High-precision surface modeling methods and systems for ecological and environmental elements considering uncertainties

Abstract

This application relates to the field of data processing technology, proposing a method and system for high-precision surface modeling of ecological and environmental elements considering uncertainty. An initial surface is obtained by interpolating ecological and environmental observation data. Then, using this initial surface as the initial field for High-Precision Surface Modeling (HASM) and the ecological and environmental observation data as the optimization control conditions for HASM, iterative simulation is performed using the HASM method. During the iteration process, the changes in the simulated surface are recorded at each iteration until a preset stopping condition is reached, resulting in a set of simulated surfaces. Based on the number of iterations of the HASM method, the final result surface, and the set of simulated surfaces, confidence interval grids are calculated using different methods, and the final result surface and confidence interval grids are output. The output confidence interval grids reflect the confidence level of the simulation results, enabling decision-makers not only to obtain the final simulation results but also to determine the reliability of the simulation results, thus helping them formulate appropriate risk measures.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a high-precision surface modeling method, system, computer-readable storage medium, and electronic device that takes into account uncertainties in ecological and environmental elements. Background Technology

[0002] In eco-informatics research, a region or its ecological environment elements can be abstracted as a mathematical "surface". Such mathematical surfaces can be used to represent ecological environment surfaces, including natural system surfaces, natural system contribution surfaces to humans, and natural system change driving force surfaces. Therefore, finding an efficient and accurate ecological environment modeling method is of great significance.

[0003] High Accuracy Surface Modeling (HASM) is a spatial simulation method for ecological and environmental elements. Due to its high accuracy and multi-scale characteristics, it is widely used in the surface simulation of ecological and environmental elements and has become an important mathematical method for simulating surface elements. Typical applications include digital ground model construction, climate change analysis, population spatial distribution, soil property spatial distribution, ecosystem spatial distribution, and forest carbon storage simulation.

[0004] Given that any model's simulation results contain errors relative to real data, and that existing HASM methods only obtain the final simulation results without simultaneously capturing the uncertainty of those results, decision-makers find it difficult to determine the reliability of the simulation results, thus hindering their ability to select appropriate risk policies and measures. On the other hand, for model researchers, the lack of knowledge about the uncertainty of the simulation results makes it difficult to accurately pinpoint the problems existing in the simulation process and, consequently, to propose targeted improvement measures.

[0005] Therefore, there is a need to provide an improved technical solution that addresses the shortcomings of the existing technology. Summary of the Invention

[0006] The purpose of this application is to provide a high-precision surface modeling method, system, computer-readable storage medium, and electronic device for ecological and environmental elements that take into account uncertainty, so as to solve or alleviate the problems existing in the prior art.

[0007] To achieve the above objectives, this application provides the following technical solution:

[0008] Firstly, this embodiment provides a high-precision surface modeling method for ecological and environmental elements that considers uncertainties, including:

[0009] Acquire ecological and environmental observation data within the study area;

[0010] The ecological environment observation data is interpolated according to a preset target resolution to obtain the initial surface of the ecological environment elements;

[0011] The initial surface is used as the initial field for high-precision surface modeling (HASM), and the ecological environment observation data is used as the optimization control conditions for HASM. The HASM method is used for iterative simulation. During the iteration process, the changes of the simulated surface in each iteration are recorded until the preset stopping condition is reached, and the iteration is completed.

[0012] Record the final result surface and the number of iterations when the iteration is completed, and at the same time, combine the simulated surfaces recorded during the iteration process into a set of simulated surfaces;

[0013] Based on the number of iterations and the final result surface, confidence interval grids are calculated using different methods, and the final result surface and the confidence interval grids are output; wherein, the confidence interval grids are used to describe the uncertainty of the final result surface.

[0014] Furthermore, based on the number of iterations and the final result surface, different methods are used to calculate the confidence interval grid, including:

[0015] The number of iterations is determined. If the number of iterations is greater than a preset iteration threshold, the confidence interval grid is calculated using the error migration method. If the number of iterations is less than or equal to the preset iteration threshold, the confidence interval grid is calculated using the guided method.

[0016] In some embodiments, the confidence interval grid is calculated using the error migration method, including:

[0017] The average value surface is obtained by averaging all the simulated surfaces in the set of simulated surfaces according to the grid points.

[0018] The difference between the grid point value of each simulated surface in the set of simulated surfaces and the value of the average value surface at that grid point is calculated to obtain the set of simulated error surfaces;

[0019] The average error surface is obtained by averaging all the simulated error surfaces in the set of simulated error surfaces according to the grid points.

[0020] Based on the set of simulated error surfaces and the average error surface, the error standard deviation surface is calculated;

[0021] Based on the average error surface and the error standard deviation surface, combined with preset confidence levels and the final result surface, the confidence interval grid for each confidence level is calculated.

[0022] In some embodiments, based on the mean error surface and the error standard deviation surface, and in conjunction with preset different confidence levels, a confidence interval grid for each confidence level is calculated, including:

[0023] Based on the values ​​of each grid point on the average error surface and the error standard deviation surface, the error variation range of each grid point under different confidence levels is calculated.

[0024] Subtract the lower limit of the error variation interval from the upper limit of each error variation interval and then divide by 2 to obtain the error variation limits at different confidence levels.

[0025] The difference between each grid point on the final result surface and the error variation limit at different confidence levels at that grid point is taken as the lower limit of that grid point on the confidence interval grid; the sum of each grid point on the final result surface and the error variation limit at different confidence levels at that grid point is taken as the upper limit of that grid point on the confidence interval grid, thus obtaining the confidence interval grid at different confidence levels.

[0026] In some embodiments, a guided method is used to calculate the confidence interval grid, including:

[0027] Each grid point of the simulated surface set is subjected to repeated sampling with replacement to obtain a sampled surface set, with each sampling corresponding to a sampled surface.

[0028] Based on the sampling data of each grid point in the sampling surface set, the upper and lower bounds of the quantiles of the grid point at different confidence levels are calculated to obtain the confidence interval grid.

[0029] In some embodiments, the preset iteration threshold is 30 times.

[0030] In some embodiments, after acquiring ecological and environmental observation data within the study area, the method further includes:

[0031] Missing values ​​are filled into the ecological and environmental observation data to obtain preprocessed ecological and environmental observation data.

[0032] Secondly, this embodiment provides a high-precision surface modeling system for ecological and environmental elements that considers uncertainties, including:

[0033] The acquisition unit is configured to acquire ecological and environmental observation data within the study area.

[0034] The interpolation unit is configured to interpolate the ecological environment observation data according to a preset target resolution to obtain the initial surface of the ecological environment elements;

[0035] The simulation unit is configured to use the initial surface as the initial field for high-precision surface modeling (HASM) and the ecological environment observation data as the optimization control conditions for HASM. It uses the HASM method to perform iterative simulation, and records the changes of the simulation surface in each iteration until the preset stopping condition is reached to complete the iteration.

[0036] The recording unit is configured to record the final result surface and the number of iterations when the iteration is completed, and to form a set of simulated surfaces recorded during the iteration process.

[0037] The calculation unit is configured to calculate the confidence interval grid using different methods based on the number of iterations and the final result surface, and output the final result surface and the confidence interval grid; wherein the confidence interval grid is used to describe the uncertainty of the final result surface.

[0038] Thirdly, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the above embodiments.

[0039] Fourthly, this embodiment provides an electronic device, including: a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any of the above embodiments.

[0040] The technical solution of this application embodiment has the following beneficial effects:

[0041] In the solution provided in this embodiment, an initial surface of ecological and environmental elements is obtained by interpolating ecological and environmental observation data. Then, the initial surface is used as the initial field for high-precision surface modeling (HASM), and the ecological and environmental observation data is used as the optimization control condition for HASM. The HASM method is used for iterative simulation, and the changes of the simulated surface in each iteration are recorded until a preset stopping condition is reached to complete the iteration. The final result surface and the number of iterations are recorded when the iteration is completed, and the simulated surfaces recorded during the iteration are combined into a set of simulated surfaces. Based on the number of iterations and the final result surface, confidence interval grids are calculated using different methods, and the final result surface and confidence interval grids are output. Each cell value on the confidence interval grid represents the reliability of the simulation result at the corresponding position on the final result surface, reflecting the likelihood that the simulation result falls within the upper and lower limits expressed by the confidence interval grid. In this way, users of the model can not only obtain the final simulation result, but also determine the reliability of the final simulation result, which helps decision-makers to judge the uncertainty of the simulation result and formulate corresponding risk measures. At the same time, the uncertainty estimation of the HASM method enables researchers to locate some problems in the simulation process to a certain extent, and then propose corresponding improvement methods, which helps to improve the theoretical system of HASM. Attached Figure Description

[0042] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. Wherein:

[0043] Figure 1 This is a flowchart illustrating a high-precision surface modeling method for ecological and environmental elements that takes into account uncertainties, provided according to some embodiments of this application.

[0044] Figure 2 This is a flowchart illustrating a high-precision surface modeling method for ecological and environmental elements that takes into account uncertainties, provided according to some embodiments of this application.

[0045] Figure 3 This is a schematic diagram of the process for calculating a confidence interval grid using the error migration method provided in some embodiments of this application.

[0046] Figure 4 This is a structural block diagram of a high-precision surface modeling system for ecological and environmental elements that takes into account uncertainties, provided according to some embodiments of this application.

[0047] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0048] Figure 6This is a hardware structure diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0049] The present application will now be described in detail with reference to the accompanying drawings and embodiments. Various examples are provided by way of explanation and not by way of limitation. In fact, those skilled in the art will recognize that modifications and variations can be made to the present application without departing from the scope or spirit thereof. For example, a feature shown or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desirable that the present application encompass such modifications and variations that fall within the scope of the appended claims and their equivalents.

[0050] In the following description, the terms "first / second / third" are used merely to distinguish similar objects and do not represent a specific order of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of this disclosure only and is not intended to limit this disclosure.

[0052] Exemplary methods

[0053] This application provides a high-precision surface modeling method for ecological and environmental elements that considers uncertainties, such as... Figures 1-2 As shown, the method includes:

[0054] Step S101: Obtain ecological and environmental observation data within the study area.

[0055] It should be noted that in the process of eco-informatics research, a region or its ecological and environmental elements are represented by a grid and abstracted as a mathematical "surface". Under the conceptual framework of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) of the United Nations, the Earth's surface system and its ecological and environmental elements are classified according to natural systems (including biodiversity, ecosystem structure and topography), the contributions of natural systems to humans (including ecosystem services such as food supply, freshwater supply and environmental pollution regulation), and the drivers of change in natural systems (including climate change, land use change, epidemics, environmental pollution and policies and regulations). The surfaces of natural systems, the surfaces of the contributions of natural systems to humans, and the surfaces of the drivers of change in natural systems are collectively referred to as ecological and environmental surfaces.

[0056] In this embodiment, the ecological environment observation data is related to the ecological and environmental elements observation data of the Earth's surface. For example, it can be digital elevation model data reflecting topography, climate element data such as temperature and precipitation reflecting climate change, or element data such as greenhouse gas and carbon dioxide concentration reflecting environmental pollution. This application does not limit the specific type of ecological environment observation data.

[0057] For ease of description, this embodiment uses high-precision surface modeling of temperature data as an example. It should be understood that the method described in this embodiment is also applicable to the modeling of other ecological and environmental elements, and the exemplary temperature data does not constitute a limitation of this application.

[0058] In this embodiment, the study area can be any geographical area on Earth, and this application does not limit the geographical location or size of the simulated study area.

[0059] The ecological environment observation data described in this embodiment refers to sampling point data related to ecological environment elements. In other words, from the perspective of geographic information expression, this data is point data, with one or more observation values ​​at each point location. Each observation value corresponds to a kind of ecological environment information, such as the temperature value at a certain moment or the precipitation value.

[0060] In this embodiment, there are multiple ways to obtain ecological environment observation data in the study area. Taking temperature observation data as an example, the corresponding observation data can be obtained from the official channels of the China Meteorological Administration, or from the shared data released by universities and research institutes, or from other channels. This application does not limit the specific acquisition method.

[0061] Step S102: Interpolate the ecological environment observation data according to the preset target resolution to obtain the initial surface of the ecological environment elements.

[0062] The HASM method is based on systems theory, surface theory and optimal control theory. Its simulation process can input an initial surface as the initial field to start and sampling points as optimal control conditions. The initial surface can be set to a fixed value, but such a setting is difficult to accurately reflect the data itself. Therefore, this embodiment uses ecological environment observation data to generate the initial surface.

[0063] As mentioned above, the ecological environment observation data obtained in step S101 is point data, while the initial surface is area data. Therefore, the corresponding area data can be obtained by interpolating the points of the ecological environment observation and used as the initial surface.

[0064] There are various methods to obtain areal data from point data through interpolation, such as inverse distance weighting, kriging interpolation, spline interpolation, etc. This embodiment does not limit the specific interpolation method.

[0065] In this embodiment, the ecological environment observation data is interpolated according to a preset target resolution to generate an initial surface for ecological environment elements. The target resolution is the resolution of the final surface obtained from the HASM simulation, and can be set according to the size of the study area, accuracy requirements, and computational performance requirements. For example, for a study area the size of a provincial province, the target resolution can be set to 1 km; for a study area the size of a county, it can be set to 500 m or other target resolutions. This embodiment does not impose any limitations on this.

[0066] Taking temperature observation data as an example, kriging interpolation can be performed on the temperature observation data to obtain the initial surface of temperature data with target resolution in the study area, and this surface can be used as the initial field of HASM.

[0067] Step S103: Using the initial surface as the initial field for high-precision surface modeling (HASM) and ecological environment observation data as the optimization control conditions for HASM, the HASM method is used for iterative simulation. During the iteration process, the changes of the simulated surface in each iteration are recorded until the preset stopping condition is reached, and the iteration is completed.

[0068] The initial field, also known as the initial driving field, refers to the planar data used to drive the simulation when the model starts, representing the spatial extent covered by the simulation dataset. The optimized control conditions refer to the point-like data used in the simulation, representing the accuracy of the dataset. HASM's simulation results are planar data. By combining the accuracy advantages of the optimized control conditions with the spatial extent advantages of the initial field, it ultimately generates a data surface with relatively high accuracy that covers the entire study area.

[0069] Treating the study area as the computational domain of the High-Precision Surface Modeling (HASM) method, the study area can be expressed as a surface equation: z = f(x, y), where x and y are the geographic coordinates of any location within the study area, and z is the surface attribute value (i.e., the value of ecological and environmental elements) at location (x, y). For the surface equation z = f(x, y), an iterative HASM equation system can be constructed, transforming the surface simulation problem into a large-scale symmetric positive definite sparse linear equation system problem. Then, using the initial surface as the initial field and ecological and environmental observation data as the optimization control conditions, the HASM method is used for iterative simulation. With each iteration, the values ​​of each grid point (also called a raster or cell) on the surface will change. In this embodiment, these changes are recorded. By recording each change of the raster surface, a surface corresponding to that iteration is formed.

[0070] The changes in the simulated surface during each iteration can be recorded in memory or output to external storage, such as a hard drive. Caching techniques can also be used to improve speed. The recorded data format can be the values ​​of each grid cell on the changed simulated surface, or it can be the difference between the simulated surface in the current iteration and the previous iteration. This embodiment does not limit the specific method described above.

[0071] When the preset stopping condition is reached, the iteration is complete, and the simulation ends, entering the result output stage. The preset stopping condition can be reaching a preset number of iterations, a preset error convergence threshold, or a preset simulation accuracy requirement; this embodiment does not limit this.

[0072] Step S104: Record the final result surface M and the iteration number N when the iteration is completed, and at the same time, form a set of simulated surfaces A (abbreviated as: surface set A) by recording the simulated surfaces during the iteration process.

[0073] Since the simulated surface of each iteration is recorded, when the HASM program finishes running, the corresponding set of simulated surfaces A, the number of iterations N, and the final result surface M will be obtained. These parameters will be recorded for subsequent use in calculating the confidence interval grid.

[0074] Step S105: Based on the number of iterations and the final result surface, calculate the confidence interval grid using different methods, and output the final result surface and the confidence interval grid.

[0075] The confidence interval grid is used to describe the uncertainty of the final result surface.

[0076] In this embodiment, each iteration is equivalent to one sampling of the simulation process. The number of iterations is the sample size of the simulation process. If the sample size (number of iterations) is sufficient, the confidence interval can be calculated using, for example, the error migration method. If the sample size (number of iterations) is insufficient, the guided method needs to be used to calculate the confidence interval. In this way, based on the number of iterations and the final result surface, different methods are used to calculate the confidence interval grid, which can fully consider the influence of the sample size on the confidence interval and ensure the accuracy and reliability of the confidence interval calculation results.

[0077] Whether the sample size is sufficient can be determined by comparing it with a preset threshold for the number of iterations.

[0078] In this embodiment, the HASM method can not only output the final result surface, but also simultaneously output confidence interval grids to describe the uncertainty of the final result surface. Using the final result surface in conjunction with the confidence interval grids is beneficial for developing more reliable risk measures. At the same time, based on the relationship between the confidence interval grids and the final result surface, the credibility of the simulation results of each grid on the final result surface can be determined. Based on this, further research and corresponding improvement measures can be taken, which will help promote the further development of HASM theory.

[0079] In some embodiments, after acquiring ecological and environmental observation data within the study area, the method further includes: filling in missing values ​​in the ecological and environmental observation data to obtain preprocessed ecological and environmental observation data.

[0080] like Figure 2 As shown, taking temperature observation data as an example, after obtaining temperature observation data from various meteorological stations in the study area, the data is preprocessed, mainly to determine whether there are missing values ​​in the temperature observation data. If a missing record is indeed found, the average value of the non-missing observation records in the same period is used to fill it in.

[0081] Preprocessing the ecological and environmental observation data obtained in the study area before starting the simulation based on the HASM method can improve the quality of the original temperature observation data and thus improve the accuracy of the simulation.

[0082] In some embodiments, confidence interval grids are calculated using different methods based on the number of iterations and the final result surface, including: judging the magnitude of the number of iterations; if the number of iterations is greater than a preset iteration threshold, then the error migration method is used to calculate the confidence interval grid; if the number of iterations is less than or equal to the preset iteration threshold, then the guided method is used to calculate the confidence interval grid.

[0083] As mentioned earlier, the number of iterations is equivalent to the sample size of the simulated surface generated in the simulation process. The number of iterations is used to determine whether the confidence interval grid is calculated. If the number of iterations is greater than the preset iteration threshold, for example, if the number of iterations is greater than 30, the error migration method is used to calculate the confidence interval grid. If the number of iterations is less than or equal to the preset iteration threshold, the guided method is used to calculate the confidence interval grid.

[0084] Figure 3 An example of the error transfer method flow is shown, such as... Figure 3 As shown, the confidence interval grid is calculated using the error migration method, which may include the following steps:

[0085] Step S221: Perform mean processing on all simulated surfaces in the simulated surface set A according to the grid points to obtain the mean surface A1; the mean surface A1 is composed of the mean value at each grid point position. That is, the value of each grid on the mean surface A1 is the mean value of all values ​​of all simulated surfaces in the simulated surface set A at that grid point position.

[0086] Step S222: Calculate the difference between the grid point value of each simulated surface in the simulated surface set and the value of the average surface at that grid point to obtain the simulated error surface set E; that is, subtract the corresponding positions of all simulated surfaces in the surface set A from A1 to obtain the simulated error surface set E.

[0087] Step S223: Calculate the average value of all simulated error surfaces in the simulated error surface set E according to the grid points to obtain the average error surface E1. It should be noted that this averaging process refers to averaging by grid position, that is, averaging the values ​​of all simulated error surfaces corresponding to the same grid point.

[0088] Step S224: Based on the simulated error surface set E and the average error surface E1, calculate the error standard deviation surface S. The calculation formula is as follows:

[0089]

[0090] In the formula, S1 represents the value at a certain grid position on the error standard deviation surface, n is the total number of surfaces in the set of simulated error surfaces, i represents the index of the surface in the set of simulated error surfaces, and x i This represents the value of the i-th surface in the set of simulated error surfaces at this grid position. This represents the value of the average error surface at that grid position.

[0091] Step S225: Based on the mean error surface and the error standard deviation surface, and combined with the preset different confidence levels and the final result surface M, calculate the confidence interval grid for each confidence level.

[0092] Different confidence levels correspond to different error variation ranges. In this embodiment, the confidence levels can be preset to 90%, 95%, and 99%, and the corresponding confidence interval grids can be calculated respectively.

[0093] Specifically, for each grid cell, based on the mean error surface and the error standard deviation surface, and combined with different preset confidence levels, the confidence interval grid cells for each confidence level are calculated, which may include the following steps:

[0094] Step S2211: Based on the values ​​of each grid point on the mean error surface E1 and the error standard deviation surface S, calculate the error variation range of each grid point under different confidence levels. The calculation formula is as follows:

[0095] E 11 ±Z i ×S1 (2)

[0096] Among them, E 11 Z represents the value at a specific grid point on the mean error surface, S1 represents the value at that grid point on the error standard deviation surface, and Z represents the value at that grid point. i Z is the deviation measured by standard deviation at confidence levels of 90%, 95%, and 99%. i The values ​​are 1.64, 1.96, and 2.58, respectively.

[0097] It should be noted that the result of formula (2) is an interval range. For any grid point, its lower limit is: E 11 -Z i ×S1, the upper limit is: E 11 +Z i ×S1.

[0098] Step S2212: Subtract the lower limit of the error variation interval from the upper limit of each error variation interval and then divide by 2 to obtain the error variation limit δ under different confidence levels.

[0099] Step S2213: The difference between each grid point on the final result surface M and the error variation limit at different confidence levels at that grid point is taken as the lower limit of that grid point on the confidence interval grid; the sum of the error variation limits between each grid point on the final result surface and the error variation limit at different confidence levels at that grid point is taken as the upper limit of that grid point on the confidence interval grid, thus obtaining the confidence interval grids at different confidence levels. The corresponding formulas are as follows:

[0100] M1±δ k (3)

[0101] In the formula, M1 is the value of any grid point on the final result surface M, and δ k It is the value of the error variation limit at the grid point position under the k-th confidence level, where k can be any of the confidence levels of 90%, 95%, and 99%.

[0102] If the number of iterations is less than or equal to a preset iteration threshold (e.g., less than 30 iterations), the guided method is used to calculate the confidence interval grid, which may include the following steps:

[0103] Step S331: Perform repeated sampling with replacement on each grid point of the simulated surface set, for example, repeating 1000 times or more, to obtain a sampled surface set AA, where each sampling corresponds to a sampled surface.

[0104] Step S332: Based on the sampling data of each grid point in the sampling surface set AA, calculate the upper and lower bounds of the quantiles of the grid point at different confidence levels to obtain the confidence interval grid.

[0105] For example, for a 95% confidence interval, we can find the quantiles corresponding to the 2.5% and 97.5% of the distribution. These two quantiles are the upper and lower bounds of the confidence interval, respectively. Other confidence levels can be calculated in the same way.

[0106] In summary, the method provided in this embodiment uses the HASM method to iteratively obtain the final result surface M. By recording the surface changes in each iteration during the simulation process, a set of simulated surfaces A is formed. Based on the number of iterations N experienced when HASM completes the iteration, different methods are used to calculate the confidence interval grid. This enables the HASM modeling process to output both the final ecological environment spatial simulation surface grid data (i.e., the final result surface M) and the confidence interval grid of the simulation results, thus obtaining the estimate of the confidence interval, in order to provide more reliable data support for model users (i.e., decision-makers).

[0107] The method provided in this embodiment proposes a confidence interval estimation approach for the spatial modeling process of ecological and environmental observation data using the HASM method. This approach enables spatial simulation of ecological and environmental observation data at any time scale, thereby obtaining spatially continuous ecological and environmental raster data and simultaneously providing the confidence interval of the simulation results.

[0108] The method provided in this embodiment uses error migration and guided methods to estimate the confidence interval in the simulation process under different conditions based on different iteration numbers, making the calculation of the confidence interval more accurate and reliable.

[0109] Exemplary System

[0110] This application provides a high-precision surface modeling system for ecological and environmental elements that considers uncertainties, such as... Figure 4 As shown, the system includes: an acquisition unit 401, an interpolation unit 402, a simulation unit 403, a recording unit 404, and a calculation unit 405. Wherein:

[0111] Acquisition unit 401 is configured to acquire ecological and environmental observation data within the study area;

[0112] Interpolation unit 402 is configured to interpolate ecological and environmental observation data according to a preset target resolution to obtain the initial surface of ecological and environmental elements;

[0113] Simulation unit 403 is configured to use the initial surface as the initial field for high-precision surface modeling HASM, and ecological environment observation data as the optimization control conditions for HASM. It uses the HASM method to perform iterative simulation, and records the changes of the simulation surface in each iteration until the preset stopping condition is reached to complete the iteration.

[0114] Recording unit 404 is configured to record the final result surface and the number of iterations when the iteration is completed, and to form a set of simulated surfaces recorded during the iteration process.

[0115] The calculation unit 405 is configured to calculate the confidence interval grid using different methods based on the number of iterations and the final result surface, and output the final result surface and the confidence interval grid; wherein, the confidence interval grid is used to describe the uncertainty of the final result surface.

[0116] The high-precision surface modeling system for ecological and environmental elements considering uncertainties provided in this embodiment can realize the steps and processes of the high-precision surface modeling method for ecological and environmental elements considering uncertainties provided in any of the above embodiments, and achieve the same technical effect, which will not be described in detail here.

[0117] Exemplary device

[0118] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to some embodiments of this application; such as Figure 5 As shown, the electronic device includes:

[0119] One or more processors 501;

[0120] A computer-readable storage medium may be configured to store one or more programs 502, which, when executed by one or more processors 501, perform the following steps:

[0121] Acquire ecological and environmental observation data within the study area;

[0122] The ecological environment observation data is interpolated according to a preset target resolution to obtain the initial surface of the ecological environment elements;

[0123] The initial surface is used as the initial field for high-precision surface modeling (HASM), and the ecological environment observation data is used as the optimization control conditions for HASM. The HASM method is used for iterative simulation. During the iteration process, the changes of the simulated surface in each iteration are recorded until the preset stopping condition is reached, and the iteration is completed.

[0124] Record the final result surface and the number of iterations when the iteration is completed, and at the same time, combine the simulated surfaces recorded during the iteration process into a set of simulated surfaces;

[0125] Based on the number of iterations and the final result surface, confidence interval grids are calculated using different methods, and the final result surface and the confidence interval grids are output; wherein, the confidence interval grids are used to describe the uncertainty of the final result surface.

[0126] Figure 6 The hardware structure of an electronic device provided according to some embodiments of this application; such as Figure 6 As shown, the hardware structure of the electronic device may include: a processor 601, a communication interface 602, a computer-readable storage medium (also known as a memory) 603, and a communication bus 604.

[0127] The processor 601, communication interface 602, and computer-readable storage medium 603 communicate with each other via communication bus 604.

[0128] Computer-readable storage medium 603 can be configured to store one or more programs.

[0129] Optionally, the communication interface 602 can be an interface of a communication module, such as the interface of a GSM module.

[0130] The processor 601 executes one or more programs that perform the following steps:

[0131] Acquire ecological and environmental observation data within the study area;

[0132] The ecological environment observation data is interpolated according to a preset target resolution to obtain the initial surface of the ecological environment elements;

[0133] The initial surface is used as the initial field for high-precision surface modeling (HASM), and the ecological environment observation data is used as the optimization control conditions for HASM. The HASM method is used for iterative simulation. During the iteration process, the changes of the simulated surface in each iteration are recorded until the preset stopping condition is reached, and the iteration is completed.

[0134] Record the final result surface and the number of iterations when the iteration is completed, and at the same time, combine the simulated surfaces recorded during the iteration process into a set of simulated surfaces;

[0135] Based on the number of iterations and the final result surface, confidence interval grids are calculated using different methods, and the final result surface and the confidence interval grids are output; wherein, the confidence interval grids are used to describe the uncertainty of the final result surface.

[0136] The processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., or it can be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0137] The electronic devices described in this application exist in various forms, including but not limited to:

[0138] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0139] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0140] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes audio and video players (e.g., iPod), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0141] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0142] (5) Other electronic devices with data interaction functions.

[0143] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0144] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code downloaded over a network that is originally stored in a remote recording medium or a non-transitory machine storage medium and will be stored in a local recording medium. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the high-precision surface modeling method for ecological and environmental elements considering uncertainties, as described herein, is implemented. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0145] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0146] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments.

[0147] The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components indicated as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0148] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A high-precision surface modeling method for ecological and environmental elements considering uncertainties, characterized in that, include: Acquire ecological and environmental observation data within the study area; The ecological environment observation data is interpolated according to a preset target resolution to obtain the initial surface of the ecological environment elements; The initial surface is used as the initial field for high-precision surface modeling (HASM), and the ecological environment observation data is used as the optimization control conditions for HASM. The HASM method is used for iterative simulation. During the iteration process, the changes of the simulated surface in each iteration are recorded until the preset stopping condition is reached, and the iteration is completed. Record the final result surface and the number of iterations when the iteration is completed, and at the same time, combine the simulated surfaces recorded during the iteration process into a set of simulated surfaces; Based on the number of iterations and the final result surface, confidence interval grids are calculated using different methods, and the final result surface and the confidence interval grids are output; wherein, the confidence interval grids are used to describe the uncertainty of the final result surface; Based on the number of iterations and the final surface, different methods are used to calculate the confidence interval grid, including: The number of iterations is determined. If the number of iterations is greater than a preset iteration threshold, the confidence interval grid is calculated using the error migration method. If the number of iterations is less than or equal to the preset iteration threshold, the guided method is used to calculate the confidence interval grid. The confidence interval grid is calculated using the error migration method, including: The average value surface is obtained by averaging all the simulated surfaces in the set of simulated surfaces according to the grid points. The difference between the grid point value of each simulated surface in the set of simulated surfaces and the value of the average value surface at that grid point is calculated to obtain the set of simulated error surfaces; The average error surface is obtained by averaging all the simulated error surfaces in the set of simulated error surfaces according to the grid points. Based on the set of simulated error surfaces and the average error surface, the error standard deviation surface is calculated; Based on the average error surface and the error standard deviation surface, combined with preset confidence levels and the final result surface, the confidence interval grid for each confidence level is calculated.

2. The method according to claim 1, characterized in that, Based on the average error surface and the error standard deviation surface, and combined with preset confidence levels and the final result surface, confidence interval grids for each confidence level are calculated, including: Based on the values ​​of each grid point on the average error surface and the error standard deviation surface, the error variation range of each grid point under different confidence levels is calculated. Subtract the lower limit of the error variation interval from the upper limit of each error variation interval and then divide by 2 to obtain the error variation limits at different confidence levels. The difference between the value of each grid point on the final result surface and the error variation limit at different confidence levels at that grid point is taken as the lower limit of that grid point on the confidence interval grid; the sum of the value of each grid point on the final result surface and the error variation limit at different confidence levels at that grid point is taken as the upper limit of that grid point on the confidence interval grid, thus obtaining the confidence interval grid at different confidence levels.

3. The method according to claim 1, characterized in that, The confidence interval grid is calculated using a guided method, including: Each grid point of the simulated surface set is subjected to repeated sampling with replacement to obtain a sampled surface set, with each sampling corresponding to a sampled surface. Based on the sampling data of each grid point in the sampling surface set, the upper and lower bounds of the quantiles of the grid point at different confidence levels are calculated to obtain the confidence interval grid.

4. The method according to any one of claims 1 to 3, characterized in that, The preset iteration threshold is 30 times.

5. The method according to claim 4, characterized in that, After obtaining ecological and environmental observation data within the study area, the following is also included: Missing values ​​are filled into the ecological and environmental observation data to obtain preprocessed ecological and environmental observation data.

6. A high-precision surface modeling system for ecological and environmental elements considering uncertainty, wherein the system, when executed, implements the steps of the method as described in any one of claims 1 to 5, characterized in that, include: The acquisition unit is configured to acquire ecological and environmental observation data within the study area. The interpolation unit is configured to interpolate the ecological environment observation data according to a preset target resolution to obtain the initial surface of the ecological environment elements; The simulation unit is configured to use the initial surface as the initial field for high-precision surface modeling (HASM) and the ecological environment observation data as the optimization control conditions for HASM. It uses the HASM method to perform iterative simulation, and records the changes of the simulation surface in each iteration until the preset stopping condition is reached to complete the iteration. The recording unit is configured to record the final result surface and the number of iterations when the iteration is completed, and to form a set of simulated surfaces recorded during the iteration process. The calculation unit is configured to calculate the confidence interval grid using different methods based on the number of iterations and the final result surface, and output the final result surface and the confidence interval grid; wherein the confidence interval grid is used to describe the uncertainty of the final result surface.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as claimed in any one of claims 1 to 5.

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