A river network hydraulic property interpolation method and related device

Abstract

The application discloses a river network hydraulic property interpolation method and related devices, and the application obtains a kernel width parameter vector that is adapted to an interpolation matrix through river network section information (i.e. section mileage and hydraulic properties of the section at different water levels), adopts a Gaussian kernel function, obtains a probability weight matrix, and converts a process of relying on logical judgment and spatial search in traditional interpolation into a weighted fusion process that is completely based on matrix operation, so that conditional judgment branches can be avoided, parallel computing potential of GUP can be fully exerted, the global rationality and stability of the interpolation result can be ensured while the computing efficiency is improved, the unity of the computing performance and the interpolation accuracy is realized, and efficient and high-precision calculation of the river network section property is realized.

Description

Technical Field

[0001] This invention relates to a method and related apparatus for interpolating the hydraulic properties of a river network, belonging to the field of river network calculation. Background Technology

[0002] In methods such as river network hydrodynamic simulation and flood forecasting, it is often necessary to extrapolate data at any location within the entire river network based on hydrological data from a limited number of monitoring stations. Traditional interpolation methods typically rely on numerous conditional statements to identify the range of a point and find neighboring cross sections when processing the points to be interpolated. This serial process, which heavily relies on logical branches, contradicts the large-scale data-parallel computing mode that GPUs (Graphics Processing Units) excel at, making it difficult to effectively utilize GPUs for acceleration. In fact, interpolation accuracy is usually related to the amount of cross-sectional information used. Introducing more information from neighboring cross sections helps improve the accuracy and stability of interpolation; however, this also makes the logical judgments and spatial searches more complex, further exacerbating the difficulty of implementing traditional interpolation methods on GPU parallel architectures. Summary of the Invention

[0003] This invention provides a method and related apparatus for interpolating the hydraulic properties of river networks, which solves the problems disclosed in the background art.

[0004] According to one aspect of this application, a method for interpolating hydraulic properties of a river network is provided, comprising:

[0005] Based on the mileage of the cross-sections in the river network and the hydraulic properties of the cross-sections at different water levels, the mileage vectors of the cross-sections and the hydraulic property matrices of each cross-section are constructed respectively.

[0006] Based on the first mileage distance and the accuracy threshold, calculate the upstream kernel width parameter vector and the downstream kernel width parameter vector; where the kernel width parameter is the kernel width in the Gaussian kernel function, the upstream kernel width parameter controls the degree of influence of the upstream section on the interpolation point, the downstream kernel width parameter controls the degree of influence of the downstream section on the interpolation point, and the first mileage distance is the difference between the downstream section mileage and the adjacent upstream section mileage in the section mileage vector;

[0007] Based on the second mileage distance, a new kernel width parameter vector corresponding to the second mileage distance is selected from the upstream kernel width parameter vector and the downstream kernel width parameter vector; wherein, the second mileage distance is the difference between the mileage of the point to be interpolated in the interpolation matrix and the mileage of the cross section in the cross section mileage vector;

[0008] Based on the second mileage distance and the new kernel width parameter vector, the probability weight matrix of the cross section to be interpolated is obtained by using the Gaussian kernel function;

[0009] Based on the hydraulic attribute matrix of each cross section and the water level of the interpolation point in the interpolation matrix, piecewise linear interpolation is performed on the hydraulic attributes of each cross section to obtain the interpolation attribute matrix; where the elements in the interpolation attribute matrix are the hydraulic attributes of the cross section at the water level of the interpolation point.

[0010] Based on the probability weight matrix and the interpolation attribute matrix, a weighted fusion calculation is performed to obtain the hydraulic attribute matrix of the point to be interpolated.

[0011] Furthermore, the upstream and downstream kernel width parameter vectors are calculated using the following formulas:

[0012] ;

[0013] ;

[0014] In the formula, σ l and σ r These are the kernel width parameter vectors in the upstream and downstream directions, respectively, where c is a constant calculated based on the precision threshold ε. M represents the number of cross-sections in the river network, d i =x i+1 -x i Let x be the mileage of the (i+1)th cross section. i+1 Mileage x at the i-th cross-section i The difference between them, 1≤i≤M-1.

[0015] Furthermore, the formula for selecting the new kernel width parameter vector is:

[0016] ;

[0017] In the formula, D jt =x qj -x t Let x be the mileage of the j-th point to be interpolated in the interpolation matrix. qj The mileage x at the t-th cross section t The difference between them, 1≤t≤M, where M is the number of cross-sections in the river network, σ l [t] and σ r [t] represents the t-th element in the upstream kernel width parameter vector and the t-th element in the downstream kernel width parameter vector, respectively, σ jt This is the element in the j-th row and t-th column of the new kernel width parameter vector.

[0018] Furthermore, the formula for obtaining the probability weight matrix is:

[0019] ;

[0020] ;

[0021] In the formula, σ is the element in the j-th row and t-th column of the probability weight matrix. jt For the element in the j-th row and t-th column of the new kernel width parameter vector, W jt for The corresponding initial weight, D jt For the interpolation matrix, the first... j Mileage of points to be interpolated x qj The mileage x at the t-th cross section t The difference between them, where M is the number of cross sections in the river network.

[0022] Furthermore, the formula for obtaining the hydraulic property matrix of the interpolation point is:

[0023] ;

[0024] In the formula, y jk Let M be the k-th hydraulic attribute of the j-th interpolation point, and M be the number of cross-sections in the river network. The element in the j-th row and t-th column of the probability weight matrix. To represent the product, This represents the k-th hydraulic attribute of the t-th cross section in the interpolation attribute matrix at the water level of the j-th interpolation point.

[0025] According to another aspect of this application, a river network hydraulic property interpolation device is provided, comprising:

[0026] The module constructs a mileage vector and a hydraulic attribute matrix for each cross-section based on the mileage of the cross-section in the river network and the hydraulic properties of the cross-section at different water levels.

[0027] The kernel width parameter vector module calculates the upstream and downstream kernel width parameter vectors based on the first mileage distance and the accuracy threshold. The kernel width parameter is the kernel width in the Gaussian kernel function. The upstream kernel width parameter controls the influence of the upstream section on the interpolation point, and the downstream kernel width parameter controls the influence of the downstream section on the interpolation point. The first mileage distance is the difference between the downstream section mileage and the adjacent upstream section mileage in the section mileage vector.

[0028] The kernel width parameter filtering module filters out a new kernel width parameter vector corresponding to the second mileage distance from the upstream kernel width parameter vector and the downstream kernel width parameter vector; wherein, the second mileage distance is the difference between the mileage of the point to be interpolated in the interpolation matrix and the mileage of the cross section in the cross section mileage vector.

[0029] The probability weight matrix module uses a Gaussian kernel function to obtain the probability weight matrix of the cross section for the interpolation points based on the second mileage distance and the new kernel width parameter vector.

[0030] The interpolation attribute matrix module performs piecewise linear interpolation on the hydraulic attributes of each cross section based on the hydraulic attribute matrix of each cross section and the water level of the point to be interpolated in the interpolation matrix, to obtain the interpolation attribute matrix; wherein, the elements in the interpolation attribute matrix are the hydraulic attributes of the cross section at the water level of the point to be interpolated;

[0031] The hydraulic attribute matrix module performs weighted fusion calculations based on the probability weight matrix and the interpolation attribute matrix to obtain the hydraulic attribute matrix of the point to be interpolated.

[0032] Furthermore, in the kernel width parameter filtering module, the formula for filtering out new kernel width parameter vectors is:

[0033] ;

[0034] In the formula, D jt =x qj -x t Let x be the mileage of the j-th point to be interpolated in the interpolation matrix. qj The mileage x at the t-th cross section t The difference between them, 1≤t≤M, where M is the number of cross-sections in the river network, σ l [t] and σ r [t] represents the t-th element in the upstream kernel width parameter vector and the t-th element in the downstream kernel width parameter vector, respectively, σ jt This is the element in the j-th row and t-th column of the new kernel width parameter vector.

[0035] Furthermore, in the probability weight matrix module, the formula for obtaining the probability weight matrix is:

[0036] ;

[0037] ;

[0038] In the formula, σ is the element in the j-th row and t-th column of the probability weight matrix. jt For the element in the j-th row and t-th column of the new kernel width parameter vector, W jt for The corresponding initial weight, D jt For the interpolation matrix, the first... j Mileage of points to be interpolated x qj The mileage x at the t-th cross section t The difference between them, where M is the number of cross sections in the river network.

[0039] According to another aspect of this application, a computer-readable storage medium is provided that stores one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform a river network hydraulic property interpolation method.

[0040] According to another aspect of this application, a computer device is provided, including one or more processors and one or more memories, wherein one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for performing a river network hydraulic property interpolation method.

[0041] The beneficial effects achieved by this invention are as follows: This invention obtains a kernel width parameter vector that matches the interpolation matrix by using river network cross-sectional information (i.e., cross-sectional mileage and hydraulic properties of the cross-section at different water levels). By employing a Gaussian kernel function, a probability weight matrix is ​​obtained. This transforms the traditional interpolation process, which relies on logical judgment and spatial search, into a weighted fusion process based entirely on matrix operations. This avoids conditional judgment branches and fully leverages the parallel computing potential of the GUP. Thus, while improving computational efficiency, it ensures the global rationality and stability of the interpolation results, achieving a balance between computational performance and interpolation accuracy. This enables efficient and high-precision calculation of river network cross-sectional properties. Attached Figure Description

[0042] Figure 1 A flowchart of a method for interpolating hydraulic properties of a river network;

[0043] Figure 2 A schematic diagram of a river network section;

[0044] Figure 3 A block diagram of a river network hydraulic property interpolation device. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this application or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0046] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0047] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0048] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0049] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0050] It should be noted that similar symbols and letters in the accompanying drawings represent similar items; therefore, once an item is defined in one accompanying drawing, it does not need to be discussed further in subsequent accompanying drawings.

[0051] Furthermore, in the description of the embodiments of this application, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Therefore, features defined with "first" or "second" may explicitly or implicitly include one or more features.

[0052] This application provides a river network hydraulic attribute interpolation method based on kernel functions and probability distribution weighting. It aims to transform the traditional interpolation process, which relies on logical judgment and spatial search, into a weighted fusion process entirely based on matrix operations, making it applicable to the field of river network computing. This interpolation method can be executed by an interpolation device, which can be a terminal device or a server. The terminal device can include, but is not limited to, mobile phones, computers, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, big data, and artificial intelligence platforms. Optionally, this interpolation method can also be executed collaboratively by multiple electronic devices with computing power. For ease of explanation, subsequent embodiments will be described as being executed by an interpolation device.

[0053] See Figure 1 , Figure 1 This is a flowchart of a river network hydraulic property interpolation method provided in an embodiment of this application. The interpolation method can be executed by an interpolation device and may include at least the following steps:

[0054] Step 1: Based on the mileage of the cross-sections in the river network and the hydraulic properties of the cross-sections at different water levels, construct the mileage vector of each cross-section and the hydraulic property matrix of each cross-section.

[0055] It should be noted that step 1 involves organizing the river network cross-sectional data into a matrix, which facilitates subsequent matrix calculations.

[0056] Assuming there are M cross-sections in the river network, the mileage (i.e., mileage coordinates) of each cross-section can form a one-dimensional vector, named the cross-section mileage vector X, which can be represented as: The hydraulic properties (including but not limited to river width, water depth, and flow velocity) of each cross-section at different water levels constitute a three-dimensional attribute matrix. The hydraulic attribute matrix V of the t-th cross-section is... t It can be represented as:

[0057] ;

[0058] In the formula, Let H be the k-th hydraulic attribute at the h-th water point of the t-th cross section, where D is the number of hydraulic attributes and H is the number of water points. t denoted as the number of water points recorded at the t-th cross section.

[0059] For ease of understanding, combined with Figure 2 The example illustrates step 1, from Figure 2 There are four river network breaks, and the resulting cross-sectional mileage vector can be expressed as: The water levels recorded at cross-sections 1, 3, and 4 are consistent, which can be represented as follows: The water level corresponding to section 2 is The water level and the water level-velocity data from the four cross-sections constitute a hydraulic attribute matrix (only the velocity attribute is retained, D=1), which can be represented as follows: , , and The units for mileage and water level are meters (m), and the unit for flow velocity is meters per second (m / s).

[0060] Step 2: Calculate the upstream and downstream kernel width parameter vectors based on the first mileage distance and accuracy threshold. The kernel width parameter is the kernel width in the Gaussian kernel function. The upstream kernel width parameter controls the influence of the upstream section on the interpolation point, and the downstream kernel width parameter controls the influence of the downstream section on the interpolation point. Together, they determine the weight distribution of each section in the interpolation calculation. The first mileage distance is the difference between the downstream section mileage and the adjacent upstream section mileage in the section mileage vector.

[0061] A constant can be calculated based on a preset precision threshold ε, which can be expressed by the formula:

[0062] ;

[0063] In the formula, c is a constant calculated based on the precision threshold ε.

[0064] Based on constants, the upstream and downstream kernel width parameter vectors can be constructed separately, which can be expressed by the formula:

[0065] ;

[0066] ;

[0067] In the formula, σ l and σ r Let d be the kernel width parameter vector in the upstream direction and the kernel width parameter vector in the downstream direction, respectively. i =x i+1 -x i Let x be the mileage of the (i+1)th cross section. i+1 Mileage x at the i-th cross-section i The difference between them, 1≤i≤M-1.

[0068] Continue to combine Figure 2 To illustrate with an example, assuming the precision threshold ε = 0.01, then... .

[0069] The upstream kernel width parameter vector and the downstream kernel width parameter vector can be:

[0070] ;

[0071] .

[0072] Step 3: Based on the second mileage distance, select a new kernel width parameter vector corresponding to the second mileage distance from the upstream kernel width parameter vector and the downstream kernel width parameter vector; wherein, the second mileage distance is the difference between the mileage of the point to be interpolated in the interpolation matrix and the mileage of the cross section in the cross section mileage vector.

[0073] It should be noted that the interpolation matrix Q can be assumed to be as follows:

[0074] ;

[0075] In the formula, N is the number of interpolation points. These are the mileage and water level of the j-th interpolation point, respectively.

[0076] Calculate the spatial distance between the point to be interpolated and each cross-section, i.e., the mileage difference D. jt =x qj -x t Let x be the mileage of the j-th point to be interpolated in the interpolation matrix. qj The mileage x at the t-th cross section t The differences between them can be used to construct a distance matrix. Based on the elements in the distance matrix and the filtering rules, a new kernel width parameter vector can be obtained. The filtering rules can be as follows:

[0077] ;

[0078] In the formula, σ jt This is the element in the j-th row and t-th column of the new kernel width parameter vector.

[0079] Continue to combine Figure 2 To illustrate, use an example of an interpolation matrix. That is, it is necessary to interpolate the flow velocities at mileage 280m and water level 2.8m and mileage 350m and water level 3.2m.

[0080] Distance matrix Based on the sign of the distance, a new kernel width parameter vector can be selected as follows: .

[0081] Step 4: Based on the second mileage distance and the new kernel width parameter vector, use the Gaussian kernel function to obtain the probability weight matrix of the cross section for the interpolation point.

[0082] Specifically, based on the second mileage distance and the new kernel width parameter vector, a Gaussian kernel function can be used to obtain the initial weight matrix. Further normalization of the initial weight matrix can yield the probability weight matrix.

[0083] The formula for obtaining the elements in the initial weight matrix can be:

[0084] ;

[0085] In the formula, W jt The initial weights for the t-th cross section to the j-th interpolation point are, i.e., the subsequent weights. The corresponding initial weights.

[0086] The normalization formula can be expressed as:

[0087] ;

[0088] In the formula, is the element in the j-th row and t-th column of the probability weight matrix.

[0089] Continue to combine Figure 2 To illustrate with an example, the initial weight matrix is: The probability weight matrix is .

[0090] Step 5: Based on the hydraulic attribute matrix of each cross section and the water level of the interpolation point in the interpolation matrix, perform piecewise linear interpolation on the hydraulic attributes of each cross section to obtain the interpolation attribute matrix; wherein, the elements in the interpolation attribute matrix are the hydraulic attributes of the cross section at the water level of the interpolation point.

[0091] It should be noted that, for each known cross-section, based on its water level sequence and hydraulic property matrix, a piecewise linear interpolation method is used to calculate the hydraulic properties of the cross-section at all interpolation points, forming an interpolation property matrix, whose elements... This represents the k-th hydraulic attribute of the t-th cross section in the interpolation attribute matrix at the water level of the j-th interpolation point.

[0092] Continue to combine Figure 2 To illustrate, we will perform piecewise linear interpolation on a known cross-section to obtain the interpolation attribute matrix (i.e., the interpolation velocity matrix). .

[0093] Step 6: Perform weighted fusion calculation based on the probability weight matrix and the interpolation attribute matrix to obtain the hydraulic attribute matrix of the point to be interpolated.

[0094] Assume the hydraulic property matrix of the point to be interpolated is ;in, In the formula, y jk For the k-th hydraulic property of the j-th interpolation point, It represents the product.

[0095] Continue to combine Figure 2 To illustrate with examples, based on The formula can be obtained .

[0096] The above method obtains a kernel width parameter vector that matches the interpolation matrix by using river network cross-sectional information (i.e., cross-sectional mileage and hydraulic properties of the cross-section at different water levels). It uses a Gaussian kernel function to obtain a probability weight matrix, transforming the traditional interpolation process that relies on logical judgment and spatial search into a weighted fusion process based entirely on matrix operations. This avoids conditional judgment branches, fully utilizes the parallel computing potential of GUP, and thus improves computational efficiency while ensuring the global rationality and stability of the interpolation results. It achieves a balance between computational performance and interpolation accuracy, enabling efficient and high-precision calculation of river network cross-sectional properties.

[0097] This application can further incorporate machine learning models to dynamically optimize the kernel width parameter vector based on meteorological, underlying surface characteristics and historical hydrological data, enabling the interpolation process to have adaptive adjustment capabilities and improving the physical rationality and environmental adaptability of the interpolation.

[0098] See Figure 3 , Figure 3 This is a block diagram of a river network hydraulic attribute interpolation device provided in an embodiment of this application. The device is a virtual device that can be loaded and executed by a computer device, which may include the aforementioned interpolation equipment. Figure 3 The apparatus may include a construction module, a kernel width parameter vector module, a kernel width parameter filtering module, a probability weight matrix module, an interpolation attribute matrix module, and a hydraulic attribute matrix module. When used to execute the above interpolation method, it can:

[0099] The module constructs a mileage vector and a hydraulic attribute matrix for each cross-section based on the mileage of the cross-section in the river network and the hydraulic properties of the cross-section at different water levels.

[0100] The kernel width parameter vector module calculates the upstream and downstream kernel width parameter vectors based on the first mileage distance and the accuracy threshold. The kernel width parameter is the kernel width in the Gaussian kernel function. The upstream kernel width parameter controls the influence of the upstream section on the interpolation point, and the downstream kernel width parameter controls the influence of the downstream section on the interpolation point. The first mileage distance is the difference between the downstream section mileage and the adjacent upstream section mileage in the section mileage vector.

[0101] The kernel width parameter filtering module filters out a new kernel width parameter vector corresponding to the second mileage distance from the upstream kernel width parameter vector and the downstream kernel width parameter vector; wherein, the second mileage distance is the difference between the mileage of the point to be interpolated in the interpolation matrix and the mileage of the cross section in the cross section mileage vector.

[0102] It should be noted that the formula for selecting a new kernel width parameter vector in the kernel width parameter filtering module is:

[0103] ;

[0104] In the formula, D jt =x qj -x t Let x be the mileage of the j-th point to be interpolated in the interpolation matrix. qj The mileage x at the t-th cross section t The difference between them, 1≤t≤M, where M is the number of cross-sections in the river network, σ l [t] and σ r [t] represents the t-th element in the upstream kernel width parameter vector and the t-th element in the downstream kernel width parameter vector, respectively, σ jt This is the element in the j-th row and t-th column of the new kernel width parameter vector.

[0105] The probability weight matrix module uses a Gaussian kernel function to obtain the probability weight matrix of the cross section for the interpolation points, based on the second mileage distance and the new kernel width parameter vector.

[0106] It should be noted that the formula for obtaining the probability weight matrix in the probability weight matrix module is:

[0107] ;

[0108] ;

[0109] In the formula, σ is the element in the j-th row and t-th column of the probability weight matrix. jt For the element in the j-th row and t-th column of the new kernel width parameter vector, W jt for The corresponding initial weight, D jtFor the interpolation matrix, the first... j Mileage of points to be interpolated x qj The mileage x at the t-th cross section t The difference between them, where M is the number of cross sections in the river network.

[0110] The interpolation attribute matrix module performs piecewise linear interpolation on the hydraulic attributes of each cross section based on the hydraulic attribute matrix of each cross section and the water level of the point to be interpolated in the interpolation matrix, to obtain the interpolation attribute matrix; wherein, the elements in the interpolation attribute matrix are the hydraulic attributes of the cross section at the water level of the point to be interpolated.

[0111] The hydraulic attribute matrix module performs weighted fusion calculations based on the probability weight matrix and the interpolation attribute matrix to obtain the hydraulic attribute matrix of the point to be interpolated.

[0112] The aforementioned device obtains a kernel width parameter vector that matches the interpolation matrix by using river network cross-sectional information (i.e., cross-sectional mileage and hydraulic properties of the cross-section at different water levels). It then uses a Gaussian kernel function to obtain a probability weight matrix, transforming the traditional interpolation process, which relies on logical judgment and spatial search, into a weighted fusion process based entirely on matrix operations. This avoids conditional judgment branches, fully leverages the parallel computing potential of the GUP, and thus improves computational efficiency while ensuring the global rationality and stability of the interpolation results. This achieves a balance between computational performance and interpolation accuracy, enabling efficient and high-precision calculation of river network cross-sectional properties.

[0113] This application also relates to a computer-readable storage medium that stores one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform a river network hydraulic property interpolation method.

[0114] This application also relates to a computer device including one or more processors and one or more memories, wherein one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for performing a river network hydraulic property interpolation method.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for interpolating hydraulic properties of a river network, characterized in that, include: Based on the mileage of the cross-sections in the river network and the hydraulic properties of the cross-sections at different water levels, the mileage vectors of the cross-sections and the hydraulic property matrices of each cross-section are constructed respectively. Based on the first mileage distance and the accuracy threshold, calculate the upstream kernel width parameter vector and the downstream kernel width parameter vector; where the kernel width parameter is the kernel width in the Gaussian kernel function, the upstream kernel width parameter controls the degree of influence of the upstream section on the interpolation point, the downstream kernel width parameter controls the degree of influence of the downstream section on the interpolation point, and the first mileage distance is the difference between the downstream section mileage and the adjacent upstream section mileage in the section mileage vector; Based on the second mileage distance, a new kernel width parameter vector corresponding to the second mileage distance is selected from the upstream kernel width parameter vector and the downstream kernel width parameter vector; wherein, the second mileage distance is the difference between the mileage of the point to be interpolated in the interpolation matrix and the mileage of the cross section in the cross section mileage vector; Based on the second mileage distance and the new kernel width parameter vector, the probability weight matrix of the cross section to be interpolated is obtained by using the Gaussian kernel function; Based on the hydraulic attribute matrix of each cross section and the water level of the interpolation point in the interpolation matrix, piecewise linear interpolation is performed on the hydraulic attributes of each cross section to obtain the interpolation attribute matrix; where the elements in the interpolation attribute matrix are the hydraulic attributes of the cross section at the water level of the interpolation point. Based on the probability weight matrix and the interpolation attribute matrix, a weighted fusion calculation is performed to obtain the hydraulic attribute matrix of the point to be interpolated.

2. The method according to claim 1, characterized in that, The formulas for calculating the kernel width parameter vectors in the upstream and downstream directions are as follows: ； ； In the formula, σ l and σ r These are the kernel width parameter vectors in the upstream and downstream directions, respectively, where c is a constant calculated based on the precision threshold ε. M represents the number of cross-sections in the river network, d i =x i+1 -x i Let x be the mileage of the (i+1)th cross section. i+1 Mileage x at the i-th cross-section i The difference between them, 1≤i≤M-1.

3. The method according to claim 1, characterized in that, The formula for selecting a new kernel width parameter vector is: ； In the formula, D jt =x qj -x t Let x be the mileage of the j-th point to be interpolated in the interpolation matrix. qj The mileage x at the t-th cross section t The difference between them, 1≤t≤M, where M is the number of cross-sections in the river network, σ l [t] and σ r [t] represents the t-th element in the upstream kernel width parameter vector and the t-th element in the downstream kernel width parameter vector, respectively, σ jt This is the element in the j-th row and t-th column of the new kernel width parameter vector.

4. The method according to claim 1, characterized in that, The formula for obtaining the probability weight matrix is: ； ； In the formula, σ is the element in the j-th row and t-th column of the probability weight matrix. jt For the element in the j-th row and t-th column of the new kernel width parameter vector, W jt for The corresponding initial weight, D jt For the interpolation matrix, the first... j Mileage of points to be interpolated x qj The mileage x at the t-th cross section t The difference between them, where M is the number of cross sections in the river network.

5. The method according to claim 1, characterized in that, The formula for obtaining the hydraulic property matrix of the interpolation point is: ； In the formula, y jk Let M be the k-th hydraulic attribute of the j-th interpolation point, and M be the number of cross-sections in the river network. The element in the j-th row and t-th column of the probability weight matrix. To represent the product, This represents the k-th hydraulic attribute of the t-th cross section in the interpolation attribute matrix at the water level of the j-th interpolation point.

6. A river network hydraulic property interpolation device, characterized in that, include: The module constructs a mileage vector and a hydraulic attribute matrix for each cross-section based on the mileage of the cross-section in the river network and the hydraulic properties of the cross-section at different water levels. The kernel width parameter vector module calculates the upstream and downstream kernel width parameter vectors based on the first mileage distance and the accuracy threshold. The kernel width parameter is the kernel width in the Gaussian kernel function. The upstream kernel width parameter controls the influence of the upstream section on the interpolation point, and the downstream kernel width parameter controls the influence of the downstream section on the interpolation point. The first mileage distance is the difference between the downstream section mileage and the adjacent upstream section mileage in the section mileage vector. The kernel width parameter filtering module filters out a new kernel width parameter vector corresponding to the second mileage distance from the upstream kernel width parameter vector and the downstream kernel width parameter vector; wherein, the second mileage distance is the difference between the mileage of the point to be interpolated in the interpolation matrix and the mileage of the cross section in the cross section mileage vector. The probability weight matrix module uses a Gaussian kernel function to obtain the probability weight matrix of the cross section for the interpolation points based on the second mileage distance and the new kernel width parameter vector. The interpolation attribute matrix module performs piecewise linear interpolation on the hydraulic attributes of each cross section based on the hydraulic attribute matrix of each cross section and the water level of the point to be interpolated in the interpolation matrix, to obtain the interpolation attribute matrix; wherein, the elements in the interpolation attribute matrix are the hydraulic attributes of the cross section at the water level of the point to be interpolated; The hydraulic attribute matrix module performs weighted fusion calculations based on the probability weight matrix and the interpolation attribute matrix to obtain the hydraulic attribute matrix of the point to be interpolated.

7. The apparatus according to claim 6, characterized in that, In the kernel width parameter filtering module, the formula for filtering out new kernel width parameter vectors is: ； In the formula, D jt =x qj -x t Let x be the mileage of the j-th point to be interpolated in the interpolation matrix. qj The mileage x at the t-th cross section t The difference between them, 1≤t≤M, where M is the number of cross-sections in the river network, σ l [t] and σ r [t] represents the t-th element in the upstream kernel width parameter vector and the t-th element in the downstream kernel width parameter vector, respectively, σ jt This is the element in the j-th row and t-th column of the new kernel width parameter vector.

8. The apparatus according to claim 6, characterized in that, In the probability weight matrix module, the formula for obtaining the probability weight matrix is: ； ； In the formula, σ is the element in the j-th row and t-th column of the probability weight matrix. jt For the element in the j-th row and t-th column of the new kernel width parameter vector, W jt for The corresponding initial weight, D jt For the interpolation matrix, the first... j Mileage of points to be interpolated x qj The mileage x at the t-th cross section t The difference between them, where M is the number of cross sections in the river network.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 5.

10. A computer device, characterized in that, include: One or more processors and one or more memories, one or more programs stored in one or more memories and configured to be executed by one or more processors, the one or more programs including instructions for performing the method of any one of claims 1 to 5.

Images