Hydrometeorological prediction method and device for watershed, computer equipment and storage medium

Abstract

The invention relates to the technical field of meteorology and climatology, in particular to a watershed hydro meteorology prediction method, which comprises the following steps of: performing multi-dimensional visual angle evaluation on a plurality of candidate satellite rainfall data of a watershed, and performing weight parameter construction on visual angle evaluation indexes of the obtained candidate satellite rainfall data; in combination with view angle evaluation indexes and weight parameters, a comprehensive evaluation system covering multiple dimension view angles such as spatial-temporal variability, extreme rainfall events, extreme rainfall intensity, rainfall probability density distribution and uncertain analysis characteristics is constructed, and the performance of multiple candidate satellite rainfall data of a drainage basin is comprehensively analyzed; a more targeted decision basis is provided for hydrometeorological prediction of the watershed, and the accuracy of hydrometeorological prediction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meteorology and climatology, and particularly relates to a hydro-meteorological prediction method and device for a river basin, a computer device and a storage medium. BACKGROUND

[0002] At present, for hydro-meteorological prediction of a river basin, satellite precipitation data of existing research is usually used. However, existing researches mainly focus on a single or a few kinds of satellite precipitation data, and lack of systematic comparison and evaluation of multiple satellite precipitation data in the same river basin, which is difficult to reflect the universal applicability of satellite precipitation data in different geographical environments, and less comprehensive analysis from multiple spatio-temporal scales, so as to fail to select the optimal satellite precipitation data for hydro-meteorological prediction of a river basin, and reduce the accuracy of hydro-meteorological prediction of a river basin. SUMMARY

[0003] Therefore, the present application aims to provide a hydro-meteorological prediction method and device for a river basin, a computer device and a storage medium, which comprehensively analyze the performance of multiple candidate satellite precipitation data of a river basin by performing multi-dimensional perspective evaluation on the multiple candidate satellite precipitation data of the river basin, constructing weight parameters of perspective evaluation indexes of the obtained candidate satellite precipitation data, combining the perspective evaluation indexes and the weight parameters, constructing a comprehensive evaluation system covering multiple dimensional perspectives of spatio-temporal variability, extreme precipitation event, extreme precipitation intensity, precipitation probability density distribution and uncertainty analysis characteristics, and providing more targeted decision basis for hydro-meteorological prediction of a river basin, thereby improving the accuracy of hydro-meteorological prediction.

[0004] In a first aspect, the present application provides a hydro-meteorological prediction method for a river basin, including the following steps:

[0005] obtaining several candidate satellite precipitation data and site measured precipitation data of a river basin;

[0006] performing multi-dimensional perspective evaluation on the several candidate satellite precipitation data and the site measured precipitation data, to obtain perspective evaluation indexes of several dimensions of the several candidate satellite precipitation data, wherein the perspective evaluation indexes include seasonal perspective evaluation indexes and extreme precipitation perspective evaluation indexes;

[0007] performing standardization processing on the perspective evaluation indexes of several dimensions of the several candidate satellite precipitation data, respectively performing weight calculation on the perspective evaluation indexes of several dimensions of the several candidate satellite precipitation data after the standardization processing, to obtain weight parameters corresponding to the perspective evaluation indexes of several dimensions of the several candidate satellite precipitation data;

[0008] The evaluation value calculation is performed according to the station measured precipitation data, the multi-dimensional perspective evaluation indicators of the plurality of candidate satellite precipitation data, and the corresponding weight parameters, to obtain evaluation values of the plurality of candidate satellite precipitation data; and the target candidate satellite precipitation data is selected from the plurality of candidate satellite precipitation data according to the evaluation values of the plurality of candidate satellite precipitation data.

[0009] The hydro-meteorological prediction is performed according to the target candidate satellite precipitation data and a preset hydro-meteorological prediction model, to obtain a hydro-meteorological prediction result of the basin.

[0010] In a second aspect, an embodiment of the present application provides a hydro-meteorological prediction device for a basin, including:

[0011] The data acquisition module is configured to obtain a plurality of candidate satellite precipitation data and station measured precipitation data of the basin.

[0012] The multi-dimensional perspective evaluation module is configured to perform multi-dimensional perspective evaluation according to the plurality of candidate satellite precipitation data and the station measured precipitation data, to obtain multi-dimensional perspective evaluation indicators of the plurality of candidate satellite precipitation data, wherein the perspective evaluation indicators include seasonal perspective evaluation indicators and extreme precipitation perspective evaluation indicators, and the seasonal perspective evaluation indicators include monthly scale correlation evaluation indicators and precipitation probability density distribution evaluation indicators.

[0013] The weight calculation module is configured to perform standardization processing on the multi-dimensional perspective evaluation indicators of the plurality of candidate satellite precipitation data, and perform weight calculation on the multi-dimensional perspective evaluation indicators of the plurality of candidate satellite precipitation data after the standardization processing, to obtain corresponding weight parameters of the multi-dimensional perspective evaluation indicators of the plurality of candidate satellite precipitation data.

[0014] The satellite precipitation data selection module is configured to perform evaluation value calculation according to the station measured precipitation data, the multi-dimensional perspective evaluation indicators of the plurality of candidate satellite precipitation data, and the corresponding weight parameters, to obtain evaluation values of the plurality of candidate satellite precipitation data; and select the target candidate satellite precipitation data from the plurality of candidate satellite precipitation data according to the evaluation values of the plurality of candidate satellite precipitation data.

[0015] The hydro-meteorological prediction module is configured to perform hydro-meteorological prediction according to the target candidate satellite precipitation data and a preset hydro-meteorological prediction model, to obtain a hydro-meteorological prediction result of the basin.

[0016] In a third aspect, an embodiment of the present application provides a computer device comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the hydrological and meteorological prediction method for the river basin as described in the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the hydrological and meteorological prediction method for a river basin as described in the first aspect.

[0018] In an embodiment of the present application, a method, apparatus, computer equipment, and storage medium for hydrological and meteorological prediction of a river basin are provided. A multi-dimensional perspective evaluation is performed on multiple candidate satellite precipitation data of the river basin, and weight parameters are constructed for the perspective evaluation indicators of the obtained candidate satellite precipitation data. By combining the perspective evaluation indicators and weight parameters, a comprehensive evaluation system covering multiple dimensional perspectives such as spatiotemporal variability, extreme precipitation events, extreme precipitation intensity, precipitation probability density distribution, and uncertainty analysis characteristics is constructed. The performance of multiple candidate satellite precipitation data of the river basin is comprehensively analyzed, providing a more targeted decision-making basis for the hydrological and meteorological prediction of the river basin, and improving the accuracy of the hydrological and meteorological prediction.

[0019] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a method for hydrological and meteorological prediction of a river basin provided in the first embodiment of the present application;

[0021] Figure 2 A schematic diagram of the flow of step S2 in the method for hydrological and meteorological prediction of a river basin provided in the first embodiment of the present application;

[0022] Figure 3 This is a flow chart of S202 in the method for hydrological and meteorological prediction of a river basin provided in the first embodiment of the present application;

[0023] Figure 4 A schematic diagram of the flow of step S2 in the method for hydrological and meteorological prediction of a river basin provided in the second embodiment of the present application;

[0024] Figure 5 A schematic diagram of the flow of step S2 in the method for hydrological and meteorological prediction of a river basin provided in the third embodiment of the present application;

[0025] Figure 6 This is a schematic diagram of the flow of S3 in the hydrological and meteorological prediction method for a river basin provided in the first embodiment of the present application;

[0026] Figure 7A flowchart of S3 in the hydro-meteorological prediction method of a river basin according to the fourth embodiment of the present application is shown in FIG. 3;

[0027] Figure 8 A flowchart of S5 in the hydro-meteorological prediction method of a river basin according to the first embodiment of the present application is shown in FIG. 1;

[0028] Figure 9 A structural diagram of the hydro-meteorological prediction device of a river basin according to the fifth embodiment of the present application is shown in FIG. 5;

[0029] Figure 10 A structural diagram of the computer device according to the sixth embodiment of the present application is shown in FIG. 6. DETAILED DESCRIPTION

[0030] The exemplary embodiments will be described in detail herein below with reference to the drawings. In the following description, the same numbers are used to denote the same elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0031] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the present application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0032] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is to be further understood that the terms "comprise," "comprising," "comprises," and / or "comprising" when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0033] Reference will now be made to Figure 1 , Figure 1 A flowchart of the hydro-meteorological prediction method of a river basin according to the first embodiment of the present application is shown in FIG. 1. The method comprises the following steps:

[0034] S1: Obtain a plurality of candidate satellite precipitation data and site measured precipitation data of a river basin.

[0035] The execution subject of the hydro-meteorological prediction method of the basin is a prediction device (hereinafter referred to as a prediction device) of the hydro-meteorological prediction method of the basin. In an optional embodiment, the prediction device can be a computer device, which can be a server or a server cluster formed by multiple computer devices.

[0036] In this embodiment, the prediction device obtains several candidate satellite precipitation data of the basin and site measured precipitation data. Specifically, the candidate satellite precipitation data includes continuously updated global mainstream multi-source fusion precipitation data products, such as CMORPH, PERSIANN, IMERG, ERA5-Land, MSWEP, and GSMaP precipitation data products. The site measured precipitation data includes CHM_PRE, CN05.1, and GSOD precipitation data products.

[0037] In an optional embodiment, in order to improve the accuracy of the evaluation of the candidate satellite precipitation data, the prediction device pre-processes the several candidate satellite precipitation data and the site measured precipitation data of the basin, wherein the pre-processing includes data completion, data correction, and data verification.

[0038] Specifically, for the possible data missing value problem in the candidate satellite precipitation data and the site measured precipitation data, the prediction device uses a multi-scale interpolation method for data completion. For the short-time scale data missing problem in the candidate satellite precipitation data and the site measured precipitation data, linear interpolation and cubic spline interpolation are used for time dimension data reconstruction. In the spatial dimension, Kriging interpolation and inverse distance weighting method are combined to fill the missing data in the sparse area of the site measured precipitation data. At the same time, a multi-source data cross-validation mechanism is introduced to improve the interpolation accuracy by using the complementarity of different candidate satellite precipitation data and reanalysis data.

[0039] For the possible extreme abnormal value problem in the candidate satellite precipitation data and the site measured precipitation data after data completion, the prediction device identifies the abnormal data in the candidate satellite precipitation data and the site measured precipitation data after data completion by combining statistical distribution analysis and physical consistency test, uses a sliding window percentile method to remove extreme abnormal values, and corrects unreasonable precipitation values based on a climatic threshold.

[0040] For data verification, the prediction device uses a multi-level verification strategy, including internal consistency test, spatial matching degree analysis of site and grid data, and time series continuity detection, to ensure that the candidate satellite precipitation data and the site measured precipitation data after data correction have high reliability and physical reasonableness, effectively improving the data quality and laying a solid foundation for subsequent satellite precipitation data performance evaluation.

[0041] S2: Perform multi-dimensional perspective evaluation based on precipitation data of several candidate satellites and measured precipitation data of the site to obtain perspective evaluation indicators of several dimensions of the precipitation data of the several candidate satellites.

[0042] In this embodiment, the prediction device performs a multi-dimensional perspective evaluation based on several candidate satellite precipitation data and the measured precipitation data of the site to obtain perspective evaluation indicators of several dimensions of the several candidate satellite precipitation data, wherein the perspective evaluation indicators include seasonal perspective evaluation indicators and extreme precipitation perspective evaluation indicators, the seasonal perspective evaluation indicators include monthly scale correlation evaluation indicators and precipitation probability density distribution evaluation indicators; the extreme precipitation perspective evaluation indicators include daily scale correlation evaluation indicators, extreme precipitation event evaluation indicators, extreme precipitation intensity evaluation indicators, uncertainty analysis evaluation indicators and daily scale precipitation probability density distribution evaluation indicators.

[0043] For the monthly scale correlation evaluation index and precipitation probability density distribution evaluation index in the seasonal perspective evaluation index, please refer to Figure 2 , Figure 2 The flowchart of S2 in the hydrological and meteorological prediction method for a river basin provided in the first embodiment of the present application includes steps S201 to S203, which are specifically as follows:

[0044] S201: Using a Pearson coefficient calculation method, Pearson coefficients are calculated based on the measured precipitation data of the station and precipitation data of several months among the precipitation data of several candidate satellites to obtain monthly-scale Pearson coefficients of the precipitation data of the several candidate satellites as the monthly-scale correlation evaluation index.

[0045] In this embodiment, the prediction device adopts the Pearson coefficient calculation method to calculate the Pearson coefficient based on the measured precipitation data of the site, the precipitation data of several months in the precipitation data of several candidate satellites, and the preset Pearson coefficient calculation algorithm, and obtains the monthly scale Pearson coefficient of the precipitation data of several candidate satellites as the monthly scale correlation evaluation index. The synchronization of the variable change is measured by standardizing the covariance. The value range of the monthly scale Pearson coefficient is [ - 1,1], the monthly scale Pearson coefficient value of 1 indicates that the candidate satellite precipitation data is more consistent with the information of the station measured precipitation data, and the reference value is higher. The Pearson coefficient calculation algorithm is:

[0046]

[0047] Where CC is the monthly scale Pearson coefficient, x i is the precipitation data of the i-th month in the candidate satellite precipitation data, is the monthly average precipitation data of candidate satellite precipitation data, yi is the precipitation data of the i-th month in the site measured precipitation data, is the monthly average precipitation data of the point measured precipitation data, and n is the number of monthly precipitation data.

[0048] S202: According to the site measured precipitation data and a plurality of candidate satellite precipitation data, respectively, the goodness of fit is calculated, and the target goodness of fit sequence of the site measured precipitation data and a plurality of candidate satellite precipitation data is constructed.

[0049] In this embodiment, the prediction device calculates the goodness of fit according to the site measured precipitation data and a plurality of candidate satellite precipitation data, respectively, and constructs the target goodness of fit sequence of the site measured precipitation data and a plurality of candidate satellite precipitation data, wherein the goodness of fit sequence includes a plurality of grid target goodness of fit.

[0050] Please refer to Figure 3 , Figure 3 is the flow chart of S202 in the hydro-meteorological prediction method of the basin provided by the first embodiment of the present application, which includes steps S2021-S2024, and specifically as follows:

[0051] S2021: According to the site measured precipitation data and a plurality of candidate satellite precipitation data, respectively, the kurtosis is calculated, and the grid kurtosis data of the site measured precipitation data and a plurality of candidate satellite precipitation data is obtained.

[0052] In this embodiment, the prediction device calculates the kurtosis according to the site measured precipitation data and a plurality of candidate satellite precipitation data, respectively, and obtains the grid kurtosis data of the site measured precipitation data and a plurality of candidate satellite precipitation data, wherein the grid kurtosis data includes a plurality of grid kurtosis.

[0053] Specifically, the kurtosis of the grid is that the prediction device respectively obtains the first L-moment and the second L-moment of the grid of the site measured precipitation data and a plurality of candidate satellite precipitation data by using a direct estimator, and the corresponding second L-moment is divided by the first L-moment to obtain, wherein the first L-moment is:

[0054]

[0055] In the formula, is the first L-moment, D is the number of samples, i.e. the number of days corresponding to the precipitation data, x (i:D) is the precipitation data of the i-th day.

[0056] The second L-moment is:

[0057]

[0058] wherein, is the second L-moment.

[0059] S2022: According to a preset intermediate parameter calculation algorithm, intermediate parameter calculation is performed based on the grid kurtosis data of the station measured precipitation data and the grid kurtosis data of the plurality of candidate satellite precipitation data respectively, to obtain grid intermediate parameter data of the station measured precipitation data and the plurality of candidate satellite precipitation data.

[0060] In this embodiment, the prediction device performs intermediate parameter calculation based on the grid kurtosis data of the station measured precipitation data and the grid kurtosis data of the plurality of candidate satellite precipitation data respectively according to a preset intermediate parameter calculation algorithm, to obtain grid intermediate parameter data of the station measured precipitation data and the plurality of candidate satellite precipitation data, wherein the grid intermediate parameter data includes deviation parameters and standard deviation parameters of a plurality of grids, and the intermediate parameter calculation algorithm is:

[0061]

[0062] wherein, B4 is the deviation parameter, N sim is the number of simulations, m is the mth simulation, is the Monte Carlo simulation parameter, τ4 is the kurtosis of the corresponding precipitation data, and σ4 is the standard deviation parameter. Specifically, the Monte Carlo simulation parameter is obtained by performing the mth Monte Carlo simulation according to the Kappa probability distribution of the station measured precipitation data.

[0063] S2023: According to a preset parameter distribution goodness-of-fit calculation algorithm, goodness-of-fit calculation is performed based on the grid kurtosis data and the grid intermediate parameter data of the station measured precipitation data, and the grid kurtosis data and the grid intermediate parameter data of the plurality of candidate satellite precipitation data respectively, to obtain grid goodness-of-fit data corresponding to a plurality of parameter distributions of the station measured precipitation data and the plurality of candidate satellite precipitation data.

[0064] In this embodiment, the prediction device performs goodness-of-fit calculation based on the grid kurtosis data and the grid intermediate parameter data of the station measured precipitation data, and the grid kurtosis data and the grid intermediate parameter data of the plurality of candidate satellite precipitation data respectively according to a preset parameter distribution goodness-of-fit calculation algorithm, to obtain grid goodness-of-fit data corresponding to a plurality of parameter distributions of the station measured precipitation data and the plurality of candidate satellite precipitation data, wherein the grid simulation goodness-of-fit data includes simulation goodness-of-fit of a plurality of grids, and the goodness-of-fit calculation algorithm is:

[0065]

[0066] wherein, is the kurtosis of the corresponding grid in the grid kurtosis data corresponding to the parameter distribution, Z DIST is the simulated goodness-of-fit of the corresponding grid in the grid goodness-of-fit data corresponding to the parameter distribution. Specifically, the parameter distribution includes a generalized extreme value distribution (GEV), a generalized logistic distribution (GLO), a generalized Pareto distribution (GPA), a Pearson type III distribution (PE3), and a generalized normal distribution (GNO).

[0067] S2024: Comparing the simulated goodness-of-fit of each grid in the grid goodness-of-fit data corresponding to the parameter distribution of the site measured precipitation data and the plurality of candidate satellite precipitation data with the standard goodness-of-fit corresponding to the corresponding parameter distribution, and determining the simulated goodness-of-fit of the target parameter distribution of each grid according to the comparison result as the target goodness-of-fit of the grid, and constructing the target goodness-of-fit sequence of the site measured precipitation data and the plurality of candidate satellite precipitation data.

[0068] In this embodiment, the prediction device compares the simulated goodness-of-fit of each grid in the grid goodness-of-fit data corresponding to the parameter distribution of the site measured precipitation data and the plurality of candidate satellite precipitation data with the standard goodness-of-fit corresponding to the corresponding parameter distribution, and determines the simulated goodness-of-fit of the target parameter distribution of each grid according to the comparison result as the target goodness-of-fit of the grid, and constructs the target goodness-of-fit sequence of the site measured precipitation data and the plurality of candidate satellite precipitation data. By comparing the simulated goodness-of-fit and the standard goodness-of-fit of multiple parameter distributions, the best goodness-of-fit of each grid is determined as the target goodness-of-fit.

[0069] S203: Using the Kendall correlation coefficient calculation method, the target goodness-of-fit sequence of each of the candidate satellite precipitation data and the goodness-of-fit sequence of the site measured precipitation data are used to calculate the Kendall correlation coefficient, and the Kendall correlation coefficient of the plurality of candidate satellite precipitation data is obtained as the precipitation probability density distribution evaluation index.

[0070] The Kendall correlation coefficient is a non-parametric statistical quantity used to measure the monotonic correlation of two groups of ordered variables, and does not depend on the distribution form of the data, and is suitable for correlation analysis of continuous variables. Compared with the Pearson correlation coefficient, the Kendall correlation coefficient is not sensitive to outliers, and pays more attention to the consistency of data ordering rather than the linear relationship of specific numerical values.

[0071] In the embodiment, the prediction device adopts the Kendall correlation coefficient calculation method, and calculates the Kendall correlation coefficient according to the target goodness-of-fit sequence of each candidate satellite precipitation data and the goodness-of-fit sequence of the station measured precipitation data, to obtain the Kendall correlation coefficient of the candidate satellite precipitation data as the precipitation probability density distribution evaluation index, wherein the Kendall correlation coefficient calculation algorithm is as follows:

[0072]

[0073] In the formula, τ is the Kendall correlation coefficient, N c is the consistent number of pairs, N d is the inconsistent number of pairs, N0 is the total number of pairs, N1 is the first adjustment term, N2 is the second adjustment term, wherein N c and N d are obtained by combining and comparing the target goodness-of-fit of a plurality of grids in the target goodness-of-fit sequence of the station measured precipitation data and the target goodness-of-fit of a plurality of grids in the target goodness-of-fit sequence of the candidate satellite precipitation data, respectively.

[0074] For the daily scale correlation evaluation index, the extreme precipitation event evaluation index and the extreme precipitation intensity evaluation index in the extreme precipitation perspective evaluation index, please refer to Figure 4 , Figure 4 is the flowchart of S2 in the hydro-meteorological prediction method of the basin provided in the second embodiment of the application, including steps S211-S215, and the details are as follows:

[0075] S211: The Pearson coefficient calculation method is adopted, and the Pearson coefficient is calculated according to the precipitation data of a plurality of days in the station measured precipitation data and a plurality of candidate satellite precipitation data, to obtain the daily scale Pearson coefficient of the candidate satellite precipitation data as the daily scale correlation evaluation index.

[0076] In the embodiment, the prediction device adopts the Pearson coefficient calculation method, and calculates the Pearson coefficient according to the precipitation data of a plurality of days in the station measured precipitation data and a plurality of candidate satellite precipitation data, to obtain the daily scale Pearson coefficient of the candidate satellite precipitation data as the daily scale correlation evaluation index. For specific embodiments, reference can be made to step S201, which will not be repeated here.

[0077] S212: According to the precipitation data of a plurality of days in the station measured precipitation data and a plurality of candidate satellite precipitation data and the preset extreme precipitation event evaluation index calculation algorithm, the extreme precipitation event evaluation index of the candidate satellite precipitation data is obtained.

[0078] The evaluation index of extreme precipitation event is equitable threat score (ETS), which is an evaluation index for binary event prediction (such as whether precipitation occurs), and the core goal is to quantify the prediction skill on the basis of random prediction. The value range of ETS is [-0.33, 1], and the higher the value is, the better the prediction skill is.

[0079] In the embodiment, the prediction device obtains the evaluation index of extreme precipitation event of the plurality of candidate satellite precipitation data according to the site measured precipitation data, precipitation data of a plurality of days in the plurality of candidate satellite precipitation data, and a preset evaluation index calculation algorithm of extreme precipitation event.

[0080]

[0081] In the formula, ETS is the evaluation index of extreme precipitation event, A is the number of precipitation events observed by the satellite and the site at the same time, B is the number of precipitation events observed by the satellite but not observed by the site, C is the number of precipitation events observed by the site but not observed by the satellite, and D is the number of precipitation events not captured by the satellite and the site, wherein A, B, C, and N d are obtained by comparing the site measured precipitation data with the precipitation data of the same month in the plurality of candidate satellite precipitation data, respectively.

[0082] S213: Obtain the extreme precipitation data of a plurality of time units of a plurality of grids of the site measured precipitation data and the plurality of candidate satellite precipitation data according to the site measured precipitation data, the precipitation data of a plurality of days of a plurality of grids of the plurality of candidate satellite precipitation data in the basin, and a preset extreme precipitation data calculation algorithm.

[0083] In the embodiment, the prediction device extracts the precipitation data of a plurality of days greater than the quantile threshold from the site measured precipitation data and the plurality of candidate satellite precipitation data according to the site measured precipitation data, the precipitation data of a plurality of days of a plurality of grids of the plurality of candidate satellite precipitation data in the basin, and a preset extreme precipitation data calculation algorithm, accumulates the precipitation data of a plurality of days greater than the quantile threshold in the site measured precipitation data corresponding to the same grid in the same time unit, and accumulates the precipitation data of a plurality of days greater than the quantile threshold in the plurality of candidate satellite precipitation data corresponding to the same grid in the same time unit, respectively, to obtain the extreme precipitation data of a plurality of time units of a plurality of grids of the site measured precipitation data and the plurality of candidate satellite precipitation data, wherein the extreme precipitation data calculation algorithm is:

[0084]

[0085] wherein R95 p is the extreme precipitation data of the current time unit of the grid, p v is the extreme precipitation data of the i-th day of the current time unit of the grid, p i is the extreme precipitation data of the i-th day of the current time unit of the grid, p 95th is the 95% quantile threshold, and n' is the number of days of the current time unit, specifically, the time unit can be a year, a month, or a preset statistical number including a plurality of days.

[0086] S214: combining the extreme precipitation data of a plurality of time units of the same grid of the site measured precipitation data respectively to construct an extreme precipitation sequence corresponding to the site measured precipitation data of a plurality of grids; combining the extreme precipitation data of a plurality of time units of the same grid based on a plurality of candidate satellite precipitation data respectively to construct an extreme precipitation sequence corresponding to the plurality of candidate satellite precipitation data of a plurality of grids.

[0087] In this embodiment, the prediction device combines the extreme precipitation data of a plurality of time units of the same grid of the site measured precipitation data respectively to construct an extreme precipitation sequence corresponding to the site measured precipitation data of a plurality of grids; combines the extreme precipitation data of a plurality of time units of the same grid based on a plurality of candidate satellite precipitation data respectively to construct an extreme precipitation sequence corresponding to the plurality of candidate satellite precipitation data of a plurality of grids.

[0088] S215: using a Pearson coefficient calculation method, performing Pearson coefficient calculation on the extreme precipitation data of a plurality of time units in the extreme precipitation sequence of the same grid and the extreme precipitation sequence corresponding to the plurality of candidate satellite precipitation data to obtain a Pearson coefficient corresponding to the plurality of candidate satellite precipitation data of a plurality of grids, accumulating and averaging the Pearson coefficients of a plurality of grids corresponding to the same candidate satellite precipitation data, and taking the obtained accumulation average result as the extreme precipitation intensity evaluation index.

[0089] In this embodiment, the prediction device uses a Pearson coefficient calculation method, performs Pearson coefficient calculation on the extreme precipitation data of a plurality of time units in the extreme precipitation sequence of the same grid and the extreme precipitation sequence corresponding to the plurality of candidate satellite precipitation data to obtain a Pearson coefficient corresponding to the plurality of candidate satellite precipitation data of a plurality of grids, accumulates and averages the Pearson coefficients of a plurality of grids corresponding to the same candidate satellite precipitation data, and takes the obtained accumulation average result as the extreme precipitation intensity evaluation index, thereby obtaining the extreme precipitation intensity evaluation index of the plurality of candidate satellite precipitation data, wherein the extreme precipitation intensity evaluation index is:

[0090]

[0091] In the formula, EPI is the Pearson coefficient of the grid, X is the number of time units in the extreme precipitation sequence, x R95p,i is the extreme precipitation data of the i-th time unit in the extreme precipitation sequence corresponding to the candidate satellite precipitation data of the grid, is the average extreme precipitation data of the extreme precipitation sequence corresponding to the candidate satellite precipitation data of the grid, y R95p,i is the extreme precipitation data of the i-th time unit in the extreme precipitation sequence corresponding to the measured precipitation data of the grid, is the average extreme precipitation data of the extreme precipitation sequence corresponding to the measured precipitation data of the grid.

[0092] For the uncertainty analysis evaluation index in the extreme precipitation perspective evaluation index, please refer to Figure 5 , Figure 5 is the flowchart of S2 in the hydro-meteorological prediction method of the basin provided by the third embodiment of the present application, comprising steps S221-S222, and the details are as follows:

[0093] S221: According to the preset number of satellite precipitation data combination data, a plurality of candidate satellite precipitation data with the corresponding number of satellite precipitation data combination data are extracted from the plurality of candidate satellite precipitation data for multi-sequence transformation combination, and a plurality of satellite precipitation data combinations are constructed.

[0094] In this embodiment, the prediction device extracts a plurality of candidate satellite precipitation data with the corresponding number of satellite precipitation data combination data from the plurality of candidate satellite precipitation data according to the preset number of satellite precipitation data combination data, and performs multi-sequence transformation combination to construct a plurality of satellite precipitation data combinations.

[0095] S222: According to the plurality of satellite precipitation data combinations and the preset uncertainty analysis evaluation index calculation algorithm, the uncertainty analysis evaluation value of the candidate satellite precipitation data at the first position index of the plurality of satellite precipitation data combinations is obtained, the uncertainty analysis evaluation values of the same candidate satellite precipitation data are accumulated and averaged as the uncertainty analysis evaluation index, and the uncertainty analysis evaluation index of the plurality of candidate satellite precipitation data is obtained.

[0096] In the embodiment, the prediction device calculates an uncertainty analysis evaluation index of a first position index candidate satellite precipitation data of a plurality of satellite precipitation data combinations according to a preset uncertainty analysis evaluation index calculation algorithm, accumulatively averages the uncertainty analysis evaluation index of the same candidate satellite precipitation data as an uncertainty analysis evaluation index, and obtains the uncertainty analysis evaluation index of a plurality of candidate satellite precipitation data, wherein the uncertainty analysis evaluation index calculation algorithm is:

[0097]

[0098] wherein ρ k is an uncertainty analysis evaluation index of a first position index candidate satellite precipitation data in a current satellite precipitation data combination, y k is precipitation data of the first position index candidate satellite precipitation data in the current satellite precipitation data combination, y i is an i-th position index other candidate satellite precipitation data in the current satellite precipitation data combination, y j is a j-th position index other candidate satellite precipitation data in the current satellite precipitation data combination, and Cov(·) is a covariance function and Var(·) is a variance function.

[0099] Specifically, taking CMORPH, PERSIANN, IMERG, ERA5-Land, MSWEP and GSMaP precipitation data products as examples, the number of satellite precipitation data combination data is set to 3, 3 candidate satellite precipitation data are extracted from a plurality of candidate satellite precipitation data for multi-sequence transformation combination, and a plurality of satellite precipitation data combinations are constructed.

[0100] According to the plurality of satellite precipitation data combinations constructed and the uncertainty analysis evaluation index calculation algorithm, an uncertainty analysis evaluation index of a first position index candidate satellite precipitation data of a plurality of satellite precipitation data combinations is obtained, the uncertainty analysis evaluation index of the same candidate satellite precipitation data is accumulatively averaged as an uncertainty analysis evaluation index, and the uncertainty analysis evaluation index of a plurality of candidate satellite precipitation data is obtained, and the uncertainty analysis evaluation index is as follows:

[0101]

[0102] wherein ρ a is an uncertainty analysis evaluation index of a first position index candidate satellite precipitation data in a current satellite precipitation data combination, y a is precipitation data of the first position index candidate satellite precipitation data in the current satellite precipitation data combination, y bother candidate satellite precipitation data for the bth position index in the current satellite precipitation data combination, y c other candidate satellite precipitation data for the cth position index in the current satellite precipitation data combination.

[0103] S3: standardize the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data, and calculate the weights of the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data respectively, to obtain the weight parameters corresponding to the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data.

[0104] In the embodiment, the prediction device standardizes the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data. Specifically, the prediction device standardizes the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data according to a preset standardization method, to obtain the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data after standardization, wherein the standardization method includes positive standardization and negative standardization, and the positive standardization is:

[0105]

[0106] and the negative standardization is:

[0107]

[0108] wherein x ij is the value of the perspective evaluation indicator of the jth dimension of the ith candidate satellite precipitation data, x j is a sequence composed of all perspective evaluation indicators of the jth dimension, min(·) is a minimum value function, and max(·) is a maximum value function.

[0109] The prediction device calculates the weights of the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data after standardization respectively, to obtain the weight parameters corresponding to the perspective evaluation indicators of the dimensions of the candidate satellite precipitation data.

[0110] For the seasonal perspective evaluation indicator, please refer to Figure 6 , Figure 6 is a flowchart of S3 in the hydro-meteorological prediction method of the basin provided in the first embodiment of the present application, including steps S301-S302, and specifically as follows:

[0111] S301: according to the seasonal perspective evaluation indicators of the candidate satellite precipitation data after standardization and a preset proportion parameter calculation algorithm, obtain the proportion parameters of the seasonal perspective evaluation indicators of the candidate satellite precipitation data.

[0112] In the embodiment, the prediction device obtains the proportion parameters of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data according to the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data after standardization and a preset proportion parameter calculation algorithm, wherein the proportion parameter calculation algorithm is:

[0113]

[0114] In the formula, p ij is the proportion parameter of the jth seasonal view evaluation sub-index in the seasonal view evaluation index of the ith candidate satellite precipitation data, L is the number of candidate satellite precipitation data, r ij is the value of the jth seasonal view evaluation index in the seasonal view evaluation index of the ith candidate satellite precipitation data after standardization.

[0115] S302: Obtain the information entropy of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data according to the proportion parameters of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data and a preset information entropy algorithm; obtain the weight parameters of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data according to the entropy redundancy of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data and a preset seasonal view weight parameter algorithm.

[0116] In the embodiment, the prediction device obtains the information entropy of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data according to the proportion parameters of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data and a preset information entropy algorithm, wherein the information entropy algorithm is:

[0117]

[0118] In the formula, e ij is the entropy redundancy of the jth seasonal view evaluation sub-index in the seasonal view evaluation index of the ith candidate satellite precipitation data, q is a preset non-ubiquity index, is the qth power of the proportion parameter of the jth seasonal view evaluation sub-index in the seasonal view evaluation index of the ith candidate satellite precipitation data.

[0119] The prediction device obtains the weight parameters of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data according to the entropy redundancy of the seasonal view evaluation indexes of the plurality of candidate satellite precipitation data and a preset seasonal view weight parameter algorithm, wherein the seasonal view weight parameter algorithm is:

[0120]

[0121] Where w ij is the corresponding weight parameter of the jth seasonal perspective evaluation sub-indicator in the seasonal perspective evaluation index of the i-th candidate satellite precipitation data, and n is the number of seasonal perspective evaluation sub-indicators.

[0122] For the extreme precipitation perspective evaluation metrics, see Figure 7 , Figure 7 The flowchart of S3 in the hydrological and meteorological prediction method for a river basin provided in the fourth embodiment of the present application includes step S311, which is specifically as follows:

[0123] S311: Obtaining weight parameters of the extreme precipitation perspective evaluation indicators of the plurality of candidate satellite precipitation data according to the standardized extreme precipitation perspective evaluation indicators and a preset extreme precipitation perspective weight calculation algorithm.

[0124] In this embodiment, the prediction device obtains weight parameters of the extreme precipitation perspective evaluation indicators of the plurality of candidate satellite precipitation data based on the standardized extreme precipitation perspective evaluation indicators and a preset extreme precipitation perspective weight calculation algorithm, wherein the extreme precipitation perspective weight calculation algorithm is:

[0125]

[0126] Where w ik is the corresponding weight parameter of the kth extreme precipitation perspective evaluation sub-index in the extreme precipitation perspective evaluation index of the i-th candidate satellite precipitation data, I ik is the value of the kth extreme precipitation perspective evaluation sub-indicator in the extreme precipitation perspective evaluation index of the i-th candidate satellite precipitation data, β is the weight adjustment factor, and m is the number of extreme precipitation perspective evaluation sub-indicators.

[0127] S4: Calculating evaluation values ​​based on the measured precipitation data of the site, perspective evaluation indicators of multiple dimensions of the candidate satellite precipitation data, and corresponding weight parameters to obtain evaluation values ​​of the multiple candidate satellite precipitation data; and selecting target candidate satellite precipitation data from the multiple candidate satellite precipitation data based on the evaluation values ​​of the multiple candidate satellite precipitation data.

[0128] In this embodiment, the prediction device calculates the evaluation value based on the measured precipitation data of the site, the perspective evaluation indicators of several dimensions of the precipitation data of several candidate satellites, and the corresponding weight parameters to obtain the evaluation values ​​of the precipitation data of several candidate satellites.

[0129] The prediction device selects, from the plurality of candidate satellite precipitation data, a candidate satellite precipitation data with the highest evaluation value as the target candidate satellite precipitation data according to the evaluation value of the plurality of candidate satellite precipitation data.

[0130] Please refer to Figure 8 , Figure 8 The flowchart of S4 of the hydro-meteorological prediction method for a river basin according to the first embodiment of the present application includes steps S41-S42, and details are as follows.

[0131] S41: Obtain the daily-scale precipitation intensity probability distribution data of the station measured precipitation data and the plurality of candidate satellite precipitation data; and obtain the precipitation probability density distribution difference data of the plurality of candidate satellite precipitation data according to the daily-scale precipitation intensity probability distribution data of the station measured precipitation data and the plurality of candidate satellite precipitation data and a preset precipitation probability density distribution difference calculation algorithm.

[0132] In the present embodiment, the prediction device obtains the daily-scale precipitation intensity probability distribution data of the station measured precipitation data and the plurality of candidate satellite precipitation data, wherein the daily-scale precipitation intensity probability distribution data includes the daily-scale precipitation intensity probability distribution data of the candidate satellite precipitation data at a plurality of levels of a histogram, specifically, the number of histogram bins is divided into five levels, and the 1-5 levels correspond to the daily-scale precipitation intensity intervals [0, 10), [10, 25), [25, 50), [50, 100) and [100, ∞), i.e., light rain, moderate rain, heavy rain, heavy rain and extremely heavy rain.

[0133] The prediction device obtains the precipitation probability density distribution difference data of the plurality of candidate satellite precipitation data according to the daily-scale precipitation intensity probability distribution data of the station measured precipitation data and the plurality of candidate satellite precipitation data and a preset precipitation probability density distribution difference calculation algorithm, wherein the precipitation probability density distribution difference calculation algorithm is:

[0134]

[0135] In the formula, D is the precipitation probability density distribution difference data, N is a preset number of histogram bins, is the daily-scale precipitation intensity probability distribution data of the candidate satellite precipitation data at the b-th level of the histogram, is the daily-scale precipitation intensity probability distribution data of the station measured precipitation data at the b-th level of the histogram.

[0136] S42: Calculate the evaluation value of the plurality of candidate satellite precipitation data according to the precipitation probability distribution difference data of the plurality of candidate satellite precipitation data, the perspective evaluation index of the plurality of dimensions, the corresponding weight parameter and a preset evaluation value calculation algorithm.

[0137] The evaluation value calculation algorithm is:

[0138]

[0139] In the formula, S is the evaluation value of the candidate satellite precipitation data, w a is the weight parameter corresponding to the perspective evaluation index of the a-th dimension of the candidate satellite precipitation data, I a is the value of the perspective evaluation index of the a-th dimension, U is the number of dimensions, and γ is a preset correction strength coefficient.

[0140] In this embodiment, the prediction device performs evaluation value calculation according to the precipitation probability distribution difference data of a plurality of candidate satellite precipitation data, the perspective evaluation indexes of a plurality of dimensions, the corresponding weight parameters, and the preset evaluation value calculation algorithm, to obtain the evaluation values of the plurality of candidate satellite precipitation data.

[0141] S5: performing hydro-meteorological prediction according to the target candidate satellite precipitation data and a preset hydro-meteorological prediction model to obtain a hydro-meteorological prediction result of the basin.

[0142] The hydro-meteorological prediction model adopts a WRF-Hydro distributed hydrological model, which is developed independently based on the WRF land surface process part and aims to simulate the interaction and process between the atmosphere and hydrology. The model is developed by using FORTRAN90 and has good expansibility and the ability to support large-scale parallel computing.

[0143] In this embodiment, the prediction device performs hydro-meteorological prediction according to the target candidate satellite precipitation data and a preset hydro-meteorological prediction model to obtain a hydro-meteorological prediction result of the basin. Through multi-dimensional perspective evaluation of a plurality of candidate satellite precipitation data of the basin, and weight parameter construction of the obtained perspective evaluation indexes of the candidate satellite precipitation data, a comprehensive evaluation system covering multiple dimensions of spatial and temporal variability, extreme precipitation events, extreme precipitation intensity, precipitation probability density distribution, and uncertainty analysis characteristics is constructed in combination with the perspective evaluation indexes and the weight parameters, the performance of the plurality of candidate satellite precipitation data of the basin is comprehensively analyzed, more targeted decision basis is provided for hydro-meteorological prediction of the basin, and the accuracy of hydro-meteorological prediction is improved.

[0144] Please refer to Figure 9 , Figure 9 FIG. 9 is a structural schematic diagram of a hydro-meteorological prediction device for a basin provided in the fifth embodiment of the present application. The device can realize all or part of the hydro-meteorological prediction device for a basin by software, hardware, or a combination of both. The device 9 includes:

[0145] The data acquisition module 91 is configured to obtain a plurality of candidate satellite precipitation data and site measured precipitation data of a basin;

[0146] The multi-dimensional perspective evaluation module 92 is configured to perform multi-dimensional perspective evaluation on the plurality of candidate satellite precipitation data and the site measured precipitation data, and obtain perspective evaluation indexes of a plurality of dimensions of the plurality of candidate satellite precipitation data, wherein the perspective evaluation indexes include seasonal perspective evaluation indexes and extreme precipitation perspective evaluation indexes.

[0147] The weight calculation module 93 is configured to perform standardization processing on the perspective evaluation indexes of the plurality of dimensions of the plurality of candidate satellite precipitation data, and perform weight calculation on the perspective evaluation indexes of the plurality of dimensions of the plurality of candidate satellite precipitation data after the standardization processing, respectively, to obtain weight parameters corresponding to the perspective evaluation indexes of the plurality of dimensions of the plurality of candidate satellite precipitation data.

[0148] The satellite precipitation data selection module 94 is configured to perform evaluation value calculation on the site measured precipitation data, the perspective evaluation indexes of the plurality of dimensions of the plurality of candidate satellite precipitation data, and the corresponding weight parameters, to obtain evaluation values of the plurality of candidate satellite precipitation data, and select target candidate satellite precipitation data from the plurality of candidate satellite precipitation data according to the evaluation values of the plurality of candidate satellite precipitation data.

[0149] The hydro-meteorological prediction module 95 is configured to perform hydro-meteorological prediction on the target candidate satellite precipitation data and a preset hydro-meteorological prediction model, to obtain a hydro-meteorological prediction result of the basin.

[0150] In the embodiment of the present application, the data acquisition module is used to obtain a plurality of candidate satellite precipitation data and site measured precipitation data of a basin; the multi-dimensional perspective evaluation module is used to perform multi-dimensional perspective evaluation according to the plurality of candidate satellite precipitation data and the site measured precipitation data, to obtain a plurality of dimension perspective evaluation indexes of the plurality of candidate satellite precipitation data, wherein the perspective evaluation indexes include seasonal perspective evaluation indexes and extreme precipitation perspective evaluation indexes; the weight calculation module is used to perform standardization processing on the plurality of dimension perspective evaluation indexes of the plurality of candidate satellite precipitation data, to respectively perform weight calculation on the plurality of dimension perspective evaluation indexes of the plurality of candidate satellite precipitation data after the standardization processing, to obtain corresponding weight parameters of the plurality of dimension perspective evaluation indexes of the plurality of candidate satellite precipitation data; the satellite precipitation data selection module is used to perform evaluation value calculation according to the site measured precipitation data, the plurality of dimension perspective evaluation indexes of the plurality of candidate satellite precipitation data and the corresponding weight parameters, to obtain evaluation values of the plurality of candidate satellite precipitation data; the target candidate satellite precipitation data is selected from the plurality of candidate satellite precipitation data according to the evaluation values of the plurality of candidate satellite precipitation data; the hydro-meteorological prediction module is used to perform hydro-meteorological prediction according to the target candidate satellite precipitation data and a preset hydro-meteorological prediction model, to obtain a hydro-meteorological prediction result of the basin. Through multi-dimensional perspective evaluation on a plurality of candidate satellite precipitation data of a basin, and weight parameter construction on the obtained perspective evaluation indexes of the candidate satellite precipitation data, a comprehensive evaluation system covering multiple dimensions of perspective, such as spatio-temporal variability, extreme precipitation event, extreme precipitation intensity, precipitation probability density distribution and uncertainty analysis characteristics, is constructed in combination with the perspective evaluation indexes and the weight parameters, the performance of a plurality of candidate satellite precipitation data of a basin is comprehensively analyzed, more targeted decision basis is provided for hydro-meteorological prediction of the basin, and the accuracy of hydro-meteorological prediction is improved.

[0151] Please refer to Figure 10 , Figure 10 The structure schematic diagram of the computer device provided in the sixth embodiment of the present application is shown in FIG. 10. The computer device 10 includes a processor 101, a memory 102, and a computer program 103 stored in the memory 102 and executable on the processor 101. The computer device can store a plurality of instructions, which are suitable for being loaded and executed by the processor 101 to perform the method steps of the first to fourth embodiments shown above. The specific execution process can refer to the specific description of the first to fourth embodiments, which will not be repeated here.

[0152] The processor 101 can include one or more processing cores. The processor 101 connects various parts within the server by various interfaces and lines, performs various functions and processes data of the hydro-meteorological prediction device 9 of the flow field by running or executing instructions, programs, code sets or instruction sets stored in the memory 102, and calling data in the memory 102. Optionally, the processor 101 can be implemented in at least one of the hardware forms of a digital signal processing (Digital Signal Processing, DSP), a field-programmable gate array (Field-Programmable Gate Array, FPGA), and a programable logic array (Programble Logic Array, PLA). The processor 101 can be integrated with one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content displayed on the touch display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 101, but can be realized by a separate chip.

[0153] The memory 102 can include a random access memory (Random Access Memory, RAM) and a read-only memory (Read-Only Memory). Optionally, the memory 102 includes a non-transitory computer-readable storage medium. The memory 102 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 102 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 102 can also be at least one storage device located away from the aforementioned processor 101.

[0154] The embodiments of the present application also provide a storage medium, which can store a plurality of instructions. The instructions are suitable for being loaded and executed by a processor to perform the method steps of the first to fourth embodiments described above. The specific execution process can refer to the specific description of the first to fourth embodiments, which will not be described here.

[0155] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for the convenience of mutual distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the above system can be referred to the corresponding process in the foregoing method embodiment, which will not be described here.

[0156] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0157] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the algorithm. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0158] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the above-mentioned apparatus / terminal device embodiments are only schematic, and the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0159] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0160] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0161] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form.

[0162] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications of the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims of the present invention and equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for hydrological and meteorological prediction of a river basin, characterized in that: The following steps are involved: Obtain several candidate satellite precipitation data and station-measured precipitation data for the basin; Performing a multi-dimensional perspective evaluation based on a plurality of candidate satellite precipitation data and the measured precipitation data at the site to obtain perspective evaluation indicators of a plurality of dimensions of the candidate satellite precipitation data, wherein the perspective evaluation indicators include a seasonal perspective evaluation indicator and an extreme precipitation perspective evaluation indicator; performing standardization processing on the viewing angle evaluation indicators of the plurality of dimensions of the plurality of candidate satellite precipitation data, performing weight calculation on the viewing angle evaluation indicators of the plurality of dimensions of the plurality of candidate satellite precipitation data after the standardization processing, and obtaining corresponding weight parameters of the viewing angle evaluation indicators of the plurality of dimensions of the plurality of candidate satellite precipitation data; Calculating evaluation values ​​based on the measured precipitation data at the site, perspective evaluation indicators of multiple dimensions of the candidate satellite precipitation data, and corresponding weight parameters to obtain evaluation values ​​of the multiple candidate satellite precipitation data; selecting target candidate satellite precipitation data from the multiple candidate satellite precipitation data based on the evaluation values ​​of the multiple candidate satellite precipitation data; A hydrological and meteorological forecast is performed based on the precipitation data of the target candidate satellite and a preset hydrological and meteorological forecast model to obtain a hydrological and meteorological forecast result for the watershed.

2. The method for hydrological and meteorological forecasting of a river basin according to claim 1, characterized in that: The seasonal perspective evaluation index includes several seasonal perspective evaluation sub-indicators, and the seasonal perspective evaluation sub-indicators include a monthly scale correlation evaluation index and a precipitation probability density distribution evaluation index; The multi-dimensional perspective evaluation is performed based on a plurality of candidate satellite precipitation data and the measured precipitation data of the site to obtain perspective evaluation indicators of a plurality of dimensions of the candidate satellite precipitation data, including the steps of: Using a Pearson coefficient calculation method, the Pearson coefficient is calculated based on the measured precipitation data of the station and the precipitation data of several months among the precipitation data of the candidate satellites, and the monthly-scale Pearson coefficients of the precipitation data of the candidate satellites are obtained as the monthly-scale correlation evaluation index; Performing goodness of fit calculations based on the site measured precipitation data and the plurality of candidate satellite precipitation data, respectively, to construct a target goodness of fit sequence of the site measured precipitation data and the plurality of candidate satellite precipitation data, wherein the goodness of fit sequence includes target goodness of fit of the plurality of grids in the watershed; The Kendall correlation coefficient calculation method is adopted to calculate the Kendall correlation coefficient according to the target goodness-of-fit sequence of each candidate satellite precipitation data and the goodness-of-fit sequence of the measured precipitation data of the station, and the Kendall correlation coefficients of the precipitation data of several candidate satellites are obtained as the precipitation probability density distribution evaluation index.

3. The method for hydrological and meteorological prediction of a river basin according to claim 2, characterized in that: The method of performing goodness of fit calculations based on the site measured precipitation data and the precipitation data of the plurality of candidate satellites, and constructing a goodness of fit sequence of the site measured precipitation data and the precipitation data of the plurality of candidate satellites, comprises the following steps: Calculating kurtosis based on the site measured precipitation data and the precipitation data of the plurality of candidate satellites to obtain grid kurtosis data of the site measured precipitation data and the precipitation data of the plurality of candidate satellites, wherein the grid kurtosis data includes kurtosis of the plurality of grids; According to a preset intermediate parameter calculation algorithm, intermediate parameter calculation is performed based on the grid kurtosis data of the station measured precipitation data and the precipitation data of the candidate satellites, respectively, to obtain grid intermediate parameter data of the station measured precipitation data and the precipitation data of the candidate satellites, wherein the grid intermediate parameter data includes deviation parameters and standard deviation parameters of the plurality of grids, wherein the intermediate parameter calculation algorithm is: Where B4 is the deviation parameter, N sim is the number of simulations, m is the mth simulation, is the Monte Carlo simulation parameter, τ4 is the kurtosis of the corresponding precipitation data, and σ4 is the standard deviation parameter; According to a preset parameter distribution fitting goodness calculation algorithm, a fitting goodness calculation is performed based on the grid kurtosis data and grid intermediate parameter data of the station measured precipitation data, and the grid kurtosis data and grid intermediate parameter data of the precipitation data of the plurality of candidate satellites, respectively, to obtain grid fitting goodness data corresponding to the parameter distributions of the station measured precipitation data and the plurality of candidate satellite precipitation data, wherein the grid simulation fitting goodness data includes the simulation fitting goodness of a plurality of grids, and the fitting goodness calculation algorithm is: Where, is the kurtosis of the corresponding grid in the grid kurtosis data corresponding to the parameter distribution, Z DIST is the simulated goodness of fit of the corresponding grid in the grid goodness of fit data corresponding to the parameter distribution; The simulated goodness of fit of several grids in the grid fit goodness data corresponding to several parameter distributions of the measured precipitation data at the site and the precipitation data of several candidate satellites are respectively compared with the standard goodness of fit corresponding to the corresponding parameter distribution. The simulated goodness of fit corresponding to the target parameter distribution of each grid is determined according to the comparison results and used as the target goodness of fit of the grid. The target goodness of fit sequence of the measured precipitation data at the site and the precipitation data of several candidate satellites is constructed.

4. The method for hydrological and meteorological forecasting of a river basin according to claim 2, characterized in that: The extreme precipitation perspective assessment index includes several extreme precipitation perspective assessment sub-indicators, and the extreme precipitation perspective assessment sub-indicators include a daily scale correlation assessment index, an extreme precipitation event assessment index, an extreme precipitation intensity assessment index, and an uncertainty analysis assessment index; The multi-dimensional perspective evaluation is performed based on a plurality of candidate satellite precipitation data and the measured precipitation data of the site to obtain perspective evaluation indicators of a plurality of dimensions of the candidate satellite precipitation data, including the steps of: Using a Pearson coefficient calculation method, the Pearson coefficient is calculated based on the measured precipitation data of the station and the precipitation data of several days among the precipitation data of the candidate satellites, and the daily-scale Pearson coefficients of the precipitation data of the candidate satellites are obtained as the daily-scale correlation evaluation index; According to the measured precipitation data of the site and the precipitation data of several days in the precipitation data of the candidate satellites, and a preset extreme precipitation event evaluation index calculation algorithm, the extreme precipitation event evaluation index of the candidate satellite precipitation data is obtained, wherein the extreme precipitation event evaluation index calculation algorithm is: Where ETS is the extreme precipitation event assessment index, A is the number of precipitation events observed by both satellites and stations, B is the number of precipitation events observed by satellites but not by stations, C is the number of precipitation events observed by stations but not by satellites, and D is the number of precipitation events not captured by both satellites and stations. According to the actual precipitation data of the station, the precipitation data of several days of several grids of the plurality of candidate satellite precipitation data in the basin, and a preset extreme precipitation data calculation algorithm, the extreme precipitation data of several time units of the actual precipitation data of the station and the plurality of grids of the plurality of candidate satellite precipitation data are obtained, wherein the extreme precipitation data calculation algorithm is: Where, R95p v is the extreme precipitation data of the current time unit of the grid, p i is the precipitation data of the i-th day of the current time unit of the grid, p 95th is the 95% quantile threshold, n′ is the number of days in the current time unit; The extreme precipitation data of several time units of the same grid of the measured precipitation data of the station are respectively combined to construct extreme precipitation sequences corresponding to the measured precipitation data of the station in the several grids; the extreme precipitation data of several time units of the same grid are respectively combined based on the precipitation data of several candidate satellites to construct extreme precipitation sequences corresponding to the precipitation data of several candidate satellites in the several grids; A Pearson coefficient calculation method is used to calculate the Pearson coefficient based on the extreme precipitation sequence corresponding to the measured precipitation data of the station in the same grid and the extreme precipitation data of several time units in the extreme precipitation sequence corresponding to the precipitation data of several candidate satellites, to obtain the Pearson coefficients corresponding to the precipitation data of several candidate satellites in several grids, to perform cumulative averaging on the Pearson coefficients of the several grids corresponding to the precipitation data of the same candidate satellite, and to use the obtained cumulative averaging result as the extreme precipitation intensity assessment indicator; According to a preset number of satellite precipitation data combination data, a number of candidate satellite precipitation data corresponding to the number of satellite precipitation data combination data are extracted from the plurality of candidate satellite precipitation data, and multiple sequence transformation and combination are performed to construct a plurality of satellite precipitation data combinations; According to the plurality of satellite precipitation data combinations and a preset uncertainty analysis evaluation index calculation algorithm, uncertainty analysis evaluation values ​​of candidate satellite precipitation data of the first position index of the plurality of satellite precipitation data combinations are obtained, and the uncertainty analysis evaluation values ​​of the same candidate satellite precipitation data are cumulatively averaged as the uncertainty analysis evaluation index to obtain uncertainty analysis evaluation indexes of the plurality of candidate satellite precipitation data, wherein the uncertainty analysis evaluation index calculation algorithm is: Where, ρ k The uncertainty analysis evaluation value of the candidate satellite precipitation data indexed by the first position in the current satellite precipitation data combination, y k The precipitation data of the candidate satellite precipitation data indexed by the first position in the current satellite precipitation data combination, y i is the other candidate satellite precipitation data indexed by the i-th position in the current satellite precipitation data combination, y j is the other candidate satellite precipitation data indexed by the j-th position in the current satellite precipitation data combination, Cov(·) is the covariance function, and Var(·) is the variance function.

5. The method for hydrological and meteorological prediction of a river basin according to claim 3, characterized in that: The weight calculation is performed on the viewing angle evaluation indicators of the plurality of dimensions of the standardized candidate satellite precipitation data to obtain corresponding weight parameters of the viewing angle evaluation indicators of the plurality of dimensions of the candidate satellite precipitation data, including the steps of: According to the seasonal perspective evaluation index of the plurality of candidate satellite precipitation data after standardization and a preset weight parameter calculation algorithm, the weight parameters of the seasonal perspective evaluation index of the plurality of candidate satellite precipitation data are obtained, wherein the weight parameter calculation algorithm is: Where p ij is the weight parameter of the jth seasonal perspective evaluation sub-index in the seasonal perspective evaluation index of the i-th candidate satellite precipitation data, L is the number of candidate satellite precipitation data, r ij is the value of the jth seasonal perspective evaluation index in the seasonal perspective evaluation index of the i-th candidate satellite precipitation data after normalization; According to the weight parameters of the seasonal perspective evaluation indicators of the satellite precipitation data of the candidate satellite precipitation products and a preset information entropy algorithm, the information entropy of the seasonal perspective evaluation indicators of the candidate satellite precipitation data is obtained; according to the entropy redundancy of the seasonal perspective evaluation indicators of the candidate satellite precipitation data and a preset seasonal perspective weight parameter algorithm, the weight parameters of the seasonal perspective evaluation indicators of the candidate satellite precipitation data are obtained, wherein the information entropy algorithm is: Where, e ij is the entropy redundancy of the jth seasonal perspective evaluation sub-index in the seasonal perspective evaluation index of the i-th candidate satellite precipitation data, q is the preset non-extensibility index, is the qth power of the weight parameter of the jth seasonal perspective evaluation sub-index in the seasonal perspective evaluation index of the i-th candidate satellite precipitation data; The seasonal perspective weight parameter algorithm is: Where w ij is the corresponding weight parameter of the jth seasonal perspective evaluation sub-indicator in the seasonal perspective evaluation index of the i-th candidate satellite precipitation data, and n is the number of seasonal perspective evaluation sub-indicators.

6. The method for hydrological and meteorological prediction of a river basin according to claim 4, characterized in that: The weight calculation is performed on the viewing angle evaluation indicators of the plurality of dimensions of the standardized candidate satellite precipitation data to obtain corresponding weight parameters of the viewing angle evaluation indicators of the plurality of dimensions of the candidate satellite precipitation data, including the steps of: According to the extreme precipitation perspective evaluation index of the plurality of candidate satellite precipitation data after standardization and a preset extreme precipitation perspective weight calculation algorithm, weight parameters of the extreme precipitation perspective evaluation index of the plurality of candidate satellite precipitation data are obtained, wherein the extreme precipitation perspective weight calculation algorithm is: Where w ik is the corresponding weight parameter of the kth extreme precipitation perspective evaluation sub-index in the extreme precipitation perspective evaluation index of the i-th candidate satellite precipitation data, I ik is the value of the kth extreme precipitation perspective evaluation sub-indicator in the extreme precipitation perspective evaluation index of the i-th candidate satellite precipitation data, β is the weight adjustment factor, and m is the number of extreme precipitation perspective evaluation sub-indicators.

7. The method for hydrological and meteorological prediction of a river basin according to claim 1, characterized in that: The step of calculating an evaluation value based on the measured precipitation data of the site, the perspective evaluation indicators of several dimensions of the precipitation data of the candidate satellites, and corresponding weight parameters to obtain the evaluation values ​​of the precipitation data of the candidate satellites comprises the following steps: Obtain daily-scale precipitation intensity probability distribution data of the site's measured precipitation data and the precipitation data of the candidate satellites; obtain precipitation probability density distribution difference data of the candidate satellite precipitation data based on the site's measured precipitation data and the daily-scale precipitation intensity probability distribution data of the candidate satellites and a preset precipitation probability density distribution difference calculation algorithm, wherein the precipitation probability density distribution difference calculation algorithm is: Where D is the precipitation probability density distribution difference data, N is the preset number of histogram boxes, is the daily-scale precipitation intensity probability distribution data of the candidate satellite precipitation data at level b of the histogram, is the daily-scale precipitation intensity probability distribution data of the station's measured precipitation data at level b of the histogram; An evaluation value calculation is performed based on the precipitation probability distribution difference data of the plurality of candidate satellite precipitation data, the perspective evaluation indicators of the plurality of dimensions, the corresponding weight parameters, and a preset evaluation value calculation algorithm to obtain evaluation values ​​of the plurality of candidate satellite precipitation data, wherein the evaluation value calculation algorithm is: Where S is the evaluation value of candidate satellite precipitation data, w a is the weight parameter corresponding to the perspective evaluation index weight of the ath dimension of the candidate satellite precipitation data, I a is the value of the viewing angle evaluation index of the ath dimension, U is the number of dimensions, and γ is the preset correction strength coefficient.

8. A hydrological and meteorological forecasting device for a river basin, characterized in that: include: The data acquisition module is used to obtain several candidate satellite precipitation data and site measured precipitation data of the basin; A multi-dimensional perspective evaluation module is used to perform multi-dimensional perspective evaluation based on a plurality of candidate satellite precipitation data and site measured precipitation data, and obtain perspective evaluation indicators of a plurality of dimensions of the candidate satellite precipitation data, wherein the perspective evaluation indicators include a seasonal perspective evaluation indicator and an extreme precipitation perspective evaluation indicator; a weight calculation module, configured to perform standardization processing on the viewing angle evaluation indicators of the plurality of dimensions of the plurality of candidate satellite precipitation data, and perform weight calculation on the viewing angle evaluation indicators of the plurality of dimensions of the plurality of candidate satellite precipitation data after the standardization processing, to obtain corresponding weight parameters of the viewing angle evaluation indicators of the plurality of dimensions of the plurality of candidate satellite precipitation data; a satellite precipitation data selection module, configured to calculate an evaluation value based on the measured precipitation data of the station, the perspective evaluation indicators of the multiple dimensions of the multiple candidate satellite precipitation data, and corresponding weight parameters to obtain evaluation values ​​of the multiple candidate satellite precipitation data; and select target candidate satellite precipitation data from the multiple candidate satellite precipitation data based on the evaluation values ​​of the multiple candidate satellite precipitation data; The hydrological and meteorological prediction module is used to perform hydrological and meteorological prediction based on the precipitation data of the target candidate satellite and a preset hydrological and meteorological prediction model to obtain the hydrological and meteorological prediction result of the basin.

9. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the method for hydrological and meteorological prediction of a river basin as described in any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for hydrological and meteorological prediction of a river basin according to any one of claims 1 to 7 are implemented.

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