Meteorological snowfall process and ecological influence simulation method, device and storage medium

Abstract

The application relates to a meteorological rain and snow process and ecological influence simulation method, equipment and a storage medium, the method comprising the following steps: acquiring meteorological data and ecological basic data and performing pretreatment; calculating a phase index according to standardized meteorological data, and determining attribute parameter values of each particle in a particle attribute parameter space; performing a life cycle simulation including different stages on each particle at a first frequency, generating a landing visual effect in a landing stage, and writing a precipitation contribution amount of each particle into a partition-level cumulative amount buffer corresponding to a landing point coordinate; reading the cumulative precipitation amount of each partition from the partition-level cumulative amount buffer at a second frequency lower than the first frequency, and performing ecological response calculation according to the ecological attribute of each partition; and outputting a simulation picture and an ecological simulation result to a display device for display. The application realizes real-time data transmission and asynchronous cooperative linkage of particle simulation and ecological response by writing the precipitation contribution amount of the particle landing stage into the partition-level buffer.

Description

Technical Field

[0001] This application relates to the field of exhibition hall technology, and in particular to a method, equipment and storage medium for simulating meteorological rain and snow processes and their ecological impacts. Background Technology

[0002] In meteorological science exhibition halls, using simulation technology to demonstrate rain and snow processes and their impact on the ecological environment is an important means of enhancing the effectiveness of science education. With the development of 3D rendering technology and interactive display equipment, the meteorological simulation system in the exhibition hall has gradually upgraded from static graphic displays and simple animation demonstrations to 3D dynamic simulations based on particle systems.

[0003] However, existing exhibition hall weather simulation systems suffer from a fundamental limitation in their architectural design: the visual simulation of rain and snow is disconnected from the simulation of ecological impacts at the data level. Specifically, the particle simulation module in existing systems is only responsible for generating visual images of rain and snow; the particle motion calculation results are discarded after the images are rendered, without transmitting any spatial distribution information to the downstream ecological simulation stage. The display of ecological impacts typically relies on independent preset animations or static parameter mappings, lacking a real-time data-driven relationship with the ongoing rain and snow simulation process. This architectural disconnect leads to a series of related problems.

[0004] First, because the spatial calculation results of particle simulation are not used by the ecological module, the display of ecological impacts cannot reflect the true spatial distribution of precipitation. For example, when a user adjusts the wind direction through an interactive device, the falling trajectory of particles on the screen will shift, but the ecological module cannot perceive this change. The response of vegetation and soil is still displayed according to a fixed pattern that is independent of wind direction, resulting in a significant visual contradiction between the rain and snow scenes and the ecological response.

[0005] Furthermore, due to the lack of a real-time data transmission path from particle simulation to ecological simulation, existing systems cannot dynamically drive the phased changes in ecological state based on the duration and accumulation of precipitation. In real-world environments, the impact of continuous rainfall on the ecosystem is not a simple linear additive process, but rather involves phased changes such as interception saturation, decreased soil infiltration, and surface water accumulation. Existing systems either ignore these phased changes and only display simple gradual effects, or use preset time-triggered animations to simulate these changes, the timing of which is independent of the actual accumulated precipitation in the simulation.

[0006] Furthermore, since the rain and snow simulation module updates particle motion visuals at a high frame rate, while the calculation of ecological impact typically requires significant computational overhead, there is an inherent mismatch in update frequency between the two. Existing systems lack an effective asynchronous coordination mechanism to resolve this frequency difference. They either reduce the frame rate of particle simulation to accommodate the speed of ecological calculations, resulting in a decrease in the smoothness of rain and snow visuals, or abandon real-time ecological calculations in favor of pre-recorded animations, causing ecological responses to become completely detached from the driving force of simulation data. Summary of the Invention

[0007] In order to establish a real-time data transmission path between particle-level simulation and ecological impact simulation, and to maintain their coordinated linkage at different update frequencies, this application provides a method, device and storage medium for simulating meteorological rain and snow processes and ecological impacts.

[0008] Firstly, this application provides a simulation method for meteorological rain and snow processes and their ecological impacts, which adopts the following technical solution: A simulation method for meteorological precipitation and snow processes and their ecological impacts includes the following steps: S1. Acquire meteorological data and basic ecological data, and preprocess the meteorological data and basic ecological data to obtain standardized meteorological data and standardized ecological data; S2. Calculate the phase index based on the standardized meteorological data, and determine the attribute parameter values ​​of each particle in the particle attribute parameter space based on the phase index; S3. Perform life cycle simulation on each particle at a first frequency. The life cycle simulation includes a launch phase, a descent phase, and a landing phase. In the landing phase, generate a landing visual effect based on the attribute parameter values ​​and write the precipitation contribution of each particle into the partition-level cumulative buffer corresponding to the partition slot of each particle's landing point coordinates. S4. Read the cumulative precipitation of each partition slot from the partition-level cumulative amount buffer at a second frequency, perform ecological response calculation on each partition according to the ecological attributes of each partition determined by the standardized ecological data, and obtain ecological simulation results, wherein the second frequency is lower than the first frequency, and the partition is divided according to the vegetation type and terrain type in the standardized ecological data; S5. Output the simulation screen of the life cycle simulation and the ecological simulation results to the display device for display.

[0009] By adopting the above technical solution, the precipitation contribution of particles is directly written into the corresponding slot of the partition-level cumulative amount buffer during the landing phase. This allows the spatial calculation results of particle simulation to be consumed by ecological response calculation in real time, rather than being discarded after rendering. The ecological impact simulation module reads the cumulative precipitation of each partition in batches from the buffer at a lower frequency than particle simulation, completing the ecological response calculation without slowing down the particle simulation frame rate. This solves the inherent mismatch in update frequency between rain and snow visual simulation and ecological impact simulation. The partition-level cumulative amount buffer acts as a decoupling intermediate layer between the two pipelines, allowing the particle simulation module and the ecological impact simulation module to run at independent frequencies without waiting for each other. This maintains the smoothness of rain and snow visuals while achieving real-time data-driven linkage between ecological response and precipitation simulation.

[0010] Optionally, step S2 includes the following sub-steps: S21. Calculate the phase index based on the temperature and humidity data in the standardized meteorological data. The phase index is a continuous value that characterizes the phase position of precipitation from pure rain to pure snow. S22. Based on the phase index, perform attribute sampling on each particle in the particle attribute parameter space to obtain the attribute parameter value of each particle, wherein the attribute parameter values ​​of different particles emitted at the same time are allowed to be different.

[0011] By employing the aforementioned technical solution, the phase index compresses the two meteorological dimensions of temperature and humidity into a continuous value ranging from pure rain to pure snow. When particles are emitted, their attribute parameter values ​​are determined by sampling in the attribute parameter space based on the phase index. Different particles emitted at the same time are allowed to have different attribute parameter values. When the phase index is between pure rain and pure snow, some particles exhibit attributes biased towards raindrop characteristics, while others exhibit attributes biased towards snowflake characteristics, thus presenting a mixed rain-snow phenomenon within the same frame. The continuity of the phase index means that the transformation from rain to snow is no longer a discrete switch between two fixed configurations, but rather a natural transition with changes in temperature and humidity.

[0012] Optionally, during the falling phase, the trajectory of each particle is affected by the wind speed vector in the standardized meteorological data, resulting in a horizontal offset. The landing point coordinates of each particle are determined according to the direction and magnitude of the wind speed vector and then written into the corresponding partition slot of the partition-level cumulative amount buffer. The partitioning method of the partition-level cumulative amount buffer and the response parameters of the ecological response calculation are not modified due to the change of the wind speed vector.

[0013] By employing the above technical solution, the wind speed vector acts on the particle's trajectory during its descent, causing a horizontal shift. The coordinates of the shifted landing point determine which partition slot the particle's precipitation contribution is written into. When wind speed changes, the cumulative precipitation in the downwind partition naturally increases, while the cumulative precipitation in the leeward partition naturally decreases. The dynamic changes in the spatial distribution of precipitation are automatically transmitted to the partition buffers by the physical calculation results of the particle trajectory. The partitioning method and response parameters of the ecological impact simulation module are not modified due to wind speed changes; that is, the ecological module is unaware of the presence or absence of wind speed. The dynamics of precipitation distribution are entirely transmitted at the particle physics layer, avoiding the additional computational overhead of reconfiguring the ecological module parameters when wind speed changes.

[0014] Optionally, the ecological response calculation includes a three-level cascaded process performed sequentially on each partition: The first-level interception layer treatment determines the interception amount and the penetration amount of precipitation from the cumulative precipitation based on the interception parameters corresponding to the vegetation type of each zone. The second-stage infiltration layer treatment divides the penetrating precipitation into infiltration amount and surface runoff based on the infiltration parameters corresponding to the soil type of each zone. The third-level visual mapping layer processes the interception amount as a leaf moisture rendering parameter, the soil moisture content corresponding to the infiltration amount as a soil color value rendering parameter, and the surface runoff as a waterlogged area rendering parameter.

[0015] By adopting the above technical solution, the ecological response calculation is upgraded from a direct mapping of precipitation to humidity in a single layer to a three-level cascaded processing of interception, infiltration, and visual mapping. Precipitation is first intercepted by the canopy; only the portion exceeding the interception capacity becomes penetrating precipitation and reaches the surface. Penetrating precipitation is then divided into infiltration and surface runoff according to soil permeability. Since different vegetation types in different zones correspond to different interception parameters, and different soil types correspond to different infiltration parameters, the same precipitation event produces differentiated response sequences in different zones. For example, in the tree zone with a high leaf area index, the interception is high in the early stage of precipitation, and the leaves become wet first, but the soil changes slowly. In contrast, in the shrub zone with a low leaf area index, penetrating precipitation reaches the surface rapidly, and the soil changes color first. Each level of the three-level cascade is implemented using simple functions, but the combination produces a differentiated visual response with a sense of physical hierarchy.

[0016] Optionally, the ecological response calculation further includes: To maintain an ecological state machine for each zone, in response to the interception amount reaching the interception capacity threshold of the corresponding zone, the ecological state machine transitions to the canopy dripping state and generates a dripping particle emission signal; Based on the emission signal of the water droplets, water droplets are generated in the corresponding partition. The life cycle simulation is performed on the water droplets, and the precipitation contribution of the water droplets in the landing stage is written into the corresponding partition slot of the partition-level cumulative buffer.

[0017] By adopting the above technical solution, when the interception amount reaches the interception capacity threshold of the corresponding zone, the ecological state machine transitions to the canopy dripping state and generates a dripping particle emission signal. The dripping particles are injected into the particle simulation module to perform the same life cycle simulation as precipitation particles. The precipitation contribution after the dripping particles land is written back into the corresponding zone slot of the zone-level accumulation buffer. The landing contribution of the dripping particles is superimposed on the penetrating precipitation, accelerating the accumulation of water content in the infiltration layer, making the appearance of surface water earlier than the calculation results without dripping closed loop. This positive feedback loop forms a causal transmission chain of "precipitation accumulation → interception saturation → canopy dripping → accelerated surface water accumulation → early appearance of water accumulation", making the temporal change of leaf dripping first and then the accelerated expansion of surface water in the display consistent with the physical phenomena in the actual rainfall process.

[0018] Optionally, the following steps may also be included: S6. Receive parameter adjustment operation sent through the interactive device, parse the parameter adjustment operation into a standardized parameter change instruction, and perform legality verification and frequency limiting on the standardized parameter change instruction; S7. Perform parameter smoothing processing on the standardized parameter change instruction that has passed the verification. Determine the corresponding transition time according to the parameter type of the standardized parameter change instruction. Transition the currently used parameter value to the target parameter value specified by the standardized parameter change instruction by interpolation according to the transition time. Different parameter types correspond to different transition times. Inject the smoothed parameter value into S3 and S4 at the same time.

[0019] By adopting the above technical solution, after the parameter adjustment operation issued by the user through the interactive device undergoes legality verification and frequency limiting, the parameter smoothing process interpolates and transitions the current parameter value to the target parameter value according to the transition duration corresponding to the parameter type. This avoids abrupt changes in particle simulation visuals and abnormal state transitions in the ecological state machine caused by parameter mutations. Different parameter types correspond to different transition durations, ensuring that the response speed of each parameter matches its visual sensitivity. The smoothed parameter value is simultaneously injected into both the particle simulation and ecological response calculation pipelines, ensuring that both pipelines receive consistent parameter input at the same time, avoiding visual contradictions between rain / snow visuals and ecological responses caused by asynchronous parameter injection timing.

[0020] Optionally, in step S6, in response to receiving multiple parameter adjustment operations simultaneously, the effective standardized parameter change instruction is determined based on the timestamps and operation ranges of the multiple parameter adjustment operations.

[0021] By adopting the above technical solution, when multiple visitors simultaneously issue parameter adjustment operations through interactive devices, the final standardized parameter change command is determined based on the timestamp and magnitude of each operation, thus avoiding parameter jitter and screen instability caused by multiple conflicting commands being injected into the simulation pipeline at the same time.

[0022] Optionally, in step S1, the acquisition of meteorological data includes a real-time sensor acquisition mode and a local preset data mode. The data output from both modes is converted to a unified format before undergoing the preprocessing.

[0023] By adopting the above technical solution, data acquisition supports two sources: real-time sensor acquisition mode and local preset data mode. Data output from both modes is converted to a unified format and then enters the same preprocessing flow, eliminating the need to distinguish data sources for subsequent particle simulation and ecological response calculations. Exhibition halls equipped with external sensors can use the real-time acquisition mode to obtain simulation effects synchronized with real weather, while exhibition halls without sensor access can operate independently using the local preset data mode, expanding the system's applicable scenarios.

[0024] Optionally, the particle attribute parameter space includes shape coefficient, drag coefficient, impact behavior coefficient, and rotation coefficient, wherein the shape coefficient controls the continuous change of particle shape from spherical to sheet-like, the drag coefficient controls the continuous change of particle shape from high-speed falling to low-speed falling, the impact behavior coefficient controls the continuous change of particle shape from impact splashing to impact accumulation, and the rotation coefficient controls the continuous change of particle shape from no flipping to high-frequency drifting.

[0025] By adopting the above technical solution, the particle attribute parameter space continuously parameterizes the visual morphology and physical behavior of particles through four dimensions: shape coefficient, drag coefficient, impact behavior coefficient, and rotation coefficient. Each of the four coefficients independently controls one behavioral dimension of the particle, so that raindrops (spherical, high-speed falling, impact splashing, no flipping) and snowflakes (flat, low-speed falling, impact accumulation, high-frequency drifting) no longer require two independent models, but are instead instances at different coordinate positions in the same parameter space.

[0026] Optionally, during the landing phase, a higher value of the ground contact behavior coefficient results in a higher sputtering ratio, and sputtering generates sub-particles; a lower value of the ground contact behavior coefficient results in a higher accumulation ratio, and accumulation increases the thickness of the snow layer on the ground; when the phase index is between pure rain and pure snow, the landing behavior of the same particle continuously transitions between sputtering and accumulation.

[0027] By adopting the above technical solution, the impact behavior coefficient controls the continuous transition from splashing to accumulation during the landing phase. Splashing generates sub-particles to enhance the visual impact, while accumulation increases the thickness of the snow layer on the ground, forming a snow cover effect. When the phase index is in the rain-snow mixture range, the landing behavior of the same particle transitions continuously between splashing and accumulation, resulting in a mixed visual effect of splashing water and a thin layer of snow on the ground in a sleet scene.

[0028] Optionally, the states of the ecological state machine include a dry state, an initial wet state, a continuous precipitation state, a saturated state, and an evaporation recovery state. Each state corresponds to a set of rendering parameters, which includes the leaf reflectivity range, the soil RGB color value range, and the transparency of the water accumulation layer.

[0029] By adopting the above technical solution, the five states of the ecological state machine cover the complete ecological process cycle from the start of precipitation to the cessation of rainfall and recovery. Each state corresponds to a set of rendering parameters including leaf reflectivity range, soil RGB color value range, and water layer transparency. The continuous gradual change of rendering parameters within a state achieves a smooth visual transition, while the switching of rendering parameter sets across states creates a staged visual leap. The combination of these two change modes allows the display of ecological impact to simultaneously possess a gradual process and qualitative change nodes.

[0030] Optionally, in the evaporation recovery state, the rollback rate of the ecological state machine is affected by the temperature parameter of the current simulation scenario. The higher the temperature parameter, the faster the rollback rate, and the lower the temperature parameter, the slower the rollback rate.

[0031] By adopting the above technical solution, the rate of evaporation recovery is adjusted by the temperature parameters of the simulated scenario. The higher the temperature, the faster the evaporation and the faster the ecological state regresses to a dry state; the lower the temperature, the slower the evaporation and the longer the humid state lasts. When users adjust the temperature parameters through interactive devices, the recovery process after the rain stops changes accordingly, enhancing the popular science demonstration effect of the causal relationship between temperature and ecological recovery.

[0032] Optionally, in the second-stage infiltration layer treatment, the surface runoff is converged to the adjacent low-lying areas according to the topographic slope of each zone, and the runoff converged to the low-lying areas is superimposed on the surface runoff of the low-lying areas.

[0033] By adopting the above technical solution, surface runoff converges towards adjacent low-lying areas according to the topographic slope of each zone. The converged runoff is added to the surface runoff of the low-lying areas, so that the water accumulation area and depth of the low-lying areas depend not only on the direct precipitation they receive, but also on the runoff from the surrounding high-slope areas. This mechanism introduces spatial coupling between zones, resulting in a topographical differentiation effect in hilly scenes, where drainage is faster at the top of the slope and water accumulates deeper in the valleys.

[0034] Optionally, in S7, the transition duration corresponding to the temperature-related parameters is greater than the transition duration corresponding to the rain / snow intensity-related parameters.

[0035] By adopting the above technical solution, the transition time of temperature parameters is longer than that of rain and snow intensity parameters. This means that when users adjust temperature and rainfall at the same time, the rain and snow intensity changes first, while the phase transition of rain and snow morphology changes later and gradually. This presents a time sequence of precipitation changing first and precipitation morphology changing later, which is consistent with the physical law that precipitation response is faster than phase transition in real meteorological processes.

[0036] Optionally, in S3, the particle budget upper limit is set for each particle, the particle emission rate is dynamically adjusted according to the current frame rate, the particle emission rate is reduced when the current frame rate is lower than the frame rate lower limit threshold, and the particle emission rate is increased when the current frame rate recovers to the frame rate recovery threshold; and the particle budget upper limit is pre-adjusted before the current frame rate decreases according to the change trend of parameter smoothing in S7.

[0037] By adopting the above technical solution, the particle budget limit dynamically adjusts the particle emission rate based on the current frame rate. When the frame rate drops, the particle density is reduced to restore smoothness, and when the frame rate recovers, the particle density is gradually increased to restore simulation accuracy. The changing trend of parameter smoothing is used to predict the upcoming load increase before the actual frame rate drop and adjust the particle budget limit in advance, upgrading budget adjustment from a passive frame rate response to an active load prediction, reducing the amplitude and duration of frame rate fluctuations.

[0038] Optionally, in S3, the meteorological parameter space is discretized into multiple parameter intervals, and particle trajectory templates are pre-calculated and cached for each parameter interval. In the life cycle simulation, the corresponding parameter interval is matched according to the current meteorological parameters, and the trajectory template is extracted from the cache and superimposed with random perturbation as the particle motion trajectory. When the meteorological parameters cross the boundary of the parameter interval, the trajectory template of the corresponding parameter interval is regenerated.

[0039] By adopting the above technical solution, the meteorological parameter space is discretized into multiple parameter intervals, and particle trajectory templates are pre-calculated for each interval. During runtime, the trajectory templates are extracted from the cache and superimposed with random perturbations to generate particle motion trajectories, eliminating the need to resolve the motion equations for each particle in every frame. The generation of trajectory templates for new intervals is only triggered when meteorological parameters cross interval boundaries. When parameters change within the same interval, the existing cache is fully reused, reducing the real-time overhead of particle motion computation.

[0040] Optionally, in response to the soil moisture content corresponding to the infiltration amount reaching the infiltration saturation threshold of the corresponding zone, the ecological state machine shifts to the surface water accumulation state. The canopy dripping state corresponding to the interception capacity threshold and the surface water accumulation state corresponding to the infiltration saturation threshold are allowed to be triggered asynchronously.

[0041] By adopting the above technical solution, the canopy dripping state triggered by the interception capacity threshold and the surface water accumulation state triggered by the infiltration saturation threshold are driven by independent conditions, allowing for asynchronous triggering. In light precipitation scenarios, the interception layer may be saturated and trigger canopy dripping, but the infiltration layer is not yet saturated and there is no surface water accumulation; in heavy precipitation scenarios, the two may trigger almost synchronously. The asynchronous triggering mechanism allows the same area to present different visual evolution sequences under different precipitation intensities, increasing the sense of scene hierarchy in the simulation display.

[0042] Secondly, the computer device provided in this application adopts the following technical solution: A computer device comprising: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: The above-mentioned simulation method for meteorological rain and snow processes and ecological impacts was implemented.

[0043] Thirdly, this application provides a computer-readable storage medium that adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and execute the methods described above; The storage medium stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the following: Such as the simulation method for meteorological rain and snow processes and their ecological impacts mentioned above.

[0044] In summary, this application includes at least one of the following beneficial technical effects: 1. By writing the precipitation contribution of particles into the corresponding partition slot of the partition-level cumulative amount buffer during the landing stage, and reading the cumulative precipitation from the buffer in batches at a second frequency lower than the particle simulation frequency to perform ecological response calculation, real-time data-driven linkage between particle simulation and ecological impact simulation at different update frequencies is realized, which solves the problem of the separation between rain and snow visual simulation and ecological impact display at the data level in the existing system.

[0045] 2. By sampling each particle separately in the particle attribute parameter space through the phase index, different particles emitted at the same time have different attribute parameter values. Combined with the horizontal offset effect of the wind speed vector on the particle falling trajectory, the spatial distribution change of the partition buffer is automatically transmitted to realize the continuous transition of rain and snow morphology and the dynamic spatial distribution of precipitation. Moreover, the partitioning method and response parameters of the ecological impact simulation module do not need to be modified due to wind speed or phase changes.

[0046] 3. By combining the three-level cascaded processing of interception, infiltration and visual mapping with a multi-stage ecological state machine and the positive feedback closed loop formed by the interception saturation trigger drip particle reinjection particle simulation module, the complete causal transmission from the microscopic landing behavior of particles to the qualitative change of the macroscopic ecological state is realized, so that different vegetation types and terrain types exhibit differentiated response time sequences and staged visual changes in the same precipitation. Attached Figure Description

[0047] Figure 1 This is a flowchart of the simulation method for meteorological rain and snow processes and ecological impacts in one embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of particle life cycle simulation in one embodiment of the present invention.

[0049] Figure 3 This is a flowchart of a three-level cascaded ecological response calculation in one embodiment of the present invention.

[0050] Figure 4 This is a schematic diagram of the state transition of the ecological state machine in one embodiment of the present invention.

[0051] Figure 5 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0052] The present application will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to limit the scope of the application.

[0053] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of the inventive concept. As part of this specification, some of the accompanying drawings of this disclosure are block diagrams illustrating structures and devices to avoid complicating the disclosed principles. For clarity, not all features of the actual embodiment need to be described. Furthermore, the language used in this disclosure has been primarily chosen for readability and instructional purposes and may not have been chosen to define or limit the subject matter of the invention, thus requiring the necessary claims to determine such inventive subject matter. References to “an embodiment” or “an embodiment” in this disclosure mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment, and multiple references to “an embodiment” or “an embodiment” should not be construed as necessarily referring to the same embodiment.

[0054] Unless explicitly defined, the terms “a,” “an,” and “the” are not intended to refer to a singular entity, but rather to include a general category whose specific examples can be used for illustration. Therefore, the use of the terms “a” or “an” can mean any number of at least one, including “a,” “one or more,” “at least one,” and “one or more.” The term “or” means any of the options and any combination of the options, including all options unless explicitly indicated that the options are mutually exclusive. The phrase “at least one of” when combined with a list of items refers to a single item in the list or any combination of items in the list. The phrase does not require all items listed unless explicitly defined as such.

[0055] This embodiment provides a simulation method for meteorological rain and snow processes and their ecological impacts, applied to an interactive display scenario in a meteorological science popularization exhibition hall. In this embodiment, the exhibition hall is the core interactive area of ​​a meteorological science popularization museum, equipped with a naked-eye 3D screen and motion-sensing interactive devices. The display area is divided into four ecological zones. Zone A is a tree-covered hilly area with a leaf area index of 5.0, loam soil, and a slope of 15°. Zone B is a shrub plain area with a leaf area index of 1.5, sandy soil, and a slope of 2°. Zone C is a grassland valley area with a leaf area index of 0.8, clay soil, and a slope of 0°, located on the downhill direction of Zones A and D, and is a runoff convergence area. Zone D is a bare soil slope area with a leaf area index of 0.1, gravelly soil, and a slope of 20°. The initial meteorological conditions in this embodiment are set as moderate rain, a temperature of 2°C, an easterly wind of level 3, and humidity of 85%.

[0056] To accommodate the meteorological science exhibition hall's ability to present the complete precipitation-interception-dripping-infiltration-water accumulation process within a minute-level display time, this embodiment employs an accelerated simulation mode. The simulated meteorological time is accelerated relative to the actual display time by a time acceleration factor α. In this embodiment, α is set to 3600, meaning 1 second of actual display time corresponds to 1 hour of simulated meteorological time. The frame rate, reading cycle, and transition duration mentioned below refer to the actual display time, while the meteorological parameters such as rain / snow intensity (mm / h), temperature change rate, and wind speed are physical quantities under simulated meteorological time. The cumulative precipitation in each zone within a single reading cycle represents the cumulative precipitation within the simulated meteorological time period corresponding to that reading cycle. Taking a baseline scenario of 12 mm / h rain as an example, one reading cycle (approximately 0.33 seconds of actual display time) corresponds to approximately 20 minutes of simulated meteorological time, and the cumulative precipitation in the zone within that reading cycle is approximately 4 mm. In accelerated simulation mode, visitors can observe the ecological response evolution of a real precipitation process that would otherwise take tens of minutes or even hours within a 1-10 second window of actual display time.

[0057] In this embodiment, the following core terms are defined as follows: The phase index is a continuous value calculated based on temperature and humidity, used to characterize the current precipitation's phase position between pure rain and pure snow, ranging from 0 to 1, where 0 represents pure rain and 1 represents pure snow. The particle attribute parameter space is a multidimensional continuous parameter domain describing the visual morphology and physical behavior of a single particle, with each particle corresponding to a set of attribute parameter values ​​in this space. The partition-level cumulative amount buffer is an intermediate data structure that divides slots according to ecological partitions. The particle simulation pipeline writes data to the buffer, and the ecological computing pipeline reads data from the buffer; the two pipelines are decoupled through the buffer. A partition slot is a data storage unit in the buffer that corresponds one-to-one with a specific spatial partition. Each partition slot stores the sum of precipitation contributions from all particles received by that partition within the current accumulation period. Lifecycle simulation is a simulation of the complete motion and behavior of a single particle from launch to landing, including three continuously executed stages: launch, descent, and landing. Ecological response calculation is the process of calculating the changes in visual rendering parameters of each zone based on the cumulative precipitation of each zone's trough and the ecological attributes of each zone.

[0058] Reference Figure 1 The method in this embodiment includes steps S1 to S5.

[0059] S1. Acquire meteorological data and basic ecological data, preprocess the meteorological data and basic ecological data to obtain standardized meteorological data and standardized ecological data.

[0060] Meteorological data includes the following fields: rainfall / snow intensity (mm / h); wind speed (m / s); wind direction (°); temperature (°C); and humidity (%). Basic ecological data includes the following fields: vegetation type code, leaf area index, soil type code, permeability coefficient (mm / h), and topographic slope (°). Meteorological data is collected at a frequency of 5 frames per second.

[0061] Meteorological data acquisition supports two modes. The first is a real-time sensor acquisition mode, which uses meteorological sensors deployed outside the exhibition hall to collect real-time data on current rain / snow intensity, wind speed, wind direction, temperature, and humidity. Taking the initial meteorological conditions in this embodiment as an example, a frame of data collected by the sensor is: rain / snow intensity 12mm / h, wind speed 4.5m / s, wind direction 90°, temperature 2℃, and humidity 85%. The second is a local preset data mode, which retrieves pre-recorded meteorological data records from a preset database according to scene tags. For example, after selecting the "moderate rain + slightly cold" scene tag, the system reads the meteorological data records associated with that tag from the database, and the data fields and format are consistent with the output of the real-time acquisition mode. The data output from both modes is converted into a unified data format before entering the preprocessing process.

[0062] The preprocessing includes three operations. The first operation is to perform sliding window mean denoising on the meteorological data, with the window width set to 5 frames. This means taking the mean of each field from the current frame and the previous 4 frames as the denoised output for the current frame. The second operation is to perform numerical normalization calibration on the ecological baseline data, mapping the original values ​​of each field to a range of 0 to 1. For example, the original value range of the leaf area index is 0 to 8; after normalization, the leaf area index of 5.0 in partition A is mapped to 0.625. The third operation is to remove outliers from two types of data. When the sampled value of a field in a frame exceeds three times the standard deviation of the mean of that field over the past 10 frames, the sampled point is marked as invalid and replaced with a valid value from the previous frame.

[0063] After preprocessing, standardized meteorological data and standardized ecological data are output. The standardized meteorological data includes noise-reduced rainfall / snow intensity, wind speed, wind direction, temperature, and humidity values. The standardized ecological data includes normalized vegetation type, leaf area index, soil type, permeability coefficient, and topographic slope values ​​for each region, along with vegetation type and topographic type codes for each region, used in subsequent steps to determine the ecological attributes and zoning of each region.

[0064] S2. Calculate the phase index based on standardized meteorological data, and determine the attribute parameter values ​​of each particle in the particle attribute parameter space based on the phase index.

[0065] The phase index is calculated using temperature and humidity data from standardized meteorological data as input. A pure rain threshold is set at 0 for temperatures greater than or equal to 5°C, and a pure snow threshold is set at 1 for temperatures less than or equal to -2°C. When the temperature is between -2°C and 5°C, the phase index is calculated using linear interpolation: Phase Index = (5 - current temperature) / (5 - (-2)) * humidity correction factor. The humidity correction factor is 1.0 when humidity is greater than or equal to 80%, and 1.0 when humidity is less than 80%. Taking the baseline scenario of this embodiment as an example, with a temperature of 2°C, humidity of 85%, and a humidity correction factor of 1.0, the phase index is equal to (5 - 2) / (5 - (-2)) × 1.0, i.e., 3 / 7 ≈ 0.43. This value falls between pure rain and pure snow, leaning towards rain but showing some snow tendency, indicating that the current precipitation is mainly rain mixed with some snowflakes.

[0066] After the phase index is calculated, the properties of each particle are sampled in the particle property parameter space according to the phase index to obtain the property parameter values ​​of each particle. The particle property parameter space includes four dimensions: shape coefficient, which controls the continuous change of particle shape from spherical to sheet-like, with a value of 0 representing a pure sphere and a value of 1 representing a pure sheet; drag coefficient, which controls the continuous change of particle falling speed from high speed to low speed, with a value of 0 representing the highest falling speed and a value of 1 representing the lowest falling speed; impact behavior coefficient, which controls the continuous change of particle landing behavior from splashing to accumulation, with a value of 0 representing pure accumulation and a value of 1 representing pure splashing; and rotation coefficient, which controls the continuous change of particle angular velocity during drift from no angular velocity to high-frequency drift, with a value of 0 representing no angular velocity and a value of 1 representing the highest angular velocity.

[0067] The phase index determines the sampling center value and sampling variance of the attribute parameters in each dimension. Taking the phase index of 0.43 in this embodiment as an example, the sampling center and sampling variance of the four dimensions are set as follows: the sampling center of the shape coefficient is 0.43, and the variance is 0.15; the sampling center of the drag coefficient is 0.40, and the variance is 0.12; the sampling center of the impact-to-ground behavior coefficient is 1-0.43=0.57, and the variance is 0.10. The higher the phase index, the stronger the accumulation tendency, so the impact-to-ground behavior coefficient and the phase index are complementary; the sampling center of the rotation coefficient is 0.38, and the variance is 0.15. Each particle samples independently from the above distribution at the time of emission, so the attribute parameter values ​​of different particles emitted at the same time are allowed to be different.

[0068] The differences in sampling results are illustrated using three particles emitted in the same frame as an example. Particle A has a shape coefficient of 0.25, drag coefficient of 0.28, impact behavior coefficient of 0.72, and rotation coefficient of 0.18, exhibiting overall rain-like characteristics. Visually, it appears as a nearly spherical water droplet falling at high speed, primarily splashing upon impact with almost no tumbling. Particle B has a shape coefficient of 0.62, drag coefficient of 0.55, impact behavior coefficient of 0.38, and rotation coefficient of 0.60, exhibiting overall snow-like characteristics. Visually, it appears as a sheet-like shape falling at a moderate speed, primarily accumulating upon impact with noticeable tumbling. Particle C has a shape coefficient of 0.40, drag coefficient of 0.38, impact behavior coefficient of 0.55, and rotation coefficient of 0.35, falling between rain and snow. The coexistence of these three particles in the same frame creates a mixed visual effect of rain and snow.

[0069] In contrast, when the phase index is 0 (pure rain state, temperature greater than or equal to 5℃), the sampling centers for the four dimensions are morphology coefficient 0.05, drag coefficient 0.05, impact behavior coefficient 0.95, and rotation coefficient 0.02, respectively. At this time, almost all particles behave as spherical, high-speed falling raindrops, producing splashing water upon landing. When the phase index is 1 (pure snow state, temperature less than or equal to -2℃), the sampling centers for the four dimensions are morphology coefficient 0.95, drag coefficient 0.90, impact behavior coefficient 0.05, and rotation coefficient 0.85, respectively. At this time, almost all particles behave as sheet-like, slowly falling snowflakes, accumulating into snow layers upon landing.

[0070] S3. Perform a lifecycle simulation on each particle at a first frequency. The lifecycle simulation includes a launch phase, a descent phase, and a landing phase. During the landing phase, generate a landing visual effect based on the attribute parameter values, and write the precipitation contribution of each particle into the partition-level cumulative buffer corresponding to the partition slot of each particle's landing point coordinates. In this embodiment, the first frequency is set to 30 frames / second.

[0071] Reference Figure 2The first stage of the lifecycle simulation is the launch phase. Particles are launched from the top of the display scene, with the launch height set at 3m above the ground in this embodiment. The launch positions of each particle are randomly distributed across the entire display area on the horizontal plane, covering the space above all four ecological zones. Taking a rainfall intensity of 12mm / h in the baseline scene as an example, 200 particles are launched per frame. Each particle acquires an initial vertical velocity upon launch, which is converted from the drag coefficient sampled in S2. The smaller the drag coefficient, the greater the initial vertical velocity; the greater the drag coefficient, the smaller the initial vertical velocity. For example, particle A with a drag coefficient of 0.28 has an initial vertical velocity of approximately 1.8m / s. For example, particle B with a drag coefficient of 0.55 has an initial vertical velocity of approximately 0.6m / s. The initial horizontal velocity of all particles is zero, and the horizontal motion is entirely driven by the wind speed vector during the descent phase.

[0072] The second stage of the lifecycle simulation is the descent stage. During the descent, each particle is affected by both gravity and the wind speed vector. Gravity accelerates the particle's descent vertically, while the wind speed vector acts as a continuous external force, pushing the particle horizontally. The vertical displacement of a particle in each frame is determined by its current vertical velocity and drag coefficient, while its horizontal displacement is determined by the wind speed vector from standardized meteorological data.

[0073] Taking a baseline scenario with a level 3 easterly wind as an example, the wind speed vector is 4.5 m / s in magnitude and direction from east to west. Taking particle B (drag coefficient 0.55, falling speed approximately 0.6 m / s) as an example, the time it takes to fall from a launch height of 3 m to the ground is approximately 5 seconds. During this 5-second fall, the simulated wind field in the exhibition hall continuously acts on the particle, with a horizontal offset of approximately 0.3 m. Assuming particle B's launch position is directly above zone A, under windless conditions, its landing point will be within zone A, and its precipitation contribution will be written into the slot of zone A. Under easterly wind conditions, particle B's landing point shifts westward by 0.3 m, falling into the range of zone B, and its precipitation contribution will be written into the slot of zone B. Zone A thus loses one particle's precipitation contribution, while zone B gains one particle's precipitation contribution. The zoning method for the zone-level cumulative buffer and the response parameters for ecological response calculation are not modified due to wind speed changes; the dynamic changes in the spatial distribution of precipitation are automatically transmitted to each zone slot by the physical calculation results of the particle trajectory.

[0074] Using particle A (drag coefficient 0.28, falling speed approximately 1.8 m / s) as a comparison, the time it takes to fall from a height of 3 m to the ground is approximately 1.7 seconds, and the horizontal offset under the same wind conditions is approximately 0.1 m. Rain particles, due to their faster falling speed and shorter wind exposure time, exhibit a smaller horizontal offset than snow particles. This means that under windy conditions, the landing point distribution of snowflake particles is more dispersed than that of raindrop particles, and the proportion of snowflake particles received by the downwind side is higher than that of raindrop particles. This difference is naturally generated by the particle physics model and requires no additional configuration.

[0075] The third stage of the lifecycle simulation is the landing stage. When a particle's vertical coordinates drop to ground height, it enters the landing stage, generating corresponding landing visual effects based on the impact behavior coefficient in the attribute parameters. A higher impact behavior coefficient results in a higher splash ratio, with splashing producing sub-particles to simulate water splashing effects; a lower impact behavior coefficient results in a higher accumulation ratio, increasing the thickness of the snow layer on the ground. When the phase index is between pure rain and pure snow states, the landing behavior of the same particle transitions continuously between splashing and accumulation.

[0076] Taking particle C (with a ground impact coefficient of 0.55) as an example, the processing during the landing phase is explained. The sputtering ratio is 0.55, and the number of sputtered sub-particles is calculated by multiplying the sputtering ratio by the baseline sputtering number. In this embodiment, it is set to 3, i.e., 0.55 × 3 ≈ 1.65, which is rounded down to 2 sputtered sub-particles. The accumulation ratio is 1 - 0.55 = 0.45, and the snow layer thickness increment is 0.45 multiplied by the baseline accumulation thickness of a single particle. The sputtered sub-particles bounce off from the landing point and exist as purely visual effect particles, not participating in the writing of precipitation contribution. The precipitation contribution carried by particle C itself (the equivalent precipitation volume calculated from the current rain and snow intensity and the total number of particles in a single frame) is written into the partition slot corresponding to the landing point coordinates.

[0077] Compared to particle A (with a ground impact coefficient of 0.72), which has a splash ratio of 0.72, producing approximately 2 splash sub-particles (0.72 × 3), and an accumulation ratio of 0.28, the increase in snow layer thickness is relatively small. Compared to particle B (with a ground impact coefficient of 0.38), which has a splash ratio of 0.38, producing approximately 1 splash sub-particle (0.38 × 3), and an accumulation ratio of 0.62, the increase in snow layer thickness is relatively large. In the same frame, particle A produces more water splashes upon landing, particle B primarily produces snow accumulation, and particle C falls somewhere in between. Together, these three particles create a mixed visual effect in a sleet scene where the ground simultaneously displays splashed water and a thin layer of snow.

[0078] In some embodiments, S3 also includes a dynamic particle budget mechanism to maintain a stable frame rate. An upper limit for the particle budget is set for the particle simulation, and the particle emission rate is dynamically adjusted based on the current frame rate. The lower frame rate threshold is set to 24 frames per second. When the current frame rate is below this threshold, it indicates excessive computational load, and the particle manager proportionally reduces the particle emission rate, for example, from 200 particles per frame to 120 particles per frame. The frame rate recovery threshold is set to 28 frames per second. When the frame rate recovers to above this threshold, the particle emission rate is gradually increased to the standard value. The lower frame rate threshold of 24 frames per second is chosen because the human eye can clearly perceive screen stuttering below this frame rate. The frame rate recovery threshold of 28 frames per second, rather than recovering directly at 24 frames per second, is chosen to leave a buffer between the two thresholds, avoiding frequent particle density switching caused by repeated frame rate fluctuations around the threshold.

[0079] When the parameter smoothing controller is transitioning the rain / snow intensity from "moderate rain" to "blizzard," the slope of the smoothing output change trend is positive and the amplitude is large. The frame rate monitor, reading this trend, determines that the upcoming particle load will increase significantly. Before the frame rate actually drops, the frame rate monitor compresses the number of particles emitted per frame down by 30% from the target value of the current smoothing interpolation output. For example, when the target emission number of the smoothing interpolation output has risen to 300 per frame, the actual emission number is compressed to 210 per frame. This compression makes the rate of increase in particle load lower than the rate of increase without compression, thus maintaining the minimum frame rate above 24 frames per second. Once the frame rate stabilizes above 28 frames per second, the compression ratio gradually returns to zero, and the emission number per frame returns to the target value of the smoothing interpolation output.

[0080] In some embodiments, S3 also employs a trajectory pre-calculation and cache reuse mechanism to reduce the real-time overhead of particle motion calculation. The meteorological parameter space is discretized into multiple parameter intervals. In this embodiment, discretization is performed along three dimensions: precipitation intensity is divided into four levels: below 5 mm / h, 5 to 15 mm / h, 15 to 30 mm / h, and 30 to 50 mm / h; wind speed is divided into four levels: calm, light wind, moderate wind, and strong wind; and phase index is divided into three levels: 0 to 0.3 for rain, 0.3 to 0.7 for mixed rain and snow, and 0.7 to 1.0 for snow, forming a total of 48 parameter intervals. During system initialization, 200 particle trajectory templates are pre-calculated and cached for each parameter interval, resulting in a total of 9600 trajectory templates cached for the 48 intervals. During runtime, the nearest parameter interval is matched based on the current meteorological parameters. One trajectory template is randomly selected from the 200 templates in that interval, and a random perturbation with a position offset within ±0.3 m and a time offset within ±0.1 s is superimposed to serve as the current particle's trajectory. The introduction of random perturbations ensures that even if multiple particles are sampled from the same trajectory template, their final trajectories will not completely overlap on the screen. When meteorological parameters change due to user interaction and cross the boundary of the parameter range, a trajectory template for the new range is generated, which takes about 0.5 seconds. During this period, the trajectory template of the old range is used to maintain the continuity of the screen.

[0081] S4. The cumulative precipitation of each zone's slots is read from the zone-level cumulative precipitation buffer at a second frequency. Ecological response calculations are performed on each zone based on the ecological attributes determined by standardized ecological data to obtain ecological simulation results. The second frequency is lower than the first frequency; in this embodiment, the second frequency is set to 3 frames / second. Zones are defined based on vegetation type and terrain type in the standardized ecological data.

[0082] Reference Figure 3 With a first frequency of 30 frames / second and a second frequency of 3 frames / second, the interval between two consecutive reads from the ecological impact simulation module is approximately 0.33 seconds. During this period, the particle simulation module performs 10 frames of particle landing calculations. In each frame, particles falling into each partition write their respective precipitation contribution to the corresponding partition slot. After 10 frames of accumulation, the value stored in each slot is the sum of the precipitation contributions received by all particles in that partition during the current period. On the Nth read, the ecological impact simulation module reads the accumulated precipitation from each partition slot. After reading, it clears each slot to zero and begins accumulating the precipitation contribution for the next period.

[0083] Ecological response calculations include a three-level cascaded process performed sequentially on each partition.

[0084] The first stage is the interception layer treatment. Based on the interception parameters corresponding to the vegetation type of each zone, the interception amount and throughfall precipitation are determined from the cumulative precipitation. The interception amount equals the cumulative precipitation multiplied by the interception coefficient, which is obtained by dividing the leaf area index by the baseline leaf area index. In this embodiment, the baseline leaf area index is set to 6.0. The throughfall precipitation equals the cumulative precipitation minus the interception amount. Taking the condition that the cumulative precipitation in a single reading cycle is 4 mm as an example, the first stage calculation is performed for zone A (tree-covered hilly area, leaf area index 5.0): the interception coefficient is 5.0 / 6.0≈0.83, the interception amount is 4×0.83≈3.3 mm, and the throughfall precipitation is 4-3.3=0.7 mm. The same calculations were performed on zone B (shrubland area, leaf area index 1.5): the interception coefficient is 1.5 / 6.0 = 0.25, the interception amount is 4 × 0.25 = 1.0 mm, and the throughfall is 4 - 1.0 = 3.0 mm. In zone A, due to the high leaf area index of the tree canopy, most of the precipitation is intercepted, with only a small amount penetrating to the ground surface; in zone B, the shrub canopy has a weak interception capacity, and most of the precipitation directly penetrates to the ground surface.

[0085] The second stage is the infiltration layer treatment. Based on the infiltration parameters corresponding to the soil type in each zone, through precipitation is divided into infiltration and surface runoff. When through precipitation is less than or equal to the maximum infiltration per cycle for that zone, all through precipitation infiltrates, and the surface runoff is zero. When through precipitation exceeds the maximum infiltration per cycle, the excess becomes surface runoff. For example, in zone A, the maximum infiltration per cycle for loam is configured as 0.7 mm, and the through precipitation is exactly 0.7 mm, so it all infiltrates, and the surface runoff is zero. In zone B, the maximum infiltration per cycle for sand is configured as 2.3 mm, and the through precipitation is 3.0 mm; the excess (3.0 - 2.3 = 0.7 mm) becomes surface runoff.

[0086] In some embodiments, during the second-level infiltration layer treatment, surface runoff converges towards adjacent low-lying areas according to the topographic slope of each zone, and the runoff converged to the low-lying areas is added to the surface runoff of the low-lying areas. Under the baseline scenario of moderate rainfall in this embodiment, the 0.7 mm surface runoff generated by zone B has an insignificant runoff convergence effect due to the slope of zone B being only 2°. When the scenario switches to continuous heavy rainfall (the cumulative rainfall in a single cycle increases to 20 mm), the penetrating rainfall in zone A increases to 20 × (1 - 0.83) = 3.4 mm, far exceeding the maximum single-cycle infiltration of loam soil of 0.7 mm, resulting in a surface runoff of 3.4 - 0.7 = 2.7 mm. The slope of zone A is 15°, and the surface runoff converges along the slope towards the adjacent low-lying zone C. Zone D (bare slope area, leaf area index 0.1, interception coefficient only 0.017) has almost no interception capacity under heavy precipitation, with a penetration precipitation of approximately 19.7 mm. Although the permeability of the gravelly soil is high, it is still insufficient to absorb all of it, and the resulting surface runoff also converges on the 20° slope towards Zone C. The direct precipitation received by Zone C itself, plus the runoff from Zones A and D, makes the total surface runoff of Zone C far exceed the runoff generated solely by its own direct precipitation.

[0087] The third level is the visual mapping layer. The interception amount is mapped to leaf wetness rendering parameters, the soil moisture content corresponding to the infiltration amount is mapped to soil color rendering parameters, and the surface runoff is mapped to waterlogged area rendering parameters. The differences in mapping results are illustrated using partitions A and B under rain conditions in the baseline scene. In partition A, the interception amount is 3.3 mm, resulting in higher leaf wetness rendering parameters and a noticeably wet sheen on the leaf surface. With an infiltration amount of 0.7 mm, the increase in soil moisture content is small, and the soil color rendering parameters remain almost unchanged, with the soil color still close to the light brown of a dry state. With zero surface runoff, the waterlogged area rendering parameters remain unchanged. In partition B, the interception amount is 1.0 mm, resulting in lower leaf wetness rendering parameters and only slight moisture on the leaf surface. With an infiltration amount of 2.3 mm, the increase in soil moisture content is significant, and the soil color rendering parameters drive the soil color from light brown to dark brown. With a surface runoff of 0.7 mm, the waterlogged area rendering parameters begin to increase, and a small, thin film of water appears on the ground. The two zones exhibited different visual evolution sequences during the same precipitation event: Zone A showed wet leaves first, then wet soil, while Zone B showed wet soil first, then wet leaves.

[0088] Reference Figure 4 The ecological response calculation also includes maintaining an ecological state machine for each zone. When the interception amount reaches the interception capacity threshold of the corresponding zone, the ecological state machine transitions to the canopy drip state and generates a drip particle emission signal. Based on the drip particle emission signal, drip particles are generated in the corresponding zone, and a life cycle simulation is performed on the drip particles. The precipitation contribution of the drip particles during the landing stage is written into the corresponding zone slot of the zone-level cumulative amount buffer.

[0089] The closed-loop conduction process is illustrated using Zone A (tree-covered hilly area) as an example. The interception capacity threshold for Zone A is set to 8 mm. Under the baseline scenario of moderate rain, the interception amount accumulates to approximately 3.3 mm per read cycle, reaching 8 mm after approximately 2.4 read cycles (approximately 0.8 seconds). At this point, the ecological state machine transitions from the initial wet state to the canopy dripping state, generating a dripping particle emission signal. The dripping particles are emitted from the canopy height (approximately 3 m), with attribute parameters of pure water droplets (phase index of 0), a ground impact behavior coefficient set to 0.9 (high splash), and an emission rate determined by the interception overflow rate. Taking an interception overflow rate of approximately 2 mm per second as an example, the dripping particles contribute approximately 2 mm of additional precipitation per second to the buffer zone of Zone A, which is added to the penetrating precipitation. The accumulation rate of water content in the infiltration layer has been improved from being driven solely by penetrating precipitation (0.7 mm per cycle) to being driven jointly by penetrating precipitation and dripping water (approximately 1.4 mm per cycle), thus accelerating the time to infiltration saturation.

[0090] In some embodiments, the states of the ecological state machine include a dry state, an initial wet state, a continuous precipitation state, a saturated state, and an evaporation recovery state. Each state corresponds to a set of rendering parameters, which includes the leaf reflectance range, the soil RGB color value range, and the transparency of the water accumulation layer. Taking zone B (shrub plain area) as an example, the rendering parameter sets for each state from the start of precipitation to recovery after the rain stops are as follows.

[0091] Dry state: Leaf reflectance is 0.6, soil RGB color value is (180, 150, 120), i.e., light brown, and water layer transparency is 0. When cumulative precipitation reaches 2 mm, it enters the initial moist state: Leaf reflectance decreases to 0.45, soil color value gradually changes to (140, 110, 85), i.e., medium brown, and water layer transparency remains 0. When cumulative precipitation reaches 10 mm, it enters the continuous precipitation state: Leaf reflectance decreases to 0.3, soil color value gradually changes to (100, 75, 55), i.e., dark brown, and water layer transparency remains 0. It enters the saturated state after the infiltration layer becomes saturated.

[0092] Saturated state: Leaf reflectivity remains at 0.3, soil color gradually changes to (70, 50, 35), approaching black, and the transparency of the water layer increases to 0.5. After the rain stops, it enters the evaporation recovery state: all rendering parameters gradually recover to the values ​​of the dry state according to the rollback rate. Within the same state, rendering parameters continuously and gradually change along the range of values ​​of the current state to achieve a smooth visual transition; when transitioning between states, the switching of the rendering parameter set produces a phased visual step, for example, when transitioning from the continuous precipitation state to the saturated state, the transparency of the water layer suddenly increases from 0 to 0.5, which is represented by the sudden appearance of the water layer in the picture.

[0093] In some embodiments, during the evaporation recovery state, the rollback rate of the ecological state machine is affected by the temperature parameters of the current simulation scene; the higher the temperature parameter, the faster the rollback rate, and the lower the temperature parameter, the slower the rollback rate. Two temperature conditions are compared as examples. Condition 1 has an air temperature of 2°C, and the evaporation rollback rate is set to roll back 0.5% of the rendering parameter change per second. It takes approximately 200 seconds to fully recover from the saturated rendering parameters to the dry rendering parameters. Condition 2 has an air temperature of 20°C, and the evaporation rollback rate is set to roll back 2.0% per second. Full recovery takes approximately 50 seconds. When the user adjusts the temperature from 2°C to 20°C via an interactive device, the recovery animation after the rain stops is significantly faster; the leaf reflectivity rises from 0.3 to 0.6 more quickly, the soil color value rolls back from near black to light brown more quickly, and the water layer recedes to 0 transparency more quickly.

[0094] In some embodiments, when the soil moisture content corresponding to the infiltration rate reaches the infiltration saturation threshold of the corresponding zone, the ecological state machine transitions to the surface waterlogging state. The canopy dripping state corresponding to the interception capacity threshold and the surface waterlogging state corresponding to the infiltration saturation threshold are allowed to be triggered asynchronously. The effect of asynchronous triggering is illustrated using two rainfall intensity scenarios as examples. Scenario 1 is light rainfall (5 mm / h). The interception capacity threshold for zone B is set to 3 mm. Approximately 2 seconds after the start of rainfall, the accumulated interception reaches 3 mm, triggering the canopy dripping state, and the leaves begin to show dripping particles. However, the sandy soil has a permeability as high as 25 mm / h, and the infiltration layer will not saturate for a long time under light rainfall, so the surface waterlogging state is not triggered. Visitors see continuous dripping from the leaves in the video, but there is never any water accumulation on the ground. Scenario 2 is heavy rainfall (30 mm / h). The interception capacity threshold is reached within 0.5 seconds of the start of rainfall, and the canopy dripping state is quickly triggered. Simultaneously, the amount of penetrating precipitation is extremely large, and the sandy soil's permeability is insufficient to absorb it all. The infiltration saturation threshold is reached 3 seconds after the start of precipitation, triggering the surface water accumulation state. The triggering times of the two states differ by approximately 2.5 seconds; visitors first see water dripping from the leaves, followed by the gradual appearance of water accumulation on the ground.

[0095] S5. Output the lifecycle simulation footage and ecological simulation results to the display device for presentation. The display device includes a naked-eye 3D screen and a motion-sensing interactive device. The naked-eye 3D screen is used to present immersive rain, snow, and ecological interaction footage, while the motion-sensing interactive device is used to receive user parameter adjustment operations. The particle simulation module outputs rain and snow particle rendering frames at a first frequency (30 frames / second), and the ecological impact simulation module outputs rendering parameter change frames at a second frequency (3 frames / second). The two outputs are combined into a single frame in the display module, where the ecological simulation results are interpolated and smoothed between adjacent updates to match the frame rate of the first frequency. For example, the ecological module outputs the soil color value of partition B as (140, 110, 85) during the Nth update and (130, 100, 75) during the N+1th update. In the 10 frames between the two updates, the display module performs linear interpolation on the soil color value, so that the soil color smoothly transitions from (140, 110, 85) to (130, 100, 75) within 10 frames, avoiding color value jumps caused by the ecological module's update frequency being lower than the frame rate.

[0096] In some embodiments, the method further includes user interaction and parameter smoothing steps.

[0097] S6. Receive parameter adjustment operations sent via the interactive device, parse the parameter adjustment operations into standardized parameter change instructions, and perform validity checks and frequency limiting on the standardized parameter change instructions. Visitors make gestures through the motion-sensing interactive device, and the system recognizes the gestures as parameter adjustment operations. For example, a visitor waving upwards is recognized as an operation to "increase rain / snow intensity," and waving to the left is recognized as an operation to "increase wind speed." The parameter adjustment operations are parsed into standardized parameter change instructions, which contain the target parameter type (such as rain / snow intensity, wind speed, temperature) and the target parameter value (such as changing rain / snow intensity from 12 mm / h to 30 mm / h). Validity checks whether the target parameter value is within the allowed range; for example, the allowed range for rain / snow intensity is 0 to 50 mm / h, and instructions exceeding the range are truncated to the boundary value. Frequency limiting sets the minimum instruction interval to 0.1 seconds; among multiple instructions arriving within a 0.1-second window, only the last one is retained.

[0098] S7. Perform parameter smoothing processing on the standardized parameter change instructions that have passed the verification. Determine the corresponding transition time according to the parameter type of the standardized parameter change instructions. Transition the currently used parameter value to the target parameter value specified by the standardized parameter change instructions by interpolation according to the transition time. Different parameter types correspond to different transition times. Inject the smoothed parameter value into S3 and S4 at the same time.

[0099] In some embodiments, the transition duration for temperature parameters is longer than that for rain / snow intensity parameters. In this embodiment, the transition duration for rain / snow intensity parameters is set to 2 seconds, the transition duration for wind speed parameters is set to 3 seconds, and the transition duration for temperature parameters is set to 5 seconds.

[0100] Taking the example of a visitor adjusting the rain / snow intensity from "moderate rain" (12 mm / h) to "blizzard" (intensity 30 mm / h, while the temperature changes from 2℃ to -5℃), the specific process of parameter smoothing is explained. In the first 2 seconds after the user's action, the rain / snow intensity transitions linearly from 12 mm / h to 30 mm / h, and the number of particles emitted per frame increases from 200 to 500. During the same period, the temperature only transitions from 2℃ to approximately -0.8℃, because only about 40% of the change is completed in 2 seconds within a 5-second transition period. The phase index slowly rises from 0.43 to approximately 0.83, and more snowflake particles begin to appear in the image, but raindrop particles still occupy a certain proportion. From the 2nd to the 5th second, the rain / snow intensity stabilizes at 30 mm / h, but the temperature continues to transition from -0.8℃ to -5℃, and the phase index continues to rise to 1.0. The proportion of snowflake particles in the image gradually increases, while raindrop particles gradually disappear, eventually transforming into a pure snow state. The visual effect is that precipitation initially increases, then gradually transitions from sleet to pure snow, presenting a temporal hierarchy where precipitation amount changes first, followed by a change in precipitation pattern. Smoothed parameter values ​​are simultaneously injected into both the particle simulation module and the ecological response calculation module in each frame. Parameters injected into the particle simulation module are used to update particle emission density, phase index, and wind speed vector; parameters injected into the ecological response calculation module are used to update temperature parameters to influence the evaporation recovery rate.

[0101] Optionally, in S6, when multiple parameter adjustment operations are received simultaneously, the effective standardized parameter change command is determined based on the timestamps and magnitudes of the multiple parameter adjustment operations. The arbitration process is illustrated using the example of two visitors simultaneously operating the motion-sensing device. Visitor A issues a command at time T=0 to "increase rain / snow intensity by 50%", a relatively large command. Visitor B issues a command at time T=0.05 to "decrease wind speed by 20%", a relatively small command. Both commands arrive within a 0.1-second frequency limiting window, indicating simultaneous reception. During arbitration, it is first determined whether the two commands target the same parameter dimension. Rain / snow intensity and wind speed belong to different parameter dimensions; therefore, the two commands do not conflict, both take effect, and each enters the smoothing controller for its respective parameter type.

[0102] When two visitors issue commands targeting the same parameter dimension, arbitration is conducted according to a rule that prioritizes timestamps and weights the magnitude of the operation. For example, if Visitor A issues a command to "increase rain / snow intensity by 50%" at time T=0, and Visitor B issues a command to "decrease rain / snow intensity by 20%" at time T=0.05, the command with the earlier timestamp is considered Visitor A's command, and the command with the larger magnitude (50% is greater than 20%) is also considered Visitor A's command. Therefore, the final effective standardized parameter change command is based on the direction of Visitor A's command. The final effective value is calculated by weighting the magnitude of the operation, i.e., 50% for the increase direction minus 20% for the decrease direction multiplied by the timestamp decay coefficient (the later the timestamp, the greater the decay), resulting in a final effective value of approximately 40% increase. This mechanism ensures the stability of the simulated image in multi-person interactive scenarios in the exhibition hall and avoids parameter jitter caused by the simultaneous injection of multiple commands in opposite directions.

[0103] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database contains data related to simulation methods for meteorological and snow processes and their ecological impacts. The network interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a simulation method for meteorological and snow processes and their ecological impacts.

[0105] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the meteorological and ecological impact simulation method of the above embodiment, for example... Figure 1 S1 to S5 are shown.

[0106] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the simulation method for meteorological rain and snow processes and ecological impacts described in the above embodiment.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments of this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0109] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A simulation method for meteorological rain and snow processes and their ecological impacts, characterized in that, Includes the following steps: S1. Acquire meteorological data and basic ecological data, and preprocess the meteorological data and basic ecological data to obtain standardized meteorological data and standardized ecological data; S2. Calculate the phase index based on the standardized meteorological data, and determine the attribute parameter values ​​of each particle in the particle attribute parameter space based on the phase index; S3. Perform life cycle simulation on each particle at a first frequency. The life cycle simulation includes a launch phase, a descent phase, and a landing phase. In the landing phase, generate a landing visual effect based on the attribute parameter values ​​and write the precipitation contribution of each particle into the partition-level cumulative buffer corresponding to the partition slot of each particle's landing point coordinates. S4. Read the cumulative precipitation of each partition slot from the partition-level cumulative amount buffer at a second frequency, perform ecological response calculation on each partition according to the ecological attributes of each partition determined by the standardized ecological data, and obtain ecological simulation results, wherein the second frequency is lower than the first frequency, and the partition is divided according to the vegetation type and terrain type in the standardized ecological data; S5. Output the simulation screen of the life cycle simulation and the ecological simulation results to the display device for display.

2. The simulation method for meteorological rain and snow processes and ecological impacts according to claim 1, characterized in that, S2 includes the following sub-steps: S21. Calculate the phase index based on the temperature and humidity data in the standardized meteorological data. The phase index is a continuous value that characterizes the phase position of precipitation from pure rain to pure snow. S22. Based on the phase index, perform attribute sampling on each particle in the particle attribute parameter space to obtain the attribute parameter value of each particle, wherein the attribute parameter values ​​of different particles emitted at the same time are allowed to be different.

3. The simulation method for meteorological rain and snow processes and ecological impacts according to claim 1, characterized in that, During the descent phase, the trajectory of each particle is affected by the wind speed vector in the standardized meteorological data, resulting in a horizontal offset. The landing point coordinates of each particle are determined according to the direction and magnitude of the wind speed vector and then written into the corresponding partition slot of the partition-level cumulative buffer. The partitioning method of the partition-level cumulative buffer and the response parameters of the ecological response calculation are not modified due to the change of the wind speed vector.

4. The simulation method for meteorological rain and snow processes and ecological impacts according to claim 1, characterized in that, The ecological response calculation includes a three-level cascaded process performed sequentially on each partition: The first-level interception layer treatment determines the interception amount and the penetration amount of precipitation from the cumulative precipitation based on the interception parameters corresponding to the vegetation type of each zone. The second-stage infiltration layer treatment divides the penetrating precipitation into infiltration amount and surface runoff based on the infiltration parameters corresponding to the soil type of each zone. The third-level visual mapping layer processes the interception amount as a leaf moisture rendering parameter, the soil moisture content corresponding to the infiltration amount as a soil color value rendering parameter, and the surface runoff as a waterlogged area rendering parameter.

5. The simulation method for meteorological precipitation and snow processes and ecological impacts according to claim 4, characterized in that, The ecological response calculation also includes: To maintain an ecological state machine for each zone, in response to the interception amount reaching the interception capacity threshold of the corresponding zone, the ecological state machine transitions to the canopy dripping state and generates a dripping particle emission signal; Based on the emission signal of the water droplets, water droplets are generated in the corresponding partition. The life cycle simulation is performed on the water droplets, and the precipitation contribution of the water droplets in the landing stage is written into the corresponding partition slot of the partition-level cumulative buffer.

6. The simulation method for meteorological rain and snow processes and ecological impacts according to claim 1, characterized in that, It also includes the following steps: S6. Receive parameter adjustment operation sent through the interactive device, parse the parameter adjustment operation into a standardized parameter change instruction, and perform legality verification and frequency limiting on the standardized parameter change instruction; wherein, the frequency limiting is set to a minimum instruction interval of 0.1 seconds, and only the last one of multiple standardized parameter change instructions arriving within the 0.1-second window is retained; S7. Perform parameter smoothing processing on the standardized parameter change instruction that has passed the verification. Determine the corresponding transition time according to the parameter type of the standardized parameter change instruction. Transition the currently used parameter value to the target parameter value specified by the standardized parameter change instruction by interpolation according to the transition time. Different parameter types correspond to different transition times. Inject the smoothed parameter value into S3 and S4 at the same time.

7. The simulation method for meteorological precipitation and snow processes and ecological impacts according to claim 6, characterized in that, In step S6, in response to receiving multiple parameter adjustment operations simultaneously, the effective standardized parameter change instruction is determined based on the timestamps and operation ranges of the multiple parameter adjustment operations.

8. The simulation method for meteorological rain and snow processes and ecological impacts according to claim 1, characterized in that, In step S1, the acquisition of meteorological data includes a real-time sensor acquisition mode and a local preset data mode. The data output from the real-time sensor acquisition mode and the local preset data mode are converted to a unified format and then subjected to the preprocessing.

9. A computer device, characterized in that, It includes: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the simulation method for meteorological rain and snow processes and ecological impacts according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the simulation method for meteorological rain and snow processes and ecological impacts as described in any one of claims 1 to 8.

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