A sediment stripping condition measurement system, measurement method, device and apparatus

Abstract

The present application relates to the technical field of pipeline monitoring, and discloses a sediment stripping condition measuring system, a measuring method, a device and equipment. In the method, firstly, a mesh cloth is used as a physical calibration reference to establish a mapping relationship between pixel coordinates and actual physical coordinates, and image data is converted into quantifiable data with real physical scales; then, continuously collected motion images are analyzed frame by frame, and the positions of tracer particles and sediment particles in a time sequence are tracked to convert discrete image frames into continuous particle motion trajectories and displacement information, so that dynamic quantification of the sediment stripping process is realized; finally, based on displacement data and length calibration parameters, the stripping amount of each position on the surface of a sediment sample is calculated, and spatial distribution data is generated, and displacement observation at the particle scale is summarized as a stripping amount distribution at the regional scale, so as to provide a refined quantitative testing means for subsequent evaluation of sediment stripping conditions.

Description

Technical Field

[0001] This invention relates to the field of pipeline monitoring technology, specifically to a sediment stripping condition determination system, measurement method, device, and equipment. Background Technology

[0002] Drainage pipes are the core transport channels of urban drainage systems. Over time, sediment gradually forms at the bottom of these pipes, reducing the flow cross-section and water transport capacity, and accelerating pipe corrosion. In severe cases, this can even lead to pipe blockage, causing sewage overflows and urban flooding. Therefore, timely treatment of sediment within the pipes is fundamental to the operation, maintenance, and disaster prevention and mitigation of drainage networks.

[0003] The drainage pipe sediment treatment method disclosed in the related technology includes a qualitative detection method based on the motion trajectory of inertial sensors. This method involves releasing a detection device equipped with an inertial measurement unit into the target pipe, using accelerometers and gyroscopes to record multidimensional data of the detection device's motion within the pipe, calculating the motion trajectory through coordinate transformation and integration, and then judging the siltation state inside the pipe based on the trajectory changes. Finally, a dredging robot is used to treat the siltation sediment inside the pipe.

[0004] However, the dredging robots disclosed in the relevant technologies mainly rely on human experience or fixed procedures for dredging. The cost of using dredging robots for sediment treatment is high and the operation cycle is long. This can easily lead to incomplete or excessive dredging of sediment in the target pipeline, making it difficult to meet the current demand for efficient and economical sediment treatment in pipelines. Summary of the Invention

[0005] This invention provides a sediment stripping condition determination system, measurement method, apparatus, and equipment to address the problem that methods disclosed in related technologies are insufficient to meet the current requirements for efficient and economical sediment treatment in pipelines.

[0006] In a first aspect, the present invention provides a sediment stripping condition determination system, the system comprising: The detection pipe is a transparent pipe for containing flowing water. One end of the pipe is connected to an external water source that provides various flow rate conditions, and the other end is connected to a water tank. The detection pipe includes a sedimentation area for placing sediment samples, and a mesh cloth is also provided in the sedimentation area as a length calibration reference. A tracer particle generator is located at one end of the detection pipe near an external water source and is used to release tracer particles into the detection pipe to follow the flow of water and mark the movement trajectory of sediment particles. An image acquisition device is installed outside the detection pipe to continuously acquire motion images of sediments and tracer particles in the deposition area of ​​the detection pipe. The data processing module is communicatively connected to the image acquisition device. It is used to receive the moving images, calibrate the moving images based on the grid size of the mesh cloth, and extract the trajectory of the tracer particles and the displacement information of the sediment particles to obtain the stripping test results of the sediment samples.

[0007] By employing the above-described implementation method, a visualized sediment testing platform is constructed using transparent detection pipes and mesh fabric, providing a clear observation window and physical calibration benchmark for image acquisition. Tracer particles are released into the water flow through a tracer particle generator, allowing the previously invisible water flow movement and sediment particle stripping process to be continuously captured by the image acquisition device, achieving visualization and traceability of the sediment stripping process. Subsequently, the data processing module performs mesh calibration, particle trajectory extraction, and particle displacement analysis on the moving images, transforming the image data into quantitative stripping data. This achieves a leap from qualitative observation to quantitative measurement, providing a repeatable and quantifiable testing method for determining sediment stripping conditions in drainage pipes, and providing reliable data support for subsequent cleaning of sediment within the pipes.

[0008] Secondly, the present invention provides a method for measuring sediment stripping conditions, the method comprising: Based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detection pipeline, the mapping relationship between the pixel coordinates in the initial motion image and the actual physical coordinates is established using the physical mapping method to obtain the length calibration parameters. Based on continuously acquired motion images at multiple times, combined with the length calibration parameters, the position change information of tracer particles and sediment particles is extracted by using a frame-by-frame particle displacement analysis method to obtain a sediment particle displacement dataset. Based on the displacement dataset of the sediment particles and the length calibration parameters, the stripping amount is calculated at each location on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipeline using the stripping amount calculation method, thereby generating sediment stripping amount distribution data.

[0009] By adopting the above implementation method, firstly, a mesh cloth is used as a physical calibration benchmark to establish a mapping relationship between pixel coordinates and actual physical coordinates, transforming image data into quantifiable data with a real physical scale; then, frame-by-frame analysis is performed on continuously acquired moving images, and by tracking the positional changes of tracer particles and sediment particles in the time series, discrete image frames are transformed into continuous particle motion trajectories and displacement information, realizing the dynamic quantification of the sediment stripping process; finally, based on displacement data and length calibration parameters, the amount of stripping at each location on the sediment sample surface is calculated and spatial distribution data is generated, summarizing the particle-scale displacement observations into a regional-scale stripping amount distribution, providing a refined quantitative testing method for subsequent assessment of sediment stripping conditions.

[0010] In one optional implementation, the motion images acquired continuously at multiple time points, combined with the length calibration parameters, are used to extract positional change information of tracer particles and sediment particles using a frame-by-frame particle displacement analysis method, resulting in a sediment particle displacement dataset, including: Based on motion images at multiple time points, image preprocessing methods are used to perform noise reduction, enhancement, and background removal on each frame of the image to obtain a preprocessed image sequence. Based on the preprocessed image sequence, the particle recognition and tracking algorithm is used to extract the position coordinates of each tracer particle in the continuous frame images, and the motion trajectory data of multiple tracer particles are obtained. Based on the preprocessed image sequence, the initial position of each sediment particle before stripping and its current position after stripping are identified using the particle boundary detection and centroid localization method, thereby obtaining the positional change information of each sediment particle. Based on the motion trajectory data of multiple tracer particles and the position change information of each sediment particle, combined with the length calibration parameters, the pixel displacement of each sediment particle between consecutive frames is converted into the actual physical displacement using the physical displacement conversion method, and then organized according to particle number and time order to obtain the displacement dataset of sediment particles.

[0011] By adopting the above implementation method, firstly, image preprocessing methods are used to denoise, enhance, and remove backgrounds from multiple frames of moving images, effectively improving image quality and providing clear input for subsequent accurate particle and grain identification. Then, particle tracking algorithms and particle boundary detection and centroid localization methods are used to simultaneously extract motion information of two different types of objects: tracer particles and sediment particles, enabling the correlation analysis of water flow dynamics and sediment response within the same time frame. Finally, pixel displacement is converted into actual physical displacement by combining length calibration parameters, and organized into a structured dataset according to particle number and time sequence, realizing the transformation from scattered image frames to ordered particle displacement records, providing a data foundation containing spatial location, displacement magnitude, and time series information for stripping calculation.

[0012] In one optional implementation, based on the preprocessed image sequence, the initial position of each sediment particle before stripping and its current position after stripping are identified using a particle boundary detection and centroid localization method, thereby obtaining positional change information for each sediment particle, including: Based on the initial frame images in the preprocessed image sequence, the boundary contours of each sediment particle are identified using an edge detection algorithm to obtain the initial boundary contour set of each sediment particle. Based on the initial boundary contour set of each sediment particle, the initial centroid coordinates of each sediment particle before stripping are determined using the centroid calculation method, thus obtaining the initial position information of each sediment particle. Based on the subsequent frames of the preprocessed image sequence, the boundary contour of the same sediment particle after stripping is tracked using edge detection and contour matching methods, and the centroid coordinates after stripping are determined using centroid calculation methods, so as to obtain the current position information of each sediment particle at each time. Based on the initial position information and the current position information of each sediment particle at each time, the position difference calculation method is used to obtain the position change information of each sediment particle before and after stripping.

[0013] By employing the above implementation method, firstly, an edge detection algorithm is used to identify the boundary contours of each sediment particle in the initial frame, establishing a recognizable contour model for each sediment particle. Then, the centroid coordinates are calculated based on the boundary contours, condensing the contour region information into precise centroid point positions, providing a stable and reliable position reference for subsequent displacement calculations, and avoiding positioning deviations caused by particle rotation or attitude changes. Simultaneously, in subsequent frames, the same particle is tracked using a contour matching method, and its current position is determined using the centroid coordinates, thereby overcoming the problem of establishing accurate correspondences between different frames after sediment particle stripping, and achieving synchronous tracking of multiple sediment particles. Finally, by calculating the difference between the initial position and the current position, the discrete inter-frame coordinate data is transformed into displacement information for each sediment particle, providing basic data at the sediment particle scale for subsequent stripping calculations.

[0014] In one optional implementation, based on the displacement dataset of the sediment particles and combined with the length calibration parameters, the stripping amount is calculated at various locations on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipe using a stripping amount calculation method, generating sediment stripping amount distribution data, including: Based on the displacement dataset of the sediment particles, combined with the length calibration parameters, the particle stripping determination method is used to filter out particles whose displacement exceeds the preset stripping threshold, thus obtaining a set of stripped particles. Based on the particle size or projected area of ​​each sediment particle in the stripped particle set, the displacement information of each sediment particle is converted into the stripping volume of a single sediment particle using the volume conversion method, thus obtaining the stripping volume data of each sediment particle. Based on the grid cells of the mesh fabric and the length calibration parameters, the spatial positioning method is used to map each stripped sediment particle to the grid cell where its initial position is located, and the stripping volume of each sediment particle is accumulated according to the grid cell to obtain the stripping amount of each grid cell. Based on the stripping amount of each grid cell, the stripping amount data of each grid cell is organized according to grid coordinates using a distribution data generation method to generate sediment stripping amount distribution data.

[0015] By employing the above implementation method, the displacement dataset is first filtered using a preset stripping threshold to eliminate weak vibrations or measurement noise below the threshold. Then, volume conversion is performed based on the particle size or projected area of ​​the stripped particles, transforming the particle displacement information observed in the two-dimensional image into a three-dimensional stripping volume. This gives the stripping amount a physical volume dimension, overcoming the limitation that image observation can only provide planar displacement and cannot directly obtain volume information. Next, using a grid cell spatial positioning method, the stripping volume of each sediment particle is mapped to the grid cell where its initial position is located, and accumulated according to grid cells. This integrates discrete particle-level data into regional distribution data, realizing the aggregation of stripping amount from a "single-particle scale" to a "spatial distribution scale," allowing the stripping amount of each grid cell to be independently quantified and compared. Finally, stripping amount distribution data is generated according to grid coordinates, providing a structured output for the spatial difference analysis of sediment stripping characteristics. This transforms scattered particle displacement information into stripping amount data with spatial distribution characteristics, providing a refined quantitative testing method for evaluating the stripping characteristics of sediments under different hydraulic conditions.

[0016] In one optional implementation, the length calibration parameters are obtained by using a physical mapping method to establish a mapping relationship between pixel coordinates in the initial moving image and actual physical coordinates based on the grid size of the mesh cloth in the received initial moving image of the deposition area of ​​the detection pipeline, including: Based on the initial motion image of the sedimentation zone of the detected pipeline, the pixel coordinates of each grid intersection point on the mesh cloth in the initial motion image are extracted using the image feature recognition method to obtain the set of grid intersection point pixel coordinates. Based on the set of pixel coordinates of the grid intersection points, the pixel distance between adjacent grid intersection points is calculated using grid spacing calculation and coordinate difference methods. Combined with the grid size of the grid cloth, the ratio between the pixel distance and the actual physical distance is established to obtain the length calibration parameters.

[0017] By adopting the above implementation method, firstly, the pixel coordinates of each intersection point on the grid cloth are extracted using image feature recognition methods, and the physical calibration benchmark of the grid cloth is transformed into a set of identifiable marker points in the image, solving the problem of lack of spatial reference objects in image measurement; then, by calculating the pixel distance between adjacent intersection points and combining it with the known fixed size of the grid cloth, a conversion ratio between pixel distance and actual physical distance is established, so that the two-dimensional pixel coordinates in the image are accurately converted into spatial coordinates with physical scale, providing an accurate length calibration benchmark for the subsequent quantitative calculation of sediment particle displacement, thereby effectively linking image information with physical scale, so that all subsequent image-based analysis results have real physical meaning and quantifiable accuracy.

[0018] In one alternative implementation, it further includes: By combining sediment stripping distribution data at multiple flow velocities, and using performance evaluation methods, a relationship curve between sediment stripping amount and flow velocity is generated, and the anti-stripping performance evaluation results of the target pipeline sediment are obtained.

[0019] By adopting the above-described implementation method, and integrating sediment stripping distribution data under multiple flow velocities, a relationship curve between stripping amount and flow velocity is established using performance evaluation methods, revealing the complete evolution law of sediment stripping amount as hydraulic conditions change. Based on the relationship curve between sediment stripping amount and flow velocity, the initiation flow velocity for sediment stripping and the gradient of stripping amount change with flow velocity are determined, thereby quantitatively evaluating the anti-stripping performance of sediments in the target pipeline. This provides a decision-making basis based on measured data for the differentiated setting of key parameters such as flushing flow rate and jet pressure during dredging operations.

[0020] Thirdly, the present invention provides a sediment stripping condition measuring device, the device comprising: The parameter calibration module is used to establish the mapping relationship between pixel coordinates in the initial motion image and actual physical coordinates based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detection pipeline using the physical mapping method, so as to obtain the length calibration parameters. The displacement extraction module is used to extract the position change information of tracer particles and sediment particles based on motion images acquired continuously at multiple times, combined with the length calibration parameters, and using a frame-by-frame particle displacement analysis method to obtain a sediment particle displacement dataset. The stripping calculation module is used to calculate the stripping amount at each position on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipeline based on the displacement dataset of the sediment particles and the length calibration parameters, using the stripping amount calculation method, and to generate sediment stripping amount distribution data.

[0021] Fourthly, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform a method for measuring sediment stripping conditions as described in the first aspect or any corresponding embodiment thereof.

[0022] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform a sediment stripping condition measurement method according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the sediment stripping condition measurement system according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a first method for measuring sediment stripping conditions according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for measuring sediment stripping conditions according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the sediment stripping condition measurement method according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the fourth process of the sediment stripping condition measurement method according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the fifth process of the sediment stripping condition measurement method according to an embodiment of the present invention; Figure 7 This is a structural block diagram of a sediment stripping condition measuring device according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention.

[0025] Explanation of reference numerals in the attached drawings: 11. Detection pipe; 111. Sedimentation zone; 12. Tracer particle generator; 13. Image acquisition device. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] In the related technologies, a method for treating sediment in drainage pipes is disclosed. A detection device equipped with an inertial measurement unit is released into the target pipe. The multidimensional data of the detection device during its movement in the pipe is recorded using an accelerometer and a gyroscope to determine the siltation status of the sediment inside the pipe. Then, a dredging robot is used to treat the sediment accumulated in the pipe.

[0030] However, the dredging robots disclosed in the relevant technologies mainly rely on human experience or fixed cycles for dredging. The cost of using dredging robots for sediment treatment is high and the operation cycle is long, which can easily lead to incomplete or excessive dredging of sediment in the target pipeline, making it difficult to meet the current pipeline's demand for high efficiency and economy in sediment treatment.

[0031] To address the shortcomings of the aforementioned related technologies, this invention provides a method for measuring sediment stripping conditions. First, a mesh cloth is used as a physical calibration benchmark to establish a mapping relationship between pixel coordinates and actual physical coordinates, transforming image data into quantifiable data with a true physical scale. Then, continuously acquired moving images are analyzed frame-by-frame. By tracking the positional changes of tracer particles and sediment particles in the time series, discrete image frames are transformed into continuous particle motion trajectories and displacement information, achieving dynamic quantification of the sediment stripping process. Finally, based on displacement data and length calibration parameters, the amount of stripping at various locations on the sediment sample surface is calculated, generating spatial distribution data. The particle-scale displacement observations are summarized into a regional-scale stripping distribution, providing a refined quantitative testing method for subsequent assessment of sediment stripping conditions.

[0032] Reference Figure 1 To facilitate the determination of stripping conditions for sediments within pipelines, this embodiment provides a sediment stripping condition determination system, comprising: The detection pipe 11 is a transparent pipe for containing flowing water. One end of the pipe is used to connect to an external water source that provides various flow rate conditions, and the other end is used to connect to a water tank. The detection pipe 11 includes a sedimentation area 111 for placing sediment samples, and a mesh cloth is also provided in the sedimentation area 111 for serving as a length calibration reference. The tracer particle generator 12 is located at one end of the detection pipe 11 near the external water source and is used to release tracer particles into the detection pipe 11 to follow the water flow and mark the movement trajectory of sediment particles. The image acquisition device 13 is installed outside the detection pipe 11 and is used to continuously acquire motion images of sediments and tracer particles in the sedimentation zone 111 of the detection pipe 11. The data processing module is connected to the image acquisition device 13 to receive moving images. At the same time, it calibrates the moving images based on the grid size of the mesh cloth and extracts the trajectory of the tracer particles and the displacement information of the sediment particles to obtain the stripping test results of the sediment sample.

[0033] The detection pipe 11 can be implemented as an acrylic transparent round pipe with a nominal diameter of 300mm and a length of 3m. One end of the pipe is used to connect to an external water source, which can be implemented as an adjustable flow water pump to provide flowing water into the detection pipe 11. The other end of the detection pipe 11 can be connected to a water tank, which is used to collect the outflowing water and stripped sediment from the detection pipe 11.

[0034] The mesh cloth can be implemented with each mesh unit being 1mm×1mm cloth. At the same time, the mesh cloth is laid inside the detection pipe 11 to simulate the roughness of the inner wall of the actual sewage pipe, and also serves as a length calibration reference during image acquisition. In addition, an image acquisition area is reserved in the deposition area 111 where the mesh cloth is set for the image acquisition device 13 to perform image acquisition.

[0035] Sediment samples can be collected directly from sediment layers with known properties, such as organic matter content, EPS content, and porosity, and spread evenly along a 30cm length from the bottom of the pipe in the direction of water flow, based on the sediment thickness distribution data obtained from actual sonar scanning on site.

[0036] The tracer particle generator 12 can be implemented as a micro-injection pump or an adjustable flow pump. Fluorescent polystyrene microspheres with a particle size of 50 μm are selected as tracer particles, and the injection port is set at the surface of the sediment sample near the sedimentation zone 111 of the monitoring pipe.

[0037] For example, the tracer particles in the tracer particle generator 12 are added in the following manner: first, they are added at a low concentration, and then the amount added is gradually increased until discrete individual particle points can be clearly distinguished in the image captured by the high-speed camera, so as to track the flow velocity distribution on the surface of the deposition layer and calculate the corresponding bottom shear force, that is, the peeling strength of the deposits in the detection pipe 11.

[0038] The image acquisition device 13 can be implemented as a high-speed camera installed above or to the side of the pipe, with the camera facing the sedimentation zone 111, for continuously recording the movement trajectory of tracer particles, the dynamic process of sediment aggregates being stripped off under the scouring of water flow, the size of the aggregate particles after stripping off, and the microscopic changes in the morphology of the sediment layer.

[0039] The camera parameters of the image acquisition device 13 can be implemented as follows: frame rate: 500~2000 fps, which can be adjusted according to the water flow speed of the flowing water to ensure that the particle displacement between adjacent frames does not exceed 1 / 10 of the field of view of the image acquisition device 13; the resolution of the image acquisition device 13 can be implemented as 1280×1024 pixels to ensure that a single fluorescent particle occupies 3 to 5 pixels; and the exposure time can be implemented as ≤1 / frame rate, and a high-power LED fill light with wavelength matching fluorescent particle excitation band is used to avoid motion blur.

[0040] When the test is started, the water pump is turned on to inject flowing water into the detection pipe 11. At the same time, the tracer particle generator 12 continuously injects tracer particles into the water layer close to the surface of the sediment layer, and the image acquisition device 13 is used to acquire continuous images. The mesh cloth in the detection pipe 11 is used as a length ruler: a static image containing the mesh cloth is taken to facilitate the establishment of spatial calibration relationship using pixel and actual length conversion coefficients.

[0041] For example, after turning on the water pump, the flow rate is slowly increased from zero in increments of 0.1 L / s. The rate of increase is such that the transient process of the sediment aggregate from rest to critical stripping can be clearly captured, and each flow rate is maintained for at least 30 seconds. Then, the tracer particle generator 12 is turned on with an initial acceleration rate of 0.01 mg / s, which is gradually increased until uniformly distributed discrete fluorescent particle points appear in the image. The image acquisition device 13 is used to record the entire process from the start of the water flow to the obvious stripping of the sediment aggregate, and at least 10 seconds are recorded at each input flow rate.

[0042] According to an embodiment of the present invention, a method for measuring sediment stripping conditions is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0043] This embodiment provides a method for measuring sediment stripping conditions, which can be used in the aforementioned data processing module. Figure 2 This is a flowchart of a sediment stripping condition measurement method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: S201, based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detected pipeline, a physical mapping method is used to establish the mapping relationship between the pixel coordinates in the initial motion image and the actual physical coordinates, and the length calibration parameters are obtained.

[0044] The sedimentation zone of the test pipeline refers to the section of the test pipeline specifically designed for placing sediment samples. Its bottom is covered with a mesh cloth to fix the sediment samples, simulate the roughness of the actual pipeline, and provide a spatial reference.

[0045] Initial motion images refer to the first frame or set of images captured by the image acquisition device before the water flow starts or before the sediment has been stripped away. They record the initial distribution of sediment particles and a clear and complete grid pattern, which serves as the reference for all subsequent spatial positioning and displacement calculations.

[0046] The grid size refers to the actual physical side length of each grid cell on the grid cloth, and it is the core parameter for establishing the conversion relationship between pixel coordinates and actual physical coordinates.

[0047] The physical mapping method is a mathematical method that maps an image coordinate system in pixels to an actual physical coordinate system. It establishes a conversion ratio by identifying grid intersections and using their known spacing.

[0048] Pixel coordinates refer to the position of each pixel in an image within the image coordinate system.

[0049] Actual physical coordinates are real spatial coordinates with the pipeline inspection site as the reference frame.

[0050] The length calibration parameter is a proportionality factor that converts pixel distance into actual physical distance, i.e., the actual length corresponding to a unit pixel, which is used for the quantitative calculation of all subsequent displacement, size and area.

[0051] By identifying the pixel coordinates of each intersection point on the mesh and combining them with the known fixed mesh size, a proportional conversion relationship between pixel distance and actual physical distance is established, so that all subsequent image-based displacement tracking and size measurement have a true physical scale.

[0052] S202, based on continuously acquired motion images at multiple times, combined with length calibration parameters, uses a frame-by-frame particle displacement analysis method to extract the positional change information of tracer particles and sediment particles, thus obtaining a sediment particle displacement dataset.

[0053] Motion images at multiple moments refer to a sequence of images captured continuously at a fixed frame rate by an image acquisition device during the entire test process under the action of flowing water. These images record the movement path of tracer particles and the complete process of sediment particles from being stationary to being stripped off and carried away by the water flow.

[0054] The frame-by-frame particle displacement analysis method refers to a method that processes each frame of the image sequence from the image acquisition device sequentially, and calculates the displacement by comparing the positional changes of the same particle or grain in adjacent frames or reference frames.

[0055] Sediment particles refer to the solid particles that make up the sediment layer in a sediment sample, and are used as the object for calculating the amount of stripping. In this method, sediment particles and tracer particles are distinguished in the image by features such as size, brightness, and shape.

[0056] Location change information refers to the amount of change in the position of each sediment particle in the image before and after stripping. It is used to reflect whether each sediment particle has been stripped and the distance and direction of movement after stripping.

[0057] Displacement datasets refer to structured data sets organized by particle number, time sequence, and spatial location after frame-by-frame analysis. They contain key information such as the coordinates, displacement, and velocity of each sediment particle at each time point.

[0058] Based on motion images from multiple time points, different identification and tracking strategies were employed for tracer particles and sediment particles. For tracer particles, a particle tracking algorithm was used for batch processing, while for sediment particles, edge detection and centroid localization methods were used for individual identification. By simultaneously extracting motion information of the two types of objects within the same time frame, the dynamics of water flow and sediment response could be correlated for analysis. Finally, the pixel displacement was converted into the actual physical displacement by combining length calibration parameters, and the data was organized into a standardized dataset according to particle number and time sequence, providing a well-structured and complete input for the next step of stripping calculation.

[0059] S203, based on the displacement dataset of sediment particles and combined with the length calibration parameters, uses the stripping amount calculation method to calculate the stripping amount at each location on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipeline, and generates sediment stripping amount distribution data.

[0060] The stripping volume calculation method is based on the displacement dataset of sediment particles. It is a complete calculation process that transforms the displacement information of each sediment particle into stripping volume and summarizes it into spatial distribution data. It includes four key steps: particle screening, volume conversion, spatial mapping, and distribution summarization.

[0061] The particle peeling determination method is a screening rule that compares the particle displacement with a preset peeling threshold to determine whether the particle has actually peeled off. Particles with displacement below the threshold are considered as weak vibrations or measurement noise and are therefore rejected.

[0062] The preset peeling threshold is the critical displacement value that distinguishes between particles that have "peeled" and those that have "not peeled". It is usually preset according to the test accuracy requirements and sediment characteristics, and is used to filter out the tiny vibrations of particles caused by water flow pulsation or image noise.

[0063] The stripping amount distribution data is a summary of the stripping amount of each grid cell organized according to grid coordinates. The stripping amount value at each location is output in the form of structured data and can be used to generate distribution heat maps or stripping amount-location curves.

[0064] Based on the displacement dataset of sediment particles, combined with length calibration parameters, a stripping volume calculation method is used to determine and convert each sediment particle: first, particles that have actually stripped are screened out by a preset threshold, and then their stripping volume is converted into the stripping volume of a single particle according to their particle size or projected area. Then, the stripping volume of each sediment particle is mapped to the grid cell where its initial position is located and accumulated by grid cell, finally generating sediment stripping volume distribution data organized by grid coordinates. This data can quantitatively reflect the stripping differences at different locations on the surface of sediment samples and provides a data basis for assessing the spatial inhomogeneity of sediment stripping characteristics.

[0065] This embodiment provides a method for measuring sediment stripping conditions. First, a mesh cloth is used as a physical calibration benchmark to establish a mapping relationship between pixel coordinates and actual physical coordinates, transforming image data into quantifiable data with a real physical scale. Then, continuously acquired moving images are analyzed frame by frame. By tracking the positional changes of tracer particles and sediment particles in the time series, discrete image frames are transformed into continuous particle motion trajectories and displacement information, realizing the dynamic quantification of the sediment stripping process. Finally, based on displacement data and length calibration parameters, the amount of stripping at each location on the sediment sample surface is calculated and spatial distribution data is generated. The particle-scale displacement observations are summarized into a regional-scale stripping amount distribution, providing a refined quantitative testing method for subsequent assessment of sediment stripping conditions.

[0066] This embodiment provides a method for measuring sediment stripping conditions, which can be used in the aforementioned data processing module. Figure 3 This is a flowchart of a sediment stripping condition measurement method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: S301, based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detection pipeline, a physical mapping method is used to establish the mapping relationship between the pixel coordinates in the initial motion image and the actual physical coordinates, and the length calibration parameters are obtained.

[0067] Specifically, S301 includes: S3011, based on the received initial motion image of the deposition area of ​​the detection pipeline, the pixel coordinates of each grid intersection point on the mesh cloth in the initial motion image are extracted using an image feature recognition method to obtain the set of grid intersection point pixel coordinates.

[0068] Image feature recognition methods refer to algorithms that automatically identify and extract points, lines, or regions with significant features from images. In this method, it specifically refers to image processing techniques used to detect the intersections of grid lines on a mesh fabric, i.e., the intersections of horizontal and vertical grid lines. This can be implemented as Harris corner detection, FAST corner detection, SIFT feature point extraction, or template matching-based intersection detection methods.

[0069] The grid intersection pixel coordinate set refers to the collection of pixel coordinates of all grid intersection points extracted from the initial moving image. It records the position information of each intersection point in the image and is usually organized in the form of a list or matrix, containing the coordinates of each intersection point and its row and column numbers in the grid.

[0070] S3012, based on the pixel coordinate set of grid intersection points, uses grid spacing calculation and coordinate difference methods to calculate the pixel distance between adjacent grid intersection points, and combines the grid size of the grid cloth to establish the ratio between the pixel distance and the actual physical distance, thus obtaining the length calibration parameters.

[0071] The grid spacing calculation and coordinate difference method refers to a calculation method based on the pixel coordinates of the identified grid intersection points, by calculating the pixel distance between adjacent grid intersection points, and combining the known actual size of the grid to establish a conversion ratio. It can be implemented to calculate the pixel distance difference between adjacent intersection points in the horizontal or vertical direction, which is used as the basis for subsequent ratio conversion.

[0072] The pixel distance between adjacent grid intersections refers to the difference in the number of pixels between two horizontally or vertically adjacent grid intersections in an image, reflecting the number of pixels occupied by a grid cell in the image space.

[0073] The length calibration parameter is a conversion parameter that transforms pixel coordinates into actual physical coordinates. It is usually a scaling factor and is used to represent the actual physical length corresponding to each pixel.

[0074] First, image feature recognition methods are used to extract the pixel coordinates of each intersection point on the mesh cloth, transforming the physical calibration benchmark of the mesh cloth into a set of identifiable marker points in the image, thus solving the problem of lack of spatial reference in image measurement. Then, by calculating the pixel distance between adjacent intersection points and combining it with the known fixed size of the mesh cloth, a conversion ratio between pixel distance and actual physical distance is established, so that the two-dimensional pixel coordinates in the image are accurately converted into spatial coordinates with physical scale. This provides an accurate length calibration benchmark for the subsequent quantitative calculation of sediment particle displacement, thereby effectively linking image information with physical scale, ensuring that all subsequent image-based analysis results have real physical meaning and quantifiable accuracy.

[0075] S302, based on continuously acquired motion images from multiple time points and combined with length calibration parameters, utilizes a frame-by-frame particle displacement analysis method to extract positional change information of tracer particles and sediment particles, obtaining a sediment particle displacement dataset. For details, please refer to [link to relevant documentation]. Figure 2 S202 of the illustrated embodiment will not be described again here.

[0076] S303, based on sediment particle displacement datasets and combined with length calibration parameters, uses a stripping amount calculation method to calculate the stripping amount at various locations on the surface of sediment aggregate samples in the sedimentation zone of the tested pipeline, generating sediment stripping amount distribution data. For details, please refer to [link to relevant documentation]. Figure 2 S203 of the illustrated embodiment will not be described again here.

[0077] This embodiment provides a method for measuring sediment stripping conditions, which can be used in the aforementioned data processing module. Figure 4 This is a flowchart of a sediment stripping condition measurement method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: S401, based on the grid size of the mesh cloth in the initial moving image of the deposition area of ​​the detection pipe, a physical mapping method is used to establish the mapping relationship between the pixel coordinates in the initial moving image and the actual physical coordinates, thus obtaining the length calibration parameters. For details, please refer to [link to documentation]. Figure 2 S201 of the illustrated embodiment will not be described again here.

[0078] S402, based on continuously acquired motion images at multiple times, combined with length calibration parameters, uses a frame-by-frame particle displacement analysis method to extract the positional change information of tracer particles and sediment particles, thus obtaining a sediment particle displacement dataset.

[0079] Specifically, S402 above includes: S4021, based on motion images at multiple times, uses image preprocessing methods to perform noise reduction, enhancement, and background removal on each frame of the image to obtain a preprocessed image sequence.

[0080] Image preprocessing methods are a combination of image processing techniques that perform a series of processes on raw moving images to improve image quality and highlight target features. Examples of image preprocessing methods include noise reduction, image enhancement, and background removal.

[0081] S4022, based on the preprocessed image sequence, uses a particle recognition and tracking algorithm to extract the position coordinates of each tracer particle in consecutive frame images, and obtains the motion trajectory data of multiple tracer particles.

[0082] Particle recognition and tracking algorithms are used to automatically identify tracer particles from image sequences and track their motion trajectories between consecutive frames. For example, a method combining brightness threshold segmentation with nearest neighbor matching or cross-correlation matching is used to establish correspondences between tracer particles in adjacent frames, generating continuous motion trajectories.

[0083] Position coordinates refer to the position of a tracer particle in the image coordinate system, in pixels. By tracking the position coordinate changes of the same tracer particle in consecutive frames, the motion path and velocity information of the same tracer particle can be obtained.

[0084] Motion trajectory data is the sequence of positions traversed by each tracer particle during the test. It is used to record the complete path of the tracer particle from its starting position to its current position and is the basic data for characterizing water flow motion and velocity field distribution.

[0085] S4023, based on the preprocessed image sequence, uses particle boundary detection and centroid localization methods to identify the initial position of each sediment particle before stripping and its current position after stripping, thus obtaining the positional change information of each sediment particle.

[0086] The particle boundary detection and centroid localization method refers to a combination of techniques used to identify the contours of sediment particles and calculate their centroid positions. First, an edge detection algorithm is used to extract the boundary contours of the particles. Then, the centroid coordinates of the contour region are calculated using the zero-order moment and the first-order moment, with the centroid representing the spatial position of the particle.

[0087] Specifically, S4023 above includes: a1, Based on the initial frame images in the preprocessed image sequence, the boundary contours of each sediment particle are identified using an edge detection algorithm to obtain the initial boundary contour set of each sediment particle; a2, Based on the initial boundary contour set of each sediment particle, the initial centroid coordinates of each sediment particle before stripping are determined using the centroid calculation method, thus obtaining the initial position information of each sediment particle; a3. Based on subsequent frames of the preprocessed image sequence, edge detection and contour matching methods are used to track the boundary contour of the same sediment particle after stripping, and centroid calculation method is used to determine the centroid coordinates after stripping, so as to obtain the current position information of each sediment particle at each time. a4. Based on the initial position information and the current position information of each sediment particle at each time, the position difference calculation method is used to obtain the position change information of each sediment particle before and after stripping.

[0088] First, an edge detection algorithm is used to identify the boundary contours of each sediment particle in the initial frame, establishing a recognizable contour model for each particle. Then, the centroid coordinates are calculated based on the boundary contours, condensing the contour region information into precise centroid positions. This provides a stable and reliable positional reference for subsequent displacement calculations, avoiding positioning deviations caused by particle rotation or attitude changes. Simultaneously, in subsequent frames, the same sediment particle is tracked using a contour matching method, and its current position is determined using the centroid coordinates. This overcomes the problem of establishing accurate correspondences between different frames after sediment particle stripping, enabling synchronous tracking of multiple sediment particles. Finally, by calculating the difference between the initial and current positions, the discrete inter-frame coordinate data is transformed into displacement information for each sediment particle, providing basic data at the sediment particle scale for subsequent stripping calculations.

[0089] S4024, based on the motion trajectory data of multiple tracer particles and the position change information of each sediment particle, combined with the length calibration parameters, uses the physical displacement conversion method to convert the pixel displacement of each sediment particle between consecutive frames into the actual physical displacement, and organizes it according to particle number and time order to obtain the displacement dataset of sediment particles.

[0090] First, image preprocessing methods are used to denoise, enhance, and remove background from multiple frames of moving images, effectively improving image quality and providing clear input for subsequent accurate particle and sediment identification. Then, particle tracking algorithms and particle boundary detection and centroid localization methods are used to simultaneously extract motion information of two different types of objects: tracer particles and sediment particles, enabling the correlation analysis of water flow dynamics and sediment response within the same time frame. Finally, pixel displacement is converted into actual physical displacement by combining length calibration parameters, and organized into a structured dataset according to particle number and time sequence, realizing the transformation from scattered image frames to ordered particle displacement records, providing a data foundation containing spatial location, displacement magnitude, and time series information for stripping calculation.

[0091] S403, based on sediment particle displacement datasets and combined with length calibration parameters, uses a stripping amount calculation method to calculate the stripping amount at various locations on the surface of sediment aggregate samples in the sedimentation zone of the tested pipe, generating sediment stripping amount distribution data. For details, please refer to [link to relevant documentation]. Figure 2 S203 of the illustrated embodiment will not be described again here.

[0092] This embodiment provides a method for measuring sediment stripping conditions, which can be used in the aforementioned data processing module. Figure 5 This is a flowchart of a sediment stripping condition measurement method according to an embodiment of the present invention, such as... Figure 5 As shown, the process includes the following steps: S501, based on the grid size of the mesh cloth in the initial moving image of the deposition area of ​​the detection pipeline, a physical mapping method is used to establish the mapping relationship between the pixel coordinates in the initial moving image and the actual physical coordinates, thus obtaining the length calibration parameters. For details, please refer to [link to documentation]. Figure 1 S203 of the illustrated embodiment will not be described again here.

[0093] S502, based on continuously acquired motion images from multiple time points and combined with length calibration parameters, uses a frame-by-frame particle displacement analysis method to extract positional change information of tracer particles and sediment particles, obtaining a sediment particle displacement dataset. For details, please refer to [link to relevant documentation]. Figure 1 S203 of the illustrated embodiment will not be described again here.

[0094] S503, based on the displacement dataset of sediment particles and combined with the length calibration parameters, uses the stripping amount calculation method to calculate the stripping amount at each location on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipeline, and generates sediment stripping amount distribution data.

[0095] Specifically, the aforementioned S503 includes: S5031, based on the displacement dataset of sediment particles, combined with the length calibration parameter, and using the particle stripping judgment method, particles whose displacement exceeds the preset stripping threshold are selected to obtain the set of stripped particles.

[0096] S5032, based on the particle size or projected area of ​​each sediment particle in the stripped particle set, the displacement information of each sediment particle is converted into the stripping volume of a single sediment particle using the volume conversion method, and the stripping volume data of each sediment particle is obtained. S5033, based on the grid cells and length calibration parameters of the mesh cloth, uses a spatial positioning method to map each stripped sediment particle to the grid cell where its initial position is located, and accumulates the stripping volume of each sediment particle according to the grid cell to obtain the stripping amount of each grid cell. S5034, based on the stripping amount of each grid cell, uses a distribution data generation method to organize the stripping amount data of each grid cell according to the grid coordinates to generate sediment stripping amount distribution data.

[0097] First, the displacement dataset is filtered using a preset stripping threshold to remove weak vibrations or measurement noise below the threshold. Then, volume conversion is performed based on the particle size or projected area of ​​the stripped particles, transforming the particle displacement information observed in the two-dimensional image into three-dimensional stripping volume. This gives the stripping amount a physical volume dimension, overcoming the limitation that image observation can only provide planar displacement and cannot directly obtain volume information. Next, using a grid cell spatial positioning method, the stripping volume of each sediment particle is mapped to the grid cell where its initial position is located, and accumulated by grid cell, integrating discrete particle-level data into regional distribution data. This realizes the aggregation of stripping amount from a "single-particle scale" to a "spatial distribution scale," allowing the stripping amount of each grid cell to be independently quantified and compared. Finally, stripping amount distribution data is generated according to grid coordinates, providing a structured output for the spatial difference analysis of sediment stripping characteristics. This transforms scattered particle displacement information into stripping amount data with spatial distribution characteristics, providing a refined quantitative testing method for evaluating the stripping characteristics of sediments under different hydraulic conditions.

[0098] This embodiment provides a method for measuring sediment stripping conditions, which can be used in the aforementioned data processing module. Figure 6 This is a flowchart of a sediment stripping condition measurement method according to an embodiment of the present invention, such as... Figure 6 As shown, the process includes the following steps: S601, based on the grid size of the mesh cloth in the initial moving image of the deposition area of ​​the detection pipeline, a physical mapping method is used to establish the mapping relationship between the pixel coordinates in the initial moving image and the actual physical coordinates, thus obtaining the length calibration parameters. For details, please refer to [link to documentation]. Figure 1 S203 of the illustrated embodiment will not be described again here.

[0099] S602, based on continuously acquired motion images from multiple time points and combined with length calibration parameters, uses a frame-by-frame particle displacement analysis method to extract positional change information of tracer particles and sediment particles, obtaining a sediment particle displacement dataset. For details, please refer to [link to relevant documentation]. Figure 1 S203 of the illustrated embodiment will not be described again here.

[0100] S603, based on sediment particle displacement datasets and combined with length calibration parameters, uses a stripping amount calculation method to calculate the stripping amount at various locations on the surface of sediment aggregate samples in the sedimentation zone of the tested pipeline, generating sediment stripping amount distribution data. For details, please refer to [link to relevant documentation]. Figure 1 S203 of the illustrated embodiment will not be described again here.

[0101] S604. By integrating sediment stripping distribution data under multiple flow velocities, and using performance evaluation methods, a relationship curve between sediment stripping amount and flow velocity is generated to obtain the anti-stripping performance evaluation results of the target pipeline sediment.

[0102] Based on the movement velocity of tracer particles at different water depths recorded by a high-speed camera, since the tracer particles are very small and their density is slightly greater than that of water, the tracer particles can be considered as the velocity of the water flow at that layer. The velocity gradient along the water depth direction is solved, and the shear force on the bottom of the pipe is calculated according to Newton's law of internal friction.

[0103] At the beginning of the test, the flow rate is slowly increased to reach the critical condition required for sediment particle movement. The critical shear stress at this point represents the minimum bed shear stress corresponding to the point at which particles begin to be scoured. The critical shear stress can be indirectly determined based on the vertical velocity profile. The relationship between the critical shear stress and the shear rate is as follows:

[0104] in, Indicates the critical shear stress; Indicates the density of flowing water; This indicates the shear velocity of the flowing water.

[0105] The shear velocity of flowing water is determined by the following:

[0106] in, This indicates the actual velocity of the tracer particles collected. Indicates the shear velocity of flowing water; This represents von Karman's constant; Indicates the height of the tracer particles within the detection channel; Indicates the roughness height.

[0107] By comparing the calculated shear force results under different flow rates and particle velocity distributions with the actual time when sediment particles begin to peel off, the critical peeling conditions of sediment aggregates were finally determined, and a correspondence was established with the size of suspended sediment particles. Thus, the critical peeling conditions for different sediment particles were obtained, which satisfy the following:

[0108]

[0109]

[0110] in, This represents the critical shear force that initiates sediment stripping. The portion representing non-sticky particulate matter can be obtained by looking up the shield curve in a table; This represents the portion corresponding to the EPS content; This represents the portion corresponding to the porosity; Indicates porosity; , , and All represent coefficients.

[0111] By integrating sediment stripping distribution data under multiple flow velocities, a relationship curve between stripping amount and flow velocity is established using performance evaluation methods, revealing the complete evolution law of sediment stripping amount as hydraulic conditions change. Based on the relationship curve between sediment stripping amount and flow velocity, the initiation flow velocity for sediment stripping and the gradient of stripping amount change with flow velocity are determined, thereby quantitatively evaluating the anti-stripping performance of sediments in the target pipeline and providing a decision-making basis based on measured data for the differentiated setting of key parameters such as flushing flow rate and jet pressure in dredging operations.

[0112] This embodiment also provides a sediment stripping condition measuring device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0113] This embodiment provides a device for measuring sediment stripping conditions, such as... Figure 7 As shown, it includes: The parameter calibration module 710 is used to establish the mapping relationship between pixel coordinates in the initial motion image and actual physical coordinates based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detection pipe using a physical mapping method, and obtain the length calibration parameters. The displacement extraction module 720 is used to extract the position change information of tracer particles and sediment particles based on motion images acquired continuously at multiple times, combined with length calibration parameters, and using a frame-by-frame analysis of particle displacement method to obtain a sediment particle displacement dataset. The stripping calculation module 730 is used to calculate the stripping amount at various locations on the surface of sediment aggregate samples in the sedimentation zone of the detection pipeline based on the displacement dataset of sediment particles, combined with the length calibration parameters, and using the stripping amount calculation method, thereby generating sediment stripping amount distribution data.

[0114] In some alternative implementations, the parameter calibration module 710 includes: The coordinate extraction unit is used to extract the pixel coordinates of each grid intersection point on the grid cloth in the initial motion image of the sedimentation area of ​​the detected pipeline based on the received initial motion image of the pipeline, and obtain the set of grid intersection point pixel coordinates. The parameter calibration unit is used to calculate the pixel distance between adjacent grid intersections based on the set of pixel coordinates of grid intersections, using grid spacing calculation and coordinate difference methods, and combined with the grid size of the grid cloth, to establish the ratio between the pixel distance and the actual physical distance, thus obtaining the length calibration parameters.

[0115] In some alternative implementations, the displacement extraction module 720 includes: The image preprocessing unit is used to perform noise reduction, enhancement, and background removal on each frame of the moving images based on multiple time points using image preprocessing methods to obtain a preprocessed image sequence. The coordinate extraction unit is used to extract the position coordinates of each tracer particle in consecutive frame images based on the preprocessed image sequence and using particle recognition and tracking algorithms, so as to obtain the motion trajectory data of multiple tracer particles. The location unit is used to identify the initial position of each sediment particle before stripping and the current position after stripping based on the preprocessed image sequence using particle boundary detection and centroid localization methods, so as to obtain the position change information of each sediment particle. The displacement conversion unit is used to convert the pixel displacement of each sediment particle between consecutive frames into the actual physical displacement based on the motion trajectory data of multiple tracer particles and the position change information of each sediment particle, combined with the length calibration parameters and the physical displacement conversion method. The data is then organized according to the particle number and time order to obtain the displacement dataset of sediment particles.

[0116] In some alternative implementations, the positioning unit is specifically used for: Based on the initial frame images in the preprocessed image sequence, the boundary contours of each sediment particle are identified using an edge detection algorithm, and the initial boundary contour set of each sediment particle is obtained. Based on the initial boundary contour set of each sediment particle, the initial centroid coordinates of each sediment particle before stripping are determined using the centroid calculation method, thus obtaining the initial position information of each sediment particle. Based on subsequent frames of the preprocessed image sequence, edge detection and contour matching methods are used to track the boundary contour of the same sediment particle after stripping, and centroid calculation method is used to determine the centroid coordinates after stripping, so as to obtain the current position information of each sediment particle at each time. Based on the initial position information and the current position information of each sediment particle at each time, the position difference calculation method is used to obtain the position change information of each sediment particle before and after stripping.

[0117] In some alternative implementations, the stripping calculation module 730 includes: The particle screening unit is used to screen out particles whose displacement exceeds a preset peeling threshold based on the displacement dataset of the sediment particles and the length calibration parameters, using a particle peeling determination method, and obtain a set of peeled particles. The volume conversion unit is used to convert the displacement information of each sediment particle into the stripping volume of a single sediment particle based on the particle size or projected area of ​​each sediment particle in the stripped particle set, and obtain the stripping volume data of each sediment particle. A spatial mapping unit is used to map each stripped sediment particle to the grid cell where its initial position is located using a spatial positioning method, based on the grid cells of the mesh cloth and the length calibration parameters, and to accumulate the stripping volume of each sediment particle according to the grid cell to obtain the stripping amount of each grid cell. The data integration unit is used to generate sediment stripping distribution data by organizing the stripping data of each grid cell according to grid coordinates based on the stripping amount of each grid cell using the distribution data generation method.

[0118] In some alternative implementations, it also includes: The boundary assessment module is used to integrate sediment stripping distribution data under multiple flow velocities, and use performance assessment methods to generate a curve showing the relationship between sediment stripping amount and flow velocity, thereby obtaining the anti-stripping performance assessment results of the target pipeline sediments.

[0119] The sediment stripping condition measuring device provided in this embodiment of the invention can execute the sediment stripping condition measuring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0120] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0121] The following is a detailed reference. Figure 8 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 801, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 802 or a program loaded from memory 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The processor 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0122] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0123] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a memory 808, or installed from a ROM 802. When the computer program is executed by the processor 801, it performs the functions defined in the sediment stripping condition measurement method of the embodiments of the present invention.

[0124] Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0125] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the deposition stripping condition measurement method shown in the above embodiments is implemented.

[0126] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0127] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A system for determining sediment stripping conditions, characterized in that, The system includes: The detection pipe (11) is a transparent pipe for containing flowing water. One end of the pipe is used to connect to an external water source that provides various flow rate conditions, and the other end is used to connect to a water tank. The detection pipe (11) includes a sedimentation area (111) for placing sediment samples, and a mesh cloth is also provided in the sedimentation area (111) as a length calibration reference. A tracer particle generator (12) is located at one end of the detection pipe (11) near an external water source and is used to release tracer particles into the detection pipe (11) to follow the flow of water and mark the movement trajectory of sediment particles. An image acquisition device (13) is installed outside the detection pipe (11) to continuously acquire motion images of sediments and tracer particles in the sedimentation zone (111) of the detection pipe (11). The data processing module is connected in communication with the image acquisition device (13) to receive the motion image, and at the same time calibrate the motion image based on the grid size of the mesh cloth, and extract the trajectory of the tracer particles and the displacement information of the sediment particles to obtain the stripping test results of the sediment sample.

2. A method for measuring sediment stripping conditions, characterized in that, The method includes: Based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detection pipeline, the mapping relationship between the pixel coordinates in the initial motion image and the actual physical coordinates is established using the physical mapping method to obtain the length calibration parameters. Based on continuously acquired motion images at multiple times, combined with the length calibration parameters, the position change information of tracer particles and sediment particles is extracted by using a frame-by-frame particle displacement analysis method to obtain a sediment particle displacement dataset. Based on the displacement dataset of the sediment particles and the length calibration parameters, the stripping amount is calculated at each location on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipeline using the stripping amount calculation method, thereby generating sediment stripping amount distribution data.

3. The method according to claim 2, characterized in that, The motion images acquired continuously at multiple time points, combined with the length calibration parameters, are used to extract positional change information of tracer particles and sediment particles using a frame-by-frame particle displacement analysis method, resulting in a sediment particle displacement dataset, including: Based on motion images at multiple time points, image preprocessing methods are used to perform noise reduction, enhancement, and background removal on each frame of the image to obtain a preprocessed image sequence. Based on the preprocessed image sequence, the particle recognition and tracking algorithm is used to extract the position coordinates of each tracer particle in the continuous frame images, and the motion trajectory data of multiple tracer particles are obtained. Based on the preprocessed image sequence, the initial position of each sediment particle before stripping and its current position after stripping are identified using the particle boundary detection and centroid localization method, thereby obtaining the positional change information of each sediment particle. Based on the motion trajectory data of multiple tracer particles and the position change information of each sediment particle, combined with the length calibration parameters, the pixel displacement of each sediment particle between consecutive frames is converted into the actual physical displacement using the physical displacement conversion method, and then organized according to particle number and time order to obtain the displacement dataset of sediment particles.

4. The method according to claim 3, characterized in that, Based on the preprocessed image sequence, the initial position of each sediment particle before stripping and its current position after stripping are identified using particle boundary detection and centroid localization methods, thereby obtaining positional change information for each sediment particle, including: Based on the initial frame images in the preprocessed image sequence, the boundary contours of each sediment particle are identified using an edge detection algorithm to obtain the initial boundary contour set of each sediment particle. Based on the initial boundary contour set of each sediment particle, the initial centroid coordinates of each sediment particle before stripping are determined using the centroid calculation method, thus obtaining the initial position information of each sediment particle. Based on the subsequent frames of the preprocessed image sequence, the boundary contour of the same sediment particle after stripping is tracked using edge detection and contour matching methods, and the centroid coordinates after stripping are determined using centroid calculation methods, so as to obtain the current position information of each sediment particle at each time. Based on the initial position information and the current position information of each sediment particle at each time, the position difference calculation method is used to obtain the position change information of each sediment particle before and after stripping.

5. The method according to claim 2, characterized in that, The displacement dataset based on the sediment particles, combined with the length calibration parameters, is used to calculate the amount of detachment at various locations on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipe using a detachment calculation method, generating sediment detachment distribution data, including: Based on the displacement dataset of the sediment particles, combined with the length calibration parameters, the particle stripping determination method is used to filter out particles whose displacement exceeds the preset stripping threshold, thus obtaining a set of stripped particles. Based on the particle size or projected area of ​​each sediment particle in the stripped particle set, the displacement information of each sediment particle is converted into the stripping volume of a single sediment particle using the volume conversion method, thus obtaining the stripping volume data of each sediment particle. Based on the grid cells of the mesh fabric and the length calibration parameters, the spatial positioning method is used to map each stripped sediment particle to the grid cell where its initial position is located, and the stripping volume of each sediment particle is accumulated according to the grid cell to obtain the stripping amount of each grid cell. Based on the stripping amount of each grid cell, the stripping amount data of each grid cell is organized according to grid coordinates using a distribution data generation method to generate sediment stripping amount distribution data.

6. The method according to claim 2, characterized in that, The mesh size of the mesh cloth in the initial moving image of the deposition area of ​​the detected pipeline is used to establish a mapping relationship between pixel coordinates in the initial moving image and actual physical coordinates using a physical mapping method, thereby obtaining length calibration parameters, including: Based on the initial motion image of the sedimentation zone of the detected pipeline, the pixel coordinates of each grid intersection point on the mesh cloth in the initial motion image are extracted using the image feature recognition method to obtain the set of grid intersection point pixel coordinates. Based on the set of pixel coordinates of the grid intersection points, the pixel distance between adjacent grid intersection points is calculated using grid spacing calculation and coordinate difference methods. Combined with the grid size of the grid cloth, the ratio between the pixel distance and the actual physical distance is established to obtain the length calibration parameters.

7. The method according to any one of claims 2 to 6, characterized in that, Also includes: By combining sediment stripping distribution data at multiple flow velocities, and using performance evaluation methods, a relationship curve between sediment stripping amount and flow velocity is generated, and the anti-stripping performance evaluation results of the target pipeline sediment are obtained.

8. A device for measuring sediment stripping conditions, characterized in that, The device includes: The parameter calibration module is used to establish the mapping relationship between pixel coordinates in the initial motion image and actual physical coordinates based on the grid size of the mesh cloth in the initial motion image of the deposition area of ​​the detection pipeline using the physical mapping method, so as to obtain the length calibration parameters. The displacement extraction module is used to extract the position change information of tracer particles and sediment particles based on motion images acquired continuously at multiple times, combined with the length calibration parameters, and using a frame-by-frame particle displacement analysis method to obtain a sediment particle displacement dataset. The stripping calculation module is used to calculate the stripping amount at each position on the surface of the sediment aggregate sample in the sedimentation zone of the detection pipeline based on the displacement dataset of the sediment particles and the length calibration parameters, using the stripping amount calculation method, and to generate sediment stripping amount distribution data.

9. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the sediment stripping condition measurement method according to any one of claims 2 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the sediment stripping condition measurement method according to any one of claims 2 to 7.

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