the rainfall condition according to the numerical forecasting result.
In the course of implementing the present application, the inventors found that the related art has at least the following problems:
the method for forecasting the rainfall condition according to the radar echo diagram is accurate in forecasting the short-term rainfall condition within 3h-6h, and has large errors in forecasting the medium-term rainfall condition within 24h, 36h or 72 h. For the method using the numerical weather forecast, due to uncertainty reasons such as the numerical method used in the numerical forecast, initial conditions, description of the mode on the nonlinear physical process, and the degree of predictability of the atmosphere, the result of the numerical forecast still has a larger error compared with the actual observation.
Disclosure of Invention
In order to solve technical problems in the related art, embodiments of the present application provide a method, an apparatus, a device, and a storage medium for rainfall forecasting.
In a first aspect, an embodiment of the present application provides a method for forecasting rainfall, where the method includes:
acquiring the information of a predicted circulating current field of a region to be predicted, wherein the information of the predicted circulating current field records the characteristics of the circulating current field at a plurality of time points in the future;
performing dimension reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimension reduction array corresponding to the predicted circulation field information;
searching out a similar historical feature dimensionality reduction array with the highest similarity to the future feature dimensionality reduction array from a pre-established historical circulation field sample library, wherein the historical circulation field sample library stores the corresponding historical feature dimensionality reduction array and the historical rainfall condition, and the dimensions of the historical feature dimensionality reduction array and the future feature dimensionality reduction array are the same;
and predicting the future rainfall condition of the region to be predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array.
Optionally, the method further includes:
acquiring a plurality of pieces of historical prediction circulation field information of the region to be predicted;
performing dimensionality reduction on historical feature arrays corresponding to the plurality of historical predicted circulating current field information according to a local linear embedding algorithm to obtain dimensionality reduction weight coefficients and historical feature dimensionality reduction arrays obtained by performing dimensionality reduction transformation on each historical feature array based on the dimensionality reduction weight coefficients;
and storing the historical characteristic dimension reduction array and the historical rainfall condition corresponding to each piece of historical forecast circulation field information into the historical circulation field sample library.
Optionally, the performing dimension reduction processing on the feature array corresponding to the predicted circular current field information to obtain a future feature dimension reduction array corresponding to the predicted circular current field information includes:
and performing dimension reduction transformation on the feature array corresponding to the predicted circulation field information based on the dimension reduction weight coefficient to obtain a future feature dimension reduction array corresponding to the predicted circulation field information.
Optionally, the circulation field characteristics include one or more of characteristics corresponding to a pressure field, a wind speed field, a temperature field, a humidity field, and a wind direction field.
Optionally, the characteristic array corresponding to the predicted circular current field information is
Wherein Pt1, Pt2.. Ptn represents pressure field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Ut1, Ut2.. Utn represents wind speed field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Tt1, Tt2.. Ttn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Rt1, Rt2.. Rtn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, and Dt1, Dt2.. Dtn represents wind direction field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information.
Optionally, the historical rainfall condition includes:
the rainfall duration and distribution condition corresponding to the historical time period, the landing condition of typhoon and rain, the river water condition, the engineering scheduling condition and one or more of the urban waterlogging and water-logging situations.
In a second aspect, an embodiment of the present application provides a rainfall forecasting device, including:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is configured to acquire predicted circulation field information of a region to be predicted, and the predicted circulation field information records circulation field characteristics of a plurality of time points in the future;
the dimension reduction module is configured to perform dimension reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimension reduction array corresponding to the predicted circulation field information;
the searching module is configured to search out a similar historical feature dimensionality reduction array with the highest similarity to the future feature dimensionality reduction array from a pre-established historical circulation field sample library, the historical circulation field sample library stores the corresponding historical feature dimensionality reduction array and the historical rainfall condition, and the dimensions of the historical feature dimensionality reduction array and the future feature dimensionality reduction array are the same;
and the prediction module is configured to predict the future rainfall condition of the region to be predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array.
Optionally, the apparatus further comprises a storage module configured to:
acquiring a plurality of pieces of historical prediction circulation field information of the region to be predicted;
performing dimensionality reduction on historical feature arrays corresponding to the plurality of historical predicted circulating current field information according to a local linear embedding algorithm to obtain dimensionality reduction weight coefficients and historical feature dimensionality reduction arrays obtained by performing dimensionality reduction transformation on each historical feature array based on the dimensionality reduction weight coefficients;
and storing the historical characteristic dimension reduction array and the historical rainfall condition corresponding to each piece of historical forecast circulation field information into the historical circulation field sample library.
Optionally, the dimension reduction module is configured to:
and performing dimension reduction transformation on the feature array corresponding to the predicted circulation field information based on the dimension reduction weight coefficient to obtain a future feature dimension reduction array corresponding to the predicted circulation field information.
Optionally, the circulation field characteristics include one or more of characteristics corresponding to a pressure field, a wind speed field, a temperature field, a humidity field, and a wind direction field.
Optionally, the characteristic array corresponding to the predicted circular current field information is
Wherein Pt1, Pt2.. Ptn represents pressure field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Ut1, Ut2.. Utn represents wind speed field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Tt1, Tt2.. Ttn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Rt1, Rt2.. Rtn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, and Dt1, Dt2.. Dtn represents wind direction field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information.
Optionally, the historical rainfall condition includes:
the rainfall duration and distribution condition corresponding to the historical time period, the landing condition of typhoon and rain, the river water condition, the engineering scheduling condition and one or more of the urban waterlogging and water-logging situations.
In a third aspect, the present application provides a computer device, where the computer device includes a processor and a memory, where the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the operations performed by the method for rainfall forecasting according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the operations performed by the method for forecasting rainfall according to the first aspect
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
according to the method provided by the embodiment of the application, the similar historical characteristic dimension reduction array with the highest similarity to the future characteristic dimension reduction array is found out in the historical circulation field sample library, and the future rainfall condition of the region to be predicted is predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array. Because the accuracy of the data corresponding to the circulation field in the numerical forecast is high, and the numerical forecast circulation field is used for forecasting and describing the weather conditions of a plurality of time points in the future, the future rainfall condition can be accurately forecasted according to the rainfall condition corresponding to the similar historical circulation field by identifying the similar historical circulation field similar to the plurality of time points in the future. Meanwhile, the method provided by the embodiment reduces the dimension of the feature array corresponding to the circulation field information, so that the calculation efficiency and accuracy of the similarity are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for forecasting rainfall according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a rainfall forecast according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In the prior art, when the method of numerical weather forecast is used for forecasting weather, the result of numerical forecast still has larger error compared with actual observation due to uncertainty reasons such as the description of the numerical method, initial conditions and modes used by the numerical forecast on a nonlinear physical process, the predictability of the atmosphere and the like. However, the predicted circulation field information in the numerical forecast can accurately describe the future weather situation, so that the historical circulation field information with the highest similarity to the predicted circulation field information is determined through the predicted circulation field information in the numerical forecast, and the future rainfall condition is accurately predicted according to the rainfall condition corresponding to the historical circulation field information.
As shown in fig. 2, an embodiment of the present application provides a method for forecasting rainfall, including:
The region to be predicted is any region. The information of the prediction circulation field of the region to be predicted is obtained in the weather forecast of the region to be predicted. In practice, weather forecast is performed every four hours by the weather station, so that real-time prediction circulation field information can be conveniently acquired.
It should be noted that the circulation field characteristics include one or more of characteristics corresponding to each of the pressure field, the wind speed field, the temperature field, the humidity field, and the wind direction field, and preferably, only one of the characteristics is acquired. Wherein the pressure field, the wind speed field, the temperature field, the humidity field, and the wind direction field are denoted by P, U, T, R and D, respectively.
In implementation, the characteristics of a pressure field, a wind speed field, a temperature field, a humidity field and a wind direction field corresponding to a plurality of future time points corresponding to the current time point of the region to be predicted are obtained, the information of the predicted circulating current field corresponding to each future time point is determined, and the information of the predicted circulating current field corresponding to the current time point is further determined.
Optionally, a feature array corresponding to the predicted circular current field information is established according to the predicted circular current field information, where the feature array is
The method includes the steps that Pt1 and Pt2.. Ptn represent pressure field characteristics corresponding to the 1 st and 2.. n future time points in predicted circulation field information, Ut1 and Ut2.. Utn represent wind speed field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Tt1 and Tt2.. Ttn represent humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Rt1 and Rt2.. Rtn represent humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, and Dt1 and Dt2.. Dtn represent wind direction field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information.
Specifically, the corresponding pressure of the future 6h, 12h, 24h, 36h and 72h of the current time point of the region to be predicted is determinedThe characteristics of a force field, a wind speed field, a temperature field, a humidity field and a wind direction field, and taking 6h, 12h, 24h, 36h and 72h in the future as a first time point, a second time point, a third time point, a fourth time point and a fifth time point respectively. Establishing a characteristic array according to the five time points and the characteristics of the pressure field, the wind speed field, the temperature field, the humidity field and the wind direction field corresponding to the five time points respectively, wherein the characteristic array is
202. And performing dimension reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimension reduction array corresponding to the predicted circulation field information.
The dimension reduction weight coefficient is an array formed by a plurality of numerical values.
Optionally, the feature array corresponding to the predicted circulating current field information is subjected to dimension reduction transformation based on the dimension reduction weight coefficient, so as to obtain a future feature dimension reduction array corresponding to the predicted circulating current field information.
In implementation, the feature array in step 202 is applied
And performing dimension reduction transformation based on the dimension reduction weight coefficient to obtain a future feature dimension reduction array corresponding to the predicted circulation field information.
203. And searching out a similar historical feature dimension reduction array with the highest similarity to a future feature dimension reduction array in a pre-established historical circulation field sample library.
The historical circulation field sample base stores corresponding historical characteristic dimension reduction arrays and historical rainfall conditions, and the historical characteristic dimension reduction arrays are obtained according to dimension reduction weight coefficients and the historical characteristic arrays. The dimension of the historical feature array is the same as that of the feature array corresponding to the predicted circulation field information, and the dimension of the future feature dimension reduction array is the same as that of the historical feature dimension reduction array because the historical feature dimension reduction array and the future feature dimension reduction array are obtained based on the same dimension reduction weight coefficient transformation.
The historical rainfall condition comprises one or more of rainfall duration and distribution conditions corresponding to the historical time period, landing conditions of typhoons and rains, river water conditions, engineering scheduling conditions and urban waterlogging and water accumulation situations.
In implementation, in a pre-established historical circulation field sample library, a similar historical feature dimension reduction array with the minimum Euclidean distance to a future feature dimension reduction array is found out. Wherein, the smaller the Euclidean distance between the two arrays, the higher the similarity of the two values.
Optionally, multiple pieces of historical predicted circulation field information of the region to be predicted are obtained. And reducing the dimension of the historical feature arrays corresponding to the plurality of pieces of historical predicted circulation field information according to a local linear embedding algorithm to obtain dimension-reduced weight coefficients and historical feature dimension-reduced arrays obtained by dimension-reduced transformation of each historical feature array based on the dimension-reduced weight coefficients. And storing the historical characteristic dimension reduction array and the historical rainfall condition corresponding to each piece of historical forecast circulation field information into a historical circulation field sample library.
The specific process of the local linear embedding algorithm is as follows: obtaining N data points { x in a high dimensional space1,x2...xNCalculate each data point xiEuclidean distance to all other data points, where data point xiIs any one of the N data points; selecting and data point xiK data points with minimum distance { xi1,xi2...xiK}. Wherein each xiCan use the nearest K data points xi1,xi2...xiKIs expressed linearly by the specific formula
And w in the formulaijSimultaneously satisfy the conditions
Wherein wijRepresents the data point xiAnd data point xjWeight coefficient between, data point xjIs associated with data point xiAny of K data points closest toA data point.
At the same time, wijAnd data point xiThe relationship between them also satisfies the formula
By the formula
When f (W) approaches zero, a weight coefficient set is obtained.
Then, the data points in the high-dimensional space are collected { x1,x2...xN}∈RDMapping to low-dimensional space through weight coefficient set to be { y1,y2...yN}∈Rd(D < D). And D represents the corresponding dimension of each data point before dimensionality reduction, and D represents the corresponding dimension of each data point after dimensionality reduction.
In an implementation, m arrays of historical features are obtained, which constitute a sample set, and may be expressed as Ω ═ θ 1, θ 2m],
Wherein Ω is a sample set composed of m historical feature arrays, θ 1, θ 2mThe wind turbine generator is represented by 1, 2.. m historical feature arrays, θ n is any one of the m historical feature arrays, Pt1, Pt2.. Ptn represents pressure field features corresponding to historical 1, 2.. n time points in historical circulation field information, Ut1, Ut2.. Utn represents wind speed field features corresponding to the historical 1, 2.. n time points in the historical circulation field information, Tt1, Tt2.. Ttn represents humidity field features corresponding to the historical 1, 2.. n time points in the historical circulation field information, Rt1, Rt2.. Rtn represents humidity field features corresponding to the historical 1, 2.. n time points in the historical circulation field information, and Dt1, Dt2.. Dtn represents wind direction features corresponding to the historical 1, 2.. n time points in the historical circulation field information. θ m is given by [ θ 1, θ 2.. θ m](wherein,
) Go on and fallAnd D, dimension reduction, namely obtaining m historical feature dimension reduction arrays after dimension reduction.
The specific dimension reduction process is as follows: step 1: obtaining any one historical characteristic array theta in m historical characteristic arraysi；
Step 2: determining a divide history feature array θiOther historical feature arrays than the previous one and the historical feature array thetaiLinear relation between them, obtaining a linear relation weight coefficient Wi＝(wi1,wi2...wi(m-1)) Linear relation weight coefficient WiAs a dimension reduction weight coefficient. Wherein, wi1For historical feature array thetaiAnd-divide historical feature array thetaiHistorical feature array theta in other historical feature arrays than others1Weight coefficient of linear relationship between wi2For historical feature array thetaiAnd-divide historical feature array thetaiHistorical feature array theta in other historical feature arrays than others2W.w. of linear relationship betweeni(m-1)For historical feature array thetaiAnd-divide historical feature array thetaiHistorical feature array theta in other historical feature arrays than othersm-1The linear relationship between them is the weighting factor.
Wherein, according to
And
determining historical feature array thetaiAnd-divide historical feature array thetaiHistorical feature array theta in other historical feature arrays than othersjThe linear relationship between them is the weighting factor.
And step 3: according to the dimensionality reduction weight coefficient Wiθ m is defined as [ θ 1, θ 2.. θ m ═ q]The mapping is η ═ 1, 2.. m]And the theta 1 and the theta 2. theta m of the high-dimensional space correspond to the 1 and the 2. theta. m of the low-dimensional space one to one respectively.
It should be noted that if θ m has s features, or θ m has s dimensions, then m after dimensionality reduction has x features, or m has x dimensions, where s > x. Meanwhile, the mapping method is the same as that in the prior art.
Although the above process reduces the dimension of the feature array corresponding to the circulation field information, the historical feature dimension reduction array after dimension reduction still has the comprehensive features of the circulation situation, so that after dimension reduction, the similarity between the dimension reduction arrays can be calculated to determine the similarity of the circulation situation.
When the feature array of the circulation field information is acquired again, dimension reduction transformation may be performed on the acquired feature array based on the existing dimension reduction weight coefficient, or dimension reduction transformation may be performed on the existing feature array again by using an LLE (local Linear Embedding) algorithm. Or when the number of the acquired feature arrays is smaller than the preset number, performing dimension reduction transformation on the feature arrays respectively based on the existing dimension reduction weight coefficients, and when the number of the acquired feature arrays is larger than the preset number, performing dimension reduction transformation on the existing feature arrays again by using the LLE algorithm. The existing feature arrays comprise a feature array of the re-acquired circulation field information and a plurality of historical feature arrays corresponding to historical predicted circulation field information.
Optionally, for each day in the historical time, obtaining historical predicted circulation field information and historical rainfall conditions in the numerical forecast for each day, and establishing a historical characteristic array corresponding to the historical predicted circulation field information for each day according to the historical predicted circulation field information for each day. Performing dimensionality reduction processing on the historical feature arrays respectively corresponding to the multiple days according to a local linear embedding algorithm to obtain historical feature dimensionality reduction arrays respectively corresponding to the multiple days; and correspondingly adding the historical characteristic dimension reduction array corresponding to each day and the historical rainfall condition corresponding to each day to a historical circulation field sample library.
Each day in the historical time can be one day in ten years of the history of the region to be predicted.
In the implementation, taking the numerical forecast of a day in the historical decade of the area to be forecasted as an example, the numerical forecast data of the day is taken as a sample, and the pressure field, the wind speed field, the temperature field, the humidity field and the wind direction field in the numerical forecast circulation field of the day and the historical rainfall condition corresponding to the day are obtained. And forecasting a pressure field, a wind speed field, a temperature field, a humidity field and a wind direction field in the circulation field according to the numerical value of the day, and establishing a historical characteristic array corresponding to the day. And acquiring the historical feature array corresponding to each day in the historical decade according to the acquisition method of the historical feature array corresponding to a certain day in the historical decade. And reducing the dimension of the historical feature array corresponding to each day in the historical decade according to a local linear embedding algorithm to obtain the dimension-reduced array of the historical feature corresponding to each day in the historical decade. And correspondingly adding the historical characteristic dimension reduction array corresponding to each day and the historical rainfall condition corresponding to the day into the historical circulation field sample library.
And each time numerical forecast data of one day is obtained, determining the characteristics corresponding to the pressure field, the wind speed field, the temperature field, the humidity field and the wind direction field corresponding to the day according to the numerical forecast data, further determining a characteristic array corresponding to the day, further obtaining a characteristic dimension reduction array corresponding to the day, and adding the characteristic dimension reduction array and the rainfall condition corresponding to the day into a historical circulation field sample library.
Since the numerical forecast annular flow field is used for forecasting and describing the future weather conditions of 6h, 12h, 24h and 36h.. 72h, the future development trend of rainfall of the future 6h, 12h, 24h and 36h 3572 h and the future moving path of a rainstorm center can be realized by identifying similar historical annular flow fields similar to the 6h, 12h, 24h and 36h … h, the change process of the rainfall can be predicted, and the prediction of the future development process of heavy rainstorm can be realized.
Optionally, the characteristics of the pressure field, the wind speed field, the temperature field, the humidity field and the wind direction field in the future 24h in the numerical prediction are obtained, including the characteristics of the pressure field, the wind speed field, the temperature field, the humidity field and the wind direction field respectively corresponding to 6h, 12h and 24h, so as to obtain the information of the predicted circulation field corresponding to the future 24 h. And establishing a characteristic array according to the predicted circulation field information corresponding to 24h in the future. And reducing the dimension of the feature array based on the dimension reduction weight coefficient, and acquiring a future feature dimension reduction array corresponding to the time group forecast. And searching out a similar historical feature dimension reduction array with the highest similarity to a future feature dimension reduction array in a pre-established historical circulation field sample library. The historical circulating current field sample base comprises historical characteristic dimension reduction arrays corresponding to a plurality of historical time points.
It should be noted that the predicted circulation field information corresponding to 24h after each time point is formed according to the characteristics of the pressure field, the wind speed field, the temperature field, the humidity field and the wind direction field corresponding to 6h, 12h and 24h after each historical time point. And (3) reducing the dimension of the historical feature array corresponding to the predicted circulating current field information corresponding to 24h in the future after each time point by using an LLE algorithm to obtain the historical feature dimension reduction array corresponding to each time point.
204. And predicting the future rainfall condition of the region to be predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array.
In implementation, the rainfall continuation and distribution condition, the typhoon and rain landing condition, the river channel water situation, the engineering scheduling condition and the urban waterlogging situation of the similar historical characteristic dimensionality reduction array corresponding to the historical time period are predicted to obtain the rainfall continuation and distribution condition, the typhoon and rain landing condition, the river channel water situation, the engineering scheduling condition and the urban waterlogging situation of the region to be predicted corresponding to the future time period.
The similar historical circulation potential field with characteristics most similar to those of a 850hpa pressure field, a wind speed field, a temperature field, a humidity field and a wind direction field in the circulation potential of the current weather process is found out, the rainfall condition corresponding to the similar historical circulation potential field is determined, and the historical rainfall condition, the typhoon landing condition, the river channel water condition, the engineering scheduling condition, the urban waterlogging and water accumulation disaster condition and the like in the rainfall condition are determined. Through the condition such as borrowing from historical rainfall, disaster information, make reasonable, objective prediction to this weather process to do various counter measures, thereby play and predict in advance and borrow from the reference effect to current rainstorm calamity development trend and waterlogging calamity, reduce the influence that the rainstorm waterlogging caused.
As shown in fig. 2, an embodiment of the present application provides a rainfall forecasting device, which includes:
an obtaining module 201, configured to obtain predicted circulation field information of a region to be predicted, where the predicted circulation field information describes circulation field characteristics at a plurality of time points in the future;
a dimension reduction module 202, configured to perform dimension reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimension reduction array corresponding to the predicted circulation field information;
the searching module 203 is configured to search out a similar historical feature dimensionality reduction array with the highest similarity to the future feature dimensionality reduction array from a pre-established historical circulation field sample library, wherein the historical circulation field sample library stores the corresponding historical feature dimensionality reduction array and the historical rainfall condition, and the dimensions of the historical feature dimensionality reduction array and the future feature dimensionality reduction array are the same;
the predicting module 204 is configured to predict future rainfall conditions of the to-be-predicted region based on historical rainfall conditions corresponding to the similar historical characteristic dimension reduction array.
Optionally, the apparatus further comprises a storage module configured to:
acquiring a plurality of pieces of historical prediction circulation field information of the region to be predicted;
performing dimensionality reduction on historical feature arrays corresponding to the plurality of historical predicted circulating current field information according to a local linear embedding algorithm to obtain dimensionality reduction weight coefficients and historical feature dimensionality reduction arrays obtained by performing dimensionality reduction transformation on each historical feature array based on the dimensionality reduction weight coefficients;
and storing the historical characteristic dimension reduction array and the historical rainfall condition corresponding to each piece of historical forecast circulation field information into the historical circulation field sample library.
Optionally, the dimension reduction module 203 is configured to:
and performing dimension reduction transformation on the feature array corresponding to the predicted circulation field information based on the dimension reduction weight coefficient to obtain a future feature dimension reduction array corresponding to the predicted circulation field information.
Optionally, the circulation field characteristics include one or more of characteristics corresponding to a pressure field, a wind speed field, a temperature field, a humidity field, and a wind direction field.
Optionally, the characteristic array corresponding to the predicted circular current field information is
Wherein Pt1, Pt2.. Ptn represents pressure field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Ut1, Ut2.. Utn represents wind speed field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Tt1, Tt2.. Ttn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Rt1, Rt2.. Rtn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, and Dt1, Dt2.. Dtn represents wind direction field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information.
Optionally, the historical rainfall condition includes:
the rainfall duration and distribution condition corresponding to the historical time period, the landing condition of typhoon and rain, the river water condition, the engineering scheduling condition and one or more of the urban waterlogging and water-logging situations.
It should be noted that: in the rainfall forecasting device provided in the above embodiment, when the future rainfall situation is forecasted, only the division of the above function modules is taken as an example, in practical application, after the functions are allocated by different function modules according to needs, the internal structure of the device may be divided into different function modules, and all or part of the functions described later may be performed. In addition, the embodiments of the method for forecasting rainfall provided by the above embodiments belong to the same concept, and the specific implementation process thereof is described in the embodiments of the method for forecasting rainfall, which is not described herein again.
Fig. 3 shows a block diagram of a terminal 300 according to an exemplary embodiment of the present application. The terminal 300 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer iv, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. The terminal 300 may also be referred to by other names such as account device, portable terminal, laptop terminal, desktop terminal, etc.
Generally, the terminal 300 includes: a processor 301 and a memory 302.
The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 301 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 301 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
In some embodiments, the terminal 300 may further include: a peripheral interface 303 and at least one peripheral. The processor 301, memory 302 and peripheral interface 303 may be connected by a bus or signal lines. Each peripheral may be connected to the peripheral interface 303 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, touch display screen 305, camera 306, audio circuitry 307, positioning components 308, and power supply 309.
The peripheral interface 303 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and peripheral interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302 and the peripheral interface 303 may be implemented on a separate chip or circuit board, which is not limited by the embodiment.
The Radio Frequency circuit 304 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 304 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 304 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 304 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, an account identity module card, and so forth. The radio frequency circuitry 304 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 304 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 305 is a touch display screen, the display screen 305 also has the ability to capture touch signals on or over the surface of the display screen 305. The touch signal may be input to the processor 301 as a control signal for processing. At this point, the display screen 305 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 305 may be one, providing the front panel of the terminal 300; in other embodiments, the display screens 305 may be at least two, respectively disposed on different surfaces of the terminal 300 or in a folded design; in still other embodiments, the display 305 may be a flexible display disposed on a curved surface or on a folded surface of the terminal 300. Even further, the display screen 305 may be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 305 may be made of LCD (liquid crystal Display), OLED (Organic Light-Emitting Diode), and the like.
The camera assembly 306 is used to capture images or video. Optionally, camera assembly 306 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 306 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The positioning component 308 is used to locate the current geographic location of the terminal 300 to implement navigation or LBS (location based Service). The positioning component 308 may be a positioning component based on the GPS (global positioning System) in the united states, the beidou System in china, the graves System in russia, or the galileo System in the european union.
The power supply 309 is used to supply power to the various components in the terminal 300. The power source 309 may be alternating current, direct current, disposable batteries, or rechargeable batteries. When the power source 309 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, the terminal 300 also includes one or more sensors 33. The one or more sensors 33 include, but are not limited to: acceleration sensor 311, gyro sensor 312, pressure sensor 313, fingerprint sensor 314, optical sensor 315, and proximity sensor 316.
The acceleration sensor 311 may detect the magnitude of acceleration in three coordinate axes of a coordinate system established with the terminal 300. For example, the acceleration sensor 311 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 301 may control the touch screen 305 to display the account interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 311. The acceleration sensor 311 may also be used for acquisition of motion data of a game or account.
The gyro sensor 312 may detect a body direction and a rotation angle of the terminal 300, and the gyro sensor 312 may cooperate with the acceleration sensor 311 to acquire a 3D motion of the account with respect to the terminal 300. The processor 301 may implement the following functions according to the data collected by the gyro sensor 312: motion sensing (such as changing the UI according to a tilt operation of the account), image stabilization while shooting, game control, and inertial navigation.
The pressure sensor 313 may be disposed on a side bezel of the terminal 300 and/or an underlying layer of the touch display screen 305. When the pressure sensor 313 is arranged on the side frame of the terminal 300, the holding signal of the account to the terminal 300 can be detected, and the processor 301 performs left-right hand identification or shortcut operation according to the holding signal collected by the pressure sensor 313. When the pressure sensor 313 is arranged at the lower layer of the touch display screen 305, the processor 301 controls the operability control on the UI interface according to the pressure operation of the account on the touch display screen 305. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 314 is used for collecting fingerprints of the account, and the identity of the account is identified by the processor 301 according to the fingerprints collected by the fingerprint sensor 314, or the identity of the account is identified by the fingerprint sensor 314 according to the collected fingerprints. When the identity of the account is found to be a trusted identity, the processor 301 authorizes the account to perform relevant sensitive operations, including unlocking a screen, viewing encrypted information, downloading software, paying, changing settings, and the like. The fingerprint sensor 314 may be disposed on the front, back, or side of the terminal 300. When a physical button or a vendor Logo is provided on the terminal 300, the fingerprint sensor 314 may be integrated with the physical button or the vendor Logo.
The optical sensor 315 is used to collect the ambient light intensity. In one embodiment, the processor 301 may control the display brightness of the touch screen display 305 based on the ambient light intensity collected by the optical sensor 315. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 305 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 305 is turned down. In another embodiment, the processor 301 may also dynamically adjust the shooting parameters of the camera head assembly 306 according to the ambient light intensity collected by the optical sensor 315.
A proximity sensor 316, also known as a distance sensor, is typically provided on the front panel of the terminal 300. The proximity sensor 316 is used to capture the distance between the account and the front face of the terminal 300. In one embodiment, the processor 301 controls the touch display screen 305 to switch from the bright screen state to the dark screen state when the proximity sensor 316 detects that the distance between the account and the front face of the terminal 300 gradually decreases; when the proximity sensor 316 detects that the distance between the account and the front face of the terminal 300 is gradually increased, the touch display screen 305 is controlled by the processor 301 to switch from a breath screen state to a bright screen state.
Those skilled in the art will appreciate that the configuration shown in fig. 3 is not intended to be limiting of terminal 300 and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
In an exemplary embodiment, a computer-readable storage medium, such as a memory, including instructions executable by a processor in a terminal to perform the method for instant messaging matching in the above embodiments is also provided. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of rain forecasting, the method comprising:
acquiring the information of a predicted circulating current field of a region to be predicted, wherein the information of the predicted circulating current field records the characteristics of the circulating current field at a plurality of time points in the future;
performing dimension reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimension reduction array corresponding to the predicted circulation field information;
searching out a similar historical feature dimensionality reduction array with the highest similarity to the future feature dimensionality reduction array from a pre-established historical circulation field sample library, wherein the historical circulation field sample library stores the corresponding historical feature dimensionality reduction array and the historical rainfall condition, and the dimensions of the historical feature dimensionality reduction array and the future feature dimensionality reduction array are the same;
and predicting the future rainfall condition of the region to be predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array.
2. The method of claim 1, further comprising:
acquiring a plurality of pieces of historical prediction circulation field information of the region to be predicted;
performing dimensionality reduction on historical feature arrays corresponding to the plurality of historical predicted circulating current field information according to a local linear embedding algorithm to obtain dimensionality reduction weight coefficients and historical feature dimensionality reduction arrays obtained by performing dimensionality reduction transformation on each historical feature array based on the dimensionality reduction weight coefficients;
and storing the historical characteristic dimension reduction array and the historical rainfall condition corresponding to each piece of historical forecast circulation field information into the historical circulation field sample library.
3. The method according to claim 2, wherein the performing dimension reduction processing on the feature array corresponding to the predicted circulating current field information to obtain a future feature dimension reduction array corresponding to the predicted circulating current field information includes:
and performing dimension reduction transformation on the feature array corresponding to the predicted circulation field information based on the dimension reduction weight coefficient to obtain a future feature dimension reduction array corresponding to the predicted circulation field information.
4. The method of claim 1, wherein the circulating field characteristics comprise one or more of pressure field, wind speed field, temperature field, humidity field, and wind direction field respectively corresponding characteristics.
5. The method of claim 4, wherein the prediction of the annular flow field information corresponds to a feature array of
Wherein Pt1, Pt2.. Ptn represents pressure field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Ut1, Ut2.. Utn represents wind speed field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Tt1, Tt2.. Ttn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, Rt1, Rt2.. Rtn represents humidity field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information, and Dt1, Dt2.. Dtn represents wind direction field characteristics corresponding to the 1 st and 2.. n future time points in the predicted circulation field information.
6. The method of claim 1, wherein the historical rainfall conditions comprise:
the rainfall duration and distribution condition corresponding to the historical time period, the landing condition of typhoon and rain, the river water condition, the engineering scheduling condition and one or more of the urban waterlogging and water-logging situations.
7. A rainfall forecast apparatus, comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is configured to acquire predicted circulation field information of a region to be predicted, and the predicted circulation field information records circulation field characteristics of a plurality of time points in the future;
the dimension reduction module is configured to perform dimension reduction processing on the feature array corresponding to the predicted circulation field information to obtain a future feature dimension reduction array corresponding to the predicted circulation field information;
the searching module is configured to search out a similar historical feature dimensionality reduction array with the highest similarity to the future feature dimensionality reduction array from a pre-established historical circulation field sample library, the historical circulation field sample library stores the corresponding historical feature dimensionality reduction array and the historical rainfall condition, and the dimensions of the historical feature dimensionality reduction array and the future feature dimensionality reduction array are the same;
and the prediction module is configured to predict the future rainfall condition of the region to be predicted based on the historical rainfall condition corresponding to the similar historical characteristic dimension reduction array.
8. The apparatus of claim 7, further comprising a storage module configured to:
acquiring a plurality of pieces of historical prediction circulation field information of the region to be predicted;
performing dimensionality reduction on historical feature arrays corresponding to the plurality of historical predicted circulating current field information according to a local linear embedding algorithm to obtain dimensionality reduction weight coefficients and historical feature dimensionality reduction arrays obtained by performing dimensionality reduction transformation on each historical feature array based on the dimensionality reduction weight coefficients;
and storing the historical characteristic dimension reduction array and the historical rainfall condition corresponding to each piece of historical forecast circulation field information into the historical circulation field sample library.
9. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction that is loaded and executed by the processor to perform operations performed by a method of rainfall forecasting according to any of claims 1 to 6.
10. A computer-readable storage medium having stored therein at least one instruction, which is loaded and executed by a processor to perform operations performed by a method of rainfall forecasting according to any one of claims 1 to 6.
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