Method and device for optimizing storm frequency map
An anomaly identification model combining two-dimensional discrete difference operators and terrain factors was developed to optimize the rainstorm frequency map. This solved the problem of difficulty in synergistically improving spatial continuity and accuracy in existing technologies, achieving higher accuracy and continuity of design rainstorm values, and supporting the reliability and practicality of flood control and disaster reduction work.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 江西省水文监测中心
- Filing Date
- 2026-05-07
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for optimizing rainstorm frequency maps are insufficient to synergistically improve spatial continuity and accuracy, and are prone to oversmoothing or undersmoothing issues, resulting in large errors in design rainstorm calculations and affecting the reliability and practicality of flood control and disaster reduction efforts.
The gradient field is calculated using a two-dimensional discrete difference operator, and anomalies are identified by combining topographic factors. An anomaly identification and correction model is constructed. By fitting the objective functional of the error term and the spatial curvature penalty term, the penalized least squares method is used to optimize the rainstorm frequency map and correct the design rainstorm value at the anomaly point.
Accurate identification and correction of anomalies improve the accuracy and spatial continuity of rainstorm frequency maps, reduce errors, and enhance the reliability and practicality of flood control and disaster reduction efforts.
Smart Images
Figure CN122415340A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of hydrological frequency analysis, and in particular to a method and apparatus for optimizing rainstorm frequency maps. Background Technology
[0002] Against the backdrop of increasingly severe global climate change and human activities, the risk of extreme rainstorms and floods in my country continues to rise, making disaster prevention and mitigation increasingly challenging. To adapt to this increasingly complex risk situation, rainstorm frequency maps, as a fundamental hydrological data source characterizing the spatial distribution of rainstorm statistical parameters, can intuitively display the design rainstorm values at any location within a watershed or region. They play a crucial role in flood control efforts, including engineering planning and design, emergency response plan development, and disaster loss assessment and prediction. They are not only a fundamental tool for building resilient flood control systems but also provide key data support for promoting the innovation of a three-pronged flood management model integrating "flood control, flood utilization, and flood shaping." The compilation process of a rainstorm frequency map typically includes: first, determining rainstorm distribution parameters based on hydrological zones; second, calculating the design rainstorm parameters for each station based on rainfall observation data within the zone; and finally, estimating the design rainstorm at grid points using spatial interpolation methods, thereby forming the rainstorm frequency map. However, due to unavoidable problems such as uneven spatial distribution of rain gauges, inconsistent hydrological zoning, and inconsistent rainfall distribution parameters across different zones, the interpolation results are prone to anomalies such as sudden changes and isolated values, limiting the practical application of the rainfall frequency map. Therefore, it is necessary to identify and correct these anomalies to further optimize the accuracy and spatial continuity of the rainfall frequency map.
[0003] Existing methods for optimizing rainstorm frequency maps, such as spatial smoothing and adjustment, focus only on optimizing single objectives like spatial continuity or local fitting accuracy, making it difficult to achieve a balance between the two. This easily leads to problems like "oversmoothing" or "undersmoothing." "Oversmoothing" excessively suppresses real spatial variability and local characteristics, resulting in significant errors in design rainstorm calculations at some extreme grid points, thus requiring further improvement in the accuracy of the optimized rainstorm frequency map. "Undersmoothing" lacks sufficient ability to correct outliers, leaving the spatial continuity of the optimized rainstorm frequency map needing improvement. Given these shortcomings, it is necessary to propose an optimization method that can synergistically improve both spatial continuity and accuracy, further enhancing the reliability and practicality of rainstorm frequency maps in flood control and disaster reduction efforts. Summary of the Invention
[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a method and apparatus for optimizing rainstorm frequency maps, which can synergistically optimize the accuracy and spatial continuity of rainstorm frequency maps, thereby further improving the reliability and practicality of rainstorm frequency maps in flood control and disaster reduction work.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for optimizing a rainstorm frequency map, comprising: Obtain the rainfall frequency map of the study area to be optimized; Input the rainstorm frequency map to be optimized into the constructed anomaly identification and correction model, and output the optimized rainstorm frequency map; The construction of the anomaly identification and correction model includes: The gradient field of the rainstorm frequency map is calculated using a two-dimensional discrete difference operator. Considering the influence of topographical environmental factors, outliers are identified based on a preset gradient mutation threshold. A search range is defined centered on each outlier, and a continuous spatial surface function to be solved is constructed within this search range. The design rainstorm values at all ground rain gauge grid points within the search range are obtained, forming a local control point set for the continuous spatial surface function. A target functional including a fitting error term and a spatial curvature penalty term is established, and the parameters of the target functional are solved using the penalized least squares method to obtain the optimal spatial surface function after co-optimization of fitting accuracy and spatial continuity. The design rainstorm values at the outliers are corrected using the optimal spatial surface function.
[0006] Furthermore, the rainstorm frequency map to be optimized in the study area includes: a spatial distribution map of the design rainstorm under a certain return period, represented in the form of gridded data; The anomaly identification of the rainstorm frequency map includes: performing a convolution operation on the rainstorm frequency map using a two-dimensional discrete difference operator to obtain the horizontal and vertical gradient components of the designed rainstorm value at each grid point in the map; calculating the gradient magnitude of all grid points based on the horizontal and vertical gradient components to generate the gradient field of the rainstorm frequency map; considering the influence of environmental factors such as terrain, constructing a gradient abrupt change threshold that incorporates terrain slope factors, and identifying grid points with gradient magnitudes exceeding the gradient abrupt change threshold as anomalies. The gradient abrupt change threshold for the fused terrain slope factor is specifically: ; in, and These represent the mean and standard deviation of the gradient amplitude in the rainstorm frequency map, respectively. These are the x and y coordinates of the grid point, respectively; Let be the terrain slope factor at the grid point to be identified. This aims to dynamically adjust the threshold to adapt to gradient changes under different terrain conditions, reflecting the characteristic that the rainfall gradient increases due to terrain variation, and avoiding misjudging reasonable gradient changes caused by terrain variation as anomalies. The expression is: ; ; in, The terrain slope at grid points is calculated using data from a digital elevation model. and The rates of change of the elevation at the grid points along the horizontal and vertical directions are respectively: and These represent the maximum and minimum slope values within the study area, respectively.
[0007] Furthermore, the search scope of the domain is: ; in, The coordinates of the spatial anomaly; Euclidean distance; search radius The adaptive function related to the density of local rain gauges is expressed as: ; in, The reference radius; This is the adjustment coefficient, and its value range is (0,1). This is the normalized value of the number of rain gauges per unit area in the study region.
[0008] Furthermore, the set of local control points is as follows: ; in, For those within the search range The first Spatial coordinates of a rain gauge station; For the first The design rainfall value for each rain gauge station; For the first The weight parameters for each rain gauge station are as follows: ; in, This is a control coefficient that reflects the attenuation of the weighting parameter as the spatial distance increases.
[0009] Furthermore, the continuous space surface function to be solved is defined over the search range, specifically as follows: ; in, It is a constrained Sobolev space, specifically , It is the function space where the second derivative exists.
[0010] Furthermore, the objective functionals of the fitting error term and the spatial curvature penalty term are specifically as follows: ; in, This is the fitting error term, used to control the fitting accuracy of the design rainfall value at the corresponding position of the local control point in the continuous spatial surface function; , is a regularization parameter used to balance fitting accuracy and spatial continuity, and is determined through generalized cross-validation. The spatial curvature penalty term is used to control the spatial continuity of a continuous spatial surface function within the neighborhood of outliers. Specifically: ; in, The gradient vector of the surface function in continuous space; The Hessian matrix describing local curvature; It is the Hilbert-Schmidt norm; It is a symmetric positive definite anisotropic tensor matrix used to describe the spatial variation trend of the gradient field, expressed as: ; in, This refers to the local orientation angle in the gradient field; This is the corresponding rotation matrix; It is a constant parameter between 0 and 1, used to maintain the continuity of the spatial variation trend of the gradient of the neighborhood grid points.
[0011] Furthermore, the penalized least squares method for solving the parameters of the objective functional specifically involves: representing the continuous space surface function as a system of linear equations consisting of radial basis functions and polynomial functions, and solving the system of linear equations using the penalized least squares method to obtain the optimal parameters of the objective functional. ; in, This is a vector of design rainfall values for a local control point set. For the radial basis functions and polynomial basis functions The joint matrix formed; Let be the coefficient vector to be solved; Let be the symmetric positive definite matrix corresponding to the curvature penalty term.
[0012] Furthermore, the design rainfall values at outlier points are corrected using the optimal spatial surface function. Specifically, the geographic coordinates of each spatial outlier point are input into the optimal spatial surface function, and the corrected design rainfall values are output. ; in, This is the corrected design rainfall value; These are the optimal parameters.
[0013] Furthermore, an optimization device for a rainstorm frequency map, implemented using the aforementioned optimization method for a rainstorm frequency map, includes: a first main module for acquiring the rainstorm frequency map to be optimized for the study area; The second main module is used to input the rainstorm frequency map to be optimized into the constructed anomaly identification and correction model, and output the optimized rainstorm frequency map. The anomaly identification and correction model includes: The gradient field of the rainstorm frequency map is calculated using a two-dimensional discrete difference operator. Considering the influence of topographical environmental factors, outliers are identified based on a preset gradient mutation threshold. A neighborhood search range is defined centered on each outlier, and a continuous spatial surface function to be solved is constructed within this range. The design rainstorm values at all ground rain gauge grid points within the search range are obtained, forming a local control point set for the continuous spatial surface function. A target functional including a fitting error term and a spatial curvature penalty term is established, and the parameters of the target functional are solved using the penalized least squares method to obtain the optimal spatial surface function after co-optimization of fitting accuracy and spatial continuity. The design rainstorm values at the outliers are corrected using the optimal spatial surface function.
[0014] The beneficial effects of this invention are as follows: by calculating the gradient field using a two-dimensional discrete difference operator and identifying gradient mutation values in conjunction with threshold conditions, abnormal points in the rainstorm frequency map can be accurately identified; by delineating the neighborhood range centered on the abnormal point, a continuous spatial surface function is constructed, and an objective functional including a fitting error term and a spatial curvature penalty term is established. The optimal spatial surface function is solved using the penalized least squares method, which can both correct abnormal points and ensure fitting accuracy and spatial continuity. While smoothing out errors, it can retain the true design rainstorm extreme values to the maximum extent, overcoming the "oversmoothing" and "undersmoothing" problems existing in the prior art. This invention can provide technical support for improving the reliability and practicality of rainstorm frequency maps in flood control, disaster reduction, and hydrological engineering design. Attached Figure Description
[0015] Figure 1 A flowchart of a method for optimizing a rainstorm frequency map; Figure 2 This is a schematic diagram illustrating the construction process of an anomaly identification and correction model for rainstorm frequency maps. Figure 3 This is a logical principle diagram of the optimization method for the rainstorm frequency map; Figure 4 A block diagram of the device for optimizing the frequency map of rainstorms; Figure 5 This is an optimized result of the rainstorm frequency map. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0017] Please see Figures 1 to 3 An optimization method for a rainstorm frequency map includes: Obtain the rainfall frequency map of the study area to be optimized; Input the rainstorm frequency map to be optimized into the constructed anomaly identification and correction model, and output the optimized rainstorm frequency map; The gradient field of the rainstorm frequency map is calculated using a two-dimensional discrete difference operator, and anomalies are identified based on a preset gradient abrupt change threshold condition; the identification of anomalies in the rainstorm frequency map includes: The rainstorm frequency map is convolved using a two-dimensional discrete difference operator, and the horizontal and vertical gradient components are specifically represented as follows: ; ; in, These are the x and y coordinates of a grid point in the rainstorm frequency graph; This is the convolution operator; It is the horizontal convolution kernel of a two-dimensional discrete difference operator; It is the vertical convolution kernel of a two-dimensional discrete difference operator; Based on the square roots of the horizontal and vertical gradient components, the gradient magnitudes of all grid points are calculated, generating the gradient field of the rainstorm frequency map. The specific gradient magnitudes are as follows: ; in, This represents the gradient magnitude at the grid point; Considering the influence of environmental factors such as topography, a gradient abrupt change threshold integrating topographic slope factors is constructed. Grid points with gradient magnitudes exceeding the gradient abrupt change threshold are identified as outliers. The specific gradient abrupt change threshold integrating topographic slope factors is as follows: ; in, and These are the mean and standard deviation of the gradient magnitude in the rainstorm assessment graph, respectively. Let be the terrain slope factor at the grid point to be identified. This aims to dynamically adjust the threshold to adapt to gradient changes under different terrain conditions, reflecting the characteristic that the rainfall gradient increases due to terrain variation, and avoiding misjudging reasonable gradient changes caused by terrain variation as anomalies. The expression is: ; ; in, The terrain slope at grid points is calculated using data from a digital elevation model. and The rates of change of the elevation at the grid points along the horizontal and vertical directions are respectively: and These represent the maximum and minimum slope values within the study area, respectively. A neighborhood search range is defined centered on each outlier point, and a continuous spatial surface function is constructed within this range for solution. The neighborhood search range is specifically defined as follows: ; in, The coordinates of the spatial anomaly point; Euclidean distance; search radius The adaptive function is related to the density of local rain gauges, specifically: ; in, The reference radius; This is an adjustment coefficient, and its value range is... ; This is the normalized value of the number of rain gauges per unit area in the study region; The continuous spatial surface function to be found is defined over the search range, specifically as follows: ; in, It is a constrained Sobolev space, specifically , The space of functions where the second derivative exists; Obtain the design rainfall values at all grid points of ground rain gauge stations within the search range, forming a local control point set for the continuous spatial surface function; the local control point set is specifically as follows: ; in, For those within the search range The first Spatial coordinates of a rain gauge station; This is the design rainfall value for this station; This is the weight parameter for the site, and its value is related to the distance between the site and spatial outliers. Specifically: ; in, This is a control coefficient that reflects the attenuation of weight parameters as spatial distance increases; A target functional including a fitting error term and a spatial curvature penalty term is established. The parameters of the target functional are solved using the penalized least squares method to obtain the optimal spatial surface function after co-optimization of fitting accuracy and spatial continuity. The target functional composed of the fitting error term and the spatial curvature penalty term is specifically expressed as follows: ; in, This is the fitting error term, used to control the fitting accuracy of the design rainstorm value at the position corresponding to the local control point in the continuous spatial surface function; , is a regularization parameter used to balance fitting accuracy and spatial continuity, and is determined through generalized cross-validation. This is a spatial curvature penalty term, used to control the spatial continuity of the continuous spatial surface function within the neighborhood of outliers, specifically: ; in, The gradient vector of the surface function in continuous space; The Hessian matrix describing local curvature; It is the Hilbert-Schmidt norm; Let be a symmetric positive definite anisotropic tensor matrix used to describe the spatial variation trend of the gradient field, specifically: ; in, This refers to the local orientation angle in the gradient field; This is the corresponding rotation matrix; It is a constant parameter between 0 and 1, used to maintain the continuity of the spatial variation trend of the gradient of the neighborhood grid points.
[0018] The parameters of the objective functional are calibrated using the penalized least squares method. Specifically, the continuous space surface function is expressed as a system of linear equations consisting of radial basis functions and polynomial basis functions. The optimal parameters of the objective functional are obtained by solving the system of linear equations using the penalized least squares method. ; in, Let be the coefficient vector to be solved; This is a vector of design rainfall values for a local control point set. For the radial basis functions and polynomial basis functions The joint matrix formed; Let be the coefficient vector to be solved; Let be the symmetric positive definite matrix corresponding to the curvature penalty term.
[0019] The design rainfall values at outlier points are corrected using an optimal spatial surface function. Specifically, the geographic coordinates of the spatial outlier points are input into the optimal spatial surface function, and the optimized design rainfall values are output. ; Please refer to Figure 4 , Figure 4 A block diagram of the storm frequency map optimization device provided in this application is shown. The device includes: a first main module 101, used to acquire the storm frequency map to be optimized in the study area; and a second main module 102, used to input the storm frequency map to be optimized into a pre-constructed anomaly identification and correction model, and output the optimized storm frequency map. The construction of the anomaly identification and correction model includes: calculating the gradient field of the storm frequency map using a two-dimensional discrete difference operator, considering the influence of environmental factors such as terrain, and identifying anomalies based on a preset gradient mutation threshold condition; defining a neighborhood search range centered on each anomaly point, and constructing a continuous spatial surface function to be solved within this range; acquiring the design storm values at all ground rain gauge grid points within the search range, forming a local control point set of the continuous spatial surface function; establishing a target functional including a fitting error term and a spatial curvature penalty term, and solving the parameters of the target functional using the penalized least squares method to obtain the optimal spatial surface function after co-optimization of fitting accuracy and spatial continuity; and correcting the design storm values at the anomalies using the optimal spatial surface function.
[0020] Please refer to Figure 5 To further illustrate the effectiveness of the rainstorm frequency map optimization method of the present invention, a rainstorm frequency map of a region in East China with a 500-year return period is used as an example to apply the rainstorm frequency map optimization method of this embodiment. Figure 5 The optimized effect diagram is shown.
[0021] Depend on Figure 5As can be seen, after optimization using the method of this invention, the number of abrupt changes and isolated abnormal grid points in the rainstorm frequency map is significantly reduced, and both accuracy and continuity are greatly improved, achieving dynamic optimization of the rainstorm frequency map. Specifically, the optimization effect of this invention is evaluated from various indicators. Regarding the improvement in accuracy, after optimization using the method of this invention, the proportion of grid points with a relative error absolute value index (design rainstorm value derived from measured data of ground rain gauges and the design rainstorm value at the grid point where the station is located) less than or equal to 10% in the rainstorm frequency map increased by 35%, and the proportion of grid points less than or equal to 20% increased by 21%. Regarding the improvement in spatial continuity, the average local variation coefficient of the rainstorm frequency map before optimization was 0.092, and the average local variation coefficient after optimization was 0.023, with an improvement rate of 75%. Therefore, considering all the results, the optimization method proposed in this invention can synergistically optimize the accuracy and spatial continuity of the rainstorm frequency map, overcoming the "oversmoothing" and "undersmoothing" problems existing in the prior art, and helping to improve the reliability and practicality of the rainstorm frequency map in flood control, disaster reduction, and hydrological engineering design.
[0022] The formula for calculating the absolute value of relative error is as follows: ; in, The design storm value is derived from the measured data of ground rain gauges; This represents the design rainfall value at the grid point where the station is located.
[0023] The formula for calculating the coefficient of local variation is: ; in, The standard deviation of the design rainfall value of a grid point in the neighborhood is calculated using a 10×10 sliding window. The mean value of the design storm value of a grid point is calculated using a 10×10 sliding window.
[0024] In summary, this invention calculates the gradient field using a two-dimensional discrete difference operator and identifies gradient abrupt changes by combining threshold conditions, enabling accurate identification of outliers in the rainstorm frequency map. By defining a neighborhood centered on the outlier, a continuous spatial surface function is constructed, and an objective functional including a fitting error term and a spatial curvature penalty term is established. The optimal spatial surface function is solved using the penalized least squares method. This approach can correct outliers while maintaining fitting accuracy and spatial continuity, smoothing errors while preserving the true design rainstorm extreme values to the maximum extent. It overcomes the "oversmoothing" and "undersmoothing" problems of existing technologies and provides technical support for improving the reliability and practicality of rainstorm frequency maps in flood control, disaster reduction, and hydrological engineering design.
[0025] The embodiments described above are merely illustrative of implementation methods of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be defined by the appended claims.
Claims
1. A method for optimizing a rainstorm frequency map, characterized in that, include: Obtain the rainfall frequency map of the study area to be optimized; Input the rainstorm frequency map to be optimized into the constructed anomaly identification and correction model, and output the optimized rainstorm frequency map; The construction of the anomaly identification and correction model includes: The gradient field of the rainstorm frequency map is calculated using a two-dimensional discrete difference operator. Considering the influence of topographical environmental factors, outliers are identified based on a preset gradient mutation threshold. A search range is defined centered on each outlier, and a continuous spatial surface function to be solved is constructed within this search range. The design rainstorm values at all ground rain gauge grid points within the search range are obtained, forming a local control point set for the continuous spatial surface function. A target functional including a fitting error term and a spatial curvature penalty term is established, and the parameters of the target functional are solved using the penalized least squares method to obtain the optimal spatial surface function after co-optimization of fitting accuracy and spatial continuity. The design rainstorm values at the outliers are corrected using the optimal spatial surface function.
2. The method for optimizing a rainstorm frequency map according to claim 1, characterized in that: The rainstorm frequency map to be optimized in the study area includes: a spatial distribution map of the design rainstorm under a certain return period, represented in the form of gridded data. The anomaly identification of the rainstorm frequency map includes: performing a convolution operation on the rainstorm frequency map using a two-dimensional discrete difference operator to obtain the horizontal and vertical gradient components of the designed rainstorm value at each grid point in the map; calculating the gradient magnitude of all grid points based on the horizontal and vertical gradient components to generate the gradient field of the rainstorm frequency map; considering the influence of environmental factors such as terrain, constructing a gradient abrupt change threshold that incorporates terrain slope factors, and identifying grid points with gradient magnitudes exceeding the gradient abrupt change threshold as anomalies. The gradient abrupt change threshold for the fused terrain slope factor is specifically: ； in, and These represent the mean and standard deviation of the gradient amplitude in the rainstorm frequency map, respectively. These are the x and y coordinates of the grid point, respectively; Let be the terrain slope factor at the grid point to be identified. This aims to dynamically adjust the threshold to adapt to gradient changes under different terrain conditions, reflecting the characteristic that the rainfall gradient increases due to terrain variation, and avoiding misjudging reasonable gradient changes caused by terrain variation as anomalies. The expression is: ； ； in, The terrain slope at grid points is calculated using data from a digital elevation model. and The rates of change of the elevation at the grid points along the horizontal and vertical directions are respectively: and These represent the maximum and minimum slope values within the study area, respectively.
3. The method for optimizing a rainstorm frequency map according to claim 2, characterized in that: The scope of the domain search is: ； in, The coordinates of the spatial anomaly; Euclidean distance; search radius The adaptive function related to the density of local rain gauges is expressed as: ； in, The reference radius; This is the adjustment coefficient, and its value range is (0,1). This is the normalized value of the number of rain gauges per unit area in the study region.
4. The method for optimizing a rainstorm frequency map according to claim 3, characterized in that: The set of local control points is as follows: ； in, For those within the search range The first Spatial coordinates of a rain gauge station; For the first The design rainfall value for each rain gauge station; For the first The weight parameters for each rain gauge station are as follows: ； in, This is a control coefficient that reflects the attenuation of the weighting parameter as the spatial distance increases.
5. The method for optimizing a rainstorm frequency map according to claim 4, characterized in that: The continuous spatial surface function to be solved is defined over the search range as follows: ； in, It is a constrained Sobolev space, specifically , It is the function space where the second derivative exists.
6. The method for optimizing a rainstorm frequency map according to claim 5, characterized in that: The objective functionals for the fitting error term and the spatial curvature penalty term are specifically: ； in, This is the fitting error term, used to control the fitting accuracy of the design rainfall value at the corresponding position of the local control point in the continuous spatial surface function; , is a regularization parameter used to balance fitting accuracy and spatial continuity, and is determined through generalized cross-validation. The spatial curvature penalty term is used to control the spatial continuity of a continuous spatial surface function within the neighborhood of outliers. Specifically: ； in, The gradient vector of the surface function in continuous space; The Hessian matrix describing local curvature; It is the Hilbert-Schmidt norm; It is a symmetric positive definite anisotropic tensor matrix used to describe the spatial variation trend of the gradient field, expressed as: ； in, This refers to the local orientation angle in the gradient field; This is the corresponding rotation matrix; It is a constant parameter between 0 and 1, used to maintain the continuity of the spatial variation trend of the gradient of the neighborhood grid points.
7. The method for optimizing a rainstorm frequency map according to claim 6, characterized in that: The penalized least squares method for solving the parameters of the objective functional specifically involves: representing the continuous space surface function as a system of linear equations consisting of radial basis functions and polynomial functions; and solving this system of linear equations using the penalized least squares method to obtain the optimal parameters of the objective functional. ； in, Let be the coefficient vector to be solved; This is a vector of design rainfall values for a local control point set. For the radial basis functions and polynomial basis functions The joint matrix formed; Let be the coefficient vector to be solved; Let be the symmetric positive definite matrix corresponding to the curvature penalty term.
8. The method for optimizing a rainstorm frequency map according to claim 7, characterized in that: The design rainfall values at outlier points are corrected using the optimal spatial surface function, specifically by inputting the geographic coordinates of each spatial outlier point into the optimal spatial surface function, and outputting the corrected design rainfall values. ； in, This is the corrected design rainfall value; These are the optimal parameters.
9. An optimization device for a rainstorm frequency map, characterized in that: The method for optimizing a rainstorm frequency map as described in any one of claims 1 to 8 is adopted, comprising: a first main module, used to obtain the rainstorm frequency map to be optimized for the study area; The second main module is used to input the rainstorm frequency map to be optimized into the constructed anomaly identification and correction model, and output the optimized rainstorm frequency map. The anomaly identification and correction model includes: The gradient field of the rainstorm frequency map is calculated using a two-dimensional discrete difference operator. Considering the influence of topographical environmental factors, outliers are identified based on a preset gradient mutation threshold. A neighborhood search range is defined centered on each outlier, and a continuous spatial surface function to be solved is constructed within this range. The design rainstorm values at all ground rain gauge grid points within the search range are obtained, forming a local control point set for the continuous spatial surface function. A target functional including a fitting error term and a spatial curvature penalty term is established, and the parameters of the target functional are solved using the penalized least squares method to obtain the optimal spatial surface function after co-optimization of fitting accuracy and spatial continuity. The design rainstorm values at the outliers are corrected using the optimal spatial surface function.