A characteristic calculation method and system for groundwater funnel area

Abstract

The present invention relates to a method and system for calculating the characteristics of a groundwater funnel area. The method comprises: calculating an isosurface of the target groundwater funnel area using a Thiessen polygon weighting calculation method based on monitoring point information of the target groundwater funnel area, determining the actual funnel range on the isosurface, and determining the actual funnel surface; performing regional grid distribution calculation on the actual funnel surface using gridding processing and a Kriging interpolation algorithm to generate gridded data; and establishing a three-dimensional model based on the gridded data; and calculating characteristic information of the actual groundwater funnel area based on the three-dimensional model. The method has the following advantages: it is more intuitive than traditional groundwater management systems, and gridding processing makes calculations more accurate. It is suitable for real-time dynamic monitoring in areas across the country where groundwater overexploitation is relatively serious, providing data support for water administration.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater monitoring, and designs a characteristic calculation method and system for a groundwater funnel area. Background Art

[0002] Traditional groundwater funnel analysis models rely solely on empirical analysis and rough estimates, but are unable to accurately calculate or accurately display the shape of the funnel, creating significant challenges for groundwater management. Therefore, it is necessary to develop an analytical calculation method and display system that can accurately calculate the funnel formed in groundwater overexploitation areas. Summary of the Invention

[0003] Based on the above problems, the present invention proposes a feature calculation method for a groundwater funnel area, comprising: based on the monitoring point information of the target groundwater funnel area, calculating the isosurface of the target groundwater funnel area by the Thiessen polygon allocation weight calculation method, and identifying the funnel area on the isosurface by the mateoinfo tool according to the water level trend change, depression structure and boundary characteristics in the isosurface, so as to determine the actual funnel range and determine the actual funnel surface; performing regional grid distribution calculation on the actual funnel surface through gridding processing and Kriging interpolation algorithm to generate gridded data; constructing a three-dimensional model based on the spatial position of each grid unit contained in the gridded data and the numerical value calculated by the Kriging interpolation algorithm, and inputting the three-dimensional model into the Echarts tool to represent the three-dimensional shape of the actual funnel surface that is continuously concave in space; and calculating the feature information of the actual groundwater funnel area according to the three-dimensional model.

[0004] Optionally, the monitoring point information includes: the horizontal and vertical coordinates of the monitoring point in the plane coordinate system, and the temperature and altitude information of the monitoring point.

[0005] Optionally, the isosurface of the target groundwater funnel area is calculated by using a Thiessen polygon allocation weight calculation method, including: dividing the target groundwater funnel area into multiple polygonal areas in a plane based on the Thiessen polygon allocation weight calculation method, and determining the vertex coordinates of each polygonal area in a plane rectangular coordinate system, and calculating the isosurface of the target groundwater funnel area according to the vertex coordinates, and the calculation formula is as follows: in, is the area of ​​the isosurface, and To represent the coordinates of multiple vertices of the polygonal area, and is the specific coordinate value of the vertex coordinate, that is, ( ) is the coordinate of the first vertex, ( ) is the Vertex coordinates, .

[0006] Optionally, the actual funnel surface is subjected to regional grid distribution calculation through gridding processing and Kriging interpolation algorithm, including: gridding the actual funnel surface through gridding processing; interpolating the coordinates of the monitoring points in the plane coordinate system with the Kriging interpolation algorithm for the gridded actual funnel surface, and the interpolation data is the monitoring point information.

[0007] Optionally, characteristic information of the actual groundwater funnel area is calculated based on the three-dimensional model, including: generating a three-dimensional stereoscopic image of the actual groundwater funnel area based on the three-dimensional model, and determining the characteristic information of the target groundwater funnel area based on the value and area of ​​the grid in the three-dimensional stereoscopic image.

[0008] Optional characteristic information, including the shape, area and volume of the actual groundwater funnel area.

[0009] Optionally, the method further includes: obtaining monitoring data of the monitoring point, inputting the monitoring data into a three-dimensional model, displaying the monitoring data based on the three-dimensional model, and generating a three-dimensional chart according to the three-dimensional model based on the monitoring data to display the monitoring data.

[0010] On the other hand, the present invention also proposes a feature calculation system for a groundwater funnel area, comprising: an initial unit, used to calculate the isosurface of the target groundwater funnel area based on the monitoring point information of the target groundwater funnel area through the Thiessen polygon allocation weight calculation method, and perform actual funnel range judgment on the isosurface to determine the actual funnel surface; a calculation unit, used to perform regional grid distribution calculation on the actual funnel surface through grid processing and Kriging interpolation algorithm, generate grid data, and establish a three-dimensional model based on the grid data; an output unit, used to calculate the feature information of the actual groundwater funnel area according to the three-dimensional model.

[0011] On the other hand, the present invention also provides a computing device, comprising: one or more processors; the processor is used to execute one or more programs; when the one or more programs are executed by the one or more processors, the method described above is implemented.

[0012] In another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] The present invention provides a method for calculating the characteristics of a groundwater funnel area, comprising: calculating an isosurface of the target groundwater funnel area using a Thiessen polygon weighting calculation method based on monitoring point information of the target groundwater funnel area, determining the actual funnel range on the isosurface, and determining the actual funnel surface; performing regional grid distribution calculation on the actual funnel surface using gridding processing and a Kriging interpolation algorithm to generate gridded data; and establishing a three-dimensional model based on the gridded data; and calculating characteristic information of the actual groundwater funnel area based on the three-dimensional model. Compared to traditional groundwater management systems, the present invention is more intuitive and uses gridding processing to achieve more accurate calculations. It is suitable for real-time dynamic monitoring in areas across the country with severe groundwater overexploitation, providing data support for water administration. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention;

[0016] Figure 2 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0018] Example 1:

[0019] This paper proposes a characteristic calculation method for groundwater funnel area, such as Figure 1 Shown, including:

[0020] Step 1: Based on the monitoring point information of the target groundwater funnel area, the isosurface of the target groundwater funnel area is calculated by the Thiessen polygon allocation weight calculation method, and the funnel area is identified on the isosurface using the spatial analysis tool or the mateoinfo tool according to the water level trend change, depression structure and boundary characteristics in the isosurface to determine the actual funnel range and the actual funnel surface.

[0021] Here, based on multiple monitoring points set within the target groundwater funnel area, the data, including their horizontal and vertical coordinates, elevation, and burial depth, are collected and integrated within a plane coordinate system to form a spatially directional raw data set. Based on this set of monitoring points, a Thiessen polygon weighting method is used to construct a number of non-overlapping, uniquely defined polygonal regions within the plane, centered around each monitoring point. This provides a preliminary spatial delineation of the target groundwater funnel area. This process automatically determines the vertex coordinates of each polygonal region, and the corresponding spatial area and spatial influence weight are derived accordingly. After completing the spatial delineation, the groundwater level contour surface of the target area is constructed within these polygonal regions by calculating and analyzing the elevation differences and spatial proximity between the monitoring point data. This is a continuous contour map depicting the changing trend of the water level distribution. Subsequently, the actual groundwater funnel area is determined on this generated contour surface. A logical analysis is performed based on the contour line shape, trend direction, and the lowest elevation point. The spatial range with typical concave features within the contour surface is ultimately determined, representing the actual groundwater funnel area.

[0022] From the above, we can see that by using the Thiessen polygon allocation weight calculation method to construct the isosurface, we can effectively solve the problem of distortion of spatial calculation results due to uneven distribution of monitoring points, so that the data of each monitoring point can play a practical role in the corresponding area, and improve the scientific nature of spatial division and the rationality of data analysis. The isosurface is used to directly reflect the changing trend of groundwater levels in the region, and combined with the contour line morphology analysis, the intelligent judgment of the funnel concave area is realized, avoiding the boundary ambiguity and insufficient precision caused by relying on manual judgment in traditional methods. While maintaining the rigor of the calculation logic, it provides a clear judgment basis and spatial structure basis, laying a precise and reliable geographical foundation for subsequent further data modeling and visualization, and helping to more accurately grasp the distribution and evolution boundaries of the funnel area in actual groundwater management.

[0023] Specifically, the monitoring point information includes the horizontal and vertical coordinates of the monitoring point in the plane coordinate system, and the temperature and altitude information of the monitoring point.

[0024] Specifically, the steps for calculating the isosurface of the target groundwater funnel area by using the Thiessen polygon allocation weight calculation method include:

[0025] Based on the Thiessen polygon weight calculation method, the target groundwater funnel area is divided into multiple polygonal areas in the plane, and the vertex coordinates of each polygonal area in the plane rectangular coordinate system are determined. The isosurface of the target groundwater funnel area is calculated according to the vertex coordinates. The calculation formula is as follows:

[0026]

[0027] Where, is the area of ​​the isosurface, and To represent the coordinates of multiple vertices of the polygonal area, and is the specific coordinate value of the vertex coordinate, that is, ( ) is the coordinate of the first vertex, ( ) is the Vertex coordinates, .

[0028] Step 2: Perform regional grid distribution calculation on the actual funnel surface through gridding processing and Kriging interpolation algorithm to generate gridded data; construct a three-dimensional model based on the spatial position of each grid unit contained in the gridded data and the numerical value calculated by the Kriging interpolation algorithm, and input the three-dimensional model into the Echarts tool to represent the three-dimensional shape of the actual funnel surface that is continuously concave in space.

[0029] Here, a continuous and uniform grid distribution calculation is performed for the funnel surface area through gridding and kriging interpolation. First, the two-dimensional plane region where the actual funnel surface lies is gridded. This spatial region is divided into multiple regular grid cells of equal size according to preset rules and spacing. Each grid cell corresponds to a spatial subregion, thus forming a structured spatial distribution framework. This not only improves the regularity and consistency of subsequent data processing but also provides a clear coordinate positioning basis for interpolation calculations. Subsequently, the monitoring point data is interpolated and filled in using the kriging interpolation algorithm within these grid cells. Specifically, based on the coordinates of the monitoring points within the funnel surface area and their water level data, the kriging interpolation algorithm calculates the corresponding attribute value for each grid node location according to the spatial correlation between their geographical locations. The interpolation process comprehensively considers the spatial autocovariance structure and distance decay of the data, resulting in smoother and more geologically meaningful interpolation results, ensuring a continuous transition of grid node data across the entire funnel surface area and a close fit with the measured trends. Finally, the interpolation results obtained are unified and integrated to generate a gridded dataset containing the spatial position of each grid cell and its water level or elevation value, providing complete spatial numerical input for the subsequent modeling process.

[0030] From the above, it can be seen that the spatial structure inside the actual funnel surface area can be modeled with high precision, solving the data gap problem caused by the discrete and uneven distribution of monitoring points, and effectively making up for the lack of spatial continuity of the measured information. Gridding processing is used to make the spatial expression inside the region more regular and analytically operable, and the use of Kriging interpolation has the advantage of handling spatial heterogeneity and directional correlation, so that the interpolation results not only meet the requirements of mathematical fitting, but also have more geological spatial interpretation significance. The generated grid data has clear coordinate attributes and numerical description capabilities, and can truly and continuously express the spatial morphological changes of the actual funnel surface, providing solid data support for the subsequent construction of a three-dimensional model based on real terrain undulations and water level distribution characteristics, so that the complex morphology of the actual groundwater funnel area can be digitally and structurally expressed.

[0031] Specifically, the steps of calculating the regional grid distribution of the actual funnel surface through grid processing and Kriging interpolation algorithm include:

[0032] The actual funnel surface is meshed through meshing processing.

[0033] For the actual funnel surface divided by gridding, the coordinates of the monitoring points in the plane coordinate system are interpolated using the Kriging interpolation algorithm, and the interpolation data is the monitoring point information.

[0034] Based on the above description, the actual funnel surface is first gridded. Based on the actual two-dimensional spatial boundary of the funnel surface, it is divided into a number of regular or semi-regular grid cells in a plane coordinate system. Each grid cell corresponds to a fixed coordinate location and spatial region. A gridding strategy of equidistant horizontal and vertical grid points is used to ensure complete coverage of the entire funnel surface and maintain the structural consistency and node addressability required for subsequent interpolation operations. After gridding is completed, the monitoring point information is interpolated and filled in within the funnel area covered by this gridded structure using the kriging interpolation algorithm. Using the coordinates of the grid points in the plane coordinate system as the target location, the kriging interpolation mathematical model is used to calculate the fitted value at each grid point, combining the spatial coordinates of the actual monitoring points and the corresponding water level values. Kriging interpolation is a geostatistical method that accounts for spatial autocorrelation. By constructing a covariance function, it assigns different weights to the data of neighboring monitoring points. The result for each interpolated grid point is synthesized based on factors such as distance and variance structure, thereby achieving a continuous numerical reconstruction of the internal space of the actual funnel surface.

[0035] As can be seen from the above, the combination of gridding and Kriging interpolation achieves high-resolution spatial filling and continuous representation of the actual groundwater funnel surface area, effectively overcoming the risk of interpolation error caused by the limited number and uneven distribution of monitoring points. This gives subsequent calculation operations a regular structure, facilitating batch operations and graphical visualization. Kriging interpolation scientifically models the spatial relationships between monitoring points based on statistical laws, ensuring smoothness while retaining actual trends in the interpolation results, improving the model's geographic accuracy and hydrological interpretation capabilities.

[0036] Step 3: Calculate the characteristic information of the actual groundwater funnel area based on the three-dimensional model.

[0037] Here, the three-dimensional model is based on the spatial grid data generated by the previous Kriging interpolation algorithm, and combined with the horizontal and vertical coordinates, elevation values, or groundwater level values ​​of each grid cell, a three-dimensional data structure is constructed to fully reflect the actual changes in the funnel surface topography. Through the geometric dimensions and numerical attributes of each grid cell in the three-dimensional model, the morphological outline of the funnel surface in three-dimensional space can be automatically extracted. Subsequently, relying on the clearly defined grid points and boundary clues in the three-dimensional model, numerical integration and geometric calculation operations are performed on the entire funnel surface area according to the set rules, and then a number of key spatial characteristic indicators including but not limited to the horizontal projection area of ​​the funnel area, the true surface area of ​​the concave surface, the length of the boundary enclosure contour, and the regional envelope volume are accurately calculated. The above-mentioned characteristic data is obtained based on the actual model and no longer relies on artificial assumptions or plane simplifications. It can fully reflect the geometric shape and water level depression of the groundwater funnel area at the current time node, becoming an important basis for quantitative analysis of the current status of groundwater overexploitation.

[0038] As can be seen from the above, the digital and precise extraction of indicators such as the shape, area, and volume of the groundwater funnel area is achieved, effectively avoiding the errors caused by simplified drawings, blurred boundaries, or manual estimation in traditional methods. The three-dimensional structural information and spatial hierarchy of the 3D model enable the feature extraction process to fully account for complex factors such as topographical undulations and water level gradients, resulting in more accurate and timely results. Furthermore, this feature information calculation process supports automation and batch operation, allowing the construction of multiple 3D models at different time points and comparative analysis, thus providing groundwater managers with a more consistent basis for assessing evolving trends. This calculation process not only clearly reveals the current status of the funnel area but also provides data support for future water resource allocation, water replenishment measures, and dynamic monitoring. Furthermore, the 3D model is updated based on monitoring point information on a polygonal basis. With each update, the polygonal areas that have changed are updated, while the polygonal areas that have not changed are locked.

[0039] Specifically, the steps for calculating characteristic information of the actual groundwater funnel area based on the three-dimensional model include:

[0040] Based on the three-dimensional model, a three-dimensional image of the actual groundwater funnel area is generated, and the characteristic information of the target groundwater funnel area is determined according to the value and area of ​​the grid in the three-dimensional image.

[0041] Based on the above specific description, a visual 3D image is generated based on the spatial grid points in the 3D model and their corresponding water level elevations or interpolated values. This 3D image faithfully reproduces the concave shape and overall distribution pattern of the target groundwater funnel area in space, clearly displaying geometric features such as the funnel area's boundaries, central depression, and slope variations, providing a graphical basis for subsequent parameter extraction. Subsequently, within the regular grid structure constructed within the 3D image, the area information and vertical amplitude of each grid cell are sequentially extracted, such as water level depth, displacement, or simulated elevation difference. By performing point-by-point accumulation or integration of the values ​​and areas of all grid cells, multiple geometric parameter characteristics of the entire funnel area are obtained. Finally, by integrating the spatial data of each grid area, characteristic information of the actual groundwater funnel area at the current time is extracted, including but not limited to the funnel's overall outline shape, horizontally projected area, concave volume, and the location of the lowest central point. This information, derived from model calculations, has physical correspondence and engineering interpretation value.

[0042] As shown above, by extracting characteristic information about the actual funnel area based on the 3D model, a quantitative representation of the groundwater depression is achieved at the geometric level. This allows the spatial morphology of the funnel area to be presented intuitively in a three-dimensional form, no longer limited to 2D contour interpretation. This allows for a more realistic fit and spatial resolution. Compared to traditional methods that rely on simplified judgments based on planar estimation or contour projection areas, the 3D model incorporates a cumulative calculation strategy based on grid values ​​and grid areas. This allows for the acquisition of key indicators such as the true coverage area of ​​the entire funnel area, the distribution of depression depths, and volumetric change trends, significantly improving the accuracy and reliability of parameter calculations.

[0043] Specifically, the characteristic information includes the shape, area and volume of the actual groundwater funnel area.

[0044] Furthermore, the feature calculation method further includes:

[0045] Acquire monitoring data from monitoring points, input the monitoring data into a three-dimensional model, display the monitoring data based on the three-dimensional model, and generate a three-dimensional chart based on the monitoring data according to the three-dimensional model to display the monitoring data.

[0046] In the present invention, the information of monitoring points is mainly used to calculate the isosurface by assigning weights through Thiessen polygons, and then the regional grid distribution calculation is completed through gridding and Kriging interpolation. After the monitoring area is circled, the funnel area is three-dimensionally displayed through a three-dimensional model and the area and volume of the corresponding groundwater over-exploitation are calculated. Compared with traditional groundwater management systems, this system is more vivid and intuitive, and the calculation is more accurate through grid processing. It is suitable for real-time dynamic supervision in areas across the country where groundwater over-exploitation is relatively serious, providing data support for water administration.

[0047] In the present invention, the shape, area, volume, etc. of the groundwater funnel area are calculated respectively by combining the monitoring data and historical data collected in real time through a model calculation method, and are presented in the form of a three-dimensional model. Combined with the three-dimensional model of the historical groundwater funnel area, we can have an intuitive understanding of the evolution process of the groundwater funnel area, and we can also use this evolution process to test whether the effectiveness of the groundwater control and restoration measures has been achieved.

[0048] The main advantages of this invention include: more accurate calculation of the funnel area; a productized funnel area model that enables a dynamic scrolling display of changes, more clearly showing the trend of the funnel area; and a more intuitive three-dimensional display of the funnel area.

[0049] The algorithm used in the present invention is described below:

[0050] Thiessen polygon calculation formula:

[0051] The Thiessen polygon method is a spatial analysis method that primarily partitions a set of points on a plane into multiple polygonal regions, where each polygon contains a point, and the distance from any point within a polygon to a point within that polygon is less than the distance to any other point within the polygon. While there's no single formula to describe the Thiessen polygon method, its construction and calculation involve some geometric and distance calculation formulas. The following is a brief introduction:

[0052] Distance formula:

[0053] In a rectangular coordinate system, two points , the calculation formula of the Euclidean distance d between them is: When constructing Thiessen polygons, this formula is needed to calculate the distance between points to determine which lines between points constitute the edges of the Thiessen polygons.

[0054] Perpendicular bisector equation:

[0055] For two points , the coordinates of the midpoint of their connecting line are .

[0056] The slope of line AB , then the slope k of the perpendicular bisector of AB is .

[0057] Using the point-slope form, we can get the equation of the perpendicular bisector AB as The edges of Thiessen polygons are composed of partial segments of these perpendicular bisectors.

[0058] Area calculation:

[0059] For each polygonal area divided by Thiessen polygons, if its vertex coordinates are known ··· , its area S can be calculated using the following formula. The formula is .

[0060] mateoinfo:

[0061] Use the mateoinfo tool to determine the funnel range on the isosurface. The specific operation process is as follows:

[0062] Data import and preprocessing: Import the isosurface data into the mateoinfo tool. After importing the data, perform necessary preprocessing operations, including data cleaning to remove noise points and outliers. These noise and outliers may be caused by measurement errors, sensor failures, or other reasons, which may affect subsequent analysis results. At the same time, standardize the data to make different types of data have a unified dimension and scale, which facilitates subsequent calculations and analysis.

[0063] Set criteria: In the mateoinfo tool, define criteria based on the characteristics of a funnel and your research needs. These criteria include the data's numerical range, changing trends, and spatial relationships. For example, to identify a funnel, you can define a gradual decrease in data values ​​within a certain area, with the rate of decrease following a specific pattern. You can also define a specific spatial geometric shape, such as an inverted cone. By setting these criteria, you can accurately filter out data that meets the characteristics of a funnel.

[0064] Execute the judgment operation: After completing data preprocessing and setting judgment criteria, perform the funnel range judgment operation in the mateoinfo tool. The tool will analyze and judge the imported data one by one according to the set conditions. During the analysis process, the tool uses its built-in algorithms and models to perform complex calculations and processing on the data. For example, spatial analysis algorithms calculate the distance and angle relationships between data points; numerical analysis algorithms determine the changing trends and patterns of the data. Based on the analysis results, the tool determines which data points fall within the funnel range and marks them.

[0065] Result output: After the judgment operation is completed, the mateoinfo tool will output the judgment results in an intuitive way. The output is in the form of text files and graphic files, which is convenient for subsequent data processing and analysis.

[0066] Kriging interpolation calculation formula:

[0067] Suppose there are n sampling points (xi,yi,zi), where xi and yi are the horizontal and vertical coordinates of the sampling points, and zi is the attribute value of the sampling point (such as temperature, altitude, etc.). Then the interpolation result z(x,y) at (x,y) can be expressed as:

[0068] z(x,y)=∑i=1nλi(x,y)zi

[0069] Where λi(xy) is the weight coefficient, which represents the contribution of the i-th sampling point to the interpolation result at (x,y). λi(x,y) can be calculated using the following formula:

[0070] λi(x,y)=∑j=lnwijl∑j=lnwij

[0071] Where w is a function of the spatial distance between the i-th sampling point and the j-th sampling point, usually a Gaussian function or an exponential function, and its formula is as follows:

[0072] wij=exp(-2h2dij2)

[0073] Among them, dij represents the spatial distance between the i-th sampling point and the j-th sampling point, and h is the interpolation parameter used to adjust the smoothness of the interpolation result.

[0074] Funnel 3D simulation:

[0075] Echarts is an open-source JavaScript-based visualization library with 3D model display capabilities. Echarts allows you to create a variety of highly interactive 3D charts and model display effects. It can be closely integrated with web applications for easy display on web pages, and has good cross-platform compatibility.

[0076] This invention analyzes and calculates the current widespread overexploitation of groundwater, providing a scientific basis for decision-making on groundwater use and replenishment. It is expected to play a role in visualizing, warning, and trend forecasting groundwater management nationwide, providing a scientific basis for preventing natural disasters such as surface subsidence.

[0077] Example 2:

[0078] The present invention also proposes a characteristic calculation system 200 for groundwater funnel area, such as Figure 2 Shown, including:

[0079] The initial unit 201 is used to calculate the isosurface of the target groundwater funnel area based on the monitoring point information of the target groundwater funnel area by using the Thiessen polygon allocation weight calculation method, and to determine the actual funnel range on the isosurface to determine the actual funnel surface.

[0080] The calculation unit 202 is used to perform regional grid distribution calculation on the actual funnel surface through grid processing and Kriging interpolation algorithm to generate grid data, and establish a three-dimensional model based on the grid data.

[0081] The output unit 203 is used to calculate characteristic information of the actual groundwater funnel area based on the three-dimensional model.

[0082] Compared with traditional groundwater management systems, the present invention is more vivid and intuitive, and makes calculations more accurate through grid processing. It is suitable for real-time dynamic supervision in areas across the country where groundwater overexploitation is relatively serious, and provides data support for water administration.

[0083] Example 3:

[0084] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the method in the above embodiment.

[0085] Example 4:

[0086] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor may load and execute the one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above-described embodiment.

[0087] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0091] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A characteristic calculation method for groundwater funnel area, characterized in that: include: Based on the monitoring point information of the target groundwater funnel area, the isosurface of the target groundwater funnel area is calculated by the Thiessen polygon allocation weight calculation method. According to the water level trend change, depression structure and boundary characteristics in the isosurface, the funnel area is identified on the isosurface using the mateoinfo tool to determine the actual funnel range and the actual funnel surface; Performing regional grid distribution calculation on the actual funnel surface through gridding processing and Kriging interpolation algorithm to generate grid data; constructing a three-dimensional model based on the spatial position of each grid cell contained in the grid data and the numerical value calculated by the Kriging interpolation algorithm, and inputting the three-dimensional model into the Echarts tool to represent the three-dimensional shape of the actual funnel surface that is continuously concave in space; Based on the three-dimensional model, characteristic information of the actual groundwater funnel area is calculated.

2. The feature calculation method according to claim 1, wherein: The monitoring point information includes the horizontal and vertical coordinates of the monitoring point in the plane coordinate system, and the buried depth and elevation information of the monitoring point.

3. The feature calculation method according to claim 1, wherein: The step of calculating the isosurface of the target groundwater funnel area by using the Thiessen polygon allocation weight calculation method includes: Based on the Thiessen polygon weight calculation method, the target groundwater funnel area is divided into multiple polygonal areas in the plane, and the vertex coordinates of each polygonal area in the plane rectangular coordinate system are determined. The area of ​​the isosurface of the target groundwater funnel area is calculated according to the vertex coordinates. The calculation formula is as follows: in, is the area of ​​the isosurface, and To represent the coordinates of multiple vertices of the polygonal area, and is the specific coordinate value of the vertex coordinate, that is, ( ) is the coordinate of the first vertex, ( ) is the Vertex coordinates, .

4. The feature calculation method according to claim 1, wherein: The step of performing regional grid distribution calculation on the actual funnel surface through grid processing and Kriging interpolation algorithm includes: Meshing the actual funnel surface by meshing processing; For the actual funnel surface divided by gridding, the coordinates of the monitoring points in the plane coordinate system are interpolated using the Kriging interpolation algorithm, and the interpolation data is the monitoring point information.

5. The feature calculation method according to claim 1, wherein: The step of calculating characteristic information of the actual groundwater funnel area based on the three-dimensional model includes: Based on the three-dimensional model, a three-dimensional image of the actual groundwater funnel area is generated, and characteristic information of the target groundwater funnel area is determined according to the values ​​and areas of the grids in the three-dimensional image.

6. The feature calculation method according to claim 1, wherein: The characteristic information includes the shape, area and volume of the actual groundwater funnel area.

7. The feature calculation method according to claim 1, wherein: Also includes: Acquire monitoring data of the monitoring points, input the monitoring data into a three-dimensional model, display the monitoring data based on the three-dimensional model, and generate a three-dimensional chart based on the monitoring data according to the three-dimensional model to display the monitoring data.

8. A system for implementing the feature calculation method according to any one of claims 1 to 7, characterized in that: include: The initial unit is used to calculate the isosurface of the target groundwater funnel area based on the monitoring point information of the target groundwater funnel area by using the Thiessen polygon allocation weight calculation method, and to determine the actual funnel range on the isosurface to determine the actual funnel surface; A calculation unit is used to perform regional grid distribution calculation on the actual funnel surface through grid processing and Kriging interpolation algorithm to generate grid data, and establish a three-dimensional model based on the grid data; The output unit is used to calculate the characteristic information of the actual groundwater funnel area based on the three-dimensional model.

9. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the feature calculation method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the feature calculation method according to any one of claims 1 to 7 is implemented.

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