A two-dimensional surface calculation method of cellular automaton

Abstract

The application relates to the technical field of computer simulation, in particular to a two-dimensional surface calculation method of a cellular automaton, which comprises the following steps: based on terrain data acquisition and recording, terrain data of multiple regions are analyzed, elevation and slope are recorded, terrain data is processed by using a cellular automaton, a terrain model is constructed, and regional terrain information is obtained. In the application, by means of terrain data acquisition and terrain model construction, the quality of basic data of the model is improved, accurate input data is provided for simulation, key positions are identified, the size and distribution of cells are adjusted, the simulation resolution of key regions is optimized, the local precision of simulation is improved, the consumption of computing resources is reduced, parameters of rainfall simulation are adjusted according to actual meteorological data, the reliability of prediction is enhanced, soil erosion and surface runoff are simulated, the erosion effect and anti-erosion capacity of multiple regions are evaluated, and the prediction accuracy and practicability of the two-dimensional surface calculation are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of computer simulation, in particular to a surface two-dimensional calculation method of a cellular automaton. BACKGROUND

[0002] The technical field of computer simulation focuses on simulating real-world processes and systems using computer technology, enabling researchers to create virtual simulations on computers through mathematical models and algorithms to predict and analyze the behavior of complex systems, which is applied to multiple disciplines such as engineering, physical science, biomedicine, environmental science and economics, helping scientists and engineers to conduct experiments and data analysis when actual physical experiments are costly and infeasible, improving research flexibility and reducing cost-effectiveness, and enabling the analysis of the impact of various conditions and parameters in a safe virtual environment.

[0003] Among them, the surface two-dimensional calculation method focuses on using computer simulation technology to study and predict changes in the earth's surface, aiming to simulate the impact of natural environment and human activities on land cover, including vegetation distribution, hydrological dynamics and urban expansion, by creating a two-dimensional digital model of the earth's surface, researchers can simulate and observe changes in the state of the earth's surface on multiple time scales, and analyze the impact of various changes on the ecological system, urban planning and geographical environment, which is applied to environmental monitoring, disaster prediction, resource management and urban development planning, providing scientific basis and support for decision-makers.

[0004] Traditional surface two-dimensional calculation methods are limited by data processing capacity and simulation accuracy when simulating large-scale changes in the earth's surface. In complex terrain and changing weather conditions, traditional models are difficult to quickly and accurately process and reflect the changes in the actual state of the earth's surface, resulting in insufficient detailed data support when conducting environmental monitoring and disaster prevention planning, affecting the effectiveness and timeliness of decision-making. When unable to accurately simulate the impact of rainfall on the earth's surface, it leads to misjudgment of flood risk, affecting the development and implementation of response measures. In terms of automatic adjustment of computing resource allocation, it is not flexible enough, resulting in insufficient computing support in key areas that require high-precision simulation, limiting the practicality and application scenarios of simulation. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art and provide a surface two-dimensional calculation method of a cellular automaton.

[0006] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme, a surface two-dimensional calculation method of a cellular automaton, comprising the following steps:

[0007] S1: Based on the terrain data collection record, the terrain data of multiple regions is analyzed, the elevation and slope are recorded, and the terrain data is processed by using cellular automata to construct a terrain model, and the regional terrain information is obtained;

[0008] S2: Based on the regional terrain information, a plurality of key positions in the simulation region are identified and the size and distribution of the cells are adjusted, the terrain resolution of the plurality of key positions is optimized, and a surface grid model is generated;

[0009] S3: Based on the surface grid model, the meteorological data of the target region is collected and rainfall simulation is performed, the rainfall mode parameters are adjusted according to the precipitation amount and precipitation frequency, the influence of rainfall on the ground surface is simulated, and a rainfall influence simulation result is generated;

[0010] S4: Using the rainfall influence simulation result, the soil erosion and surface runoff are simulated and calculated to evaluate the erosion resistance and erosion effect of multiple regions, and soil erosion simulation information is generated;

[0011] S5: Based on the soil erosion simulation information, the soil erosion and runoff dynamic data are analyzed in time series to evaluate the ground surface state under multiple environmental change scenarios, and ground surface state prediction information is obtained;

[0012] S6: Based on the ground surface state prediction information, the stability of multiple ground surface regions is evaluated to predict ground surface deformation events, calculate regional risk and disaster occurrence probability, and generate a two-dimensional calculation result of the ground surface.

[0013] As a further scheme of the present application, the regional terrain information includes elevation data, slope data, and terrain structure characteristics, the surface grid model includes grid size information, cell distribution parameters, and terrain resolution information of key regions, the rainfall influence simulation result includes precipitation simulation data, precipitation distribution map, and influence analysis result of precipitation on the ground surface, the soil erosion simulation information includes erosion location map, erosion depth data, and erosion range analysis result, the ground surface state prediction information includes ground surface change trend, environmental change scenario analysis data, and state change prediction phenomenon, and the ground surface two-dimensional calculation result includes ground surface deformation prediction result, regional risk evaluation information, and disaster occurrence probability calculation information.

[0014] As a further scheme of the present application, based on the terrain data collection record, the terrain data of multiple regions is analyzed, the elevation and slope are recorded, and the terrain data is processed by using cellular automata to construct a terrain model, and the regional terrain information is obtained, and the step specifically comprises:

[0015] S101: Based on the terrain data collection record, the terrain data of multiple regions is analyzed, the elevation and slope information is identified, and the data is formatted and standardized to generate a data formatting processing result;

[0016] S102: Based on the data formatting processing results, analyze the terrain features of multiple regions, including height changes and slope differences, identify key terrain features, and generate terrain feature information records;

[0017] S103: Based on the recorded terrain feature information, the terrain data is processed using cellular automata to simulate the response of the terrain data under various environmental conditions, and a terrain model is constructed to generate regional terrain information.

[0018] As a further aspect of the present invention, the steps of identifying multiple key locations in the simulated area and adjusting the size and distribution of cells based on the regional terrain information, optimizing the terrain resolution of multiple key locations, and generating a surface grid model are as follows:

[0019] S201: Based on the regional terrain information, identify multiple key locations in the target area, including river valleys, mountain ridges, and urban areas; record the location and extent information of the target area; and generate key area marker information.

[0020] S202: Based on the key area marking information, optimize the terrain resolution and computational efficiency of key locations by adjusting the size and distribution parameters of cells, and generate grid configuration parameters;

[0021] S203: Based on the grid configuration parameters, construct a surface grid model of the target simulation area, and adjust the terrain resolution of key areas to match the accuracy requirements, thereby generating a surface grid model.

[0022] As a further aspect of the present invention, based on the aforementioned surface grid model, meteorological data of the target area is collected and rainfall simulation is performed. Rainfall model parameters are adjusted according to precipitation amount and frequency to simulate the impact of rainfall on the surface, and the specific steps for generating rainfall impact simulation results are as follows:

[0023] S301: Based on the surface grid model, collect meteorological data of the target area, including precipitation and precipitation frequency information, and combine the surface model to simulate the water flow distribution under various rainfall conditions to generate real-time meteorological input data;

[0024] S302: Based on the real-time meteorological input data, adjust the rainfall simulation parameters, including adjusting the rainfall intensity and distribution pattern, simulate surface areas with various terrains and soil conditions, and generate rainfall pattern simulation information;

[0025] S303: Based on the rainfall pattern simulation information, the Manning formula is used to simulate and analyze the impact of rainfall events on the land surface, calculate the water flow velocity, flow direction and water accumulation area, assess the degree of change of the terrain caused by rainfall, and generate simulation results of rainfall impact.

[0026] As a further aspect of the present invention, the Manning formula is as follows:

[0027]

[0028] Calculate the water flow velocity;

[0029] Wherein, v represents the water flow velocity, which indicates the rate at which water passes through the target cross section within the target time. It is a key parameter for assessing the impact force and erosion potential of water flow. n represents the Manning coefficient, which characterizes the frictional resistance of water flow under target surface conditions and affects the magnitude of water flow velocity. R represents the hydraulic radius, which is the ratio of the cross-sectional area of ​​the water flow to the wetted perimeter. It reflects the flow capacity of the water flow. A larger hydraulic radius indicates that the water flow is more concentrated and the flow velocity is higher. S represents the water flow gradient, which is the degree of water level reduction per unit length. It directly determines the potential energy and dynamics of the water flow and is a fundamental factor driving water flow. By combining topography and rainfall conditions, it provides a computational basis for simulating water flow dynamics.

[0030] As a further aspect of the present invention, the steps of using the rainfall impact simulation results to simulate and calculate soil erosion and surface runoff, assess the erosion resistance and erosion effect of multiple regions, and generate soil erosion simulation information are as follows:

[0031] S401: Using the rainfall impact simulation results, assess the scouring effect of water flow on the soil based on soil type and slope factors, analyze the erosion probability of soil in multiple areas, and generate scouring effect calculation data.

[0032] S402: Based on the scouring calculation data, perform erosion simulation on various soil types, and calculate the depth, width and range of erosion to generate erosion simulation data;

[0033] S403: Based on the erosion simulation data, by analyzing the erosion simulation information of multiple regions, including erosion location, depth and range, the erosion resistance and erosion effect of multiple regions are evaluated, and soil erosion simulation information is generated.

[0034] As a further aspect of the present invention, based on the soil erosion simulation information, the specific steps for evaluating the surface state under various environmental change scenarios and obtaining surface state prediction information by performing time series analysis on dynamic data of soil erosion and runoff are as follows:

[0035] S501: Based on the soil erosion simulation information, classify the simulation data according to the time information, record the soil erosion degree and runoff information at multiple time points, and generate time-classified erosion data;

[0036] S502: Based on the time-classified erosion data, a multivariate regression analysis method is used to perform trend analysis on the soil erosion data, identify key factors affecting surface changes, including the impact of rainfall changes on erosion and runoff, and generate trend analysis results;

[0037] S503: Based on the trend analysis results, simulate the changes in the land surface state under various environmental change scenarios, including continuous drought and frequent rainfall, predict the land surface state under various scenarios, and generate land surface state prediction information.

[0038] As a further aspect of the present invention, the multiple linear regression analysis method is based on the formula:

[0039] E=β0+β1R+β2S+β3C+∈

[0040] Calculate soil erosion;

[0041] Where E represents the predicted soil erosion, which is the target variable of the model and is used to measure the overall impact of different factors on soil erosion. R represents annual rainfall, which is the main climatic factor affecting erosion and is directly related to soil erosion. S represents slope percentage, which reflects the degree of terrain inclination and is closely related to water flow velocity and erosion rate. C represents vegetation cover index, which shows the effect of vegetation in protecting soil from erosion. β0 is the intercept term, which represents the basic erosion in the absence of rainfall, slope, and vegetation cover. β1, β2, and β3 are the regression coefficients corresponding to rainfall, slope, and vegetation cover, used to quantify the impact of the target factor on erosion. ∈ is the error term, which takes into account random fluctuations caused by other factors that the model cannot fully explain.

[0042] As a further aspect of the present invention, the steps of generating two-dimensional surface calculation results based on the surface state prediction information, by assessing the stability of multiple surface regions, predicting surface deformation events, calculating regional risks and disaster occurrence probabilities, and generating surface two-dimensional calculation results are as follows:

[0043] S601: Based on the surface condition prediction information, analyze and identify surface stability data for multiple regions, record multiple risk areas, including landslide and subsidence risk locations, and generate surface stability analysis data;

[0044] S602: Based on the surface stability analysis data, perform deformation event prediction, predict the location and scale of landslides and subsidence in multiple areas, and generate deformation event prediction data;

[0045] S603: Based on the deformation event prediction data, assess the probability of disaster occurrence in multiple regions, calculate the risk level of various disaster types, and generate two-dimensional surface calculation results.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In this invention, by collecting terrain data and constructing a terrain model, the quality of the model's basic data is improved, and accurate input data is provided for the simulation. Key locations are identified and cell size and distribution are adjusted to optimize the simulation resolution of key areas, improve the local accuracy of the simulation, and reduce computational resource consumption. The parameters of the rainfall simulation are adjusted according to actual meteorological data to enhance the reliability of the prediction. By simulating soil erosion and surface runoff, the erosion effects and erosion resistance of various regions are evaluated, thereby improving the prediction accuracy and practicality of two-dimensional surface calculation. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the main steps of the present invention;

[0049] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0050] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0051] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0052] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0053] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0054] Figure 7 This is a detailed schematic diagram of S6 of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0057] Please see Figure 1 This invention provides a technical solution: a method for two-dimensional calculation of the Earth's surface using cellular automata, comprising the following steps:

[0058] S1: Based on the terrain data collection and recording, analyze the terrain data of multiple regions, record the elevation and slope, and use cellular automata to process the terrain data, construct a terrain model, and obtain regional terrain information;

[0059] S2: Based on regional terrain information, identify multiple key locations in the simulated area and adjust the size and distribution of cells to optimize the terrain resolution of multiple key locations and generate a surface grid model.

[0060] S3: Based on the surface grid model, meteorological data of the target area is collected and rainfall simulation is performed. The rainfall model parameters are adjusted according to the precipitation amount and frequency to simulate the impact of rainfall on the surface and generate rainfall impact simulation results.

[0061] S4: Using the simulation results of rainfall impact, soil erosion and surface runoff are simulated and calculated to assess the erosion resistance and erosion effect of multiple regions and generate soil erosion simulation information;

[0062] S5: Based on soil erosion simulation information, by performing time series analysis on dynamic data of soil erosion and runoff, the surface state under various environmental change scenarios is evaluated, and surface state prediction information is obtained.

[0063] S6: Based on surface condition prediction information, by assessing the stability of multiple surface regions, predicting surface deformation events, calculating regional risks and disaster occurrence probabilities, and generating two-dimensional surface calculation results.

[0064] Regional topographic information includes elevation data, slope data, and topographic structure features; the surface grid model includes grid size information, cell distribution parameters, and topographic resolution information for key areas; rainfall impact simulation results include simulated precipitation data, precipitation distribution maps, and analysis results of the impact of precipitation on the surface; soil erosion simulation information includes erosion location maps, erosion depth data, and analysis results of erosion range; surface condition prediction information includes surface change trends, environmental change scenario analysis data, and predicted phenomena of condition changes; and two-dimensional surface calculation results include surface deformation prediction results, regional risk assessment information, and disaster occurrence probability calculation information.

[0065] Please see Figure 2 Based on topographic data collection and recording, the topographic data of multiple regions is analyzed, elevation and slope are recorded, and cellular automata are used to process the topographic data to construct a topographic model, thereby obtaining regional topographic information. The specific steps are as follows:

[0066] S101: Based on terrain data collection and recording, analyze terrain data from multiple regions, identify elevation and slope information, format and standardize the data, and generate data formatting processing results;

[0067] In sub-step S101, based on terrain data acquisition records, terrain data is collected through a terrain data acquisition system. The data includes information such as surface elevation and slope, and is a three-dimensional dataset in the format of latitude, longitude, and corresponding elevation values. Through terrain data preprocessing algorithms, including data cleaning and interpolation methods, data gaps are filled and the impact of outliers is reduced to ensure the integrity and accuracy of the data. A digital elevation model (DEM) construction program is applied to convert the collected point data into a continuous elevation field, improving the efficiency of data application and providing standardized and formatted elevation data for subsequent analysis. Based on the elevation data, the slope information of each data point is determined through a slope calculation algorithm. The results are stored in a unified format for easy subsequent processing and analysis. The generated formatted terrain data result file provides basic data for terrain feature analysis.

[0068] S102: Based on the data formatting processing results, analyze the terrain features of multiple regions, including height changes and slope differences, identify key terrain features, and generate terrain feature information records;

[0069] In sub-step S102, based on the data formatting processing results, terrain data from multiple regions are analyzed using terrain feature analysis tools. An elevation change analysis algorithm is used to analyze elevation data within the region, identifying significant elevation differences, including key terrain features such as ridges or valleys. A slope difference analysis program is employed to compare slope changes in different areas, revealing the slope characteristics of the terrain, identifying key features, and quantifying terrain changes. The analysis results are compiled and recorded in a terrain feature information record file, used to assess the terrain characteristics of each region and providing crucial information for terrain simulation and environmental assessment.

[0070] S103: Based on the recording of terrain feature information, cellular automata are used to process terrain data, simulate the response of terrain data under various environmental conditions, construct a terrain model, and generate regional terrain information;

[0071] In sub-step S103, based on the recorded terrain feature information, a cellular automata model is used to process and simulate the terrain data. By setting differentiated environmental parameters, including rainfall and soil type, the response of the terrain under various environmental conditions is simulated. By defining the initial state of each cell, including elevation and slope, and updating the state of the current cell according to the environmental input and the state of neighboring cells, the dynamic response of the terrain to environmental changes is shown through iterative calculation. The generated regional terrain information model is used to visualize the terrain response process and provides decision support in multiple aspects such as urban planning and disaster prevention.

[0072] Please see Figure 3 Based on regional terrain information, the specific steps for identifying multiple key locations in the simulated area, adjusting cell size and distribution, optimizing terrain resolution at multiple key locations, and generating a surface mesh model are as follows:

[0073] S201: Based on regional terrain information, identify multiple key locations in the target area, including river valleys, mountain ridges, and urban areas, record the location and extent information of the target area, and generate key area marking information;

[0074] In sub-step S201, based on regional topographic information, multiple key locations within the target area are identified, including valleys, ridges, and urban areas. The location and extent of the target area are recorded. Specifically, a digital elevation model (DEM) is used to extract topographic features by calculating the slope in the DEM data, according to the formula...

[0075]

[0076] Calculate the slope G at each point j ,

[0077] In the formula, h east h represents the elevation of the point on the east side, and represents the elevation value of the adjacent point on the east side. central h represents the elevation of the center point, and h represents the elevation value of the analysis point. south is the elevation of the southern point, representing the elevation value of the adjacent southern point; D is the horizontal distance between the two points, representing the distance between adjacent pixels in the DEM data.

[0078] Detailed explanation of the formula and its calculation derivation:

[0079] Set the elevation of the center point to 200 meters, the elevation of the east point to 205 meters, and the elevation of the south point to 202 meters. Each pixel represents an actual distance of 10 meters. First, calculate the elevation difference between the east and center points, and the elevation difference between the south and center points:

[0080] h east -h central =205-200=5m

[0081] h south -h central =202-200=2m

[0082] D = 10m

[0083] Substitute the parameter values ​​into the formula to calculate:

[0084]

[0085] This indicates that the slope at this point is 0.54, identifying multiple key topographic features of the ridge and valley, providing data support for topographic analysis and regional planning.

[0086] S202: Based on key region marking information, the terrain resolution and computational efficiency at key locations are optimized by adjusting the size and distribution parameters of cells, and mesh configuration parameters are generated.

[0087] In substep S202, based on key region marking information, the cell size and distribution parameters in the cellular automata model are adjusted to optimize the terrain resolution and computational efficiency at key locations. Different simulation accuracies are required for different terrain and landform features, including using smaller cell sizes in river valleys and ridge areas to improve simulation refinement, and increasing cell sizes in urban and plain areas to improve computational speed. By modifying the cell distribution parameters, including adjusting cell density and interaction rules, the computational resources of the model are ensured to be used efficiently. The generated mesh configuration parameters are saved in text or XML format, recording the cell size and distribution of multiple regions, and optimizing the level of detail and running efficiency of terrain simulation.

[0088] S203: Based on the grid configuration parameters, construct a surface grid model of the target simulation area, and adjust the terrain resolution of key areas to match the accuracy requirements, thereby generating a surface grid model;

[0089] In substep S203, a surface grid model of the target simulation area is constructed using grid configuration parameters. Computer-aided design software and terrain modeling tools are used to adjust the grid density of differentiated key areas according to the resolution requirements of the terrain, matching the simulation needs of the terrain. This includes increasing the terrain grid density in valleys and ridges to capture subtle terrain changes, and using a lower grid density in flat urban or farmland areas to optimize computational efficiency, matching the accuracy requirements of the actual terrain. The generated surface grid model is stored in a 3D model file format, providing an accurate 3D terrain foundation for subsequent terrain analysis, environmental simulation, or visual display.

[0090] Please see Figure 4Based on a surface grid model, meteorological data of the target area is collected and rainfall simulation is performed. Rainfall model parameters are adjusted according to precipitation amount and frequency to simulate the impact of rainfall on the surface and generate rainfall impact simulation results. The specific steps are as follows:

[0091] S301: Based on the surface grid model, collect meteorological data of the target area, including precipitation and precipitation frequency information, and combine the surface model to simulate the water flow distribution under various rainfall conditions to generate real-time meteorological input data;

[0092] In sub-step S301, based on the surface grid model, meteorological data of the target area, including precipitation and precipitation frequency information, is collected to perform water flow distribution simulation. Meteorological simulation software is used to input the real-time collected precipitation data into the model. GIS technology is used to combine the meteorological data with the surface grid model to simulate water flow distribution under differentiated rainfall conditions. The data is recorded in a standard time series database, including the start time, end time, total precipitation, and distribution of each rainfall event. During the simulation, the software dynamically adjusts the water flow path and water accumulation area based on the input meteorological data, generating real-time meteorological input data. This data is saved in GIS format or as dynamic simulation video for analyzing the impact of rainfall on topography and providing a scientific basis for urban drainage and flood prevention.

[0093] S302: Based on real-time meteorological input data, adjust rainfall simulation parameters, including adjusting rainfall intensity and distribution patterns, to simulate surface areas with various terrains and soil conditions and generate rainfall pattern simulation information;

[0094] In sub-step S302, based on real-time meteorological input data, rainfall simulation is performed on surface areas with different terrain and soil conditions by adjusting rainfall simulation parameters, including rainfall intensity and distribution patterns. Surface water flow simulation software is used to adjust simulation parameters according to the elevation and slope information of the terrain model to reflect the spatial distribution and intensity changes of rainfall in the real world. The adjusted parameters include setting the percentage of rainfall intensity and distribution patterns for different areas, including uniform and random distribution, to simulate the impact of rainfall on the terrain. Through simulation, rainfall pattern simulation information is generated and recorded in the simulation log and result data file to evaluate water flow behavior under various parameter settings.

[0095] S303: Based on rainfall pattern simulation information, the Manning formula is used to simulate and analyze the impact of rainfall events on the land surface, calculate water flow velocity, flow direction and water accumulation area, assess the degree of change of rainfall on topography, and generate simulation results of rainfall impact;

[0096] Manning's formula, according to the formula:

[0097]

[0098] Calculate the water flow velocity;

[0099] Wherein, v represents the water flow velocity, which indicates the rate at which water passes through the target cross section within the target time. It is a key parameter for assessing the impact force and erosion potential of water flow. n represents the Manning coefficient, which characterizes the frictional resistance of water flow under target surface conditions and affects the magnitude of water flow velocity. R represents the hydraulic radius, which is the ratio of the cross-sectional area of ​​the water flow to the wetted perimeter. It reflects the flow capacity of the water flow. A larger hydraulic radius indicates that the water flow is more concentrated and the flow velocity is higher. S represents the water flow gradient, which is the degree of water level reduction per unit length. It directly determines the potential energy and dynamics of the water flow and is a fundamental factor driving water flow. By combining topography and rainfall conditions, it provides a computational basis for simulating water flow dynamics.

[0100] formula:

[0101]

[0102] Detailed explanation of the formula and its calculation derivation:

[0103] Parameter definition and acquisition method:

[0104] n is the Manning coefficient, representing the frictional resistance of the water flow. It is a dimensionless coefficient whose value depends on the surface roughness of the waterway and is set to n = 0.013.

[0105] R is the hydraulic radius, which is the ratio of the cross-sectional area of ​​the flow to the wetted perimeter. The formula for its calculation is: Where A is the cross-sectional area of ​​the water flow, P is the wetted perimeter, and R is set to 0.5 meters.

[0106] S is the water flow gradient, which represents the rate at which the water flow height decreases along the flow direction. It is determined by measuring the water level difference and horizontal distance between two points and is set to S = 0.001.

[0107] Substitute the parameters into the formula to calculate:

[0108]

[0109] The result v≈1.532m / s indicates that, given the channel conditions and flow characteristics, the flow velocity is approximately 1.532 m / s. This helps engineers design flood control measures, detect flood risks, and conduct environmental impact assessments. By calculating the flow velocity, the propagation speed and erosion degree of floods can be analyzed, and flood events can be predicted and managed.

[0110] Please see Figure 5 The specific steps for generating soil erosion simulation information by using rainfall impact simulation results to simulate and calculate soil erosion and surface runoff, assessing the erosion resistance and erosion effects of multiple regions, are as follows:

[0111] S401: Using the simulation results of rainfall impact, based on soil type and slope factors, assess the erosion effect of water flow on soil, analyze the erosion probability of soil in multiple areas, and generate erosion calculation data.

[0112] In the S401 sub-step, the soil erosion effect is assessed by combining the simulation results of rainfall impact with soil type and slope factors. Using a soil and water conservation model, the velocity and flow data in the rainfall simulation results are analyzed in combination with the erosion sensitivity and slope impact specific to soil type. This includes calculating the potential erosion rate of each soil type under different slope conditions, considering multiple attributes of the soil such as water retention capacity, mechanical composition, and organic matter content, and recording the erosion probability of soils in various regions.

[0113] S402: Based on scour calculation data, erosion simulation is performed on various soil types, and the depth, width and range of erosion are calculated to generate erosion simulation data;

[0114] In sub-step S402, based on the scouring data, the erosion process is simulated using the general soil loss equation to calculate the depth, width, and extent of erosion. The formula for calculating the erosion amount E1 is as follows:

[0115] E1 = R1 × K × LS × C1 × P

[0116] Calculate the amount of erosion.

[0117] Where E1 represents annual soil erosion, R1 is the rainfall erosivity factor, K is the soil erodibility factor, LS is the slope length and gradient factor, C1 is the vegetation cover management factor, and P is the supporting practice factor.

[0118] Detailed explanation of the formula and its calculation derivation:

[0119] Suppose a region has the following parameters:

[0120] Rainfall erosivity factor R1 = 100 MJ / mm / ha / hour / year, soil erodibility factor K = 0.2 tons / ha / h / MJ / mm, slope length / gradient factor LS = 1.3, vegetation cover management factor C1 = 0.04, and supporting practice factor P = 0.8.

[0121] Substitute into the formula for calculation:

[0122] E1 = 100 × 0.2 × 1.3 × 0.04 × 0.8

[0123] E1 = 100 × 0.2 × 1.3 × 0.032

[0124] E1 = 100 × 0.0832

[0125] E1 = 8.32 tons / hectare / year

[0126] This result indicates that, under given management and environmental conditions, the estimated annual soil loss in the region is 8.32 tons per hectare, assessing the severity of soil loss under current land management and environmental conditions.

[0127] S403: Based on erosion simulation data, by analyzing erosion simulation information from multiple regions, including erosion location, depth, and extent, the erosion resistance and erosion effect of multiple regions are assessed, and soil erosion simulation information is generated.

[0128] In substep S403, based on erosion simulation data, soil erosion simulation information for multiple regions is analyzed to assess the erosion resistance and erosion effects of multiple regions. Through statistical analysis methods, the location, depth, and extent of erosion are analyzed to evaluate the effectiveness of different soil treatment and protection measures. Soil erosion simulation information is generated and saved in the form of data tables and maps to show the distribution and severity of soil erosion. Standard environmental scientists and land managers assess areas vulnerable to erosion and identify effective management measures.

[0129] Please see Figure 6 Based on soil erosion simulation information, the specific steps for assessing land surface status under various environmental change scenarios and obtaining land surface status prediction information by performing time series analysis on dynamic data of soil erosion and runoff are as follows:

[0130] S501: Based on soil erosion simulation information, the simulation data is classified according to time information, and the soil erosion degree and runoff information at multiple time points are recorded to generate time-classified erosion data;

[0131] In sub-step S501, soil erosion simulation information is used to classify data according to time information, recording the degree of soil erosion and runoff information at different time points. Through data processing technology, time series analysis methods are used to sort and group the erosion data according to the date and time of rainfall events. The data at each time point are summarized, including erosion depth, erosion width, soil loss, and runoff. The data is stored in database form, including an SQL database and a time series database, for dynamic analysis and comparison with historical data, generating time-classified erosion data, recording the temporal dynamics of erosion, and providing a data foundation for analyzing the erosion process and predicting future erosion trends.

[0132] S502: Based on time-classified erosion data, multiple regression analysis is used to conduct trend analysis on soil erosion data, identify key factors affecting surface changes, including the impact of rainfall changes on erosion and runoff, and generate trend analysis results.

[0133] The multiple linear regression analysis method, according to the formula:

[0134] E=β0+β1R+β2S+β3C+∈

[0135] Calculate soil erosion;

[0136] Where E represents the predicted soil erosion, which is the target variable of the model and is used to measure the overall impact of different factors on soil erosion. R represents annual rainfall, which is the main climatic factor affecting erosion and is directly related to soil erosion. S represents slope percentage, which reflects the degree of terrain inclination and is closely related to water flow velocity and erosion rate. C represents vegetation cover index, which shows the effect of vegetation in protecting soil from erosion. β0 is the intercept term, which represents the basic erosion in the absence of rainfall, slope, and vegetation cover. β1, β2, and β3 are the regression coefficients corresponding to rainfall, slope, and vegetation cover, used to quantify the impact of the target factor on erosion. ∈ is the error term, which takes into account random fluctuations caused by other factors that the model cannot fully explain.

[0137] formula:

[0138] E=β0+β1R+β2S+β3C+∈

[0139] Detailed explanation of the formula and its calculation derivation:

[0140] Parameter definition and acquisition method:

[0141] β0 is the intercept term, representing the baseline erosion amount, which is usually assumed to be the baseline erosion rate in the environment unaffected by any factors, and is set to 0.5 tons / year.

[0142] β1 is the regression coefficient of rainfall, representing the degree of influence of rainfall on erosion. It is set to 0.03 by analyzing historical meteorological and soil erosion data, which means that for every 1 mm increase in rainfall, the erosion increases by 0.03 tons / year.

[0143] R represents the annual rainfall, which is the average annual rainfall in the observation area and is obtained from the records of the local meteorological station. It is assumed to be 850 mm.

[0144] β2 is the regression coefficient of slope, representing the influence of slope percentage on erosion. It is set to 0.4 based on topographic data analysis, which means that for every 1% increase in slope, the amount of erosion increases by 0.4 tons / year.

[0145] S represents the slope percentage, obtained through topographic measurements, and is assumed to be 15%.

[0146] β3 is the regression coefficient of vegetation cover, representing the protective effect of vegetation cover on erosion. Based on field observation and analysis of vegetation cover data, it is set to -0.02, which means that for every 1% increase in vegetation cover, the amount of erosion decreases by 0.02 tons / year.

[0147] C is the vegetation cover index, obtained through satellite imagery and ground sample surveys, assumed to be 65%.

[0148] ∈ represents the error term, which considers random variation that the model cannot fully explain. It is usually assumed to be a small value of a normal distribution, such as 0.3, reflecting the influence of other factors not considered in the model.

[0149] Substitute the parameters into the formula to calculate:

[0150] E=0.5+0.03×850+0.4×15-0.02×65+0.3

[0151] E = 0.5 + 25.5 + 6 - 1.3 + 0.3

[0152] E = 31 tons / year

[0153] The calculation result E = 31 tons / year indicates that after considering multiple factors such as rainfall, slope, and vegetation cover, the predicted annual soil erosion is 31 tons, which is used to analyze the erosion risk of the target area under the current environmental conditions.

[0154] S503: Based on trend analysis results, simulate the changes in land surface state under various environmental change scenarios, including continuous drought and frequent rainfall, predict the land surface state under various scenarios, and generate land surface state prediction information.

[0155] In the S503 sub-step, based on the trend analysis results, the changes in the land surface state under various environmental change scenarios are simulated. Using environmental simulation software, different environmental scenarios are set, including continuous drought and frequent rainfall conditions. Meteorological parameters, including rainfall frequency and intensity, are adjusted in the simulation to simulate the impact of various extreme weather events on the land surface, including the impact on soil erosion and runoff. The simulation results are used to generate land surface state prediction information through a land surface state prediction model, including predicted erosion depth, erosion range, and change trends. The information is saved in graphical and textual forms, providing a predictive tool for environmental management and disaster prevention planning, and helping decision-makers and researchers understand and respond to the impact of environmental changes on the land surface.

[0156] Please see Figure 7 Based on surface condition prediction information, the steps for predicting surface deformation events, calculating regional risks and disaster occurrence probabilities, and generating two-dimensional surface calculation results by assessing the stability of multiple surface regions are as follows:

[0157] S601: Based on the surface condition prediction information, analyze and identify surface stability data in multiple regions, record multiple risk areas, including landslide and subsidence risk locations, and generate surface stability analysis data;

[0158] In substep S601, surface stability in multiple regions is analyzed and evaluated using surface state prediction information. Specifically, a simplified Bishop method is used to analyze and identify surface stability data for multiple regions, according to the formula...

[0159]

[0160] Calculate the safety factor FS.

[0161] In the formula, c′ represents effective cohesion, l i W represents the length of the i-th segment on the sliding surface. i θ represents the weight of the i-th segment. i Let φ' represent the slope angle of the i-th segment on the sliding surface, and let φ′ represent the effective internal friction angle of the soil.

[0162] Detailed explanation of the formula and its calculation derivation:

[0163] Assuming the landslide analysis involves 5 segments, the specific parameters are set as follows:

[0164] c′=20kPa (effective cohesion) Length of each segment l i =Weight W per 10m segment i Assuming the soil density is 1800 kg / m³ 3 Each segment has a volume of 10m³. 3 Then W i =18000kg slope angle θ i =15° Internal friction angle φ′=25°

[0165] Substitute the parameters into the formula to calculate:

[0166]

[0167] The results indicate that the safety factor FS = 1.88, which is greater than 1, means that the slope in the area is relatively stable under the current conditions, reflecting a low probability of landslides, and thus quantifying and assessing the landslide risk.

[0168] S602: Based on surface stability analysis data, perform deformation event prediction, predict the location and scale of landslides and subsidence in multiple areas, and generate deformation event prediction data;

[0169] In substep S602, based on surface stability analysis data, a deformation prediction model is used to predict landslide and subsidence events in multiple regions. According to topography, soil conditions, and predicted extreme weather conditions, model parameters are set to predict the location and scale of landslides and subsidence in different regions. Finite element analysis is used to simulate the deformation response of the surface under various environmental pressures. The generated deformation event prediction data describes the location, scale, and occurrence time of the predicted landslide and subsidence events, and is presented in the form of digital models and visual charts. It is provided to land management departments and disaster early warning systems for real-time monitoring and prevention planning.

[0170] S603: Based on deformation event prediction data, assess the probability of disaster occurrence in multiple regions, calculate the risk level of various disaster types, and generate two-dimensional surface calculation results;

[0171] In substep S603, the probability of disaster occurrence in multiple regions is assessed based on deformation event prediction data, and the risk level of different disaster types is calculated. Using a disaster risk assessment framework, combined with geological data, historical disaster records, and current deformation predictions, the disaster types and risk levels of each region are classified and determined. Using probability theory and risk quantification models, the probability of occurrence and the degree of impact of various disasters are calculated. The generated two-dimensional surface calculation results are saved in the form of a multidimensional dataset, including the GIS coordinates of the risk areas, disaster types, risk levels, and probability distributions.

[0172] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for two-dimensional calculation of the Earth's surface using cellular automata, characterized in that, Includes the following steps: Based on the terrain data collection and recording, the terrain data of multiple regions are analyzed, the elevation and slope are recorded, and the terrain data are processed using cellular automata to construct a terrain model and obtain regional terrain information. Based on the terrain information of the region, multiple key locations in the simulated area are identified and the size and distribution of cells are adjusted to optimize the terrain resolution of multiple key locations and generate a surface grid model. Based on the surface grid model, meteorological data of the target area are collected and rainfall simulation is performed. The rainfall model parameters are adjusted according to the precipitation amount and frequency to simulate the impact of rainfall on the surface and generate rainfall impact simulation results. Using the rainfall impact simulation results, soil erosion and surface runoff are simulated and calculated to assess the erosion resistance and erosion effect of multiple regions, and to generate soil erosion simulation information. Based on the soil erosion simulation information, by performing time series analysis on soil erosion and runoff dynamic data, the surface state under various environmental change scenarios is evaluated, and surface state prediction information is obtained. Based on the surface condition prediction information, the stability of multiple surface regions is assessed, surface deformation events are predicted, regional risks and disaster occurrence probabilities are calculated, and two-dimensional surface calculation results are generated.

2. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, The regional topographic information includes elevation data, slope data, and topographic structure features. The surface grid model includes grid size information, cell distribution parameters, and topographic resolution information for key areas. The rainfall impact simulation results include simulated precipitation data, precipitation distribution maps, and analysis results of the impact of precipitation on the surface. The soil erosion simulation information includes erosion location maps, erosion depth data, and analysis results of erosion range. The surface state prediction information includes surface change trends, environmental change scenario analysis data, and predicted phenomena of state changes. The two-dimensional surface calculation results include surface deformation prediction results, regional risk assessment information, and disaster occurrence probability calculation information.

3. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, Based on topographic data collection and recording, the topographic data of multiple regions is analyzed, elevation and slope are recorded, and cellular automata are used to process the topographic data to construct a topographic model, thereby obtaining regional topographic information. The specific steps are as follows: Based on topographic data collection and recording, topographic data from multiple regions are analyzed to identify elevation and slope information, and the data is formatted and standardized to generate data formatting processing results; Based on the data formatting processing results, the terrain features of multiple regions are analyzed, including height changes and slope differences, key terrain features are identified, and terrain feature information records are generated. Based on the recorded terrain feature information, cellular automata are used to process the terrain data, simulate the response of the terrain data under various environmental conditions, construct a terrain model, and generate regional terrain information.

4. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, Based on the aforementioned regional terrain information, the specific steps for identifying multiple key locations in the simulated area, adjusting the size and distribution of cells, optimizing the terrain resolution at these key locations, and generating a surface mesh model are as follows: Based on the regional terrain information, multiple key locations in the target area are identified, including river valleys, mountain ridges, and urban areas. The location and extent information of the target area are recorded, and key area marker information is generated. Based on the key region marking information, the terrain resolution and computational efficiency at key locations are optimized by adjusting the size and distribution parameters of the cells, and mesh configuration parameters are generated. Based on the grid configuration parameters, a surface grid model of the target simulation area is constructed, and the terrain resolution of key areas is adjusted to match the accuracy requirements to generate a surface grid model.

5. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, Based on the aforementioned surface grid model, the specific steps for collecting meteorological data of the target area and simulating rainfall, adjusting rainfall model parameters according to precipitation amount and frequency, simulating the impact of rainfall on the surface, and generating rainfall impact simulation results are as follows: Based on the aforementioned surface grid model, meteorological data of the target area is collected, including precipitation and precipitation frequency information. Combined with the surface model, the water flow distribution under various rainfall conditions is simulated to generate real-time meteorological input data. Based on the real-time meteorological input data, the rainfall simulation parameters are adjusted, including adjusting the rainfall intensity and distribution pattern, to simulate surface areas with various terrains and soil conditions, and to generate rainfall pattern simulation information. Based on the rainfall pattern simulation information, the Manning formula is used to simulate and analyze the impact of rainfall events on the land surface, calculate water flow velocity, flow direction and water accumulation area, assess the degree of change of topography caused by rainfall, and generate simulation results of rainfall impact.

6. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 5, characterized in that, The Manning formula is as follows: Calculate the water flow velocity; Wherein, v represents the water flow velocity, which indicates the rate at which water passes through the target cross section within the target time. It is a key parameter for assessing the impact force and erosion potential of water flow. n represents the Manning coefficient, which characterizes the frictional resistance of water flow under target surface conditions and affects the magnitude of water flow velocity. R represents the hydraulic radius, which is the ratio of the cross-sectional area of ​​the water flow to the wetted perimeter. It reflects the flow capacity of the water flow. A larger hydraulic radius indicates that the water flow is more concentrated and the flow velocity is higher. S represents the water flow gradient, which is the degree of water level reduction per unit length. It directly determines the potential energy and dynamics of the water flow and is a fundamental factor driving water flow. By combining topography and rainfall conditions, it provides a computational basis for simulating water flow dynamics.

7. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, Using the rainfall impact simulation results, the steps for simulating and calculating soil erosion and surface runoff, evaluating the erosion resistance and erosion effects of multiple regions, and generating soil erosion simulation information are as follows: Using the simulation results of the rainfall impact, the scouring effect of water flow on the soil is assessed based on soil type and slope factors, the erosion probability of soil in multiple regions is analyzed, and scouring effect calculation data is generated. Based on the scouring data, erosion simulations were performed on various soil types, and the depth, width, and extent of erosion were calculated to generate erosion simulation data. Based on the erosion simulation data, by analyzing the erosion simulation information of multiple regions, including erosion location, depth and range, the erosion resistance and erosion effect of multiple regions are evaluated, and soil erosion simulation information is generated.

8. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, Based on the soil erosion simulation information, the specific steps for evaluating the land surface state under various environmental change scenarios and obtaining land surface state prediction information by performing time series analysis on soil erosion and runoff dynamic data are as follows: Based on the soil erosion simulation information, the simulation data is classified according to time information, and the soil erosion degree and runoff information at multiple time points are recorded to generate time-classified erosion data. Based on the time-classified erosion data, a multivariate regression analysis method was used to perform trend analysis on the soil erosion data, identify key factors affecting surface changes, including the impact of rainfall changes on erosion and runoff, and generate trend analysis results. Based on the trend analysis results, the changes in land surface state under various environmental change scenarios are simulated, including continuous drought and frequent rainfall, and the land surface state under various scenarios is predicted to generate land surface state prediction information.

9. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 8, characterized in that, The aforementioned multiple linear regression analysis method, according to the formula: E=β0+β1R+β2S+β3C+∈ Calculate soil erosion; Where E represents the predicted soil erosion, which is the target variable of the model and is used to measure the overall impact of different factors on soil erosion. R represents annual rainfall, which is the main climatic factor affecting erosion and is directly related to soil erosion. S represents slope percentage, which reflects the degree of terrain inclination and is closely related to water flow velocity and erosion rate. C represents vegetation cover index, which shows the effect of vegetation in protecting soil from erosion. β0 is the intercept term, which represents the basic erosion in the absence of rainfall, slope, and vegetation cover. β1, β2, and β3 are the regression coefficients corresponding to rainfall, slope, and vegetation cover, used to quantify the impact of the target factor on erosion. ∈ is the error term, which takes into account random fluctuations caused by other factors that the model cannot fully explain.

10. The method for two-dimensional calculation of the Earth's surface using cellular automata according to claim 1, characterized in that, Based on the surface state prediction information, the specific steps for generating two-dimensional surface calculation results by assessing the stability of multiple surface regions, predicting surface deformation events, calculating regional risks and disaster occurrence probabilities, and generating surface two-dimensional calculation results are as follows: Based on the surface condition prediction information, surface stability data of multiple regions are analyzed and identified, multiple risk areas are recorded, including landslide and subsidence risk locations, and surface stability analysis data is generated. Based on the surface stability analysis data, deformation event prediction is performed to predict the location and scale of landslides and subsidence in multiple regions, generating deformation event prediction data. Based on the deformation event prediction data, the probability of disaster occurrence in multiple regions is assessed, the risk level of various disaster types is calculated, and two-dimensional surface calculation results are generated.

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