A river channel bank silting thickness detection method and system based on hydraulic engineering

Abstract

This application relates to the technical field of intelligent detection, providing a method and system for detecting siltation thickness on riverbanks and reservoirs used in hydraulic engineering. The method includes: first, acquiring siltation thickness information corresponding to multiple target detection points based on a preset first detection area template; then, effectively determining target detection direction lines based on the siltation thickness information; next, quickly determining core detection area information based on multiple target detection direction lines and a preset second detection area template; and finally, efficiently generating and executing a second detection command based on the core detection area information. This application can accurately locate the core detection area with the most concentrated anomalies by identifying the spatial distribution characteristics of siltation thickness, and perform secondary detection on this area to obtain higher-density verification data, thereby effectively improving the reliability and robustness of the final siltation thickness distribution model.

Description

Technical Field

[0001] This application relates to the technical field of intelligent detection, and more specifically, to a method and system for detecting the siltation thickness of river and reservoir banks used in water conservancy projects. Background Technology

[0002] River channels and reservoir banks are the most active areas for reservoir siltation. Silt deposits often extend in strips and are distributed very unevenly. If timely detection and corresponding measures are not taken, the continued development of siltation will directly encroach on the effective reservoir capacity and threaten downstream irrigation and water supply security.

[0003] Currently, river and reservoir bank siltation detection typically employs a completely random sampling method. However, due to the non-uniformity and directional characteristics of siltation distribution, the completely random sampling method struggles to cover the actual siltation area, easily leading to missed detections and resulting in low reliability of the detection results. Further improvements are needed. Summary of the Invention

[0004] Based on this, this application provides a method and system for detecting the siltation thickness of river and reservoir banks used in water conservancy projects, in order to solve the problem of low reliability of detection results in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for detecting the siltation thickness of riverbanks used in water conservancy projects, the method comprising: Based on the preset first detection area template information, the sediment thickness information corresponding to multiple target detection points is obtained; Based on the siltation thickness information, the target detection direction line information is determined; Based on the target detection direction line information and the preset second detection area template information, the core detection area information is determined; Based on the core detection area information, a re-detection instruction is generated and executed, wherein the re-detection instruction is used to instruct the core detection area information to be detected.

[0006] Compared with existing technologies, the beneficial effects are as follows: The method for detecting siltation thickness on riverbanks and reservoirs used in water conservancy projects provided in this application embodiment allows the terminal device to first obtain siltation thickness information corresponding to multiple target detection points based on a preset first detection area template. Then, based on the siltation thickness information, the target detection direction line information is effectively determined. Next, based on the multiple target detection direction lines and the preset second detection area template information, the core detection area information is quickly determined. Finally, based on the core detection area information, a re-detection command information is efficiently generated and executed. This enables the accurate identification of the core detection area with the most concentrated siltation anomalies and the secondary detection of it, which helps to improve the reliability of the detection results and, to a certain extent, solves the problem of low reliability of current detection results.

[0007] Secondly, embodiments of this application provide a system for detecting siltation thickness on riverbanks and reservoirs used in water conservancy projects, the system comprising: The sediment thickness information acquisition module is used to acquire sediment thickness information corresponding to multiple target detection points based on the preset first detection area template information. Target detection direction line information determination module: used to determine the target detection direction line information based on the siltation thickness information; Core detection area information determination module: used to determine core detection area information based on multiple target detection direction line information and preset second detection area template information; The re-probe instruction information generation module is used to generate and execute re-probe instruction information based on the core probe area information, wherein the re-probe instruction information is used to instruct the core probe area information to be probed.

[0008] Thirdly, embodiments of this application provide a terminal 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.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0010] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0012] Figure 1 This is a schematic flowchart of a method for detecting sediment thickness provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the process before step S100 in the siltation thickness detection method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the first shoreline sampling point information provided in an embodiment of this application; Figure 4 This is a flowchart illustrating step S100 in a siltation thickness detection method provided in an embodiment of this application; Figure 5 This is a schematic diagram of the detection area information provided in one embodiment of this application; Figure 6 This is a schematic diagram of the first detection point provided in an embodiment of this application; Figure 7 This is a flowchart illustrating step S200 in a siltation thickness detection method provided in an embodiment of this application; Figure 8 This is a flowchart illustrating step S300 in a siltation thickness detection method provided in an embodiment of this application; Figure 9 This is a schematic diagram of target detection direction line information provided in an embodiment of this application; Figure 10 This is a block diagram of a siltation thickness detection system provided in one embodiment of this application; Figure 11 This is a schematic diagram of a terminal device provided in an embodiment of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0016] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0017] Please see Figure 1 , Figure 1This is a flowchart illustrating a method for detecting siltation thickness on riverbanks and reservoirs used in water conservancy projects, as provided in this embodiment. In this embodiment, the execution entity of the siltation thickness detection method is a terminal device. It is understood that the types of terminal devices include, but are not limited to, tablet computers, laptops, Ultra-Mobile Personal Computers (UMPCs), netbooks, and Personal Digital Assistants (PDAs). This embodiment does not impose any restrictions on the specific type of terminal device.

[0018] Please see Figure 1 The method for detecting sediment thickness provided in this application includes, but is not limited to, the following steps: In S100, based on the preset first detection area template information, the sediment thickness information corresponding to multiple target detection points is obtained.

[0019] Specifically, the terminal device can first quickly obtain the sediment thickness information corresponding to multiple target detection points based on the preset first detection area template information, thereby initially determining the overall sediment situation.

[0020] For some possible implementations, please refer to [link to relevant documentation] for accurate acquisition of sediment thickness information. Figure 2 Before step S100, the method further includes, but is not limited to, the following steps: In S101, real-time image information of the river and reservoir banks to be detected is acquired based on the preset drone.

[0021] Specifically, the terminal equipment can first acquire real-time image information of the river and reservoir banks to be detected based on a pre-set drone. The drone can be pre-installed with a camera, ultrasonic probe, and lightweight drilling rig. The river and reservoir banks to be detected are used to describe the river and reservoir banks where siltation is to be monitored. The real-time image information is used to describe the real-time images obtained by taking pictures of the river and reservoir banks to be detected.

[0022] For example, the terminal device can first control the drone to fly to a position above the river or reservoir bank, and then control the drone to take pictures of the river or reservoir bank through the camera, thereby obtaining real-time image information.

[0023] In S102, shoreline boundary information is generated based on real-time image information and a preset edge detection algorithm.

[0024] Specifically, after the terminal device acquires real-time image information, it can perform edge line detection processing on the real-time image information based on a preset edge detection algorithm to effectively generate shoreline boundary information. The edge detection algorithm can be an edge detection algorithm based on the Sober operator, an edge detection algorithm based on the Laplacian operator, or an edge detection algorithm based on the Canny operator. The shoreline boundary information is used to describe the boundary between the water area and the bank slope.

[0025] In S103, for any given shoreline boundary information, multiple first shoreline sampling point information are generated based on a preset first sampling interval value.

[0026] Specifically, after the terminal device generates shoreline boundary information, the terminal device can perform this processing on any shoreline boundary information. Based on a preset first sampling interval value, it generates multiple first shoreline sampling point information, where the first shoreline sampling point information is used to describe the sampling points located on the shoreline boundary information; the distance between two adjacent first shoreline sampling point information is the first sampling interval value, and the specific value and number of the first sampling interval value can be predefined.

[0027] For example, please refer to Figure 3 , Figure 3 The dots marked "A1", "B1" and "C1" inside the middle represent the information of the first shoreline sampling point.

[0028] In S104, for each first shoreline sampling point information, the second shoreline sampling point information corresponding to the first shoreline sampling point information is determined according to the preset cross-sectional marking line information.

[0029] Specifically, after the terminal device generates multiple first shoreline sampling point information, the terminal device can determine the second shoreline sampling point information corresponding to each first shoreline sampling point information based on the preset cross-sectional marking line information. The extension direction of the cross-sectional marking line information is perpendicular to the direction of river flow. One endpoint of the cross-sectional marking line information is the first shoreline sampling point information, and the other endpoint of the cross-sectional marking line information is the second shoreline sampling point information. The second shoreline sampling point information is located on another shoreline boundary information.

[0030] For example, please refer to Figure 3 , Figure 3 The dashed lines in the text represent cross-sectional marking lines. Figure 3 The dots marked "A2", "B2" and "C2" inside the middle represent the second shoreline sampling point information.

[0031] In S105, the river center point information is determined based on the information of the first shoreline sampling point and the information of the second shoreline sampling point corresponding to the information of the first shoreline sampling point.

[0032] Specifically, after the terminal device determines the second shoreline sampling point information, the terminal device can determine the river channel center point information based on the first shoreline sampling point information and the second shoreline sampling point information corresponding to the first shoreline sampling point information. The river channel center point information is used to describe the midpoint between the first shoreline sampling point information and the second shoreline sampling point information. The specific determination of the midpoint between the two location points can be based on existing sampling techniques, so it will not be elaborated here.

[0033] For example, please refer to Figure 3 , Figure 3 The dots marked "A3", "B3" and "C3" inside the channel indicate the center point of the river.

[0034] In S106, the river centerline information is determined based on information from multiple river center points.

[0035] Specifically, after the terminal device determines the center point information of the river channel, it can determine the center line information of the river channel based on multiple center point information. The center line information is a straight line that passes through multiple center point information simultaneously.

[0036] In some possible implementations, for accurate acquisition of sediment thickness information, please refer to [link / reference]. Figure 4 Step S100 includes, but is not limited to, the following steps: In S110, based on a preset random sampling algorithm, regional anchor point information is generated according to the river centerline information.

[0037] Specifically, the terminal device can first determine a sampling point randomly based on the river centerline information using a preset random sampling algorithm, and effectively generate regional anchor point information. The random sampling algorithm can be a completely random sampling algorithm, a low-difference sequence algorithm, or a Markov chain Monte Carlo algorithm.

[0038] In S120, based on the preset first detection area template information, the detection area information is determined according to the area anchor point information and the shoreline boundary information.

[0039] Specifically, after the terminal device generates the area anchor point information, the terminal device can quickly determine the detection area information based on the preset first detection area template information, the area anchor point information, and the shoreline boundary information. The first detection area template information describes a rectangular area template with a fixed aspect ratio. The detection area information describes the area determined by aligning the center point of the first detection area template information with the area anchor point information and scaling the first detection area template information proportionally until the boundary of the first detection area template information intersects with the shoreline boundary information.

[0040] For example, please refer to Figure 5 Assuming the region anchor point information is a circle marked "B3" inside, then Figure 5 The bold rectangle in the image represents the detection area information.

[0041] In S130, multiple first detection points are determined based on the detection area information.

[0042] Without loss of generality, the target detection points include the first detection point and the second detection point.

[0043] Specifically, after the terminal device determines the detection area information, it can efficiently determine multiple first detection points based on the detection area information. The first detection points are used to describe the corner points of the detection area information.

[0044] For example, please refer to Figure 6 , Figure 6 The dots marked "A4", "B4", "C4" and "D4" inside the center all represent the first detection point.

[0045] In S140, for each first detection point, multiple second detection points are generated according to the preset second sampling interval value.

[0046] Specifically, after the terminal device determines multiple first detection points, the terminal device can perform this process for each first detection point. According to the preset second sampling interval value, multiple second detection points are quickly generated. The distance between the second detection point and the first detection point is the second sampling interval value. The interval between each second detection point is equal, and each second detection point is located within the detection area information. Multiple second detection points determined based on the same first detection point are in the same group. The specific interval between the second detection points in the same group can be predefined, so it will not be elaborated here.

[0047] For example, please refer to Figure 6 , Figure 6 The dots marked "A5", "A6", "A7" and "A8" inside the middle all represent the second detection point and belong to the same group; Figure 6 The dots marked "B5", "B6", "B7" and "B8" inside the middle all represent the second detection point and belong to the same group; Figure 6 The dots marked "C5", "C6", "C7" and "C8" inside the middle all represent the second detection point and belong to the same group; Figure 6 The dots marked "D5", "D6", "D7" and "D8" inside the middle all represent the second detection point and belong to the same group.

[0048] In S150, based on preset marker points, the first physical coordinate information corresponding to each first detection point and the second physical coordinate information corresponding to each second detection point are determined.

[0049] Specifically, after the terminal device generates multiple second detection points, it can determine the first physical coordinate information corresponding to each first detection point based on preset marker points and the relative distance between each first detection point and the marker points. It can also determine the second physical coordinate information corresponding to each second detection point based on the relative distance between each second detection point and the marker points. The marker points can be pre-marked in the real-time image information and are associated with pixel coordinates in the real-time image information, as well as specific physical coordinates. It should be noted that existing technologies can be used to determine the physical coordinates of other detection points based on a marker point in the image whose specific physical coordinates are known; therefore, this will not be elaborated upon.

[0050] In S160, a sedimentation thickness information acquisition instruction is generated based on the first physical coordinate information and the second physical coordinate information to obtain sedimentation thickness information corresponding to multiple target detection points.

[0051] Specifically, after the terminal device determines the first and second physical coordinates, it can generate a sediment thickness information acquisition command based on these coordinates to obtain the sediment thickness information corresponding to each target detection point. This allows the drone to reach the first physical coordinates and then use an ultrasonic probe to acquire the sediment thickness information corresponding to the first detection point. The drone then moves to the second physical coordinates and uses the same ultrasonic probe to acquire the sediment thickness information corresponding to the second detection point. The sediment thickness information acquisition command instructs the drone to reach both the first and second physical coordinates to acquire the sediment thickness information corresponding to the first and second detection points. It should be noted that existing technologies can be used to calculate the sediment layer thickness using an ultrasonic probe, which will not be elaborated upon here. For example, the depth to which the ultrasonic wave reaches the sediment layer surface can be calculated first, followed by the distance to the hard bottom after the ultrasonic wave penetrates the sediment layer. The vertical thickness of the sediment layer itself can be determined by subtracting the depth to the sediment layer surface from the hard bottom distance.

[0052] In one possible implementation, while the drone uses an ultrasonic probe to collect information on the thickness of the sediment deposit, a lightweight drilling rig can be used simultaneously for drilling and sampling operations.

[0053] In S200, the target detection direction line information is determined based on the siltation thickness information.

[0054] Specifically, after the terminal device acquires the sediment thickness information, it can effectively determine the target detection direction line information based on the sediment thickness information, thereby indicating the main expansion direction of the sediment thickness abnormal area to guide subsequent detection.

[0055] In some possible implementations, for effectively determining the target detection direction line information, please refer to [link / reference needed]. Figure 7 Step S200 includes, but is not limited to, the following steps: In S210, the core direction detection point information is determined based on multiple second detection points in the same group.

[0056] Specifically, after the terminal device acquires the accumulation thickness information, it can quickly determine the core direction detection point information based on multiple second detection points in the same group. The core direction detection point information is used to describe the second detection point corresponding to the largest accumulation thickness information among the multiple second detection points.

[0057] In S220, target detection direction line information is generated based on the core direction detection point information and the first detection point in the same group.

[0058] Specifically, after the terminal device determines the core direction detection point information, the terminal device can generate target detection direction line information based on the core direction detection point information and the first detection point in the same group. The target detection direction line information is used to describe the ray that starts from the first detection point and passes through the core direction detection point information.

[0059] In S300, the core detection area information is determined based on multiple target detection direction line information and preset second detection area template information.

[0060] Specifically, after the terminal device generates target detection direction line information, it can effectively determine the core detection area information based on multiple target detection direction line information and preset second detection area template information. This allows for precise identification of the core detection area with the most concentrated siltation anomalies, guiding secondary detection sampling to be concentrated in this area, effectively improving the detection efficiency and reliability of the detection results for the true distribution of siltation.

[0061] In some possible implementations, for effectively determining core probe area information, please refer to [link / reference]. Figure 8 Step S300 includes, but is not limited to, the following steps: In S310, it is determined whether there is target intersection information within the detection area information.

[0062] Specifically, after the terminal device determines the target detection direction line information, the terminal device can determine whether there is target intersection information within the detection area information. The target intersection information is used to describe the intersection between different target detection direction line information.

[0063] In S320, if target intersection information exists within the detection area information, the core detection area information is determined based on the second detection area template information and the target intersection information.

[0064] Specifically, if target intersection information exists within the detection area information, the terminal device can effectively determine the core detection area information based on the second detection area template information and the target intersection information. The second detection area template information describes a circular area template with fixed size and shape. The center point of the core detection area information is the target intersection information, the shape of the core detection area information is the shape of the second detection area template information, and the size of the core detection area information is the size of the second detection area template information.

[0065] For example, please refer to Figure 9 , Figure 9 The straight dashed lines in the diagram represent target detection direction information. Figure 9 The circles enclosed by dashed lines represent information about the core detection area.

[0066] In the S400, based on the core detection area information, a re-detection command is generated and executed.

[0067] Specifically, after the terminal device determines the core detection area information, it can quickly generate and execute a re-detection instruction based on the core detection area information. The re-detection instruction is used to instruct the core detection area information to be detected, thereby obtaining the accumulation thickness information of the core detection area information and performing valuable sampling processing.

[0068] The implementation principle of the method for detecting siltation thickness on riverbanks and reservoirs used in water conservancy projects in this application embodiment is as follows: The terminal device can first obtain siltation thickness information corresponding to multiple target detection points based on the preset first detection area template information. Then, based on the siltation thickness information, the target detection direction line information is effectively determined. Then, based on the multiple target detection direction line information and the preset second detection area template information, the core detection area information is quickly determined. Finally, based on the core detection area information, the re-detection command information is efficiently generated and executed, thereby accurately determining the core detection area with the most concentrated siltation anomaly and conducting secondary detection on it, which is beneficial to improve the reliability of the detection results.

[0069] It should be noted that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0070] Embodiments of this application also provide a system for detecting siltation thickness on riverbanks and reservoirs used in hydraulic engineering. For ease of explanation, only the parts relevant to this application are shown, such as... Figure 10 As shown, the system 100 includes: 101 Module for Acquiring Accumulation Thickness Information: Used to acquire accumulation thickness information corresponding to multiple target detection points based on preset first detection area template information; Target detection direction line information determination module 102: used to determine target detection direction line information based on siltation thickness information; Core detection area information determination module 103: used to determine core detection area information based on multiple target detection direction line information and preset second detection area template information; The re-probe instruction information generation module 104 is used to generate and execute re-probe instruction information based on the core probe area information, wherein the re-probe instruction information is used to instruct the probe of the core probe area information.

[0071] Optionally, the system 100 also includes: Real-time image information acquisition module: used to acquire real-time image information of the river and reservoir banks to be detected based on a preset UAV; Shoreline boundary information generation module: This module generates shoreline boundary information based on real-time image information and a preset edge detection algorithm. The shoreline boundary information describes the boundary between the water area and the bank slope. First shoreline sampling point information generation module: used to generate multiple first shoreline sampling point information based on a preset first sampling interval value for any shoreline boundary information, wherein the distance between two adjacent first shoreline sampling point information is the first sampling interval value; The second shoreline sampling point information determination module is used to determine the second shoreline sampling point information corresponding to each first shoreline sampling point information based on the preset cross-sectional marker line information. The extension direction of the cross-sectional marker line information is perpendicular to the direction of river flow. One endpoint of the cross-sectional marker line information is the first shoreline sampling point information, and the other endpoint of the cross-sectional marker line information is the second shoreline sampling point information. The second shoreline sampling point information is located on another shoreline boundary information. River center point information determination module: used to determine the river center point information based on the first shoreline sampling point information and the second shoreline sampling point information corresponding to the first shoreline sampling point information; River centerline information determination module: used to determine the river centerline information based on multiple river center point information.

[0072] Optionally, the target detection points include a first detection point and a second detection point; the above-mentioned siltation thickness information acquisition module 101 includes: Regional anchor point information generation submodule: used to generate regional anchor point information based on the river centerline information using a preset random sampling algorithm; The detection area information determination submodule is used to determine the detection area information based on the preset first detection area template information, the area anchor point information, and the shoreline boundary information. The first detection area template information describes a rectangular area template with a fixed aspect ratio. The detection area information describes the area determined by aligning the center point of the first detection area template information with the area anchor point information and scaling the first detection area template information proportionally until the boundary of the first detection area template information intersects with the shoreline boundary information. First detection point determination submodule: used to determine multiple first detection points based on the detection area information, wherein the first detection points are used to describe the corner points of the detection area information; The second detection point generation submodule is used to generate multiple second detection points for each first detection point according to a preset second sampling interval value. The distance between the second detection point and the first detection point is the second sampling interval value. The interval between each second detection point is equal, and each second detection point is located within the detection area information. The second physical coordinate information determination submodule is used to determine the first physical coordinate information corresponding to each first detection point and the second physical coordinate information corresponding to each second detection point based on preset marker points. The sediment thickness information acquisition instruction generation submodule is used to generate sediment thickness information acquisition instructions based on the first physical coordinate information and the second physical coordinate information, so as to obtain sediment thickness information corresponding to multiple target detection points. The sediment thickness information acquisition instructions are used to instruct the UAV to reach the first physical coordinate information to obtain the sediment thickness information corresponding to the first detection point, and to instruct the UAV to reach the second physical coordinate information to obtain the sediment thickness information corresponding to the second detection point.

[0073] Optionally, the aforementioned core detection area information determination module 103 includes: Target intersection information judgment submodule: used to determine whether there is target intersection information within the detection area information, where target intersection information is used to describe the intersection between different target detection direction line information; The core detection area information determination submodule is used to determine the core detection area information based on the second detection area template information and the target intersection information if the detection area information contains target intersection information. The second detection area template information describes a circular area template with fixed size and shape. The center point of the core detection area information is the target intersection information, the shape of the core detection area information is the shape of the second detection area template information, and the size of the core detection area information is the size of the second detection area template information.

[0074] Optionally, the aforementioned core detection area information determination module 103 includes: The detection area information judgment submodule is used to determine whether there is target intersection information within the detection area information. The target intersection information is used to describe the intersection between different target detection direction line information. The core detection area information determination submodule is used to determine the core detection area information based on the second detection area template information and the target intersection information if the detection area information contains target intersection information. The second detection area template information describes a circular area template with fixed size and shape. The center point of the core detection area information is the target intersection information, the shape of the core detection area information is the shape of the second detection area template information, and the size of the core detection area information is the size of the second detection area template information.

[0075] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0076] This application also provides a terminal device, such as... Figure 11 As shown, the terminal device 110 of this embodiment includes: a processor 111, a memory 112, and a computer program 113 stored in the memory 112 and executable on the processor 111. When the processor 111 executes the computer program 113, it implements the steps described in the above-described embodiment of the siltation thickness detection method, for example... Figure 1 Steps S100 to S400 are shown; or, when processor 111 executes computer program 113, it implements the functions of each module in the above-described device, for example... Figure 10 The functions of modules 101 to 104 are shown.

[0077] The terminal device 110 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The terminal device 110 includes, but is not limited to, a processor 111 and a memory 112. Those skilled in the art will understand that... Figure 11This is merely an example of terminal device 110 and does not constitute a limitation on terminal device 110. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device 110 may also include input / output devices, network access devices, buses, etc.

[0078] The processor 111 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0079] The memory 112 can be an internal storage unit of the terminal device 110, such as the hard disk or memory of the terminal device 110. The memory 112 can also be an external storage device of the terminal device 110, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 110. Furthermore, the memory 112 can include both internal storage units and external storage devices of the terminal device 110. The memory 112 can also store computer program 113 and other programs and data required by the terminal device 110. The memory 112 can also be used to temporarily store data that has been output or will be output.

[0080] One embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0081] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the methods, principles and structures of this application should be covered within the scope of protection of this application.

Claims

1. A method for detecting the siltation thickness of river and reservoir banks used in water conservancy projects, characterized in that, The method includes: Based on the preset first detection area template information, the sediment thickness information corresponding to multiple target detection points is obtained; Based on the siltation thickness information, the target detection direction line information is determined; Based on the target detection direction line information and the preset second detection area template information, the core detection area information is determined; Based on the core detection area information, a re-detection instruction is generated and executed, wherein the re-detection instruction is used to instruct the core detection area information to be detected.

2. The method according to claim 1, characterized in that, Before acquiring the sediment thickness information corresponding to multiple target detection points based on the preset first detection area template information, the method further includes: Based on the pre-set drone, real-time image information of the river and reservoir banks to be detected is obtained; Based on the real-time image information and the preset edge detection algorithm, shoreline boundary information is generated, wherein the shoreline boundary information is used to describe the boundary between the water area and the bank slope; For any one of the shoreline boundary information, multiple first shoreline sampling point information are generated based on a preset first sampling interval value, wherein the distance between two adjacent first shoreline sampling point information is the first sampling interval value; For each of the first shoreline sampling point information, the second shoreline sampling point information corresponding to the first shoreline sampling point information is determined according to the preset cross-sectional marking line information. The extension direction of the cross-sectional marking line information is perpendicular to the river flow direction. One end of the cross-sectional marking line information is the first shoreline sampling point information, and the other end of the cross-sectional marking line information is the second shoreline sampling point information. The second shoreline sampling point information is located on another shoreline boundary information. Based on the information of the first shoreline sampling point and the information of the second shoreline sampling point corresponding to the information of the first shoreline sampling point, the information of the river center point is determined; Based on the information of multiple river center points, the river centerline information is determined.

3. The method according to claim 2, characterized in that, The target detection points include a first detection point and a second detection point; the acquisition of accumulation thickness information corresponding to multiple target detection points based on preset first detection area template information includes: Based on a preset random sampling algorithm, regional anchor point information is generated according to the river centerline information; Based on the preset first detection area template information, the detection area information is determined according to the area anchor point information and the shoreline boundary information. The first detection area template information is used to describe a rectangular area template with a fixed aspect ratio. The detection area information is used to describe the area determined by aligning the center point of the first detection area template information with the area anchor point information and scaling the first detection area template information proportionally until the boundary of the first detection area template information intersects with the shoreline boundary information. Based on the detection area information, multiple first detection points are determined, wherein the first detection points are used to describe the corner points of the detection area information; For each of the first detection points, multiple second detection points are generated according to a preset second sampling interval value. The distance between the second detection point and the first detection point is the second sampling interval value. The interval between each second detection point is equal, and each second detection point is located within the detection area information. Based on preset marker points, determine the first physical coordinate information corresponding to each of the first detection points and the second physical coordinate information corresponding to each of the second detection points; Based on the first physical coordinate information and the second physical coordinate information, a sediment thickness information acquisition instruction is generated to obtain sediment thickness information corresponding to multiple target detection points. The sediment thickness information acquisition instruction is used to instruct the UAV to reach the first physical coordinate information to obtain sediment thickness information corresponding to the first detection point, and to instruct the UAV to reach the second physical coordinate information to obtain sediment thickness information corresponding to the second detection point.

4. The method according to claim 3, characterized in that, The step of determining the target detection direction line information based on the siltation thickness information includes: Based on multiple second detection points in the same group, core direction detection point information is determined, wherein the core direction detection point information is used to describe the second detection point corresponding to the largest accumulation thickness information among the multiple second detection points; Based on the core direction detection point information and the first detection point in the same group, target detection direction line information is generated, wherein the target detection direction line information is used to describe a ray that starts from the first detection point and passes through the core direction detection point information.

5. The method according to claim 1, characterized in that, The step of determining the core detection area information based on multiple target detection direction line information and preset second detection area template information includes: Determine whether target intersection information exists within the detection area information, wherein the target intersection information is used to describe the intersection between different target detection direction line information; If target intersection information exists within the detection area information, then core detection area information is determined based on the second detection area template information and the target intersection information. The second detection area template information describes a circular area template with fixed size and shape. The center point of the core detection area information is the target intersection information. The shape of the core detection area information is the shape of the second detection area template information. The size of the core detection area information is the size of the second detection area template information.

6. A system for detecting siltation thickness on river and reservoir banks used in water conservancy projects, characterized in that, The system includes: The sediment thickness information acquisition module is used to acquire sediment thickness information corresponding to multiple target detection points based on the preset first detection area template information. Target detection direction line information determination module: used to determine the target detection direction line information based on the siltation thickness information; Core detection area information determination module: used to determine core detection area information based on multiple target detection direction line information and preset second detection area template information; The re-probe instruction information generation module is used to generate and execute re-probe instruction information based on the core probe area information, wherein the re-probe instruction information is used to instruct the core probe area information to be probed.

7. The system according to claim 6, characterized in that, The core detection area information determination module includes: Target intersection information determination submodule: used to determine whether there is target intersection information within the detection area information, wherein the target intersection information is used to describe the intersection between different target detection direction line information; The core detection area information determination submodule is used to determine the core detection area information based on the second detection area template information and the target intersection information if the detection area information contains target intersection information. The second detection area template information describes a circular area template with fixed size and shape. The center point of the core detection area information is the target intersection information. The shape of the core detection area information is the shape of the second detection area template information. The size of the core detection area information is the size of the second detection area template information.

8. A terminal 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 the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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