Evaluation method for erosion and deposition changes based on the seabed scouring mechanism of the Yellow River subaqueous delta

Abstract

The present invention discloses a method for evaluating scouring and silting changes based on the seabed scouring mechanism of the Yellow River subaqueous delta, which relates to the technical field of seabed scouring analysis. Multisource environmental data of the target area are collected, and the environmental data are preprocessed to obtain a multisource environmental data set. Based on the Navier-Stokes equation and the sediment transport model, combined with the disturbance of the seabed pipeline to the water flow and the scouring of the seabed pipeline by sediments, a multi-factor coupling model of waves, tides, and sediment transport is constructed. By constructing the multi-factor coupling model, the present invention comprehensively considers the interaction of various factors such as waves, tides, sediment characteristics, and pipeline status, and can more comprehensively reflect the actual process of scouring and silting changes. On this basis, an abnormal scouring and silting change model is further constructed, effectively avoiding the errors caused by single-factor judgment, significantly improving the accuracy of abnormal detection of scouring and silting changes, and helping to take measures in advance to avoid damage to the seabed pipeline due to scouring.

Description

Technical Field

[0001] The present invention relates to the technical field of submarine scour analysis, and particularly to an evaluation method for scouring and silting changes based on the submarine scour mechanism of the Yellow River subaqueous delta. Background Art

[0002] The Yellow River Delta is the estuary area of the Yellow River, the river with the highest sediment content in the world. Its sediment sources are rich and complex. The Chengdao sea area in the Yellow River Delta is the main oil production area of Shengli Oilfield, with a large number of marine engineering facilities, including 107 various platforms, 162 submarine pipelines, and 123 submarine cables. The stability and safety of these facilities are directly related to the economic benefits and environmental safety of marine oil and gas development. Problems such as pipeline suspension and seabed instability caused by submarine scour have made the seabed scouring and silting process in this area complex and changeable.

[0003] In the prior art, scouring and silting changes involve the coupling of multiple factors such as waves, tides, sediment characteristics, and pipeline status. The complexity of the coupling of multiple factors and their interactions will lead to a reduction in the detection accuracy of abnormal scouring and silting changes. Therefore, how to improve the detection accuracy of abnormal scouring and silting changes and issue early warning signals is the problem we need to solve. For this reason, an evaluation method for scouring and silting changes based on the submarine scour mechanism of the Yellow River subaqueous delta is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an evaluation method for scouring and silting changes based on the submarine scour mechanism of the Yellow River subaqueous delta to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] An evaluation method for scouring and silting changes based on the submarine scour mechanism of the Yellow River subaqueous delta, comprising the following steps:

[0007] Step 1: Collect multi-source environmental data of the target area, including water depth and terrain data, sea current and wave observation data, submarine sediment characteristic data, submarine pipeline status data, and meteorological data, and preprocess the environmental data to obtain a multi-source environmental data set;

[0008] Step 2: Based on the Navier-Stokes equation and sediment transport model, combined with the disturbance of the submarine pipeline to the water flow and the scour of the sediment to the submarine pipeline, construct a multi-factor coupling model of waves, tides, and sediment transport;

[0009] Step 3: Use the constructed multi-factor coupling model to simulate the scouring and silting change process of the target area, and then identify the scouring and silting change characteristics;

[0010] Step 4: Based on the simulation results and the identified erosion and deposition change characteristics, construct an abnormal erosion and deposition change model to identify abnormal situations in the erosion and deposition changes;

[0011] Step 5: Arrange monitoring stations in the target area, obtain real-time monitoring data of the target area, and deploy the abnormal erosion and deposition change model to detect abnormal situations in the erosion and deposition changes in the target area;

[0012] Step 6: Classify and evaluate the detected abnormal erosion and deposition changes, determine their potential impacts on the safety of submarine pipelines, and match corresponding early warning measures according to the erosion and deposition change characteristics.

[0013] A further improvement of the technical solution of the present invention lies in: in the above Step 1, the process of obtaining the multi-source environmental data set is as follows:

[0014] Collect environmental data of the target area in the Yellow River subaqueous delta, including water depth and terrain data, sea current and wave observation data, seabed sediment property data, submarine pipeline status data, and meteorological data;

[0015] Preprocess the collected multi-source environmental data of the target area, including data cleaning, data alignment, and data interpolation;

[0016] Integrate the pre-cleaned, aligned, and interpolated multi-source environmental data into a unified data set, and store the data set in a MySQL database, creating multiple tables to store water depth and terrain data, sea current and wave data, sediment property data, pipeline status data, and meteorological data respectively, facilitating data query and management.

[0017] A further improvement of the technical solution of the present invention lies in: in the above Step 2, the construction process of the multi-factor coupling model includes:

[0018] Define the scope and boundary conditions of the target area, divide the computational grid according to the terrain of the target area and the distribution of submarine pipelines, define the boundary conditions of the computational area, including wave incident boundary, tidal current boundary, and sediment boundary, and determine the physical processes to be simulated by the model, including waves, tidal currents, sediment transport, and the disturbance of the submarine pipeline to the water flow and the scouring of sediments;

[0019] Based on the Navier-Stokes equation, analyze the resistance and disturbance effects of the pipeline on the water flow, introduce the pipeline resistance coefficient and disturbance term, simulate the influence of the pipeline on the water flow, and establish an equation describing the water flow movement around the submarine pipeline, considering the interaction of physical quantities such as water flow velocity, pressure, and viscosity;

[0020] Considering the influence of comprehensive factors such as water flow velocity, sediment particle size and density on sediment transport, a sediment transport equation describing the movement and distribution of sediment under the action of water flow is established. Combining the sediment transport equation with the pipeline force analysis, and considering the factors of erosion rate and erosion depth, an equation describing the scouring effect of sediment on submarine pipelines is established, taking into account the impact of scouring on the safety and stability of the pipeline.

[0021] Couple the hydrodynamic equation, sediment transport equation and pipeline scouring equation to form a multi-factor coupling model, ensuring the physical quantities between each equation are coordinated with each other to form a complete mathematical model.

[0022] Use the finite volume method to solve the multi-factor coupling model. By solving the multi-factor coupling model, the results of the water flow velocity field, sediment concentration field and pipeline scouring depth are obtained. According to the simulation results, analyze the safety and stability of the pipeline and evaluate the impact of scouring on the pipeline.

[0023] A further improvement of the technical solution of the present invention lies in: the process of solving the multi-factor coupling model is as follows:

[0024] Divide the computational domain (the target area of the Yellow River subaqueous delta) into a finite number of control volumes (grid cells). Each control volume represents a computational node, and the average value of physical quantities within this volume is stored at the node. Physical quantities such as fluid velocity, pressure and sediment concentration are assigned to each control volume, and boundary conditions of waves, tides and sediment are applied on the boundary of the computational domain.

[0025] Integrate the Navier-Stokes equation, sediment transport equation and pipeline scouring equation over the control volume to obtain discretized equations, which include flux terms and source terms on the control volume interface.

[0026] Select a time step to ensure the stability and accuracy of the model during the solution process. On each control volume interface, calculate the flux terms according to the current velocity field and sediment concentration field. Using the discretized equation, update the values of unknown variables at the control volume nodes according to the flux terms and source terms. Check whether the update amount of the unknown variables is less than the set convergence criterion. If so, the iteration ends; otherwise, return to the flux calculation step to continue the iteration, and then solve to obtain the water flow velocity field, sediment concentration field and pipeline scouring depth.

[0027] Analyze the simulated water flow velocity field to understand the water flow distribution characteristics around the pipeline. Analyze the simulated sediment concentration field to understand the distribution and migration of sediments around the pipeline. By comparing the sediment concentration fields at different time steps, evaluate the scouring effect of sediments on the pipeline. Based on the simulated pipeline scouring depth results, evaluate the impact of the scouring effect on the safety and stability of the pipeline. If the scouring depth exceeds the allowable range of the pipeline, it may lead to pipeline failure or damage. Furthermore, comprehensively analyze the results of the water flow velocity field, sediment concentration field, and pipeline scouring depth to conduct a comprehensive assessment of the safety and stability of the pipeline.

[0028] A further improvement of the technical solution of the present invention lies in that: in the third step, the process of identifying the scouring and silting change characteristics includes:

[0029] Based on the iterative solution of the multi-factor coupling model, obtain the water flow velocity field, sediment concentration field, and pipeline scouring depth that change with time. Use the obtained water flow velocity field and sediment concentration field to simulate the scouring and silting change process of the target area. By comparing the simulation results at different time steps, analyze the spatial distribution and temporal evolution law of the scouring and silting changes, and identify the key time periods and regions of the scouring and silting changes, that is, the periods and locations where the scouring or sedimentation effects are significantly enhanced;

[0030] According to the simulation results, identify the scouring depth distribution around the pipeline, analyze the maximum value, minimum value, and average value of the scouring depth to quantify the intensity of the scouring effect, and evaluate the impact of the scouring effect on the safety of the pipeline, including whether the scouring depth exceeds the allowable range of the pipeline, and whether the scouring effect may lead to pipeline failure or damage;

[0031] According to the scouring depth and the pipeline burial depth, calculate the suspended length of the pipeline, identify the maximum value, minimum value, and distribution characteristics of the suspended length, and evaluate its impact on the stability of the pipeline, including whether the suspended length is too large, and whether the suspended part is prone to shaking or damage under the action of water flow or external forces;

[0032] According to the solution results of the sediment transport equation, calculate the transport rate of sediments, identify the maximum value, minimum value, and distribution characteristics of the sediment transport rate, analyze the spatial distribution and temporal evolution law of the sediment transport rate, and evaluate its impact on the topographic changes around the pipeline, including whether sediments will accumulate near the pipeline to form new topographic features, and whether these topographic features will have an adverse impact on the safety and stability of the pipeline;

[0033] Comprehensively analyze the simulation results to determine the characteristics of scouring and silting changes, including the change rate of scouring depth, the change rate of suspended length, the change rate of sediment transport rate, the sediment concentration gradient, and the water flow velocity gradient, and then analyze the interaction between the characteristics of scouring and silting changes. Among them, the increase in scouring depth will lead to an increase in suspended length, which in turn affects the stability of the pipeline, and the change in sediment transport rate will affect the distribution of scouring depth.

[0034] A further improvement of the technical solution of the present invention lies in: in the step four, the construction process of the abnormal scouring and silting change model includes:

[0035] Collect the simulation results of the multi-factor coupling model, including the water flow velocity field, sediment concentration field, pipeline scouring depth, suspended length, and sediment transport rate, and extract the characteristics of scouring and silting changes including the change rate of scouring depth, the change rate of suspended length, the change rate of sediment transport rate, the sediment concentration gradient, and the water flow velocity gradient from the simulation results for anomaly detection;

[0036] Statistically analyze the historical scouring and silting data of the target area, identify the range and law of normal scouring and silting changes, based on the statistical analysis results, establish a model of normal scouring and silting changes, determine the normal range and change trend of the characteristics of scouring and silting changes, and integrate the relevant data of the obtained characteristics of scouring and silting changes, mark the abnormal scouring and silting characteristics among them, obtain a characteristic data set, and then divide the integrated data set into a training set and a test set;

[0037] Use the training set to train the K-means clustering algorithm to construct an abnormal scouring and silting change model. According to the normal mode, determine the number of cluster centers, calculate the distance from each data point to the cluster center, assign the data points to the nearest cluster center to form clusters, use the test set to verify the trained model, and evaluate the anomaly detection accuracy and stability of the model through indicators such as the confusion matrix and ROC curve;

[0038] Apply the constructed abnormal scouring and silting change model to perform anomaly detection on the scouring and silting change data, output whether the scouring and silting change data is abnormal, and calculate the abnormal scouring and silting change index to identify the abnormal scouring and silting change characteristics and severity.

[0039] A further improvement of the technical solution of the present invention lies in: the process of anomaly detection of the scouring and silting change data is as follows:

[0040] Input the scouring and silting change characteristic data including the change rate of scouring depth, the change rate of suspended length, the change rate of sediment transport rate, the sediment concentration gradient, and the water flow velocity gradient into the trained abnormal scouring and silting change model, and the model outputs whether each data point is abnormal, that is, the cluster label (normal or abnormal) of each data point;

[0041] Determine the weight of each feature according to the importance of each feature, and determine the reference value and standard deviation of each feature according to historical data;

[0042] For each data point, calculate the abnormal erosion and deposition change index by synthesizing the current value, weight, reference value and standard deviation of each erosion and deposition change feature;

[0043] According to the output of the model, identify the abnormal erosion and deposition change features, and evaluate the severity of the abnormality according to the value of the abnormal erosion and deposition change index. When the abnormal erosion and deposition change index approaches 0, it indicates that the data point approaches the normal range and the degree of abnormality is low. When the abnormal erosion and deposition change index increases, it indicates that the data point is far from the normal range and the degree of abnormality increases.

[0044] A further improvement of the technical solution of the present invention lies in: in step five, the process of detecting abnormal conditions in the erosion and deposition change of the target area includes:

[0045] Divide the monitoring area according to the topography of the target area, the distribution of submarine pipelines and the erosion and deposition change characteristics, and set up monitoring stations near the pipelines and in areas with severe erosion and deposition changes to ensure that the entire target area is covered;

[0046] Select monitoring equipment to conduct bathymetric topography monitoring, ocean current and wave monitoring, sediment concentration monitoring and pipeline status monitoring on the target area. Among them, for bathymetric topography monitoring, use a single-beam or multi-beam echosounder to monitor the water depth change in real time. For ocean current and wave monitoring, use an ADCP (Acoustic Doppler Current Profiler) and a wave buoy to monitor the ocean current velocity, direction, wave height and wave period in real time. For sediment concentration monitoring, use an optical sensor or an acoustic sensor to monitor the sediment concentration in real time. For pipeline status monitoring, use a side-scan sonar and a pipeline inspection robot to monitor the suspended length and scour depth of the pipeline in real time, and establish a data transmission network to transmit the monitoring data to the data center in real time;

[0047] Set the sampling frequency of the monitoring equipment to collect once every 10 minutes, deploy the trained abnormal erosion and deposition change model to the data center, configure the model operation parameters to ensure that the model can process the monitoring data in real time, and establish a data interface between the monitoring data and the model to ensure that the data can be input into the model in real time;

[0048] The abnormal erosion and deposition change model receives the real-time monitoring data and conducts abnormal detection on it. If an abnormal situation is detected, the model outputs the abnormal erosion and deposition change index and the abnormal erosion and deposition change characteristics.

[0049] A further improvement of the technical solution of the present invention lies in: in step six, the process of matching corresponding warning measures includes:

[0050] Classify the detected abnormal scouring and silting change characteristics, including abnormal scouring depth, abnormal suspended length, abnormal sediment transport rate, abnormal sediment concentration, and abnormal water flow velocity, and analyze and evaluate each abnormal scouring and silting change characteristic to determine its abnormal degree;

[0051] Divide the abnormal degree of the abnormal scouring and silting change characteristics into different abnormal levels, namely mild abnormality, moderate abnormality, and severe abnormality. Mild abnormality means that the change in the scouring and silting change characteristic is within 10% of the normal range, moderate abnormality means that the change in the scouring and silting change characteristic is within 10%-30% of the normal range, and severe abnormality means that the change in the scouring and silting change characteristic exceeds 30% of the normal range. Among them, the abnormal scouring depth is manifested as a sudden increase or decrease in the scouring depth, the abnormal suspended length is manifested as a sudden increase or decrease in the suspended length, the abnormal sediment transport rate is manifested as a sudden increase or decrease in the sediment transport rate, the abnormal sediment concentration is manifested as a sudden increase or decrease in the sediment concentration, and the abnormal water flow velocity is manifested as a sudden increase or decrease in the water flow velocity;

[0052] According to the analysis results of the abnormal degree, match the warning measures corresponding to this abnormal level.

[0053] Due to the adoption of the above technical solution, the technical progress achieved by the present invention compared with the prior art is:

[0054] The present invention provides a method for evaluating scouring and silting changes based on the seabed scouring mechanism of the Yellow River subaqueous delta. By constructing a multi-factor coupling model and comprehensively considering the interactions of various factors such as waves, tides, sediment characteristics, and pipeline conditions, it can more comprehensively reflect the actual process of scouring and silting changes. On this basis, an abnormal scouring and silting change model is further constructed and trained using simulation results and historical data, enabling the model to accurately identify abnormal characteristics, effectively avoiding errors caused by single-factor judgment, significantly improving the accuracy of abnormal scouring and silting change detection, and helping to take measures in advance to avoid damage to submarine pipelines due to scouring.

[0055] The present invention provides a method for evaluating scouring and silting changes based on the seabed scouring mechanism of the Yellow River subaqueous delta. Through numerical simulation, it can more accurately identify key scouring and silting change characteristics such as scouring depth, suspended length, and sediment transport rate, providing a basis for early warning and response. It not only helps to timely discover potential safety hazards but also effectively avoids false alarms and missed alarms, improving the safety and stability of marine engineering facilities. Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0057] Figure 1 It is a schematic flow chart of the method of the present invention;

[0058] Figure 2 It is a schematic flow chart of the construction process of the abnormal erosion and deposition change model of the present invention. Specific embodiments

[0059] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0060] Embodiment 1, as Figure 1 shown, the present invention provides an evaluation method for erosion and deposition changes based on the seabed scouring mechanism of the Yellow River subaqueous delta, including the following steps:

[0061] Step 1: Collect multi-source environmental data of the target area, including water depth topography data, ocean current and wave observation data, seabed sediment characteristic data, seabed pipeline status data and meteorological data, and pre-process the environmental data to obtain a multi-source environmental data set. Collect environmental data of the target area of ​​the Yellow River underwater delta, including water depth topography data, ocean current and wave observation data, seabed sediment characteristic data, seabed pipeline status data and meteorological data. Among them, use a multi-beam bathymetric system to obtain water depth topography data, including water depth value, measuring point coordinates and measurement time. Use an acoustic Doppler current profiler to obtain ocean current wave observation data, including flow velocity, flow direction, wave height, wave period, wave direction and observation time. Use a grab sampler to obtain seabed surface sediments. Sediment samples are collected and sediment samples of different depths are obtained through drilling. The physical and mechanical properties of the collected sediment samples are tested to obtain the characteristic data of seabed sediments, including sediment type, particle size distribution, density, shear strength and porosity. The side-scan sonar is used to obtain the lateral image of the submarine pipeline, identify the exposed, suspended and buried status of the pipeline, and combine the multi-beam bathymetric data to determine the location and suspension height of the pipeline to obtain the submarine pipeline status data, including the pipeline location coordinates, buried depth, suspension height, suspension length, pipeline diameter and laying year. Meteorological data, including wind speed, wind direction, air pressure, temperature and rainfall, are obtained through meteorological stations and satellite remote sensing. The multi-source environmental data collected in the target area are preprocessed, including data cleaning, Data alignment and data interpolation, including removing noise and outliers through data cleaning. For water depth data, remove outliers caused by instrument errors or sudden changes in seabed topography. For ocean current and wave data, remove abnormal wave heights or velocity values ​​caused by equipment failure or signal interference. For sediment characteristic data, check the rationality of sample test results and remove obviously erroneous data. For pipeline status data, exclude unreasonable suspension heights or lengths caused by measurement errors or equipment failures. For meteorological data, remove outliers caused by sensor failures or transmission errors. Unify all data into the WGS-84 geographic coordinate system, unify time series data into the same time base, and perform georeferencing on spatial data to ensure the spatial consistency of data. Consistency is ensured, and then data gaps are filled through data interpolation. For water depth and topography data, Kriging interpolation or inverse distance weighted (IDW) interpolation methods are used to fill the blank areas between measurement points. For ocean current and wave data, time series interpolation methods are used to fill the data gaps within the observation time interval. For sediment characteristic data, spatial interpolation methods are used to estimate the sediment characteristics of blank areas based on the spatial distribution of known sample points. The cleaned, aligned and interpolated multi-source environmental data are integrated into a unified data set, and the data set is stored in a MySQL database. Multiple tables are created to store water depth and topography data, ocean current and wave data, sediment characteristic data, pipeline status data and meteorological data, respectively, to facilitate data query and management;

[0062] Step 2: Based on the Navier-Stokes equations and sediment transport models, considering the disturbance of the seabed pipeline to the water flow and the scouring of the sediment on the seabed pipeline, construct a multi-factor coupling model of waves, tides, and sediment transport. Define the scope and boundary conditions of the target area. According to the topography of the target area and the distribution of the seabed pipeline, divide the computational grid and define the boundary conditions of the computational area, including wave incident boundaries, tidal boundaries, and sediment boundaries. Determine the physical processes to be simulated by the model, including waves, tides, sediment transport, the disturbance of the seabed pipeline to the water flow, and the scouring of the sediment. Among them, the wave incident boundary means determining parameters such as the source, direction, period, and wave height of the waves and setting the corresponding boundary conditions; the tidal boundary means setting the incident boundary conditions of the tide according to the tidal data; the sediment boundary means determining the source, type, particle size distribution, etc. of the sediment and setting the input or output boundary conditions of the sediment. Based on the Navier-Stokes equations, analyze the resistance and disturbance effects of the pipeline on the water flow, introduce the pipeline resistance coefficient and disturbance term, simulate the influence of the pipeline on the water flow, and establish an equation describing the water flow movement around the seabed pipeline. Considering the interaction of physical quantities such as water flow velocity, pressure, and viscosity, comprehensively consider the influence of water flow velocity, sediment particle size, and density factors on sediment transport, establish a sediment transport equation describing the movement and distribution of sediment under the action of water flow, and combine the sediment transport equation and pipeline force analysis. Considering factors such as scouring rate and scouring depth, establish an equation describing the scouring effect of sediment on the seabed pipeline. Considering the influence of scouring on the safety and stability of the pipeline, couple the hydrodynamic equation, sediment transport equation, and pipeline scouring equation to form a multi-factor coupling model, ensure the physical quantities between the equations are coordinated with each other, and form a complete mathematical model. Use the finite volume method to solve the multi-factor coupling model. By solving the multi-factor coupling model, obtain the results of the water flow velocity field, sediment concentration field, and pipeline scouring depth, and analyze the safety and stability of the pipeline according to the simulation results, and evaluate the influence of the scouring effect on the pipeline;

[0063] The process of solving the multi-factor coupling model is as follows:

[0064] The computational domain (the target area of the Yellow River subaqueous delta) is divided into a finite number of control volumes (grid cells). Each control volume represents a computational node, and the average physical quantities within the volume are stored at the node. Physical quantities such as fluid velocity, pressure, and sediment concentration are assigned to each control volume. Boundary conditions of waves, tidal currents, and sediment are imposed on the boundaries of the computational domain. The Navier-Stokes equations, sediment transport equations, and pipeline scour equations are integrated over the control volumes to obtain discretized equations. The discretized equations contain flux terms and source terms on the control volume interfaces. A time step is selected to ensure the stability and accuracy of the model during the solution process. On each control volume interface, the flux terms are calculated based on the current velocity field and sediment concentration field. Using the discretized equations, the unknown variable values at the control volume nodes are updated based on the flux terms and source terms. Check whether the updated amount of the unknown variable is less than the set convergence criterion. If so, the iteration ends; otherwise, return to the flux calculation step to continue the iteration. Thus, the water flow velocity field, sediment concentration field, and pipeline scour depth are solved. Analyze the simulated water flow velocity field to understand the water flow distribution characteristics around the pipeline. Analyze the simulated sediment concentration field to understand the distribution and migration of sediments around the pipeline. By comparing the sediment concentration fields at different time steps, evaluate the scour effect of sediments on the pipeline. Based on the simulated pipeline scour depth results, evaluate the impact of the scour effect on the safety and stability of the pipeline. If the scour depth exceeds the allowable range of the pipeline, it may lead to pipeline failure or damage. Thus, comprehensively analyze the results of the water flow velocity field, sediment concentration field, and pipeline scour depth to conduct a comprehensive assessment of the safety and stability of the pipeline;

[0065] The water flow simulation based on the Navier-Stokes equations, which includes the mass conservation equation and the momentum conservation equation, is as follows:

[0066] Mass conservation equation: ;

[0067] Momentum conservation equation: ;

[0068] In the equations, u is the water flow velocity, p is the pressure, is the fluid density, is the dynamic viscosity, and f is the external force (such as gravity);

[0069] Regarding the resistance and perturbation effects of the pipeline on the water flow, a pipeline resistance coefficient (such as the Darcy resistance coefficient) is introduced to describe the resistance of the pipeline to the water flow. A perturbation term is introduced into the Navier-Stokes equations to consider the influence of the pipeline on the water flow velocity and pressure fields. The equations are as follows:

[0070] ;

[0071] In the equations, is the disturbing force of the pipeline on the water flow, which can be determined through experiments or theoretical analysis;

[0072] Sediment transport equation: ;

[0073] In the formula, is the sediment concentration, D is the diffusion coefficient, and S is the source term (such as erosion and deposition);

[0074] According to the critical shear stress judge whether the sediment starts to move:

[0075] ;

[0076] In the formula, is the shear velocity, is the critical shear stress. When the water flow shear stress is less than the critical shear stress, sediment deposition occurs;

[0077] According to the water flow velocity and sediment concentration, an erosion rate equation is established, and the equation is as follows:

[0078] ;

[0079] In the formula, is the erosion depth, k is the erosion coefficient, and n is the empirical exponent. By integrating the erosion rate equation, the change of the erosion depth with time is calculated;

[0080] Pipeline erosion equation: ;

[0081] In the formula, is the recovery time constant, which describes the re-deposition process of sediments;

[0082] The equation of the multi-factor coupling model is:

[0083] ;

[0084] Step 3: Using the constructed multi-factor coupling model, simulate the erosion and deposition change process in the target area, and then identify the erosion and deposition change characteristics. Based on the iterative solution of the multi-factor coupling model, obtain the water flow velocity field, sediment concentration field, and pipeline scouring depth that change with time. Use the obtained water flow velocity field and sediment concentration field to simulate the erosion and deposition change process in the target area. By comparing the simulation results at different time steps, analyze the spatial distribution and temporal evolution law of the erosion and deposition changes, identify the key time periods and regions of the erosion and deposition changes, that is, the periods and locations where the scouring or deposition action is significantly enhanced. According to the simulation results, identify the scouring depth distribution around the pipeline, analyze the maximum, minimum, and average values of the scouring depth to quantify the intensity of the scouring action, and evaluate the impact of the scouring action on the pipeline safety, including whether the scouring depth exceeds the allowable range of the pipeline and whether the scouring action may cause the pipeline to fail or be damaged. According to the scouring depth and pipeline burial depth, calculate the suspended length of the pipeline, identify the maximum, minimum, and distribution characteristics of the suspended length, and evaluate its impact on the pipeline stability, including whether the suspended length is too large and whether the suspended part is prone to shaking or damage under the action of water flow or external forces. According to the solution results of the sediment transport equation, calculate the transport rate of the sediment, identify the maximum, minimum, and distribution characteristics of the sediment transport rate, analyze the spatial distribution and temporal evolution law of the sediment transport rate, and evaluate its impact on the terrain change around the pipeline, including whether the sediment will accumulate near the pipeline to form new terrain features and whether these terrain features will have an adverse impact on the pipeline safety and stability. Comprehensively analyze the simulation results to determine the erosion and deposition change characteristics, including the scouring depth change rate, suspended length change rate, sediment transport rate change rate, sediment concentration gradient, and water flow velocity gradient, and then analyze the interaction between the erosion and deposition change characteristics. Among them, the increase in the scouring depth will lead to an increase in the suspended length, which will in turn affect the pipeline stability, and the change in the sediment transport rate will affect the scouring depth distribution;

[0085] Step 4: Based on the simulation results and the identified erosion and deposition change characteristics, construct an abnormal erosion and deposition change model to identify abnormal situations in the erosion and deposition changes;

[0086] Step 5: Arrange monitoring stations in the target area to obtain real-time monitoring data of the target area, and deploy the abnormal erosion and deposition change model to detect abnormal situations in the erosion and deposition changes in the target area;

[0087] Step 6: Classify and evaluate the detected abnormal erosion and deposition changes, determine their potential impact on the safety of the submarine pipeline, and match corresponding early warning measures according to the erosion and deposition change characteristics.

[0088] Example 2, as Figure 2 shown, on the basis of Example 1, the present invention provides a technical solution: Preferably, in Step 4, the construction process of the abnormal erosion and deposition change model includes:

[0089] Collect the simulation results of the multi-factor coupling model, including the water flow velocity field, sediment concentration field, pipeline scour depth, suspension length, and sediment transport rate, and extract the erosion and deposition change characteristics including the scour depth change rate, suspension length change rate, sediment transport rate change rate, sediment concentration gradient, and water flow velocity gradient from the simulation results for anomaly detection. Among them, the water flow velocity field represents the water flow velocity distribution at each time step, the sediment concentration field represents the sediment concentration distribution at each time step, the pipeline scour depth represents the pipeline scour depth at each time step, the suspension length represents the pipeline suspension length at each time step, the sediment transport rate represents the sediment transport rate at each time step, the scour depth change rate represents the change rate of the scour depth over time, the suspension length change rate represents the change rate of the suspension length over time, the sediment transport rate change rate represents the change rate of the sediment transport rate over time, the sediment concentration gradient represents the spatial gradient of the sediment concentration, and the water flow velocity gradient represents the spatial gradient of the water flow velocity. Conduct statistical analysis on the historical erosion and deposition data of the target area to identify the range and pattern of normal erosion and deposition changes. Based on the statistical analysis results, establish a model of normal erosion and deposition changes, determine the normal range and change trend of the erosion and deposition change characteristics, and integrate the relevant data of the obtained erosion and deposition change characteristics to mark the abnormal erosion and deposition characteristics among them to obtain a characteristic data set. Furthermore, divide the integrated data set into a training set and a test set, use the training set to train the K-means clustering algorithm to construct an abnormal erosion and deposition change model. According to the normal pattern, determine the number of cluster centers, calculate the distance from each data point to the cluster center, and assign the data points to the nearest cluster center to form clusters. Use the test set to verify the trained model and evaluate the anomaly detection accuracy and stability of the model through indicators such as the confusion matrix and ROC curve. Apply the constructed abnormal erosion and deposition change model to detect anomalies in the erosion and deposition change data, output whether the erosion and deposition change data is abnormal, and calculate the abnormal erosion and deposition change index to identify the abnormal erosion and deposition change characteristics and severity;

[0090] The process of anomaly detection for erosion and deposition change data is as follows:

[0091] Input the data of scouring and silting change characteristics, including the change rate of scouring depth, the change rate of suspended length, the change rate of sediment transport rate, the sediment concentration gradient, and the water flow velocity gradient, into the trained abnormal scouring and silting change model. The model outputs whether each data point is abnormal, that is, the clustering label (normal or abnormal) of each data point. Determine the weight of each feature according to the importance of each feature, and determine the reference value and standard deviation of each feature according to historical data. For each data point, calculate the abnormal scouring and silting change index by synthesizing the current value, weight, reference value, and standard deviation of each scouring and silting change feature. According to the output of the model, identify the abnormal scouring and silting change features, and evaluate the severity of the abnormality according to the value of the abnormal scouring and silting change index. When the abnormal scouring and silting change index approaches 0, it indicates that the data point approaches the normal range and the degree of abnormality is low. When the abnormal scouring and silting change index increases, it indicates that the data point is far from the normal range and the degree of abnormality increases;

[0092] The expression of the abnormal scouring and silting change index is:

[0093] ;

[0094] In the formula, A is the abnormal scouring and silting change index, n is the number of scouring and silting change characteristics, is the weight of the i-th scouring and silting change feature. The sum of the weights is 1, indicating the importance of each scouring and silting change feature in the abnormal scouring and silting change index, is the value of the i-th scouring and silting change feature, is the reference value (mean of the normal range) of the i-th scouring and silting change feature, is the standard deviation of the i-th scouring and silting change feature, indicating the degree of dispersion of the feature values. The value range of A is , when is close to , is close to 0, is close to 1, and A is close to 0, indicating that the data point is close to the normal range. When is far from , increases, increases, and A increases, indicating that the data point is far from the normal range and the degree of abnormality increases;

[0095] In step five, the process of detecting abnormal conditions in the scouring and silting change of the target area includes:

[0096] According to the terrain, submarine pipeline distribution, and erosion and deposition change characteristics of the target area, divide the monitoring area, and set up monitoring stations near the pipeline and in areas with severe erosion and deposition changes to ensure coverage of the entire target area. Select monitoring equipment to conduct water depth and terrain monitoring, sea current and wave monitoring, sediment concentration monitoring, and pipeline status monitoring on the target area. Among them, for water depth and terrain monitoring, use a single-beam or multi-beam depth sounder to monitor the water depth change in real time. For sea current and wave monitoring, use an ADCP (Acoustic Doppler Current Profiler) and wave buoys to monitor the sea current velocity, direction, wave height, and wave period in real time. For sediment concentration monitoring, use an optical sensor or an acoustic sensor to monitor the sediment concentration in real time. For pipeline status monitoring, use a side-scan sonar and a pipeline inspection robot to monitor the suspended length and scour depth of the pipeline in real time. Establish a data transmission network to transmit the monitoring data to the data center in real time. Set the sampling frequency of the monitoring equipment to collect once every 10 minutes. Deploy the trained abnormal erosion and deposition change model to the data center, configure the model operation parameters to ensure that the model can process the monitoring data in real time, and establish a data interface between the monitoring data and the model to ensure that the data can be input into the model in real time. The abnormal erosion and deposition change model receives the real-time monitoring data and conducts abnormal detection on it. If an abnormal situation is detected, the model outputs abnormal erosion and deposition change indicators and abnormal erosion and deposition change characteristics;

[0097] In step six, the process of matching corresponding early warning measures includes:

[0098] Classify the detected abnormal erosion and deposition change characteristics, including abnormal scour depth, abnormal suspended length, abnormal sediment transport rate, abnormal sediment concentration, and abnormal water flow velocity. Analyze and evaluate each abnormal erosion and deposition change characteristic to determine its degree of abnormality. Divide the degree of abnormality of the abnormal erosion and deposition change characteristics into different abnormal levels, namely mild abnormality, moderate abnormality, and severe abnormality. Mild abnormality means that the change in the erosion and deposition change characteristic is within 10% of the normal range; moderate abnormality means that the change in the erosion and deposition change characteristic is within 10%-30% of the normal range; severe abnormality means that the change in the erosion and deposition change characteristic exceeds 30% of the normal range. Among them, the abnormal scour depth is manifested as a sudden increase or decrease in the scour depth; the abnormal suspended length is manifested as a sudden increase or decrease in the suspended length; the abnormal sediment transport rate is manifested as a sudden increase or decrease in the sediment transport rate; the abnormal sediment concentration is manifested as a sudden increase or decrease in the sediment concentration; the abnormal water flow velocity is manifested as a sudden increase or decrease in the water flow velocity. According to the analysis results of the degree of abnormality, match the early warning measures corresponding to this abnormal level. For mild abnormality, increase the monitoring frequency, closely monitor the development trend of the erosion and deposition change, conduct a risk assessment of the safety status of the pipeline, and determine whether further measures need to be taken. For moderate abnormality, issue an early warning notice to relevant departments and personnel, remind them to pay attention to pipeline safety, organize professional personnel to conduct on-site inspections, evaluate the specific impact of the erosion and deposition change on pipeline safety, and take necessary engineering measures according to the on-site inspection results, such as strengthening the pipeline, setting up protective facilities, etc. For severe abnormality, immediately issue an emergency early warning notice to relevant departments and personnel, start the emergency plan, organize professional personnel for emergency disposal, and take emergency engineering measures, such as repairing damaged pipelines, changing the pipeline direction, etc., to ensure pipeline safety.

[0099] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An evaluation method for scouring and silting changes based on the seabed scouring mechanism of the Yellow River subaqueous delta, characterized in that, The method includes the following steps: Step 1: Collect multi-source environmental data of the target area, and preprocess the environmental data to obtain a multi-source environmental data set; Step 2: Based on the Navier-Stokes equation and the sediment transport model, combined with the disturbance of the seabed pipeline to the water flow and the scouring of the seabed pipeline by sediments, construct a multi-factor coupling model of waves, tides, and sediment transport; Step 3: Use the constructed multi-factor coupling model to simulate the erosion and deposition change process of the target area, and then identify the erosion and deposition change characteristics; Step 4: Based on the simulation results and the identified erosion and deposition change characteristics, construct an abnormal erosion and deposition change model to identify abnormal situations in the erosion and deposition changes. The construction process of the abnormal erosion and deposition change model includes: Collect the simulation results of the multi-factor coupling model, including the water flow velocity field, sediment concentration field, pipeline scouring depth, suspension length, and sediment transport rate, and extract the erosion and deposition change characteristics including the scouring depth change rate, suspension length change rate, sediment transport rate change rate, sediment concentration gradient, and water flow velocity gradient from the simulation results; Conduct statistical analysis on the historical erosion and deposition data of the target area, identify the range and pattern of normal erosion and deposition changes. Based on the statistical analysis results, establish a normal erosion and deposition change pattern, determine the normal range and change trend of the erosion and deposition change characteristics, and integrate the relevant data of the obtained erosion and deposition change characteristics, mark the abnormal erosion and deposition characteristics among them, obtain a characteristic data set, and then divide the integrated data set into a training set and a test set; Use the training set to train the K-means clustering algorithm to construct an abnormal erosion and deposition change model. According to the normal pattern, determine the number of cluster centers, calculate the distance from each data point to the cluster center, assign the data points to the nearest cluster center to form clusters, and use the test set to verify the trained model to evaluate the abnormal detection accuracy and stability of the model; Apply the constructed abnormal erosion and deposition change model to conduct abnormal detection on the erosion and deposition change data, output whether the erosion and deposition change data is abnormal, and calculate the abnormal erosion and deposition change index to identify the abnormal erosion and deposition change characteristics and severity. The process of abnormal detection of the erosion and deposition change data is: Input the erosion and deposition change characteristic data including the scouring depth change rate, suspension length change rate, sediment transport rate change rate, sediment concentration gradient, and water flow velocity gradient into the trained abnormal erosion and deposition change model, and the model outputs whether each data point is abnormal, that is, the cluster label of each data point; Determine the weight of each feature according to the importance of each feature, and determine the reference value and standard deviation of each feature according to the historical data; For each data point, calculate the abnormal erosion and deposition change index by comprehensively considering the current value, weight, reference value, and standard deviation of each erosion and deposition change characteristic; According to the output of the model, identify the abnormal erosion and deposition change characteristics, and evaluate the severity of the abnormality according to the value of the abnormal erosion and deposition change index. When the abnormal erosion and deposition change index approaches 0, it indicates that the data point approaches the normal range and the degree of abnormality is low. When the abnormal erosion and deposition change index increases, it indicates that the data point is far from the normal range and the degree of abnormality increases; Step 5: Deploy monitoring stations within the target area to obtain real-time monitoring data of the target area, and deploy an abnormal erosion and deposition change model to detect abnormal conditions in the erosion and deposition changes of the target area; Step 6: Classify and evaluate the detected abnormal erosion and deposition changes, determine their potential impacts on the safety of submarine pipelines, and match corresponding warning measures according to the characteristics of erosion and deposition changes.

2. The method for evaluating erosion and deposition changes based on the seabed scouring mechanism of the Yellow River subaqueous delta according to claim 1, wherein: In Step 1, the process of obtaining the multi-source environmental data set is as follows: Collect environmental data of the target area in the Yellow River subaqueous delta, including water depth and topography data, ocean current and wave observation data, submarine sediment property data, submarine pipeline status data, and meteorological data; Preprocess the multi-source environmental data of the collected target area, including data cleaning, data alignment, and data interpolation; Integrate the pre-cleaned, aligned, and interpolated multi-source environmental data into a unified data set, store the data set in a MySQL database, and create multiple tables to store water depth and topography data, ocean current and wave data, sediment property data, pipeline status data, and meteorological data respectively.

3. The method for evaluating erosion and deposition changes based on the seabed scouring mechanism of the Yellow River subaqueous delta according to claim 2, characterized in that: In Step 2, the process of constructing the multi-factor coupling model includes: Define the scope and boundary conditions of the target area, divide the computational grid according to the topography of the target area and the distribution of submarine pipelines, define the boundary conditions of the computational area, including wave incident boundary, tidal current boundary, and sediment boundary, and determine the physical processes to be simulated by the model, including waves, tidal currents, sediment transport, and the disturbance of the submarine pipeline to the water flow and the erosion of sediments; Based on the Navier-Stokes equation, analyze the resistance and disturbance effects of the pipeline on the water flow, introduce the pipeline resistance coefficient and disturbance term, simulate the influence of the pipeline on the water flow, and establish an equation describing the water flow movement around the submarine pipeline; Integrate the effects of water flow velocity, sediment particle size, and density on sediment transport, establish a sediment transport equation describing the movement and distribution of sediment under the action of water flow, and combine the sediment transport equation and the pipeline force analysis to establish an equation describing the erosion effect of sediment on the submarine pipeline by integrating the erosion rate and erosion depth factors; Couple the hydrodynamic equation, sediment transport equation, and pipeline erosion equation to form a multi-factor coupling model; Solve the multi-factor coupling model using the finite volume method. By solving the multi-factor coupling model, obtain the results of the water flow velocity field, sediment concentration field, and pipeline erosion depth, and analyze the safety and stability of the pipeline according to the simulation results, and evaluate the impact of the erosion effect on the pipeline.

4. The method for evaluating erosion and deposition changes based on the seabed scouring mechanism of the Yellow River subaqueous delta according to claim 3, characterized in that: The process of solving the multi-factor coupling model is as follows: Divide the computational domain into a finite number of control volumes, each control volume representing a computational node, and assign physical quantities such as fluid velocity, pressure, and sediment concentration to each control volume, and apply the boundary conditions of waves, tidal currents, and sediments on the boundary of the computational domain; Integrate the Navier-Stokes equation, sediment transport equation, and pipeline erosion equation over the control volume to obtain a discretized equation, and the discretized equation contains flux terms and source terms on the interface of the control volume; Select the time step. On each control volume interface, calculate the flux terms based on the current velocity field and sediment concentration field. Use the discretized equation to update the values of the unknown variables at the control volume nodes according to the flux terms and source terms. Check whether the updated amount of the unknown variables is less than the set convergence criterion. If so, end the iteration; otherwise, return to the flux calculation step to continue the iteration, and then solve to obtain the water flow velocity field, sediment concentration field, and pipeline scouring depth. Analyze the simulated water flow velocity field to understand the water flow distribution characteristics around the pipeline. Analyze the simulated sediment concentration field to understand the distribution and migration of sediments around the pipeline. By comparing the sediment concentration fields at different time steps, evaluate the scouring effect of sediments on the pipeline. According to the simulated pipeline scouring depth results, evaluate the impact of the scouring effect on the safety and stability of the pipeline. Then, comprehensively evaluate the safety and stability of the pipeline based on the analysis results of the water flow velocity field, sediment concentration field, and pipeline scouring depth.

5. The scouring and silting change evaluation method based on the submarine scouring mechanism of the Yellow River subaqueous delta according to claim 4, wherein: In step three, the identification process of the erosion and deposition change characteristics includes: Based on the iterative solution of the multi-factor coupling model, obtain the water flow velocity field, sediment concentration field, and pipeline scouring depth that change with time. Use the obtained water flow velocity field and sediment concentration field to simulate the erosion and deposition change process in the target area. By comparing the simulation results at different time steps, analyze the spatial distribution and temporal evolution law of the erosion and deposition changes, and identify the key time periods and areas of the erosion and deposition changes. According to the simulation results, identify the scouring depth distribution around the pipeline, analyze the maximum value, minimum value, and average value of the scouring depth to quantify the intensity of the scouring effect, and evaluate the impact of the scouring effect on the pipeline safety. According to the scouring depth and pipeline burial depth, calculate the suspended length of the pipeline, identify the maximum value, minimum value, and distribution characteristics of the suspended length, and evaluate its impact on the pipeline stability. According to the solution results of the sediment transport equation, calculate the transport rate of sediments, identify the maximum value, minimum value, and distribution characteristics of the sediment transport rate, analyze the spatial distribution and temporal evolution law of the sediment transport rate, and evaluate its impact on the topographic changes around the pipeline. Comprehensively analyze the simulation results to determine the erosion and deposition change characteristics, including the scouring depth change rate, suspended length change rate, sediment transport rate change rate, sediment concentration gradient, and water flow velocity gradient, and then analyze the interaction between the erosion and deposition change characteristics.

6. The method for evaluating scouring and silting changes based on the seabed scouring mechanism of the Yellow River subaqueous delta according to claim 1, wherein: In step five, the process of detecting abnormal conditions in the erosion and deposition changes in the target area includes: According to the topography of the target area, the distribution of submarine pipelines, and the erosion and deposition change characteristics, divide the monitoring area and set up monitoring stations near the pipeline and in areas with intense erosion and deposition changes. Select monitoring equipment to conduct bathymetric topography monitoring, ocean current and wave monitoring, sediment concentration monitoring, and pipeline status monitoring on the target area, and establish a data transmission network to transmit the monitoring data to the data center in real time. Set the sampling frequency of the monitoring equipment to collect once every 10 minutes, deploy the trained abnormal erosion and deposition change model to the data center, configure the model operation parameters, and establish a data interface between the monitoring data and the model. The abnormal erosion and deposition change model receives real-time monitoring data and conducts abnormal detection on it. If an abnormal situation is detected, the model outputs abnormal erosion and deposition change indicators and abnormal erosion and deposition change characteristics.

7. The method for evaluating scouring and silting changes based on the seabed scouring mechanism of the Yellow River subaqueous delta according to claim 6, wherein: In step six mentioned above, the process of matching corresponding warning measures includes: Classify the detected abnormal erosion and deposition change characteristics, including abnormal scour depth, abnormal suspended length, abnormal sediment transport rate, abnormal sediment concentration, and abnormal water flow velocity, and analyze and evaluate each abnormal erosion and deposition change characteristic to determine its degree of abnormality; Divide the degree of abnormality of the abnormal erosion and deposition change characteristics into different abnormal levels, namely mild abnormality, moderate abnormality, and severe abnormality. Mild abnormality means that the change in the erosion and deposition change characteristic is within 10% of the normal range, moderate abnormality means that the change in the erosion and deposition change characteristic is within 10%-30% of the normal range, and severe abnormality means that the change in the erosion and deposition change characteristic exceeds 30% of the normal range; According to the analysis result of the degree of abnormality, match the warning measure corresponding to this abnormal level.

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