Method for determining enteromorpha green tide full life cycle wind action coefficient

Abstract

The invention discloses an enteromorpha green tide full life cycle wind action coefficient determination method, and belongs to the field of enteromorpha green tide drift prediction. The method comprises the following steps: S1, acquiring satellite remote sensing images of enteromorpha green tide in different development stages, and interpreting the satellite remote sensing images to obtain enteromorpha green tide distribution data; and / or acquiring sea drift tracking observation data of enteromorpha green tide at different development stages; s2, constructing an enteromorpha green tide short-term drift numerical model; s3, acquiring surface ocean current and sea surface wind data of the research sea area; s4, determining optimal wind action coefficients of the enteromorpha green tide in different development stages; and S5, performing quadratic polynomial fitting, establishing a wind action coefficient dynamic model suitable for the enteromorpha green tide full life cycle, and further determining an enteromorpha green tide full life cycle wind action coefficient value. According to the method, the prediction precision of the enteromorpha green tide drift path can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of green tide of Enteromorpha prolifera drift prediction, in particular to a method for determining wind action coefficient of green tide of Enteromorpha prolifera in whole life cycle. BACKGROUND

[0002] Green tide of Enteromorpha prolifera has the characteristics of high biomass, long distance migration process and great influence, which has caused serious influence on marine ecological environment, coastal tourism, marine sports and aquaculture. The prevention and control of green tide of Enteromorpha prolifera has become a major task in the field of ocean. Existing researches have found that the occurrence and development process of green tide of Enteromorpha prolifera mainly includes the following stages: the floating occurrence stage of green tide algae from suspended green algae, sporadic green algae and small patches to strip and large patches, and the scale green tide formation stage. The surface of green tide patch of Enteromorpha prolifera has a certain roughness, and its drift is mainly driven by sea surface wind and sea current. The wind action coefficient refers to the ratio of the drift speed of the floating green tide patch of Enteromorpha prolifera to the wind speed under the action of sea surface wind only, which reflects the drag effect of wind on the green tide patch of Enteromorpha prolifera. The patch area and floating state of green tide of Enteromorpha prolifera are significantly different in different development stages, resulting in different wind action coefficients of green tide of Enteromorpha prolifera in different development stages. However, at present, the wind action coefficient in the prediction of green tide of Enteromorpha prolifera is mainly determined by empirical data, sea trial and other methods, and is generally a constant. Obviously, the dynamic changes of wind action coefficient in different development stages are not fully considered, which to some extent limits the accuracy of the prediction of drift path. SUMMARY

[0003] Based on the above technical problems, the present application provides a method for determining wind action coefficient of green tide of Enteromorpha prolifera in whole life cycle.

[0004] The technical solution adopted by the present application is as follows: A method for determining wind action coefficient of green tide of Enteromorpha prolifera in whole life cycle, comprising the following steps: S1, dividing the different development stages of green tide of Enteromorpha prolifera into early stage, outbreak stage and extinction stage; obtaining satellite remote sensing images of different development stages of green tide of Enteromorpha prolifera, and interpreting the satellite remote sensing images to obtain green tide distribution data; and / or obtaining sea drift tracking observation data of different development stages of green tide of Enteromorpha prolifera; S2, constructing a short-term drift numerical model of green tide of Enteromorpha prolifera; S3, obtaining surface current and sea surface wind data of the study area; S4, determining the optimal wind action coefficient; The distribution data of Enteromorpha prolifera green tide in different development stages is obtained by interpreting satellite remote sensing images in S1, and / or the data of the sea drifting tracking observation, and the surface current and sea surface wind data obtained in S3, and the short-term drifting numerical model of the Enteromorpha prolifera green tide is combined to determine the optimal wind action coefficient of the Enteromorpha prolifera green tide in different development stages. S5, a dynamic model of the wind action coefficient in the whole life cycle of the Enteromorpha prolifera green tide is established. According to the optimal wind action coefficient of the Enteromorpha prolifera green tide in different development stages determined in S4, a dynamic model of the wind action coefficient suitable for the whole life cycle of the Enteromorpha prolifera green tide is established by quadratic polynomial fitting, and the wind action coefficient value in the whole life cycle of the Enteromorpha prolifera green tide is determined according to the dynamic model of the wind action coefficient.

[0005] The beneficial technical effects of the present application are as follows: (1) The present application breaks through the limitation that the traditional wind action coefficient is a constant, and a dynamic model of the wind action coefficient suitable for the whole life cycle of the Enteromorpha prolifera green tide is established; that is, by dividing the different development stages of the Enteromorpha prolifera green tide, based on satellite remote sensing monitoring and sea tracking data, and combined with the short-term drifting numerical model of the Enteromorpha prolifera green tide, a quantitative model of the dynamic change of the wind action coefficient with the different development stages of the Enteromorpha prolifera green tide is creatively established. The present application can significantly improve the prediction accuracy of the drifting of the Enteromorpha prolifera green tide.

[0006] (2) The traditional business prediction model uses a fixed wind action coefficient (usually based on limited experience or specific stage test results), ignoring the significant difference in patch area and floating state of the Enteromorpha from the early sporadic patches, the large-scale aggregation in the outbreak period to the process of the Enteromorpha in the dying period. The present application establishes a quantitative functional relationship between the wind action coefficient and the growth and death of the Enteromorpha. The obtained dynamic quantitative model reflects the physical evolution law of the wind driving force with the growth, aggregation and death of the Enteromorpha, and can significantly improve the prediction accuracy of the drifting path and coverage of the Enteromorpha, providing more reliable decision support for the three lines of defense of “sea salvage, near-shore interception and shore cleaning” for disaster prevention and reduction.

[0007] (3) The present application uses the speed fitting method and the trajectory similarity optimization method for combination, which can reasonably determine the optimal wind action coefficient; that is, through two-stage optimization strategy: the first stage is based on the dynamic sub-process (speed matching) to determine the initial value of the parameter, to ensure that the solution is located in the physically feasible neighborhood; the second stage is to fine-tune the overall similarity of the trajectory as the ultimate index, effectively suppressing the error accumulation effect, so as to obtain the optimal wind action coefficient which is consistent with the observation in the dynamic response and motion form.

[0008] (4) The present application provides a new method for quantitatively analyzing the relative contribution and coupling mechanism of the sea current and the wind field in the long-distance transport process of the Enteromorpha prolifera green tide, which has important scientific significance for elucidating the environmental driving mechanism of the Enteromorpha prolifera green tide. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 A flowchart of the method for determining the wind action coefficient of Enteromorpha green tide throughout its life cycle according to the present application is shown in the figure. Figure 2 A comparison chart of the prediction results and the measured results of the satellite remote sensing monitoring of Enteromorpha green tide under the optimal wind action coefficient in the specific application example of the present application is shown in the figure. Figure 3 A comparison chart of the tracking drift trajectory of Enteromorpha green tide at sea and the prediction results under the optimal wind action coefficient in the specific application example of the present application is shown in the figure. Figure 4 A quadratic polynomial fitting curve of the wind action coefficient of Enteromorpha green tide in the specific application example of the present application is shown in the figure. Figure 5 A comparison chart of the drift trajectory of Enteromorpha green tide predicted based on the wind action coefficient throughout the life cycle of Enteromorpha green tide and the traditional wind action coefficient is shown in the figure. DETAILED DESCRIPTION

[0010] The patch area and floating state of Enteromorpha at different development stages are significantly different, resulting in different wind action coefficients of Enteromorpha green tide at different development stages. The wind action coefficient in the current Enteromorpha green tide drift prediction model is a constant at different development stages, which does not fully consider its dynamic changes, which to some extent limits the accuracy of the drift path prediction. Based on this, the present application proposes a method for determining the wind action coefficient of Enteromorpha green tide throughout its life cycle, which can determine the dynamic wind action coefficient of Enteromorpha green tide throughout its life cycle, which not only helps to improve the prediction accuracy of the drift path of Enteromorpha green tide, but also has important scientific significance for in-depth understanding of the different contributions of sea currents and wind fields and the coupling mechanism in the long-distance transport process of Enteromorpha green tide.

[0011] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0012] As shown in the figure, a method for determining the wind action coefficient of Enteromorpha green tide throughout its life cycle includes the following steps: Figure 1 S1, obtaining satellite remote sensing images and sea drift tracking observation data of Enteromorpha green tide at different development stages. S1, obtaining satellite remote sensing images and sea drift tracking observation data of Enteromorpha green tide at different development stages.

[0013] Collect at least 5 years of Enteromorpha green tide distribution characteristic data, such as annual coverage area time series and first discovery of Enteromorpha sporadic floating time. Calculate the growth rate of Enteromorpha green tide based on the coverage area. Calculate the average starting date of Enteromorpha sporadic floating based on the first discovery of Enteromorpha sporadic floating time.

[0014] Early stage refers to the period before the bloom scales up (coverage area > 5 km2), outbreak stage refers to the period from the bloom scales up to the biomass significantly decreases (growth rate < -10%), generally from May to early July, and decline stage refers to the period from the biomass significantly decreases to the bloom basically disappears, generally after early July.

[0015] Meanwhile, based on the collected first discovery of scattered floating Enteromorpha prolifera each year, the average starting time of scattered floating Enteromorpha prolifera is calculated.

[0016] High-resolution satellite remote sensing images of different development stages (early stage, outbreak stage and decline stage) of Enteromorpha prolifera bloom in different sea areas (such as the sea area near the North Jiangsu Shoal, the sea area near 35 degrees, the sea area north of 35 degrees, and the sea area near Yantai and Weihai) under sunny weather conditions are selected for interpretation to obtain Enteromorpha prolifera bloom distribution data.

[0017] The tracking observation data or ship positioning observation data of Enteromorpha prolifera bloom in different sea areas and different development stages are collected to obtain long-time series data of Enteromorpha prolifera drift, generally not less than 24 h in length and not more than 1 h in each time interval.

[0018] The above collected data sets (cases) are not less than 100, considering that early monitoring data is less and it is difficult to track the drift of Enteromorpha prolifera bloom, other time periods are easier to track the drift of Enteromorpha prolifera bloom, among which the outbreak stage and decline stage are not less than 30.

[0019] S2, construct a short-term drift numerical model of Enteromorpha prolifera bloom.

[0020] Based on the Lagrangian particle tracking method, without considering the growth and decline process of Enteromorpha, considering the drag effect of wind and current on Enteromorpha prolifera bloom patches, a short-term drift numerical model of Enteromorpha prolifera bloom is constructed.

[0021] ; In the formula, is the position of the i th Enteromorpha prolifera patch at time t; is the surface current velocity, is the 10 m wind speed on the sea surface; is the flow coefficient; is the wind coefficient, representing the direct drag effect of wind on Enteromorpha prolifera patch; represents the change of wind on the direction of Enteromorpha prolifera patch movement; the x-axis direction is , and the y-axis direction is , wherein is the angle between the wind and the x-axis direction, in degrees, is the wind drag deflection angle, in degrees.

[0022] S3, obtain the surface current and sea surface wind data of the study sea area.

[0023] Obtain the hydro-meteorological data of the surface current and sea surface wind of the study sea area where the data set (case) is located; the main sources are observation data or numerical simulation data, wherein the time interval of the numerical simulation data is generally 1 h, and the time length is longer than that of the observation.

[0024] S4, obtain the optimal wind action coefficient of each case (each data set).

[0025] S41, for satellite remote sensing data, the position of the initial moment of the satellite remote sensing feature point, based on the numerical model of green tide drift constructed in S2 and the surface current and sea surface wind data (environmental field) of the study sea area obtained in S3, carry out numerical sensitivity test of wind action coefficient. The flow action coefficient R1 in the short-term drift numerical model of green tide is set to 1, and the value range of the wind action coefficient R2 is 0.001 to 0.030. The distance error between the predicted position and the measured position under each test scheme is calculated (that is, the distance error corresponding to the change of the value of the wind action coefficient R2 is calculated). The minimum distance error is taken as the judgment criterion to determine the optimal wind action coefficient in the tracking process of each particle or green tide patch.

[0026] S42, for the sea drift tracking observation data, the drift time sequence of the green tide is obtained. Based on the numerical simulation data of the sea surface wind field and the flow field and the drift position sequence of the green tide, the optimal wind action coefficient is determined by using the velocity fitting method and the trajectory similarity optimization method. The specific method is as follows: S421, velocity fitting method: preliminary parameter optimization based on velocity error minimization; This method aims to obtain the initial estimated value R 2,init of the wind action coefficient R2 by minimizing the simulation error of the drift velocity. The specific steps are as follows: (a) Construct the velocity error objective function; The velocity error objective function is the root mean square error of the residual between the simulated velocity and the observed velocity, as follows: ; In the formula: t = 1, 2, … n, represents different time; is the velocity error objective function; is the simulated velocity; is the observed velocity.

[0027] (b) Gradient descent iteration; The gradient descent method is used to optimize the velocity error objective function, and the specific steps include: Initialization: Set the initial guess of the wind action coefficient R2(0)=0.001 in the range of 0<R2<0.03.

[0028] Gradient calculation: In the kth iteration, calculate the gradient (first derivative) of the velocity error objective function with respect to the parameter (R2); ; In the formula: The gradient of the velocity error objective function with respect to the parameter indicates the rate of change and trend of the objective function with the increase of R2.

[0029] (c) Parameter update: Adjust the parameter in the opposite direction of the gradient, and the update formula is: ; In the formula: H is the learning rate (step size), which can be set to a value comparable to the magnitude of the parameter itself, such as 0.01. If the gradient of the velocity error objective function with respect to the parameter is positive, then decrease R2 to reduce the error.

[0030] (d) Convergence criterion: When the following conditions are met: (Gradient threshold, generally set to 10 -4 ), or (Parameter change threshold, generally set to 10 -5 ), or the maximum number of iterations is reached, the iteration is terminated. At this time, the obtained R 2,init is the local optimal solution in the sense of minimizing the velocity error, which is the initial estimate of the wind action coefficient.

[0031] S422, Fine parameter optimization based on trajectory similarity criterion; After obtaining the initial estimate R 2,init , further fine-tune the wind action coefficient based on the overall trajectory fitting criterion, and the specific process is as follows: (a) Construct the trajectory similarity objective function; Construct the trajectory similarity objective function, and take the root mean square error of the simulated trajectory and the observed trajectory as the evaluation index; ; In the formula: t=1, 2, …n, represents different time; is the simulated trajectory; is the observed trajectory.

[0032] (b) Gradient descent iteration; Initialization: Take the R 2,init obtained in the previous step as the starting point of this stage of optimization, which can effectively improve the convergence efficiency and avoid false local minimum points.

[0033] Gradient approximation: The commonly used finite difference method is expressed as follows: ; where δ is a small perturbation. High-precision gradient calculation can also be achieved by using adjoint model or automatic differentiation technique.

[0034] (c) Parameter update: iterative update along the opposite direction of the gradient: ; where β is the learning rate of this stage, which should be smaller than the learning rate (step size) of the first stage, and is usually set to 0.001.

[0035] (d) Convergence criterion: When the trajectory error changes tend to be stable, such as , is the set threshold (usually set to 10 -4 ), or the maximum number of iterations is reached, the iteration is terminated. The wind action coefficient R 2,optimal obtained finally is the optimal parameter estimation value on the trajectory scale, that is, the optimal wind action coefficient.

[0036] The initial time position of the satellite remote sensing feature point and the green tide drift position sequence are all from the data set.

[0037] The two-stage optimization strategy of the application has physical rationality and computational robustness: the first stage determines the initial value of the parameter based on the dynamic sub-process (velocity matching), ensuring that the solution is located in the physically feasible neighborhood; the second stage fine-tunes the whole trajectory similarity as the ultimate indicator, effectively suppressing the error accumulation effect, so as to obtain the optimal wind action coefficient that is consistent with the observation in both short-term dynamic response and long-term motion form.

[0038] S5, a dynamic model of the wind action coefficient of the whole life cycle of green tide is established.

[0039] The variation law of the optimal wind action coefficient determined in S4 with time is analyzed, the optimal wind action coefficients obtained in different time periods are fitted by a quadratic polynomial, and a dynamic model of the wind action coefficient suitable for the whole life cycle of green tide is established.

[0040] Specifically, the dynamic model of the wind action coefficient is as follows: ; Where A, B, and C are to-be-fitted numerical values; and x is the number of days from the starting date of sporadic drifting of green tide. The specific values of A, B, and C in the dynamic model of the wind action coefficient are determined by fitting, and the wind action coefficients in different time periods can be calculated.

[0041] The wind action coefficient dynamic model is combined with the Enteromorpha prolifera green tide short-term drift numerical model to perform the Enteromorpha prolifera green tide drift prediction, that is, the optimal wind action coefficient of different time periods is obtained in real time according to the wind action coefficient dynamic model, and then the wind action coefficient is substituted into the Enteromorpha prolifera green tide short-term drift numerical model to obtain the Enteromorpha prolifera green tide drift prediction result.

[0042] In summary, the to-be-fitted values of the wind action coefficient dynamic model are determined by using the method of the application through historical data. When performing prediction, the initial date of the sporadic floating of Enteromorpha prolifera in the current year is determined, and then the wind action coefficient corresponding to the corresponding day from the initial date of the sporadic floating of Enteromorpha prolifera is predicted. The wind action coefficient is substituted into the Enteromorpha prolifera green tide short-term drift numerical model, and the prediction result is obtained in combination with the surface current and sea surface wind data.

[0043] The application will be further described below in combination with specific application examples.

[0044] 1. Collecting characteristic data of Enteromorpha prolifera green tide, satellite remote sensing and sea tracking test data in different development stages (early stage, outbreak period and extinction period).

[0045] The distribution characteristic data of Enteromorpha prolifera green tide in the Yellow Sea from 2021 to 2025 are collected, such as the total coverage area time sequence in the whole period and the time of first discovery of sporadic floating of Enteromorpha prolifera each year. Considering that it is relatively difficult to obtain the coverage area before large-scale outbreak, the growth rate of Enteromorpha prolifera green tide is calculated based on the coverage area time sequence each day, and the early stage, outbreak period and extinction period of Enteromorpha prolifera green tide in the current year are divided according to the combination of the coverage area and the growth rate. The early stage of Enteromorpha prolifera green tide generally refers to the period from the sporadic Enteromorpha prolifera monitored by a ship to the period before the outbreak (the coverage area reaches 5 square kilometers); the outbreak period refers to the period from the outbreak to the period when the biomass is significantly reduced (the growth rate is less than-10%), generally from May to early July; the extinction period refers to the period from the significant reduction of the biomass to the disappearance of the Enteromorpha prolifera green tide, generally after early July. The average initial date of sporadic floating of Enteromorpha prolifera green tide is calculated based on the time of first discovery of sporadic floating of Enteromorpha prolifera.

[0046] Taking 2024 as an example, sporadic floating green algae was first discovered by a ship in the northern coastal waters of Sheyang on April 15; the coverage area reached 5 square kilometers on May 13; the growth rate of Enteromorpha prolifera green tide was less than-10% since July 3, and the Enteromorpha prolifera green tide basically disappeared on August 14. Therefore, the early stage is from April 15 to May 12, the outbreak period is from May 13 to July 2, and the extinction period is from July 3 to August 14. The earliest time of sporadic floating Enteromorpha prolifera monitored by a ship from 2021 to 2025 is April 15 on average, so the average initial date of sporadic floating of Enteromorpha prolifera is April 15.

[0047] (1) Select high-resolution satellite remote sensing images of different sea areas of Enteromorpha green tide at different development stages (early stage, outbreak period and decline period) under sunny weather conditions from 2021 to 2025. According to the distribution characteristics of Enteromorpha, select feature points to construct a data set with more than two monitoring data.

[0048] (2) Collect Enteromorpha green tide tracer tracking observation and ship positioning observation data of different sea areas at different development stages and different sea areas (such as the shoal of northern Jiangsu, the sea area near 35 degrees, the sea area north of 35 degrees, Yantai and Weihai, etc.) to obtain long-time series data of Enteromorpha green tide drift, with a time length of more than 24 hours in most cases and a time interval of not more than 1 hour.

[0049] There are a total of 115 satellite remote sensing and tracking observation data sets, 7 data sets in the early stage, 64 data sets in the outbreak period, and 44 data sets in the decline period.

[0050] The above 115 data sets are divided into training set and validation set, of which the training set is 100 and the validation set is 15. The training set and the validation set both include different development stages of Enteromorpha green tide.

[0051] 2. Construct a short-term drift prediction model of Enteromorpha green tide.

[0052] Based on the Lagrangian particle tracking method, without considering the growth and decline process of Enteromorpha, considering the drag effect of wind and current on Enteromorpha green tide patches, a short-term drift numerical model of Enteromorpha green tide is constructed.

[0053] ; In the formula, is the position of the i-th Enteromorpha green tide patch at time t; is the surface current velocity, is the 10 m wind speed on the sea surface; is the flow coefficient; is the wind coefficient, representing the direct drag effect of wind on Enteromorpha green tide patches; represents the change of wind on the movement direction of Enteromorpha green tide patches; the x-axis direction is , and the y-axis direction is , where is the angle between the wind and the x-axis direction, with a unit of degree, is the wind drag deflection angle, with a unit of degree.

[0054] 3. Obtain the surface current and sea surface wind data of the study area.

[0055] Obtain the hydro-meteorological data of surface current and sea surface wind in the nearby sea area of the case (data set). The main source is numerical simulation data, and the time interval of numerical simulation data is generally 1 h, and the time length is longer than the observation time length. In the example, the sea surface wind mainly comes from the WRF operational meteorological forecasting system; the surface current mainly comes from the ROMS three-dimensional current prediction system, and the data time interval is 1 h. The data space range is the Yellow Sea, which is larger than the research area.

[0056] 4. Obtain the optimal wind action coefficient of each case (each data set).

[0057] (1) For satellite remote sensing data, carry out numerical sensitivity test of wind action coefficient. Take May 13, 2024 as an example, based on 5 satellite remote sensing feature points (green points) near the North Jiangsu Shoal, carry out numerical sensitivity test, set the flow action coefficient R1 as 1, the wind drag deflection angle as 20°, and the wind action coefficient R2 as 0.001 to 0.030 with an interval of 0.005. Calculate the minimum distance absolute error between the simulated position (red point) and the measured position (blue point) under each test scheme, and obtain the optimal wind action coefficient for each feature point. Figure 2 Figure 2 Figure 2

[0058] (2) For the data of the sea drift tracking test, the time series of the drift position of Enteromorpha prolifera green tide is obtained. Set the flow action coefficient R1 as 1 and the wind drag deflection angle as 20°. Based on the numerical simulation data of sea surface wind field and current field and the time series of the drift position of Enteromorpha prolifera green tide, determine the optimal wind action coefficient by using the velocity fitting method and the trajectory similarity optimization method. Take June 13, 2025 as an example, based on the initial point of the tracking trajectory as the simulation initial time, based on the numerical simulation data of sea surface wind field and current field, and using a hypothetical wind action coefficient 0.01, use the short-term drift numerical model of Enteromorpha prolifera green tide to calculate the simulated drift velocity and drift trajectory (). Figure 3

[0059] Then establish the drift velocity error objective function, and take the square sum of the difference between the predicted drift velocity and the true drift velocity as the evaluation index; set 0 < R2 < 0.03, and use the gradient descent method to solve the wind action coefficient that minimizes the drift velocity error objective function. Take the wind action coefficient calculated in the previous step as the initial value of the trajectory similarity method, and construct the trajectory similarity evaluation function; by calculating the root mean square error of the deviation between the predicted drift trajectory and the observation, also use the gradient descent method to determine the optimal wind action coefficient value.

[0060] Finally, 100 optimal wind action coefficients are obtained at different development stages (early stage, outbreak stage and extinction stage). ​​​​

[0061] 5. Analyze the distribution characteristics of wind action coefficient and construct a wind action coefficient model for the entire life cycle of Ulva prolifera green tide.

[0062] The distribution characteristics of the wind influence coefficient over time (different development stages) were analyzed. At different development stages, the wind influence coefficient of the *Ulva prolifera* green tide exhibited obvious dynamic changes, which were consistent with the life cycle variation of *Ulva prolifera*. In the early stage, when the *Ulva prolifera* was in a suspended state, the wind influence coefficient was 0; later, when the *Ulva prolifera* was mainly in a floating state, the wind influence coefficient was relatively small, generally below 0.01. After entering the outbreak stage, the wind influence coefficient gradually increased, mainly distributed between 0.01 and 0.02, reaching its peak around June 15th. During the decline stage, the wind influence coefficient decreased significantly, eventually approaching 0. Figure 4 ).

[0063] By performing quadratic polynomial fitting on 100 optimal wind action coefficients for different time periods (early stage, outbreak stage, and decline stage), a dynamic model of wind action coefficients applicable to the entire life cycle of Ulva prolifera green tides was constructed.

[0064] ; Where x is the number of days since the first sporadic floating of seaweed.

[0065] 6. Model validation; Fifteen validation sets that were not involved in the fitting were used, namely satellite data on the characteristic points or drift tracking of Ulva prolifera green tides at different development stages (early stage, outbreak stage, and decline stage). Combined with environmental field data, the drift trajectory of Ulva prolifera green tides was predicted based on the drift numerical model, and the distance error was calculated to verify the prediction effect of the model after using the parameters.

[0066] like Figure 5 As shown, taking the marine drift tracking experiment conducted using a seaweed tracker in June 2025 as an example, based on the initial position of the seaweed tracker (red dot) at 11:00 on June 12th, the wind action coefficient obtained using the full-cycle wind action coefficient model and the traditional wind action coefficient (generally 0.01) were used to forecast the drift of the seaweed green tide using a numerical model. Two drift trajectories of the seaweed green tide patches were obtained and compared. The results show that the drift distance error of the drift trajectory obtained based on the full-cycle wind action coefficient model is significantly smaller at each moment than that obtained using the traditional wind action coefficient. Comparison with other monitoring data and forecast results shows that the full-cycle wind action coefficient model can better reflect the dynamic periodic characteristics of wind-induced seaweed green tide patch drift, and its forecast distance error is smaller than that obtained using the traditional wind action coefficient.

[0067] In summary, the method of the present invention, by constructing a dynamic wind action coefficient that can determine the entire life cycle of the Ulva prolifera green tide, can help improve the prediction accuracy of the Ulva prolifera green tide drift path.

[0068] The parts not mentioned in the above manner can be realized by taking or referring to the existing technology.

[0069] Of course, the above description is only for the preferred embodiment of the present application, and the present application is not limited to the above-mentioned embodiments. It should be noted that any person skilled in the art can make all equivalent substitutions and obvious modifications under the teaching of the present application, which shall fall within the scope of the present application and shall be protected by the present application.

Claims

1. A method for determining the wind action coefficient throughout the entire life cycle of *Ulva prolifera* green tides, characterized in that... Includes the following steps: S1. Divide the different development stages of the green tide of Ulva prolifera into the early stage, the outbreak stage and the extinction stage; Acquire satellite remote sensing images of different development stages of the green tide of Ulva prolifera and interpret the satellite remote sensing images to obtain distribution data of the green tide of Ulva prolifera; and / or acquire marine drift tracking observation data of different development stages of the green tide of Ulva prolifera. S2. Construct a numerical model for the short-term drift of the green tide of *Ulva prolifera*; S3. Obtain surface current and sea surface wind data for the study area; S4. Determine the optimal wind action coefficient; The distribution data of the green tide of Ulva prolifera obtained by interpreting satellite remote sensing images of different development stages in S1, and / or the drift tracking observation data at sea, as well as the surface current and sea surface wind data of the study area obtained in S3, and combined with the constructed numerical model of short-term drift of the green tide of Ulva prolifera, the optimal wind action coefficient of the green tide of Ulva prolifera at different development stages was determined. S5. Establish a dynamic model of the wind action coefficient throughout the entire life cycle of Ulva prolifera green tide; Based on the optimal wind action coefficients for different development stages of the Ulva prolifera green tide determined in S4, a quadratic polynomial fitting was performed to establish a dynamic model of wind action coefficients applicable to the entire life cycle of the Ulva prolifera green tide. The wind action coefficient values ​​for the entire life cycle of the Ulva prolifera green tide were determined based on this dynamic model.

2. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 1, characterized in that, In S1: Collect data on the distribution characteristics of Ulva prolifera green tide, including the annual coverage area time series, and calculate the growth rate of Ulva prolifera green tide based on the coverage area time series; divide the different development stages of Ulva prolifera green tide into early stage, outbreak stage and extinction stage according to the coverage area and the growth rate of Ulva prolifera green tide; at the same time, determine the starting date of sporadic floating of Ulva prolifera based on the collected data on the distribution characteristics of Ulva prolifera green tide.

3. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 2, characterized in that, In S1: Select high-resolution satellite remote sensing images of different development stages of the green tide of Ulva prolifera under clear weather conditions, and select at least two monitoring data points based on the distribution and morphological characteristics of the green tide of Ulva prolifera to form a data set; Collect data from tracking observations or ship positioning observations of green tides of Ulva prolifera at different development stages to obtain long-term series data of green tide drift of Ulva prolifera, with a time length of not less than 24 hours and each time interval not greater than 1 hour, and form a data set. Set up no fewer than 100 data groups, with no fewer than 30 data groups covering both the outbreak and decline phases.

4. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 3, characterized in that, In S2: Based on the Lagrange particle tracking method, considering the dragging effect of wind and current on the green tide patches of Ulva prolifera, a numerical model of short-term drift of Ulva prolifera green tide is constructed. ； In the formula, Let i be the position of the i-th green algae patch at time t; Surface flow velocity, The wind speed at sea surface is 10 m. The flow action coefficient; The wind effect coefficient represents the direct drag effect of wind on the green tide patches of Ulva prolifera; This indicates the effect of wind on altering the direction of movement of the green tide patches of *Ulva prolifera*; the x-axis direction is... The y-axis direction is ,in The angle between the wind and the x-axis coordinate axis, in degrees. The deflection angle caused by wind drag is measured in degrees.

5. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 4, characterized in that, In S3: Obtain hydrological and meteorological data on surface current velocity and sea surface wind in the sea area where the data set is located; the source of this hydrological and meteorological data is observation data or numerical simulation data.

6. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 5, characterized in that, In S4: For the distribution data of Ulva prolifera green tide obtained from satellite remote sensing image interpretation, based on the constructed numerical model of short-term drift of Ulva prolifera green tide and surface current and sea surface wind data of the study sea area, a numerical sensitivity experiment of wind action coefficient is carried out. The distance error between the predicted position and the actual monitoring position under each experimental scheme is calculated. The optimal wind action coefficient for each data group is determined by minimizing the distance error as the evaluation criterion.

7. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 6, characterized in that, In S4: For the offshore drift tracking and observation data, obtain the drift position sequence of the Enteromorpha prolifera green tide; based on the surface current and sea surface wind data in the study area and the drift position sequence of the Enteromorpha prolifera green tide, use the velocity fitting method and the trajectory similarity optimization method to determine the optimal wind action coefficient.

8. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 7, characterized in that, The specific steps of the velocity fitting method are as follows: a. Construct a velocity error objective function; The velocity error objective function is the root mean square error of the residual between the simulated velocity and the observed velocity, as shown in the following formula: ； In the formula: t = 1, 2, ... n, representing different times; Let the velocity error be the objective function; To simulate speed; For observation speed; b. Use the gradient descent method to optimize the velocity error objective function, including: Initialization: Set the initial guess value of the wind action coefficient R2(0) = 0.001 within the range of 0 < R2 < 0.03; Gradient calculation: In the k-th iteration, calculate the gradient of the velocity error objective function with respect to the parameter; ； In the formula: This represents the gradient of the velocity error objective function with respect to the parameters; the gradient direction indicates the rate of change and trend of the objective function as R² increases. c. Adjust the parameter along the opposite direction of the gradient, and the update formula is: ； In the formula: H is the learning rate; if the gradient of the velocity error objective function with respect to the parameter is positive, then reduce R2 to reduce the error; d. Convergence criterion: When the following conditions are met... , Gradient threshold; or , The parameter variation threshold is used; or the iteration terminates when the maximum number of iterations is reached; at this point, the obtained R... 2,init That is, the local optimum solution in the sense of minimizing velocity error; In obtaining R 2,init Subsequently, the wind effect coefficient was further refined using the trajectory similarity optimization method. The specific steps are as follows: e. Construct a trajectory similarity objective function, and use the root mean square error of the spatial positions between the simulated trajectory and the observed trajectory as the evaluation index; ； In the formula: t = 1, 2, ... n, representing different times; To simulate the trajectory; For observation trajectory; f. Perform gradient descent iteration, including: Initialization: R obtained in the previous step 2,init This serves as the starting point for optimization in this phase; The finite difference formula is as follows: ； In the formula: δ is a small perturbation; g. Perform iterative update along the opposite direction of the gradient: ； In the formula, β is the learning rate of this stage; h. Convergence determination: The iteration terminates when the trajectory error stabilizes or the maximum number of iterations is reached; the final wind action coefficient R is obtained. 2,optimal This is the optimal wind action coefficient for the corresponding data set.

9. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 8, characterized in that, In S5, the dynamic model of the wind action coefficient is as follows: ； In the formula, A, B, and C are numerical values to be fitted; x is the number of days from the starting date of the sporadic floating of Enteromorpha prolifera.

10. The method for determining the wind action coefficient of the entire life cycle of *Ulva prolifera* green tide according to claim 9, characterized in that: Combine the dynamic model of the wind action coefficient with the short-term drift numerical model of the Enteromorpha prolifera green tide to predict the drift of the Enteromorpha prolifera green tide.

Images