A red tide short-term trend prediction method considering extinction process under high wind speed
By constructing a stochastic diffusion model that considers the properties of algal masses and a red tide prediction method based on the high-wind-speed dissipation process, the uncertainty in red tide migration prediction under high sea states was resolved, and accurate prediction of red tide distribution and impact range was achieved, providing decision support for the scientific management of red tides.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIHAI FORECASTING CENT OF STATE OCEANIC ADMINISTRATION ((QINGDAO MARINE FORECASTING STATION OF STATE OCEANIC ADMINISTRATION) (QINGDAO MARINE ENVIRONMENT MONITORING CENT OF STATE OCEANIC ADMINISTRATION))
- Filing Date
- 2026-04-30
- Publication Date
- 2026-06-02
Smart Images
Figure CN122132677A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of red tide development trend prediction, and more specifically to a method for predicting the short-term trend of red tides that takes into account the dissipation process under high wind speeds. Background Technology
[0002] Red tides are an abnormal ecological phenomenon in which microscopic plankton in the ocean proliferate or aggregate to a certain level under specific environmental conditions, causing water discoloration or harming other marine life. Predicting the migration of red tide algal masses is crucial for red tide forecasting. The formation, dissipation, and migration processes of red tides involve complex ecological and dynamic nonlinear couplings and are related to the properties of the algal species, leading to uncertainties in the diffusion process and affecting prediction results. Furthermore, under sustained high wind speeds, the churning caused by high sea states can trigger the gradual dissipation of algal aggregates, ultimately leading to the demise of the red tide.
[0003] Existing predictions of red tide migration in the ocean mainly consider the effects of sea surface winds and surface currents, using the Lagrange motion of particles to simulate the drift of red tide algae with ocean currents, and forming predictions of the spatial location of red tide algae over time based on forecast data from ocean dynamic and meteorological models. These models have obvious limitations: (1) they generally use random motion to represent the convective diffusion process of fluids, without considering the differences in random motion caused by the characteristics of algae; (2) these models cannot simulate the actual phenomenon of red tides partially receding or gradually disappearing in time or space under high sea states. Summary of the Invention
[0004] Based on the above-mentioned technical problems, this invention proposes a method for predicting the short-term trend of red tides that takes into account the dissipation process under high wind speeds.
[0005] The technical solution adopted in this invention is: A method for predicting the short-term trend of red tides that takes into account the dissipation process under high wind speeds includes the following steps: S1. Obtain the initial distribution area of the red tide, characterize the red tide with multiple algal clusters, and determine the initial number of algal clusters and the initial location of each algal cluster; S2. Determine the wind speed threshold and duration threshold; acquire wind field and flow field forecast data; determine the state of algal cluster extinction during algal cluster drift and update the current count of surviving algal clusters. S3. Construct an algal floc migration model. For algal flocs identified as surviving in S2, update their positions based on the constructed algal floc migration model. S4. Repeat S2 and S3 to complete the short-term trend prediction of red tide considering the dissipation process under high wind speed.
[0006] The beneficial technical effects of the present invention are as follows: This invention establishes a short-term trend prediction method for red tides that considers the dissipation process under high wind speeds, for forecasting the development trend and affected area of red tides. This method considers the gradual dissipation of red tides under high wind speeds and takes into account the random diffusion of algae during location updates, thus enabling more accurate short-term trend predictions of algal cluster distribution and the range of red tide influence. Furthermore, this method also delineates the red tide affected area based on spatiotemporal accumulation rate to more intuitively reflect the red tide change trend. This invention can provide decision-making assistance and technical support for the scientific and effective management of red tides. Attached Figure Description
[0007] Figure 1 This is a flowchart illustrating the red tide short-term trend prediction method that takes into account the dissipation process under high wind speeds, as described in this invention. Figure 2 This is a schematic diagram of spatial grid division and grid point probability distribution in a specific application example of the present invention; Figure 3a This is a schematic diagram illustrating the prediction of algal cluster location in Experiment 1, a specific application example of the present invention. Figure 3b This is a schematic diagram illustrating the predicted location of algal clusters in Experiment 2, a specific application example of the present invention. Figure 3c This is a schematic diagram illustrating the prediction of algal cluster location in Experiment 3, a specific application example of the present invention. Figure 4a This is a schematic diagram of the main affected area of red tide in Experiment 1, a specific application example of the present invention; Figure 4b This is a schematic diagram of the main affected area of red tide in Experiment 2, a specific application example of the present invention; Figure 4c This is a schematic diagram of the main affected area of red tide in Experiment 3, a specific application example of the present invention. Detailed Implementation
[0008] Large-scale algal blooms are typically caused by a single algal species (such as *Ulva prolifera* green tides). These algae are relatively large, with some parts floating above the sea surface, and are directly dragged by wind and driven by currents. This invention, however, targets microscopic planktonic organisms that are completely suspended in the water, with cell sizes ranging from approximately 1 micrometer to 1 centimeter, and are not a single algal species. Due to their small size, diverse species, and frequent subsurface suspension, drift prediction models for large species such as green tides are not applicable.
[0009] Based on this, the present invention proposes a red tide trend prediction method. This method constructs models for the main links in red tide migration prediction (considering the randomness of algal diffusion during algal mass migration, the gradual disappearance of red tide under high sea states, and the quantitative probability distribution of red tide affected areas) and provides corresponding processing steps. Moreover, it fully considers the uncertainties in the process, thereby realizing the simulation and short-term prediction of the spatial distribution range of red tide areas, which can be applied in business.
[0010] This method uses a red tide region range vector as the basic input and a set of small particles to represent the initial distribution of red tide algae. The migration, diffusion, and extinction model considers the influence of wind and hydrodynamic processes on red tide algae, uses ocean dynamics and meteorological model forecast data as the driving field, and develops a stochastic diffusion algorithm that considers the properties of algae (algal characteristic scale and swimming characteristics) to simulate the diffusion process of different algal species. It also develops an algae extinction algorithm under high wind speeds to simulate the gradual and partial extinction of a large number of algal cells in the water surface. Based on the hourly spatial location of algae output by the model, the migration trend is tracked, and an algorithm for delineating the influence area based on the spatial probability cumulative rate is developed to guide the prediction of the influence range of red tide.
[0011] Specifically, this invention simulates the horizontal migration and random diffusion of red tide algal masses under marine dynamics by constructing a model, and introduces a dissipation parameterization scheme that considers high wind speeds and their duration to describe the gradual dissipation process of algal masses under high sea states. The model predicts the location of the algal masses after a period of time by calculating and integrating the movement distance at each time step. When certain high wind speed conditions are met, high sea states cause the algal masses to dissipate and die, reducing the number of surviving algal masses. The model sequentially performs dissipation condition judgment and algal mass location update within each time step, and finally outputs the spatiotemporal location and quantity changes of the algal masses. Through spatiotemporal statistics, the migration trend and affected area prediction results are formed.
[0012] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0013] like Figure 1 As shown, a method for predicting the short-term trend of red tides that considers the dissipation process under high wind speeds includes the following steps: S1. Obtain the initial distribution area of the red tide, characterize the red tide with multiple algal clusters, and determine the initial number of algal clusters and the initial location of each algal cluster.
[0014] The red tide area is defined by remote sensing or on-site monitoring. One particle represents a relatively dense algal mass, and the particles (i.e., algal masses) are evenly distributed within the red tide area. That is, algal mass particles are evenly released within the outer boundary of the red tide area defined by remote sensing or on-site monitoring.
[0015] S2. Determine the wind speed threshold and duration threshold; during the algal floc drift process, determine the algal floc extinction status and update the current surviving algal floc count.
[0016] Specifically, wind speed threshold Used to determine whether algal blooms are affected by high wind speeds, and is related to the characteristic area of red tide zones. Relative concentration of algal cells Generally speaking, the larger the red tide area and the higher the relative concentration of algal cells, the higher the wind speed threshold required for algal cell extinction. Therefore, the setting needs to be based on the current red tide conditions. A reference setting formula is: ; in, The unit is square kilometers; This represents the total area of the red tide region. If the area is less than 1 square kilometer, the value is 1; if the area is greater than 1000 square kilometers, the value is 1000.
[0017] ,in The concentration of dominant algal cells (unit: cells / L) is measured in the field. The baseline concentration of dominant algae (unit: cells / liter) is determined based on the baseline concentration of each algal species in the industry standard.
[0018] Duration threshold This is used to specify the duration of high wind speed's effect on the current algal bloom, measured in hours. The algal bloom will gradually disappear only when the duration of its high wind influence exceeds a certain threshold. Characteristic area of red tide zone Relative concentration of algal cells Generally speaking, the larger the red tide area and the higher the relative concentration of algal cells, the longer the high wind speed needs to last; therefore, settings need to be adjusted based on the current red tide conditions. A reference setting formula is: .
[0019] When an algal clump is affected by a high wind speed exceeding the wind speed threshold, and the duration of the impact exceeds the duration threshold, the condition for the algal clump to disappear is met, and it is then determined that the algal clump will disappear.
[0020] After the algal bloom triggers the conditions for its extinction, it does not disappear immediately, but gradually dissipates over a period of time. This period is defined as the extinction duration. The unit is hours. Characteristic area of red tide zone Relative concentration of algal cells Generally speaking, the larger the red tide area and the higher the relative concentration of algal cells, the longer it takes for the algae to disappear; settings need to be adjusted based on the current red tide conditions. A reference setting formula is: .
[0021] initial At time , the initial algal cluster count was , The algal cluster count at time was , No. The initial position vector of each algal cluster is , No. Individual algal clusters The position vector at time is denoted as The predicted total duration is Generally, it should not exceed 72 hours.
[0022] Each algal cluster is assigned a disappearance marker. Demise Triggering Time The moment of extinction When the sign of extinction This indicates that the algal bloom has not disappeared, when This indicates that the algal cluster has died.
[0023] Initialize the extinction flag Initialize the extinction trigger time. Initialize the extinction moment The initial extinction trigger time and initial extinction time are both set to the total predicted duration, indicating that the algae clusters had not yet extinct at the time of initialization. If the high wind speed condition is met during the calculation, the extinction trigger time and extinction time will be updated. If the high wind speed condition is not met until the end of the calculation, it means that none of the algae clusters have extincted within the predicted duration, and the initial values are retained, without affecting the output results.
[0024] By time step Perform a loop. .
[0025] During the development of algal blooms, strong weather events such as cold waves, strong winds, and typhoons can cause the dense areas of the algal blooms to disperse, deform, or decrease in density due to agitation, and some may gradually disappear. This is represented in the model as the wind speed exceeding a certain threshold. And the duration exceeds the duration threshold. The algal clusters gradually disappeared in the model.
[0026] The magnitude of wind speed is denoted as ,like and Then check the past At that moment, algae clusters Do the wind speeds at all locations meet the wind speed threshold conditions? If all wind speed threshold conditions are met, the algal cluster is marked as entering the extinction process, and the extinction trigger time is updated. If the wind speed threshold condition is not met at least at any given moment, the extinction process will not be triggered.
[0027] The duration of algal blooms entering the extinction process Random extinction occurs within the body, and the specific time of extinction is... ,in , is a uniformly random number; The value is taken as the nearest whole hour.
[0028] exist Update the extinction markers regularly. At the same time, this algal cluster will no longer participate in subsequent position calculations, and the current count of surviving algal clusters will be updated. .
[0029] S3. Construct an algal cluster migration model. For algal clusters identified as surviving in S2, update their positions based on the constructed algal cluster migration model.
[0030] exist At any given moment, for each currently surviving algal cluster Perform the following calculations: Algal blooms were obtained by bilinear interpolation of the wind field at the current moment. Wind speed vector at location The algal flocs were obtained by bilinear interpolation of the surface flow field at the current moment. Surface velocity vector at location .
[0031] The horizontal migration speed of algal clusters driven by environmental dynamics is: ,in and These are the parameters corresponding to the wind speed and flow velocity vectors, respectively. The value is typically between 0.005 and 0.02. The value is typically taken as 0.5~1. The horizontal migration speed of algal clusters is caused by the randomness of the dynamic-ecological coupling process. ,in For random action parameters, , The random velocity of the algal cluster represents the random diffusion process of the algal cluster.
[0032] With the dynamic driving speed of algal clusters And related to the properties of the algal community; the properties of the algal community include whether the dominant algal species has the ability to swim and the characteristic scale of the algal cells. (Unit: meters)
[0033]
[0034] The horizontal migration velocity of algal clusters is the vector sum of the environmentally driven velocity and the coupled stochastic velocity, i.e. .
[0035] go through With time step, the algal flocs shift due to horizontal migration. , Location updated to .
[0036] Obtaining Algal Clusters The wind speed vector, surface velocity vector, and algal cell characteristic scale at the location are given. , , The value was determined using the constructed algal floc migration model to analyze the algal floc. Perform a location update.
[0037] S4. Repeat S2 and S3 to complete the short-term trend prediction of red tide considering the dissipation process under high wind speeds. Record and output each hourly time. Algal cluster location set and surviving algal cluster count .
[0038] Furthermore, the method also includes predictions of migration trends and the scope of impact.
[0039] S5. Predict the migration trend of red tides; Select all algal cluster centroids as representative points, or alternatively, the red tide region center point, etc., output hourly positions and connect them to predict the migration direction of the red tide.
[0040] S6. Prediction of the scope of impact; The main impact range of red tide is defined as the range in which red tide algal masses appear with a certain probability. This method determines the impact range based on the area that 90% of the algal masses may affect. This removes the low-probability red tide area, reduces the uncertainty error in observation and calculation to a certain extent, and improves the focus of subsequent emergency response.
[0041] Divide the computational region into The grid, in At that moment, the The number of algal clusters in each grid is Calculate the probability of algal clusters appearing within the grid. .
[0042] Convert all grid probabilities into a one-dimensional array and sort them in descending order: ,Right now .
[0043] Calculate the cumulative rate , k=1,2,…,MN.
[0044] Find the threshold for the 90% cumulative rate, i.e., the smallest index that meets the following conditions. Corresponding probability threshold .by Plotting contour lines on a probability space distribution map represents... The predicted distribution range of red tides at any given time.
[0045] The first array index that satisfies a cumulative rate greater than 0.9 is denoted as k. 90 Its corresponding probability value is P. k90 Set the probability threshold for plotting to P. k90The value of .
[0046] The calculation is performed iteratively at various time points to provide the hourly distribution range of the red tide within the predicted time frame, and the distribution area is also calculated. Based on the hourly changes in the distribution range and area, the spatial location and regional trends of the red tide's impact are predicted.
[0047] The invention will be further explained below with reference to specific application examples.
[0048] Satellite remote sensing detected an anomaly in water color in a certain body of water at 11:00 AM on September 19, 2024, covering an area of approximately 100 square kilometers. On-site verification revealed that the water was brownish-red, and the dominant algal species identified in the samples was *Hacana hemangiosus*, with an average algal cell concentration of 48 × 10⁻⁶ cells at each sampling point. 4 The red tide concentration was [number] particles per liter. Subsequently, most of the area was affected by a sustained period of strong winds. Monitoring conditions improved in the area on September 21, but at 13:00 on the 21st, satellite remote sensing detected abnormal water color in some areas. Using the red tide short-term trend prediction method established in this invention, a 72-hour short-term trend simulation was conducted to provide predicted conclusions on the changes in the red tide migration direction and impact range, which were then compared with the actual satellite measurements.
[0049] (1) Parameter settings; The forecast will begin at 11:00 AM on September 19, 2024, and will end at 10:00 AM on September 22, 2024. Hours. Initial algal clusters within the range of water color anomaly. Input the latitude and longitude of the initial algal cluster and record it as a position vector. The sign of the complete disappearance of algal blooms. Demise Triggering Time Initialize the extinction moment .
[0050] Characteristic area of red tide zone According to standards, the baseline concentration of *Hacochloa hemlock* red tide is... 50×10 4 Algal cell concentration per liter, relative concentration of algal cells The calculated wind speed threshold Duration threshold Hours, Duration of Extinction Hour.
[0051] (2) Iterative calculation; from The cycle begins at a specific time, based on the wind field predicted by the numerical model. Surface flow field After bilinear interpolation, the algal bloom at time t can be obtained. of and Then the power drive speed can be calculated. The characteristic cell length of *Hacakaris hemlock* is approximately 50 mm. It has a certain swimming ability, and the algal clusters move at random speeds. The position of the algal cluster changes with the velocity of the driving field at different times. Under the combined effects of dynamic driving and random diffusion, the algal cluster undergoes positional changes.
[0052] (3) Prediction of migration trends and scope of impact; Output the location of the algal clusters at each time point. Divide the computational domain according to resolution. The grid is divided into sections. The number of algal clusters and their occurrence probability are calculated hourly on the spatial grid, such as... Figure 2 As shown. Calculate the probability distribution at all grid points in space and find the probability value corresponding to a cumulative rate of 90%. .by Draw contour lines to determine the spatial extent and distribution area at different times.
[0053] (4) Comparison of results from different methods; Three sets of experiments were designed to compare the differences between random diffusion considering algal cluster properties and simple random diffusion proposed in this invention, as well as the improvement in prediction performance by adding a high-wind-speed extinction module.
[0054] Experiment 1: Using the random diffusion algorithm and high wind speed extinction algorithm that take into account the properties of algal clusters proposed in this invention.
[0055] Experiment 2: Using a simple random diffusion algorithm and the high wind speed extinction algorithm proposed in this invention.
[0056] Experiment 3: Using the random diffusion algorithm proposed in this invention that considers the properties of algal clusters, without considering high wind speeds causing algal cluster extinction.
[0057] Figure 3a This is a schematic diagram illustrating the predicted location of algal clusters in Experiment 1. Figure 3b This is a schematic diagram illustrating the predicted location of algal clusters in Experiment 2. Figure 3c This is a schematic diagram illustrating the predicted location of the algal clusters in Experiment 3. The red line represents the range of water color anomalies observed by satellite at 11:00 on September 19, 2024, and the green line represents the range of water color anomalies at 13:00 on the 21st. The scatter plots represent the predicted locations of the algal clusters at 13:00 on the 21st from the three sets of experiments, and the purple-red line represents the line connecting the centroids of the algal clusters hourly. Figure 4a This is a schematic diagram of the main affected areas of the red tide predicted in Experiment 1. Figure 4b This is a schematic diagram showing the main areas affected by the predicted red tide in Experiment 2. Figure 4cThis diagram illustrates the main affected area of the red tide predicted in Experiment 3. The green line represents the area of water color anomaly observed at 13:00 on the 21st, and the orange line represents the predicted red tide impact area at that time using the three sets of experiments. As shown in the diagram, the algal mass predicted using the method of this invention (Experiment 1) migrated generally southwestward. Due to the influence of high wind speeds, some particles perished during migration and distributed in nearshore waters. Experiment 2, using simple random diffusion, was overly influenced by dynamic forces, resulting in excessive concentration near the shoreline. Experiment 3, failing to consider the high-wind-speed dissipation process, had an excessively large migration range. In comparison, the method proposed in this invention is more consistent with actual measurements regarding algal mass distribution and the red tide impact area.
[0058] For any parts not mentioned above, existing technologies can be adopted or referenced.
[0059] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.
Claims
1. A method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, characterized in that... Includes the following steps: S1. Obtain the initial distribution area of the red tide, characterize the red tide with multiple algal clusters, and determine the initial number of algal clusters and the initial location of each algal cluster; S2. Determine the wind speed threshold and duration threshold; Acquire wind field and flow field forecast data, determine the state of algal cluster extinction during algal cluster drift, and update the count of currently surviving algal clusters; S3. Construct an algal floc migration model. For algal flocs identified as surviving in S2, update their positions based on the constructed algal floc migration model. S4. Repeat S2 and S3 to complete the short-term trend prediction of red tide considering the dissipation process under high wind speed.
2. The method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 1, is characterized in that... In S2: The formula for calculating the wind speed threshold is as follows: ； In the formula, The wind speed threshold, This represents the total area of the red tide region, expressed in square kilometers. This represents the relative concentration of algal cells, dimensionless. ； In the formula, d represents the concentration of dominant algal cells monitored on-site, in units of cells / liter; The standard concentration of dominant algae is expressed in cells / liter. The formula for calculating the duration threshold is as follows: ； In the formula, This is a duration threshold, in hours; This represents the total area of the red tide region, expressed in square kilometers. This represents the relative concentration of algal cells, dimensionless. When an algal clump is affected by a high wind speed exceeding the wind speed threshold, and the duration of the effect exceeds the duration threshold, the condition for the algal clump to disappear, namely the high wind speed condition, is met, and it is determined that the algal clump will disappear.
3. The method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 2, is characterized in that: After the algal bloom meets the conditions for extinction, it does not disappear immediately, but gradually dissipates over a period of time. This period is defined as the extinction duration, and the calculation formula is as follows: ； In the formula, Duration of extinction, in hours; This represents the total area of the red tide region, expressed in square kilometers. This represents the relative concentration of algal cells, dimensionless.
4. The method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 3, is characterized in that: initial At time , the initial algal cluster count was , The algal cluster count at time was , No. The initial position vector of each algal cluster is denoted as... ;No. Individual algal clusters The position vector at time is denoted as ; Each algal cluster is assigned a disappearance flag, a disappearance trigger time, and a disappearance time, where the first... The disappearance of an algal mass is marked by The time of extinction is The moment of extinction is When the sign of extinction This indicates that the algal bloom has not disappeared, when This indicates that the algal cluster has died out; Initialize the extinction flag Initialize the extinction trigger time. Initialize the extinction moment ; To predict the total duration; If the high wind speed condition is met during the calculation, the extinction trigger time and extinction time will be updated; if the high wind speed condition is not met until the end of the calculation, it means that none of the algal clusters have extincted within the total predicted duration, and the initial values will be retained. Algal mass The wind speed at the location is recorded as ,like and Then check the past At that moment, algae clusters Do the wind speeds at all locations meet the wind speed threshold conditions? If all wind speed threshold conditions are met, the algal cluster is marked as entering the extinction process, and the extinction trigger time is updated. If the wind speed threshold condition is not met at least once, the extinction process will not be triggered.
5. A method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 4, is characterized in that... For algal clusters entering the process of disintegration, the specific time of disintegration is calculated using the following formula: ； In the formula, ;Will The value is taken as the nearest whole hour. exist Update the extinction markers regularly. At the same time, this algal cluster will no longer participate in subsequent position calculations, and the current count of surviving algal clusters will be updated. .
6. A method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 5, is characterized in that... In S3: The algal floc migration model is as follows: go through With time step, the algal flocs shift due to horizontal migration. , Location updated to ; In the formula, The horizontal migration speed of algal clusters is calculated using the following formula: ; In the formula, The migration speed of algal clusters is driven by environmental dynamics. The migration speed of algal clusters is caused by the randomness of the dynamic-ecological coupling process; ； In the formula, The action parameter corresponding to the wind speed vector. The parameter corresponding to the flow velocity vector. algal clusters Wind speed vector at location algal clusters The surface velocity vector at the location; ； In the formula, For random action parameters, , The random velocity of the algal cluster represents the random diffusion process of the algal cluster. The calculation formula is as follows: ； In the formula: Meters are the characteristic scale of algal cells. Obtaining Algal Clusters The wind speed vector, surface velocity vector, and algal cell characteristic scale at the location were used to analyze the algal floc migration model constructed. Perform a location update.
7. A method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 6, is characterized in that... In S4: While repeating S2 and S3, record and output the set of algal cluster locations at each full hour. and surviving algal cluster count .
8. A method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 7, is characterized in that... It also includes the following steps: S5. Predict the migration trend of red tides; Select all algal cluster centroids as representative points, output their hourly positions and connect them to predict the migration direction of the red tide.
9. A method for predicting the short-term trend of red tides considering the dissipation process under high wind speeds, as described in claim 8, is characterized in that... It also includes the following steps: S6. Predict the impact range of red tide; The region to be predicted is divided into: Each grid, in At that moment, the The number of algal clusters in each grid is Calculate the probability of algal clusters appearing within the grid. ; Convert all grid probabilities into a one-dimensional array and sort them in descending order: Calculate the cumulative rate k=1,2,…,MN; The first array index that satisfies a cumulative rate greater than or equal to 90% is denoted as k. 90 The corresponding probability value is ; And set a probability threshold. ; by Plotting contour lines on a probability space distribution map represents... The predicted distribution range of red tides at any given time; The calculation is performed iteratively at various times to give the hourly red tide distribution range within the predicted time range and calculate the distribution area; based on the hourly distribution range and area changes, the spatial location and regional change trend of the red tide impact are predicted.