Floating object track prediction method and device, equipment and storage medium

Abstract

The invention provides a floating object track prediction method, device and equipment and a storage medium, and relates to the technical field of marine environment scientificity, and the floating object track prediction method comprises the steps: obtaining the state information of a target floating object and the information of the environment where the target floating object is located; processing the state information and the environment information by using a Lagrange particle tracking method, and determining a first moving trajectory of the target floating object; the first moving track comprises displacement in a three-dimensional direction; correcting the first moving trajectory based on a biological influence factor to obtain a target moving trajectory; the biological influence factors comprise displacement of the target floating object in the direction perpendicular to the sea level caused by the gathering effect of biological fouling or ocean snow, and the loss rate of the target floating object in the running process. According to the invention, the Lagrange particle tracking method is utilized to realize accurate tracking of the target floating object in the transportation process in the ocean three-dimensional space, and the accuracy of simulating the migration path of the target floating object is improved.

Description

Technical Field

[0001] The present invention relates to the field of marine environmental science and technology, and in particular to a floating object trajectory prediction method, device, equipment and storage medium. Background Art

[0002] Microplastics are plastic particles with a diameter of less than 5 mm and are the main carrier of marine pollution. Microplastics continue to accumulate, break and weather in the ocean, and can adsorb and aggregate with other pollutants, posing a serious threat to the survival of marine life. These microplastic particles are difficult to biodegrade, easily accumulate in the environment, and cause great harm to marine life and human health through the transmission of the food chain. Given that current technical means cannot completely remove microplastics from water bodies, it is particularly urgent to study their distribution, migration and transformation processes in the environment. At present, the relevant research on the simulation of marine microplastic migration mainly focuses on two-dimensional simulation using Lagrangian particle tracking methods, and three-dimensional simulation methods have not yet been fully developed. Although two-dimensional simulation can describe the horizontal transport of microplastics on the ocean surface, it cannot provide dynamic changes in the vertical direction, which makes it difficult for existing models to truly reflect the transport process and diffusion trend of microplastics in the three-dimensional space of the ocean, especially under the action of complex physical and biodynamic processes. Summary of the invention

[0003] The purpose of the present invention is to solve the problem that the current simulation of marine microplastic migration is still imperfect, inaccurate and incomplete. The present invention provides a floating object trajectory prediction method, device, equipment and storage medium.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] A first aspect of an embodiment of the present application provides a method for predicting the trajectory of a floating object, comprising:

[0006] Obtain the status information and environment information of the target floating object;

[0007] The state information and the environment information are processed by using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object; the first running trajectory includes a displacement in a three-dimensional direction;

[0008] The first running trajectory is corrected based on biological impact factors to obtain a target running trajectory; the biological impact factors include the displacement of the target floating object in a direction vertical to the sea level caused by biological fouling or aggregation of marine snow, and the loss rate of the target floating object during operation.

[0009] Optionally, the state information and the environmental information are processed by using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object, and the corresponding equation is:

[0010]

[0011] where u, v, and w correspond to the advection velocities in the x, y, and z directions, respectively. r 、v r and w r is the random velocity caused by turbulent diffusion effect, Δt is the time step;

[0012] The calculation equation for advection velocity is:

[0013]

[0014] Among them, u c 、v c and w c is the velocity output by the Ocean Circulation Model (OGCM), u a and v a is the wind speed at 10 meters above sea level, α and β are the wind drift coefficient and deviation angle, u s and v s The Stokes drift velocity provided for the wave model, w d is the settling velocity of floating objects.

[0015] Optionally, the settling velocity w of the floating object d The calculation formula is:

[0016]

[0017] Among them, ρ tot is the density of floating objects, ρ sw is the density of the surrounding seawater, g is the acceleration due to gravity, ω * is the dimensionless sedimentation velocity, v sw is the kinematic viscosity of seawater.

[0018] Optionally, the state information and the environment information are processed by a Lagrangian particle tracking method to determine a first running trajectory of the target floating object, further comprising:

[0019] The Lagrangian trajectory equation is solved based on the Runge-Kutta fourth-order method to determine the first running trajectory of the target floating object.

[0020] Optionally, the correcting the first running trajectory based on the biological influencing factor to obtain a target running trajectory includes:

[0021] Obtaining density parameters of the target floating object during operation;

[0022] Determine the buoyancy parameter corresponding to the target floating object based on the density parameter;

[0023] The first running trajectory is corrected based on the buoyancy parameter to obtain a target running trajectory.

[0024] Optionally, the method of correcting the first running trajectory based on the biological influencing factor to obtain a target running trajectory further includes: creating a floating object gradual change model to determine the density parameter of the target floating object during the running process; the floating object gradual change model is:

[0025]

[0026]

[0027]

[0028] in, G represents the increment of biofilm attached to the surface of target floating objects over time. coll represents the rate at which the target floating particles collide with microorganisms and are colonized, G grow represents the growth rate of attached microbial cells, L graze represents the loss rate of biofilm or marine snow by zooplankton grazing, L resp represents the loss rate of microorganisms through respiration, L nonlin is a nonlinear loss term, including the virus lysis process, represents the rate of change of biofilm increment below the photic zone, L diss is the dissolution rate of the microbial cell wall, represents the rate of change of the concentration of marine snow bound to the target floating particles, G upt is the rate at which target floating particles aggregate with marine snow, L rel Represents the release rate of non-biodegradable target floating debris particles.

[0029] A second aspect of the embodiment of the present application provides a floating object trajectory prediction device, comprising: an acquisition module, a determination module and a correction module, wherein:

[0030] The acquisition module is configured to acquire status information and environment information of the target floating object;

[0031] The determination module is configured to process the state information and the environmental information using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object; the first running trajectory includes a displacement in a three-dimensional direction;

[0032] The correction module is configured to correct the first running trajectory based on biological impact factors to obtain a target running trajectory; the biological impact factors include the displacement of the target floating object in a direction vertical to the sea level caused by biological fouling or aggregation of marine snow, and the loss rate of the target floating object during operation.

[0033] Optionally, the correction module is specifically configured as follows:

[0034] Obtaining density parameters of the target floating object during operation;

[0035] Determine the buoyancy parameter corresponding to the target floating object based on the density parameter;

[0036] The first running trajectory is corrected based on the buoyancy parameter to obtain a target running trajectory.

[0037] A third aspect of an embodiment of the present application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program implements the floating object trajectory prediction method described in the first aspect when executed by the processor.

[0038] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0039] Compared with the prior art, the technical solution provided by this application has the following beneficial effects:

[0040] The present invention provides a method, device, equipment and storage medium for predicting the trajectory of floating objects. By acquiring the state information and the environment information of the target floating object, the state information and the environment information are processed by the Lagrangian particle tracking method, the displacement trajectory of the target floating object in the three-dimensional direction is determined, and then the running trajectory is corrected based on the biological influence factor to obtain the target running trajectory. The present invention fully considers the influence of biological influence factors such as biological fouling, the aggregation of marine snow and zooplankton feeding on the vertical transport of floating objects, and uses the Lagrangian particle tracking method to accurately track the transport process of floating objects in the three-dimensional space of the ocean, thereby improving the accuracy and comprehensiveness of the simulated floating object migration path. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a flow chart of a floating object trajectory prediction method provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the structure of a floating object trajectory prediction device provided in an embodiment of the present application;

[0043] Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] Below, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present application.

[0045] The terms used herein are only for describing specific embodiments and are not intended to limit the present application. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0046] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0047] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some blocks or combinations thereof in the block diagrams and / or flow charts may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions, when executed by the processor, may create a device for implementing the functions / operations described in these block diagrams and / or flow charts.

[0048] In some embodiments, see Figure 1 , Figure 1 A schematic diagram of a flow chart of a method for predicting the trajectory of floating objects provided in an embodiment of the present application; the method for predicting the trajectory of floating objects provided in an embodiment of the present application comprises:

[0049] S110, obtaining status information and environment information of the target floating object.

[0050] In this embodiment, the target floating objects may include microplastics floating on the ocean surface, or other floating objects, such as wood product debris and rubber product debris, which are not enumerated. Take the target floating objects as microplastics as an example, which may be in the form of particles. The state information of the target floating objects may include their own density parameters, current location information, and category information, etc.; the environmental information may include current speed, wind speed, turbulence, etc.

[0051] Under the influence of ocean currents, waves and wind, target floating particles can spread to sea areas far from the shore or be washed onto the beach. Biological action changes the particle size and density of the particles, thereby changing their force area and buoyancy. As the density gradually increases, some particles will sink to the bottom of the sea. Therefore, particles have three states: particles floating on the surface of the ocean, particles attached to biofilms or aggregated marine snow that sink to the bottom of the sea, and particles washed to the coast. The particles described in the latter two states do not participate in subsequent trajectory calculations. In the initial stage of the simulation, all released target floating particles are in a state of floating on the surface of the ocean. Subsequently, each particle can change its state under the influence of various factors.

[0052] S120, using a Lagrangian particle tracking method to process the state information and the environmental information to determine a first running trajectory of the target floating object; the first running trajectory includes displacement in a three-dimensional direction.

[0053] In the Lagrangian particle tracking method, the movement of each target floating object particle can be regarded as a tiny fluid particle, and its motion trajectory can be solved by integrating ordinary differential equations. It can be understood that the target floating object will not only move on the sea surface due to the influence of ocean currents and wind, but also change from floating state to suspended underwater or sinking due to changes in its own gravity and buoyancy.

[0054] In some embodiments, S120, the state information and the environment information are processed using the Lagrangian particle tracking method to determine the first running trajectory of the target floating object. The corresponding equation is:

[0055]

[0056] where u, v, and w correspond to the advection velocities in the x, y, and z directions, respectively. r 、v r and w r is the random velocity caused by turbulent diffusion effect, Δt is the time step;

[0057] The calculation equation for advection velocity is:

[0058]

[0059] Among them, u c 、v c and w c is the velocity output by the Ocean Circulation Model (OGCM), u a and v a is the wind speed at 10 meters above sea level, α and β are the wind drift coefficient and deviation angle, u s and v s The Stokes drift velocity provided for the wave model, wd is the settling velocity of floating objects.

[0060] In some embodiments, the settling velocity w of the floating object d The calculation formula is:

[0061]

[0062] Among them, ρ tot is the density of the target floating object, ρ sw is the density of the surrounding seawater, g is the acceleration due to gravity, ω * is the dimensionless sedimentation velocity, v sw is the kinematic viscosity of seawater.

[0063] In this embodiment, the total density ρ proposed by the present invention is tot The calculation formula is:

[0064]

[0065] Among them, V pl and ρ pl are the volume and density of the target floating object, V pl Calculated from the radius of the target floating particle, assuming the particle radius is r pl ,but Diatoms are the main contributors to primary production in the ocean. They are a type of unicellular algae with chromatophores. As the most common plankton in the ocean, they inhabit almost all aquatic environments. Assuming that only diatoms are attached to the target floating objects, V bfd is the volume of dead biofilm, ρ fd is the density of diatom frustules, V bfa is the volume of the living biofilm, ρ fa is the density of the biofilm, V snow represents the volume of marine snow aggregated with the target floating object, m snow is the mass of marine snow that aggregates with the target float.

[0066] Among them, V bfd 、V bfa and V snow The calculation formula is:

[0067] V bfd =(V a -V cy )A fd θ pl ;

[0068] V bfa =V a A0 p] ;

[0069] V snow =V ms MSθ pl ;

[0070] θ pl is the surface area of ​​the target floating object, which is calculated from the particle radius of the target floating object, that is, V a and V cy are the volume of a single diatom cell and the volume of a single diatom cytoplasm, V a -V cy is the volume of diatom shells. Diatom cells are composed of cytoplasm and siliceous cell walls (diatom shells). A is the total amount of biofilm attached to the target floating particles. A fd is the total amount of siliceous cell wall left by dead diatom cells, V ms is the volume of a single marine snow particle, calculated from the particle radius, assuming that the radius of the marine snow particle is r ms ,but MS represents the total amount of marine snow aggregated with the target drift particles.

[0071] In some embodiments, S120, using the Lagrangian particle tracking method to process the state information and the environmental information to determine the first running trajectory of the target floating object, further comprising:

[0072] The Lagrangian trajectory equation is solved based on the Runge-Kutta fourth-order method to determine the first running trajectory of the target floating object.

[0073] Runge-Kutta methods are an important class of implicit or explicit iterative methods for solving nonlinear ordinary differential equations. Here, the Runge-Kutta fourth-order method is used to solve the Lagrangian trajectory equation to ensure the calculation accuracy of the particle motion trajectory.

[0074] S130, correcting the first running trajectory based on biological impact factors to obtain a target running trajectory; the biological impact factors include displacement of the target floating object in a direction perpendicular to the sea level caused by biological fouling or aggregation of marine snow, and loss rate of the target floating object during operation.

[0075] Target floating objects can provide microorganisms with an attachment matrix, which can colonize and form biofilms on these target floating object particles. As the thickness of the film increases, the particle size and density of the target floating objects will also increase, and then sink to the seabed or become suspended. In addition to the biofouling process, the aggregation of target floating objects with marine snow is also a key mechanism that changes their buoyancy characteristics and causes them to sink. Marine snow is an aggregate rich in organic matter, composed of zooplankton excrement, protists, marine debris, bacterial mucus, etc. They play a very important role in the marine ecosystem, providing an important food source for marine organisms such as zooplankton and fish distributed at different ocean depths, and serving as a carrier for the vertical transport of carbon and nutrients. The settling velocity of target floating object particles w d Depends on two factors: the density difference between the biofouled target float particles and the target float particles captured by marine snow and the surrounding seawater, and the size and shape of the target float particles.

[0076] In some embodiments, S130, correcting the first running trajectory based on the biological influencing factor to obtain a target running trajectory includes:

[0077] Obtain density parameters of target floating objects during operation;

[0078] Determine the buoyancy parameter corresponding to the target floating object based on the density parameter;

[0079] The first running trajectory is corrected based on the buoyancy parameter to obtain a target running trajectory.

[0080] In this embodiment, the current state of the target floating object can be preliminarily determined based on the density of the target floating object and the density of the seawater. If the target floating object has not reached the coast, if the density difference between the target floating object and the seawater is less than 0, the current state of the target floating object is still floating; if the density difference between the target floating object and the seawater is equal to 0, the current state of the target floating object is suspended in the seawater; if the density difference between the target floating object and the seawater is greater than 0, the current state of the target floating object is sinking.

[0081] In some embodiments, S130, the first running trajectory is corrected based on the biological impact factor to obtain the target running trajectory, and further includes: creating a floating object gradual change model to determine the density parameter of the target floating object during the running process; the floating object gradual change model is:

[0082]

[0083]

[0084]

[0085] in, G represents the increment of biofilm attached to the surface of target floating objects over time. coll represents the rate at which the target floating particles collide with microorganisms and are colonized, G grow represents the growth rate of attached microbial cells, L graze represents the loss rate of biofilm or marine snow by zooplankton grazing, L resp represents the loss rate of microorganisms through respiration, L nonlin is a nonlinear loss term, including the virus lysis process, represents the rate of change of biofilm increment below the photic zone, L diss is the dissolution rate of the microbial cell wall, represents the rate of change of the concentration of marine snow bound to the target floating particles, G upt is the rate at which target floating particles aggregate with marine snow, L rel Represents the release rate of non-biodegradable target floating particles. Since diatoms are the main contributors to marine primary producers and are extremely widely distributed, it can be assumed here that only diatoms are attached to the target floating objects, that is, diatoms are used as organisms attached to the target floating particles.

[0086] Based on the Monod equation, the rate G at which target floating particles are converted into target floating particles aggregated in marine snow can be calculated. upt .

[0087]

[0088] Among them, C pl is the concentration of target floating particles, k p is the rate constant for the aggregation of target floating objects by marine snow, It means that as the concentration of target floating matter increases, the aggregation rate will not increase infinitely, but tend to a maximum value, source (D m ) represents the rate of marine snow formation, F m is the aggregation probability of target floating objects and marine snow, which indicates what proportion of marine snow can aggregate target floating objects, ranging from 0 to 1.

[0089] L rel =μ m C MS ;

[0090] Among them, μ m is the remineralization rate of debris, which is mainly affected by temperature. It can be calculated that μ0 is the reaction rate constant, T is the surrounding seawater temperature, and T b is the temperature change required to increase the reaction rate to e times its initial value, C MSis the concentration of target floating particles aggregated in marine snow.

[0091] The target floating particles rich in biofilm are an efficient food source for low-trophic-level organisms such as zooplankton larvae and fish larvae. For predators, the target floating particles are nutrient-rich food. Zooplankton in the ocean ingests the target floating particles and then excretes them through fecal particles. The Holling II type functional response function can be used to calculate the rate at which marine snow that aggregates target floating objects is ingested by zooplankton during the sedimentation process. Considering the zooplankton's intake preference for different food sources, the food-weighted scaling ratio ψ is used. i To control the feeding preference of zooplankton, the sum of all feeding preferences is 1. The feeding rate of zooplankton L graze The calculation formula is:

[0092]

[0093] in, is the maximum potential predation rate, Z is the population density of zooplankton, ψ pl The ingestion preferences of zooplankton for target floating objects were CA, PH, Diaz and Der tot They represent different types of food sources, CA represents calcified phytoplankton, PH represents ordinary phytoplankton, and Diaz represents nitrogen-fixing organisms, usually ψ Diaz =0.1,Detr tot represents the sum of the debris, k z is the half-saturation constant.

[0094] In one example, the target floating objects are microplastics, and diatoms are used as organisms attached to the outside of the target floating object particles. The Lagrangian particle tracking method is used to calculate the migration trajectory of microplastics, and the microplastic particles are modeled as virtual particles, whose transport process is jointly affected by wind, currents, waves and biological factors. Assuming that the simulation area is in a coastal area, the emission of microplastic particles is estimated based on the degree of industrialization and coastal population, and Lagrangian particles are released at different emission rates in estuaries and major shipping routes. These particles represent spherical particles with a radius between 1μm and 5mm, with different initial densities.

[0095] Under the influence of ocean currents, waves and wind, particles can spread to sea areas far from the shore or be washed onto the beach. Biological action changes the particle size and density, and thus changes its force area and buoyancy. As the density gradually increases, some particles will sink to the seabed. Based on the initial conditions, the changes in the horizontal and vertical positions of the particles in each time step are calculated, and the new position, travel distance and survival time of each particle are recorded. In this process, due to biofouling or the aggregation of marine snow, particles with positive buoyancy become neutrally buoyant or negatively buoyant. The density difference between the composite particles and the surrounding seawater determines whether the particles float or sink. The rate at which diatom cells attach to the particle surface is calculated, and the average number of diatom cells attached to the particles per unit time is quantified. As the thickness of the biofilm continues to increase, when the density of the biofilm exceeds the density of the surrounding seawater, the microplastics that originally had positive buoyancy begin to sink. When they sink to the area below the photic zone, due to the limitation of light conditions, the organic matter in the biofilm gradually decomposes or is consumed, while the siliceous cell walls of diatoms are strong and porous and will not decompose, so they can continue to attach to the surface of microplastics. The following formula can be used for quantitative calculation: Among them, A A represents the concentration of surrounding diatoms, β A Represents the frequency with which particles collide with diatoms in the surrounding water as they travel through the ocean.

[0096] As an important material transport carrier, marine snow plays a key role in transporting microplastics to the seabed. The formation process of marine snow is not restricted by light conditions. When marine snow aggregates with microplastic particles, it will change the vertical migration behavior of microplastics and accelerate their sedimentation rate. Due to the limitations of the resolution of existing ocean models in nearshore waters, global-scale ocean current datasets have deviations in ocean units adjacent to land. Therefore, microplastics stranded on the beach are usually parameterized as a random process in coastal areas. Here, the nearshore is defined as an area extending from the coastline to a few kilometers offshore. During the simulation period, the probability p of a particle being washed ashore is b The calculation formula is:

[0097]

[0098] Where d is the distance from the particle to the nearest coastal unit, D is the nearshore boundary. Only when the particle is within the area within D from the coast can it be washed ashore, dt is the integration time step, and λ b is the average time required for particles to migrate in the coastal area and eventually be washed ashore, b The larger the value, the longer the particle exists in the ocean. It may move away from the coastline due to the influence of factors such as ocean currents, wind and waves, so a sensitivity analysis is needed.

[0099] After these particles are released from the ocean surface, it is determined whether the particles sink into the seabed sediments and become part of the marine sediments; or are washed to the coastline by ocean currents and waves, and then enter the terrestrial ecosystem. During the simulation process, the time and spatial coordinates of the particles from the release point to the final destination are recorded. If a particle moves outside the simulation area, the particle is deleted, and the Lagrange trajectory equation is solved using the Runge-Kutta fourth-order method to ensure the calculation accuracy of the particle motion trajectory. Before the simulation cycle reaches the predetermined length, the model will continue to calculate the position and velocity changes of the particles based on the real-time data of physical and biological factors. After the simulation cycle is completed, the trajectory data of each particle is output, which records in detail the changes in the time and spatial position of the particle during the simulation.

[0100] After that, data visualization technology can be used to convert the particle trajectory data output by the model into three-dimensional animations and interactive charts, so as to more intuitively display the migration of particles, draw the spatiotemporal distribution density map of particles, and show the concentration and distribution pattern of microplastics in the ocean at different time nodes. Based on the spatiotemporal distribution density map of particles, hot spots where microplastics gather can be further identified. The concentration of microplastics in these areas is significantly higher than that in other areas, indicating potential pollution risk areas. These areas need to be given priority for environmental management and protection. Summarize the distribution characteristics of microplastics in three-dimensional space, and explore their migration laws under the action of ocean circulation, wind fields and biogeochemical processes. According to the analysis results, targeted management measures and suggestions are proposed to reduce the pollution impact of microplastics on the marine environment, and provide a scientific basis for the cleanup and mitigation of marine floating pollution.

[0101] This embodiment obtains the state information and the environment information of the target floating object, processes the state information and the environment information by using the Lagrangian particle tracking method, determines the displacement trajectory of the target floating object in the three-dimensional direction, and then corrects its movement trajectory based on the biological influence factor to obtain the target movement trajectory. The influence of biological influence factors such as biological fouling, aggregation of marine snow and zooplankton feeding on the vertical transport of floating objects is fully considered. The Lagrangian particle tracking method is used to accurately track the transportation process of floating objects in the three-dimensional space of the ocean, thereby improving the accuracy and comprehensiveness of the simulated floating object migration path.

[0102] In some embodiments, see Figure 2 , Figure 2 The structure diagram of the floating object trajectory prediction device provided in the embodiment of the present application is as follows; the floating object trajectory prediction device 200 provided in the embodiment of the present application comprises: an acquisition module 210, a determination module 220 and a correction module 230, wherein:

[0103] An acquisition module 210 is configured to acquire state information and environment information of a target floating object;

[0104] The determination module 220 is configured to process the state information and the environment information using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object; the first running trajectory includes a displacement in a three-dimensional direction;

[0105] The correction module 230 is configured to correct the first running trajectory based on biological impact factors to obtain a target running trajectory; the biological impact factors include the displacement of the target floating object in a direction vertical to the sea level caused by biological fouling or the aggregation of marine snow, and the loss rate of the target floating object during the operation.

[0106] In some embodiments, the equation corresponding to the determination module 220 is:

[0107]

[0108] where u, v, and w correspond to the advection velocities in the x, y, and z directions, respectively. r 、v r and w r is the random velocity caused by turbulent diffusion effect, Δt is the time step;

[0109] The calculation equation for advection velocity is:

[0110]

[0111] Among them, u c 、v c and w c is the velocity output by the Ocean Circulation Model (OGCM), u a and v a is the wind speed at 10 meters above sea level, α and β are the wind drift coefficient and deviation angle, u s and v s The Stokes drift velocity provided for the wave model, w d is the settling velocity of floating objects.

[0112] Optionally, the settling velocity w of the floating object d The calculation formula is:

[0113]

[0114] Among them, ρ tot is the density of floating objects, ρ sw is the density of the surrounding seawater, g is the acceleration due to gravity, ω * is the dimensionless sedimentation velocity, v sw is the kinematic viscosity of seawater.

[0115] In some embodiments, the determination module 220 is specifically configured as follows:

[0116] The Lagrangian trajectory equation is solved based on the Runge-Kutta fourth-order method to determine the first running trajectory of the target floating object.

[0117] In some embodiments, the correction module 230 is specifically configured as follows:

[0118] Obtain density parameters of target floating objects during operation;

[0119] Determine the buoyancy parameter corresponding to the target floating object based on the density parameter;

[0120] The first running trajectory is corrected based on the buoyancy parameter to obtain a target running trajectory.

[0121] In some embodiments, the correction module 230 is further configured to: create a floating object gradual change model to determine the density parameters of the target floating object during operation; the floating object gradual change model is:

[0122]

[0123]

[0124]

[0125] in, G represents the increment of biofilm attached to the surface of target floating objects over time. coll represents the rate at which the target floating particles collide with microorganisms and are colonized, G grow represents the growth rate of attached microbial cells, L graze represents the loss rate of biofilm or marine snow by zooplankton grazing, L resp represents the loss rate of microorganisms through respiration, L nonlin is a nonlinear loss term, including the virus lysis process, represents the rate of change of biofilm increment below the photic zone, L diss is the dissolution rate of the microbial cell wall, represents the rate of change of the concentration of marine snow bound to the target floating particles, G upt is the rate at which target floating particles aggregate with marine snow, L rel Represents the release rate of non-biodegradable target floating debris particles.

[0126] The floating object trajectory prediction device provided in the embodiment of the present application is based on the above-mentioned floating object trajectory prediction method, which will not be described again here to avoid repetition.

[0127] It should be noted that the floating object trajectory prediction device provided in the embodiment of the present application and the floating object trajectory prediction method provided in the embodiment of the present application are based on the same application concept, so the specific implementation of this embodiment can refer to the implementation of the aforementioned floating object trajectory prediction method, and the repeated parts will not be repeated.

[0128] In some embodiments, see Figure 3 , Figure 3 The electronic device 300 provided in the embodiment of the present application includes a processor 310 and a memory 320; the memory 320 stores a computer program, wherein the computer program implements the above-mentioned floating object trajectory prediction method when executed by the processor.

[0129] Specifically, the processor 310 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 310 may also include an onboard memory for cache purposes. The processor 310 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiment of the present application.

[0130] The memory 320 may be any medium capable of containing, storing, conveying, propagating or transmitting instructions. For example, the memory 320 may include, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device, component or propagation medium. Specific examples of the memory 320 include: a magnetic storage device, such as a magnetic tape or a hard disk (HDD); an optical storage device, such as a compact disk (CD-ROM); a random access memory (RAM) or flash memory; and / or a wired / wireless communication link.

[0131] The present application also provides a computer-readable medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned floating object trajectory prediction method is implemented. The computer-readable medium may be included in the device / apparatus / system described in the above-mentioned embodiment; or it may exist independently without being assembled into the device / apparatus / system. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed, the method according to the embodiment of the present application is implemented.

[0132] According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signal, etc., or any suitable combination of the above.

[0133] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-described embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A method for predicting the trajectory of floating objects, characterized in that: include: Obtain the status information and environment information of the target floating object; The state information and the environment information are processed by using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object; the first running trajectory includes a displacement in a three-dimensional direction; The first running trajectory is corrected based on biological impact factors to obtain a target running trajectory; the biological impact factors include the displacement of the target floating object in a direction vertical to the sea level caused by biological fouling or aggregation of marine snow, and the loss rate of the target floating object during operation.

2. The method for predicting the trajectory of floating objects according to claim 1, characterized in that: The state information and the environment information are processed by the Lagrangian particle tracking method to determine the first running trajectory of the target floating object. The corresponding equation is: where u, v, and w correspond to the advection velocities in the x, y, and z directions, respectively. r 、v r and w r is the random velocity caused by turbulent diffusion effect, Δt is the time step; The calculation equation for advection velocity is: Among them, u c 、v c and w c is the velocity output by the Ocean Circulation Model (OGCM), u a and v a is the wind speed at 10 meters above sea level, α and β are the wind drift coefficient and deviation angle, u s and v s The Stokes drift velocity provided for the wave model, w d is the settling velocity of floating objects.

3. The floating object trajectory prediction method according to claim 2, characterized in that: The settling speed of floating objects w d The calculation formula is: Among them, ρ tot is the density of floating objects, ρ sw is the density of the surrounding seawater, g is the acceleration due to gravity, ω * is the dimensionless sedimentation velocity, u sw is the kinematic viscosity of seawater.

4. The floating object trajectory prediction method according to claim 1, characterized in that: The state information and the environment information are processed by using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object, and further comprising: The Lagrangian trajectory equation is solved based on the Runge-Kutta fourth-order method to determine the first running trajectory of the target floating object.

5. The floating object trajectory prediction method according to claim 1, characterized in that: The step of correcting the first running trajectory based on the biological influencing factor to obtain a target running trajectory includes: Obtaining density parameters of the target floating object during operation; Determine the buoyancy parameter corresponding to the target floating object based on the density parameter; The first running trajectory is corrected based on the buoyancy parameter to obtain a target running trajectory.

6. The floating object trajectory prediction method according to claim 5, characterized in that: The method of correcting the first running trajectory based on the biological influencing factor to obtain the target running trajectory also includes: creating a floating object gradual change model to determine the density parameter of the target floating object during the running process; the floating object gradual change model is: in, G represents the increase of biofilm attached to the surface of floating objects over time. coll represents the rate at which the target floating particles collide with microorganisms and are colonized, G grow represents the growth rate of attached microbial cells, L graze represents the loss rate of biofilm or marine snow by zooplankton grazing, L resp represents the loss rate of microorganisms through respiration, L nonlin is a nonlinear loss term, including the virus lysis process, represents the rate of change of biofilm increment below the photic zone, L diss is the dissolution rate of the microbial cell wall, represents the rate of change of the concentration of marine snow bound to the target floating particles, G upt is the rate at which target floating particles aggregate with marine snow, L rel Represents the release rate of non-biodegradable target floating debris particles.

7. A floating object trajectory prediction device, characterized in that: include: Acquisition module, determination module and correction module, wherein, The acquisition module is configured to acquire status information and environment information of the target floating object; The determination module is configured to process the state information and the environmental information using a Lagrangian particle tracking method to determine a first running trajectory of the target floating object; the first running trajectory includes a displacement in a three-dimensional direction; The correction module is configured to correct the first running trajectory based on biological impact factors to obtain a target running trajectory; the biological impact factors include the displacement of the target floating object in a direction vertical to the sea level caused by biological fouling or aggregation of marine snow, and the loss rate of the target floating object during operation.

8. The floating object trajectory prediction device according to claim 7, characterized in that: The correction module is specifically configured as follows: Obtaining density parameters of the target floating object during operation; Determine the buoyancy parameter corresponding to the target floating object based on the density parameter; The first running trajectory is corrected based on the buoyancy parameter to obtain a target running trajectory.

9. An electronic device comprising a processor and a memory; the memory stores a computer program, wherein: When the computer program is executed by the processor, the computer program implements the floating object trajectory prediction method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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