A structure perception driven long time sequence point-plane data fusion method and device

Abstract

The application relates to the technical field of spatial information processing and data fusion, and discloses a structure perception driven long-time sequence point-surface data fusion method and equipment. The method extracts spatial structure information in precipitation products, elevation model products and slope data by using a spatial feature extraction module, and combines a sequence feature analysis module to model time sequence change characteristics, so that the nonlinear relationship between product data and measured stations is comprehensively captured, and high-quality enhanced station data is generated. Finally, the measured and enhanced stations are fused, long-time sequence sparse observation data is high-precision converted into continuous spatial information, the spatial coverage capability and dynamic response capability of the data are effectively improved, and more continuous, accurate and reliable data support is provided for disaster monitoring and early warning of mountainous basins.

Description

Technical Field

[0001] This invention relates to the field of spatial information processing and data fusion technology, and in particular to a structure-aware-driven long-time-series point-area data fusion method and device. Background Technology

[0002] In fields such as geographic information processing, environmental monitoring, and meteorological simulation, observational data is often collected in the form of stations. However, due to limitations in terrain conditions, economic costs, and operation and maintenance, these stations generally exhibit sparse, uneven, or even locally missing spatial layouts. This heterogeneity not only limits the spatial coverage of observational data but also poses a significant challenge to subsequent spatial analysis and modeling. Especially when constructing high spatial resolution areal data (such as precipitation, temperature, or surface deformation), traditional interpolation methods often fail to fully capture spatial heterogeneity and local feature variations, leading to decreased prediction accuracy and blurred ground feature distribution, thereby affecting the reliability of refined simulation and decision support systems.

[0003] To enhance the expressive power of sparse observation data, existing research has attempted to incorporate multi-source auxiliary data, such as remote sensing imagery, digital elevation models (DEMs), and meteorological reanalysis products, into modeling to improve the spatial continuity and rationality of prediction results as background information. However, these methods generally have shortcomings: on the one hand, they fail to fully leverage the information support role of the observation points themselves, resulting in insufficient enhancement of modeling effects; on the other hand, for non-observation areas, there is a lack of effective completion mechanisms, making it difficult to accurately reconstruct data for missing areas. These problems are particularly prominent in areas with complex natural environments, such as mountainous watersheds. For example, in monitoring hydropower disaster-causing factors in watersheds, station data is one of the core information sources. However, due to the difficulty of construction and complex environment in mountainous areas, the distribution of observation stations is sparse and uneven, making it difficult to accurately capture key disaster-causing factors such as sudden and localized heavy rainfall, thus restricting the identification of regional disaster risks and early warning capabilities.

[0004] Therefore, improving the spatial continuity and dynamic adaptability of station data, and constructing a high-precision precipitation expression model, have become key issues that urgently need to be addressed. Summary of the Invention

[0005] This invention provides a structure-aware driven long-time-series point-area data fusion method and device, aiming to solve the problems of poor spatial continuity and dynamic adaptability of site data and lack of high-precision precipitation expression models in the prior art.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] In a first aspect, the present invention provides a structure-aware driven long-time-series point-area data fusion method, comprising:

[0008] Spatiotemporal training samples and spatiotemporal test samples are constructed using existing daily precipitation observation data from meteorological stations, daily precipitation product data, and digital elevation model product data.

[0009] An enhanced site generation model is constructed, which includes a spatial feature enhancement perception module, a spatial feature extraction module, and a sequence feature analysis module.

[0010] The nth group of data in the spatiotemporal training samples The enhanced features are obtained by inputting them into the spatial feature enhancement perception module. After the enhanced features are processed by the spatial feature extraction module and the sequence feature analysis module, the predicted daily precipitation data is obtained.

[0011] The optimal enhanced station generation model was determined based on daily precipitation data and spatiotemporal test samples.

[0012] Enhanced station daily precipitation data are obtained based on daily precipitation product data, digital elevation model product data, and the best-performing enhanced station generation model.

[0013] The daily precipitation data of existing meteorological stations are combined with the daily precipitation data of enhanced stations to obtain the expanded mixed station daily precipitation data. The expanded mixed station daily precipitation data are then accumulated month by month to obtain the mixed station monthly precipitation data.

[0014] The daily precipitation product data is accumulated month by month to obtain the monthly precipitation product data. Array points are generated according to the spatial resolution of the monthly precipitation product data. The values ​​of the monthly precipitation product data are extracted into the array points to obtain the point-like monthly precipitation product data.

[0015] Using primarily point-based monthly precipitation product data, supplemented by mixed station monthly precipitation data, and under the constraint of spatial resolution, a set algorithm is employed to complete the spatial transformation of dense point-based precipitation data into continuous areal precipitation data, thereby achieving point-area fusion.

[0016] In a second aspect, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0017] Beneficial effects:

[0018] This invention provides a structure-aware-driven long-term point-area data fusion method. First, a two-dimensional Pascal array sensing mechanism is introduced through a spatial feature enhancement sensing module to construct an enhancement matrix with local weighting characteristics. This facilitates subsequent structured spatial enhancement of key areas in remote sensing images. Then, adaptive spatial importance analysis highlights the changing characteristics of complex terrain areas. Next, a spatial feature extraction module extracts spatial structure information from precipitation products, elevation model products, and slope data. Combined with a sequence feature analysis module, time-series variation characteristics are modeled to comprehensively capture the nonlinear relationship between product data and measured stations, generating high-quality enhanced station data. Finally, the measured and enhanced station data are fused to achieve high-precision conversion of long-term sparse observation data into continuous spatial information, effectively improving the spatial coverage and dynamic response capabilities of the data. This provides more continuous, accurate, and reliable data support for disaster monitoring and early warning in mountainous watersheds. Attached Figure Description

[0019] Figure 1 This is a flowchart of a structure-aware driven long-time-series point-area data fusion method provided by a preferred embodiment of the present invention. Detailed Implementation

[0020] To facilitate understanding of the present invention, the present invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of protection of the present invention is not limited to the following specific embodiments.

[0021] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of the invention.

[0022] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0023] Please see Figure 1 This application provides a structure-aware driven long-term time-series point-area data fusion method, including:

[0024] Step 1: Construct spatiotemporal training samples and spatiotemporal test samples using existing daily precipitation observation data, daily precipitation product data, and digital elevation model product data from meteorological stations;

[0025] Step 2: Construct an enhanced site generation model, which includes a spatial feature enhancement perception module, a spatial feature extraction module, and a sequence feature analysis module;

[0026] Step 3: Extract the data from the nth group of the spatiotemporal training samples. The enhanced features are obtained by inputting them into the spatial feature enhancement perception module. After the enhanced features are processed by the spatial feature extraction module and the sequence feature analysis module, the predicted daily precipitation data is obtained.

[0027] Step 4: Determine the optimal enhanced station generation model based on daily precipitation data and spatiotemporal test samples;

[0028] Step 5: Obtain enhanced station daily precipitation data based on daily precipitation product data, digital elevation model product data, and the best-performing enhanced station generation model;

[0029] Step 6: Combine the existing daily precipitation observation data from meteorological stations with the enhanced daily precipitation data from stations to obtain the expanded mixed daily precipitation data. Then, accumulate the expanded mixed daily precipitation data month by month to obtain the mixed monthly precipitation data.

[0030] Step 7: Accumulate the daily precipitation product data month by month to obtain monthly precipitation product data, generate array points according to the spatial resolution of the monthly precipitation product data; extract the values ​​of the monthly precipitation product data into the array points to obtain point-like monthly precipitation product data.

[0031] Step 8: Using point-based monthly precipitation product data as the main source and mixed station monthly precipitation data as a supplement, under the constraint of spatial resolution, a set algorithm is used to complete the spatial transformation of dense point-based precipitation data into continuous areal precipitation data, thereby achieving point-area fusion.

[0032] It is worth explaining that in this application, x represents the serial number; Data represents the obtained training sample data; gpm represents the data in the GPM product; dem represents the data in the DEM (Digital Elevation Model); and slope represents the slope data. These three represent data from three different sources.

[0033] The aforementioned structure-aware-driven long-term point-area data fusion method first introduces a two-dimensional Pascal array sensing mechanism through a spatial feature enhancement sensing module to construct an enhancement matrix with local weighting characteristics. This facilitates subsequent structured spatial enhancement of key areas in remote sensing images. Then, adaptive spatial importance analysis highlights the changing characteristics of complex terrain areas. Subsequently, a spatial feature extraction module extracts spatial structure information from precipitation products, elevation model products, and slope data. Combined with a sequence feature analysis module, time-series variation characteristics are modeled to comprehensively capture the nonlinear relationship between product data and measured stations, generating high-quality enhanced station data. Finally, the measured and enhanced station data are fused to achieve high-precision conversion of long-term sparse observation data into continuous spatial information, effectively improving the spatial coverage and dynamic response capabilities of the data. This provides more continuous, accurate, and reliable data support for disaster monitoring and early warning in mountainous watersheds.

[0034] Furthermore, the specific implementation of step 1 includes the following sub-steps:

[0035] Step 1-1: Calculate slope information: Read the DEM data Z using the GDAL third-party library in Python. For any location DEM data Z(a,b), obtain its 9-neighbor DEM data Z' in a 3×3 window. 3×3 , where a represents the row number and b represents the column number.

[0036]

[0037] First, calculate the row direction gradient S. a :

[0038]

[0039] Where Δa is the spatial resolution of the pixel in the row direction.

[0040] Then, calculate the column directional gradient S. b :

[0041]

[0042] Where Δb is the spatial resolution of the pixel in the row direction.

[0043] Finally, calculate the slope (in degrees):

[0044]

[0045] Steps 1-2, Data Preprocessing: It is worth noting that the daily precipitation observation data from the meteorological stations are in vector format.

[0046] First, daily precipitation data is read using the OGR third-party library in Python. in, This represents the precipitation data for the i-th station on day t. Let represent the precipitation data for the i-th station on day t. The upper limit of t is determined by the number of stations i. The upper limit of t is determined by the number of days of data t.

[0047] Then, according to Data Station The latitude and longitude coordinates {Lng} of the i-th station are stored in the middle. i ,Lat i Get the row and column numbers of precipitation product data, DEM data, and slope data for day t. By row and column number respectively Centered on the target area, and with a set width of 32 pixels and a height of 32 pixels, obtain the corresponding precipitation product slice data:

[0048]

[0049] DEM tile data

[0050] In the formula, This represents the daily precipitation product slice data for the i-th station on day t. This represents the digital elevation model product slice data for the i-th station.

[0051] slope slice data

[0052] Among them, Data Station Data gpm These consist of daily precipitation observation data from meteorological stations and precipitation product slice data, totaling i×t items; Data gpm-i Data slope-i These are the precipitation product slice data and the slope slice data of the i-th station, respectively.

[0053] Steps 1-3: Dataset Partitioning Station Data gpm The data is for each day, and its quantity is i×t; Data gpm-i Data slope-i The quantity is i (spatial dimension), and the Data... gpm-i Data slope-i By replicating t copies to achieve alignment and expansion on the time scale, we obtain:

[0054]

[0055] In the formula, This represents the elevation model product slice data for the i-th site on day t. This represents the slope slice data of the i-th station on the t-th day.

[0056] {Data Station Data gpm Data dem Data slope The spatiotemporal samples D were randomly divided according to a 7:3 ratio. Train and D Test D Train As spatiotemporal training samples, D Test This is a spatiotemporal test sample.

[0057] In this application, by calculating the slope of the DEM and extracting precipitation products, DEM and slope slices, the precise spatial and temporal alignment of multi-source data is achieved. The static features of the terrain and the dynamic information of daily precipitation are integrated into a complete spatiotemporal sample. The scientific nature of the model training and the generalization ability of the prediction are ensured by dividing the training set and the test set, thereby providing a reliable and information-rich input basis for subsequent enhancement of site generation and interpolation.

[0058] It is worth noting that the spatial feature enhancement perception module of the enhanced site generation model includes a two-dimensional Pascal array enhancement layer, a layer normalization layer, an importance analysis layer, a first convolutional layer, and a Sigmoid activation function. The spatial feature extraction module consists of a second convolutional layer, a first ReLU activation function, a first max pooling layer, a third convolutional layer, a first ReLU activation function, and a first max pooling layer. Specifically, the second convolutional layer has 3 input channels, 16 output channels, a kernel size of 3, a stride of 1, and padding of 1; the third convolutional layer has 16 input channels, 32 output channels, a kernel size of 3, a stride of 1, and padding of 1. The sequence feature analysis module includes a first linear layer, a Long Short-Term Memory (LSTM) layer, and a second linear layer. Specifically, the first linear layer has an input size of 32 and an output size of 32; the LSTM layer has an input size of 32 and an output size of 64; and the second linear layer has an input size of 64 and an output size of 1.

[0059] In the specific working process, the two-dimensional Pascal array reinforcement layer constructs a 4×4 weight coefficient matrix W using Pascal triangles. yang According to the properties of Pascal's Triangle:

[0060]

[0061] Then, W yang Dividing by the maximum value yields the normalized weight coefficient matrix:

[0062]

[0063] Based on spatiotemporal sample D Train In the nth slice of data A weight coefficient matrix W, consisting entirely of 1s, is generated with a width of 32 pixels and a height of 32 pixels. one The dimensions are width × Height.

[0064] W' yang Add to W one At the center 4×4 position, the two-dimensional Pascal array reinforcement coefficient matrix was obtained. Will and Multiplying them together yields enhanced precipitation product slice data.

[0065] Enhanced precipitation product slice data Spatiotemporal test sample D Train In the nth group of data Enhanced hybrid data is obtained by splicing data along channels and then passing it through a layer normalization layer.

[0066] The importance analysis layer includes maximum, average, and minimum value calculations. This will enhance the analysis of mixed data. The maximum value is calculated according to the channel dimension to obtain the feature. The features are obtained by calculating the average value according to the channel dimension. The minimum value is calculated according to the channel dimension to obtain the feature. Then, the features are concatenated according to the channel dimension to obtain the importance mixture feature.

[0067] Then, the importance of mixed features is... The input is fed into the second convolutional layer for feature extraction, resulting in spatial features F. 2 The second convolutional layer has 9 input channels, 3 output channels, a kernel size of 1, and a stride of 1.

[0068] Next, the spatial feature F 2 The feature probability map F is obtained by using the Sigmoid activation function. 2-1 .

[0069] Finally, the feature probability map F 2-1 With enhanced hybrid data Element-wise multiplication yields the enhanced feature F3.

[0070] Furthermore, the enhanced feature F3 is input into the second convolutional layer to obtain spatial features. Then spatial features The input is fed into the first ReLU activation function to obtain spatial features. Next, spatial features Spatial features are obtained after the first max pooling layer.

[0071] Finally, spatial features The input is fed into the third convolutional layer to obtain spatial features. Then, spatial features The input is fed into the second ReLU activation function to obtain spatial features. Next, spatial features Spatial features are obtained after the second maximum pooling layer.

[0072] In one example, The dimensions are 16×32×32. The dimensions are 16×16×16. The dimensions are 32×16×16. The dimensions are 32×8×8.

[0073] Furthermore, spatial features Expanding along the width and height dimensions yields the sequence features. Its size is 32×64. Then, the sequence features... By exchanging channels and expanding the width and height dimensions, sequence features are obtained. Its dimensions are 64×32. Next, sequence features... The input is fed into the first linear layer to obtain sequence features. Next, sequence features The input is fed into an LSTM layer to obtain sequence features. sequence features The average value is calculated along the channel dimension and then input into the second linear layer to obtain the predicted daily precipitation data F. P .

[0074] In one example, The dimensions are 32×32. The dimensions are 64×64, F P The size is 1.

[0075] Furthermore, the specific implementation of step 4 includes the following sub-steps:

[0076] Step 4-1: Calculate the predicted daily precipitation data F using the mean square loss function MSELoss. P With the xth group of data The losses between them.

[0077] Step 4-2: Iteratively train the augmented site generation model using the Adam optimizer and MSELoss. After training, use D... Test The optimized augmented site generation model was evaluated and compared. The parameter weights with the smallest root mean square value were selected as the best-performing augmented site generation model. This ensures efficient and stable training while selecting model parameters with the strongest generalization ability and most reliable prediction results, consistent with the evaluation metrics.

[0078] Furthermore, the specific implementation of step 5 includes the following sub-steps:

[0079] Step 5-1: Randomly generate N stations to be augmented within the spatial range of daily precipitation product data and DEM product data. p , where p∈{1,2,3,…,N}. According to Station p The latitude and longitude coordinates are calculated from the row and column numbers of daily precipitation product data, DEM product data, and slope data, according to the width = 32 pixels and the height = 32 pixels, to obtain the Station. p Corresponding slice data In the formula, This represents the precipitation product slice data on day t for the p-th proposed enhancement site. This represents the elevation model product slice data for the p-th site to be enhanced. This represents the slope slice data of the p-th station to be reinforced.

[0080] Step 5-2, The best-performing augmented site generation model is used to obtain daily precipitation data for N augmented sites.

[0081] Furthermore, the specific implementation of step 8 includes the following sub-steps:

[0082] Step 8-1: Extract point-based monthly precipitation product data The coordinates of each point are used to obtain coordinate data C. gpm Extract monthly precipitation data from mixed stations. The coordinates of each point are used to obtain coordinate data C. Station .

[0083] Then, the coordinate data is standardized to obtain the standardized coordinates C'. gpm and C' Station .

[0084]

[0085] Next, the precipitation data is normalized to obtain the normalized point-based monthly precipitation product data, Data'. gpm Normalized mixed site monthly precipitation data Station .

[0086]

[0087] In subsequent interpolation:

[0088]

[0089] Step 8-2: Calculate the parameters of the covariance model using the least squares method:

[0090]

[0091] Wherein, C(x) i ,x j ) represents x i and x j The covariance between them, x i and x j All represent known points, where i and j take values ​​of 1, 2, ..., n; and i ≠ j. C0 is the gap effect, C1 and a are the fitting parameters for the covariance, and ||x i -x j || represents x i and x j The Euclidean distance between them.

[0092] Step 8-3: For each unobserved point x0 and n known points {x1, x2, x3, ..., x...} n First, construct an n×n covariance matrix to represent the covariance of the known points:

[0093]

[0094] Then, construct an n×1 vector to represent the covariance between known points and unobserved points, specifically C(x i ,x0), where i=1,2,…,n.

[0095]

[0096] Next, the solution is obtained using the weight calculation formula:

[0097]

[0098] λ=C -1 ·C0;

[0099] In the formula, λ={λ1,λ2,…,λ iThe} represents the weighting coefficients calculated based on the covariance model. These are used to measure the influence of each known observation point on the target interpolation point.

[0100] Step 8-4: Interpolate for each unobserved point to obtain the interpolation result Z(x0) in the normalized space. Reconstruct the interpolation result into raster data according to the specified spatial resolution.

[0101]

[0102] In the formula, λ i Z(x) represents the weights calculated based on the covariance model. i ) represents the normalized observation (from GPM or site data).

[0103] In this system, raster data is in matrix form, and each element (point) in the matrix is ​​considered an unknown point. This formula can be used to extrapolate the results for any unknown point. Once all points are calculated, the entire interpolated raster data is obtained.

[0104] In this implementation, by weighted averaging of the observations of known points, the optimal estimate of unobserved points can be obtained under the constraint of minimum variance unbiasedness. This ensures that the interpolation results fully utilize neighboring information while guaranteeing the accuracy and reliability of spatial prediction. It can fully utilize multi-source observation information at a uniform scale, obtaining optimal weights through covariance constraints, thereby achieving stable, highly generalizable, and physically consistent precipitation interpolation results.

[0105] The method of this application will be described and verified below with a specific experiment:

[0106] The study area, encompassing a county in a certain city and its surrounding region (96.374°W, 33.684°N – 102.766°W, 28.696°N), was selected. Daily precipitation data from November 2018 (GPM) and daily precipitation observation data from 10 meteorological stations within the region were used as experimental data. The proposed method was compared with Kriging interpolation and a point-area fusion method based on co-Kriging. Experimental results show that the root mean square error (RMSE) of the proposed method is reduced to 5.392 mm, significantly better than the comparative methods. This indicates that the proposed method has a higher ability to characterize the spatial distribution of precipitation under sparse observation conditions, effectively improving fusion accuracy and spatial continuity, and has strong adaptability and promotional value for precipitation estimation in complex terrain areas.

[0107]

[0108] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described method. This computer device can implement various embodiments of the structure-aware driven long-time-series point-area data fusion method described above, and can achieve the same beneficial effects, which will not be elaborated here.

[0109] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A structure-aware driven long-term time-series point-area data fusion method, characterized in that, include: Spatiotemporal training samples and spatiotemporal test samples are constructed using existing daily precipitation observation data from meteorological stations, daily precipitation product data, and digital elevation model product data. An enhanced site generation model is constructed, which includes a spatial feature enhancement perception module, a spatial feature extraction module, and a sequence feature analysis module. The first in the spatiotemporal training samples Group data The enhanced features are obtained by inputting them into the spatial feature enhancement sensing module. These enhanced features are then processed by the spatial feature extraction module and the sequence feature analysis module to obtain the predicted daily precipitation data. For daily precipitation product data; For digital elevation model product data, Based on elevation model product data The calculated slope data; Based on daily precipitation data and Determine the optimal enhanced site generation model; Enhanced station daily precipitation data are obtained based on daily precipitation product data, digital elevation model product data, and the optimal enhanced station generation model. The daily precipitation data of existing meteorological stations are combined with the daily precipitation data of enhanced stations to obtain the expanded mixed station daily precipitation data. The expanded mixed station daily precipitation data are then accumulated month by month to obtain the mixed station monthly precipitation data. Daily precipitation data is accumulated monthly to obtain monthly precipitation data, which is then analyzed according to the spatial resolution of the monthly precipitation data. Generate array points; extract the values ​​of monthly precipitation product data into the array points to obtain point-like monthly precipitation product data; Primarily using point-based monthly precipitation product data, supplemented by mixed site-based monthly precipitation data, with high spatial resolution. Under the constraints, a set algorithm is used to complete the spatial transformation of dense point precipitation data into continuous areal precipitation data, thereby achieving point-area fusion; The spatial feature enhancement perception module includes a two-dimensional Pascal array enhancement layer, a layer normalization layer, an importance analysis layer, a first convolutional layer, and a Sigmoid activation function; The first in the spatiotemporal training samples Group data The input to the spatial feature enhancement perception module yields enhanced features, including: The two-dimensional Pascal array reinforcement layer constructs a 4×4 weight coefficient matrix using Pascal's triangle. as follows: ； Will The normalized weight coefficient matrix is ​​obtained by dividing by its maximum value as follows: ； Based on spatiotemporal samples The Middle Group slice data width Pixels, High Pixels generate a weight coefficient matrix where all values ​​are 1 The size is ; Will Add to At the center 4×4 position, the two-dimensional Pascal array reinforcement coefficient matrix was obtained. ,Will and Multiplying them together yields enhanced precipitation product slice data. ; Enhanced precipitation product slice data Spatiotemporal test samples The Middle Group data Enhanced hybrid data is obtained by splicing data along channels and then passing it through a layer normalization layer. ; The importance analysis layer will enhance the analysis of mixed data. The maximum value is calculated according to the channel dimension to obtain the feature. Calculate the average value according to the channel dimension to obtain the features. ; Calculate the minimum value according to the channel dimension to obtain the feature. Then, the features are concatenated according to the channel dimension to obtain the mixed importance features. ; Mixed features of importance The input is fed into the first convolutional layer for feature extraction to obtain spatial features. ; spatial features The feature probability map is obtained by using the Sigmoid activation function. ; Feature probability map With enhanced hybrid data Enhanced features are obtained by element-wise multiplication. ; The data primarily consists of point-based monthly precipitation product data, supplemented by mixed site-based monthly precipitation data, with a high spatial resolution. Under constraints, a set algorithm is used to complete the spatial transformation of dense point precipitation data into continuous areal precipitation data, achieving point-area fusion, including: Extracting point-based monthly precipitation product data The coordinates of each point are obtained, thus acquiring coordinate data. Extract monthly precipitation data from mixed stations. The coordinates of each point are obtained, thus acquiring coordinate data. ; For coordinate data and Standardization is performed to obtain standardized coordinates. and ,as follows: ； ； Precipitation data are normalized to obtain normalized point-based monthly precipitation product data. Normalized mixed site monthly precipitation data as follows: ； ； In subsequent interpolation: ； Calculate the parameters of the covariance model using the least squares method: ； in, express and Covariance between and Both represent known points, where i and j take values ​​of 1, 2, ..., n; and i ≠ j. It is a gap effect. and These are the fitting parameters for the covariance. express and Euclidean distance between them; For each unobserved point and Known points First, build a The covariance matrix, representing the covariance of the known points, is as follows: ； Then, construct a The vector represents the covariance between known points and unobserved points, specifically: ,in ,as follows: ； Solve using the weight calculation formula: ； ； In the formula, This represents the weighting coefficients calculated based on the covariance model; Interpolation is performed at each unobserved point to obtain the interpolation result in the normalized space. The interpolation results are then reshaped into raster data according to the specified spatial resolution, as follows: ； In the formula, This represents the weights calculated based on the covariance model. This represents the normalized observation value.

2. The structure-aware driven long-time-series point-area data fusion method according to claim 1, characterized in that, The construction of spatiotemporal training and test samples using existing daily precipitation observation data from meteorological stations, daily precipitation product data, and digital elevation model product data includes: Calculate slope information: Read digital elevation model (DEM) product data; for any location, read the DEM product data. ,by Retrieving 9-neighborhood digital elevation model product data using large and small windows ,in The index indicating the row direction. The column direction index is as follows: ； In the formula, Z represents the digital elevation model product data; Calculate the gradient in the row direction ,as follows: ； in, Indicates the spatial resolution of a pixel in the row direction; Calculate the gradient in the column direction ,as follows: ； in, The spatial resolution of a pixel in the row direction; Calculate slope ,as follows: ； The daily precipitation data is as follows: ； in, Indicates the first The first site Rainfall data, where i takes values ​​of 1, 2, ...; t takes values ​​of 1, 2, ...; according to The first stored in the middle Latitude and longitude coordinates of each station Get the Rainfall product data, DEM data, and slope row and column numbers of the data By row and column number respectively Centered on, with a set width Pixels, High Pixels acquire corresponding precipitation product slice data as follows: ； DEM tile data ; In the formula, This represents the daily precipitation product slice data for the i-th station on day t. This represents the digital elevation model product slice data for the i-th site; slope The slice data is as follows: ； in, These are daily precipitation observation data from meteorological stations and precipitation product slice data, totaling [number]. indivual; Let i be the precipitation product slice data and the slope slice data of the i-th station, respectively. Copy each The alignment and expansion on the time scale are achieved respectively, resulting in: ； ； In the formula, This represents the elevation model product slice data for the i-th site on day t. This represents the slope slice data of the i-th station on day t. Will Spatiotemporal training samples are obtained by randomly dividing the data according to a set ratio. and spacetime test samples .

3. The structure-aware driven long-time-series point-area data fusion method according to claim 1, characterized in that, The spatial feature extraction module includes a second convolutional layer, a first ReLU activation function, a first max pooling layer, a third convolutional layer, a first ReLU activation function, and a first max pooling layer; the sequence feature analysis module includes a first linear layer, a long short-term memory (LSTM) network layer, and a second linear layer.

4. The structure-aware driven long-time-series point-area data fusion method according to claim 3, characterized in that, The process of processing the enhanced features through a spatial feature extraction module and a sequence feature analysis module to obtain predicted daily precipitation data includes: Enhance features The input is fed into the first convolutional layer to obtain spatial features. Then spatial features The input is fed into the first ReLU activation function to obtain spatial features. Spatial features Spatial features are obtained after the first max pooling layer. ; spatial features The input is fed into the second convolutional layer to obtain spatial features. Spatial features The input is fed into the second ReLU activation function to obtain spatial features. Spatial features Spatial features are obtained after the second maximum pooling layer. ; spatial features Expanding along the width and height dimensions yields the sequence features. sequence features By exchanging channels and expanding the width and height dimensions, sequence features are obtained. ; sequence features The input is fed into the first linear layer to obtain sequence features. sequence features The input is fed into the LSTM layer of a Long Short-Term Memory (LSTM) network to obtain sequence features. sequence features The average value is calculated along the channel dimension and then input into the second linear layer to obtain the predicted daily precipitation data. .

5. The structure-aware driven long-time-series point-area data fusion method according to claim 1, characterized in that, The Daily precipitation data and Determine the optimal augmented site generation model, including: The mean square loss function is used to calculate the loss between predicted daily precipitation data and observed daily precipitation data from meteorological stations. Based on this loss, an enhanced station generation model is trained using the gradient backpropagation principle to obtain an optimized enhanced station generation model. After training, it is used... The performance of the optimized enhanced site generation model was evaluated and compared, and the parameter weight with the smallest root mean square value was selected as the best-performing enhanced site generation model.

6. The structure-aware driven long-time-series point-area data fusion method according to claim 1, characterized in that, The enhanced station daily precipitation data, obtained based on daily precipitation product data, digital elevation model product data, and the best-performing enhanced station generation model, includes: Randomly generated from the spatial range of daily precipitation product data and digital elevation model product data. One proposed site ,in ; according to Calculation of latitude and longitude coordinates The proposed enhancement stations will utilize daily precipitation product data, digital elevation model product data, and slope data. Data row and column numbers, according to width Pixels, High Pixels, Get The corresponding slice data is as follows: ； In the formula, This represents the precipitation product slice data on day t for the p-th proposed enhancement site. This represents the elevation model product slice data for the p-th site to be enhanced. This represents the slope slice data for the p-th station to be reinforced; Will Slice data corresponding to each proposed enhancement site The input is fed into the best-performing augmented site generation model to obtain Daily precipitation data for each enhanced station .

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the steps of any one of the methods of claims 1 to 6 above.

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