Floating fan and ship collision risk dynamic assessment method

Abstract

The invention discloses a floating fan and ship collision risk dynamic assessment method, and relates to the technical field of collision risk dynamic assessment. According to the floating fan and ship collision risk dynamic assessment method, ship AIS time sequence data, floating fan monitoring time sequence data and marine environment time sequence data are continuously obtained; based on the marine environment time sequence data, analyzing a marine environment interference factor of each time point; analyzing a ship reachable set and a floating fan safety domain by combining ship AIS time sequence data and floating fan monitoring time sequence data respectively; according to the ship fan collision risk value analysis method, comprehensive analysis is carried out through the ship fan overlapping characteristic values in combination with the finite element simulation structure vulnerability curve stored in the database, and the ship fan collision risk value is obtained, so that the risk analysis accuracy is improved, the deviation between an evaluation result and an actual state is effectively avoided, and the risk analysis accuracy is improved. And safe operation of the ship under complex sea conditions is ensured.

Description

Technical Field

[0001] This invention relates to the field of dynamic collision risk assessment technology, specifically a method for dynamic assessment of collision risk between floating wind turbines and ships. Background Technology

[0002] In recent years, with the advancement of large-scale construction of offshore wind farms, a large number of floating wind turbines have been deployed in deep-sea areas. Compared with fixed wind turbines, floating wind turbines achieve floating support through mooring systems, which has advantages such as convenient construction and strong environmental adaptability. However, they also face a more complex marine dynamic environment. As shipping vessels frequently pass through the waters near wind farms, their motion is affected by environmental factors such as wind, current, and waves, and their tracks are prone to drift and deviation. If they overlap with the safety boundary zone of the floating wind turbine, it is very easy to cause safety accidents such as structural collisions and mooring instability.

[0003] The limitations of existing technologies include at least the following problems: existing technologies are difficult to automatically adjust according to the dynamic changes in the marine environment and the ship's motion state, making it difficult for risk assessments to reflect changes in risk levels under different sea conditions. In addition, floating wind turbines will experience attitude changes such as rolling and pitching, as well as tension fluctuations in the mooring system under complex sea conditions. However, existing assessment methods ignore the impact of dynamic attitude changes and anchor drift, resulting in a deviation between the actual position of the wind turbine's safety boundary and the assessment model. When ships approach or pass through wind farms, the possibility of collision is easily underestimated, and the potential damage consequences are not quantitatively analyzed in conjunction with the structural stress characteristics, which can easily lead to insufficient accuracy in the assessment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic assessment method for collision risks between floating wind turbines and ships, which solves the problem that existing risk assessments do not consider attitude changes and environmental time-varying factors, leading to large deviations in results.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic assessment method for collision risk between floating wind turbines and ships, comprising the following steps: continuously acquiring time-series data of ship AIS, floating wind turbine monitoring, and marine environment within a designated sea area; analyzing marine environment interference factors at each time point within the designated sea area based on the marine environment time-series data; analyzing the ship reachability set within the designated sea area based on the ship AIS time-series data and the marine environment interference factors at each time point; analyzing the floating wind turbine safety domain within the designated sea area based on the floating wind turbine monitoring time-series data and the marine environment interference factors at each time point; analyzing the ship-wind turbine overlap characteristic value within the designated sea area based on the ship reachability set and the floating wind turbine safety domain, and performing a comprehensive analysis in conjunction with the finite element simulation structural vulnerability curves stored in the database to obtain the ship-wind turbine collision risk value within the designated sea area.

[0006] Furthermore, the specific steps for analyzing the marine environmental disturbance factors at each time point within the designated sea area are as follows: read the marine environmental time series data within the designated sea area and perform standardization processing; based on the standardized marine environmental time series data within the designated sea area, analyze the marine environmental disturbance factors at each time point within the designated sea area.

[0007] Furthermore, the ship AIS time-series data includes the ship's position, speed, and heading angle at each time point. The specific steps for analyzing the ship reachability set within the designated sea area are as follows: Based on the ship AIS time-series data within the designated sea area, analyze the ship fluctuation range dataset within the designated sea area; based on the ship fluctuation range dataset within the designated sea area, and combined with the marine environmental interference factors at each time point, construct a ship dynamic displacement prediction model within the designated sea area; perform time-series advancement processing on the ship dynamic displacement prediction model within the designated sea area, and analyze the ship reachability set within the designated sea area.

[0008] Furthermore, the specific steps for analyzing the ship fluctuation range dataset within the designated sea area are as follows: perform trend statistical processing on the ship speed values ​​at each time point within the designated sea area to obtain the ship speed fluctuation range within the designated sea area; perform angle fluctuation statistical processing on the ship heading angle values ​​at each time point within the designated sea area to obtain the ship heading fluctuation range within the designated sea area.

[0009] Furthermore, the specific steps of the time-series advancement process are as follows: iteratively process the ship dynamic displacement prediction model within the designated sea area to obtain the ship prediction position set within the designated sea area; perform spatial aggregation processing on the ship prediction position set within the designated sea area to obtain the ship reachability set within the designated sea area.

[0010] Furthermore, the floating wind turbine monitoring time series data includes the float roll angle, float pitch angle, mooring line tension, and anchor point displacement values ​​at each time point. The specific steps for analyzing the floating wind turbine safety zone within the designated sea area are as follows: Read the marine environmental disturbance factors at each time point within the designated sea area, and combine them with the floating wind turbine monitoring time series data to analyze several sets of floating wind turbine change rate sets at adjacent time points within the designated sea area; perform trend evolution processing based on each set of floating wind turbine change rate sets at adjacent time points within the designated sea area to obtain the wind turbine motion trend set within the designated sea area; perform spatial envelope processing on the wind turbine motion trend set within the designated sea area to extract the floating wind turbine safety zone within the designated sea area.

[0011] Furthermore, the specific steps to obtain the set of wind turbine motion trends within the designated sea area are as follows: Integrate the set of floating wind turbine change rates at several adjacent time points within the designated sea area to obtain the set of wind turbine trends within the designated sea area; Extrapolate the set of wind turbine trends within the designated sea area to obtain the set of wind turbine motion trends within the designated sea area.

[0012] Furthermore, the specific steps of spatial envelope processing are as follows: spatial coordinate mapping processing is performed on the set of wind turbine motion trends within the designated sea area to obtain the set of wind turbine coordinates within the designated sea area; boundary envelope fitting processing is performed on the set of wind turbine coordinates within the designated sea area to obtain the safe domain of floating wind turbines within the designated sea area.

[0013] Further, the specific steps for analyzing the overlapping characteristics of ships and wind turbines within a designated sea area are as follows: read the ship reachability set and floating wind turbine safety domain within the designated sea area, and perform rasterization processing; based on the rasterized ship reachability set and floating wind turbine safety domain within the designated sea area, extract the spatial overlap rate value, spatial intrusion rate value, and cluster density value within the designated sea area, and analyze the overlapping characteristics of ships and wind turbines within the designated sea area.

[0014] Furthermore, the specific steps to obtain the collision risk value of ships and wind turbines within the designated sea area are as follows: Based on the overlapping characteristic value of ships and wind turbines within the designated sea area, the external load kinetic energy of ships within the designated sea area is extracted; Based on the vulnerability curve of the finite element simulation structure stored in the database, the external load kinetic energy of ships within the designated sea area is mapped and processed to obtain the collision risk value of ships and wind turbines within the designated sea area.

[0015] The present invention has the following beneficial effects:

[0016] (1) The dynamic assessment method for collision risk between floating wind turbines and ships sets the time-series data of ship AIS, floating wind turbine monitoring and marine environment in the sea area, and analyzes the ship reachability set and the floating wind turbine safety domain. This method can adaptively reflect the comprehensive impact of wind, wave and current changes on ships and floating bodies. Through this time-varying dynamic modeling method, the real-time update and predictability assessment of the floating wind turbine safety domain are realized, which significantly improves the accuracy of risk analysis, effectively avoids the deviation between the assessment results and the actual state, and ensures the safe operation of ships in complex sea conditions.

[0017] (2) The dynamic assessment method for collision risk between floating wind turbines and ships analyzes the ship reachability set through AIS time series data analysis and combines marine environmental interference factors to perform time series extrapolation and spatial aggregation processing on the future movement trend of ships. This enables the prediction of the dynamic reachability range of ships under multiple disturbance conditions such as ocean currents and waves, so that the ship's motion characteristics remain continuous and traceable in space and time. This allows for a comprehensive reflection of the deviation law of ship motion trajectory and reachability boundary under different environmental conditions. In this way, it is possible to predict the spatial distribution area that ships may reach in real time under different sea conditions and navigation conditions, and to dynamically compare it with the safety domain of floating wind turbines, thereby realizing the early identification of potential approach or intrusion events and improving the reliability of dynamic risk assessment.

[0018] (3) The dynamic assessment method for collision risk between floating wind turbines and ships establishes a risk quantification mechanism based on spatial overlap rate, intrusion rate and aggregation density by integrating the wind turbine safety domain and the ship reachability set in the spatial domain through gridded analysis. It analyzes the external load kinetic energy of the ship and obtains the wind turbine structural damage probability and collision risk value through vulnerability curve mapping. Thus, it realizes the risk characterization of the whole process from spatial encroachment to structural response, and makes the risk assessment not only stop at the geometric level of proximity judgment, but also extend to the risk analysis based on mechanical response, thereby improving the accuracy of dynamic risk assessment.

[0019] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0020] Figure 1 This is a flowchart of a dynamic assessment method for collision risk between a floating wind turbine and a ship, according to the present invention.

[0021] Figure 2 This is a flowchart illustrating the specific steps involved in analyzing the reachability set of ships within a designated sea area in a dynamic assessment method for collision risk between floating wind turbines and ships according to the present invention. Detailed Implementation

[0022] Please see Figure 1This invention provides a technical solution: a method for dynamic assessment of collision risk between floating wind turbines and ships, comprising the following steps: within a set assessment period (e.g., 5-10 minutes), continuously acquiring ship AIS time-series data, floating wind turbine monitoring time-series data, and marine environment time-series data within a set sea area (it should be noted that each type of time-series data is the corresponding data at each time point, and the time interval between two adjacent time points is the minimum time step of the sampling period of each type of time-series data as the unified sampling time interval, and low-frequency data is supplemented by interpolation).

[0023] Based on the marine environmental time series data within the designated sea area (it should be noted that the marine environmental time series data is a set of environmental parameters at representative monitoring points within the designated sea area to characterize the overall environmental load conditions of the sea area. The representative monitoring points within the designated sea area can be the floating wind turbine itself or marine buoy monitoring points deployed near the floating wind turbine. Since the ship and the floating wind turbine are located in the same evaluation sea area, the spatial scale of this sea area is much smaller than the spatial correlation scale of environmental loads such as wind, current, and waves. Therefore, it can be assumed that the ship and the floating wind turbine are under the same marine environmental conditions during the evaluation period. At this time, the environmental time series data can be used for both the ship and the floating wind turbine), the marine environmental disturbance factors at each time point within the designated sea area are analyzed.

[0024] Based on the time-series AIS data of ships within the designated sea area, and combined with the marine environmental interference factors at each time point, the reachability set of ships within the designated sea area is analyzed; based on the time-series monitoring data of floating wind turbines within the designated sea area, and combined with the marine environmental interference factors at each time point, the safety domain of floating wind turbines within the designated sea area is analyzed; based on the reachability set of ships and the safety domain of floating wind turbines within the designated sea area, the overlap characteristic value of ships and wind turbines within the designated sea area is analyzed, and combined with the vulnerability curves of finite element simulation structures stored in the database, a comprehensive analysis is performed to obtain the collision risk value of ships and wind turbines within the designated sea area.

[0025] The marine environmental time-series data includes wind speed values ​​(obtained in real time by an ultrasonic anemometer on a marine buoy), ocean current velocity values ​​(obtained by an ocean current profiler, which uses the Doppler effect to measure the velocity components of water at different depths and performs a weighted average of the near-surface velocity data to obtain a representative ocean current velocity value), and wave height values ​​(obtained by a wave height meter installed on a buoy). The specific steps for analyzing the marine environmental disturbance factors at each time point within a designated sea area are as follows: Read the marine environmental time-series data within the designated sea area and perform standardization processing (i.e., standardize the wind speed, ocean current velocity, and wave height values ​​at each time point within the designated sea area to remove units); Based on the standardized marine environmental time-series data within the designated sea area, analyze the marine environmental disturbance factors at each time point within the designated sea area. Specifically, perform weighted processing on the standardized wind speed, ocean current velocity, and wave height values ​​at each time point within the designated sea area to obtain the corresponding marine environmental disturbance factor for that time point.

[0026] It should be noted that in this implementation example, the weight coefficients of each parameter in the weighted processing can be obtained using sample entropy weighting. Taking the weighted processing of marine environmental disturbance factors as an example, the wind speed, ocean current speed, and wave height values ​​at each time point are read and standardized (units are removed, and their corresponding values ​​are mapped to the range of 0-1). The corresponding information entropy values ​​are then extracted and transformed using the reciprocal suppression mapping function f(x)=1 / (1+x), such as 1 / (1+wind speed information entropy value). The results are then summed to obtain the information entropy sum. The transformed information entropy values ​​are then compared with the information entropy sum to obtain the weight coefficients corresponding to each parameter.

[0027] Specifically, such as Figure 2 As shown, the ship AIS time-series data includes the ship's position, speed, and heading angle at each time point. The specific steps for analyzing the reachability set of ships within a designated sea area are as follows: Based on the ship AIS time-series data within the designated sea area, analyze the ship fluctuation range dataset within the designated sea area; based on the ship fluctuation range dataset within the designated sea area, and combined with the marine environmental interference factors at each time point, construct a ship dynamic displacement prediction model within the designated sea area, which is specifically as follows:

[0028] Based on the ship speed fluctuation range and ship heading fluctuation range within a defined sea area, the ship's self-displacement components within the defined sea area are constructed. That is, several discrete values ​​are selected within the ship speed fluctuation range and ship heading fluctuation range, and the displacement components under each combination are calculated according to the pairwise combination of speed and heading. That is, the lateral displacement component = ship speed value × cos(heading value) × time step; the longitudinal displacement component = ship speed value × sin(heading value) × time step; each combination of speed value and heading value generates a two-dimensional displacement component (i.e., a coordinate increment pair composed of lateral displacement component and longitudinal displacement component). All combination results constitute a sample set of self-propulsion displacement components. In this implementation example, the upper and lower limits of the ship speed fluctuation range and ship heading fluctuation range are combined in pairs.

[0029] Based on the marine environmental disturbance factors at each time point within the designated sea area, the mean and standard deviation of the marine environmental disturbance factors are extracted to obtain the marine environmental disturbance fluctuation range. Specifically, the lower limit of the marine environmental disturbance fluctuation range is the difference between the mean and the standard deviation, and the upper limit is the sum of the mean and the standard deviation. The marine environmental disturbance fluctuation range is then used as an environmental correction term, with the upper and lower limits serving as the boundaries for the correction term. The lower limit corresponds to the weakest environmental disturbance, and the upper limit corresponds to the strongest environmental disturbance. Multi-point discrete sampling is performed between these two conditions. In this implementation example, the upper and lower limits of the marine environmental disturbance fluctuation range are selected for sampling the environmental correction term.

[0030] Based on the ship positions at each time point within a designated sea area, the initial predicted positions of ships within the designated sea area are statistically analyzed (i.e., the ship position at the last time point in the cycle is used as the initial predicted position). Combined with the ship's self-displacement components and environmental correction terms within the designated sea area, a dynamic displacement prediction model for ships is constructed to obtain the predicted ship positions at several prediction time points. Its representation is as follows: ;in, To set the first in the sea area Predicted ship position at each predicted time point (when...) (At this time, this is the predicted position of the ship at the first predicted time point). To set the first in the sea area Predicted ship position at each predicted time point (when...) At that time, the predicted initial position of the ship is selected. To define the ship's self-displacement components within the sea area, To set environmental correction items within the marine area, The displacement adjustment coefficients are stored in the database. These are the marine environmental disturbance adjustment coefficients stored in the database. , To predict the number of time points;

[0031] It should be noted that the displacement adjustment coefficients stored in the database The steps are as follows: Read the ship's position at each time point within the designated sea area and substitute it into the ship dynamic displacement prediction model. Solve the model using the ship displacement adjustment coefficient as the unknown quantity to obtain several values ​​for the displacement adjustment coefficient. Take the average value and use it as the displacement adjustment coefficient. ;

[0032] Displacement adjustment coefficients stored in the database The acquisition steps are as follows: Read the marine environmental disturbance factors at each time point within the designated sea area. Perform trend analysis on the marine environmental disturbance factors at adjacent time points sequentially. This involves processing the difference between two adjacent time points and comparing the result with the marine environmental disturbance factor at the second of those adjacent time points to obtain several sets of marine environmental disturbance change rates between adjacent time points. Take the average of these averages and use it as the marine environmental disturbance adjustment coefficient. The dynamic displacement prediction model of ships within the designated sea area is subjected to time-series processing to analyze the reachability set of ships within the designated sea area.

[0033] The specific steps for analyzing the ship speed fluctuation range dataset within a designated sea area are as follows: Perform trend statistical processing on the ship speed values ​​at each time point within the designated sea area to obtain the ship speed fluctuation range within the designated sea area. Specifically, based on the ship speed values ​​at each time point within the designated sea area, extract the mean ship speed and the standard deviation of ship speed within the designated sea area, and use these to obtain the ship speed fluctuation range. That is, use the mean ship speed minus the standard deviation of ship speed as the lower limit of the ship speed fluctuation range, and use the mean ship speed plus the standard deviation of ship speed as the upper limit of the ship speed fluctuation range.

[0034] The ship heading angle values ​​at each time point within the designated sea area are statistically processed to obtain the ship heading fluctuation range within the designated sea area. Specifically, based on the ship heading angle values ​​at each time point within the designated sea area, the mean ship heading angle and the range ship heading angle within the designated sea area are extracted (i.e., the maximum and minimum ship heading angle values ​​are statistically analyzed, and the difference is processed, taking half of the difference result). The ship heading fluctuation range is obtained from this, that is, the lower limit of the ship heading fluctuation range is the mean ship heading angle minus the range ship heading angle, and the upper limit of the ship heading fluctuation range is the mean ship heading angle plus the range ship heading angle.

[0035] The specific steps of the time-series advancement process are as follows: The ship dynamic displacement prediction model within the designated sea area is iteratively processed to obtain the ship prediction position set within the designated sea area. Specifically, the ship prediction initial position at the last time point of the current evaluation cycle is used as the iteration starting point. The ship dynamic displacement prediction model is solved in this way to obtain a set of prediction positions for several prediction time points. Taking the prediction position set at the first prediction time point as an example, each sample in the ship self-displacement component and the sample in the environmental correction term are randomly combined to obtain several sets of values ​​for the ship self-displacement component and the environmental correction term. These values ​​are then substituted into the ship dynamic displacement prediction model for solution to obtain several ship prediction positions at the first prediction time point. Then, for the prediction position set at the second prediction time point, the above steps are repeated with each ship prediction position at the first prediction time point as the new starting point to obtain the prediction position set for each prediction time point within the designated sea area. The prediction position set at the last time point of the next cycle is taken as the set of ship prediction positions within the designated sea area.

[0036] Spatial aggregation processing is performed on the set of predicted ship positions within a designated sea area to obtain the ship reachable set within the designated sea area. Specifically, the Euclidean distance between any two adjacent predicted ship positions is calculated. When the distance is less than a preset aggregation threshold, the two predicted ship positions are grouped into the same aggregation region. The search is then expanded sequentially to include all predicted ship positions that meet the adjacency condition, forming a connected region from all interconnected predicted ship positions. For each predicted ship position within a connected region, its two-dimensional coordinate outer envelope boundary is calculated. This can be done using a convex hull algorithm, Delaunay triangulation, or a boundary generation method based on the minimum rectangular envelope to obtain the minimum outer envelope region corresponding to the connected region. All minimum outer envelope regions are then spatially merged to form the ship reachable region within the designated sea area, which is then used as the ship reachable set within the designated sea area.

[0037] In this implementation scheme, by performing hierarchical deconstruction and dynamic modeling of ship AIS time-series data, high-precision prediction of ship reachability and spatial boundary quantification can be achieved. Fluctuation intervals are established in the speed and heading dimensions, and a set of self-propulsion displacement components is formed by multi-point discrete value taking, so that the randomness and diversity of ship motion characteristics can be fully expressed. Secondly, combined with marine environmental interference factors, an environmental correction term is constructed and displacement adjustment coefficients and environmental regulation coefficients are introduced to achieve adaptive correction of wind and current coupling disturbances in the model, ensuring that the prediction results still have continuity and physical consistency under complex sea conditions. Furthermore, through time-series advancement and spatial aggregation processing, a dynamic reachability set can be formed in two-dimensional coordinate space, accurately representing the spatial activity range of ships at different prediction time points. This process effectively solves the error accumulation problem caused by multiple disturbances and nonlinear coupling in ship motion prediction, and realizes continuous and environmentally adaptive ship reachability prediction based on AIS data.

[0038] Specifically, the floating wind turbine monitoring time series data includes the float roll angle, float pitch angle, mooring line tension, and anchor point displacement values ​​at each time point. The specific steps for analyzing the floating wind turbine safety zone within the designated sea area are as follows: Read the marine environmental disturbance factors at each time point within the designated sea area, and combine this with the floating wind turbine monitoring time series data to analyze several sets of floating wind turbine change rate sets at adjacent time points within the designated sea area. Specifically:

[0039] For the buoy roll angle value at each time point within the designated sea area, the buoy roll angle value at two adjacent time points is obtained by subtracting the roll angle value of the previous time point from the roll angle value of the later time point and dividing by the sampling time interval between the two time points. This yields the initial buoy roll angle change rate values ​​for several sets of adjacent time points within the designated sea area. Similarly, the initial buoy pitch angle change rate value, initial mooring line tension change rate value, initial anchor point displacement change rate value, and marine environmental disturbance change rate value within the designated sea area are obtained.

[0040] Based on the rate of change of marine environmental disturbance, the initial rate of change of the initial buoy roll angle, the initial rate of change of the initial buoy pitch angle, the initial rate of change of the initial mooring line tension, and the initial rate of change of the initial anchor point displacement at each adjacent time point in the designated sea area are corrected. Taking the initial rate of change of the initial buoy pitch angle as an example, the initial rate of change of the buoy roll angle is multiplied by (1 + the initial rate of change of the buoy roll angle × the adjustment coefficient). The adjustment coefficient can be in the range of 0.1 to 0.3 to obtain the rate of change of the buoy pitch angle. Similarly, the rate of change of the buoy pitch angle, the rate of change of the mooring line tension, and the rate of change of the anchor point displacement can be obtained.

[0041] Trend evolution processing is performed on the set of floating wind turbine change rates at each adjacent time point within the designated sea area to obtain the set of wind turbine motion trends within the designated sea area; spatial envelope processing is then performed on the set of wind turbine motion trends within the designated sea area to extract the safe domain of floating wind turbines within the designated sea area.

[0042] The set of floating wind turbine change rates includes the change rate values ​​of the floating body pitch angle, the floating body roll angle, the mooring line tension, and the anchor point displacement. The specific steps to obtain the set of wind turbine motion trends in a set sea area are as follows: Integrate the set of floating wind turbine change rates at several adjacent time points in the set sea area to obtain the wind turbine trend set in the set sea area. In the integration process, the sampling time interval between two adjacent time points is used as the integration step size, and each change rate value is accumulated point by point in chronological order to ensure the temporal continuity of the trend curve, so as to obtain the trend values ​​of the floating body roll angle, the floating body pitch angle, the mooring tension, and the anchor point displacement in the set sea area.

[0043] Extrapolate the wind turbine trend set within the designated sea area to obtain the wind turbine motion trend set within the designated sea area. Specifically, read the buoy roll angle value, pitch angle value, mooring tension value, and anchor point displacement value at the end of the current evaluation cycle (i.e., the first time point of week), and define them as the initial values ​​for roll angle prediction, pitch angle prediction, mooring force prediction, and anchor displacement prediction, respectively.

[0044] Using each trend value as the rate of change for the current period, the parameter values ​​for each predicted time point in the next evaluation period are extrapolated step by step according to the set time step (consistent with the time step of the adjacent time point in the current period): For example, the predicted roll angle value = the initial predicted roll angle value + the trend value of the buoy roll angle × the time step. Similarly, the predicted roll angle value, predicted pitch angle value, predicted mooring force value, and predicted anchor displacement value can be obtained. Through the above calculation, the prediction results of each time step are superimposed on the time series to obtain the predicted roll angle sequence, predicted pitch angle sequence, predicted mooring force sequence, and predicted anchor displacement sequence for all predicted time points in the next evaluation period, that is, the predicted roll angle value, predicted pitch angle value, predicted mooring force value, and predicted anchor displacement value for each predicted time point.

[0045] To prevent the accumulation of attitude shifts or displacement drifts caused by continuous superposition during multi-step prediction, attitude and displacement attenuation coefficients are set to gradually attenuate the increments of consecutive prediction steps. For each prediction time point within the set sea area, the attitude increment (e.g., the trend value of the float's roll angle × time step, the trend value of the float's pitch angle × time step) and the displacement increment (the trend value of the anchor point displacement × time step) of the current step are multiplied by the attenuation coefficient, respectively. The attenuation coefficient ranges from 0.85 to 0.95 and decreases exponentially with the increase of the prediction time step. In addition, in the prediction results of attitude, force, and displacement... If the predicted value exceeds the set physical upper limit, a truncation process is performed. For example, if the absolute value of the predicted roll angle exceeds ±10°, it is limited to ±10°; if the predicted mooring force exceeds 1.2 times the rated value (which can be obtained from the floating wind turbine design parameter set stored in the database, including rated mooring tension, rated anchor chain length, and design safety factor, etc.), it is adjusted to 1.2 times the rated value; if the predicted mooring displacement exceeds 2.0 meters, it is limited to 2.0 meters. The truncated prediction results are used as the initial input value for the next time point to avoid prediction divergence or numerical non-convergence caused by abnormal peak values.

[0046] The specific steps of spatial envelope processing are as follows: Spatial coordinate mapping processing is performed on the set of wind turbine motion trends within the set sea area to obtain the set of wind turbine coordinates within the set sea area. Specifically, the predicted values ​​of roll angle, pitch angle, mooring force, and anchor displacement at each prediction time point within the set sea area are processed for change. For example, the difference between the predicted roll angle at the first prediction time point and the roll angle at the last time point of the current cycle is processed, and the ratio is processed with the predicted roll angle at the first prediction time point to obtain the predicted change value of the roll angle at the first prediction time point. The predicted change value of the roll angle at each prediction time point is obtained. Similarly, the predicted change values ​​of pitch angle, mooring force, and anchor displacement at each prediction time point can be obtained.

[0047] A two-dimensional sea-level coordinate system based on the mean sea level is established, where the X-axis represents the east-west drift component of the floating body, and the Y-axis represents the north-south drift component. The origin of the coordinate system is the position of the floating body center at the end of the current evaluation period. The height from the center of the floating body structure to the top of the tower and the length from the center of the floating body to the mean waterline are extracted from the data. The predicted roll angle change and the predicted pitch angle change are geometrically mapped to calculate the horizontal component offset caused by the change in floating body attitude. For example, the lateral attitude offset = height from the center of the floating body structure to the top of the tower × sin(predicted roll angle change), and the longitudinal attitude offset = length from the center of the floating body to the mean waterline × sin(predicted pitch angle change). The two are combined to obtain the horizontal composite offset radius caused by the attitude change, i.e., √((lateral attitude offset)² + (longitudinal attitude offset)²).

[0048] For the predicted change value of mooring force, the force mapping is performed based on the equivalent stiffness of the mooring line (which is the force-displacement linearization characteristic of the mooring system within the working tensile range, and its value can be determined by fitting the mooring line tension and displacement monitoring data in the current evaluation period, and can be updated when the period is switched) and the water entry angle (the angle between the water entry point of the mooring line and the horizontal plane, which remains stable within a single evaluation period and can be updated when the period is switched). That is, the mooring tensile offset = (predicted change value of mooring force / equivalent stiffness of mooring line) × cos(water entry angle). For the predicted change value of mooring displacement, it is directly used as the overall translational offset term of the floating body, and weighted and summed with the horizontal composite offset radius and the mooring tensile offset to obtain the comprehensive horizontal drift radius at each prediction time point.

[0049] Combined with the azimuth angle corresponding to the pitch direction of the floating body, the sea-level spatial coordinates of each predicted time point are calculated: x-coordinate = x-coordinate value of the center position of the floating body + comprehensive horizontal drift radius × cos(azimuth angle), y-coordinate = x-coordinate value of the center position of the floating body + comprehensive horizontal drift radius × sin(azimuth angle). By performing the above calculations sequentially on all predicted time points in the next evaluation cycle, the spatial coordinate points corresponding to each predicted time point in the set sea area are obtained, and spatial coordinate transformation processing (such as UTM projection or Mercator approximation) is performed to map the spatial coordinate points under the local sea-level coordinate system to the geographic coordinate system (consistent with the coordinate system of the ship's latitude and longitude). The set of all coordinate points is used as the wind turbine coordinate set in the set sea area, that is, the position coordinates of each predicted time point.

[0050] It should be noted that the azimuth is the spatial orientation of the buoy's pitch direction in the sea-level coordinate system. The wind direction angle values ​​at each time point within the current assessment period are obtained (with true north as the reference and angles counted clockwise). The arithmetic mean of all wind direction angle values ​​is taken to obtain the average wind direction angle value for the current period. The mooring system layout orientation angle value (the angle between the mooring main cable and true north) is read from the database; this orientation angle remains constant during wind turbine operation. The average wind direction angle and the mooring orientation angle are summed, and when the total angle exceeds 360°, it is rounded down to 360°. The calculated azimuth is used as the reference azimuth angle for the buoy's pitch direction within the current prediction period, remaining stable throughout the entire prediction period. When entering the next assessment period, the wind direction data is reread to update the azimuth angle.

[0051] The boundary envelope fitting process is performed on the set of wind turbine coordinates within the designated sea area to obtain the safety domain of the floating wind turbine within the designated sea area. Specifically, the position coordinates of each predicted time point within the designated sea area are spatially aggregated, and adjacent predicted points are connected in chronological order to form a set of motion trajectories of the floating wind turbine within the prediction period. Spatial envelope operation is then performed, which can be done using a convex hull algorithm or an equivalent minimum circumscribed boundary fitting method. A minimum closed region is generated with all predicted position points as vertices. This closed region is defined as the spatial motion envelope domain of the floating wind turbine. The boundary of the spatial motion envelope domain is then expanded with a safety margin, that is, it is expanded along the normal direction of the outer boundary of the envelope domain by a preset distance. The preset distance can be determined according to 10% to 30% of the horizontal projection radius of the floating structure to compensate for the uncertainty caused by attitude extreme value deviation and structural scale. The expanded region is the safety domain of the floating wind turbine within the designated sea area.

[0052] In this implementation scheme, by extracting the rate of change, integrating the trend, and extrapolating the multi-dimensional time-series monitoring data such as floating body attitude parameters (roll and pitch angles), mooring forces, and anchor displacement, continuous prediction of the future cycle attitude and force evolution of the wind turbine can be achieved. This avoids the error accumulation problem caused by neglecting dynamic response in traditional static analysis. Furthermore, the method introduces attitude and displacement attenuation coefficients and a physical upper limit truncation mechanism in the prediction stage to ensure the convergence of numerical iteration and the physical rationality of the results, effectively suppressing drift superposition and abnormal fluctuations. Subsequently, through spatial geometric mapping and azimuth transformation, the predicted attitude and force changes are transformed into the two-dimensional spatial position of the floating body in the sea-level coordinate system, realizing the physical mapping process from attitude change to spatial drift. Finally, through boundary envelope fitting and safety margin expansion, a safe domain region for the floating wind turbine in the next cycle is formed, realizing the dynamic updating and spatial expression of the safety boundary. This provides high-precision, dynamically adjustable basic data of the safe region for subsequent ship-wind turbine spatial overlap analysis and risk assessment.

[0053] Specifically, the ship-accessible area and floating wind turbine safety zone within the designated sea area are read and rasterized. Specifically, the spatial area under the geographic coordinate system is divided into several regular grid units according to a preset resolution (e.g., 0.5m × 0.5m), and the grid nodes belonging to the ship-accessible area and the wind turbine safety zone are marked respectively.

[0054] Based on the rasterized ship accessibility set and floating wind turbine safety zone within the designated sea area, the spatial overlap rate, spatial intrusion rate, and clustering density values ​​are extracted within the designated sea area. The ship-wind turbine overlap characteristic values ​​within the designated sea area are analyzed. Specifically, the number of grid cells simultaneously covered by both the ship accessibility set and the wind turbine safety zone is counted, and this number is compared with the total number of grid cells in the wind turbine safety zone to obtain the spatial overlap rate of the wind turbine safety zone. Simultaneously, the number of overlapping grid cells is compared with the total number of grid cells in the ship accessibility set to obtain the spatial intrusion rate of the ship accessibility set. The center coordinates of the overlapping grid cells are read, and density clustering based on a distance threshold (e.g., 2.0 meters) is performed. The number of overlapping grid cells per unit area is counted, and this number is compared with the total number of grid cells in the entire wind turbine safety zone to obtain the clustering density value, which characterizes the concentration of ship activity around the wind turbine.

[0055] The specific steps for calculating the overlap characteristic value of ship-driven wind turbines within a specified sea area are as follows: ;in, To set the characteristic value of ship-wind turbine overlap in the sea area, To set the spatial overlap rate value within the sea area, This is the spatial overlap adjustment coefficient stored in the database (its value range can be 0.3 to 0.5). To set the spatial intrusion rate value within the sea area, This is the space intrusion adjustment coefficient stored in the database (its value range can be 0.2 to 0.4). To set the aggregation density value within the sea area, This refers to the cluster density adjustment coefficient stored in the database (its value can range from 0.1 to 0.3). .

[0056] The specific steps to obtain the ship-wind turbine collision risk value within a designated sea area are as follows: Based on the ship-wind turbine overlap characteristic value within the designated sea area, the ship's external load kinetic energy within the designated sea area is extracted. Specifically, the ship's mass (read from the AIS database) and average speed (i.e., the average speed value of the ship at each time point in the cycle) within the designated sea area are obtained and comprehensively analyzed with the ship-wind turbine overlap characteristic value. That is, ship-wind turbine overlap characteristic value × ship mass × (average speed of the ship² / 2) × kinetic energy conversion factor (used to convert the degree of spatial overlap into the intensity of kinetic energy action, with a value range of 0.3 to 0.7) are used to obtain the ship's external load kinetic energy within the designated sea area.

[0057] Based on the vulnerability curves of the finite element simulation structure stored in the database, the external kinetic energy of ships in the designated sea area is mapped to obtain the collision risk value of ships and wind turbines in the designated sea area, which is as follows:

[0058] The finite element simulation structural vulnerability curves stored in the database are used to characterize the response characteristics and damage probability distribution of floating wind turbine structures under different external impact loads. These curves are obtained through finite element numerical simulation analysis of key structural components of the floating wind turbine. Based on the structural design parameters of the floating wind turbine (including tower wall thickness, float dimensions, mooring line material properties, and mooring arrangement parameters), different levels of external impact energy are applied to the finite element simulation model, and structural response parameters such as tower buckling deformation, peak float stress, and maximum mooring line tension are calculated. A mapping relationship between external impact energy and structural damage probability is established through multiple sets of simulation results. Structural vulnerability curves are generated, with external load kinetic energy as the abscissa and structural damage probability as the ordinate, reflecting the possibility of damage or failure occurring in a specific structural part. After simulation verification and comparison with experimental data, the vulnerability curves are stored in a database and classified and indexed according to structural type (tower, floating body, mooring system) for quick retrieval and matching calculation in subsequent risk assessment. In this embodiment, each vulnerability curve in the database is obtained by fitting no less than 20 sets of finite element simulation sample points, and logistic regression or log-normal distribution function is used for fitting to ensure the continuity of the curve and the physical rationality of the probability distribution.

[0059] The equivalent external load kinetic energy is substituted into the vulnerability curve of the corresponding structural part in the database to obtain the structural damage probability at that kinetic energy level. When multiple structural parts (including tower, floating body, and mooring system) are involved, the damage probability of each part is weighted and summed according to the structural importance coefficient (e.g., 0.5-0.7 for tower, 0.2-0.4 for floating body, and 0.1-0.3 for mooring system) to obtain the overall structural damage probability. Based on the overall structural damage probability and the overlap characteristic value of the ship and wind turbine, the collision risk value of the ship and wind turbine is calculated, which is the overall structural damage probability × (1 + risk amplification coefficient × ship and wind turbine overlap characteristic value). The risk amplification coefficient is used to reflect the amplification effect of the degree of spatial overlap on the risk, and its value ranges from 0.1 to 0.4.

[0060] In this implementation scheme, by performing high-resolution rasterization processing on the ship's reachability set and the wind turbine's safety domain, the spatial relationship between the two in the geographic coordinate system can be accurately characterized. By using indicators such as spatial overlap rate, intrusion rate, and cluster density to comprehensively reflect the spatial approximation characteristics of ship motion on wind turbine, the spatial distribution of risk is refined and quantified. Furthermore, the method introduces multi-dimensional adjustment coefficients stored in the database into the calculation of overlap characteristics, making the influence of different spatial indicators on risk contribution adaptively adjustable, thus enhancing the model's adaptability and sensitivity. Subsequently, by combining ship mass, average speed, and kinetic energy conversion coefficient, the degree of spatial overlap is converted into external load kinetic energy, realizing the integrated characterization of spatial collision probability and physical impact energy. Finally, based on the structural vulnerability curve obtained from finite element simulation, the external load kinetic energy is mapped to the structural damage probability to obtain the overall collision risk value, significantly improving the accuracy of ship-floating wind turbine collision risk assessment.

[0061] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0062] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for dynamic assessment of collision risk between floating wind turbines and ships, characterized in that, Includes the following steps: Continuously acquire time-series data of ship AIS, floating wind turbine monitoring, and marine environment within a designated sea area; Based on the time series data of the marine environment in the designated sea area, the marine environmental disturbance factors at each time point in the designated sea area are analyzed. Based on the time-series AIS data of ships within the designated sea area, and combined with the marine environmental interference factors at each time point, the reachability set of ships within the designated sea area is analyzed. Based on the time-series monitoring data of floating wind turbines in the designated sea area, and combined with the marine environmental disturbance factors at each time point, the safety zone of floating wind turbines in the designated sea area is analyzed. Based on the reachability of ships and the safety domain of floating wind turbines within a defined sea area, the overlapping characteristic values ​​of ships and wind turbines within the defined sea area are analyzed. Combined with the vulnerability curves of finite element simulation structures stored in the database, a comprehensive analysis is conducted to obtain the collision risk value of ships and wind turbines within the defined sea area.

2. The method for dynamic assessment of collision risk between floating wind turbines and ships according to claim 1, characterized in that, The specific steps for analyzing marine environmental disturbance factors at each time point within the defined sea area are as follows: Read the marine environmental time-series data within the designated sea area and perform standardization processing; Based on the standardized marine environmental time-series data within the designated sea area, we analyze the marine environmental disturbance factors at each time point within the designated sea area.

3. The method for dynamic assessment of collision risk between floating wind turbines and ships according to claim 1, characterized in that, The ship AIS time-series data includes the ship's position, speed, and heading angle at each time point. The specific steps for analyzing and defining the ship reachability set within the sea area are as follows: Based on the time-series AIS data of ships within a designated sea area, analyze the data set of ship fluctuation range within the designated sea area; Based on a dataset of ship movement ranges within a defined sea area, and combined with marine environmental disturbance factors at each time point, a prediction model for ship dynamic displacement within the defined sea area is constructed. The dynamic displacement prediction model of ships within a designated sea area is subjected to time-series processing to analyze the reachability set of ships within the designated sea area.

4. The dynamic assessment method for collision risk between floating wind turbines and ships according to claim 3, characterized in that, The specific steps for analyzing the dataset of ship movement ranges within a defined sea area are as follows: By performing trend statistical processing on the ship speed values ​​at each time point within a designated sea area, the fluctuation range of ship speed within the designated sea area can be obtained. By performing statistical processing on the ship's heading angle value at each time point within the designated sea area, the ship's heading fluctuation range within the designated sea area is obtained.

5. The dynamic assessment method for collision risk between floating wind turbines and ships according to claim 3, characterized in that, The specific steps of the time-series advancement process are as follows: The dynamic displacement prediction model of ships within a designated sea area is iteratively processed to obtain the predicted position set of ships within the designated sea area. Spatial aggregation processing is performed on the predicted ship locations within a designated sea area to obtain the ship reachability set within the designated sea area.

6. The method for dynamic assessment of collision risk between floating wind turbines and ships according to claim 1, characterized in that, The time-series monitoring data for the floating wind turbine includes the buoy roll angle, buoy pitch angle, mooring line tension, and anchorage point displacement at each time point. The specific steps for analyzing and setting the safe zone for the floating wind turbine within the sea area are as follows: Read the marine environmental disturbance factors at each time point within the designated sea area, and combine them with the time series data of floating wind turbine monitoring to analyze the set of floating wind turbine change rates at several adjacent time points within the designated sea area; Trend evolution processing is performed on the set of floating wind turbine change rates at each adjacent time point within the designated sea area to obtain the set of wind turbine movement trends within the designated sea area; Spatial envelope processing is performed on the set of wind turbine movement trends within a designated sea area to extract the safety domain of floating wind turbines within the designated sea area.

7. The dynamic assessment method for collision risk between floating wind turbines and ships according to claim 6, characterized in that, The specific steps to obtain the set of wind turbine movement trends within a designated sea area are as follows: By integrating the set of floating wind turbine change rates at several adjacent time points within a designated sea area, the trend set of wind turbines within the designated sea area is obtained. Extrapolate the wind turbine trend set within the designated sea area to obtain the wind turbine motion trend set within the designated sea area.

8. The dynamic assessment method for collision risk between floating wind turbines and ships according to claim 6, characterized in that, The specific steps for spatial envelope processing are as follows: Spatial coordinate mapping is performed on the set of wind turbine movement trends within a designated sea area to obtain the set of wind turbine coordinates within the designated sea area; The boundary envelope fitting process is performed on the set of wind turbine coordinates within the designated sea area to obtain the safe zone of the floating wind turbine within the designated sea area.

9. The method for dynamic assessment of collision risk between floating wind turbines and ships according to claim 1, characterized in that, The specific steps for analyzing and setting the overlap characteristic values ​​of ship wind turbines within the sea area are as follows: Read the safe zones of ships that can reach collection and floating wind turbines within the designated sea area and perform gridding processing; Based on the rasterized ship reachability cluster and floating wind turbine safety zone within the designated sea area, the spatial overlap rate, spatial intrusion rate, and cluster density values ​​within the designated sea area are extracted, and the ship-wind turbine overlap characteristic values ​​within the designated sea area are analyzed.

10. The method for dynamic assessment of collision risk between floating wind turbines and ships according to claim 1, characterized in that, The specific steps to obtain the collision risk value of ships and wind turbines within a designated sea area are as follows: Based on the overlapping characteristic values ​​of ship wind turbines within a designated sea area, the external kinetic energy of ships within the designated sea area is extracted. Based on the finite element simulation structural vulnerability curves stored in the database, the external kinetic energy of ships in a designated sea area is mapped to obtain the collision risk value of ships and wind turbines in the designated sea area.

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