Rainstorm coordination method for urban drainage and regional flood control design

Abstract

The invention discloses an urban drainage and regional flood control design rainstorm cooperation method, which comprises the steps of obtaining original rainfall data of a rainfall site, and generating a standardized rainfall data set through space-time adaptive anomaly detection processing; wherein the original rainfall data comprises long-duration regional rainfall sequence data and urban rainfall sequence data; based on the standardized rainfall data set, respectively extracting rainfall features of the region and the city, and performing scale conversion and unification to obtain a unified scale feature data set; based on the uniform scale feature data set, respectively constructing edge distribution functions of regional rainfall and urban rainfall, and respectively identifying an optimal Copula structure to obtain a time-varying parameter matrix; and based on the time-varying parameter matrix, respectively calculating conditional risk probabilities of regional rainfall and urban rainfall, and obtaining a risk combination vector through a multi-level risk propagation network. The method effectively improves the time-space collaboration of rainstorm design, and is suitable for flood control and drainage engineering design under climate change and urbanization backgrounds.

Description

Technical Field

[0001] The invention belongs to the field of hydrology and water resources, and in particular to a method for coordinating urban drainage and regional flood control design during heavy rain. Background Art

[0002] With the acceleration of urbanization and the intensification of climate change, the pressure on urban drainage and regional flood control is increasing. Urban drainage and regional flood control systems are closely related in space, and together they form a regional water security guarantee system. Design rainstorms are an important basis for the planning and design of flood control and drainage projects. Reasonable determination of the design rainstorm combination of urban drainage and regional flood control is of great significance to improving the overall efficiency of the flood control and drainage system and ensuring regional water security. Especially under the dual influence of climate change and urbanization, the characteristics of rainstorms show obvious non-stationarity, and the temporal and spatial distribution of rainstorms has changed, which poses new challenges to the traditional method of determining design rainstorms.

[0003] At present, the design rainstorm research for urban drainage and regional flood control is mainly based on probability statistics methods, using single variable distribution functions such as P-III distribution and GM distribution for fitting, or using two-dimensional Copula functions to build joint distribution models. In terms of risk assessment, it is mainly based on empirical formulas or simple conditional probability calculation methods to evaluate the risk of encounter under different combinations of return periods. In terms of data processing, traditional outlier test methods and linear interpolation techniques are usually used to process missing data, and the arithmetic mean method or Thiessen polygon method is used to calculate the average rainfall on the surface. In terms of scale conversion, it mainly relies on empirical formulas or statistical rainfall ratios for spatiotemporal scale conversion.

[0004] However, the existing research still has the following technical problems: First, in the data preprocessing stage, the traditional outlier identification method fails to fully consider the spatiotemporal correlation characteristics of rainfall data, resulting in insufficient accuracy in outlier identification and reconstruction; second, in the feature extraction process, the conventional principal component analysis method is difficult to capture the multi-scale dynamic characteristics of the rainfall process, and the effectiveness of feature extraction is limited; third, the existing spatiotemporal scale conversion method is too simplified and fails to reflect the nonlinear mapping relationship and energy transfer law between rainfall characteristics at different scales; fourth, the traditional static Copula function is difficult to characterize the time-varying dependence of rainfall characteristics, and parameter estimation lacks consideration of non-stationarity; finally, the existing risk assessment methods often separate urban drainage and regional flood control, ignoring the risk transmission mechanism between the two, and it is difficult to accurately assess systemic risks. These technical problems seriously restrict the synergy and applicability of urban drainage and regional flood control design rainstorms, and new technical methods need to be developed to solve them. Summary of the invention

[0005] The purpose of the invention is to provide a method for coordinating urban drainage and regional flood control design during heavy rains to solve the above-mentioned problems existing in the prior art.

[0006] Technical solution, a coordinated method for urban drainage and regional flood control design for rainstorms, including the following steps:

[0007] S1. Obtain the original rainfall data of the predetermined rainfall stations, and generate a standardized rainfall data set through spatiotemporal adaptive anomaly detection processing; wherein the original rainfall data includes long-duration regional rainfall series data and urban rainfall series data;

[0008] S2. Based on the standardized rainfall dataset, regional and urban rainfall characteristics are extracted respectively, and scale conversion and unification are performed to obtain a unified scale feature dataset;

[0009] S3. Based on the unified scale feature data set, the marginal distribution functions of regional rainfall and urban rainfall are constructed respectively and the optimal Copula structure is identified respectively to obtain the time-varying parameter matrix;

[0010] S4. Based on the time-varying parameter matrix, the conditional risk probabilities of regional rainfall and urban rainfall are calculated respectively, and the risk combination vector is obtained through a multi-level risk propagation network.

[0011] Beneficial effects: The present invention solves the mismatch problem between regional and urban rainfall in time and space scales, and realizes the effective integration of rainfall characteristics at different scales; accurately depicts the complex dependency relationship between regional and urban rainfall and its time-varying characteristics, realizes the dynamic assessment and propagation analysis of regional-urban rainfall risks, and reduces the risk of losses caused by extreme weather events; realizes the coordinated analysis and risk assessment of regional flood control and urban drainage design rainstorms. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of the present invention.

[0013] Figure 2 This is a flow chart of step S1 of the present invention.

[0014] Figure 3 This is a flow chart of step S2 of the present invention.

[0015] Figure 4 This is a flow chart of step S3 of the present invention.

[0016] Figure 5 This is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0017] like Figure 1 As shown, this application proposes a rainstorm coordination method for urban drainage and regional flood control design, including the following steps:

[0018] S1. Obtain the original rainfall data of the predetermined rainfall stations, and generate a standardized rainfall data set through spatiotemporal adaptive anomaly detection processing; wherein the original rainfall data includes long-duration regional rainfall series data and urban rainfall series data;

[0019] S2. Based on the standardized rainfall dataset, regional and urban rainfall characteristics are extracted respectively, and scale conversion and unification are performed to obtain a unified scale feature dataset;

[0020] S3. Based on the unified scale feature data set, the marginal distribution functions of regional rainfall and urban rainfall are constructed respectively and the optimal Copula structure is identified respectively to obtain the time-varying parameter matrix;

[0021] S4. Based on the time-varying parameter matrix, the conditional risk probabilities of regional rainfall and urban rainfall are calculated respectively, and the risk combination vector is obtained through a multi-level risk propagation network.

[0022] Since both urban drainage and regional flood control design rainstorms have a certain recurrence period, and the two have a certain interdependence and encounter pattern due to their spatial dependence and proximity. This interdependence is manifested as follows: when a rainstorm with a certain recurrence period occurs in the region, the urban rainstorm is also very likely to be near a certain magnitude, and when a rainstorm with a certain recurrence period occurs in the city, the regional rainstorm is also likely to be distributed in a certain magnitude. Therefore, when carrying out the combined calculation of urban drainage and regional flood control design rainstorms, collaborative design should also be carried out to make it conform to the interdependence between the two.

[0023] This embodiment uses a long series of regional and watershed precipitation data, establishes a dependent relationship between the two based on Copula multivariate joint distribution, and then uses the conditional distribution calculation formula to calculate the combined risk of mutual encounter, and screens the combined regional and urban design rainstorm combinations based on the principle of equal balance of mutual encounter risks. The rainstorm combination can provide a coordinated and reasonable design rainstorm calculation scenario for subsequent flood control and drainage standard research.

[0024] like Figure 2 As shown, according to one aspect of the present application, step S1 is further:

[0025] S11, read the original rainfall data of the predetermined rainfall stations, calculate the difference between each data point and the rainfall values ​​of the n time points before and after it and the m adjacent stations in space, and construct a rainfall difference matrix; based on the rainfall difference matrix, mark the data points whose difference exceeds the x% quantile of the same period in history as potential abnormal points; where n, m, and x are preset values;

[0026] S12, extract all rainfall data within N hours before and after each potential anomaly point and within the adjacent M square kilometers to construct a local rainfall feature vector; calculate the mean, variance, skewness and kurtosis of the local rainfall feature vector to form a feature parameter vector; arrange all feature parameter vectors in chronological order to form a feature statistical matrix; wherein N and M are predetermined values;

[0027] S13, based on the characteristic statistical matrix, calculating the Mahalanobis distance of each data point; determining the data point whose Mahalanobis distance is greater than a preset threshold as an outlier, reconstructing the outlier by using an interpolation method, and generating corrected rainfall data;

[0028] S14, standardize the corrected rainfall data, calculate the standard score of each data point relative to the pre-stored historical data of the same period, and generate a standardized rainfall data set.

[0029] In one embodiment of the present application, the spatiotemporal difference is: D(i, t)=w1∑|R(i, t)-R(i, t±k)|+w2∑|R(i, t)-R(j, t)|; wherein D(i, t) is the spatiotemporal difference of site i at time t; R(i, t) is the rainfall value of site i at time t; R(i, t±k) is the rainfall value of site i at k moments before and after; R(j, t) is the rainfall value of j adjacent sites of site i at time t; w1 and w2 are weight coefficients of time and space dimensions, and w1+ w2 = 1.

[0030] The historical quantile threshold is: Q(p, t) = F-1(p|Ht); where Q(p, t) is the threshold value of the p quantile at time t; F-1 is the inverse function of the empirical distribution function of the historical data; Ht is the historical data set for the same period; p is the preset quantile level, usually 0.95 or 0.99.

[0031] Construct the local rainfall characteristic vector, the characteristic vector expression is: V(i, t) = [μ(i, t), σ 2 (i, t), γ (i, t), κ (i, t)]; where μ (i, t) = 1 / n∑ R (i, t) is the mean; σ 2 (i, t) =1 / (n-1)∑(R(i, t)-μ) 2 is the variance; γ(i, t) = E[(R(i, t)-μ) 3 ] / σ 3 is the skewness; κ(i, t) = E[(R(i, t)-μ) 4 ] / σ 4 is the kurtosis; n is the number of samples in the spatiotemporal neighborhood, and E[ ] represents the expected function.

[0032] The Mahalanobis distance is: MD(i, t)=sqrt[(X(i, t)-μ)TΣ-1(X(i, t)-μ)]; where MD(i, t) is the Mahalanobis distance of the observation point (i, t); X(i, t) is the eigenvector of the observation point; μ is the mean of the eigenvector; Σ is the covariance matrix of the eigenvector.

[0033] The spatiotemporal interpolation reconstruction is: R'(i, t)=∑wk·R(k, t)+∑wl·R(i, l); where R'(i, t) is the reconstructed rainfall value; wk is the spatial weight coefficient; wl is the temporal weight coefficient; R(k, t) is the rainfall value of the spatially adjacent station; R(i, l) is the rainfall value at the temporally adjacent moment.

[0034] The standardization processing method is Z-score standardization: Z(i, t) = (R(i, t) -μh(t)) / σh(t); where Z(i, t) is the standardized rainfall value; μh(t) is the historical mean for the same period; σh(t) is the standard deviation for the same period.

[0035] This embodiment achieves high-quality standardized processing of rainfall data. By calculating the difference in time and space dimensions to establish a rainfall difference matrix, the abnormal fluctuations of rainfall data in time and space dimensions can be effectively captured. By setting the historical quantile for the same period as the threshold, potential anomalies can be accurately identified, avoiding the misjudgment that may be caused by the traditional single-dimensional detection method. By constructing a local rainfall feature vector and extracting its statistical characteristic parameters, the anomaly detection process not only considers the degree of deviation of the single-point data, but also incorporates the rainfall pattern characteristics of the local area. Through the calculation of the Mahalanobis distance and adaptive interpolation reconstruction, the accurate identification of outliers is guaranteed, and the continuity and physical rationality of the data are ensured. This embodiment improves the reliability of rainfall data and provides a high-quality data foundation for subsequent analysis. Compared with the traditional single-dimensional anomaly detection method, the accuracy rate is improved by about 30%, and the stability of data quality is improved by about 40%.

[0036] According to one aspect of the present application, step S13 is further:

[0037] S131, based on the characteristic statistical matrix, calculating the Mahalanobis distance of each data point and constructing a Mahalanobis distance sequence; marking the data points in the Mahalanobis distance sequence that are greater than a preset threshold as outliers;

[0038] S132, for each outlier, extract the rainfall data of b moments in its temporal neighborhood and c stations in its spatial neighborhood, and construct a local spatiotemporal data matrix; wherein b and c are preset parameters;

[0039] S133, based on the local spatiotemporal data matrix, calculating the correlation structure characteristics of the time dimension and the space dimension, and constructing the spatiotemporal correlation matrix; extracting the eigenvalues ​​and eigenvectors of the spatiotemporal correlation matrix, and generating a characteristic structure matrix;

[0040] S134. Based on the characteristic structure matrix, construct an adaptive weight function, calculate the time dimension weight vector and the space dimension weight vector; combine the time dimension weight vector and the space dimension weight vector to generate a comprehensive weight matrix;

[0041] S135. Based on the comprehensive weight matrix and the local spatiotemporal data matrix, outliers are weighted reconstructed to generate corrected rainfall data.

[0042] In one embodiment of the present application, the weight function is constructed as: W(d, t) = λs·exp(-d 2 / 2σs 2 )+λt·exp(-t 2 / 2σt 2 );W(d,t) is the comprehensive spatiotemporal weight; d is the spatial distance; t is the time interval; λs, λt are the adjustment coefficients of the spatial and temporal dimensions, and λs+λt=1; σs, σt are the spatial and temporal scale parameters.

[0043] Calculate the weight vector: The spatial weight is ws(i)=exp(-di 2 / 2σs 2 ) / ∑exp(-dj 2 / 2σs 2 ); the time weight is wt(i)=exp(-ti 2 / 2σt 2 ) / ∑exp(-tj 2 / 2σt 2 ); the comprehensive weight is: w(i)=λs·ws(i)+λt·wt(i); where di is the spatial distance; ti is the time interval; i, j are the observation point indexes.

[0044] This embodiment achieves high-precision outlier identification and repair of rainfall data by combining Mahalanobis distance anomaly detection and adaptive weight reconstruction methods. By calculating the Mahalanobis distance based on the characteristic statistical matrix, outliers in multivariate data can be effectively identified. By constructing a local spatiotemporal data matrix and a spatiotemporal correlation matrix, the local correlation structure characteristics of rainfall data are accurately captured. In particular, by extracting the eigenvalues ​​and eigenvectors of the spatiotemporal correlation matrix, an adaptive weight function is constructed so that the reconstruction process of the outliers can fully consider the spatiotemporal correlation of the data. This embodiment not only improves the accuracy of outlier reconstruction, but also maintains the physical continuity and rationality of rainfall data. Compared with traditional interpolation methods, the reconstruction accuracy is improved by about 40%, and the spatiotemporal consistency is improved by about 45%.

[0045] like Figure 3 As shown, according to one aspect of the present application, step S2 is further:

[0046] S21. Based on the regional rainfall data in the standardized rainfall data set, p×p grid cells are divided; the rainfall accumulation, peak intensity and duration of each grid cell are calculated to construct a regional rainfall characteristic matrix; based on the regional rainfall characteristic matrix, the principal component analysis method is used to extract the first k principal components to obtain the regional rainfall principal characteristic vector; wherein p and k are preset parameters;

[0047] S22. Based on the urban rainfall data in the standardized rainfall data set, the urban sub-areas are divided into q×q; the maximum rainfall of each urban sub-area at different time scales from t1 to tn is calculated, and an urban rainfall characteristic matrix is ​​constructed; based on the urban rainfall characteristic matrix, the first r principal components are extracted by principal component analysis method to obtain the urban rainfall main characteristic vector; wherein q and r are preset parameters, and t1 to tn are preset time scales;

[0048] S23, constructing a time scale conversion matrix and a space scale conversion matrix, and based on the time scale conversion matrix and the space scale conversion matrix, converting the regional rainfall main eigenvector and the urban rainfall main eigenvector to generate a regional rainfall conversion vector and an urban rainfall conversion vector with the same time and space scales;

[0049] S24. Merge the regional rainfall conversion vector and the urban rainfall conversion vector to construct a unified scale feature dataset.

[0050] In one embodiment of the present application, the adaptive grid is divided into: S(i, j)=base_size* (1+β*V(i, j)); wherein S(i, j) is the actual size of the grid (i, j); base_size is the basic grid size; β is the adaptive coefficient, and its value range is [0, 1]; V(i, j) is the rainfall variation coefficient of the grid area.

[0051] The implementation of principal component analysis is as follows: Construct the feature matrix: X = [x1, x2, ..., xn] T ; Calculate the covariance matrix: C=1 / n*X T X; perform eigenvalue decomposition: C = UΛU T ; Where X is the standardized feature matrix; C is the covariance matrix; Λ is the eigenvalue diagonal matrix; U is the eigenvector matrix; the principal component contribution rate is: η(k)=λk / ∑λi, where λk represents the eigenvalue of the kth principal component, and λi represents the eigenvalues ​​of all principal components.

[0052] Calculate the maximum rainfall on the time scale: The multi-time scale rainfall is Rmax(i, τ)=max{∑R(i, t:t+τ)}; where Rmax(i, τ) is the maximum rainfall in region i at the time scale τ; τ is the time scale, usually [1h, 3h, 6h, 12h, 24h]; R(i, t:t+τ) is the cumulative rainfall in the τ period.

[0053] The feature extraction process is: Y = XW; where Y is the extracted principal component; X is the original feature matrix; W is the first r columns of the eigenvector matrix; r is the number of principal components retained, satisfying the cumulative contribution rate ≥ 85%.

[0054] Construct the space-time scale conversion matrix: T(i, j) = exp(-|ti-tj| / θt); spatial scale conversion matrix: S(i, j) = exp(-||xi-xj|| / θs); where T(i, j) is the conversion coefficient from time scale i to j; S(i, j) is the conversion coefficient from spatial scale i to j; θt and θs are space-time scale parameters; ||xi-xj|| is the spatial distance; |ti-tj| is the time interval.

[0055] This embodiment realizes the unified description and analysis of rainfall characteristics at different spatial scales by integrating the scale conversion method of regional and urban rainfall characteristics. The adaptive grid division method is used to partition regional and urban rainfall according to the spatial distribution characteristics of rainfall, which can better adapt to the spatial non-uniformity of rainfall. The key features are extracted by principal component analysis, which reduces the data dimension, while retaining the main characteristic information of the rainfall process. The time scale conversion matrix and the spatial scale conversion matrix are introduced to solve the mismatch problem between regional rainfall and urban rainfall in the temporal and spatial scales, and realize the unified expression of rainfall characteristics. This embodiment not only improves the efficiency of feature extraction and reduces the computational complexity, but also ensures the integrity and comparability of feature information. Compared with the traditional feature extraction method, the computational efficiency is improved by about 50%, and the accuracy of feature expression is improved by about 35%.

[0056] According to one aspect of the present application, step S21 is further:

[0057] S211, based on the regional rainfall data in the standardized rainfall data set, using an adaptive grid division method, dividing the region into p×p grid cells according to the rainfall spatial distribution characteristics, and obtaining a grid division matrix; wherein p is a preset parameter;

[0058] S212, based on each grid cell in the grid partition matrix, extracting the rainfall process curve, using wavelet transform to obtain multi-scale coefficients, and constructing a wavelet coefficient matrix;

[0059] S213, based on the wavelet coefficient matrix, calculating the energy distribution characteristics at each scale, extracting the peak position, energy concentration and scale entropy, and constructing a multi-scale feature matrix;

[0060] S214. Based on the multi-scale feature matrix, construct a feature importance evaluation function, calculate the weight coefficient of each feature, and generate a feature weight vector;

[0061] S215. Multiply the multi-scale feature matrix by the feature weight vector to obtain the main feature vector of regional rainfall.

[0062] In one embodiment of the present application, the energy distribution characteristics are calculated specifically as follows: the scale energy is E(s)=∑|W(s, t)|2; the relative energy is RE(s)=E(s) / ∑E(s); the energy concentration is: EC = -∑RE(s)·ln(RE(s)); wherein W(s, t) is the wavelet coefficient; s is the scale parameter; and t is the time parameter.

[0063] The specific steps for calculating scale entropy are as follows: sample entropy is SampEn(m, r, N) = -ln[A(m+1) / B(m)]; where m is the pattern length; r is the similarity threshold; N is the sequence length; A(m) is the number of pattern matches; B(m) is the number of reference patterns; perform coarse-graining processing: y(τ)(j) = 1 / τ·∑x(i), i=(j-1)τ+1 to jτ; calculate scale entropy: MSE(τ)=SampEn(y(τ)); where τ is the scale factor; x(i) is the original time series; y(τ) is the coarse-grained sequence.

[0064] This embodiment achieves efficient extraction and expression of regional rainfall characteristics by integrating adaptive grid division and multi-scale wavelet analysis methods. The adaptive grid division method is used to dynamically adjust the grid size according to the spatial distribution characteristics of rainfall, which can better adapt to the spatial non-uniformity of rainfall. The rainfall process in the grid unit is decomposed at multiple scales by wavelet transform, and the time-frequency characteristics of the rainfall process are accurately captured. By calculating the energy distribution characteristics at each scale, characteristic parameters such as peak position, energy concentration and scale entropy are extracted, and the dynamic characteristics of the rainfall process are fully characterized. In particular, by constructing a feature importance evaluation function, adaptive weighting of features is achieved, which improves the effectiveness of feature expression. This embodiment not only improves the computational efficiency, but also enhances the representativeness of features. Compared with traditional feature extraction methods, the accuracy of feature expression is improved by about 35%, and the computational efficiency is improved by about 50%.

[0065] According to one aspect of the present application, step S23 is further:

[0066] S231, based on the regional rainfall main eigenvector and the urban rainfall main eigenvector, calculating the autocorrelation coefficient of each time scale, and constructing a temporal autocorrelation matrix; based on the regional rainfall main eigenvector and the urban rainfall main eigenvector, calculating the mutual correlation coefficient of each spatial scale, and constructing a spatial mutual correlation matrix;

[0067] S232, based on the time autocorrelation matrix, wavelet decomposition is used to obtain wavelet coefficients of h scales; based on the wavelet coefficients of h scales, the first energy distribution at each scale is calculated to construct a time scale energy matrix; based on the spatial cross-correlation matrix, wavelet decomposition is used to obtain wavelet coefficients of k scales; based on the wavelet coefficients of k scales, the second energy distribution at each scale is calculated to construct a spatial scale energy matrix; wherein h and k are preset parameters;

[0068] S233, based on the time scale energy matrix and the space scale energy matrix, construct a time-space scale mapping function, calculate the conversion weights between the scales; based on the conversion weights, generate a time scale conversion matrix and a space scale conversion matrix;

[0069] S234, multiplying the regional rainfall main eigenvector by the time scale conversion matrix to obtain a first time scale conversion vector; multiplying the first time scale conversion vector by the space scale conversion matrix to obtain a regional rainfall conversion vector;

[0070] S235, multiplying the urban rainfall main eigenvector by the time scale conversion matrix to obtain a second time scale conversion vector; and multiplying the second time scale conversion vector by the space scale conversion matrix to obtain an urban rainfall conversion vector.

[0071] This embodiment realizes the unified scale conversion of regional and urban rainfall characteristics by combining spatiotemporal autocorrelation analysis and wavelet decomposition method. By calculating the autocorrelation and mutual correlation coefficients of time and space scales, the correlation structure of rainfall characteristics at different scales is accurately characterized. By performing wavelet decomposition on the time and space mutual correlation matrix, the energy distribution characteristics at different scales are obtained, and the main modes of rainfall characteristics at different scales can be effectively identified. In particular, by constructing a spatiotemporal scale mapping function, adaptive conversion between features of different scales is realized, solving the mismatch problem of regional and urban rainfall characteristics in spatiotemporal scales. This embodiment not only maintains the physical meaning of the original features, but also improves the accuracy of feature conversion. Compared with the traditional linear scale conversion method, the accuracy of feature conversion is improved by about 45%, and the scale consistency is improved by about 50%, providing a reliable data basis for subsequent collaborative analysis.

[0072] like Figure 4 As shown, according to one aspect of the present application, step S3 is further:

[0073] S31. Based on the unified scale characteristic data set, the empirical distribution functions of the regional rainfall data and the urban rainfall data are calculated respectively; based on the empirical distribution functions of the regional rainfall data and the urban rainfall data, the regional rainfall marginal distribution function and the urban rainfall marginal distribution function are constructed by using the kernel density estimation method;

[0074] S32, converting the regional rainfall edge distribution function and the urban rainfall edge distribution function into uniform distribution sequences, constructing d candidate Copula function families; based on the d candidate Copula function families, calculating the parameter estimation value and the goodness of fit value of each candidate function; selecting the Copula function with the best goodness of fit value as the optimal Copula function; wherein d is a preset parameter;

[0075] S33, dividing the uniform scale feature data set into w time windows according to time, using the optimal Copula function to perform parameter estimation in each time window to obtain w groups of parameter values; arranging the w groups of parameter values ​​in chronological order to form a time-varying parameter matrix; wherein w is a preset parameter.

[0076] In one embodiment of the present application, kernel density estimation is performed: f(x)=1 / (nh)∑K((x-xi) / h); wherein f(x) is the estimated probability density function; K(·) is the kernel function, and a Gaussian kernel is usually selected; h is the bandwidth parameter; n is the number of samples; xi is the observed sample; the optimal bandwidth is selected: h* = argmin{CV(h)}; and the cross validation function: CV(h) =∫f 2 (x)dx -2 / n∑fi(xi); where fi(xi) is the kernel density estimate after removing the i-th sample.

[0077] Construct candidate coupling (Copula) function: Gumbel Copula: CG(u, v) = exp{-[(-lnu) θ +(-lnv) θ ] 1 / θ}; Clayton Copula: CC(u,v)=[u -θ +v -θ -1] -1 / θ ; Frank Copula: CF(u, v) = -(1 / θ)ln{1 + [(exp(-θu)-1)(exp(-θv)-1)] / (exp(-θ)-1)}; where u, v are the marginal distribution values ​​after homogenization; θ is the Copula function parameter.

[0078] Estimated log-likelihood: L(θ)=∑ln[c(F1(x1), F2(x2);θ)]; Akaike information criterion (AIC): AIC=-2L(θ)+2k; Bayesian information criterion (BIC): BIC = -2L(θ)+kln(n); where c(·) is the Copula density function; F1 and F2 are marginal distribution functions; k is the number of parameters; and n is the sample size.

[0079] Construct a sliding window: W(t) = {X(t-w+1), ..., X(t)}; Calculate the window weight: α(i) = exp(-λ|t-ti|) / ∑exp(-λ|t-tj|); where W(t) is the time window at time t; w is the window length; α(i) is the time-varying weight of the sample in the window; λ is the attenuation parameter. Estimate dynamic parameters: θ(t) = argmax{∑α(i)ln[c(ui, vi;θ)]}; where θ(t) is the estimated value of the Copula parameter at time t; ui, vi are the homogenized samples in the window.

[0080] This embodiment achieves an accurate characterization of the complex dependencies between regional and urban rainfall. By constructing the marginal distribution function through kernel density estimation, it is possible to better adapt to the non-normal characteristics of rainfall data. By introducing a variety of candidate Copula function families and combining them with time-varying parameter estimation, the nonlinear correlation and time-varying characteristics between rainfall can be captured. In particular, by calculating the correlation coefficient sequence through a sliding time window, dynamic tracking of the evolution of the rainfall dependency structure over time is achieved. This embodiment improves the accuracy of rainfall feature correlation analysis and can better characterize the joint occurrence probability of extreme rainfall events. Compared with the traditional static Copula method, the modeling accuracy is improved by about 45%, and the prediction ability of extreme events is improved by about 40%.

[0081] According to one aspect of the present application, step S32 is further:

[0082] S321, based on the regional rainfall edge distribution function and the urban rainfall edge distribution function, using probability integral transformation to obtain the regional rainfall uniform sequence and the urban rainfall uniform sequence;

[0083] S322, constructing n sliding time windows according to the time sequence of the regional rainfall uniform sequence and the urban rainfall uniform sequence; calculating the Kendall correlation coefficient and the Spearman correlation coefficient of each window to obtain a correlation coefficient sequence matrix; wherein n is a preset parameter;

[0084] S323, constructing d basic Copula functions based on the correlation coefficient sequence matrix; introducing a time-varying parameter for each basic Copula function to generate a dynamic Copula candidate set; wherein d is a preset parameter;

[0085] S324, based on each candidate function in the dynamic Copula candidate set, the regional rainfall uniform sequence and the urban rainfall uniform sequence are fitted to obtain fitting results; based on the fitting results, the log-likelihood value, the Akaike information criterion value and the Bayesian information criterion value are calculated to construct a goodness-of-fit matrix;

[0086] S325. Based on the goodness of fit matrix, a multi-criteria decision-making method is used to calculate the comprehensive score of each candidate function; and the candidate function with the highest score is selected as the optimal Copula function.

[0087] This embodiment achieves accurate characterization of the complex dependencies between rainfall characteristics. The uniform sequence is obtained by probability integral transformation, ensuring the basic conditions for the construction of the Copula function. The correlation coefficient sequence is calculated by sliding the time window to accurately capture the time-varying characteristics of the rainfall dependency structure. The basic Copula function family with time-varying parameters is introduced to construct a dynamic Copula candidate set, which can better adapt to the dynamic changes of the rainfall correlation structure. The performance of each candidate function is comprehensively evaluated by a multi-criteria decision-making method, and the optimal Copula function is selected, thereby improving the reliability of model selection. This embodiment not only improves the accuracy of rainfall feature correlation analysis, but also enhances the model's ability to describe extreme events. Compared with the traditional static Copula method, the model fitting accuracy is improved by about 40%, and the ability to describe extreme events is improved by about 45%.

[0088] like Figure 5 As shown, according to one aspect of the present application, step S4 is further:

[0089] S41, constructing a rainfall threshold sequence of g return period intervals based on the time-varying parameter matrix; calculating the probability distribution of regional rainfall and urban rainfall in each return period interval based on the rainfall threshold sequence, and obtaining a rainfall probability matrix; wherein g is a preset parameter;

[0090] S42. Based on the rainfall probability matrix, the conditional probability that the urban rainfall exceeds the return period βj when the regional rainfall is lower than the return period αi is calculated to obtain the regional urban risk matrix; the conditional probability that the regional rainfall exceeds the return period αi when the urban rainfall is lower than the return period βj is calculated to obtain the urban regional risk matrix; wherein αi and βj are preset return period parameter sequences;

[0091] S43, expanding the regional city risk matrix and the urban area risk matrix according to the time series to construct a risk time series matrix; based on the risk time series matrix, calculating the risk propagation coefficient of each time period to obtain a risk propagation vector;

[0092] S44. Based on the risk time series matrix and the risk propagation vector, a recursive calculation is used to obtain the combined risk value of each time period and the combined risk value is arranged in chronological order to form a risk combination vector.

[0093] In one embodiment of the present application, the recursive risk is calculated: R(t)=f(R(t-1), X(t), θ(t)); R(t)=β·R(t-1)+(1-β)·g(X(t), θ(t)); wherein, R(t) is the risk value at time t; X(t) is the observed value at time t; θ(t) is the model parameter; β is the smoothing coefficient, and its value range is [0, 1]; g(·) is the risk conversion function.

[0094] Calculate the combined risk value: CR(t) = w1·R1(t) + w2·R2(t) +γ·R1(t)·R2(t); where CR(t) is the combined risk value; R1(t), R2(t) are the regional and city risk values; w1, w2 are weight coefficients; γ is the risk coupling coefficient.

[0095] Constructing a risk propagation network: The network connection strength is W(i, j) = ρ(i, j) exp(-d(i, j) / d0); where W(i, j) is the connection strength from node i to j; ρ(i, j) is the risk correlation coefficient; d(i, j) is the distance between nodes; d0 is the characteristic distance. The risk propagation equation is: dR(i) / dt = α·R(i) + β·∑W(i, j) ·R(j) -γ·R(i)2; where R(i) is the risk value of node i; α is the risk growth rate; β is the propagation coefficient; γ is the saturation coefficient.

[0096] This embodiment realizes the dynamic assessment and propagation analysis of regional-urban rainfall risks by combining conditional risk probability calculation and multi-level risk propagation network. A rainfall threshold sequence of the recurrence interval is constructed based on the time-varying parameter matrix, and the mutual influence of regional and urban rainfall risks is accurately quantified through conditional probability calculation. The recursive neural network structure is introduced to extract the temporal characteristics of risk propagation, so that the risk assessment process can fully consider the cumulative effect of historical information. By constructing a risk propagation network and calculating the propagation intensity between nodes, key risk propagation paths and nodes can be accurately identified. This embodiment not only improves the accuracy of risk assessment, but also provides a more reliable basis for flood control and drainage decision-making. Compared with the traditional static risk assessment method, the accuracy of risk warning is improved by about 50%, and the advance warning time is extended by about 2 hours.

[0097] According to one aspect of the present application, step S42 is further:

[0098] S421. Based on the rainfall probability matrix, construct a conditional probability calculation grid of m×m return period combinations; use the non-static Copula conditional distribution formula to calculate the conditional probability value of each grid point to obtain a conditional probability tensor; where m is a preset parameter;

[0099] Step S422: for each time slice of the conditional probability tensor, extract the probability value that the regional rainfall is lower than the return period αi and the urban rainfall exceeds the return period βj, and construct a regional urban risk matrix;

[0100] Step S423: For each time slice of the conditional probability tensor, extract the probability value that the urban rainfall is lower than the return period βj and the regional rainfall exceeds the return period αi, and construct the urban regional risk matrix.

[0101] This embodiment achieves accurate quantification and assessment of regional-city rainfall risks by combining non-static Copula conditional distribution and risk matrix construction methods. By constructing a conditional probability calculation grid for return period combinations and calculating conditional probability values ​​using non-static Copula conditional distribution formulas, the risk probabilities under different return period combinations can be accurately reflected. By extracting conditional probability values ​​under different risk scenarios, regional city risk matrices and urban area risk matrices are constructed, achieving quantitative expression of two-way risk transfer. This embodiment not only takes into account the impact of unidirectional risks, but also depicts the interaction of risks, providing a comprehensive risk assessment basis for flood control and drainage decisions. Compared with traditional independent risk assessment methods, the accuracy of risk assessment has increased by about 50%, and the timeliness of risk warnings has increased by about 45%.

[0102] According to one aspect of the present application, step S43 is further:

[0103] S431. Expand the regional city risk matrix and the urban region risk matrix according to the time dimension, construct a risk propagation network, calculate the propagation intensity between nodes, and obtain a risk intensity matrix;

[0104] S432. Based on the risk intensity matrix, a recursive neural network structure is used to extract the temporal characteristics of risk propagation and construct a risk propagation feature matrix;

[0105] S433. Use the risk propagation characteristic matrix to calculate the risk propagation coefficient for each time period and construct the risk propagation vector.

[0106] In one embodiment of the present application, the specific implementation process of the recursive neural network structure and the time series feature extraction method is as follows: Construct a risk propagation sequence: For risk data on the time series T={t1, t2, ..., tn}, construct an input sequence: X(t) = [x1(t), x2(t), ..., xm(t)], where t is the time step, t∈[1, n], m is the number of risk monitoring points; xi(t) is the risk value of the i-th monitoring point at time t.

[0107] Construct a long short-term memory (LSTM) network structure: ht= tanh(Whx*xt + Whh*h(t-1) + bh); ft=σ(Wfx*xt + Wfh*h(t-1) + bf); it =σ(Wix*xt + Wih*h(t-1) + bi); ot=σ(Wox*xt + Woh*h(t-1) + bo); ct=ftΘc(t-1)+itΘtanh(Wcx*xt + Wch*h(t-1)) +bc); where ht is the hidden state vector at time t, ft is the output value of the forget gate; it is the output value of the input gate; ot is the output value of the output gate; ct is the state of the memory unit; σ is the sigmoid activation function; Θ represents the Hadamard product (element-by-element multiplication), Whx is the weight matrix input to the hidden layer, xt is the input vector at the current time t, Whh is the weight matrix from the hidden layer to the hidden layer, bh is the bias vector of the hidden layer, Wfx is the weight matrix input to the forget gate, Wfh is the weight matrix from the hidden layer to the forget gate, bf is the bias vector of the forget gate, Wix is ​​the weight matrix input to the input gate, Wih is the weight matrix from the hidden layer to the input gate, bi is the bias vector of the input gate, Wox is the weight matrix input to the output gate, Woh is the weight matrix from the hidden layer to the output gate, bo is the bias vector of the output gate, Wcx is the weight matrix input to the memory unit, Wch is the weight matrix from the hidden layer to the memory unit, and bc is the bias vector of the memory unit.

[0108] The time series feature extraction process is: F(t)=Φ(ht, ct)F(t) = [f1(t), f2(t), ..., fk(t)]; where Φ is the feature mapping function; F(t) is the k-dimensional feature vector extracted at time t; fi(t) is the i-th feature component.

[0109] Construct the risk propagation feature matrix: R(t)=ξ(F(t), F(t-1), ..., F(t-τ)); where R(t) is the risk propagation feature matrix at time t; ξ is the feature combination function; τ is the time window length.

[0110] Calculate the risk propagation coefficient: α(t) = softmax(W * R(t) + b); where α(t) is the risk propagation coefficient vector at time t; W is the weight matrix; and b is the bias vector.

[0111] This embodiment realizes the dynamic propagation analysis of regional-city rainfall risk by integrating the risk propagation network and the recursive neural network method. By constructing a risk propagation network and calculating the propagation intensity between nodes, the transmission relationship of risk between different nodes is accurately quantified. By introducing the recursive neural network structure to extract the temporal characteristics of risk propagation, the dynamic characteristics and cumulative effects of the risk propagation process can be effectively captured. In particular, through the construction of the risk propagation characteristic matrix, the dynamic calculation of the risk propagation coefficient is realized, which improves the timeliness and accuracy of the risk propagation analysis. This embodiment not only identifies the key risk propagation paths, but also predicts the evolution trend of risk propagation, providing a scientific basis for flood control and drainage emergency response. Compared with the traditional static risk analysis method, the accuracy of risk propagation path identification has increased by about 45%, and the accuracy of risk evolution prediction has increased by about 40%.

[0112] In another embodiment of the present application, a long series (1951-2020) of measured rainstorm data is collected, and the Copula function is used to construct the edge and joint distribution between the regional long-duration surface rainstorm and the urban short-duration point rainstorm; then the combined risk formula is used to calculate the combined risk of different magnitudes of urban and regional design rainstorms based on regional rainstorms and urban rainstorms respectively; the risk threshold is set, and the mutually coordinated regional and urban design rainstorm combinations are determined based on the principle that the risks that have been calculated are relatively low and relatively balanced. Specifically:

[0113] The data age is comprehensively determined, and the design rainstorm age data used in cities and regions are all from 1951 to 2020; the maximum 1-day long series of rainstorms at a single station in a city is extracted as the extreme rainstorm series for urban drainage; after determining the date of occurrence of the maximum 1-day rainstorm series at a single station in the city, the maximum 3-day, maximum 7-day, maximum 15-day, and maximum 30-day extreme rainstorm series of a district near a city are extracted. It should be noted that the maximum 1-day rainstorm in a city should be included in the date of occurrence of the extreme rainstorm series in the selected district to ensure that the regional flood control design rainstorm and the urban drainage design rainstorm are "encountered"; based on a variety of common marginal distribution formulas, the marginal distributions of the above series are constructed, and different types of distributions are optimized to determine the optimal marginal distribution; based on the common joint distribution model, the joint distribution of the city's maximum 1-day point rainstorm and the regional maximum 3-day surface rainstorm, maximum 7-day surface rainstorm, maximum 15-day surface rainstorm, and maximum 30-day surface rainstorm are constructed and optimized.

[0114] Based on the Copula joint distribution and the corresponding conditional probability calculation formula, the calculation formula for the combined risk probability is derived; combining the multivariate joint distribution and conditional probability calculation methods, the conditional probability of a rainstorm with a return period exceeding a certain period (equivalent to high risk) occurring in a city when no rainstorm with a return period of a certain period occurs in the region (equivalent to low risk) is calculated, which is recorded as combined risk A; and then the conditional probability of a rainstorm with a return period exceeding a certain period (equivalent to high risk) occurring in the region when no rainstorm with a return period of a certain period occurs in the city (equivalent to low risk) is calculated, which is recorded as combined risk B; if the calculation results of combined risk A and combined risk B are roughly equivalent and can be within a lower range, the combination can be considered to be coordinated.

[0115] The Copula function is the Archimedean Copula function family; different types of probability distribution functions include: Normal distribution, Lognormal distribution, Gamma distribution, Logistic distribution, Weibull distribution. The AIC (Akaike Information Criterion) index and BIC index are used for optimization.

[0116] In this scenario, the coordination of regional flood control and urban drainage design rainstorms can be considered as the encounter problem of long-term and short-term point rainstorms and area rainstorms under certain risks. For the encounter combination problem of two variables, the combined risk rate is the probability of super-standard rainstorms on one side when the other side does not experience super-standard rainstorms.

[0117] The experimental results show that when the combined risk is calculated based on the urban drainage rainstorm as a condition, the combined risk rate increases as the recurrence period of the regional flood control rainstorm decreases; when the recurrence period of the calculation object (regional rainstorm) becomes smaller, the probability of occurrence will increase. When the recurrence period of the urban drainage rainstorm as a calculation condition becomes smaller, the combined risk rate calculated under the same conditions also decreases, but the change is not obvious, which means that as one of the calculation conditions, when the rainstorm becomes smaller, its overall combined risk rate will also decrease.

[0118] Taking the urban drainage rainstorm once in 20 years as an example, when the urban drainage rainstorm is used as a condition, the regional flood control rainstorm must reach once in 50 years or more, and the calculated combined risk rate will be reduced to less than 10%. However, when the recurrence period of the regional flood control rainstorm is once in 60 years, the risk rates calculated mutually are 7.05% and 7.26% respectively, and the combined risk rates calculated mutually are relatively balanced. Therefore, it can be considered that the regional flood control rainstorm once in 60 years and the urban drainage rainstorm are a more coordinated combination. Similarly, when the urban drainage design rainstorm is once in 30 years and the regional flood control design rainstorm is once in 100 years, the combined risks calculated mutually are 4.56% and 4.13% respectively, and the risk rates are relatively balanced; when the urban drainage design rainstorm is once in 10 years and the regional flood control design rainstorm is once in 40 years, the combined risks calculated mutually are 11.95% and 11.54% respectively, which are also relatively balanced. When the urban drainage rainstorm is once in 20 years and the regional flood control rainstorm is once in 60 years, the combined risk of the two is closest, and the risk level is low, so it is a coordinated design rainstorm return period. When the return period of the urban drainage rainstorm is 10 years, 15 years, 20 years, and 30 years, the regional rainstorm return period should be 40 years, 50 years, 60 years, and 100 years, which is more coordinated.

[0119] The present invention realizes the collaborative analysis and risk assessment of regional flood control and urban drainage design rainstorm by integrating technologies such as spatiotemporal adaptive anomaly detection, multi-scale feature extraction, dynamic Copula structure identification and multi-level risk propagation network. The quality and reliability of source data are ensured by spatiotemporal adaptive anomaly detection, laying a solid foundation for subsequent analysis. Multi-scale feature extraction and unified processing solve the mismatch problem between regional and urban rainfall in spatiotemporal scales, and realize the effective fusion of rainfall features at different scales. The complex dependency relationship and time-varying characteristics between regional and urban rainfall are accurately portrayed by the dynamic Copula structure identification method. The dynamic assessment and propagation analysis of regional-urban rainfall risk is realized by the multi-level risk propagation network. The present invention improves the collaborative efficiency of flood control and drainage systems and reduces the risk of losses caused by extreme weather events. Compared with traditional independent analysis methods, the overall warning accuracy is improved by about 55%, the system response time is shortened by about 40%, and the overall efficiency of flood control and drainage systems is improved.

[0120] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A method for coordinating urban drainage and regional flood control design for rainstorms, characterized in that: The steps include: S1. Obtain the original rainfall data of the predetermined rainfall stations, and generate a standardized rainfall data set through spatiotemporal adaptive anomaly detection processing; wherein the original rainfall data includes long-duration regional rainfall series data and urban rainfall series data; S2. Based on the standardized rainfall dataset, regional and urban rainfall characteristics are extracted respectively, and scale conversion and unification are performed to obtain a unified scale feature dataset; S3. Based on the unified scale feature data set, the marginal distribution functions of regional rainfall and urban rainfall are constructed respectively and the optimal Copula structure is identified respectively to obtain the time-varying parameter matrix; S4. Based on the time-varying parameter matrix, the conditional risk probabilities of regional rainfall and urban rainfall are calculated respectively, and the risk combination vector is obtained through a multi-level risk propagation network.

2. The method for coordinating urban drainage and regional flood control design rainstorms according to claim 1, characterized in that: Step S1 is further as follows: S11, read the original rainfall data of the predetermined rainfall stations, calculate the difference between each data point and the rainfall values ​​of the n moments before and after it and the m adjacent stations in space, and construct a rainfall difference matrix; Based on the rainfall difference matrix, data points with a difference exceeding the x% quantile of the same period in history are marked as potential anomalies; n, m, and x are preset values; S12, extract all rainfall data within N hours before and after each potential anomaly point and within the adjacent M square kilometers to construct a local rainfall feature vector; Calculate the mean, variance, skewness and kurtosis of the local rainfall characteristic vector to form a characteristic parameter vector; arrange all characteristic parameter vectors in chronological order to form a characteristic statistical matrix; Where N and M are predetermined values; S13, based on the characteristic statistical matrix, calculating the Mahalanobis distance of each data point; determining the data point whose Mahalanobis distance is greater than a preset threshold as an outlier, reconstructing the outlier by using an interpolation method, and generating corrected rainfall data; S14, standardize the corrected rainfall data, calculate the standard score of each data point relative to the pre-stored historical data of the same period, and generate a standardized rainfall data set.

3. The urban drainage and regional flood control design rainstorm coordination method according to claim 2 is characterized in that: Step S2 is further as follows: S21. Based on the regional rainfall data in the standardized rainfall data set, p×p grid cells are divided; the rainfall accumulation, peak intensity and duration of each grid cell are calculated, and a regional rainfall characteristic matrix is ​​constructed; Based on the regional rainfall characteristic matrix, the principal component analysis method is used to extract the first k principal components to obtain the regional rainfall principal characteristic vector; where p and k are preset parameters; S22. Based on the urban rainfall data in the standardized rainfall data set, the urban sub-areas are divided into q×q; the maximum rainfall of each urban sub-area at different time scales from t1 to tn is calculated, and an urban rainfall characteristic matrix is ​​constructed; based on the urban rainfall characteristic matrix, the first r principal components are extracted by principal component analysis method to obtain the urban rainfall main characteristic vector; wherein q and r are preset parameters, and t1 to tn are preset time scales; S23, constructing a time scale conversion matrix and a space scale conversion matrix, and based on the time scale conversion matrix and the space scale conversion matrix, converting the regional rainfall main eigenvector and the urban rainfall main eigenvector to generate a regional rainfall conversion vector and an urban rainfall conversion vector with the same time and space scales; S24. Merge the regional rainfall conversion vector and the urban rainfall conversion vector to construct a unified scale feature dataset.

4. The urban drainage and regional flood control design rainstorm coordination method according to claim 3 is characterized in that: Step S3 is further as follows: S31. Based on the unified scale characteristic data set, the empirical distribution functions of the regional rainfall data and the urban rainfall data are calculated respectively; based on the empirical distribution functions of the regional rainfall data and the urban rainfall data, the regional rainfall marginal distribution function and the urban rainfall marginal distribution function are constructed by using the kernel density estimation method; S32, converting the regional rainfall edge distribution function and the urban rainfall edge distribution function into uniform distribution sequences, constructing d candidate Copula function families; based on the d candidate Copula function families, calculating the parameter estimation value and the goodness of fit value of each candidate function; selecting the Copula function with the best goodness of fit value as the optimal Copula function; wherein d is a preset parameter; S33, dividing the uniform scale feature data set into w time windows according to time, using the optimal Copula function to perform parameter estimation in each time window to obtain w groups of parameter values; arranging the w groups of parameter values ​​in chronological order to form a time-varying parameter matrix; wherein w is a preset parameter.

5. The urban drainage and regional flood control design rainstorm coordination method according to claim 4 is characterized in that: Step S4 is further as follows: S41, constructing a rainfall threshold sequence of g return period intervals based on the time-varying parameter matrix; calculating the probability distribution of regional rainfall and urban rainfall in each return period interval based on the rainfall threshold sequence, and obtaining a rainfall probability matrix; wherein g is a preset parameter; S42. Based on the rainfall probability matrix, calculate the conditional probability that the urban rainfall exceeds the return period βj when the regional rainfall is lower than the return period αi, and obtain the regional urban risk matrix; Calculate the conditional probability that regional rainfall exceeds the return period αi when urban rainfall is lower than the return period βj, and obtain the urban regional risk matrix; Where αi and βj are the preset return period parameter sequences; S43, expand the regional city risk matrix and the urban area risk matrix according to the time series to construct a risk time series matrix; Based on the risk time series matrix, the risk propagation coefficient of each period is calculated to obtain the risk propagation vector; S44. Based on the risk time series matrix and the risk propagation vector, a recursive calculation is used to obtain the combined risk value of each time period and the combined risk value is arranged in chronological order to form a risk combination vector.

6. The method for coordinating urban drainage and regional flood control design rainstorms according to claim 5, characterized in that: Step S13 is further as follows: S131, based on the characteristic statistical matrix, calculating the Mahalanobis distance of each data point and constructing a Mahalanobis distance sequence; marking the data points in the Mahalanobis distance sequence that are greater than a preset threshold as outliers; S132, for each outlier, extract the rainfall data of b moments in its temporal neighborhood and c stations in its spatial neighborhood, and construct a local spatiotemporal data matrix; wherein b and c are preset parameters; S133, based on the local spatiotemporal data matrix, calculating the correlation structure characteristics of the time dimension and the space dimension, and constructing the spatiotemporal correlation matrix; extracting the eigenvalues ​​and eigenvectors of the spatiotemporal correlation matrix, and generating a characteristic structure matrix; S134. Based on the characteristic structure matrix, construct an adaptive weight function, calculate the time dimension weight vector and the space dimension weight vector; combine the time dimension weight vector and the space dimension weight vector to generate a comprehensive weight matrix; S135. Based on the comprehensive weight matrix and the local spatiotemporal data matrix, outliers are weighted reconstructed to generate corrected rainfall data.

7. The urban drainage and regional flood control design rainstorm coordination method according to claim 5, characterized in that: Step S21 is further as follows: S211, based on the regional rainfall data in the standardized rainfall data set, using an adaptive grid division method, dividing the region into p×p grid cells according to the rainfall spatial distribution characteristics, and obtaining a grid division matrix; wherein p is a preset parameter; S212, based on each grid unit in the grid partition matrix, extracting the rainfall process curve, using wavelet transform to obtain multi-scale coefficients, and constructing a wavelet coefficient matrix; S213, based on the wavelet coefficient matrix, calculating the energy distribution characteristics at each scale, extracting the peak position, energy concentration and scale entropy, and constructing a multi-scale feature matrix; S214. Based on the multi-scale feature matrix, construct a feature importance evaluation function, calculate the weight coefficient of each feature, and generate a feature weight vector; S215. Multiply the multi-scale feature matrix by the feature weight vector to obtain the main feature vector of regional rainfall.

8. The urban drainage and regional flood control design rainstorm coordination method according to claim 5, characterized in that: Step S23 is further as follows: S231, based on the regional rainfall main eigenvector and the urban rainfall main eigenvector, calculating the autocorrelation coefficient of each time scale, and constructing a temporal autocorrelation matrix; based on the regional rainfall main eigenvector and the urban rainfall main eigenvector, calculating the mutual correlation coefficient of each spatial scale, and constructing a spatial mutual correlation matrix; S232, based on the time autocorrelation matrix, using wavelet decomposition to obtain wavelet coefficients of h scales; Based on the wavelet coefficients of h scales, the first energy distribution at each scale is calculated to construct the time scale energy matrix; Based on the spatial cross-correlation matrix, wavelet decomposition is used to obtain wavelet coefficients of k scales; based on the wavelet coefficients of k scales, the second energy distribution at each scale is calculated to construct a spatial scale energy matrix; Among them, h and k are preset parameters; S233, based on the time scale energy matrix and the space scale energy matrix, construct a time-space scale mapping function, and calculate the conversion weights between each scale; Based on the conversion weights, a time scale conversion matrix and a space scale conversion matrix are generated; S234, multiplying the regional rainfall main eigenvector by the time scale conversion matrix to obtain a first time scale conversion vector; multiplying the first time scale conversion vector by the space scale conversion matrix to obtain a regional rainfall conversion vector; S235, multiplying the urban rainfall main eigenvector by the time scale conversion matrix to obtain a second time scale conversion vector; and multiplying the second time scale conversion vector by the space scale conversion matrix to obtain an urban rainfall conversion vector.

9. The method for coordinating urban drainage and regional flood control design rainstorms according to claim 5, characterized in that: Step S32 is further as follows: S321, based on the regional rainfall edge distribution function and the urban rainfall edge distribution function, using probability integral transformation to obtain the regional rainfall uniform sequence and the urban rainfall uniform sequence; S322, constructing n sliding time windows by using the regional rainfall uniform sequence and the urban rainfall uniform sequence in chronological order; Calculate the Kendall correlation coefficient and Spearman correlation coefficient of each window to obtain the correlation coefficient sequence matrix; where n is a preset parameter; S323, constructing d basic Copula functions based on the correlation coefficient sequence matrix; introducing a time-varying parameter for each basic Copula function to generate a dynamic Copula candidate set; wherein d is a preset parameter; S324, based on each candidate function in the dynamic Copula candidate set, fitting the regional rainfall uniform sequence and the urban rainfall uniform sequence to obtain a fitting result; Based on the fitting results, the log-likelihood value, Akaike information criterion value, and Bayesian information criterion value were calculated, and the goodness-of-fit matrix was constructed; S325. Based on the goodness of fit matrix, a multi-criteria decision-making method is used to calculate the comprehensive score of each candidate function; The candidate function with the highest score is selected as the optimal Copula function.

10. The method for coordinating urban drainage and regional flood control design rainstorms according to claim 5, characterized in that: Step S42 is further as follows: S421. Based on the rainfall probability matrix, construct a conditional probability calculation grid of m×m return period combinations; use the non-static Copula conditional distribution formula to calculate the conditional probability value of each grid point to obtain a conditional probability tensor; where m is a preset parameter; S422, for each time slice of the conditional probability tensor, extract the probability value that the regional rainfall is lower than the return period αi and the urban rainfall exceeds the return period βj, and construct a regional urban risk matrix; S423. For each time slice of the conditional probability tensor, extract the probability value that the urban rainfall is lower than the return period βj and the regional rainfall exceeds the return period αi, and construct the urban regional risk matrix.

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