A method for analyzing seabed sand wave migration for risk management of seabed facilities

Abstract

The present invention discloses a method for analyzing seabed sand wave migration applied to seabed facility risk management, which relates to the technical field of sand wave migration analysis and includes the following steps: collecting seabed sand wave migration data, dividing the seabed sand wave migration data into real-time data and historical data, preprocessing the collected seabed sand wave migration data, identifying the topological structure in the seabed sand wave, determining the sand wave morphology, extracting sand wave migration influencing factors, wherein the sand wave migration influencing factors are ocean dynamic influencing factors and sediment influencing factors, and constructing a sand wave migration prediction model based on the extracted sand wave migration influencing factors. The present invention can accurately predict the long-term trend and short-term changes of sand wave migration by collecting ocean dynamic influencing factors and sediment influencing factors in real time, which not only improves the accuracy of risk prediction, but also timely captures the dynamic changes of sand wave migration, and at the same time provides early warning and formulates emergency response measures.

Description

Technical Field

[0001] The invention relates to the technical field of sand wave migration analysis, and in particular to a seabed sand wave migration analysis method applied to seabed facility risk management. Background Art

[0002] Submarine sand waves are a common nearshore landform that is commonly developed on the seafloor of the world, especially in the shelf area. Coasts, straits, breakwaters, shelves, bays, tidal channels, estuaries, bays and straits, beam areas near the shore of the shelf, and all shelf sea areas with directional flow rates are suitable environments for the formation of sand waves. The migration of submarine sand waves is an important phenomenon in the marine geological process, and its stability has a vital impact on the safety of submarine facilities such as submarine pipelines and submarine cables. In the outer shelf area of ​​the northern South China Sea, submarine sand waves are widely distributed, and because the area is affected by typhoons, internal waves and other dynamic factors, the migration characteristics of sand waves and their dynamic mechanisms are very complex. In addition, with the continuous development and utilization of marine resources, the construction scale of submarine facilities such as submarine pipelines is expanding, and the requirements for the stability of submarine topography and landforms are becoming higher and higher. The migration of submarine sand waves may cause the suspension, burial or damage of submarine facilities, bringing huge risks to marine engineering. Therefore, in-depth research on the migration of submarine sand waves in this area is of great significance for ensuring the safe operation of submarine facilities.

[0003] In the existing technology, the submarine sand wave migration analysis method is affected by uncertain factors such as submarine topography, wave conditions, and sediment characteristics, which will lead to deviations in its evaluation results. It is difficult to comprehensively and accurately consider the combined effects of various dynamic factors, thereby affecting the accuracy and completeness of the evaluation results. Therefore, improving the accuracy of the evaluation results and reducing the impact of uncertain factors on the submarine sand wave migration analysis are the problems we need to solve now. A submarine sand wave migration analysis method applied to submarine facility risk management is proposed. Summary of the invention

[0004] The present invention aims to provide a seabed sand wave migration analysis method applied to seabed facility risk management 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:

[0006] A method for analyzing seabed sand wave migration applied to seabed facility risk management comprises the following steps:

[0007] Step 1: Collect seabed sand wave migration data and divide it into real-time data and historical data. The real-time data is used for immediate monitoring and analysis, and the historical data is used for long-term trend analysis and model building.

[0008] Step 2, preprocessing the collected seabed sand wave migration data, using geographic information system and image processing technology to identify the topological structure of the seabed sand waves, determine the sand wave morphology, extract the sand wave migration influencing factors, where the sand wave migration influencing factors are ocean dynamics influencing factors and sediment influencing factors, and analyze the seabed sand wave migration trend;

[0009] Step 3: construct a sand wave migration prediction model based on the extracted sand wave migration influencing factors, obtain the ocean dynamics impact index and sediment impact index, analyze and evaluate the impact of the sand wave migration influencing factors on sand wave migration and growth, and calculate the migration distance and direction of the seabed sand waves;

[0010] Step 4: Calculate the sand wave migration assessment coefficient based on the ocean dynamic impact index and the sediment impact index, analyze the dynamic mechanism of seabed sand wave migration, including the influence of near-bottom residual current, waves, tidal currents and other factors on sand wave migration, predict sand wave migration, and assess the risk of seabed facilities based on the migration characteristics of seabed sand waves and the dynamic mechanism of seabed sand wave migration, determine the risk level and potential impact range of seabed facilities, and output an assessment report, including risk level, possible damaged parts, and expected degree of damage;

[0011] Step 5: Based on the assessment report on seabed sand wave migration, determine the risk level of the current seabed facilities, generate corresponding early warning information, and formulate corresponding risk management strategies, including design optimization of seabed facilities, formulation of monitoring plans, and planning of emergency measures.

[0012] A further improvement of the technical solution of the present invention is that the process of acquiring the seabed sand wave migration data in step 1 is:

[0013] Step 101, using ocean detection technology, a meteorological data platform, and an in-situ real-time observation device to obtain real-time data of seabed sand wave migration data, wherein the real-time data includes seabed topography data, ocean dynamics data, sediment movement data, and sand wave in-situ data;

[0014] Step 102, obtaining historical data from previous seafloor exploration missions, weather data platform archive data, and historical observation data from scientific research projects, the historical data including historical changes in seafloor topography, sediment distribution maps, sand wave morphology and position changes, and historical records of ocean dynamic conditions, such as waves, tides, wind fields, etc.;

[0015] Step 103, the acquired real-time data and historical data are transmitted to the data monitoring center via wireless communication, and the data are classified and stored according to time sequence and measurement area. At the same time, a data backup and recovery mechanism is established to ensure rapid recovery when the data is lost or damaged.

[0016] A further improvement of the technical solution of the present invention is that in step 2, the process of determining the sand wave morphology and extracting the sand wave migration influencing factor is as follows:

[0017] Step 201, preprocessing the collected real-time data and historical data to remove noise and outliers, and gridding the data to improve the spatial resolution and accuracy of the data;

[0018] Step 202, performing digital terrain analysis on the seabed sand wave migration data, such as slope analysis, curvature analysis, etc., to identify the topological structural characteristics of the seabed sand waves, and determine the crest and trough positions and ridgeline direction of the sand waves by calculating the slope change of the terrain surface; using curvature analysis to detect the bending degree and morphological changes of the sand waves, so as to accurately outline the topological structural contour of the seabed sand waves and determine the sand wave morphology;

[0019] Step 203, based on the pre-processed real-time data and historical data, feature extraction is performed on the current velocity, direction, wave height, period, and tide to obtain the ocean dynamics influencing factors, analyze the relationship between the ocean dynamics influencing factors and the sand wave migration rate, direction, etc., determine the impact of ocean dynamics on sand wave migration, and at the same time, feature extraction is performed on the particle size distribution, bulk density, particle size, and sorting coefficient of the sediment to obtain the sediment influencing factors; study the relationship between the sediment supply, particle size, and sand wave formation and migration.

[0020] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the ocean dynamic impact index and the sediment impact index is as follows:

[0021] Step 301, based on the extraction of sand wave migration influencing factors, analyzing the correlation data of ocean dynamic influencing factors and sediment influencing factors, and constructing a sand wave migration prediction model;

[0022] Step 302, collating historical data, ocean dynamics influencing factors and sediment influencing factors to form a unified data set;

[0023] Step 303, dividing the data set into a training set, a validation set, and a test set, using the training data set to train the sand wave migration prediction model, adjusting the model parameters to minimize the prediction error, using the validation set to verify the sand wave migration prediction model, and combining the test set to evaluate the prediction performance of the model, and optimizing the model according to the results, such as adjusting feature selection, increasing the amount of data, etc.;

[0024] Step 304, using the trained sand wave migration prediction model, analyzing the model parameters or feature importance to obtain the ocean dynamics impact index and the sediment impact index, respectively evaluating the impact of the ocean dynamics impact index and the sediment impact index on the migration and growth of sand waves, and the correlation between the ocean dynamics impact factor and the sediment impact factor, and visual analysis can be performed by drawing scatter plots, trend lines, etc.;

[0025] Step 305 , based on the sand wave migration prediction model and combined with historical data, the migration distance and migration direction of the sand wave are obtained, a migration trajectory map can be drawn for visual display, and the sand wave migration trend is analyzed.

[0026] A further improvement of the technical solution of the present invention is that the calculation formula of the ocean dynamic impact index is:

[0027] ;

[0028] Among them, MDI is the ocean dynamics impact index, which ranges from 0 to 1, and n is the number of ocean dynamics impact factors. is the current observed value of the i-th ocean dynamic influencing factor, such as flow velocity, flow direction intensity, etc., is the baseline value of the ith ocean dynamics factor, used for comparison with the current observation value. is the adjustment coefficient of the i-th ocean dynamics influencing factor, which is used to adjust the contribution of this factor to MDI;

[0029] The calculation formula of the sediment impact index is:

[0030] ;

[0031] Among them, SDI is the sediment impact index, which ranges from 0 to 1, and m is the number of sediment impact factors. is the current observed value of the jth sediment influencing factor, such as sediment particle size, water content, etc., is the baseline value of the jth sediment impact factor, which is used to compare with the current observation value. is the adjustment coefficient of the jth sediment influencing factor, which is used to adjust the contribution of this factor to SDI.

[0032] A further improvement of the technical solution of the present invention is that in step 4, the process of obtaining the sand wave migration assessment coefficient and the risk assessment of the submarine facilities is as follows:

[0033] Step 401, combining the ocean dynamics impact index and the sediment impact index with the historical sand wave migration data, and analyzing the correlation between the sand wave migration situation and the ocean dynamics impact index and the sediment impact index;

[0034] Step 402, assigning different weights to the ocean dynamics impact index and the sediment impact index, and performing weighted summation of the ocean dynamics impact index and the sediment impact index to obtain a sand wave migration assessment coefficient;

[0035] Step 403, based on the real-time data and the sand wave migration prediction model, analyzing the dynamic mechanism of seabed sand wave movement, including sand wave formation, sand wave evolution and sand wave migration law, and obtaining the dynamic mechanism analysis result;

[0036] Step 404, predicting the migration trend of sand waves, including the migration direction, speed and possible impact area, based on the sand wave migration assessment coefficient and the dynamic mechanism analysis results, assessing the risk of sand wave migration to submarine facilities, such as scouring, burial, deformation, etc., and generating an assessment report, while dividing the risk of submarine facilities into different risk levels, namely high risk level, medium risk level and low risk level;

[0037] Step 405, based on the assessment report, the determined different risk levels are matched with the sand wave migration assessment coefficients, corresponding risk assessment thresholds are set for different risk levels, and the scope and extent of the impact on the submarine facilities are assessed, including the number, type, location, and potential economic losses and environmental impacts of the affected facilities.

[0038] A further improvement of the technical solution of the present invention is that the calculation formula of the sand wave migration assessment coefficient is:

[0039] ;

[0040] Among them, SMC is the sand wave migration assessment coefficient, MDI is the ocean dynamic impact index, is the benchmark value of the ocean dynamic impact index, which is used for comparison with the current MDI. SDI is the sediment impact index. is the baseline value of the sediment impact index, which is used for comparison with the current SDI. It is an adjustment coefficient used to adjust the contribution of MDI and SDI to SMC.

[0041] A further improvement of the technical solution of the present invention is that: the multiple risk levels correspond to multiple risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold;

[0042] The multiple risk levels and the multiple risk assessment thresholds satisfy the following relationship:

[0043] Low risk level ;

[0044] Medium risk level ;

[0045] High risk level ;

[0046] Among them, SMC is the sand wave migration assessment coefficient, S1 is the upper threshold corresponding to the low risk level and the lower threshold corresponding to the medium risk level, and S2 is the upper threshold corresponding to the medium risk level and the lower threshold of the high risk level.

[0047] A further improvement of the technical solution of the present invention is that in step 5, the process of determining the risk level of the current submarine facility and generating corresponding warning information is as follows:

[0048] Step 501, based on the ocean dynamics impact factor and sediment impact factor in the real-time data, and inputting them into the sand wave migration prediction model, respectively obtain the ocean dynamics impact index and sediment impact index, and predict and analyze the long-term trend and short-term changes of sand wave migration. The long-term trend analysis involves seasonal changes, multi-year average migration rate, etc., and the short-term change analysis involves factors that affect sand wave migration in the short term, such as storms and tidal changes;

[0049] Step 502, combining the ocean dynamic impact index and the sediment impact index to obtain a sand wave migration assessment coefficient and an assessment report, and determining a corresponding risk level according to a corresponding risk assessment threshold to assess the degree of physical damage caused by sand wave migration to submarine facilities;

[0050] Step 503 generates corresponding warning information according to the current corresponding risk level, including the current status of sand wave migration, potential risks, recommended response measures and emergency contact information, and synchronizes it to the marine management department, such as using SMS, e-mail, automated telephone system and other channels to send warning information to relevant managers and operators, and formulates emergency response measures, including personnel evacuation, equipment protection and rapid repair measures, such as strengthening facility monitoring, adjusting facility operation status or performing temporary reinforcement.

[0051] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0052] The present invention provides a method for analyzing seabed sand wave migration applied to risk management of seabed facilities. By collecting ocean dynamic influencing factors and sediment influencing factors in real time, the long-term trend and short-term changes of sand wave migration can be accurately predicted, which not only improves the accuracy of risk prediction and timely captures the dynamic changes of sand wave migration, but also effectively reduces the potential damage to seabed facilities caused by sand wave migration through early warning and formulation of emergency response measures, thereby ensuring the safe operation of the facilities.

[0053] The present invention provides a submarine sand wave migration analysis method applied to submarine facility risk management. Through real-time monitoring and early warning, the risks that may be caused by sand wave migration can be discovered and responded to in a timely manner, the development of risks can be effectively controlled, and the occurrence of damage can be reduced. At the same time, a risk assessment report based on the submarine sand wave migration analysis method can provide a scientific basis for the formulation of risk management strategies for submarine facilities. By analyzing the current status, predicted trends and potential risks of sand wave migration, a more reasonable and effective risk management strategy can be formulated.

[0054] The present invention provides a submarine sand wave migration analysis method applied to submarine facility risk management. By comprehensively analyzing ocean dynamic influencing factors and sediment influencing factors and building a sand wave migration prediction model based on the analysis, the long-term trend and short-term changes of sand wave migration and the evolution of its morphology can be accurately predicted, and potential risks can be fully understood, so as to formulate maintenance and prevention strategies in a targeted manner. For submarine oil and gas pipelines, accurate prediction of the direction and speed of sand wave migration is helpful to determine whether the pipeline will be threatened by scouring, burial or suspension by sand waves, and then plan pipeline rerouting, reinforcement or protection measures in advance, avoid serious accidents such as pipeline rupture and oil and gas leakage caused by sand wave migration, and ensure the safe exploitation and transportation of marine oil and gas resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] 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.

[0056] Figure 1 is a flow chart of the method of the present invention;

[0057] Figure 2 The present invention is a flow chart for obtaining the ocean dynamics impact index and the sediment impact index;

[0058] Figure 3 The present invention is a flow chart for obtaining the sand wave migration assessment coefficient and assessing the risk of submarine facilities. DETAILED DESCRIPTION

[0059] 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.

[0060] Embodiment 1, as Figures 1 to 3As shown, the present invention provides a method for analyzing seabed sand wave migration applied to seabed facility risk management, comprising the following steps:

[0061] Step 1, collect seabed sand wave migration data, and divide the seabed sand wave migration data into real-time data and historical data. Real-time data is used for immediate monitoring and analysis, and historical data is used for long-term trend analysis and model building. The process of acquiring seabed sand wave migration data is as follows: use ocean detection technology, meteorological data platform and in-situ real-time observation device to obtain real-time data of seabed sand wave migration data, where real-time data includes seabed topography data, ocean dynamics data, sediment movement data and sand wave in-situ data. The specific ocean detection technology is as follows: through the multi-beam bathymetric system, high-precision data of seabed topography can be obtained, including the shape and position of sand waves, which is used to analyze the migration of sand waves. Side-scan sonar is used to scan the seabed surface to provide a two-dimensional image of the seabed topography, which is used to identify the shape and migration path of sand waves. Underwater photography or video equipment is used to observe the shape and dynamic changes of seabed sand waves to provide intuitive sand wave images. The meteorological data platform obtains ocean dynamics data related to seabed sand wave migration, such as water flow rate. The in-situ real-time observation device is based on fiber Bragg grating floating soil weight sensor, sediment movement monitor, imaging sonar and other technologies to observe the migration of seabed sand waves for a long time, continuously and in-situ, and is used to record key parameters such as elevation change, migration speed and direction of sand waves, and measure the movement state of sediments, such as vibration and displacement. It also obtains historical data from previous seabed exploration missions, archived data of meteorological data platforms and historical observation data in scientific research projects. The historical data includes historical changes in seabed topography, sediment distribution maps, changes in sand wave morphology and position, and historical records of ocean dynamic conditions, such as waves, tides, wind fields, etc. The real-time data and historical data obtained are transmitted to the data monitoring center through wireless communication, and the data are classified and stored according to time sequence and measurement area. At the same time, a data backup and recovery mechanism is established to ensure rapid recovery when the data is lost or damaged.

[0062] Step 2, pre-processing the collected seabed sand wave migration data, using geographic information system and image processing technology to identify the topological structure of the seabed sand waves, determine the sand wave morphology, extract the sand wave migration influencing factors, where the sand wave migration influencing factors are ocean dynamics influencing factors and sediment influencing factors, and analyze the seabed sand wave migration trend; the process of determining the sand wave morphology and extracting the sand wave migration influencing factors is as follows: pre-processing the collected real-time data and historical data, removing noise and outliers, and gridding the data to improve the spatial resolution and accuracy of the data, performing digital terrain analysis on the seabed sand wave migration data, such as slope analysis, curvature analysis, etc., identifying the topological structure characteristics of the seabed sand waves, and calculating the terrain The slope changes of the surface are used to determine the crests, troughs and ridge directions of the sand waves; curvature analysis is used to detect the bending degree and morphological changes of the sand waves, so as to accurately outline the topological structure of the seabed sand waves and determine the sand wave morphology. Based on the pre-processed real-time data and historical data, feature extraction is performed on the current velocity, direction, wave height, period, and tide to obtain the ocean dynamics influencing factors, and the relationship between the ocean dynamics influencing factors and the sand wave migration rate and direction is analyzed to determine the impact of ocean dynamics on the sand wave migration. At the same time, feature extraction is performed on the particle size distribution, bulk density, particle size and sorting coefficient of the sediment to obtain the sediment influencing factors; the relationship between the sediment supply, particle size and sand wave formation and migration is studied;

[0063] Step 3: construct a sand wave migration prediction model based on the extracted sand wave migration influencing factors, obtain the ocean dynamics impact index and sediment impact index, analyze and evaluate the impact of sand wave migration influencing factors on sand wave migration and growth, and calculate the migration distance and direction of seabed sand waves; use three-dimensional terrain reconstruction technology to identify the seabed sand wave topological structure, determine its morphological parameters, such as wave height, wavelength, ridge direction, slope, etc., extract ocean dynamics influencing factors, such as tidal flow velocity, flow direction, wave parameters, residual flow characteristics, etc., and sediment influencing factors, particle size distribution, particle shape, density, viscosity, etc., analyze the seabed sand wave migration trend through statistical analysis and trend fitting, and provide a basis for subsequent modeling;

[0064] The process of obtaining the ocean dynamic impact index and the sediment impact index is as follows: based on the extraction of sand wave migration influencing factors, the correlation data of ocean dynamic impact factors and sediment impact factors are analyzed, and a sand wave migration prediction model is constructed at the same time. The historical data, ocean dynamic impact factors and sediment impact factors are sorted out to form a unified data set, and the data set is divided into a training set, a validation set and a test set. The training data set is used to train the sand wave migration prediction model, and the model parameters are adjusted to minimize the prediction error. The sand wave migration prediction model is verified using the validation set, and the prediction performance of the model is evaluated in combination with the test set, and the model is optimized according to the results. , such as adjusting feature selection, increasing the amount of data, etc., using the trained sand wave migration prediction model, analyzing the model parameters or feature importance to obtain the ocean dynamics impact index and the sediment impact index, respectively evaluating the impact of the ocean dynamics impact index and the sediment impact index on the migration and growth of sand waves, as well as the correlation between the ocean dynamics impact factor and the sediment impact factor. Visual analysis can be performed by drawing scatter plots, trend lines, etc. Based on the sand wave migration prediction model and combined with historical data, the migration distance and migration direction of the sand wave can be obtained, the migration trajectory map can be drawn for visual display, and the sand wave migration trend can be analyzed;

[0065] Step 4: Calculate the sand wave migration assessment coefficient based on the ocean dynamic impact index and the sediment impact index, analyze the dynamic mechanism of seabed sand wave migration, including the influence of near-bottom residual current, waves, tidal currents and other factors on sand wave migration, predict sand wave migration, and assess the risk of seabed facilities based on the migration characteristics of seabed sand waves and the dynamic mechanism of seabed sand wave migration, determine the risk level and potential impact range of seabed facilities, and output an assessment report, including risk level, possible damaged parts, and expected degree of damage;

[0066] The process of obtaining the sand wave migration assessment coefficient and assessing the risk of seabed facilities is as follows: combining the ocean dynamic impact index and the sediment impact index with the historical sand wave migration data, analyzing the correlation between the sand wave migration situation and the ocean dynamic impact index and the sediment impact index, assigning different weights to the ocean dynamic impact index and the sediment impact index, and performing weighted summation of the ocean dynamic impact index and the sediment impact index to obtain the sand wave migration assessment coefficient. Based on real-time data and the sand wave migration prediction model, the dynamic mechanism of seabed sand wave movement is analyzed, including sand wave formation, sand wave evolution and sand wave migration laws, and the dynamic mechanism analysis results are obtained. According to the sand wave migration assessment coefficient Based on the results of the dynamic mechanism analysis, the migration trend of sand waves is predicted, including the migration direction, speed and possible impact area. The risk of sand wave migration to submarine facilities, such as scouring, burial, deformation, etc., is evaluated, and an evaluation report is generated. At the same time, the risk of submarine facilities is divided into different risk levels, namely high risk level, medium risk level and low risk level. Based on the evaluation report, the determined different risk levels are matched with the sand wave migration assessment coefficients, and corresponding risk assessment thresholds are set for different risk levels. At the same time, the scope and degree of the impact on submarine facilities are evaluated, including the number, type, location and potential economic losses and environmental impacts of the affected facilities;

[0067] Step 5, according to the assessment report of submarine sand wave migration, determine the risk level of the current submarine facilities, generate corresponding early warning information, and formulate corresponding risk management strategies, including design optimization of submarine facilities, formulation of monitoring plans and planning of emergency measures; the process of determining the risk level of the current submarine facilities and generating corresponding early warning information is as follows: based on the ocean dynamics influencing factors and sediment influencing factors in real-time data, and inputting them into the sand wave migration prediction model, respectively obtain the ocean dynamics impact index and sediment impact index, and predict and analyze the long-term trend and short-term changes of sand wave migration. The long-term trend analysis involves seasonal changes, multi-year average migration rate, etc., and the short-term change analysis involves storms, tidal changes and other factors that affect sand wave migration in the short term. The sand wave migration assessment coefficient and assessment report are obtained by combining the index and the sediment impact index, and the corresponding risk level is determined according to the corresponding risk assessment threshold. The degree of physical damage caused by sand wave migration to submarine facilities, such as suspended pipelines and submarine landslides, is assessed. According to the current corresponding risk level, corresponding early warning information is generated, including the current status of sand wave migration, potential risks, recommended response measures and emergency contact information, etc., and synchronized to the marine management department, such as using SMS, e-mail, automated telephone system and other channels to send early warning information to relevant managers and operators, and formulate emergency response measures, including personnel evacuation, equipment protection and rapid repair measures, such as strengthening facility monitoring, adjusting the operating status of facilities or performing temporary reinforcement.

[0068] Embodiment 2, as Figures 1 to 3As shown, based on Example 1, the present invention provides a technical solution: Preferably, the calculation formula of the ocean dynamic impact index is:

[0069] ;

[0070] Among them, MDI is the ocean dynamics impact index, which ranges from 0 to 1, and n is the number of ocean dynamics impact factors. is the current observed value of the i-th ocean dynamic influencing factor, such as flow velocity, flow direction intensity, etc., is the baseline value of the ith ocean dynamics factor, used for comparison with the current observation value. is the adjustment coefficient of the i-th ocean dynamics influencing factor, which is used to adjust the contribution of this factor to MDI;

[0071] The calculation formula of sediment impact index is:

[0072] ;

[0073] Among them, SDI is the sediment impact index, which ranges from 0 to 1, and m is the number of sediment impact factors. is the current observed value of the jth sediment influencing factor, such as sediment particle size, water content, etc., is the baseline value of the jth sediment impact factor, which is used to compare with the current observation value. is the adjustment coefficient of the jth sediment influencing factor, which is used to adjust the contribution of this factor to SDI;

[0074] The calculation formula of sand wave migration assessment coefficient is:

[0075] ;

[0076] Among them, SMC is the sand wave migration assessment coefficient, MDI is the ocean dynamic impact index, is the benchmark value of the ocean dynamic impact index, which is used for comparison with the current MDI. SDI is the sediment impact index. is the baseline value of the sediment impact index, which is used for comparison with the current SDI. It is the adjustment coefficient, which is used to adjust the contribution of MDI and SDI to SMC;

[0077] Multiple risk levels correspond to multiple risk assessment thresholds, where the risk assessment thresholds include an upper threshold and a lower threshold;

[0078] Multiple risk levels and multiple risk assessment thresholds satisfy the following relationship:

[0079] Low risk level ;

[0080] Medium risk level ;

[0081] High risk level ;

[0082] Among them, SMC is the sand wave migration assessment coefficient, S1 is the upper threshold corresponding to the low risk level and the lower threshold corresponding to the medium risk level, and S2 is the upper threshold corresponding to the medium risk level and the lower threshold of the high risk level.

[0083] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for analyzing seabed sand wave migration applied to seabed facility risk management, characterized by: The following steps are involved: Step 1, collecting seabed sand wave migration data, and dividing the seabed sand wave migration data into real-time data and historical data; Step 2, preprocessing the collected seabed sand wave migration data, identifying the topological structure of the seabed sand waves, determining the sand wave morphology, and extracting the sand wave migration influencing factors, wherein the sand wave migration influencing factors are the ocean dynamics influencing factors and the sediment influencing factors; Step 3: construct a sand wave migration prediction model based on the extracted sand wave migration influencing factors, obtain the ocean dynamics impact index and sediment impact index, analyze and evaluate the impact of the sand wave migration influencing factors on sand wave migration and growth, and calculate the migration distance and direction of the seabed sand waves; The calculation formula of the ocean dynamic impact index is: Where MDI is the ocean dynamics impact index, n is the number of ocean dynamics impact factors, V i is the current observed value of the i-th ocean dynamics influencing factor, V 0i is the benchmark value of the i-th ocean dynamics influencing factor, α i is the adjustment coefficient of the i-th ocean dynamics influencing factor; The calculation formula of the sediment impact index is: Among them, SDI is the sediment impact index, m is the number of sediment impact factors, S j is the current observed value of the jth sediment influencing factor, S 0j is the baseline value of the jth sediment impact factor, β j is the adjustment coefficient of the jth sediment impact factor; Step 4: Calculate the sand wave migration assessment coefficient based on the ocean dynamic impact index and the sediment impact index, analyze the flow mechanism of the seabed sand wave movement, predict the sand wave migration situation, and assess the risk of the seabed facilities according to the migration characteristics of the seabed sand waves and the flow mechanism of the seabed sand wave movement, determine the risk level and potential impact range of the seabed facilities, and output the assessment report; The calculation formula of the sand wave migration assessment coefficient is: Among them, SMC is the sand wave migration assessment coefficient, MDI is the ocean dynamic impact index, MDI0 is the benchmark value of the ocean dynamic impact index, SDI is the sediment impact index, SDI0 is the benchmark value of the sediment impact index, and γ is the adjustment coefficient; Step 5: According to the assessment report of seabed sand wave migration, determine the risk level of the current seabed facilities, generate corresponding early warning information, and formulate corresponding risk management strategies.

2. The method for analyzing seabed sand wave migration applied to seabed facility risk management according to claim 1, characterized in that: In step 1, the process of acquiring seabed sand wave migration data is as follows: Step 101, using ocean detection technology, a meteorological data platform, and an in-situ real-time observation device to obtain real-time data of seabed sand wave migration data, wherein the real-time data includes seabed topography data, ocean dynamics data, sediment movement data, and sand wave in-situ data; Step 102, obtaining historical data from previous seafloor exploration missions, weather data platform archive data, and historical observation data from scientific research projects, the historical data including historical changes in seafloor topography, sediment distribution maps, sand wave morphology and position changes, and historical records of ocean dynamic conditions; Step 103, the acquired real-time data and historical data are transmitted to the data monitoring center via wireless communication, and the data are classified and stored according to time sequence and measurement area, and a data backup and recovery mechanism is established.

3. The method for analyzing seabed sand wave migration applied to seabed facility risk management according to claim 2, characterized in that: In step 2, the process of determining the sand wave morphology and extracting the sand wave migration influencing factors is as follows: Step 201, cleaning the collected real-time data and historical data, removing noise and outliers, and performing data gridding processing; Step 202, performing digital terrain analysis on the seabed sand wave migration data, identifying the topological structure characteristics of the seabed sand waves, and determining the sand wave morphology; Step 203, based on the pre-processed real-time data and historical data, feature extraction is performed on the current velocity, direction, wave height, period, and tide to obtain the ocean dynamics influencing factors. At the same time, feature extraction is performed on the particle size distribution, bulk density, particle size, and sorting coefficient of the sediment to obtain the sediment influencing factors.

4. The method for analyzing seabed sand wave migration applied to seabed facility risk management according to claim 3, characterized in that: In step 3, the process of obtaining the ocean dynamic impact index and the sediment impact index is as follows: Step 301, based on the extraction of sand wave migration influencing factors, analyzing the correlation data of ocean dynamic influencing factors and sediment influencing factors, and constructing a sand wave migration prediction model; Step 302, collating historical data, ocean dynamics influencing factors and sediment influencing factors to form a unified data set; Step 303, dividing the data set into a training set, a validation set and a test set, using the training data set to train the sand wave migration prediction model, using the validation set to verify the sand wave migration prediction model, and combining the test set to evaluate the prediction performance of the model; Step 304, using the trained sand wave migration prediction model, obtain the ocean dynamics impact index and the sediment impact index, and respectively evaluate the impact of the ocean dynamics impact index and the sediment impact index on the migration and growth of sand waves, as well as the correlation between the ocean dynamics impact factor and the sediment impact factor; Step 305 , based on the sand wave migration prediction model and in combination with historical data, the migration distance and migration direction of the sand wave are obtained, and the migration trend of the sand wave is analyzed.

5. The method for analyzing seabed sand wave migration applied to seabed facility risk management according to claim 4, characterized in that: In step 4, the process of obtaining the sand wave migration assessment coefficient and the risk assessment of the submarine facilities is as follows: Step 401, combining the ocean dynamics impact index and the sediment impact index with the historical sand wave migration data, and analyzing the correlation between the sand wave migration situation and the ocean dynamics impact index and the sediment impact index; Step 402, assigning different weights to the ocean dynamics impact index and the sediment impact index, and performing weighted summation of the ocean dynamics impact index and the sediment impact index to obtain a sand wave migration assessment coefficient; Step 403, based on the real-time data and the sand wave migration prediction model, analyzing the flow mechanism of seabed sand wave movement, including sand wave formation, sand wave evolution and sand wave migration law, and obtaining the flow mechanism analysis result; Step 404, predicting the sand wave migration trend according to the sand wave migration assessment coefficient and the flow mechanism analysis result, assessing the risk of sand wave migration to the submarine facilities, and generating an assessment report, and at the same time classifying the risk of the submarine facilities into different risk levels, namely high risk level, medium risk level and low risk level; Step 405, based on the assessment report, the determined different risk levels are matched with the sand wave migration assessment coefficients, corresponding risk assessment thresholds are set for different risk levels, and the scope and degree of the impact on the submarine facilities are assessed.

6. The method for analyzing seabed sand wave migration applied to seabed facility risk management according to claim 5, characterized in that: The plurality of risk levels correspond to the plurality of risk assessment thresholds, wherein the risk assessment thresholds include an upper threshold and a lower threshold; The multiple risk levels and the multiple risk assessment thresholds satisfy the following relationship: Low risk level SMC ≤ S1; Medium risk level S1 <SMC≤S2; High risk level SMC>S2; Among them, SMC is the sand wave migration assessment coefficient, S1 is the upper threshold corresponding to the low risk level and the lower threshold corresponding to the medium risk level, and S2 is the upper threshold corresponding to the medium risk level and the lower threshold of the high risk level.

7. The method for analyzing seabed sand wave migration for seabed facility risk management according to claim 6, characterized in that: In step 5, the process of determining the risk level of the current submarine facility and generating corresponding warning information is as follows: Step 501, based on the ocean dynamics impact factor and sediment impact factor in the real-time data, input them into the sand wave migration prediction model, obtain the ocean dynamics impact index and sediment impact index respectively, and predict and analyze the long-term trend and short-term change of sand wave migration; Step 502, combining the ocean dynamic impact index and the sediment impact index to obtain a sand wave migration assessment coefficient and an assessment report, and determining a corresponding risk level according to a corresponding risk assessment threshold to assess the degree of physical damage caused by sand wave migration to submarine facilities; Step 503, generate corresponding warning information according to the current corresponding risk level, synchronize it to the marine management department, and formulate emergency response measures.

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