A river channel identification method and related device

Abstract

The present invention discloses a river channel shoal identification method and related devices, the method comprising: decomposing a river channel section into a plurality of sine basis functions of different frequency domains and amplitudes to obtain a sine basis function cluster; screening out a low-frequency high-amplitude sine basis function cluster and a high-frequency low-amplitude sine basis function cluster from the sine basis function cluster; identifying the river channel area and the shoal area of ​​the river channel section according to the low-frequency high-amplitude sine basis function cluster and the high-frequency low-amplitude sine basis function cluster. Compared with the traditional artificial division, the efficiency is greatly improved, and the subjectivity and contingency of the artificial division are avoided, and the division is more accurate.

Description

Technical Field

[0001] The invention relates to a river channel shoal identification method and related devices, belonging to the technical field of water conservancy engineering. Background Art

[0002] At present, numerical simulation methods are often used to obtain the hydraulics of open channels and plain river networks. The one-dimensional hydrodynamic model based on the Saint-Venant equations has been widely used in the calculation or forecasting of water levels in open channels or plain river networks. The riverbed roughness is one of the very important sensitive parameters, and its numerical value is the key to the one-dimensional numerical simulation of river flow. However, the cross-sectional shape of natural river channels is often not a simple rectangular or parabolic open channel cross-section. It is generally composed of multiple river channels and beaches. The use of a single roughness for the entire river section is likely to result in low applicability of the hydraulic numerical model and difficulty in meeting the accuracy requirements.

[0003] In response to the above problems, there are two main existing methods or measures: one is to give a comprehensive roughness value of the cross section, and the other is to give the roughness of the river channel and the beach separately. Both the comprehensive roughness and the separately given roughness are closely related to the distinction between the river channel and the beach. However, due to the complexity of the natural river channel, the artificial demarcation of the river channel and the beach based on the existing measured data is inefficient and subjective, and it is often impossible to accurately distinguish between the river channel and the beach. Summary of the invention

[0004] The present invention provides a river channel identification method and related devices, which solve the problems disclosed in the background technology.

[0005] According to one aspect of the present disclosure, a method for identifying a river channel is provided, comprising:

[0006] Decompose the river section into multiple sinusoidal basis functions with different frequency domains and amplitudes to obtain a sinusoidal basis function cluster;

[0007] Selecting a low-frequency and high-amplitude sine basis function cluster and a high-frequency and low-amplitude sine basis function cluster from the sine basis function cluster;

[0008] According to the low-frequency and high-amplitude sinusoidal basis function clusters and the high-frequency and low-amplitude sinusoidal basis function clusters, the river channel area and beach area of ​​the river section are identified.

[0009] Furthermore, a low-frequency and high-amplitude sinusoidal basis function cluster and a high-frequency and low-amplitude sinusoidal basis function cluster are screened out from the sinusoidal basis function cluster, including:

[0010] Traverse the sine basis function cluster and select the low-frequency sine basis functions with amplitude greater than The low-frequency high-sine basis functions are combined to form a low-frequency high-amplitude sine basis function cluster, and the high-frequency sine basis functions are selected to select the ones with amplitude less than The high-frequency sine basis functions form a high-frequency low-amplitude sine basis function cluster; among them, As parameters, is the maximum amplitude in the sine basis function cluster, is the minimum amplitude in the cluster of sine basis functions.

[0011] Furthermore, the river channel area and beach area of ​​the river section are identified based on the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster, including:

[0012] Traverse the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster, and calculate the phase point coordinates of each sinusoidal basis function within the range of the horizontal coordinate value of the river section;

[0013] Merge multiple phase points with the same horizontal coordinates into one phase point, and sort the phase points according to the size of the horizontal coordinates; wherein the vertical coordinate of the merged phase point is the sum of the vertical coordinates of the phase points with the same horizontal coordinates;

[0014] In the phase point sorting, the phase point with the smallest ordinate is taken as the low point of the river channel, the phase point with the largest ordinate on the left side of the river channel low point is taken as the left channel vertex, and the phase point with the largest ordinate on the right side of the river channel low point is taken as the right channel vertex. The stationary points are selected from the remaining phase points, and the range formed by the horizontal coordinates of all stationary points is taken as the beach area, and the rest of the range is taken as the river channel area.

[0015] Furthermore, the stationary points are selected from the remaining phase points, including: if (v j+1 -v j )×(v j-1 -v j )>0, then the jth phase point among the remaining phase points is a stationary point.

[0016] According to another aspect of the present disclosure, a river channel identification device is provided, comprising:

[0017] The decomposition module decomposes the river section into multiple sinusoidal basis functions with different frequency domains and amplitudes to obtain a sinusoidal basis function cluster;

[0018] A screening module is used to screen out low-frequency and high-amplitude sinusoidal basis function clusters and high-frequency and low-amplitude sinusoidal basis function clusters from the sinusoidal basis function clusters;

[0019] The recognition module identifies the river channel area and the beach area of ​​the river section according to the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster.

[0020] Furthermore, the screening module can be configured to: traverse the sine basis function cluster and screen out the low-frequency sine basis functions with amplitudes greater than The low-frequency high-sine basis functions are combined to form a low-frequency high-amplitude sine basis function cluster, and the high-frequency sine basis functions are selected to select the ones with amplitude less than The high-frequency sine basis functions form a high-frequency low-amplitude sine basis function cluster; among them, As parameters, is the maximum amplitude in the sine basis function cluster, is the minimum amplitude in the cluster of sine basis functions.

[0021] Furthermore, the identification module can be configured as:

[0022] Traverse the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster, and calculate the phase point coordinates of each sinusoidal basis function within the range of the horizontal coordinate value of the river section;

[0023] Merge multiple phase points with the same horizontal coordinates into one phase point, and sort the phase points according to the size of the horizontal coordinates; wherein the vertical coordinate of the merged phase point is the sum of the vertical coordinates of the phase points with the same horizontal coordinates;

[0024] In the phase point sorting, the phase point with the smallest ordinate is taken as the low point of the river channel, the phase point with the largest ordinate on the left side of the river channel low point is taken as the left channel vertex, and the phase point with the largest ordinate on the right side of the river channel low point is taken as the right channel vertex. The stationary points are selected from the remaining phase points, and the range formed by the horizontal coordinates of all stationary points is taken as the beach area, and the rest of the range is taken as the river channel area.

[0025] Furthermore, in the identification module, the stationary points are selected from the remaining phase points, including: if (v j+1 -v j )×(v j-1 -v j )>0, then the jth phase point among the remaining phase points is a stationary point.

[0026] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes a river channel identification method.

[0027] According to another aspect of the present disclosure, a computer device is provided, comprising one or more processors and one or more memories, wherein one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a river channel identification method.

[0028] The beneficial effects achieved by the present invention are as follows: the present invention decomposes a river section into a sine basis function cluster, selects a low-frequency, high-amplitude sine basis function cluster and a high-frequency, low-amplitude sine basis function cluster from the sine basis function cluster, and based on the low-frequency, high-amplitude sine basis function cluster and the high-frequency, low-amplitude sine basis function cluster, the river channel area and the beach area of ​​the river section can be identified. Compared with traditional artificial division, the efficiency is greatly improved, and the subjectivity and randomness of artificial division are avoided, and the division is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of the river channel identification method;

[0030] Figure 2 This is a cross-section diagram of the river;

[0031] Figure 3 This is a txt text diagram of the river section;

[0032] Figure 4 is the frequency domain diagram corresponding to the river channel;

[0033] Figure 5 Flow chart for identifying the channel area and beach area of ​​the river section;

[0034] Figure 6 Schematic diagram for beach channel identification;

[0035] Figure 7 This is the cross-section diagram of the river channel after adding random disturbance;

[0036] Figure 8 In order to use the river channel identification method Figure 7 the results of the processing;

[0037] Fig. 9 It is a cross-section diagram of a river channel connected by multiple sections of shoals and channels;

[0038] Fig.10 In order to use the river channel identification method Fig. 9 the results of the processing;

[0039] Fig.11 This is a block diagram of a river channel identification device. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. It is obvious that the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0041] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0042] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0043] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0044] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0045] It should be noted that similar symbols and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it does not require further discussion in subsequent figures.

[0046] In order to solve the problem of low efficiency and poor accuracy in the existing artificial demarcation of river channels, the present invention proposes a river channel identification method and related devices, which specifically processes the river section based on fast Fourier transform to achieve automatic identification of the river channel.

[0047] Figure 1 It is a schematic diagram of an embodiment of the river channel identification method disclosed in the present invention. Figure 1 The embodiment of can be executed by the identification terminal.

[0048] like Figure 1 As shown, step 1 of the embodiment is to decompose the river section into multiple sinusoidal basis functions of different frequency domains and amplitudes to obtain a sinusoidal basis function cluster.

[0049] It should be noted that the river channel here refers to the natural river channel, and the river channel section refers to the measured data of the river channel section (such as scattered point data). The measured data can be stored in software such as CAD, Geostudio, Office, etc., and can specifically include the section mileage, the horizontal coordinates and vertical coordinates of the scattered points of the section, etc. The river channel section decomposition here is to draw a cross-section diagram by connecting the scattered point data, which can be seen in Figure 2 ( Figure 2 In the figure, the horizontal axis represents the width of the river cross section, the vertical axis represents the elevation, and the units of width and elevation are both meters), and then further Fourier transform decomposition is performed based on the cross-section diagram.

[0050] In order to facilitate decomposition, the measured data of the river section can be processed into txt text first, see Figure 3 (The scattered points in the figure are the scattered points before decomposition). Using the Fourier transform method, the river section is decomposed into multiple sinusoidal basis functions with different frequency domains and amplitudes, and all sinusoidal basis functions are defined as sinusoidal basis function clusters, represented by F. The corresponding frequency domain diagram is shown in Figure 4 ( Figure 4 The specific formula is as follows:

[0051] ;

[0052] In the formula, N is the number of scattered points obtained after decomposition, f i represents the elevation corresponding to the i-th scattered point after decomposition, x i A represents the width of the river cross section corresponding to the i-th scattered point after decomposition. i is the amplitude corresponding to the i-th scattered point after decomposition, B i is the frequency corresponding to the i-th scattered point after decomposition.

[0053] Fourier transform is a very important transform and plays an important role in different fields, such as modern acoustics, image processing, earthquake, sonar, voice communication, and even biomedical engineering. Here, the Fourier transform method is used to represent the complex cross-section trends that are difficult to describe mathematically with a cluster of sinusoidal basis functions, which is convenient for beach and trough identification.

[0054] return Figure 1 , step 2 of the embodiment, screening out a low-frequency, high-amplitude sinusoidal basis function cluster and a high-frequency, low-amplitude sinusoidal basis function cluster from the sinusoidal basis function cluster.

[0055] In step 2, low-pass filtering strategy and high-pass filtering strategy are used for screening respectively. The low-pass filtering strategy is to limit the amplitude on the basis of filtering out high-frequency functions. Specifically, the maximum amplitude in F is first obtained. , traverse the sine basis function cluster and select the low-frequency sine basis functions with amplitude greater than The low-frequency high-amplitude sine basis functions form a low-frequency high-amplitude sine basis function cluster F c ; A similar high-pass filtering strategy is to limit the amplitude on the basis of filtering out low-frequency functions, specifically, first obtain the minimum amplitude in F , traverse the sine basis function cluster and select the high-frequency sine basis functions with amplitude less than The high-frequency sine basis functions form a high-frequency low-amplitude sine basis function cluster F b ;in, is the parameter, low frequency and high amplitude sine basis function cluster F c It can represent the channel characteristics of the river section, high-frequency and low-amplitude sinusoidal basis function cluster F b It can represent the beach characteristics of the river section.

[0056] Will are set to 0.75 and 1.25 respectively, and the low-pass filter is based on the sine basis function of 75% after filtering out the maximum amplitude in F, and the low-frequency and high-amplitude sine basis function cluster F is obtained. c , which can be expressed as:

[0057] ;

[0058] Where N c is the number of low-frequency, high-amplitude sinusoidal basis functions, The i c The elevation and amplitude corresponding to a low-frequency, high-amplitude sine basis function.

[0059] High-pass filtering is to remove the first 75% of the sine basis functions based on the minimum amplitude in F to obtain a high-frequency, low-amplitude sine basis function cluster F b , which can be expressed as:

[0060] ;

[0061] Where N b is the number of high-frequency low-amplitude sinusoidal basis functions, The i b The height and amplitude corresponding to a high-frequency, low-amplitude sine basis function.

[0062] The low-pass filtering and high-pass filtering strategies here can effectively separate the function clusters representing the characteristics of the river channel and the beach. Low-pass filtering is a filter for a cluster of sinusoidal functions. Its filtering rule is that low-frequency functions can pass normally, while high-frequency functions above a certain threshold are weakened or blocked, and then the amplitude is further screened, that is, the high amplitude is passed. High-pass filtering is the opposite of low-pass filtering, and is also controlled by the user's own threshold; among them, the thresholds of low-pass filtering and high-pass filtering are generally set to allow the first 30% of low-frequency and high-frequency sinusoidal functions to pass.

[0063] return Figure 1 In step 3 of the embodiment, the river channel area and the beach area of ​​the river section are identified according to the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster.

[0064] It should be noted that to determine the river channel area and the beach area, it is necessary to first determine the stationary point and the extreme point (i.e., the minimum point, the left maximum point, and the right maximum point) based on the function cluster. In some embodiments, see Figure 5 , the process of identifying the channel area and beach area of ​​the river section can be as follows:

[0065] Traverse the low-frequency high-amplitude sine basis function cluster and the high-frequency low-amplitude sine basis function cluster, and calculate the value of each sine basis function within the range of the horizontal coordinate of the river section. Phase point coordinates; where n represents the order or serial number of the sine basis function, the coordinate set P corresponding to the phase point can be expressed as {(s i ,v i ),i=1,2…N},s i is the width of the river cross section corresponding to the i-th phase point, v i is the elevation corresponding to the i-th phase point, and N represents the total number of phase points, which is consistent with the number of scattered points obtained after decomposition.

[0066] The horizontal axis (i.e. s i ) are merged into one phase point, and the phase points are sorted according to the size of the horizontal coordinates; wherein the vertical coordinate of the merged phase point is the sum of the vertical coordinates of the phase points with the same horizontal coordinates.

[0067] In the phase point sorting, the phase point with the smallest ordinate is taken as the low point of the river channel, the phase point with the largest ordinate on the left side of the low point of the river channel is taken as the left channel vertex, and the phase point with the largest ordinate on the right side of the low point of the river channel is taken as the right channel vertex. The stationary points are selected from the remaining phase points, see Figure 6 ( Figure 6 The horizontal coordinate in the middle represents the width of the river cross section, and the vertical coordinate represents the elevation. The units of width and elevation are both meters. The minimum point is the low point of the river channel, the maximum point on the left is the vertex of the left river channel, and the maximum point on the right is the vertex of the right river channel). The range formed by the horizontal coordinates of all stationary points is taken as the beach area, and the rest of the range is taken as the river channel area.

[0068] The stationary point is selected from the remaining phase points, which can be expressed by the formula:

[0069] K=(v j+1 -v j )×(v j-1 -v j );

[0070] If K>0, the jth phase point among the remaining phase points is a stationary point.

[0071] Figure 7 In order to add random disturbances to the river section, Figure 7 The horizontal axis represents the width of the river cross section, and the vertical axis represents the elevation. The units of width and elevation are both meters. The above method is used to identify the river cross section with random disturbance. The results can be seen Figure 8 ( Figure 8 The horizontal axis represents the width of the river cross section, and the vertical axis represents the elevation. The units of width and elevation are both meters). It can be seen that the above method is effective for identifying river sections with strong disturbances.

[0072] Fig. 9 It is a river section connected by multiple sections of shoals and channels. Fig. 9 The horizontal axis represents the width of the river cross section, and the vertical axis represents the elevation. The units of width and elevation are both meters. The above method is also used to identify the river cross section connected by multiple sections of shoals and channels. The results obtained can be seen Fig.10 ( Fig.10 The horizontal axis represents the width of the river cross section, and the vertical axis represents the elevation. The units of width and elevation are both meters). It can be seen that the above method is effective for identifying river sections connected by multiple shoals and channels.

[0073] The above method realizes the identification of river channel and shoal, with clear physical concepts, simple calculations, strong operability, and can process large quantities of cross-sectional data. It can be directly applied to hydraulic calculations in water conservancy projects, and can process river sections with strong disturbances and river sections with complex multi-section shoal connections. It has high accuracy and greatly improves efficiency compared to traditional artificial divisions. It avoids the subjectivity and contingency of artificial divisions, and the division is more accurate. In addition, this method is universal and can be applied to most natural rivers, and can be simply implemented through the Python programming language.

[0074] Fig.11 This is a schematic diagram of an embodiment of the river channel identification device disclosed in the present invention. Fig.11 The embodiment is a virtual device that can be loaded and executed by a computer device, and the computer device may include the above-mentioned identification terminal, Fig.11 The device may include a decomposition module, a screening module and an identification module, which, when used to execute the above-mentioned river channel and shoal identification method, can:

[0075] The decomposition module is used to decompose the river section into multiple sinusoidal basis functions with different frequency domains and amplitudes to obtain a sinusoidal basis function cluster.

[0076] The screening module is used to screen out low-frequency and high-amplitude sinusoidal basis function clusters and high-frequency and low-amplitude sinusoidal basis function clusters from the sinusoidal basis function clusters.

[0077] The screening module is also used to traverse the sine basis function cluster and screen out the low-frequency sine basis functions with amplitudes greater than The low-frequency high-sine basis functions are combined to form a low-frequency high-amplitude sine basis function cluster, and the high-frequency sine basis functions are selected to select the ones with amplitude less than The high-frequency sine basis functions form a high-frequency low-amplitude sine basis function cluster; among them, As parameters, is the maximum amplitude in the sine basis function cluster, is the minimum amplitude in the cluster of sine basis functions.

[0078] The identification module is used to identify the river channel area and the beach area of ​​the river section according to the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster.

[0079] The screening module is further used to traverse the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster, and calculate the phase point coordinates of each sinusoidal basis function within the range of the horizontal coordinate value of the river section; merge multiple phase points with consistent horizontal coordinates into one phase point, and sort the phase points according to the size of the horizontal coordinates; in the phase point sorting, the phase point with the smallest vertical coordinate is used as the low point of the river channel, the phase point with the largest vertical coordinate on the left side of the river channel low point is used as the left river channel vertex, and the phase point with the largest vertical coordinate on the right side of the river channel low point is used as the right river channel vertex, and the stationary points are screened out from the remaining phase points, and the range formed by the horizontal coordinates of all the stationary points is used as the beach area, and the remaining range is used as the river channel area; wherein the vertical coordinate of the merged phase point is the sum of the vertical coordinates of the phase points with consistent horizontal coordinates; screening the stationary points from the remaining phase points includes: if (v j+1 -v j )×(v j-1 -v j )>0, then the jth phase point among the remaining phase points is a stationary point.

[0080] The device of this embodiment decomposes the river section into a sine basis function cluster, and selects a low-frequency, high-amplitude sinusoidal basis function cluster and a high-frequency, low-amplitude sinusoidal basis function cluster from the sinusoidal basis function cluster. Based on the low-frequency, high-amplitude sinusoidal basis function cluster and the high-frequency, low-amplitude sinusoidal basis function cluster, the river channel area and the beach area of ​​the river section can be identified. Compared with traditional artificial division, the efficiency is greatly improved, the subjectivity and randomness of artificial division are avoided, and the division is more accurate.

[0081] The present disclosure also relates to a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes a river channel identification method.

[0082] The present disclosure also relates to a computer device, including one or more processors and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a river channel identification method.

[0083] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0087] 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 in the scope of the claims of the present invention to be approved.

Claims

1. A method for identifying a river channel, characterized in that: include: Decomposing the river section into multiple sinusoidal basis functions of different frequency domains and amplitudes to obtain a sinusoidal basis function cluster; wherein the river section decomposition includes connecting the measured data of the river section into a cross-section diagram, and performing Fourier transform decomposition based on the cross-section diagram; Selecting a low-frequency and high-amplitude sine basis function cluster and a high-frequency and low-amplitude sine basis function cluster from the sine basis function cluster; Traverse the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster, and calculate the phase point coordinates of each sinusoidal basis function within the range of the horizontal coordinate value of the river section; Merge multiple phase points with the same horizontal coordinates into one phase point, and sort the phase points according to the size of the horizontal coordinates; wherein the vertical coordinate of the merged phase point is the sum of the vertical coordinates of the phase points with the same horizontal coordinates; In the phase point sorting, the phase point with the smallest ordinate is taken as the low point of the river channel, the phase point with the largest ordinate on the left side of the river channel low point is taken as the left channel vertex, and the phase point with the largest ordinate on the right side of the river channel low point is taken as the right channel vertex. The stationary points are selected from the remaining phase points, and the range formed by the horizontal coordinates of all stationary points is taken as the beach area, and the rest of the range is taken as the river channel area.

2. The method according to claim 1, characterized in that Filter out low-frequency and high-amplitude sine basis function clusters and high-frequency and low-amplitude sine basis function clusters from the sine basis function clusters, including: Traverse the sine basis function cluster and select the low-frequency sine basis functions with amplitude greater than The low-frequency high-sine basis functions are combined to form a low-frequency high-amplitude sine basis function cluster, and the high-frequency sine basis functions are selected to select the ones with amplitude less than The high-frequency sine basis functions form a high-frequency low-amplitude sine basis function cluster; among them, As parameters, is the maximum amplitude in the sine basis function cluster, is the minimum amplitude in the cluster of sine basis functions.

3. The method according to claim 1, characterized in that Select the stationary points from the remaining phase points, including: if ( v j+1 - v j )×( v j-1 - v j )>0, then the remaining phase points j The phase point is the stationary point.

4. A river channel identification device, characterized in that: include: The decomposition module decomposes the river section into multiple sinusoidal basis functions of different frequency domains and amplitudes to obtain a sinusoidal basis function cluster; the river section decomposition includes connecting the measured data of the river section into a cross-section diagram, and performing Fourier transform decomposition based on the cross-section diagram; A screening module is used to screen out low-frequency and high-amplitude sinusoidal basis function clusters and high-frequency and low-amplitude sinusoidal basis function clusters from the sinusoidal basis function clusters; The identification module traverses the low-frequency high-amplitude sinusoidal basis function cluster and the high-frequency low-amplitude sinusoidal basis function cluster, and calculates the phase point coordinates of each sinusoidal basis function within the range of the horizontal coordinate value of the river section; merges multiple phase points with consistent horizontal coordinates into one phase point, and sorts the phase points according to the size of the horizontal coordinates; in the phase point sorting, the phase point with the smallest vertical coordinate is taken as the low point of the river channel, the phase point with the largest vertical coordinate on the left side of the river channel low point is taken as the left river channel vertex, and the phase point with the largest vertical coordinate on the right side of the river channel low point is taken as the right river channel vertex, and the stationary points are selected from the remaining phase points, and the range formed by the horizontal coordinates of all stationary points is taken as the beach area, and the remaining range is taken as the river channel area; wherein the vertical coordinate of the merged phase point is the sum of the vertical coordinates of the phase points with consistent horizontal coordinates.

5. The device according to claim 4, characterized in that The filtering module can be configured as follows: traverse the sine basis function cluster and filter out the low-frequency sine basis functions with amplitudes greater than The low-frequency high-sine basis functions are combined to form a low-frequency high-amplitude sine basis function cluster, and the high-frequency sine basis functions are selected to select the ones with amplitude less than The high-frequency sine basis functions form a high-frequency low-amplitude sine basis function cluster; among them, As parameters, is the maximum amplitude in the sine basis function cluster, is the minimum amplitude in the cluster of sine basis functions.

6. The device according to claim 4, characterized in that In the identification module, the stationary points are selected from the remaining phase points, including: if ( v j+1 - v j )×( v j-1 - v j )>0, then the remaining phase points j The phase point is the stationary point.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes any one of the methods of claims 1 to 3.

8. A computer device, characterized in that: include: One or more processors, and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 1 to 3.

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