Ground surface two-dimensional calculation method of cellular automaton

Abstract

The invention relates to the technical field of computer simulation, in particular to an earth surface two-dimensional calculation method based on a cellular automaton, which comprises the following steps of: analyzing topographic data of a plurality of areas based on topographic data acquisition records, recording elevation and gradient, processing the topographic data by using the cellular automaton, constructing a topographic model, and calculating the topographic model by using the cellular automaton. And obtaining regional terrain information. According to the method, through terrain data acquisition and terrain model construction, the basic data quality of the model is improved, accurate input data is provided for simulation, a key position is identified, the size and distribution of cells are adjusted, the simulation resolution of a key area is optimized, the local precision of simulation is improved, and the consumption of computing resources is reduced; the rainfall simulation parameters are adjusted according to the actual meteorological data, the prediction reliability is enhanced, the erosion effect and anti-erosion ability of various areas are evaluated by simulating soil erosion and surface runoff, and the prediction accuracy and practicability of surface two-dimensional calculation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer simulation, and in particular to a two-dimensional surface calculation method of a cellular automaton. Background Art

[0002] The field of computer simulation technology focuses on applying computer technology to simulate real-world processes and systems, allowing researchers to create virtual simulations on computers through mathematical models and algorithms, predict and analyze the behavior of complex systems, and apply it to multiple disciplines such as engineering, physical science, biomedicine, environmental science, and economics. It helps scientists and engineers conduct experiments and data analysis when actual physical experiments are costly and impractical, improves research flexibility and reduces cost-effectiveness, and enables analysis of the effects of multiple conditions and parameters in a safe virtual environment.

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

[0004] Traditional two-dimensional surface calculation methods are limited by data processing capabilities and simulation accuracy when simulating large-scale surface changes. Under complex terrain and changing meteorological conditions, traditional models find it difficult to quickly and accurately process and reflect changes in actual surface conditions, resulting in an inability to provide sufficiently detailed data support during environmental monitoring and disaster prevention planning, affecting the effectiveness and timeliness of decision-making. Failure to accurately simulate the impact of rainfall on the surface can lead to misjudgment of flood risks, affecting the formulation and implementation of response measures. The method is not flexible enough in automatically adjusting the allocation of computing resources, resulting in a lack of sufficient computing support in key areas that require high-precision simulations, limiting the practicality and application scenarios of the simulation. Summary of the invention

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

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a two-dimensional surface calculation method of cellular automaton, comprising the following steps:

[0007] S1: 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;

[0008] S2: Based on the regional terrain information, identify multiple key locations in the simulation area and adjust the size and distribution of cells, optimize the terrain resolution of the multiple key locations, and generate a surface grid model;

[0009] S3: Based on the surface grid model, collect meteorological data of the target area and perform rainfall simulation, adjust rainfall pattern parameters according to precipitation amount and precipitation frequency, simulate the impact of rainfall on the surface, and generate rainfall impact simulation results;

[0010] S4: using the rainfall impact simulation results, simulating and calculating soil erosion and surface runoff, evaluating the anti-erosion capacity and erosion effects of multiple regions, and generating soil erosion simulation information;

[0011] S5: 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 to obtain surface state prediction information;

[0012] S6: Based on the surface state prediction information, by evaluating the stability of multiple surface areas, predicting surface deformation events, calculating regional risks and disaster probability, and generating surface two-dimensional calculation results.

[0013] As a further solution of the present invention, the regional terrain information includes elevation data, slope data, and terrain structure characteristics; the surface grid model includes grid size information of key areas, cellular distribution parameters, and terrain resolution information; the rainfall impact simulation results include precipitation simulation data, precipitation distribution map, and precipitation impact analysis results on the surface; the soil erosion simulation information includes erosion location map, erosion depth data, and erosion range analysis results; the surface state prediction information includes surface change trends, environmental change scenario analysis data, and state change prediction phenomena; the surface two-dimensional calculation results include surface deformation prediction results, regional risk assessment information, and disaster occurrence probability calculation information.

[0014] As a further solution of the present invention, based on terrain data collection and recording, terrain data of multiple regions are analyzed, elevation and slope are recorded, and terrain data are processed using cellular automata to construct a terrain model. The steps of obtaining regional terrain information are specifically as follows:

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

[0016] S102: Analyze the terrain features of multiple regions based on the data formatting processing results, including height change and slope difference information, identify key terrain features, and generate terrain feature information records;

[0017] S103: Based on the terrain feature information record, the terrain data is processed using a cellular automaton 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 solution of the present invention, based on the regional terrain information, multiple key positions in the simulation area are identified and the size and distribution of cells are adjusted to optimize the terrain resolution of the multiple key positions. The steps of generating a surface grid model are specifically as follows:

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

[0020] S202: Based on the key area marking information, the size and distribution parameters of the cells are adjusted to optimize the terrain resolution and calculation efficiency of the key positions, and generate grid configuration parameters;

[0021] S203: Based on the grid configuration parameters, a surface grid model of the target simulation area is constructed, and the terrain resolution of the key area is adjusted to match the accuracy requirement to generate a surface grid model.

[0022] As a further solution of the present invention, based on the surface grid model, meteorological data of the target area is collected and rainfall simulation is performed, rainfall pattern parameters are adjusted according to precipitation amount and precipitation frequency, and the impact of rainfall on the surface is simulated. The steps of generating rainfall impact simulation results are specifically as follows:

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

[0024] S302: Based on the real-time meteorological input data, adjusting rainfall simulation parameters, including adjusting rainfall intensity and distribution pattern, simulating surface areas of various terrains and soil conditions, and generating 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 ground surface, calculate the water flow speed, flow direction and water accumulation area, and evaluate the degree of change of the terrain caused by rainfall, and generate rainfall impact simulation results.

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

[0027]

[0028] Calculate water velocity;

[0029] Among them, 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 evaluating the impact force and erosion potential of the water flow. n represents the Manning coefficient, which characterizes the friction resistance of the water flow under the target surface conditions and affects the magnitude of the water flow velocity. R represents the hydraulic radius, which refers to the ratio of the water flow cross-sectional area to the wetted perimeter, reflecting the flow capacity of the water flow. A larger hydraulic radius indicates that the water flow is more concentrated and the flow rate is higher. S represents the water flow slope, that is, the degree of water level reduction per unit length, which directly determines the potential energy and power of the water flow and is the basic factor driving the water flow movement. By combining the terrain and rainfall conditions, it provides a calculation basis for simulating the dynamics of the water flow.

[0030] As a further solution of the present invention, the steps of generating soil erosion simulation information by simulating and calculating soil erosion and surface runoff using the rainfall impact simulation results to evaluate the anti-erosion capacity and erosion effects of multiple regions are as follows:

[0031] S401: using the rainfall impact simulation results, evaluating the scouring effect of water flow on soil according to soil type and slope factors, analyzing the erosion probability of soil in multiple regions, and generating scouring effect calculation data;

[0032] S402: Based on the scouring action 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;

[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 to generate soil erosion simulation information.

[0034] As a further solution of the present invention, 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 the steps of obtaining surface state prediction information are specifically 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 surface conditions under various environmental change scenarios, including continuous drought and frequent rainfall, predict the surface conditions under various scenarios, and generate surface condition prediction information.

[0038] As a further solution of the present invention, the multivariate linear regression analysis method is according to the formula:

[0039] E=β0+β1R+β2S+β3C+∈

[0040] Calculate soil erosion;

[0041] Among them, E represents the predicted soil erosion amount, which represents 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 the slope percentage, which reflects the slope of the terrain and is closely related to the water flow velocity and erosion rate. C represents the vegetation cover index, which shows the effect of vegetation in protecting soil from erosion. β0 is the intercept term, which represents the basic erosion amount in the absence of any rainfall, slope and vegetation cover. β1, β2, and β3 are the regression coefficients corresponding to rainfall, slope and vegetation cover, which are used to quantify the impact of the target factor on erosion. ∈ is the error term, which takes into account the random fluctuations caused by other factors that are not fully explained by the model.

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

[0043] S601: Analyze and identify surface stability data of multiple regions based on the surface state prediction information, record multiple risk areas, including landslide and subsidence risk locations, and generate surface stability analysis data;

[0044] S602: performing deformation event prediction based on the surface stability analysis data, predicting the location and scale information of landslides and settlements in multiple regions, and generating deformation event prediction data;

[0045] S603: Based on the deformation event prediction data, the probability of disaster occurrence in multiple regions is evaluated, and the risk levels of multiple disaster types are calculated to generate a two-dimensional surface calculation result.

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

[0047] In the present invention, by collecting terrain data and constructing a terrain model, the basic data quality of the model is improved and accurate input data is provided for the simulation, key locations are identified and the cell size and distribution are adjusted, the simulation resolution of key areas is optimized, the local accuracy of the simulation is improved, and the consumption of computing resources is reduced. 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 anti-erosion capabilities of various regions are evaluated, thereby improving the prediction accuracy and practicality of the two-dimensional surface calculation. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0049] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0050] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0051] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0052] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

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

[0054] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0057] See also Figure 1 The present invention provides a technical solution, a two-dimensional surface calculation method of a cellular automaton, comprising the following steps:

[0058] S1: 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;

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

[0060] S3: Based on the surface grid model, collect meteorological data of the target area and perform rainfall simulation, adjust rainfall pattern parameters according to precipitation amount and precipitation frequency, simulate the impact of rainfall on the surface, and generate rainfall impact simulation results;

[0061] S4: Using the rainfall impact simulation results, the soil erosion and surface runoff are simulated and calculated to evaluate the anti-erosion capacity and erosion effects of multiple regions and generate soil erosion simulation information;

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

[0063] S6: Based on the surface state prediction information, by evaluating the stability of multiple surface areas, predicting surface deformation events, calculating regional risks and disaster probability, and generating surface two-dimensional calculation results.

[0064] 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 areas. Rainfall impact simulation results include precipitation simulation data, precipitation distribution map, and precipitation impact analysis results on the surface. Soil erosion simulation information includes erosion location map, erosion depth data, and erosion range analysis results. Surface state prediction information includes surface change trends, environmental change scenario analysis data, and state change prediction phenomena. Surface two-dimensional calculation results include surface deformation prediction results, regional risk assessment information, and disaster occurrence probability calculation information.

[0065] See also Figure 2 ,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 by cellular automata to construct a terrain model. The specific steps to obtain regional terrain information are as follows:

[0066] S101: Based on the terrain data collection records, the terrain data of multiple regions are analyzed, the elevation and slope information are identified, and the data are formatted and standardized to generate data formatting processing results;

[0067] In sub-step S101, terrain data is collected through a terrain data collection system based on terrain data collection records. The data includes information such as surface elevation and slope, and is in the form of a three-dimensional data set of longitude and latitude and corresponding elevation values. A terrain data preprocessing algorithm, including data cleaning and interpolation methods, is used to fill data gaps and reduce the impact of outliers to ensure data integrity and accuracy. A digital elevation model (DEM) is used to build a program to convert the collected point data into a continuous elevation field, improve data application efficiency, and provide 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 to facilitate 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, analyzing the terrain characteristics of multiple areas, including height change and slope difference information, identifying key terrain characteristics, and generating terrain characteristic information records;

[0069] In sub-step S102, based on the data formatting processing results, the terrain data of multiple areas are analyzed by terrain feature analysis tools, and the elevation data in the area is analyzed by height change analysis algorithms to identify significant height differences in the terrain, including ridges or valleys and other key terrain features. The slope difference analysis program is used to compare slope changes in different areas, reveal the slope characteristics of the terrain, identify key features in the terrain, and quantitatively describe the terrain changes. The analysis results are organized and recorded in a terrain feature information recording file, which is used to evaluate the terrain characteristics of each area and provide key information for terrain simulation and environmental assessment.

[0070] S103: Based on the terrain feature information record, the terrain data is processed using a cellular automaton to simulate the response of the terrain data under various environmental conditions, and a terrain model is constructed to generate regional terrain information;

[0071] In sub-step S103, based on the terrain feature information records, the cellular automaton model is used to process and simulate the terrain data. By setting differentiated environmental parameters, including rainfall, soil type, etc., 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 the neighboring cells, the dynamic response of the terrain to environmental changes is displayed through iterative calculation. The generated regional terrain information model is used to visualize the response process of the terrain and provide decision support in many aspects such as urban planning and disaster prevention.

[0072] See also Figure 3 Based on the regional terrain information, multiple key locations in the simulation area are identified and the size and distribution of cells are adjusted to optimize the terrain resolution of multiple key locations. The specific steps for generating the surface grid model are as follows:

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

[0074] In sub-step S201, based on the regional terrain information, multiple key locations of the target area are identified, including river valleys, ridges, and urban areas, and the location and range information of the target area are recorded. The digital elevation model is used to extract the terrain 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 is the elevation of the east point, representing the elevation value of the adjacent point on the east side, h central is the elevation of the center point, representing the elevation value of the analysis point, h south is the elevation of the south point, representing the elevation value of the adjacent point on the south side, D is the horizontal distance between the two points, representing the distance between adjacent pixel points in the DEM data,

[0078] Detailed explanation of the formula and the process of formula calculation and 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 side and the center point and the elevation difference between the south side and the center point:

[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 means that the slope at this point is 0.54, identifying a variety of key terrain features such as ridges and valleys, providing data support for terrain analysis and regional planning.

[0086] S202: Based on the key area marking information, the size and distribution parameters of the cells are adjusted to optimize the terrain resolution and calculation efficiency of the key positions, and the grid configuration parameters are generated;

[0087] In sub-step S202, based on the key area marking information, the cell size and distribution parameters in the cellular automaton model are adjusted to optimize the terrain resolution and computational efficiency of 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 the simulation fineness, and increasing the cell size in urban and plain areas to improve the calculation speed. By modifying the distribution parameters of the cells, including adjusting the density and interaction rules of the cells, the computing resources of the model are ensured to be efficiently utilized. The generated grid configuration parameters are saved in text or XML format to record the cell size and distribution of multiple areas, and optimize the level of detail and operating efficiency of the terrain simulation.

[0088] S203: constructing a surface grid model of the target simulation area based on the grid configuration parameters, and adjusting the terrain resolution of the key area to match the accuracy requirements to generate a surface grid model;

[0089] In sub-step S203, the grid configuration parameters are used to construct a surface grid model of the target simulation area. Computer-aided design software and terrain modeling tools are used to adjust the grid density of the differentiated key areas according to the resolution requirements of the terrain to match the simulation requirements of the terrain, including increasing the terrain grid density in valleys and ridge areas to capture subtle terrain changes, and using a lower grid density in flat urban or farmland areas to optimize computing efficiency and match the accuracy requirements of the actual terrain. The generated surface grid model is stored in a three-dimensional model file format to provide an accurate three-dimensional terrain foundation for subsequent terrain analysis, environmental simulation, or visual presentation.

[0090] See also Figure 4,Based on the surface grid model, the meteorological data of the target area is collected and rainfall simulation is performed.,The rainfall pattern parameters are adjusted according to the precipitation amount and precipitation frequency, and the impact of rainfall on the surface is simulated.,The specific steps for generating rainfall impact simulation results are as follows:

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

[0092] In sub-step S301, based on the surface grid model, by collecting meteorological data of the target area, including precipitation and precipitation frequency information, water flow distribution simulation is performed, and meteorological simulation software is used to input the real-time collected precipitation data into the model. The meteorological data is combined with the surface grid model through GIS technology to simulate the water flow distribution under differentiated rainfall conditions and record it in a standard time series database, including the start time, end time, total precipitation and distribution of each rainfall. During the simulation process, the software dynamically adjusts the water flow path and water accumulation area according to the input meteorological data, generates real-time meteorological input data, and saves it in the form of GIS format or dynamic simulation video, which is used to analyze the impact of rainfall on the terrain and provide a scientific basis for urban drainage and flood prevention.

[0093] S302: Based on the real-time meteorological input data, adjusting rainfall simulation parameters, including adjusting rainfall intensity and distribution pattern, simulating surface areas of various terrains and soil conditions, and generating rainfall pattern simulation information;

[0094] In sub-step S302, based on real-time meteorological input data, rainfall simulation is performed for surface areas with differentiated terrain and soil conditions by adjusting rainfall simulation parameters, including rainfall intensity and distribution pattern. 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 rainfall intensity percentage and distribution pattern in different areas, including uniform distribution and random distribution, simulating the impact of rainfall on the terrain, and generating rainfall pattern simulation information through simulation, recording it in the simulation log and result data file, and evaluating the water flow behavior under various parameter settings.

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

[0096] Manning formula, according to the formula:

[0097]

[0098] Calculate water velocity;

[0099] Among them, 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 evaluating the impact force and erosion potential of the water flow. n represents the Manning coefficient, which characterizes the friction resistance of the water flow under the target surface conditions and affects the magnitude of the water flow velocity. R represents the hydraulic radius, which refers to the ratio of the water flow cross-sectional area to the wetted perimeter, reflecting the flow capacity of the water flow. A larger hydraulic radius indicates that the water flow is more concentrated and the flow rate is higher. S represents the water flow slope, that is, the degree of water level reduction per unit length, which directly determines the potential energy and power of the water flow and is the basic factor driving the water flow movement. By combining the terrain and rainfall conditions, it provides a calculation basis for simulating the dynamics of the water flow.

[0100] formula:

[0101]

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

[0103] Parameter definition and acquisition method:

[0104] n is the Manning coefficient, which represents the friction resistance of 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 water flow cross-sectional area to the wetted perimeter. The calculation formula is: Where A is the water flow cross-sectional area, P is the wetted perimeter, which is set to R = 0.5 m.

[0106] S is the water flow slope, which indicates the ratio of the decrease of water flow height 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 for calculation:

[0108]

[0109] The result v≈1.532m / s indicates that under given channel conditions and water flow characteristics, the water flow velocity is approximately 1.532m / s, which helps engineers design flood control measures, detect flood risks and conduct environmental impact assessments. By calculating water flow velocity, they can analyze flood propagation speed and erosion degree, and predict and manage flood events.

[0110] See also Figure 5 , using the rainfall impact simulation results, by simulating and calculating soil erosion and surface runoff, the anti-erosion capacity and erosion effects of multiple regions are evaluated. The specific steps for generating soil erosion simulation information are as follows:

[0111] S401: Using the rainfall impact simulation results, evaluate the scouring effect of water flow on soil according to soil type and slope factors, analyze the erosion probability of soil in multiple areas, and generate scouring effect calculation data;

[0112] In sub-step S401, soil scouring is evaluated by combining rainfall simulation results with soil type and slope factors. Using a soil and water conservation model, velocity and flow data from rainfall simulation results are analyzed in combination with soil type-specific erosion sensitivity and slope effects. This includes calculating the potential erosion rate of each soil type under different slope conditions, considering soil water retention capacity, mechanical composition, organic matter content and other properties, and recording soil erosion probabilities in various regions.

[0113] S402: Based on the scouring 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 calculation data, the erosion process is simulated using the general soil loss equation in the calculation of the depth, width and range of erosion. The calculation formula of the erosion amount E1 is as follows:

[0115] E1=R1×K×LS×C1×P

[0116] Calculate the amount of erosion,

[0117] Among them, E1 represents annual soil loss, R1 is the rainfall erosivity factor, K is the soil erodibility factor, LS is the slope length and slope factor, C1 is the vegetation cover management factor, and P is the support practice factor.

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

[0119] Assume that the parameters of a region are as follows:

[0120] Rainfall erosivity factor R1 = 100 MJmm / ha / hour / year, soil erodibility factor K = 0.2 tons hah / haMJmm, slope length and slope factor LS = 1.3, vegetation cover management factor C1 = 0.04, support practice factor P = 0.8,

[0121] Substitute into the formula to calculate:

[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 area is 8.32 tons / hectare, assessing the severity of soil loss under current land management and environmental conditions.

[0127] S403: Based on the erosion simulation data, by analyzing the erosion simulation information of multiple regions, including erosion location, depth and range, evaluating the anti-erosion capacity and erosion effect of multiple regions, and generating soil erosion simulation information;

[0128] In sub-step S403, based on the erosion simulation data, the soil erosion simulation information of multiple regions is analyzed, the erosion resistance and erosion effects of multiple regions are evaluated, and the erosion location, depth and range data are analyzed through statistical analysis methods 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 evaluate areas susceptible to erosion and identify effective management measures.

[0129] See also Figure 6 Based on the soil erosion simulation information, the soil erosion and runoff dynamic data are analyzed in time series to evaluate the surface state under various environmental change scenarios. The specific steps to obtain the surface state prediction information are as follows:

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

[0131] In sub-step S501, the data is classified according to the time information through the soil erosion simulation information, and the soil erosion degree and runoff information at different time points are recorded. Through data processing technology and using time series analysis methods, the erosion data are sorted and grouped according to the date and time of the rainfall event, and the data at each time point are summarized, including erosion depth, erosion width, soil loss, and runoff. The data is saved in the form of a database, including an SQL database and a time series database, which is used for dynamic analysis and historical data comparison, to generate time-classified erosion data, record the time dynamics of erosion, and provide a data basis for analyzing the erosion process and predicting future erosion trends.

[0132] S502: Based on the time-classified erosion data, a multivariate regression analysis method is used to perform 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] Multiple linear regression analysis method, according to the formula:

[0134] E=β0+β1R+β2S+β3C+∈

[0135] Calculate soil erosion;

[0136] Among them, E represents the predicted soil erosion amount, which represents 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 the slope percentage, which reflects the slope of the terrain and is closely related to the water flow velocity and erosion rate. C represents the vegetation cover index, which shows the effect of vegetation in protecting soil from erosion. β0 is the intercept term, which represents the basic erosion amount in the absence of any rainfall, slope and vegetation cover. β1, β2, and β3 are the regression coefficients corresponding to rainfall, slope and vegetation cover, which are used to quantify the impact of the target factor on erosion. ∈ is the error term, which takes into account the random fluctuations caused by other factors that are not fully explained by the model.

[0137] formula:

[0138] E=β0+β1R+β2S+β3C+∈

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

[0140] Parameter definition and acquisition method:

[0141] β0 is the intercept term, representing the base erosion amount, which is usually assumed to be the baseline erosion rate in the environment not affected by any factors and is set to 0.5 tons / year.

[0142] β1 is the regression coefficient of rainfall, representing the impact of rainfall on erosion. By analyzing historical meteorological and soil erosion data, it is set to 0.03, indicating that for every increase of 1 mm of rainfall, the erosion increases by 0.03 tons / year.

[0143] R is the annual rainfall, which represents the average annual rainfall in the observation area, obtained through the records of regional meteorological stations, and 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 terrain data analysis, indicating that for every 1% increase in slope, erosion increases by 0.4 tons / year.

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

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

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

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

[0149] Substitute the parameters into the formula for calculation:

[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, indicating that after considering multiple factors such as rainfall, slope, and vegetation coverage, the predicted annual soil erosion is 31 tons, which is used to analyze the erosion risk of the target area under current environmental conditions.

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

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

[0156] See also Figure 7 Based on the surface state prediction information, by evaluating the stability of multiple surface areas, predicting surface deformation events, calculating regional risks and disaster probability, the specific steps of generating surface two-dimensional calculation results are as follows:

[0157] S601: Analyze and identify surface stability data of multiple regions based on surface state prediction information, record multiple risk areas, including landslide and subsidence risk locations, and generate surface stability analysis data;

[0158] In sub-step S601, the surface stability of multiple regions is analyzed and evaluated based on the surface state prediction information, wherein the surface stability data of multiple regions are analyzed and identified using the simplified Bishop method, and the surface stability data of multiple regions are analyzed and evaluated based on the formula

[0159]

[0160] Calculate the safety factor FS,

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

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

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

[0164] c'=20kPa (effective cohesion) length of each segment l i =10m weight per section W i , assuming the soil density is 1800kg / m 3 , each section has a volume of 10m 3 , then W i =18000kg slope angle θ i =15°Internal friction angle φ′=25°

[0165] Substitute the parameters into the formula for calculation:

[0166]

[0167] The results show that the safety factor FS = 1.88, which is greater than 1, indicating that the slopes in the area are relatively stable under current conditions, reflecting a low probability of landslides and quantifying and evaluating the landslide risk.

[0168] S602: Based on the surface stability analysis data, deformation event prediction is performed to predict the location and scale information of landslides and settlements in multiple regions, and deformation event prediction data is generated;

[0169] In sub-step S602, based on the surface stability analysis data, the deformation prediction model is used to predict landslides and subsidence events in multiple regions. The model parameters are set according to the terrain, soil conditions and predicted extreme weather conditions 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, which are provided to land management departments and disaster warning systems for real-time monitoring and prevention planning.

[0170] S603: Based on the deformation event prediction data, the probability of disaster occurrence in multiple regions is evaluated, and the risk levels of multiple disaster types are calculated to generate a two-dimensional surface calculation result;

[0171] In sub-step S603, based on the deformation event prediction data, the probability of disaster occurrence in multiple regions is evaluated, and the risk levels of different disaster types are calculated. The disaster risk assessment framework is used to classify the disaster type and determine the risk level in each region in combination with geological data, historical disaster records and current deformation predictions. The probability of occurrence of various disasters and the degree of impact caused are calculated using probability theory and risk quantification models. The generated two-dimensional surface calculation results are saved in the form of a multidimensional data set, including the GIS coordinates of the risk area, disaster type, risk level, and probability distribution.

[0172] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A two-dimensional surface calculation method of cellular automata, characterized in that: The following steps are involved: Based on the terrain data collection records, 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 regional terrain information, multiple key locations in the simulation area are identified and the size and distribution of cells are adjusted to optimize the terrain resolution of the multiple key locations and generate a surface grid model; Based on the surface grid model, meteorological data of the target area is collected and rainfall simulation is performed, rainfall pattern parameters are adjusted according to precipitation amount and precipitation frequency, the impact of rainfall on the surface is simulated, and rainfall impact simulation results are generated; Using the rainfall impact simulation results, soil erosion and surface runoff are simulated and calculated to evaluate the anti-erosion capacity and erosion effects of multiple regions and 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 to obtain surface state prediction information; Based on the surface state prediction information, by evaluating the stability of multiple surface areas, predicting surface deformation events, calculating regional risks and disaster occurrence probabilities, and generating surface two-dimensional calculation results.

2. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: The regional terrain information includes elevation data, slope data, and terrain structure characteristics; the surface grid model includes grid size information of key areas, cell distribution parameters, and terrain resolution information; the rainfall impact simulation results include precipitation simulation data, precipitation distribution maps, and precipitation impact analysis results on the surface; the soil erosion simulation information includes erosion location maps, erosion depth data, and erosion range analysis results; the surface state prediction information includes surface change trends, environmental change scenario analysis data, and state change prediction phenomena; the surface two-dimensional calculation results include surface deformation prediction results, regional risk assessment information, and disaster occurrence probability calculation information.

3. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: 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. The specific steps to obtain regional terrain information are as follows: Based on the terrain data collection records, the terrain data of multiple regions are analyzed, the elevation and slope information are identified, and the data is formatted and standardized to generate data formatting processing results; Based on the data formatting processing results, analyzing the terrain characteristics of multiple areas, including height change and slope difference information, identifying key terrain features, and generating terrain feature information records; Based on the terrain feature information records, 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.

4. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: Based on the regional terrain information, multiple key locations in the simulation area are identified and the size and distribution of cells are adjusted to optimize the terrain resolution of multiple key locations. The steps of generating a surface grid model are specifically as follows: Based on the regional terrain information, identifying multiple key locations of the target area, including river valleys, mountain ridges, and urban areas, recording the location and range information of the target area, and generating key area marking information; Based on the key area marking information, by adjusting the size and distribution parameters of the cells, the terrain resolution and calculation efficiency of the key positions are optimized, and the grid 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 the key area is adjusted to match the accuracy requirements to generate a surface grid model.

5. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: Based on the surface grid model, meteorological data of the target area is collected and rainfall simulation is performed, rainfall pattern parameters are adjusted according to precipitation amount and precipitation frequency, and the impact of rainfall on the surface is simulated. The steps of generating rainfall impact simulation results are specifically as follows: Based on the surface grid model, meteorological data of the target area is collected, including precipitation amount and precipitation frequency information, and combined with the surface model, water flow distribution under various rainfall conditions is simulated to generate real-time meteorological input data; Based on the real-time meteorological input data, adjusting rainfall simulation parameters, including adjusting rainfall intensity and distribution pattern, simulating surface areas of various terrain and soil conditions, and generating 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 surface, calculate the water flow speed, flow direction and water accumulation area, and evaluate the degree of change in the terrain caused by rainfall, thereby generating rainfall impact simulation results.

6. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 5, characterized in that: The Manning formula, according to the formula: Calculate water velocity; Among them, 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 evaluating the impact force and erosion potential of the water flow. n represents the Manning coefficient, which characterizes the friction resistance of the water flow under the target surface conditions and affects the magnitude of the water flow velocity. R represents the hydraulic radius, which refers to the ratio of the water flow cross-sectional area to the wetted perimeter, reflecting the flow capacity of the water flow. A larger hydraulic radius indicates that the water flow is more concentrated and the flow rate is higher. S represents the water flow slope, that is, the degree of water level reduction per unit length, which directly determines the potential energy and power of the water flow and is the basic factor driving the water flow movement. By combining the terrain and rainfall conditions, it provides a calculation basis for simulating the dynamics of the water flow.

7. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: Using the rainfall impact simulation results, soil erosion and surface runoff are simulated and calculated to evaluate the anti-erosion capacity and erosion effects of multiple regions. The steps for generating soil erosion simulation information are as follows: Using the rainfall impact simulation results, the scouring effect of water flow on soil is evaluated according to 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 action 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; 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 effects of multiple regions are evaluated to generate soil erosion simulation information.

8. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: Based on the soil erosion simulation information, the steps of evaluating the surface state under various environmental change scenarios by performing time series analysis on soil erosion and runoff dynamic data to obtain the surface state prediction information are as follows: Based on the soil erosion simulation information, the simulation data is classified according to the time information, the soil erosion degree and runoff information at multiple time points are recorded, and the time-classified erosion data is generated; Based on the time-classified erosion data, a multivariate regression analysis method is used to perform 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; Based on the trend analysis results, the changes in surface conditions under various environmental change scenarios are simulated, including continuous drought and frequent rainfall, the surface conditions under various scenarios are predicted, and surface condition prediction information is generated.

9. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 8, characterized in that: The multivariate linear regression analysis method is according to the formula: E=β0+β1R+β2S+β3C+∈ Calculate soil erosion; Among them, E represents the predicted soil erosion amount, which represents 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 the slope percentage, which reflects the slope of the terrain and is closely related to the water flow velocity and erosion rate. C represents the vegetation cover index, which shows the effect of vegetation in protecting soil from erosion. β0 is the intercept term, which represents the basic erosion amount in the absence of any rainfall, slope and vegetation cover. β1, β2, and β3 are the regression coefficients corresponding to rainfall, slope and vegetation cover, which are used to quantify the impact of the target factor on erosion. ∈ is the error term, which takes into account the random fluctuations caused by other factors that are not fully explained by the model.

10. The cellular automation method for calculating the two-dimensional surface of the earth according to claim 1, characterized in that: Based on the surface state prediction information, the steps of generating the surface two-dimensional calculation results by evaluating the stability of multiple surface areas, predicting surface deformation events, calculating regional risks and disaster occurrence probabilities are specifically as follows: Analyzing and identifying surface stability data of multiple regions based on the surface state prediction information, recording multiple risk areas, including landslide and subsidence risk locations, and generating surface stability analysis data; According to the surface stability analysis data, deformation event prediction is performed to predict the location and scale information of landslides and settlements in multiple regions, and deformation event prediction data is generated; Based on the deformation event prediction data, the probability of disaster occurrence in multiple regions is evaluated, and the risk levels of various disaster types are calculated to generate two-dimensional surface calculation results.

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