Particle rendering method and system for simulating deep sea snow settlement process

Abstract

The invention relates to a particle rendering method and system for simulating a deep sea snow settlement process. The method comprises the following steps: dividing a three-dimensional scene space into a plurality of uniform grid units, and constructing a sea snow particle model containing multi-attribute features; synchronously driving a particle motion process based on three-dimensional marine environment field data and simulating a biochemical process including polymerization, decomposition and ingestion mechanisms, and updating and solving a particle state by utilizing parallel computing; and performing three-dimensional scene rendering based on the updated particle state and simulating an optical special effect in the deep sea environment. By adopting the method, efficient and accurate simulation of the deep sea snow settlement process can be realized, and the accuracy and authenticity of sea snow settlement visualization are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of deep-sea environment simulation, in particular to a particle rendering method and system for simulating deep-sea sea snow settling process. BACKGROUND

[0002] Deep-sea sea snow is a kind of suspended particles mainly composed of organic suspended matter such as the remains of plankton and excretion, as a key carrier of the marine carbon cycle "biological pump", the simulation and visualization of its settling process is of great significance for understanding the potential of marine carbon sink, predicting climate change and exploring the evolution of deep-sea life.

[0003] For the visualization simulation of sea snow particles, the current visualization technology has the following problems: first, the current mainstream ocean science visualization software mainly focuses on the presentation of static or quasi-static field data (such as temperature, salinity, chlorophyll concentration plane distribution), and usually cannot simulate the aggregation, decomposition, and predation of sea snow particles, resulting in a simplified view and low scientific fidelity. Secondly, the particle system in the field of computer graphics is usually only a point with basic physical properties, lacks a series of biochemical properties, cannot support the calculation of sea snow ecological processes, and cannot be coupled with marine environmental data for driving, resulting in a disconnection between the simulation results and the real marine physical environment. In addition, when the particle scale reaches more than one billion, limited by the computing architecture, the current technology cannot realize real-time interactive simulation while maintaining scientific accuracy.

[0004] In summary, the current simulation method has technical bottlenecks in simultaneously realizing high-fidelity biochemical mechanism simulation and large-scale real-time rendering of deep-sea sea snow settling process, resulting in insufficient accuracy and real-time performance of the visualization results. SUMMARY

[0005] Therefore, it is necessary to provide a particle rendering method and system for simulating deep-sea sea snow settling process that can accurately and efficiently simulate deep-sea sea snow settling process.

[0006] In a first aspect, the present application provides a particle rendering method for simulating deep-sea sea snow settling process, comprising:

[0007] dividing a three-dimensional scene space into a plurality of uniform grid cells, and initializing a plurality of sea snow particle models in the three-dimensional scene space, the sea snow particle models including physical properties, biochemical properties, and ecological properties;

[0008] based on input three-dimensional marine environment field data, using parallel computing to drive the sea snow particle models in each grid cell to perform motion process simulation and biochemical process simulation, and to update and solve, the biochemical process simulation including aggregation mechanism, decomposition mechanism, and predation mechanism;

[0009] Based on the updated state of the sea snow particle model, rendering is performed in the three-dimensional scene while simulating optical effects in a deep-sea environment.

[0010] In one of the embodiments, the driving the sea snow particle model to perform motion process simulation based on the input three-dimensional marine environment field data comprises:

[0011] determining a grid cell in which the sea snow particle model is currently located, and obtaining flow velocity vectors of vertices of the grid cell;

[0012] calculating local coordinates of the sea snow particle model inside the grid cell;

[0013] based on the local coordinates, performing trilinear interpolation calculation on the flow velocity vectors of the vertices to obtain a flow velocity vector of the current position of the sea snow particle;

[0014] updating the position of the sea snow particle model according to the flow velocity vector through a kinematic equation.

[0015] In one of the embodiments, the physical attribute, biochemical attribute and ecological attribute of the sea snow particle model comprise:

[0016] the physical attribute comprises position, velocity, mass, volume, density and formation time;

[0017] the biochemical attribute comprises organic carbon content, viscosity coefficient and particle type identifier;

[0018] the ecological attribute comprises a parameter for controlling prey-priority.

[0019] In one of the embodiments, the aggregation mechanism comprises:

[0020] determining a target grid cell in which the current sea snow particle model is located;

[0021] searching for other sea snow particle models in a local search area constituted by the target grid cell and a plurality of grid cells spatially adjacent to the target grid cell;

[0022] when there is another sea snow particle model in the local search area and the distance between the two sea snow particle models is less than a preset aggregation threshold, calculating an aggregation probability according to the viscosity coefficients of the two sea snow particle models;

[0023] if the aggregation probability is greater than a preset threshold, merging the two sea snow particle models into a new particle, and calculating and updating the physical attribute and biochemical attribute of the new particle according to the physical attribute and biochemical attribute of the two sea snow particle models.

[0024] In one of the embodiments, the disaggregation mechanism comprises:

[0025] For the marine snow particle model within each grid cell, the particle mass decays over time according to the formation time of the marine snow particle model and the preset decomposition rate.

[0026] When the particle mass is lower than the decomposition threshold, the splitting probability is calculated according to a preset splitting probability model;

[0027] If the splitting probability meets the preset condition, the snow particle model is split into multiple sub-particle models, and the attributes of each sub-particle model are inherited from the original particle.

[0028] If the splitting probability does not meet the preset condition, the sea snow particle model is removed to simulate the degradation process.

[0029] In one embodiment, the feeding mechanism includes:

[0030] One or more specific cell grids in the three-dimensional scene space are defined as virtual predator regions;

[0031] When the sea snow particle model enters the grid cell corresponding to the virtual predator area through the movement process, the sea snow particle model is removed according to a preset probability to simulate the feeding process.

[0032] In one embodiment, the step of using parallel computing to update the state of the sea snow particle model includes:

[0033] The three-dimensional scene space is divided into multiple subdomains, and each subdomain corresponds to a computing node;

[0034] The snow particle models located in different subdomains are distributed and stored on the corresponding computing nodes;

[0035] Each computing node computes the state of the snow particle model in the corresponding subdomain in parallel.

[0036] Data exchange of the sea snow particle model located at the subdomain boundary is performed between adjacent computing nodes through the message passing interface.

[0037] In one embodiment, the optical effects simulating a deep-sea environment include:

[0038] A virtual camera and a simulated searchlight source are set in the three-dimensional scene space;

[0039] Based on the position of the virtual camera and the direction of the searchlight source, the light scattering intensity of the sea snow particle model is calculated according to the Tyndall effect model:

[0040] ;

[0041] wherein, is the angle between the position of the virtual camera and the direction of the spotlight light source, d is the distance from the sea snow particle model to the virtual camera, and r is the radius of the sea snow particle, is the scattering coefficient related to the particle material and refractive index;

[0042] In one embodiment, the fusing of the light scattering intensity and the base color of the sea snow particle model to obtain a final pixel color and output a rendering result comprises:

[0043] rendering fragments of all sea snow particle models in the three-dimensional scene space are sorted according to depth;

[0044] According to the sorting result, in order from far to near, the sea snow particle model color fused with the light scattering intensity is mixed with a target color based on an Alpha blending formula to output a final pixel color, wherein the Alpha blending formula is:

[0045] ;

[0046] wherein, is the source color of the sea snow particle model calculated by the Zeeman effect, is a transparency parameter, is a target color already existing on the screen, is an output color.

[0047] In a second aspect, the present application also provides a particle rendering system for simulating the deep sea snow sedimentation process, comprising:

[0048] a model construction module for dividing a three-dimensional scene space into a plurality of uniform grid cells and initializing a plurality of sea snow particle models in the three-dimensional scene space, the sea snow particle models including physical properties, biochemical properties and ecological properties;

[0049] a simulation module for driving the sea snow particle models in each grid cell to perform motion process simulation and biochemical process simulation based on input three-dimensional marine environment field data using parallel computing, and performing update solving, wherein the biochemical process simulation includes aggregation mechanism, decomposition mechanism and feeding mechanism;

[0050] a real-time rendering module for rendering in a three-dimensional scene based on the updated state of the sea snow particle models, while simulating optical special effects in a deep sea environment.

[0051] The particle rendering method and system for simulating the deep-sea snow sinking process can realize accurate quantitative characterization of the complex ecological behavior of the snow by constructing a snow particle model, introducing aggregation, decomposition and predation mechanisms, improving the efficiency of large-scale snow particle visualization by introducing a parallel computing architecture, and improving the authenticity of the visual performance of the particles in the deep-sea light environment in the three-dimensional simulation by integrating optical special effect simulation in three-dimensional scene rendering. The method can significantly improve the behavior authenticity, calculation efficiency and visual performance of the simulation of the snow sinking process. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative labor.

[0053] Figure 1 An application environment diagram of the particle rendering method for simulating the deep-sea snow sinking process in an embodiment;

[0054] Figure 2 A flowchart of the particle rendering method for simulating the deep-sea snow sinking process in an embodiment;

[0055] Figure 3 A flowchart of the step of updating the motion process of the snow particle model by trilinear interpolation in an embodiment;

[0056] Figure 4 A flowchart of the method of simulating the optical special effect in the deep-sea environment in another embodiment;

[0057] Figure 5 A structural block diagram of the particle rendering system for simulating the deep-sea snow sinking process in an embodiment;

[0058] Figure 6 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0060] The particle rendering method for simulating the deep-sea snow sinking process provided by the embodiments of the present application can be applied to, for example, Figure 1In the application environment shown, terminal 102 (e.g., a high-performance graphics workstation) communicates with server 104 (e.g., a database server storing marine environmental field data) via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud. Specifically, terminal 102 can be a server for scientific computing, and the data storage system can be used to store three-dimensional marine environmental field data and simulation results. It should be noted that this embodiment only uses a graphics workstation as an example; in practical applications, terminal 102 can also be, but is not limited to, various personal computers, laptops, server clusters, etc. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0061] This embodiment provides a particle rendering method for simulating the settling process of deep-sea snow. Figure 2 This is a flowchart of a high-performance particle rendering method provided in an embodiment of this application. The execution subject of this method can be an electronic device. Optionally, the electronic device can be a server or a terminal device, but this application is not limited thereto. Specifically, as shown... Figure 2 As shown, the process includes the following steps:

[0062] Step S201: Divide the three-dimensional scene space into several uniform mesh units, and initialize several snow particle models in the three-dimensional scene space.

[0063] First, a uniform mesh space is used to divide the 3D scene space into several cubic mesh units. Specifically, the entire 3D simulation space can be divided into units of size [missing information]. A cubic mesh, in which Let be the maximum particle radius, and c be an adjustable parameter. It can be understood that after meshing, for a particle located in the grid (X, Y, Z), the search can be completed by searching and interacting with particles in its 26 neighboring grids. Preferably, the grid can be reduced to 8 grids. By meshing, the computational complexity can be reduced from O(N²) of the exhaustive search method to O(N), where N is the number of particles.

[0064] Then, in the memory of the computing device, the structure of the marine snow particle model is constructed, and several marine snow particle models are initialized.

[0065] It should be noted that, in addition to the particle model constructed during system initialization, a particle generation volume can be defined in the 0-100 meter depth region of the simulated water area to continuously generate new sea snow particles. For example, the generation of new particles can be based on two modes:

[0066] (1) Fixed rate: M new particles are generated per time step. This approach is suitable for maintaining system activity.

[0067] (2) Dynamic rate coupled with external data: The generation rate G is calculated from the surface chlorophyll concentration and net primary productivity data, with the formula where Input is the input data and k is a fixed proportionality coefficient. In this way, the generation rate of sea snow caused by diurnal temperature difference, ocean currents, and monsoon events can be simulated.

[0068] In some embodiments, the sea snow particle model includes physical properties, biochemical properties, and ecological properties. The physical properties include position, velocity, mass, volume, density, and formation time; the biochemical properties include organic carbon content, viscosity coefficient, and particle type identifier, where the particle types include mayfly plant debris, fecal pellets, and other types; and the ecological properties include parameters for controlling the priority of being preyed upon.

[0069] During initialization, the positions of the sea snow particle models can be randomly distributed within the generation area, with an initial velocity of a random value close to 0, and the direction of the velocity is set to be driven by gravity. The initial mass, volume, and viscosity coefficient are randomly generated by a normal distribution to simulate the natural distribution characteristics of the particles. The formation time is the time when the particle is generated. Upper and lower limits of the number of particles allowed by the system are set to prevent memory overflow or depletion of examples. When the total number of particles exceeds the upper limit, some particles can be removed according to rules, for example, particles that have settled to the deepest part and contribute the least to vision are removed first.

[0070] Step S202, based on the input three-dimensional marine environment field data, using parallel computing, driving each grid cell of the sea snow particle model to perform motion process simulation and biochemical process simulation, and updating the solution.

[0071] The three-dimensional marine environment field data is read from a data file, including but not limited to three-dimensional flow field, temperature field, geographic information (latitude, longitude, depth) data, and marine biological distribution data.

[0072] For each sea snow particle model, the environmental parameters at its current position coordinates are obtained from the three-dimensional environment field by a spatial interpolation method, and the particle position and velocity are updated based on the physical kinematics equation, where the environmental parameters at least include flow velocity and temperature.

[0073] Based on this, biochemical process simulations are performed simultaneously. The aggregation mechanism calculates the aggregation probability based on the surface characteristic parameters of the particle models when the spatial distance between two particles is less than a preset aggregation threshold. When the aggregation conditions are met, the two particle models are merged into a new particle model, whose attributes are calculated by weighting or superimposing the attributes of the original particle models. The decomposition mechanism causes particle mass to decay over time according to a preset decomposition kinetic model based on the particle model's formation time and environmental parameters. When the particle mass falls below the decomposition threshold, the particle model splits or disappears. The feeding mechanism sets a virtual predator action area in three-dimensional space. When a particle enters this area, it is marked as consumed according to a preset feeding probability model, and corresponding metabolic product particles are optionally generated.

[0074] Understandably, due to the large number of particles and the interactions between them during the snowfall process, the computational complexity of simulating the aforementioned motion and biochemical processes is high. Therefore, a data-parallel distributed computing framework can be constructed to decompose the large-scale cluster state update task into multiple independently executable subtasks, and the parallel computing capabilities of the Graphics Processing Unit (GPU) can be used to achieve parallel solutions.

[0075] Specifically, all sea snow particle models are divided into multiple subsets and assigned to different computational units, each comprising multiple threads. Within each computational unit, synchronous state update calculations are performed on its assigned subset. These state updates include at least updates to the motion process and the detection and calculation of interactions between particle models, including neighbor particle model search and aggregation judgment in the aggregation mechanism, predation zone intrusion detection in the feeding mechanism, and particle attribute decay calculation and lifecycle state judgment in the decomposition mechanism.

[0076] Step S203: Based on the updated state of the sea snow particle model, render in the 3D scene while simulating optical effects in the deep sea environment.

[0077] Traditional ocean particle visualization methods are mostly based on general-purpose graphics engines, and their rendering effects often do not match the real physical and optical characteristics of the deep-sea environment, especially failing to accurately represent the unique light scattering phenomenon of sea snow particles under artificial searchlights and other light sources. In this step, our solution couples particle data with a physically based optical model.

[0078] Specifically, the updated sea snow particle model state data is transmitted to a graphics rendering domain, and in a shading stage of a rendering process, simulation of deep-sea optical effects is synchronously performed. According to spatial positions of a virtual camera and a simulated light source, a set relationship of each particle model relative to the light source and a line of sight is calculated, and based on the geometric relationship, optical scattering model is used to calculate light scattering intensity generated by the particle model. The calculated scattering light intensity is fused with a basic color of the particle, and a final output pixel color is generated.

[0079] In the above embodiment, by constructing a sea snow particle model, introducing aggregation, decomposition and feeding mechanisms, the problem that a traditional particle model cannot accurately represent complex ecological behaviors of sea snow is solved; by synchronously driving a motion process and a biochemical process simulation based on three-dimensional ocean environment field data, technical defects of existing methods that the unique ecological interaction mechanism simulation of sea snow is incomplete are overcome; by using parallel computing, the efficiency of large-scale sea snow particle visualization is improved; by integrating optical effect simulation in three-dimensional scene rendering, the authenticity of particle visual performance in a deep-sea lighting environment in three-dimensional simulation is improved.

[0080] In some exemplary embodiments, based on input three-dimensional ocean environment field data, the sea snow particle model is driven to perform motion process simulation, such as Figure 3 As shown, the method comprises the following steps:

[0081] Step S301, determining a grid cell in which a current position of a sea snow particle model is located, and acquiring flow velocity vectors of each vertex of the grid cell.

[0082] It can be understood that the flow velocity suffered by the point is obtained from the environment field data by trilinear interpolation, and the position of the particle is updated accordingly. In advection motion, the formula is as follows:

[0083] ;

[0084] Wherein, is the position of particle i at time t, is a flow velocity vector of the position of the particle obtained from the three-dimensional flow field, is a simulated time step. The formula describes the basic physical process of the sea snow particles being carried by the ocean current.

[0085] However, in computer simulation, physical fields (such as three-dimensional flow field, temperature field) are discretely stored at the vertices of the three-dimensional grid, while the position of the particle model is continuous and can be located inside the grid cell. Therefore, the motion process simulation of the sea snow particle model is calculated by using trilinear interpolation method in the embodiment, and the key is to solve the flow velocity vector of any point P(x, y, z) .

[0086] First, for a three-dimensional flow field, the mesh size is NX×NY×NZ, and each mesh point (i, j, k) stores a flow velocity vector value V(i, j, k). For any particle model position P(x, y, z), assuming the mesh cell spacing is 1, the mesh cells surrounding particle P(x, y, z) are:

[0087] ;

[0088] ;

[0089] ;

[0090] It should be noted that if the grid spacing is not 1, a normalization step is required before subsequent calculations.

[0091] Once the grid cells are determined, a particle can be considered to be surrounded by 8 vertices, which are: , , , , , , .

[0092] Step S302: Calculate the local coordinates of the snow particle model within the grid cells.

[0093] Then, the local coordinates of the particle within this unit grid cell are calculated, i.e., the weighting factor: Then (dx, dy, dz) are the interpolation weights in the three directions, with values ​​ranging from [0, 1).

[0094] Step S303: Based on the local coordinates, perform trilinear interpolation calculation on the velocity vector of each vertex to obtain the velocity vector of the current position of the snow particle model.

[0095] Next, a cubic linear interpolation step is performed. First, interpolation is performed along the X-axis (on four lines parallel to the X-axis). Layer, along The line is and Interpolation between ,along The line is and Interpolation between Similarly, in Layer, along The line is and Interpolation between; Layer, along line in and is interpolated. Thus, the estimated flow velocity of the particle along the X-axis direction when the particle is at the Y-Z plane coordinate of is obtained. Similarly, interpolation is performed on the Y-axis and Z-axis to obtain the final flow velocity vector of the particle:

[0096] .

[0097] At step S304, the position of the sea snow particle model is updated according to the flow velocity vector through kinematic equations.

[0098] The above steps are integrated, that is, the trilinear interpolation formula is obtained, and the same calculation is independently performed on each component (Vx, Vy, Vz) of the flow velocity vector:

[0099] .

[0100] It can be understood that, assuming that the physical field is continuous and linearly changed within a single grid cell, the above method estimates the field value at the position of the particle model by weightedly averaging the known values at the 8 corner points of the unit grid where the particle model is located. The weights (dx, dy, dz) reflect the spatial proximity of the particle model to each corner point, and the closer to a corner point, the greater the weight of the value of the corner point on the final result.

[0101] In the above embodiment, the motion process of each sea snow particle model is simulated through the trilinear interpolation algorithm, which can ensure the accuracy of obtaining continuous spatial field values from discrete grid data, thereby improving the accuracy of the description of the motion process of the sea snow particle.

[0102] Since the sea snow particles will collide due to turbulence, Brownian motion, etc. during the settling process, and will adhere and aggregate when certain conditions are met, this process will affect the particle size distribution and settling rate. To simulate this ecological process, in some exemplary embodiments, the aggregation mechanism can be implemented in the following manner:

[0103] At step S401, the target grid cell where the current sea snow particle model is located is determined.

[0104] At step S402, other sea snow particle models are searched in a local search area formed by the target grid cell and a plurality of grid cells spatially adjacent thereto.

[0105] At step S403, when there are other sea snow particle models in the local search area, and the distance between the two sea snow particle models is less than a preset aggregation threshold, the aggregation probability is calculated according to the viscosity coefficients of the two sea snow particle models.

[0106] Specifically, each sea snow particle model is traversed and its neighboring particles are searched for. The aggregation threshold is related to the physical size of the particles. In one embodiment, for original particles j and k, r is the equivalent radius of the particles, and the judgment is as follows:

[0107]

[0108] wherein, is a threshold coefficient, used to simulate the effective range of the particle model.

[0109] When the distance condition is met, a probabilistic aggregation judgment is made based on the surface characteristics of the particle models. Specifically, the aggregation probability is calculated according to the viscosity coefficients of the two sea snow particle models:

[0110]

[0111] wherein, are the viscosity coefficients of the two particle models, respectively, and A is a rate constant related to the particle collision cross section and the environmental turbulence intensity. The specific value can be calibrated by comparing field observation data, or configured according to the physical characteristics of the target simulation area.

[0112] In one embodiment, the order of magnitude of the rate constant A can be estimated by the formula wherein, and are the equivalent radii of the interacting particle models j and k, respectively. which approximately represents the product of the collision cross section and the relative motion speed. Larger particles have larger collision target areas and higher relative speeds in turbulence. is the turbulent dissipation rate of seawater, and the aggregation efficiency coefficient can be calibrated by experimental data within a reasonable range of to . is the time step, indicating that the probability is related to the action time. This formula macroscopically quantifies the microscopic adhesion as a random event.

[0113] Step S404, if the aggregation probability is greater than the preset threshold, the two sea snow particle models are merged into a new particle, and the physical properties and biochemical properties of the new particle model are calculated and updated according to the physical properties and biochemical properties of the two sea snow particle models.

[0114] The new particle model merged has each attribute obtained by calculating the two original particle models. For original particle models j and k, the mass of the merged new particle model is , and the center of mass is ​​The position is the center of mass of the two-particle model, and its density and other physical properties are calculated similarly. The viscosity coefficient of the new particle model i is output by weighting the two particle models according to their surface areas. The velocity of the new particle model i is determined by the law of conservation of momentum. Calculations are performed, with the formation time set at age=0, and the formation position at the average of the displacement vectors of particles j and k before their aggregation. The particle type can be determined using a preset inheritance rule, which identifies the new particle type.

[0115] It can be understood that the decomposition mechanism simulates the material decay and morphological evolution process of marine snow particles during sedimentation due to microbial degradation, hydrolysis, and other processes. In some exemplary embodiments, the decomposition mechanism is implemented according to the following steps:

[0116] Step S501: For the marine snow particle model in each grid cell, the particle mass is made to decay over time according to the formation time of the marine snow particle model and the preset decomposition rate.

[0117] Specifically, for each snowflake particle model, its mass is reduced based on its travel time and a set decomposition rate. When the mass falls below a threshold, the particle model is marked for deletion or split into multiple smaller particle models. For example, an exponential decay model is used to simulate this process:

[0118] ;

[0119] in, Let i be the mass of particle model i at the current time step. The decomposition rate constant is The time step is the decomposition rate constant. It can be correlated with environmental parameters, such as decreasing with increasing water depth or decreasing temperature. A decomposition event is triggered when the particle model mass is detected to be below a preset decomposition threshold. The decomposition threshold can be set as a fixed percentage of the initial particle model mass, or dynamically adjusted according to the particle model type.

[0120] Step S502: When the particle mass is lower than the decomposition threshold, the splitting probability is calculated according to the preset splitting probability model.

[0121] When the decomposition condition is met, the particle model randomly decomposes into two parts or disappears. Preferably, in one implementation, the system determines the decomposition based on a preset probability. This determines whether the particle model disappears or splits.

[0122] When generating random numbers Or the particle mass is already extremely small (e.g. ). This particle model is removed directly from the system to simulate the complete degradation of organic matter into dissolved state, or the case where the particle is too small to be resolved at the simulation scale.

[0123] At step S503, if the splitting probability satisfies a preset condition, the sea snow particle model is split into a plurality of sub-particle models, and the properties of each sub-particle model are inherited from the original particle.

[0124] When , the decomposition is performed, and is defined as a random allocation factor. For example, it can take ~U(0.3, 0.7), that is, it is subject to a random distribution between 0.3 and 0.7. After splitting, the new particle inherits the material density of the original particle model and follows the law of conservation of momentum:

[0125] ;

[0126] ;

[0127] wherein is a random velocity vector representing a small impulse generated at the time of splitting, the direction is random, and the size can be set to . The position of the new particle model is obtained according to the following manner:

[0128]

[0129] ;

[0130] wherein offset is a displacement vector, the direction is random, and the size is proportional to the merging radius of the two new particle models, to avoid the generation of two new particle models at the same position, resulting in visual overlap and abnormal physical calculation.

[0131] The formation time of the new particle model is 0, the type is inherited from the type of the original particle, and the viscosity coefficient can be fine-tuned according to the surface area or volume ratio of the new particle model, for example , to simulate the case where the freshly fractured surface may have different viscosities.

[0132] At step S504, if the splitting probability does not satisfy the preset condition, the sea snow particle model is removed to simulate the degradation process.

[0133] In the above embodiment, by introducing the decomposition mechanism, the defect that the traditional particle simulation method cannot reflect the biological degradation process of sea snow particles is solved.

[0134] In some exemplary embodiments, due to the spatial heterogeneity and randomness of the distribution of predators and their feeding behavior, a feeding mechanism is used to simulate the ecological characteristics of sea snow particles: one or more specific unit grids in the three-dimensional scene space are defined as virtual predator areas; when the sea snow particle model passes through the movement process and enters the grid cell corresponding to the virtual predator area, the sea snow particle model is removed according to a preset probability to simulate the feeding process.

[0135] Specifically, virtual predator agents with ecological attributes are set in the three-dimensional space. For each agent, sea snow particle models within the predator activity range are detected. When a particle model enters any virtual predator area, it is determined whether feeding occurs according to a preset feeding probability model.

[0136] In one possible implementation, a fixed probability value is used for judgment; in another more complex implementation, the feeding probability is related to one or more factors such as particle type, particle size, and predator density in the area. If it is determined that feeding occurs, the sea snow particle model is removed from the system.

[0137] In a preferred embodiment, the feeding mechanism includes a metabolite generation step. When a sea snow particle model is removed to simulate the feeding process, a new particle representing predator excrement is generated at the location of the fed particle.

[0138] Specifically, the total mass of the consumed substance is calculated . Part of it (AE is assimilation efficiency) is assimilated and permanently removed from the system. The remaining part is accumulated. When the accumulated mass reaches a threshold T, a new excrement particle is generated near the agent location. The new particle type has two labels, "fecal pellet" and "new type", and its mass is equal to the accumulated excrement mass. The density and viscosity coefficient can be set to specific values according to biological knowledge, and the age is reset to 0.

[0139] In some embodiments, the step S202 uses parallel computing to drive the sea snow particle models in each grid cell to perform movement process simulation and biochemical process simulation, and to update and solve, which can be realized by the parallel computing capability of a GPU cluster. Specifically, it can include the following steps:

[0140] Step S601 divides the three-dimensional scene space into multiple subdomains, each subdomain corresponding to a computing node.

[0141] An initialization of a high-performance computing cluster environment, usually based on Message Passing Interface (MPI) for multi-node communication. The three-dimensional scene space is logically divided into multiple non-overlapping or partially overlapping sub-domains. The entire three-dimensional ocean simulation space is decomposed in three-dimensional spatial domain, and the global computing domain is divided into multiple sub-domains, each of which is assigned to a computing node in the cluster (each node can contain multiple GPUs). On each computing node, the computing environment of its local GPU is further initialized.

[0142] The strategy of spatial division can be uniform grid division or non-uniform division based on particle density distribution to ensure load balancing of each computing node.

[0143] Step S602, the sea snow particle models located in different sub-domains are distributed and stored in the corresponding computing nodes.

[0144] According to the position of the particle model in the three-dimensional space, it is assigned and stored in the corresponding sub-domain computing node. Each computing node only maintains and processes the particle data set within its local sub-domain. Within each computing node, the particle data assigned to the node is transferred from its host memory to the global memory of one or more GPUs on the node.

[0145] Step S603, each computing node parallelly calculates the state of the sea snow particle model in the corresponding sub-domain.

[0146] On each node, the dimensions of thread grid and thread block are configured for its GPU, and the kernel function is started in parallel to process all particle models in the local sub-domain. This update process includes the simulation of the movement process and the simulation logic of all biochemical processes such as aggregation, decomposition and feeding. Each node uses its local computing resources to perform synchronous calculation on the particle models belonging to it, achieving task-level parallelism.

[0147] Step S604, through the Message Passing Interface, data exchange of sea snow particle models located at the sub-domain boundary is performed between adjacent computing nodes.

[0148] An MPI communication step is inserted before or after the execution of the kernel function. Specifically, each computing node identifies the particles in the boundary region of its sub-domain, packs the state data of these particles and sends them to the adjacent node, and receives the boundary particle data from the adjacent node.

[0149] Through the above distributed parallel calculation, large-scale particle update calculation can be dispersed to multiple computing units, thereby significantly improving the simulation speed.

[0150] In some embodiments, as shown in Figure 4 the simulation of optical special effects in deep sea environment is realized by the following way:

[0151] Step S701, setting a virtual camera and a simulated spotlight light source in a three-dimensional scene space.

[0152] The virtual camera is used to define the viewpoint position, view angle and orientation of an observer, and the simulated spotlight light source simulates an illumination device used in a deep-sea environment, having specific spatial position, illumination direction and light intensity and other attributes.

[0153] Step S702, based on the position of the virtual camera and the direction of the spotlight light source, calculating the light scattering intensity of the sea snow particle model according to the Tyndall effect model.

[0154] According to the Tyndall effect model, the light scattering intensity coefficient can be calculated as:

[0155] ;

[0156] wherein, is the included angle between the observation direction and the light source direction, is a simplified approximation of the anisotropic term of the Rayleigh scattering function, indicating that forward and backward scattering is stronger than lateral scattering, and d is the distance from the particle to the camera, represents the square distance decay of light intensity, is the scattering cross-sectional area of the particle, and the larger the area, the stronger the scattered light. is the scattering coefficient related to the particle material and refractive index. It should be noted that the specific mathematical expression of the Tyndall effect model can have multiple forms, which is not limited in the present application.

[0157] Step S703, fusing the light scattering intensity with the base color of the sea snow particle model to obtain the final pixel color and output the rendering result.

[0158] In the rendering shading stage, the light scattering intensity is fused with the base color of the sea snow particle model, and the fusion method is exemplarily:

[0159] ;

[0160] wherein, Color_final is the current particle color, Color_particle is the particle color at the previous time, and the initial particle color is randomly generated according to the particle type value, is the attenuation coefficient of the water body, is the depth of the particle.

[0161] In the above embodiment, by adding the deep-sea optical physical characteristics into the rendering process of the deep-sea sea snow particle model, the accuracy and realism of the visualization result can be improved, thereby supporting researchers to make accurate visual analysis and judgment.

[0162] It is understandable that, since the snow particles are semi-transparent objects, they need to be blended from back to front during the final rendering to achieve the correct visual effect. In one embodiment, a parallel depth-sorting synthesis algorithm is used to render the snow particle model, specifically including the following steps:

[0163] First, the rendering fragments of all the sea snow particle models in the three-dimensional scene space are sorted by depth.

[0164] For example, when using parallel computing, the master node divides the image into multiple regions and assigns them to different nodes. Each node sends the fragment data of its assigned image region to the corresponding node. Each node receives all the fragments belonging to its own image region and sorts the locally rendered fragments by depth value z.

[0165] Then, based on the sorting results, in order from far to near, the color of the snow particle model after light scattering intensity fusion is mixed with the target color according to the Alpha mixing formula to output the final pixel color.

[0166] The Alpha mixing formula is:

[0167] ;

[0168] in, The source color represents the color calculated by the Tyndall effect from a single sea snow particle model; it is a vector containing RGB components. For source transparency, exemplarily, A value of 1 indicates complete opacity. A value of 0 indicates complete transparency. The target color represents the existing color on the screen. When drawing the first particle, this value is the background color; when drawing subsequent particles, it is the color that is already displayed on the screen after all previous particles have been blended. To output color.

[0169] For example, during parallel computing, each node sends the synthesized local image region back to the master node, which then stitches all regions together to form the final image.

[0170] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other sequences. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of the steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0171] Based on the same inventive concept, the embodiments of the present application also provide a particle rendering system for simulating deep-sea sea snow sedimentation process for implementing the above-mentioned method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more particle rendering system embodiments for simulating deep-sea sea snow sedimentation process provided below can refer to the limitations of the corresponding method in the above, which will not be repeated here.

[0172] In an exemplary embodiment, as shown in Figure 5 a particle rendering system 800 for simulating deep-sea sea snow sedimentation process is provided, comprising a model construction module 801, a simulation simulation module 802 and a real-time rendering module 803, wherein:

[0173] The model construction module 801 is configured to divide a three-dimensional scene space into a plurality of uniform grid cells, and initialize a plurality of sea snow particle models in the three-dimensional scene space, wherein the sea snow particle models include physical properties, biochemical properties and ecological properties.

[0174] The simulation simulation module 802 is configured to drive the sea snow particle models in each of the grid cells to perform motion process simulation and biochemical process simulation based on input three-dimensional ocean environment field data using parallel computing, and perform update solving, wherein the biochemical process simulation includes aggregation mechanism, decomposition mechanism and feeding mechanism.

[0175] The real-time rendering module 803 is configured to perform rendering in a three-dimensional scene based on the updated state of the sea snow particle models, while simulating optical special effects in a deep-sea environment.

[0176] The various modules in the particle rendering system for simulating deep-sea sea snow sedimentation process described above can be implemented in whole or in part by software, hardware, and combinations thereof. The various modules described above can be embedded in the processor in the computer device in hardware form or independent of the processor in the computer device, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the various modules.

[0177] In one embodiment, a computer device is provided, which can be a server or a graphics workstation, and an internal structure diagram thereof can be as shown in Figure 6 The computer device includes a processor, a graphics processor (GPU), a memory, and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium (such as a hard disk) and a memory (such as a random access memory). The non-volatile storage medium stores an operating system, a computer program, and a database (for storing marine environment field data). The memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with an external data server through a network connection. The computer program is executed by the processor and implements the high-performance particle rendering method described above.

[0178] Those skilled in the art can understand that Figure 6 The structure shown in the above

[0179] In one exemplary embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program, which is executed by the processor to implement the steps in the method embodiments described above.

[0180] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0181] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0182] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A particle rendering method for simulating deep-sea sea snow settling process, characterized in that, The method comprises: dividing a three-dimensional scene space into a plurality of uniform grid cells, and initializing a plurality of sea snow particle models in the three-dimensional scene space, the sea snow particle models including physical attributes, biochemical attributes, and ecological attributes; based on input three-dimensional marine environment field data, driving the sea snow particle models in each grid cell to perform motion process simulation and biochemical process simulation, and performing update solving, the biochemical process simulation including aggregation mechanism, decomposition mechanism, and predation mechanism; based on the updated state of the sea snow particle models, rendering in the three-dimensional scene while simulating optical special effects in a deep sea environment.

2. The method of claim 1, wherein, The driving of the sea snow particle models to perform motion process simulation based on the input three-dimensional marine environment field data comprises: determining a grid cell in which the current position of the sea snow particle model is located, and obtaining flow velocity vectors of vertices of the grid cell; calculating local coordinates of the sea snow particle model inside the grid cell; based on the local coordinates, performing trilinear interpolation calculation on the flow velocity vectors of the vertices to obtain a flow velocity vector of the current position of the sea snow particle; updating the position of the sea snow particle model according to the flow velocity vector through a kinematic equation.

3. The method of claim 1, wherein, The physical attributes, biochemical attributes, and ecological attributes of the sea snow particle model comprise: the physical attributes include position, velocity, mass, volume, density, and formation time; the biochemical attributes include organic carbon content, viscosity coefficient, and particle type identifier; the ecological attributes include a parameter for controlling predation priority.

4. The method of claim 3, wherein, The aggregation mechanism comprises: determining a target grid cell in which the current sea snow particle model is located; searching for other sea snow particle models in a local search area constituted by the target grid cell and a plurality of grid cells spatially adjacent to the target grid cell; when there are other sea snow particle models in the local search area and the distance between the two sea snow particle models is less than a preset aggregation threshold, calculating an aggregation probability according to the viscosity coefficients of the two sea snow particle models; if the aggregation probability is greater than a preset threshold, merging the two sea snow particle models into a new particle; calculating and updating the physical attributes and biochemical attributes of the new particle according to the physical attributes and biochemical attributes of the two sea snow particle models.

5. The method of claim 3, wherein, The decomposition mechanism comprises: for each sea snow particle model in a grid cell, causing particle mass to decay over time according to the formation time of the sea snow particle model and a preset decomposition rate; when the particle mass is lower than a decomposition threshold, calculating a splitting probability according to a preset splitting probability model; if the splitting probability meets a preset condition, splitting the sea snow particle model into a plurality of sub-particle models, the attributes of each sub-particle model being inherited from the original particle; if the splitting probability does not meet the preset condition, removing the sea snow particle model to simulate a degradation process.

6. The method of claim 1, wherein, The predation mechanism comprises: defining one or more specific unit grids in the three-dimensional scene space as a virtual predator area; When the sea snow particle model passes through the movement process and enters a grid cell corresponding to the virtual predator area, the sea snow particle model is removed according to a preset probability to simulate a feeding process.

7. The method of claim 1, wherein, The updating and solving of the state of the sea snow particle model by using parallel computing includes: dividing the three-dimensional scene space into a plurality of sub-domains, each sub-domain corresponding to a computing node; distributing the sea snow particle models located in different sub-domains in the corresponding computing nodes in a distributed manner; each computing node parallelly calculates the state of the sea snow particle model in the corresponding sub-domain; the data exchange of the sea snow particle models located at the boundaries of the sub-domains is performed between adjacent computing nodes through a message passing interface.

8. The method of claim 1, wherein, The simulation of the optical special effect in the deep sea environment includes: setting a virtual camera and a simulated searchlight light source in the three-dimensional scene space; based on the position of the virtual camera and the direction of the searchlight light source, calculating the light scattering intensity of the sea snow particle model according to a Tyndall effect model; ； wherein, is the angle between the position of the virtual camera and the direction of the spotlight light source, d is the distance of the sea snow particle model to the virtual camera, r is the radius of the sea snow particle, is the scattering coefficient related to the particle material, refractive index; fusing the light scattering intensity and the base color of the sea snow particle model to obtain a final pixel color and output a rendering result.

9. The method of claim 8, wherein, The fusion of the light scattering intensity and the base color of the sea snow particle model to obtain a final pixel color and output a rendering result includes: sorting all the rendering fragments of the sea snow particle models in the three-dimensional scene space according to depth; according to the sorting result, in the order from far to near, mixing the sea snow particle model color after the light scattering intensity fusion and a target color based on an Alpha blending formula to output a final pixel color, wherein the Alpha blending formula is: ； wherein, is the source color calculated by the Tyndall effect for the sea snow particle model, is the transparency parameter, is the target color already present on the screen, is the output color.

10. A particle rendering system for simulating deep-sea sea snow settling processes, characterized in that The system includes: a model construction module configured to divide a three-dimensional scene space into a plurality of uniform grid cells and initialize a plurality of sea snow particle models in the three-dimensional scene space, the sea snow particle models including physical properties, biochemical properties, and ecological properties; a simulation module configured to, based on input three-dimensional marine environment field data, drive the sea snow particle models in each of the grid cells to perform movement process simulation and biochemical process simulation by using parallel computing and to update and solve, the biochemical process simulation including aggregation mechanism, decomposition mechanism, and feeding mechanism; a real-time rendering module configured to, based on the updated state of the sea snow particle models, perform rendering in a three-dimensional scene and simulate optical special effects in a deep sea environment.

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