Scouring and silting change evaluation method based on seabed scouring mechanism of Yellow River underwater delta

Abstract

The invention discloses an erosion and deposition change evaluation method based on a yellow river underwater delta seabed scour mechanism, and relates to the technical field of seabed scour analysis, and the method comprises the steps: collecting multi-source environment data of a target region, and carrying out the preprocessing of the environment data, and obtaining a multi-source environment data set; on the basis of a Navier-Stokes equation and a sediment transportation model, a multi-factor coupling model of wave, tidal current and sediment transportation is constructed in combination with disturbance of a submarine pipeline to water flow and scouring of sediment to the submarine pipeline. According to the method, the actual process of erosion and deposition change can be reflected more comprehensively by constructing the multi-factor coupling model and comprehensively considering the interaction of multiple factors such as waves, tidal currents, sediment characteristics and pipeline states, on the basis, the abnormal erosion and deposition change model is further constructed, errors caused by single-factor judgment are effectively avoided, and the accuracy of the abnormal erosion and deposition change is improved. The precision of erosion and deposition change anomaly detection is remarkably improved, measures can be taken in advance, and the submarine pipeline is prevented from being damaged due to erosion.

Description

Technical Field

[0001] The invention relates to the technical field of seabed scour analysis, and in particular to a method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater 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 waters in the Yellow River Delta are the main oil production area of ​​Shengli Oilfield, with a large number of marine engineering facilities, including 107 platforms of various types, 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 suspended pipelines and seabed instability caused by seabed scouring have led to complex and changeable seabed scouring and siltation processes in this area.

[0003] In the existing technology, scouring and silting changes involve the coupling of multiple factors such as waves, tides, sediment characteristics, and pipeline status. The complexity and interaction of the coupling of multiple factors will lead to a decrease 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. To this end, a scouring and silting change evaluation method based on the seabed scouring mechanism of the Yellow River underwater delta is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: The evaluation method of scouring and silting changes based on the seabed scouring mechanism of the Yellow River underwater delta includes the following steps: Step 1: Collect multi-source environmental data of the target area, including water depth and topography data, ocean current and wave observation data, seabed sediment characteristics data, seabed pipeline status data, and meteorological data, and pre-process the environmental data to obtain a multi-source environmental data set; 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 scouring of the submarine pipeline by the sediment, a multi-factor coupling model of wave, tidal and sediment transport is constructed; 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; Step 4: Based on the simulation results and the identified scouring and silting change characteristics, an abnormal scouring and silting change model is constructed to identify abnormal conditions in scouring and silting changes; Step 5: Deploy monitoring stations in the target area, obtain real-time monitoring data of the target area, and deploy an abnormal scouring and silting change model to detect abnormal conditions in the scouring and silting changes in the target area; Step 6: Classify and evaluate the detected abnormal scouring and silting changes, determine their potential impact on the safety of submarine pipelines, and match corresponding early warning measures according to the characteristics of scouring and silting changes.

[0006] A further improvement of the technical solution of the present invention is that in step 1, the process of acquiring the multi-source environmental data set is: Collect environmental data of the target area of ​​the Yellow River underwater delta, including water depth and topography data, ocean current and wave observation data, seabed sediment characteristics data, seabed pipeline status data, and meteorological data; Preprocess the collected multi-source environmental data of the target area, including data cleaning, data alignment and data interpolation; The cleaned, aligned and interpolated multi-source environmental data are integrated into a unified dataset, and the dataset 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, which facilitates data query and management.

[0007] A further improvement of the technical solution of the present invention is that in step 2, the construction process of the multi-factor coupling model includes: Clarify 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 the wave incidence boundary, tidal 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 water flow and sediment scouring caused by submarine pipelines; Based on the Navier-Stokes equation, the resistance and disturbance of the pipeline on the water flow are analyzed, the pipeline resistance coefficient and disturbance term are introduced, the influence of the pipeline on the water flow is simulated, and the equation describing the water flow around the submarine pipeline is established, considering the interaction between the water flow velocity, pressure and viscosity physical quantities; The effects of water velocity, sediment particle size and density on sediment transport are comprehensively considered to establish a sediment transport equation that describes the movement and distribution of sediment under the action of water flow. In combination with the sediment transport equation and pipeline force analysis, the scouring rate and scouring depth factors are comprehensively considered to establish an equation that describes the scouring effect of sediment on submarine pipelines, taking into account the impact of scouring on pipeline safety and stability; The fluid dynamics equation, sediment transport equation and pipeline scour equation are coupled to form a multi-factor coupling model to ensure that the physical quantities between the equations are coordinated to form a complete mathematical model; The finite volume method is used to solve the multi-factor coupling model. By solving the multi-factor coupling model, the results of water velocity field, sediment concentration field and pipeline scouring depth are obtained. Based on the simulation results, the safety and stability of the pipeline are analyzed, and the impact of scouring on the pipeline is evaluated.

[0008] A further improvement of the technical solution of the present invention is that the process of solving the multi-factor coupling model is: The computational domain (the target area of ​​the Yellow River underwater delta) is divided into a finite number of control volumes (grid cells). Each control volume represents a computational node, which stores the average value of the physical quantities in the volume. The fluid velocity, pressure and sediment concentration physical quantities are distributed on each control volume, and the boundary conditions of waves, tidal currents and sediments are applied on the boundaries of the computational domain. The Navier-Stokes equation, sediment transport equation and pipeline scour equation are integrated on the control volume to obtain the discretized equation, which contains the flux term and source term on the control volume interface; Select the time step to ensure the stability and accuracy of the model during the solution process. On each control volume interface, calculate the flux term according to the current velocity field and sediment concentration field. Use the discretized equation to update the unknown variable value on the control volume node according to the flux term and source term. Check whether the update amount of the unknown variable is less than the set convergence standard. If so, the iteration ends. Otherwise, return to the flux calculation step to continue the iteration, and then solve the water velocity field, sediment concentration field and pipeline scour depth. Analyze the simulated water 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 scouring on the safety and stability of the pipeline. If the scouring depth exceeds the allowable range of the pipeline, it may cause pipeline failure or damage. Then, based on the analysis results of the water velocity field, sediment concentration field and pipeline scouring depth, conduct a comprehensive assessment of the safety and stability of the pipeline.

[0009] A further improvement of the technical solution of the present invention is that in step 3, the identification process of the scouring and silting change characteristics includes: Based on the iterative solution of the multi-factor coupling model, the water velocity field, sediment concentration field and pipeline scouring depth that change with time are obtained. The water velocity field and sediment concentration field obtained by the solution are used to simulate the scouring and deposition change process of the target area. By comparing the simulation results of different time steps, the spatial distribution and time evolution law of scouring and deposition changes are analyzed, and the key time periods and areas of scouring and deposition changes, that is, the periods and locations where scouring or deposition effects are significantly enhanced, are identified. Based on the simulation results, the scour depth distribution around the pipeline is identified, and the maximum, minimum and average scour depths are analyzed to quantify the intensity of the scour effect and assess the impact of the scour effect on pipeline safety, including whether the scour depth exceeds the allowable range of the pipeline and whether the scour effect may cause pipeline failure or damage; According to the scouring depth and buried depth of the pipeline, calculate the suspended length of the pipeline, identify the maximum, minimum 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 susceptible to shaking or damage due to water flow or external forces; Based on the solution of the sediment transport equation, calculate the sediment transport rate, identify the maximum, minimum and distribution characteristics of the sediment transport rate, analyze the spatial distribution and temporal evolution of the sediment transport rate, and evaluate its impact on the terrain changes around the pipeline, including whether sediments will accumulate near the pipeline to form new terrain features, and whether these terrain features will have an adverse effect on the safety and stability of the pipeline; The simulation results are comprehensively analyzed to determine the characteristics of scour and deposition changes, including the change rate of scour depth, the change rate of suspended length, the change rate of sediment transport rate, the sediment concentration gradient and the water velocity gradient, and then the interaction between the scour and deposition change characteristics is analyzed. Among them, the increase in scour depth will lead to an increase in suspended length, which in turn affects the stability of the pipeline. The change in sediment transport rate will affect the distribution of scour depth.

[0010] A further improvement of the technical solution of the present invention is that in step 4, the construction process of the abnormal scouring and silting change model includes: Collect the simulation results of the multi-factor coupling model, including water velocity field, sediment concentration field, pipeline scouring depth, suspended length and sediment transport rate, and extract the scouring and deposition change characteristics including scouring depth change rate, suspended length change rate, sediment transport rate change rate, sediment concentration gradient and water velocity gradient from the simulation results for anomaly detection; Statistical analysis is performed on the historical scouring and silting data of the target area to identify the scope and regularity of normal scouring and silting changes. Based on the statistical analysis results, a normal scouring and silting change model is established to determine the normal scope and change trend of scouring and silting change characteristics. The relevant data of scouring and silting change characteristics obtained are integrated, and abnormal scouring and silting characteristics are marked to obtain a feature data set. The integrated data set is then divided into a training set and a test set. The K-means clustering algorithm is trained using the training set to build an abnormal scouring and silting change model. According to the normal mode, the number of cluster centers is determined, the distance from each data point to the cluster center is calculated, and the data points are assigned to the nearest cluster center to form clusters. The trained model is verified using the test set, and the model is evaluated through indicators such as confusion matrix and ROC curve to evaluate the accuracy and stability of abnormal detection. The constructed abnormal scouring and silting change model is used to detect anomalies in the scouring and silting change data, output whether the scouring and silting change data is abnormal, calculate the abnormal scouring and silting change indicators, and identify the abnormal scouring and silting change characteristics and severity.

[0011] A further improvement of the technical solution of the present invention is that the process of detecting abnormality of scouring and silting change data is as follows: The scouring and deposition change characteristic data including the scouring depth change rate, the hanging length change rate, the sediment transport rate change rate, the sediment concentration gradient and the water flow velocity gradient are input into the trained abnormal scouring and deposition 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 based on its importance, and determine the benchmark value and standard deviation of each feature based on historical data; For each data point, the abnormal scouring and silting change index is calculated by combining the current value, weight, baseline value and standard deviation of each scouring and silting change feature; According to the output of the model, the abnormal scouring and silting change characteristics are identified, and the severity of the anomaly is evaluated according to the value of the abnormal scouring and silting change index. When the abnormal scouring and silting change index is close to 0, it means that the data point is close to the normal range and the degree of anomaly is low. When the abnormal scouring and silting change index increases, it means that the data point is far away from the normal range and the degree of anomaly increases.

[0012] A further improvement of the technical solution of the present invention is that in step 5, the process of detecting abnormal conditions in the scouring and silting changes in the target area includes: The monitoring area is divided according to the topography, distribution of submarine pipelines and scouring and silting change characteristics of the target area, and monitoring stations are deployed near pipelines and in areas with drastic scouring and silting changes to ensure coverage of the entire target area; Select monitoring equipment to conduct water depth and terrain monitoring, ocean current and wave monitoring, sediment concentration monitoring and pipeline status monitoring in the target area. For water depth and terrain monitoring, use single-beam or multi-beam echo sounders to monitor water depth changes in real time. For ocean current and wave monitoring, use ADCP (Acoustic Doppler Current Profiler) and wave buoys to monitor ocean current speed, direction, wave height and wave period in real time. For sediment concentration monitoring, use optical sensors or acoustic sensors to monitor sediment concentration in real time. For pipeline status monitoring, use side-scan sonar and pipeline inspection robots to monitor the suspended length and scouring depth of pipelines in real time. Establish a data transmission network to transmit monitoring data to the data center in real time. Set the sampling frequency of the monitoring equipment to once every 10 minutes, deploy the trained abnormal erosion and siltation 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 scouring and silting change model receives real-time monitoring data and performs anomaly detection on it. If an abnormal situation is detected, the model outputs abnormal scouring and silting change indicators and abnormal scouring and silting change characteristics.

[0013] A further improvement of the technical solution of the present invention is that in step 6, the process of matching corresponding early warning measures includes: The abnormal scouring and silting change characteristics detected are classified, including abnormal scouring depth, abnormal hanging length, abnormal sediment transport rate, abnormal sediment concentration and abnormal water flow velocity, and each abnormal scouring and silting change characteristic is analyzed and evaluated to determine its abnormal degree; The abnormal degree of abnormal scouring and deposition change characteristics is divided into different abnormal levels, namely mild abnormality, moderate abnormality and severe abnormality. Mild abnormality means that the change of scouring and deposition change characteristics is within 10% of the normal range, moderate abnormality means that the change of scouring and deposition change characteristics is between 10% and 30% of the normal range, and severe abnormality means that the change of scouring and deposition change characteristics exceeds 30% of the normal range. Among them, the abnormality of scouring depth is manifested as a sudden increase or decrease in scouring depth, the abnormality of suspended length is manifested as a sudden increase or decrease in suspended length, the abnormality of sediment transport rate is manifested as a sudden increase or decrease in sediment transport rate, the abnormality of sediment concentration is manifested as a sudden increase or decrease in sediment concentration, and the abnormality of water flow velocity is manifested as a sudden increase or decrease in water flow velocity. According to the analysis results of the abnormality level, the early warning measures corresponding to the abnormality level are matched.

[0014] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: The present invention provides an evaluation method for scouring and silting changes based on the submarine scouring mechanism of the Yellow River underwater delta. By constructing a multi-factor coupling model and comprehensively considering the interaction of multiple factors such as waves, tides, sediment characteristics, and pipeline status, the actual process of scouring and silting changes can be more comprehensively reflected. On this basis, an abnormal scouring and silting change model is further constructed, and the simulation results and historical data are used for training, so that the model can accurately identify abnormal features, effectively avoiding the errors caused by single factor judgment, and significantly improving the accuracy of abnormal detection of scouring and silting changes, which is helpful to take measures in advance to avoid damage to submarine pipelines due to scouring.

[0015] The present invention provides an evaluation method for scour and siltation changes based on the submarine scour mechanism of the Yellow River underwater delta. Through numerical simulation, it can more accurately identify key scour and siltation change characteristics such as scour 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 can effectively avoid false alarms and missed alarms, and improve the safety and stability of marine engineering facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the construction process of the abnormal scouring and silting change model of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, as Figure 1 As shown, the present invention provides a method for evaluating scouring and silting changes based on the seabed scouring mechanism of the Yellow River underwater delta, comprising the following steps: 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; Step 2: Based on the Navier-Stokes equations and sediment transport model, combined with the disturbance of the submarine pipeline on the water flow and the scouring of the submarine pipeline by the sediment, a multi-factor coupling model of wave, tidal and sediment transport is constructed, the scope and boundary conditions of the target area are clarified, and the calculation grid is divided according to the topography of the target area and the distribution of the submarine pipeline. The boundary conditions of the calculation area are defined, including the wave incident boundary, tidal boundary and sediment boundary, and the physical processes to be simulated by the model are determined, including waves, tidal, sediment transport and the disturbance of the submarine pipeline on the water flow and the scouring of the sediment. Among them, the wave incident boundary means determining the source, direction, period, wave height and other parameters of the wave, and setting the corresponding boundary conditions. The tidal boundary means setting the incident boundary conditions of the tidal current 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, the resistance and disturbance of the pipeline on the water flow are analyzed. Introduce pipeline resistance coefficient and disturbance term, simulate the influence of pipeline on water flow, establish equations to describe water flow around submarine pipeline, consider the interaction of water velocity, pressure and viscosity physical quantities, integrate the influence of water velocity, sediment particle size and density on sediment transport, establish sediment transport equation to describe the movement and distribution of sediment under water flow, and combine sediment transport equation with pipeline force analysis, integrate scouring rate and scouring depth factors, establish equation to describe sediment scouring effect on submarine pipeline, consider the influence of scouring on pipeline safety and stability, couple fluid dynamics equation, sediment transport equation and pipeline scouring equation to form a multi-factor coupling model, ensure the coordination of physical quantities between equations, form a complete mathematical model, use finite volume method to solve multi-factor coupling model, by solving multi-factor coupling model, obtain the results of water velocity field, sediment concentration field and pipeline scouring depth, analyze pipeline safety and stability according to simulation results, and evaluate the influence of scouring on pipeline; The process of solving the multi-factor coupling model is: The computational domain (the target area of ​​the Yellow River underwater delta) is divided into a finite number of control volumes (grid units). Each control volume represents a computational node, and the node stores the average value of the physical quantities in the volume. The physical quantities of fluid velocity, pressure and sediment concentration are distributed on each control volume. The boundary conditions of waves, tidal currents and sediments are applied to the boundaries of the computational domain. The Navier-Stokes equations, sediment transport equations and pipeline scour equations are integrated on the control volume to obtain discretized equations. The discretized equations contain flux terms and source terms on the control volume interface. The time step is selected to ensure the stability and accuracy of the model during the solution process. On each control volume interface, the flux term is calculated based on the current velocity field and sediment concentration field. The unfixed value on the control volume node is updated based on the flux term and source term using the discretized equation. Know the variable value, check whether the update amount of the unknown variable is less than the set convergence standard, if so, the iteration ends, otherwise, return to the flux calculation step to continue the iteration, and then solve the water velocity field, sediment concentration field and pipeline scouring depth, analyze the simulated water velocity field, understand the water flow distribution characteristics around the pipeline, analyze the simulated sediment concentration field, understand the distribution and migration of sediments around the pipeline, and evaluate the scouring effect of sediments on the pipeline by comparing the sediment concentration fields of different time steps. According to the simulated pipeline scouring depth results, evaluate the impact of scouring on pipeline safety and stability. If the scouring depth exceeds the allowable range of the pipeline, it may cause pipeline failure or damage, and then comprehensively evaluate the safety and stability of the pipeline by combining the analysis results of the water velocity field, sediment concentration field and pipeline scouring depth; Water flow simulation based on the Navier-Stokes equations, which includes the mass conservation equation and the momentum conservation equation, is as follows: The mass conservation equation: ; Momentum conservation equation: ; In the formula, u is the water velocity, p is the pressure, is the fluid density, is the dynamic viscosity, f is the external force (such as gravity); The resistance and disturbance of the pipeline to the water flow, the pipeline resistance coefficient (such as Darcy resistance coefficient) is introduced to describe the resistance of the pipeline to the water flow, and the disturbance term is introduced into the Navier-Stokes equation to consider the influence of the pipeline on the water flow velocity and pressure field. The equation is as follows: ; In the formula, It is the disturbance force of the pipe on the water flow, which can be determined by experiment or theoretical analysis; Sediment transport equation: ; In the formula, is the sediment concentration, D is the diffusion coefficient, and S is the source term (such as erosion and deposition); According to the critical shear stress Determine whether the sediment is started: ; In the formula, is the shear velocity, is the critical shear stress. When the shear stress of the water flow is less than the critical shear stress, sediment will be deposited. According to the water flow velocity and sediment concentration, the scouring rate equation is established, which is as follows: ; In the formula, is the scour depth, k is the scour coefficient, and n is the empirical exponent. By integrating the scour rate equation, the change of scour depth over time is calculated; Pipe scour equation: ; In the formula, is the recovery time constant, describing the redeposition process of sediments; The equation of the multi-factor coupling model is: ; 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. Based on the iterative solution of the multi-factor coupling model, the water velocity field, sediment concentration field and pipeline scouring depth that change with time are obtained. The water velocity field and sediment concentration field obtained by the solution are used to simulate the scouring and silting change process of the target area. By comparing the simulation results of different time steps, the spatial distribution and time evolution law of scouring and silting changes are analyzed, and the key time periods and areas of scouring and silting changes, that is, the periods and places where scouring or deposition are significantly enhanced, are identified. According to the simulation results, the scouring depth distribution around the pipeline is identified, and the maximum, minimum and average values ​​of the scouring depth are analyzed 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 cause pipeline failure or damage. According to the scouring depth and the buried depth of the pipeline, the suspended length of the pipeline is calculated, and the maximum and minimum values ​​of the suspended length are identified. and distribution characteristics, evaluate their impact on pipeline stability, including whether the suspended length is too large, and whether the suspended part is easily shaken or damaged by water flow or external forces; calculate the sediment transport rate based on the solution of the sediment transport equation, identify the maximum, minimum and distribution characteristics of the sediment transport rate, analyze the spatial distribution and time evolution of the sediment transport rate, and evaluate its impact on the terrain changes around the pipeline, including whether sediments will accumulate near the pipeline to form new terrain features, and whether these terrain features will have an adverse effect on the safety and stability of the pipeline; comprehensively analyze the simulation results to determine the scouring and silting change characteristics, including the scouring depth change rate, the suspension length change rate, the sediment transport rate change rate, the sediment concentration gradient and the water velocity gradient, and then analyze the interaction between the scouring and silting change characteristics, among which the increase in scouring depth will lead to an increase in the suspension length, which will affect the stability of the pipeline, and the change in sediment transport rate will affect the distribution of scouring depth; Step 4: Based on the simulation results and the identified scouring and silting change characteristics, an abnormal scouring and silting change model is constructed to identify abnormal conditions in scouring and silting changes; Step 5: Deploy monitoring stations in the target area, obtain real-time monitoring data of the target area, and deploy an abnormal scouring and silting change model to detect abnormal conditions in the scouring and silting changes in the target area; Step 6: Classify and evaluate the detected abnormal scouring and silting changes, determine their potential impact on the safety of submarine pipelines, and match corresponding early warning measures according to the characteristics of scouring and silting changes.

[0020] Embodiment 2, as Figure 2 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 4, the process of constructing the abnormal scouring and silting change model includes: The simulation results of the multi-factor coupling model are collected, including the water velocity field, sediment concentration field, pipeline scouring depth, suspended length and sediment transport rate, and the scouring and sedimentation change characteristics including the scouring depth change rate, suspended length change rate, sediment transport rate change rate, sediment concentration gradient and water velocity gradient are extracted from the simulation results for anomaly detection. Among them, the water velocity field represents the distribution of water velocity at each time step, the sediment concentration field represents the distribution of sediment concentration at each time step, the pipeline scouring depth represents the pipeline scouring depth at each time step, the suspended length represents the pipeline suspended length at each time step, the sediment transport rate represents the sediment transport rate at each time step, the scouring depth change rate represents the change rate of the scouring depth over time, the suspended length change rate represents the change rate of the suspended 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 velocity gradient represents the spatial gradient of the water velocity. Statistical analysis is performed on historical scouring and silting data to identify the scope and laws of normal scouring and silting changes. Based on the statistical analysis results, a normal scouring and silting change pattern is established to determine the normal scope and change trend of scouring and silting change characteristics, and the relevant data of scouring and silting change characteristics obtained are integrated to mark the abnormal scouring and silting characteristics to obtain a feature data set. The integrated data set is then divided into a training set and a test set. The K-means clustering algorithm is trained using the training set to construct an abnormal scouring and silting change model. According to the normal pattern, the number of cluster centers is determined, the distance from each data point to the cluster center is calculated, and the data points are assigned to the nearest cluster center to form clusters. The trained model is verified using the test set, and the accuracy and stability of the model's anomaly detection are evaluated through indicators such as confusion matrix and ROC curve. The constructed abnormal scouring and silting change model is used 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 indicators to identify the abnormal scouring and silting change characteristics and severity. The process of anomaly detection of scouring and silting change data is as follows: The scouring and silting change characteristic data including the scouring depth change rate, the hanging length change rate, the sediment transport rate change rate, the sediment concentration gradient and the water flow velocity gradient are input 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. The weight of each feature is determined according to the importance of each feature, and the benchmark value and standard deviation of each feature are determined according to historical data. For each data point, the abnormal scouring and silting change index is calculated by combining the current value, weight, benchmark value and standard deviation of each scouring and silting change feature. According to the output of the model, the abnormal scouring and silting change features are identified, and the severity of the anomaly is evaluated according to the value of the abnormal scouring and silting change index. When the abnormal scouring and silting change index is close to 0, it means that the data point is close to the normal range and the degree of anomaly is low. When the abnormal scouring and silting change index increases, it means that the data point is far away from the normal range and the degree of anomaly increases. The expression of abnormal scouring and silting change index is: ; Where 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, and 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 ith scouring and silting change characteristic, is the baseline value of the ith scouring and silting change characteristic (the mean value of the normal range), is the standard deviation of the ith scouring and silting change characteristic, indicating the degree of dispersion of the characteristic value. The value range of A is ,when near hour, Close to 0, Close to 1, A close to 0, indicating that the data point is close to the normal range. keep away hour, Increase, Increases,A increases, indicating that the data point is far away from the normal range and the degree of abnormality increases; In step 5, the process of detecting abnormal conditions in the scouring and silting changes in the target area includes: According to the topography of the target area, the distribution of submarine pipelines and the characteristics of scouring and silting changes, the monitoring area is divided, and monitoring stations are set up near the pipelines and in areas with drastic scouring and silting changes to ensure coverage of the entire target area. Monitoring equipment is selected to conduct water depth and topography monitoring, ocean current and wave monitoring, sediment concentration monitoring and pipeline status monitoring in the target area. For water depth and topography monitoring, single-beam or multi-beam echo sounders are used to monitor water depth changes in real time. For ocean current and wave monitoring, ADCP (Acoustic Doppler Current Profiler) and wave buoys are used to monitor ocean current speed, direction, wave height and wave period in real time. For sediment concentration monitoring, optical sensors or acoustic sensors are used to monitor sediment concentration in real time. For pipeline status monitoring, side-scan sonar and pipeline inspection robots are used to monitor the suspended length and scouring depth of the pipeline in real time, and a data transmission network is established to transmit the monitoring data to the data center in real time. The sampling frequency of the monitoring equipment is set to collect data once every 10 minutes. The trained abnormal scouring and silting change model is deployed to the data center, and the model operation parameters are configured to ensure that the model can process the monitoring data in real time. A data interface is established between the monitoring data and the model to ensure that the data can be input into the model in real time. The abnormal scouring and silting change model receives the real-time monitoring data and performs anomaly detection on it. If an abnormal situation is detected, the model outputs abnormal scouring and silting change indicators and abnormal scouring and silting change characteristics; In step six, the process of matching corresponding early warning measures includes: 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. Analyze and evaluate each abnormal scouring and silting change characteristic to determine its degree of abnormality. Divide the degree of abnormality 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. 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 scouring and silting changes, 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 scouring and silting changes 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 to conduct emergency disposal, and take emergency engineering measures, such as repairing damaged pipelines, changing the pipeline route, etc., to ensure pipeline safety.

[0021] The above 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. A method for evaluating scour and siltation changes based on the seafloor scour mechanism of the Yellow River underwater delta, characterized in that: The following steps are involved: Step 1: Collect multi-source environmental data of the target area and pre-process the environmental data to obtain a multi-source environmental data set; 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 scouring of the submarine pipeline by the sediment, a multi-factor coupling model of wave, tidal and sediment transport is constructed; 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; Step 4: Based on the simulation results and the identified scouring and silting change characteristics, an abnormal scouring and silting change model is constructed to identify abnormal conditions in scouring and silting changes; Step 5: Deploy monitoring stations in the target area, obtain real-time monitoring data of the target area, and deploy an abnormal scouring and silting change model to detect abnormal conditions in the scouring and silting changes in the target area; Step 6: Classify and evaluate the detected abnormal scouring and silting changes, determine their potential impact on the safety of submarine pipelines, and match corresponding early warning measures according to the characteristics of scouring and silting changes.

2. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 1 is characterized by: In step 1, the process of acquiring the multi-source environment data set is as follows: Collect environmental data of the target area of ​​the Yellow River underwater delta, including water depth and topography data, ocean current and wave observation data, seabed sediment characteristics data, seabed pipeline status data, and meteorological data; Preprocess the collected multi-source environmental data of the target area, including data cleaning, data alignment and data interpolation; The cleaned, aligned and interpolated multi-source environmental data are integrated into a unified dataset, and the dataset is stored in a MySQL database. Multiple tables are created to store water depth and topography data, ocean current and wave data, sediment characteristics data, pipeline status data and meteorological data.

3. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 2 is characterized by: In the step 2, the construction process of the multi-factor coupling model includes: Clarify 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 the wave incidence boundary, tidal 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 water flow and sediment scouring caused by submarine pipelines; Based on the Navier-Stokes equation, the resistance and disturbance of the pipeline on the water flow are analyzed, the pipeline resistance coefficient and disturbance term are introduced, the influence of the pipeline on the water flow is simulated, and the equation describing the water flow around the submarine pipeline is established; The effects of water velocity, sediment particle size and density on sediment transport are comprehensively considered to establish a sediment transport equation that describes the movement and distribution of sediment under the action of water flow. In addition, the sediment transport equation is combined with pipeline force analysis, and the scouring rate and scouring depth factors are comprehensively considered to establish an equation that describes the scouring effect of sediment on submarine pipelines. The fluid dynamics equation, sediment transport equation and pipeline scour equation are coupled to form a multi-factor coupling model; The finite volume method is used to solve the multi-factor coupling model. By solving the multi-factor coupling model, the results of water velocity field, sediment concentration field and pipeline scouring depth are obtained. Based on the simulation results, the safety and stability of the pipeline are analyzed, and the impact of scouring on the pipeline is evaluated.

4. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 3 is characterized by: The process of solving the multi-factor coupling model is: The computational domain is divided into a finite number of control volumes, each of which represents a computational node. Physical quantities such as fluid velocity, pressure, and sediment concentration are distributed on each control volume, and boundary conditions of waves, tidal currents, and sediments are imposed on the boundaries of the computational domain. The Navier-Stokes equation, sediment transport equation and pipeline scour equation are integrated on the control volume to obtain the discretized equation, which contains the flux term and source term on the control volume interface; Select the time step, calculate the flux term on each control volume interface according to the current velocity field and sediment concentration field, use the discretized equation to update the unknown variable value on the control volume node according to the flux term and source term, and check whether the update amount of the unknown variable is less than the set convergence standard. If so, the iteration ends, otherwise, return to the flux calculation step to continue the iteration, and then solve the water flow velocity field, sediment concentration field and pipeline scour depth; Analyze the simulated water 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, based on the analysis results of the water velocity field, sediment concentration field and pipeline scouring depth, conduct a comprehensive assessment of the safety and stability of the pipeline.

5. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 4 is characterized by: In step 3, the identification process of scouring and silting change characteristics includes: Based on the iterative solution of the multi-factor coupling model, the water velocity field, sediment concentration field and pipeline scouring depth that change with time are obtained. The water velocity field and sediment concentration field obtained by the solution are used to simulate the scouring and silting change process of the target area. By comparing the simulation results of different time steps, the spatial distribution and time evolution law of scouring and silting changes are analyzed, and the key time periods and areas of scouring and silting changes are identified; Based on the simulation results, the scour depth distribution around the pipeline is identified, and the maximum, minimum and average scour depths are analyzed to quantify the intensity of the scour effect and evaluate the impact of the scour effect on pipeline safety; According to the scouring depth and pipeline burial depth, the suspended length of the pipeline is calculated, the maximum, minimum and distribution characteristics of the suspended length are identified, and its impact on pipeline stability is evaluated; According to the solution of sediment transport equation, the sediment transport rate is calculated, the maximum, minimum and distribution characteristics of sediment transport rate are identified, the spatial distribution and temporal evolution of sediment transport rate are analyzed, and its impact on the terrain changes around the pipeline is evaluated; The simulation results were comprehensively analyzed to determine the characteristics of scour and deposition changes, including the change rate of scour depth, change rate of suspended length, change rate of sediment transport rate, sediment concentration gradient and water velocity gradient, and then the interaction between the scour and deposition change characteristics was analyzed.

6. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 5 is characterized by: In step 4, the construction process of the abnormal scouring and silting change model includes: Collect the simulation results of the multi-factor coupling model, including water velocity field, sediment concentration field, pipeline scouring depth, suspended length and sediment transport rate, and extract the scouring and deposition change characteristics including scouring depth change rate, suspended length change rate, sediment transport rate change rate, sediment concentration gradient and water velocity gradient from the simulation results; Statistical analysis is performed on the historical scouring and silting data of the target area to identify the scope and regularity of normal scouring and silting changes. Based on the statistical analysis results, a normal scouring and silting change model is established to determine the normal scope and change trend of scouring and silting change characteristics. The relevant data of scouring and silting change characteristics obtained are integrated, and abnormal scouring and silting characteristics are marked to obtain a feature data set. The integrated data set is then divided into a training set and a test set. The K-means clustering algorithm is trained using the training set to build an abnormal scouring and silting change model. According to the normal mode, the number of cluster centers is determined, the distance from each data point to the cluster center is calculated, and the data points are assigned to the nearest cluster center to form clusters. The trained model is verified using the test set to evaluate the model's anomaly detection accuracy and stability. The constructed abnormal scouring and silting change model is used to detect anomalies in the scouring and silting change data, output whether the scouring and silting change data is abnormal, calculate the abnormal scouring and silting change indicators, and identify the abnormal scouring and silting change characteristics and severity.

7. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 6 is characterized by: The process of detecting abnormality of scouring and silting change data is as follows: The scouring and deposition change characteristic data including the scouring depth change rate, the hanging length change rate, the sediment transport rate change rate, the sediment concentration gradient and the water flow velocity gradient are input into the trained abnormal scouring and deposition change model. The model outputs whether each data point is abnormal, that is, the cluster label of each data point. Determine the weight of each feature based on its importance, and determine the benchmark value and standard deviation of each feature based on historical data; For each data point, the abnormal scouring and silting change index is calculated by combining the current value, weight, baseline value and standard deviation of each scouring and silting change feature; According to the output of the model, the abnormal scouring and silting change characteristics are identified, and the severity of the anomaly is evaluated according to the value of the abnormal scouring and silting change index. When the abnormal scouring and silting change index is close to 0, it means that the data point is close to the normal range and the degree of anomaly is low. When the abnormal scouring and silting change index increases, it means that the data point is far away from the normal range and the degree of anomaly increases.

8. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 7 is characterized by: In step 5, the process of detecting abnormal conditions in the scouring and silting changes in the target area includes: The monitoring area is divided according to the topography, distribution of submarine pipelines and scouring and silting change characteristics of the target area, and monitoring stations are deployed near pipelines and in areas with drastic scouring and silting changes; Select monitoring equipment to monitor water depth and topography, ocean current and wave, sediment concentration and pipeline status in the target area, and establish a data transmission network to transmit monitoring data to the data center in real time; Set the sampling frequency of the monitoring equipment to once every 10 minutes, deploy the trained abnormal erosion and siltation 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 scouring and silting change model receives real-time monitoring data and performs anomaly detection on it. If an abnormal situation is detected, the model outputs abnormal scouring and silting change indicators and abnormal scouring and silting change characteristics.

9. The method for evaluating scour and siltation changes based on the seabed scour mechanism of the Yellow River underwater delta according to claim 8 is characterized by: In step 6, the process of matching corresponding early warning measures includes: The abnormal scouring and silting change characteristics detected are classified, including abnormal scouring depth, abnormal hanging length, abnormal sediment transport rate, abnormal sediment concentration and abnormal water flow velocity, and each abnormal scouring and silting change characteristic is analyzed and evaluated to determine its abnormal degree; The abnormal degree of abnormal scouring and silting change characteristics is divided into different abnormal levels, namely mild abnormality, moderate abnormality and severe abnormality. Mild abnormality means that the change of scouring and silting change characteristics is within 10% of the normal range, moderate abnormality means that the change of scouring and silting change characteristics is between 10% and 30% of the normal range, and severe abnormality means that the change of scouring and silting change characteristics exceeds 30% of the normal range. According to the analysis results of the abnormality level, the early warning measures corresponding to the abnormality level are matched.

Images