Method for defining a green tide fishing cloth defense zone

Abstract

This invention discloses a method for delineating deployment zones for the harvesting of *Ulva prolifera* (a type of algae) green tides, relating to the field of *Ulva prolifera* green tide disaster prevention and control. The method includes the following steps: initially determining the harvesting area; acquiring satellite remote sensing images of the harvesting area and interpreting them to obtain the distribution location and range of the *Ulva prolifera* green tide; setting up grid cells within the distribution range of the *Ulva prolifera* green tide; constructing an ecodynamic forecasting model for the *Ulva prolifera* green tide; acquiring future forecast data from sensitive targets of the *Ulva prolifera* green tide disaster to the sea area near the grid cells, and combining this with the ecodynamic forecasting model to obtain the drift position and biomass of *Ulva prolifera* green tide particles at each future moment; constructing a deployment level model for the harvesting of *Ulva prolifera* green tides from three aspects: nearshore threat, biomass accumulation, and beaching trend; and delineating the deployment zones for the harvesting of *Ulva prolifera* green tides. This invention, by scientifically and accurately delineating deployment zones for the harvesting of *Ulva prolifera* green tides, can provide guidance for the priority order of *Ulva prolifera* green tide harvesting.

Description

Technical Field

[0001] This invention relates to the field of prevention and control of green tide disasters caused by seaweed, specifically to a method for delineating deployment zones for seaweed harvesting and prevention. Background Technology

[0002] Ulva prolifera green tides are characterized by high biomass, long-distance transport, and significant impact. Marine salvage, near-shore interception, and shoreline cleanup constitute the three lines of defense against Ulva prolifera green tides. Among these, marine salvage is currently a crucial means of on-site control. However, during the salvage process, there is no clear methodology to determine which Ulva prolifera tides should be prioritized for salvage when facing threats from different stages of development, varying biomass, and different distances from sensitive targets.

[0003] Chinese invention patent application CN116467565A discloses a method for predicting the optimal search area for *Ulva prolifera* green tide patches. This method constructs a Monte Carlo probabilistic drift model for *Ulva prolifera* green tide patches that considers uncertainties such as drift, aggregation, and splitting. Based on the possible arrival time of salvage vessels, it statistically analyzes the probability distribution to predict the optimal search area. However, it does not address the priority determination of *Ulva prolifera* green tide salvage. Summary of the Invention

[0004] Based on the above-mentioned technical problems, this invention proposes a method for delineating deployment zones for the harvesting of green algae tides.

[0005] The technical solution adopted in this invention is:

[0006] A method for delineating deployment zones for harvesting *Ulva prolifera* (green algae) tides includes the following steps:

[0007] a. Based on the prevention and control needs, identify sensitive targets for the green tide disaster caused by seaweed, and preliminarily determine the salvage area based on the location of the sensitive targets.

[0008] b. Obtain satellite remote sensing images of the salvage area initially determined in step a, and interpret the distribution location and range of the seaweed green tide in the salvage area;

[0009] c. Set up grid units within the distribution area of ​​the green tide of *Ulva prolifera*;

[0010] d. Construct an ecodynamic forecasting model for the green tide of *Ulva prolifera*;

[0011] e. Obtain future forecast data of sea areas near grid cells that are sensitive to the green tide disaster of Ulva prolifera; Based on the distribution location and range of Ulva prolifera green tide obtained in step b, determine the location and biomass of Ulva prolifera green tide particles, and then combine the Ulva prolifera green tide ecodynamic forecast model constructed in step d, as well as the obtained forecast data, to obtain the drift location and biomass of Ulva prolifera green tide particles at each future moment.

[0012] f. Based on the drift position and biomass of the green tide particles at each future moment obtained in step e, construct a green tide salvage deployment level model from three aspects: nearshore threat of green tide, biomass accumulation and beach landing trend.

[0013] g. Delineate the deployment zone for the green tide salvage of seaweed based on the deployment level model for seaweed green tide salvage constructed in step f.

[0014] The beneficial technical effects of the present invention are as follows:

[0015] This invention scientifically and precisely delineates the deployment zones for harvesting *Ulva prolifera* (green algae) tides, thereby providing guidance for prioritizing harvesting efforts and offering key technical support for harvesting decisions. Specifically:

[0016] (1) This method is the first to integrate three core indicators for salvage scenarios: nearshore threat, biomass accumulation, and beaching trend, which differs from current research that mainly uses the density of Ulva prolifera green tides for disaster risk warning or assessment. The offshore distance hazard index quantifies the risk decay characteristics of Ulva prolifera at offshore distance, solving the boundary effect problem of the inverse distance method; the biomass accumulation index integrates future forecast data and uses a time-weighted mechanism to highlight the cumulative pressure of biomass; the beaching trend index correlates the drift direction with the angle between the shoreline and the shoreline, accurately predicting the probability of landing. The model dynamically couples multiple dimensions represented by the above indices, such as spatial distance, temporal evolution, ecological load, and landing trend, to achieve a leap from static warning to dynamic risk assessment, improving the scientificity and comprehensiveness of salvage decisions.

[0017] (2) The innovation of this method lies in the construction of a dynamic weight adjustment mechanism based on context awareness. This mechanism simulates practical decision-making thinking and can automatically switch the focus of the assessment strategy according to the different development stages of the seaweed (far, middle, and near shore), thereby achieving a smooth transition from "long-term source suppression" to "short-term risk avoidance". For example, when the sensitive shoreline is threatened, the system will automatically enter "emergency mode" and increase the weight of the near shore threat index. This dynamic focusing capability ensures that the assessment results are always consistent with the prevention and control objectives, and improves the timeliness and scientific nature of resource allocation.

[0018] (3) This method innovatively transforms the salvage priority index into a four-level action command, dynamically dividing the key salvage area, salvage patrol area, monitoring and warning area, and general patrol area through quantiles. Based on the comprehensive index ranking of all grid units, a four-color deployment map of red, orange, blue, and green is generated to intuitively guide the deployment of ships and interception nets. This system not only solves the problem of ambiguous priorities in traditional manual decision-making, but also achieves seamless connection from numerical forecasting to visualized command, providing commanders with immediate and operable action plans, and improving the response speed and control effectiveness of emergency salvage. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0020] Figure 1 This is a flowchart illustrating the method for delineating the deployment area for harvesting seaweed in the green tide according to the present invention.

[0021] Figure 2 A schematic diagram of the nearshore threat index at different distances from the nearshore shore;

[0022] Figure 3 To preliminarily determine the distribution map of the green tide of *Ulva prolifera* within the salvage area using satellite remote sensing monitoring;

[0023] Figure 4 This is a diagram illustrating the nearshore threat index.

[0024] Figure 5 This is a schematic diagram of the biomass aggregation index;

[0025] Figure 6 Map showing the deployment levels for salvage operations. Detailed Implementation

[0026] like Figure 1 As shown, this invention proposes a method for delineating deployment zones for the salvage of *Ulva prolifera* (a type of algae bloom) during green tides. This method, by determining the deployment zone levels for *Ulva prolifera* green tide salvage, provides decision support for emergency salvage operations by command personnel in charge of disaster prevention and control.

[0027] The technical solution adopted in this invention is:

[0028] A method for determining the deployment zone for combating seaweed blooms includes the following steps:

[0029] a. Identify disaster-sensitive targets;

[0030] Based on prevention and control needs, sensitive targets for the Ulva prolifera green tide disaster were identified. The main pathways through which the Ulva prolifera green tide disaster might affect these sensitive targets were collected and compiled, and preliminary salvage areas were determined.

[0031] b. Interpreting satellite remote sensing and UAV monitoring data;

[0032] Obtain satellite remote sensing images or monitoring data from drones in the salvage area to determine the location and extent of the green tide of seaweed.

[0033] The steps for interpreting satellite remote sensing images are as follows:

[0034] Specifically, after acquiring satellite remote sensing images, scattered points of Ulva prolifera green tides are extracted based on operational green tide information inversion algorithms. First, the remote sensing images are preprocessed, including radiometric calibration and atmospheric correction, to obtain atmospheric low-level radioactivity products. Then, Ulva prolifera green tide remote sensing detection algorithms, such as Normalized Diffusion Vibration Index (NDVI), are used to calculate the preprocessed images. Finally, the calculated images are visually interpreted, and boundary values ​​that can be identified as Ulva prolifera green tide pixels are selected. The detection index is used as a threshold for Ulva prolifera green tide correlation extraction.

[0035]

[0036] In the formula, and These are the reflectance values ​​in the near-infrared and infrared bands, respectively. The presence or absence of *Ulva prolifera* is determined by setting a threshold T0. The T value is jointly determined by visual interpretation using NDVI threshold segmentation and false-color composite images. The theoretical value of the threshold T0 is 0, but it fluctuates due to various factors (shallow water, water depth, etc.).

[0037] c. Set the grid;

[0038] Grid units should be set up according to the distribution of the green tide of seaweed within the possible salvage area and the needs of emergency prevention and control. In principle, the area of ​​a grid unit should not exceed 10km×10km.

[0039] d. Construct an ecodynamic forecasting model for the green tide of *Ulva prolifera*;

[0040] Based on the "Lagrange particle tracking" method, floating seaweed green tide patches are discretized into a certain number of seaweed green tide particles, taking into account the drift of seaweed green tide patches and the process of seaweed growth and decay. Within each model time step, the drift of particles is calculated based on wind speed and flow velocity, and the particles grow or die with the marine and atmospheric environmental factors and the particle's own state.

[0041] d1. Considering the dragging effect of wind and current on the green tide patches of Ulva prolifera, a dynamic drift submodule is constructed. The motion equation of Ulva prolifera green tide particles is shown in the following equation (1).

[0042] (1)

[0043] In equation (1), Let be the position of the i-th green tide particle of *Ulva prolifera* at time t; Surface flow velocity, The wind speed at sea surface is 10m. The flow action coefficient; The wind effect coefficient; This indicates the effect of wind on the direction of motion of particles in the green tide of *Ulva prolifera*; the x-axis direction is... The y-axis direction is ,in The angle between the wind and the x-axis is expressed in degrees. The deflection angle caused by wind drag is measured in degrees.

[0044] d2. Considering the crucial roles of temperature, light, and nutrients in the growth and decline of *Ulva prolifera* green tides, an ecological submodule for biomass changes in *Ulva prolifera* green tides was constructed, with the biomass of *Ulva prolifera* green tide particles from time t0 to t (…). The biomass change over time is shown in equation (2);

[0045] (2)

[0046] In equation (2), For the daily biomass change of the j-th *Ulva prolifera* green tide particle, For the j-th green tide particle of *Ulva prolifera*, the daily biomass increase is... This represents the daily decrease in the biomass of the j-th green tide of *Ulva prolifera* particles.

[0047] (3)

[0048] In equation (3), For the j-th green tide particle biomass of *Ulva prolifera*, This represents the maximum daily growth rate of the green tide of *Ulva prolifera*. , , These are the dimensionless limiting coefficients for the effects of temperature, light, and nutrients on the growth rate of *Ulva prolifera* green tides.

[0049] (4)

[0050] In equation (4), The maximum mortality rate; This is the dimensionless limiting coefficient of temperature on mortality. Let be the biomass of the j-th *Ulva prolifera* green tide particle.

[0051] d3. Construct an ecological dynamic prediction model for Ulva prolifera green tide by combining the Ulva prolifera green tide dynamic drift submodule and the Ulva prolifera green tide biomass change ecological submodule.

[0052] e. Acquire environmental data and conduct 7-day numerical weather prediction;

[0053] Data on surface currents, sea surface temperature, and nutrients for the next 7 days in the sea area near the grid unit from the disaster-sensitive target to the forecast sea area were obtained based on a three-dimensional marine ecological dynamics model; data on sea surface wind and solar shortwave radiation for the next 7 days in the forecast sea area were obtained based on a meteorological forecast model.

[0054] Based on the biomass per unit area of ​​*Ulva prolifera* green tide and the particle coverage area of ​​*Ulva prolifera* green tide monitored concurrently or historically, the particle biomass of *Ulva prolifera* green tide was obtained. Based on the monitored particle location and biomass of *Ulva prolifera* green tide, a *Ulva prolifera* green tide ecodynamic forecasting model was used to conduct numerical forecasts of drift and growth over the next 7 days, obtaining the drift location and biomass of *Ulva prolifera* green tide particles at each moment over the next 7 days.

[0055] f. Construct a disaster relief and deployment level model for *Ulva prolifera* green tide disaster;

[0056] A deployment level model for the salvage of *Ulva prolifera* tides was constructed based on three aspects: nearshore threats, biomass accumulation, and beach landing trends.

[0057] (1) Offshore distance risk index;

[0058] The nearshore threat of *Ulva prolifera* green tides is represented by the offshore distance hazard index, or nearshore threat index. The offshore distance hazard index is calculated using a piecewise exponential function. The specific process is as follows: First, based on the monitored distribution location of the *Ulva prolifera* green tides, the average distribution location (center of mass) within each grid cell is calculated. Simultaneously, shoreline vector data is acquired, and the distance between the mass center of the *Ulva prolifera* green tide and the nearest shoreline within each grid cell is calculated. The initial hazard index is calculated based on the piecewise function and then normalized across the entire area to obtain the standardized index. Specific calculations are shown in formulas (5) and (6).

[0059] (5)

[0060] (6)

[0061] In equation (5), k is the shore segment number, k=1,2,…,m; m is the total number of shore segments; i is the grid cell number, i=1,2,…,n; n is the total number of all grid cells; d ik D is the shortest distance from the mass center of all green tides of *Ulva prolifera* within the i-th grid cell to the k-th shore segment. i DK represents the initial hazard index of the i-th grid cell. i This represents the normalized offshore distance hazard index.

[0062] like Figure 2 To set the distance from the nearshore to 0-100km with an interval of 1km, the nearshore threat index was calculated based on formulas (5) and (6).

[0063] (2) Biomass aggregation index of *Ulva prolifera* green tide;

[0064] The biomass accumulation degree of *Ulva prolifera* green tides is quantified using the *Ulva prolifera* green tide biomass accumulation index; the calculation steps of this index are as follows: First, based on the *Ulva prolifera* green tide ecodynamic prediction model, the biomass changes of all *Ulva prolifera* green tide particles in the future period are obtained (that is, step 4 obtains the biomass changes of each particle). Then, the ratio of biomass to distribution area of ​​all *Ulva prolifera* green tide particles in each grid cell is calculated for each hour, which is the *Ulva prolifera* green tide biomass density; the biomass density of the i-th grid cell at the three key forecast times (T1=24h, T2=72h, T3=168h) is calculated, and the weighted composite biomass aggregation baseline value W is obtained. i The specific calculations are as shown in formulas (7) and (8);

[0065] (7)

[0066] (8)

[0067] In the formula, For the i-th grid cell at the predicted time T k Biomass density; The total biomass of all *Ulva prolifera* green tides within the i-th grid cell at the initial time. The initial time of all Ulva prolifera green tide biomass located in the i-th grid cell up to T k The cumulative biomass increment over time is calculated as follows: T1=24h, T2=72h, T3=168h, where A is the area of ​​the grid cell. As weighting coefficients, C1, C2, and C3 are set to 50%, 30%, and 20%, respectively.

[0068] Then, the baseline value of biomass accumulation was normalized to obtain the biomass accumulation index of *Ulva prolifera* green tide:

[0069] (9)

[0070] (3) Beach Landing Trend Index;

[0071] The beach landing trend index is used to represent the beach landing trend of the green tide of Ulva prolifera. This index reflects the consistency between the drift direction of the main body of Ulva prolifera particles and the direction of the shoreline towards the shore in the next three days: the smaller the angle between the two, the greater the risk of affecting the beach landing in the nearshore waters.

[0072] For the i-th grid cell, the index calculation steps are as follows: First, calculate the main drift direction of the *Ulva prolifera* particles within the cell based on their distribution positions at the initial time and after 72 hours; then, calculate the angle between this direction and the shoreline normal for each shore segment; and take the optimal (minimum angle) case as the beaching trend index for that grid cell. Beaching Trend Index The calculation is as shown in formula (10);

[0073] (10)

[0074] In equation (10), Let be the angle between the drift direction of the green tide particles of the i-th grid cell and the direction towards the shore of the k-th shoreline (k=1,2,…,m).

[0075] Based on the weights of three indicators—offshore distance hazard index, Ulva prolifera biomass accumulation index, and beaching trend index—a priority index for Ulva prolifera harvesting is calculated. Construct a priority model for harvesting seaweed green tides, as shown in formula (11).

[0076] (11)

[0077] In equation (11), R1, R2 and R3 represent weights, and the sum of the three is 1.

[0078] Different weights are assigned based on the different stages of the green tide development of Ulva prolifera (distance from sensitive targets).

[0079] When the closest distance of the green tide particles of Ulva prolifera to the shore is greater than 100km, the control measures mainly consider the distance to the shore and the biomass, and set R1=0.5, R2=0.4, and R3=0.1.

[0080] When the closest distance between the green algae particles and the shore is less than 100km and greater than 30km, the distance from the shore is more important than other factors. R1=0.4, R2=0.3, R3=0.3.

[0081] When the closest distance of the green algae particles to the shore is less than 30km, the distance to the shore becomes significantly more important than other factors. R1=0.6, R2=0.2, R3=0.2.

[0082] g. Delineate the deployment area for the salvage of *Ulva prolifera* based on the constructed deployment level model for the salvage of *Ulva prolifera*.

[0083] Based on the priority index for the removal of *Ulva prolifera* (a type of algae) from all grids. The salvage deployment area was divided into four levels according to all grids, and then a salvage deployment map was developed. The specific level division is shown in Table 1 below.

[0084] Table 1

[0085]

[0086] in The 75th, 50th, and 25th percentiles of Hi values ​​for all grid cells.

[0087] This method can be used for monitoring data such as daily satellite remote sensing of Ulva prolifera green tides. Based on the occurrence and development of Ulva prolifera green tides, the method achieves "dynamic classification" of the salvage level based on the dynamic Ulva prolifera green tide salvage priority index Hi value.

[0088] The invention will be further explained below with reference to specific application examples.

[0089] a. Identify disaster-sensitive targets;

[0090] Identify sensitive targets for the green tide disaster caused by *Ulva prolifera*. Collect and organize the main channels through which the green tide disaster may affect sensitive targets, and determine that the main areas for potential salvage are the sea areas north of 35° to 36°10' and east of 121°.

[0091] b. Interpreting satellite remote sensing and UAV monitoring data;

[0092] like Figure 3 As shown, based on the HY-1E satellite remote sensing image taken on June 22, 2025, under clear weather conditions, the distribution location and coverage area of ​​the green tide of Ulva prolifera were obtained using operational extraction methods.

[0093] c. Set the grid;

[0094] Grid cells are set according to the distribution of the green tide of seaweed within the possible salvage range, with a grid cell resolution of 1 / 15°.

[0095] d. Construct an ecodynamic forecasting model for the green tide of Ulva prolifera.

[0096] e. Obtain environmental data and conduct 7-day numerical forecasts.

[0097] Acquire marine and meteorological environmental data (sea surface wind, surface current, sea surface temperature, solar shortwave radiation, and surface nutrients) from disaster-sensitive targets to the sea area near the grid unit; the forecast duration is 7 days.

[0098] Based on the coverage area of ​​*Ulva prolifera* green tide particles and the biomass per unit area from *Ulva prolifera* green tide monitoring or historical data, the biomass of each particle was obtained. Based on the location and biomass of *Ulva prolifera* green tide particles monitored on the current day, a *Ulva prolifera* green tide ecodynamic forecasting model was used to conduct numerical forecasts of drift and growth over the next 7 days. The drift location and biomass of each *Ulva prolifera* green tide particle at different times over the next 7 days were obtained, with a time interval of 1 hour between two consecutive forecast results.

[0099] f. Construct a disaster relief and deployment level model for *Ulva prolifera* green tide disaster;

[0100] like Figure 4 , Figure 5 As shown, considering the prevention and control objective of "no more or less landfall" of the green tide of Ulva prolifera, a deployment level model for the salvage of green tide of Ulva prolifera is constructed from three aspects: nearshore threat of green tide of Ulva prolifera, biomass accumulation and landfall trend.

[0101]

[0102] In the formula, R1, R2, and R3 represent weights, and the sum of the three is 1. The closest distance from the shore to the green tide of seaweed is less than 30km, so R1=0.6, R2=0.2, and R3=0.2 are set to calculate the salvage priority index.

[0103] Based on the priority index for the removal of *Ulva prolifera* (a type of algae) from all grids. The salvage deployment area is divided into four levels according to all grids, and a salvage deployment map is created, such as... Figure 6 As shown.

[0104] Based on the salvage priority index value ( Based on the quantitative assessment results, the areas affected by the green algae bloom were scientifically divided into four response levels, each corresponding to a differentiated action strategy and visual identification system. Specifically: Level I key retrieval area ( Q1=0.51) is marked in red, indicating that the highest priority all-weather salvage operation is required; Level II salvage patrol area ( (Q2=0.37) marked in orange indicates the need for moderate-intensity control measures combining ship patrols and biomass suppression; Level III monitoring and alert zone ( The area marked in blue (Q3=0.29) emphasizes that this area is primarily focused on refined monitoring and risk prediction; Level IV patrol area ( (Marked in green) indicates routine remote sensing scanning and data monitoring. This system uses quantile thresholds ( The system achieves precise grading by using the 75th, 50th, and 25th percentiles of Hi values ​​for all grid cells, and combined with color-coded management, it provides a systematic and operable decision-making basis for emergency response to seaweed disasters.

Claims

1. A method for delineating deployment zones for harvesting *Ulva prolifera* (green algae) tides, characterized in that... Includes the following steps: a. Based on the prevention and control needs, identify sensitive targets for the green tide disaster caused by seaweed, and preliminarily determine the salvage area based on the location of the sensitive targets. b. Obtain satellite remote sensing images of the salvage area initially determined in step a, and interpret the distribution location and range of the seaweed green tide in the salvage area; c. Set up grid units within the distribution area of ​​the green tide of *Ulva prolifera*; d. Construct an ecodynamic forecasting model for the green tide of *Ulva prolifera*; e. Obtain future forecast data of sea areas near grid cells that are sensitive to the green tide disaster of Ulva prolifera; Based on the distribution location and range of Ulva prolifera green tide obtained in step b, determine the location and biomass of Ulva prolifera green tide particles, and then combine the Ulva prolifera green tide ecodynamic forecast model constructed in step d, as well as the obtained forecast data, to obtain the drift location and biomass of Ulva prolifera green tide particles at each future moment. f. Based on the drift position and biomass of the green tide particles at each future moment obtained in step e, construct a green tide salvage deployment level model from three aspects: nearshore threat of green tide, biomass accumulation and beach landing trend. In step f: f1. The nearshore threat of Ulva prolifera green tide is represented by the offshore distance hazard index, which is calculated using a piecewise exponential function. The specific process is as follows: First, based on the monitored distribution location of Ulva prolifera green tide, the mass center of Ulva prolifera green tide in each grid cell is calculated. At the same time, shoreline vector data is obtained, and the distance between the mass center of Ulva prolifera green tide in each grid cell and the shoreline is calculated one by one. The initial hazard index is calculated according to the piecewise exponential function and then normalized across the entire area to obtain the offshore distance hazard index, as shown in formula (5) and formula (6). （5） （6） In equation (5), k is the shore segment number, k=1,2,…,m; m is the total number of shore segments; i is the grid cell number, i=1,2,…,n; n is the total number of all grid cells; d ik D is the shortest distance from the mass center of all green tides of *Ulva prolifera* within the i-th grid cell to the k-th shore segment. i DK represents the initial hazard index of the i-th grid cell. i Represents the normalized offshore distance hazard index; f2. The biomass aggregation degree of *Ulva prolifera* green tide is quantified using the *Ulva prolifera* green tide biomass aggregation index. The calculation steps of this index are as follows: First, based on the *Ulva prolifera* green tide ecodynamic forecasting model, the biomass changes of all *Ulva prolifera* green tide particles in the future time period are obtained. Then, the ratio of biomass to distribution area of ​​all *Ulva prolifera* green tide particles in each grid cell is calculated for each hour to obtain the *Ulva prolifera* green tide biomass density. The biomass density of the *Ulva prolifera* green tide in the i-th grid cell at the three forecast times T1=24h, T2=72h, and T3=168h is statistically analyzed, and the weighted composite biomass aggregation baseline value W is obtained. i The specific calculations are as shown in formulas (7) and (8); （7） （8） In the formula For the i-th grid cell at the predicted time T k Biomass density; The total biomass of all *Ulva prolifera* green tides within the i-th grid cell at the initial time. The initial time of all Ulva prolifera green tide biomass located in the i-th grid cell up to T k The cumulative biomass increment over time is given by T1=24h, T2=72h, and T3=168h, where A is the area of ​​the grid cell. As weighting coefficients, C1, C2, and C3 are set to 50%, 30%, and 20%, respectively. Then, the baseline value of biomass accumulation was normalized to obtain the biomass accumulation index of *Ulva prolifera* green tide: （9） f3. The trend of seaweed green tides on the beach is represented by the beach landing trend index; This beach landing trend index reflects the consistency between the main drift direction of the seaweed green tide particles and the shoreline direction over the next three days: the smaller the angle between the two, the greater the risk of affecting the beach landing in nearshore waters. For the i-th grid cell, the calculation steps for its beaching trend index are as follows: First, calculate the main drift direction of the *Ulva prolifera* green tide particles within the grid cell based on their distribution positions at the initial time and after 72 hours; then, calculate the angle between this direction and the shoreline normal for each shore segment; and take the minimum angle as the beaching trend index for that grid cell; The calculation is as shown in formula (10); （10） In equation (10), Let k = 1, 2, ..., m be the angle between the drift direction of the green tide particles of the i-th grid cell and the shore-to-shore direction of the k-th shoreline. Based on the offshore distance hazard index, the Ulva prolifera biomass accumulation index, and the beach landing trend index, a weighted fusion calculation was performed to determine the Ulva prolifera harvesting priority index. , as in formula (11); （11） In equation (11), R1, R2 and R3 represent weights, and the sum of the three is 1; g. Delineate the deployment zone for the green tide salvage of seaweed based on the deployment level model for seaweed green tide salvage constructed in step f.

2. The method for delineating deployment zones for harvesting *Ulva prolifera* tides according to claim 1, characterized in that, In step c: the size of each grid cell is no larger than 10km × 10km.

3. The method for delineating deployment zones for harvesting *Ulva prolifera* tides according to claim 1, characterized in that... In step d: considering the drag effect of wind speed and flow velocity on the green tide particles of Ulva prolifera, a dynamic drift submodule of Ulva prolifera is constructed. The motion equation of Ulva prolifera particles is shown in the following equation (1): （1） In equation (1), Let be the position of the i-th green tide particle of *Ulva prolifera* at time t; Surface flow velocity, The wind speed at sea surface is 10m. The velocity coefficient is used to determine the effect of the flow rate. This is the wind speed effect coefficient; This indicates the effect of wind on the direction of motion of particles in the green tide 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. The wind pulls the deflection angle.

4. The method for delineating deployment zones for harvesting *Ulva prolifera* tides according to claim 3, characterized in that: Considering the crucial roles of temperature, light, and nutrients in the growth and decline of *Ulva prolifera* green tides, an ecological submodule for biomass changes in *Ulva prolifera* green tides was constructed, and *Ulva prolifera* green tide particles were observed in... The change in biomass over time is shown in equation (2): （2） In equation (2), For the daily biomass change of the j-th *Ulva prolifera* green tide particle, For the j-th green tide particle of *Ulva prolifera*, the daily biomass increase is... The daily decrease in particle biomass during the j-th green tide of *Ulva prolifera*; （3） In equation (3), For the j-th green tide particle biomass of *Ulva prolifera*, This represents the maximum daily growth rate of the green tide of *Ulva prolifera*. , , These are the dimensionless limiting coefficients for the effects of temperature, light, and nutrients on the growth rate of *Ulva prolifera* green tides, respectively. （4） In equation (4), The maximum mortality rate; This is the dimensionless limiting coefficient of temperature on mortality. The biomass of the j-th green tide particle of *Ulva prolifera*; By combining the constructed sub-modules of Ulva prolifera green tide dynamic drift and biomass change ecology, the Ulva prolifera green tide ecological dynamic prediction model is obtained.

5. The method for delineating deployment zones for harvesting *Ulva prolifera* tides according to claim 1, characterized in that, In step e: the future forecast data includes surface currents, sea surface temperature, and nutrient data for the next 7 days in the sea area near the grid cell from the disaster-sensitive target obtained based on a three-dimensional marine ecological dynamics model; Based on the meteorological forecasting model, data on sea surface wind and solar shortwave radiation from disaster-sensitive targets to the vicinity of grid units were obtained for the next 7 days.

6. The method for delineating deployment zones for harvesting *Ulva prolifera* tides according to claim 1, characterized in that, In step f3: when the closest distance of the green tide particles of Ulva prolifera to the shore is greater than 100km, the control measures mainly consider the distance to the shore and the biomass, and set R1=0.5, R2=0.4, and R3=0.

1. When the closest distance between the green algae particles and the shore is less than 100km and greater than 30km, the distance from the shore is more important than other factors. R1=0.4, R2=0.3, R3=0.

3. When the closest distance of the green algae particles to the shore is less than 30km, the distance to the shore becomes significantly more important than other factors. R1=0.6, R2=0.2, R3=0.

2.

7. The method for delineating deployment zones for harvesting *Ulva prolifera* tides according to claim 1, characterized in that, In step g: Based on the priority index H of the algae bloom harvesting for all grid cells. i The salvage deployment area is divided into four levels according to all grid units, and then different colors are assigned to different levels of areas to create a salvage deployment map. The four levels mentioned above are Level I Key Salvage Zone, Level II Salvage Patrol Zone, Level III Monitoring and Warning Zone, and Level IV Patrol Zone; among them, when At that time, it was designated as a Level I key salvage area; when At that time, it was designated as a Level II salvage patrol zone; when At that time, it was divided into a Level III monitoring and alert zone; when At that time, it was classified as a Level IV cruise zone; in These are the 75th, 50th, and 25th quantiles of Hi values ​​for all grid cells, respectively.

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