Soil-groundwater organic pollutant spatial distribution multi-element visualization technology and method

Abstract

The application is suitable for the technical field of environmental geological information visualization modeling, and provides a soil-groundwater organic pollutant spatial distribution multi-element volume visualization technical method, which comprises the following steps: taking a GBIM data management and a WebGL visualization cloud platform as a basic platform, integrating multiple function modules, coupling three-dimensional geological structure online modeling technology and organic pollutant migration simulation technology to realize online modeling of a pollution site, based on three-dimensional scene volume visualization technology, assigning different colors and transparencies to each volume pixel in the volume model through a light projection algorithm, and adjusting the visibility of multiple elements in the rendering process by using different conversion functions to obtain the spatial distribution effect of the surface and the interior of the organic pollutant. Therefore, the migration track and the multi-element distribution characteristics of the pollutant in the geological body can be more clearly displayed, and scientific, intuitive and dynamic decision support can be provided for site pollution assessment and management scheme design.

Description

Technical Field

[0001] The present application belongs to the field of environmental geological information visualization modeling technology, and in particular relates to a multi-factor volume visualization technology method for the spatial distribution of soil-groundwater organic pollutants. Background Art

[0002] During site environmental surveys and pollution assessments, the spatial distribution and migration of organic pollutants in soil and groundwater are key factors influencing the development of remediation strategies. Related technologies typically use two-dimensional graphic overlays or cross-sectional rendering to display the distribution of pollutants in soil and groundwater. These methods lack a direct representation of the complex three-dimensional structure of the subsurface and cannot dynamically reflect the migration of pollutants within the soil-water medium. Consequently, these methods can make it difficult to identify the internal structure of pollutants and inaccurately determine risk areas. Summary of the Invention

[0003] The embodiment of the present application provides a multi-factor volumetric visualization technology method for the spatial distribution of organic pollutants in soil and groundwater, which can solve the problems of difficulty in identifying the internal structure of pollutants and inaccurate risk area judgment caused by the current use of two-dimensional graphic overlay or cross-sectional rendering to display the distribution of pollutants in soil and groundwater.

[0004] In a first aspect, an embodiment of the present application provides a multi-factor volume visualization technology method for the spatial distribution of organic pollutants in soil and groundwater, including: S1, collecting geological structure data and pollutant monitoring data of the target site; S2, using the GBIM cloud data management system to integrate and manage the geological structure data and pollutant monitoring data; S3, processing the integrated pollutant monitoring data according to the three-dimensional spatial interpolation method to obtain continuous spatial data, and converting the continuous spatial data into the data format required by the organic pollutant spatial distribution modeling and model visualization platform; S4, based on the continuous spatial data, calling the Internet Geographic Information System (WebGIS) and three-dimensional online modeling technology to construct a multi-factor model of the spatial distribution of organic pollutants in soil and groundwater; S5, based on the model constructed in step S4, dividing each geological unit grid into voxels and assigning corresponding attribute values ​​to establish a three-dimensional volume data model; S6, a dynamic volume visualization method based on the network graphics library WebGL, assigning color and opacity to each voxel of the three-dimensional volume data model according to a preset conversion function in the ray casting algorithm, so as to convert the three-dimensional volume data model into a three-dimensional volume visualization image, thereby realizing the volume visualization expression of the multi-factor spatial information of organic pollutants in soil and groundwater.

[0005] In a possible implementation of the first aspect, step S2 includes:

[0006] The GBIM cloud data management system is used to divide geological structure data and pollutant monitoring data into two types: offline image terrain service data and site and user-related data;

[0007] Use MinIO, an open source distributed object storage service, to store and publish offline image terrain service data;

[0008] The PostgreSQL database is used to store venue and user-related data.

[0009] Optionally, in another possible implementation of the first aspect, the three-dimensional space interpolation method in step S3 is as follows:

[0010] Using 3D hypersurface spline function to perform 3D space interpolation, expression of 3D hypersurface spline function

[0011] The formula is:

[0012] (1)

[0013] Where W is the attribute value at the target interpolation point (x, y, z) in space, , , , , F i is the unknown coefficient, n is the number of monitoring points where pollutant monitoring data are collected, To adjust the empirical parameters of the surface curvature, r i Interpolate the target point (x, y, z) to the i-th monitoring data point The Euclidean distance between The expression is:

[0014] (2)

[0015] 、 、 、 、 F i By solving formula (3), we can obtain:

[0016] (3)

[0017] Where c j is the weighted value of the smoothness of the surface, c j =16 D / k j , D is the bending stiffness, k j is the elastic constant about point j, is the Euclidean distance from point j to point i;

[0018] Let c j =0, so that the obtained three-dimensional hypersurface spline function is consistent with the pollutant monitoring data after integrated management at the known points. The matrix expression of formula (3) is:

[0019] (4)

[0020] Where, (5)

[0021] (6)

[0022] Where T represents the matrix transpose.

[0023] Optionally, in another possible implementation of the first aspect, step S4 includes:

[0024] Based on the 3D GIS map in WebGIS, the site model boundary is selected and the points within the boundary are encrypted to obtain multiple encrypted points;

[0025] Based on the site model boundary, multiple encryption points and borehole data, grid generation is performed to obtain the basic network structure;

[0026] Match and fuse the stratigraphic layer file with the basic network structure to generate a three-dimensional stratigraphic model corresponding to the stratigraphic layer of the pollutant migration model;

[0027] Select characteristic pollutants corresponding to solute transport analysis based on the actual pollution data of the site;

[0028] Set the initial conditions of the pollutant transport model and uniformly add and manage the material parameters in the model;

[0029] The geological unit grid of the three-dimensional stratigraphic model is partitioned and assigned values, point boundary conditions and surface boundary conditions of the pollutant transport simulation model are added centrally, and multiple encrypted points are used as pollutant leakage points;

[0030] The pollutant migration model is solved according to the preset calculation parameters to obtain simulation result data, and a multi-factor model of the spatial distribution of soil-groundwater organic pollutants is constructed based on the simulation result data.

[0031] Optionally, in another possible implementation of the first aspect, the preset conversion function in the ray casting algorithm in step S6 is specifically as follows:

[0032] (7)

[0033] (8)

[0034] (9)

[0035] (10)

[0036] in, is the sampling value of the light, is the scalar value of volume V, is the opacity, s is the total number of images in the mosaic, is the color space, For a given The color is defined by the transfer function, is the light factor, is the color of any pixel in the k-th step of the light, is the alpha component of the pixel in ray step k that is set to 1 at the end of the rendering process.

[0037] On the second aspect, the embodiment of the present application provides a multi-factor volume visualization technology device for the spatial distribution of organic pollutants in soil and groundwater, including: an acquisition module for acquiring geological structure data and pollutant monitoring data of the target site; an integrated management module for integrating and managing geological structure data and pollutant monitoring data using the GBIM cloud data management system; a first conversion module for processing the integrated pollutant monitoring data according to a three-dimensional spatial interpolation method to obtain continuous spatial data, and converting the continuous spatial data into the data format required by the organic pollutant spatial distribution modeling and model visualization platform; a construction module for calling the Internet geographic information system based on the continuous spatial data. The information system WebGIS and three-dimensional online modeling technology are used to construct a multi-factor model of the spatial distribution of soil-groundwater organic pollutants; a module is established to divide each geological unit grid into voxels based on the multi-factor model of the spatial distribution of soil-groundwater organic pollutants, and assign corresponding attribute values ​​to establish a three-dimensional volume data model; the second conversion module is used to assign color and opacity to each voxel of the three-dimensional volume data model according to the preset conversion function in the ray casting algorithm based on the dynamic volume visualization method of the network graphics library WebGL, so as to convert the three-dimensional volume data model into a three-dimensional volume visualization image, thereby realizing the volume visualization expression of the multi-factor spatial information of soil-groundwater organic pollutants.

[0038] Beneficial Effects: This application achieves high-precision modeling and visualization of pollutants in underground 3D media by integrating WebGIS 3D online modeling technology with WebGL-based volumetric visualization methods. Using a ray casting algorithm combined with multiple preset transformation functions, the transparency and color of pollutants in 3D images can be adjusted based on attribute values, accurately reflecting the surface and internal distribution of pollutants. Compared to traditional 2D or surface rendering methods, this application can more clearly demonstrate the migration trajectory and multi-factor distribution characteristics of pollutants within geological structures, providing scientific, intuitive, and dynamic decision-making support for site pollution assessment and remediation plan design. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 This is a flow chart of a multi-factor volume visualization technology method for spatial distribution of organic pollutants in soil and groundwater provided by an embodiment of the present application;

[0041] Figure 2 This is a visualization effect diagram of a mixture of two pollutants provided by an embodiment of the present application;

[0042] Figure 3 This is a spatial distribution map of pollutants within the indicator threshold range provided in one embodiment of the present application;

[0043] Figure 4 This is a volumetric image that integrates visualization and post-processing of multiple models provided by an embodiment of the present application;

[0044] Figure 5 It is a structural schematic diagram of a multi-factor volume visualization technology device for spatial distribution of soil-groundwater organic pollutants provided in one embodiment of the present application. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0046] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0047] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0048] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0049] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0051] The following describes in detail the multi-factor volume visualization technology method, device, terminal equipment and storage medium for the spatial distribution of soil-groundwater organic pollutants provided by this application with reference to the accompanying drawings.

[0052] Figure 1 A flow chart of a multi-factor volume visualization technology method for spatial distribution of organic pollutants in soil and groundwater provided in an embodiment of the present application is shown.

[0053] like Figure 1 As shown, the multi-factor volume visualization technology method for spatial distribution of soil-groundwater organic pollutants includes the following steps:

[0054] S1. Collect geological structure data and pollutant monitoring data of the target site;

[0055] As a possible implementation method, the geological structure data in the above step S1 includes the hydrogeological conditions of the site, the spatial distribution of aquifers and aquicludes, lithology, rock layer thickness, groundwater recharge and drainage conditions, groundwater level dynamics, soil and groundwater monitoring data.

[0056] S2. Use the GBIM cloud data management system to integrate and manage geological structure data and pollutant monitoring data;

[0057] Furthermore, in the embodiment of the present application, the above step S2 includes:

[0058] The GBIM cloud data management system is used to divide geological structure data and pollutant monitoring data into two types: offline image terrain service data and site and user-related data;

[0059] Use MinIO, an open source distributed object storage service, to store and publish offline image terrain service data;

[0060] The PostgreSQL database is used to store venue and user-related data.

[0061] S3. Process the integrated pollutant monitoring data using a three-dimensional spatial interpolation method to obtain continuous spatial data, and convert the continuous spatial data into the data format required by the organic pollutant spatial distribution modeling and model visualization platform;

[0062] Specifically, the three-dimensional space interpolation method in step S3 is as follows:

[0063] Using 3D hypersurface spline function to perform 3D space interpolation, expression of 3D hypersurface spline function

[0064] The formula is:

[0065] (1)

[0066] Where W is the attribute value at the target interpolation point (x, y, z) in space, such as the temperature in the temperature field, the flow velocity in the flow velocity field, etc. , , , , F i ( i =1,2,..., n ) is the unknown coefficient, This is an empirical parameter for adjusting the curvature of the surface. The characteristics of this value are the same as those in the surface spline function expression.r i Interpolate the target point (x, y, z) to the i-th monitoring data point The Euclidean distance between The expression is:

[0067] (2)

[0068] above 、 、 、 、 F i ( i =1,2,..., n ) is obtained by solving formula (3):

[0069] (3)

[0070] Where c j is the weighted value of the smoothness of the surface, c j =16 D / k j , D is the bending stiffness, k j is the elastic constant about point j, if ,but , is the Euclidean distance from point j to point i;

[0071] Let c j =0, so that the obtained three-dimensional hypersurface spline function is consistent with the pollutant monitoring data after integrated management at the known points. The matrix expression of formula (3) is:

[0072] (4)

[0073] Where, (5)

[0074] (6)

[0075] Where T represents the matrix transpose.

[0076] S4. Based on continuous spatial data, we use the Internet Geographic Information System (WebGIS) and 3D online modeling technology to construct a multi-factor model of the spatial distribution of organic pollutants in soil and groundwater;

[0077] Furthermore, in the embodiment of the present application, the above step S4 includes:

[0078] Based on the 3D GIS map in WebGIS, the site model boundary is selected and the points within the boundary are encrypted to obtain multiple encrypted points;

[0079] Based on the site model boundary, multiple encryption points and borehole data, grid generation is performed to obtain the basic network structure;

[0080] Match and fuse the stratigraphic layer file with the basic network structure to generate a three-dimensional stratigraphic model corresponding to the stratigraphic layer of the pollutant migration model;

[0081] Select characteristic pollutants corresponding to solute transport analysis based on the actual pollution data of the site;

[0082] Set the initial conditions of the pollutant transport model and uniformly add and manage the material parameters in the model;

[0083] The geological unit grid of the three-dimensional stratigraphic model is partitioned and assigned values, point boundary conditions and surface boundary conditions of the pollutant transport simulation model are added centrally, and multiple encrypted points are used as pollutant leakage points;

[0084] The pollutant migration model is solved according to the preset calculation parameters to obtain simulation result data, and a multi-factor model of the spatial distribution of soil-groundwater organic pollutants is constructed based on the simulation result data.

[0085] S5. Based on the model constructed in step S4, each geological unit grid is divided into voxels and assigned corresponding attribute values ​​to establish a three-dimensional volume data model;

[0086] S6. A dynamic volume visualization method based on the WebGL web graphics library assigns color and opacity to each voxel of the 3D volume data model according to the preset conversion function in the ray casting algorithm to convert the 3D volume data model into a 3D volume visualization image, thereby realizing the volume visualization expression of multiple elements of the spatial information of soil-groundwater organic pollutants.

[0087] Specifically, the preset conversion function in the ray casting algorithm in step S6 is as follows:

[0088] (7)

[0089] (8)

[0090] (9)

[0091] (10)

[0092] in, is the sampling value of the light, is the scalar value of volume V, is the opacity, s is the total number of images in the mosaic, is the color space, For a given The color is defined by the transfer function, is the light factor, is the color of any pixel in the k-th step of the light, is the alpha component of the pixel in ray step k that is set to 1 at the end of the rendering process.

[0093] The present application provides a multi-factor volume visualization technology method for the spatial distribution of organic pollutants in soil and groundwater. The method first collects geological structure data and pollutant monitoring data of the target site, then integrates and manages the geological structure data and pollutant monitoring data using the GBIM cloud data management system. The integrated pollutant monitoring data is then processed using a three-dimensional spatial interpolation method to obtain continuous spatial data, which is then converted into the data format required by the organic pollutant spatial distribution modeling and model visualization platform. Based on the continuous spatial data, the Internet Geographic Information System (WebGIS) and three-dimensional online modeling technology are then called to construct a multi-factor model for the spatial distribution of organic pollutants in soil and groundwater. Based on the multi-factor model for the spatial distribution of organic pollutants in soil and groundwater, each geological unit grid is divided into voxels and assigned corresponding attribute values ​​to establish a three-dimensional volume data model. Finally, based on a dynamic volume visualization method using the WebGL web graphics library, a color and opacity are assigned to each voxel of the three-dimensional volume data model according to a preset conversion function in the ray casting algorithm to convert the three-dimensional volume data model into a three-dimensional volume visualization image, thereby realizing a volume visualization expression of the multi-factor spatial information of high-risk organic pollutants in soil and groundwater. In this way, the migration trajectory and multi-factor distribution characteristics of pollutants within geological bodies can be more clearly demonstrated, providing scientific, intuitive and dynamic decision-making support for site pollution assessment and treatment plan design.

[0094] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0095] Another embodiment will be used below to illustrate the multi-factor volume visualization technology method for the spatial distribution of soil-groundwater organic pollutants provided by this application.

[0096] 1) Conceptual Model

[0097] The 3D visualization software system for fine-grained characterization of DNAPLs contamination in soil and groundwater incorporates a variety of pollutant attributes, including stratigraphic properties, gas saturation, water saturation, NAPL phase saturation, mass fraction of organic pollutants in the water phase, gas phase density, and NAPL phase density. Therefore, a DNAPLs migration and diffusion model was designed. The calculated results include information such as the concentration and saturation of each phase of the pollutants in soil and groundwater. Because the calculation process does not require detailed cell size information, only the location of the cell center and the relative positions of adjacent cell centers are required, the calculated results do not include the size of the model cells, but only the location and attributes of the cell center. Using WebGIS and 3D online modeling technology, a 3D Delaunay tetrahedron was generated for the entire model based on the cell center points, constructing a multi-factor model for the spatial distribution of organic pollutants in soil and groundwater. Each stratigraphic cell was then partitioned into voxels, and lithology and contamination attribute values ​​were assigned to the geological cell grids in the volume model to establish a volume data model.

[0098] 2) Ray projection algorithm and conversion function design

[0099] This method emits a ray from each pixel on the screen and selects m equidistant sampling points along the ray, based on the viewing direction of the 3D volume data field. Spatial cubic linear interpolation is used to calculate the opacity and color of each sampling point based on the color and opacity values ​​of the eight vertices of the hexahedron where the sampling point is located. After obtaining the color and opacity values ​​for each sampling point, the final color value of the pixel that emitted the ray is obtained by superimposing each sampling point from front to back. Finally, this method is applied to every pixel in the image to obtain a 3D image of the entire volume data field with transparency.

[0100] The design of the conversion function is crucial to the final visualization effect. The conversion function used this time is as follows:

[0101] (11)

[0102] (12)

[0103] (13)

[0104] (14)

[0105] The purpose of volume visualization is to hide the parts that are not of interest and highlight the parts that are of interest. The transparency is higher in the parts with low concentration and lower in the parts with high concentration. Therefore, the final volume visualization effect should be a combination of multiple attributes. Figure 2This is a volumetric visualization of a mixture of two pollutants. If attributes are deeply nested, they can be displayed individually or combined with formation attributes to reveal the spatial distribution of each phase within the concentration threshold of interest. Alternatively, multiple attributes can be weighted to create a single attribute (the indicator of interest) and visualized volumetrically to reveal the spatial distribution of pollutants within the threshold of interest. Figure 3 Shown is the volumetric visualization of the XVOCW (mass fraction of organic contaminants in the water phase), SG (gas saturation), SW (water phase saturation), and SO (NAPL phase saturation) attributes within the geological volume.

[0106] 3) Model post-processing

[0107] Volume visualization is only a display of certain properties of pollutants. It is usually necessary to combine volume visualization with multiple methods such as translucency, cross-section, and isosurface extraction to obtain a more comprehensive display effect. These can all be accomplished in model processing. Figure 4 The model shown uses a variety of visualization effects, including surface rendering model translucency, model sectioning, isosurface screening, model screening and sectioning, etc., which more clearly shows the internal structure of the geological body and the multi-factor distribution of organic pollutants in the soil-groundwater system.

[0108] Corresponding to the multi-factor volume visualization technology method for spatial distribution of soil-groundwater organic pollutants in the above embodiment, Figure 5 A structural block diagram of a multi-factor volume visualization technology device for spatial distribution of organic pollutants in soil and groundwater provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0109] Reference Figure 5 , the apparatus 500 comprises:

[0110] The acquisition module 501 is used to collect geological structure data and pollutant monitoring data of the target site;

[0111] An integrated management module 502 is used to integrate and manage geological structure data and pollutant monitoring data using the GBIM cloud data management system;

[0112] The first conversion module 503 is used to process the integrated pollutant monitoring data according to the three-dimensional spatial interpolation method to obtain continuous spatial data, and convert the continuous spatial data into the data format required by the organic pollutant spatial distribution modeling and model visualization platform;

[0113] A construction module 504 is used to construct a multi-factor model of spatial distribution of organic pollutants in soil and groundwater based on continuous spatial data by calling the Internet Geographic Information System (WebGIS) and three-dimensional online modeling technology;

[0114] Establishing module 505, for dividing each geological unit grid into voxels based on the multi-factor model of spatial distribution of organic pollutants in soil and groundwater, and assigning corresponding attribute values ​​to establish a three-dimensional volume data model;

[0115] The second conversion module 506 is used to assign color and opacity to each volume pixel of the three-dimensional volume data model according to a preset conversion function in the ray casting algorithm based on a dynamic volume visualization method of the web graphics library WebGL, so as to convert the three-dimensional volume data model into a three-dimensional volume visualization image, thereby realizing the volume visualization expression of multiple elements of the spatial information of soil-groundwater organic pollutants.

[0116] The multi-factor volume visualization technology device for the spatial distribution of organic pollutants in soil and groundwater provided in this application first collects geological structure data and pollutant monitoring data of the target site, then integrates and manages the geological structure data and pollutant monitoring data using the GBIM cloud data management system. The integrated pollutant monitoring data is then processed using a three-dimensional spatial interpolation method to obtain continuous spatial data, and the continuous spatial data is converted into the data format required by the organic pollutant spatial distribution modeling and model visualization platform. Based on the continuous spatial data, the Internet Geographic Information System (WebGIS) and three-dimensional online modeling technology are then called to construct a multi-factor model for the spatial distribution of organic pollutants in soil and groundwater. Based on the multi-factor model for the spatial distribution of organic pollutants in soil and groundwater, each geological unit grid is divided into voxels and assigned corresponding attribute values ​​to establish a three-dimensional volume data model. Finally, based on a dynamic volume visualization method using the network graphics library WebGL, a color and opacity are assigned to each voxel of the three-dimensional volume data model according to a preset conversion function in the ray casting algorithm to convert the three-dimensional volume data model into a three-dimensional volume visualization image, thereby realizing a volume visualization expression of the multi-factor spatial information of high-risk organic pollutants in soil and groundwater. In this way, the migration trajectory and multi-factor distribution characteristics of pollutants within geological bodies can be more clearly demonstrated, providing scientific, intuitive and dynamic decision-making support for site pollution assessment and treatment plan design.

[0117] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A multi-factor volumetric visualization method for the spatial distribution of organic pollutants in soil and groundwater, characterized by: The method comprises the following steps: S1. Collect geological structure data and pollutant monitoring data of the target site; S2. Use the GBIM cloud data management system to integrate and manage geological structure data and pollutant monitoring data; S3. Process the integrated pollutant monitoring data using a three-dimensional spatial interpolation method to obtain continuous spatial data, and convert the continuous spatial data into the data format required by the organic pollutant spatial distribution modeling and model visualization platform; S4. Based on continuous spatial data, we use the Internet Geographic Information System (WebGIS) and 3D online modeling technology to construct a multi-factor model of the spatial distribution of organic pollutants in soil and groundwater; S5. Based on the model constructed in step S4, each geological unit grid is divided into voxels and assigned corresponding attribute values ​​to establish a three-dimensional volume data model; S6. A dynamic volume visualization method based on the WebGL web graphics library assigns color and opacity to each voxel of the 3D volume data model according to a preset conversion function in the ray casting algorithm to convert the 3D volume data model into a 3D volume visualization image, thereby realizing a volume visualization expression of multiple elements of spatial information of soil and groundwater organic pollutants; The step S4 comprises: Based on the 3D GIS map in WebGIS, the site model boundary is selected and the points within the boundary are encrypted to obtain multiple encrypted points; Based on the site model boundary, multiple encryption points and borehole data, grid generation is performed to obtain the basic network structure; Match and fuse the stratigraphic layer file with the basic network structure to generate a three-dimensional stratigraphic model corresponding to the stratigraphic layer of the pollutant migration model; Select characteristic pollutants corresponding to solute transport analysis based on the actual pollution data of the site; Set the initial conditions of the pollutant transport model and uniformly add and manage the material parameters in the model; The geological unit grid of the three-dimensional stratigraphic model is partitioned and assigned values, point boundary conditions and surface boundary conditions of the pollutant transport simulation model are added centrally, and multiple encrypted points are used as pollutant leakage points; The pollutant migration model is solved according to the preset calculation parameters to obtain simulation result data, and a multi-factor model of the spatial distribution of soil-groundwater organic pollutants is constructed based on the simulation result data.

2. The multi-factor volume visualization technology method for spatial distribution of organic pollutants in soil and groundwater according to claim 1 is characterized in that: The step S2 includes: The GBIM cloud data management system is used to divide geological structure data and pollutant monitoring data into two types: offline image terrain service data and site and user-related data; The open source distributed object storage service MinIO is used to store and publish the offline image terrain service data; A PostgreSQL database is used to store the venue and user related data.

3. The multi-factor volume visualization technology method for spatial distribution of organic pollutants in soil and groundwater according to claim 2 is characterized in that: The three-dimensional space interpolation method in step S3 is as follows: Using 3D hypersurface spline function to perform 3D space interpolation, expression of 3D hypersurface spline function The formula is: （1） Where W is the attribute value at the target interpolation point (x, y, z) in space, , , , , F i is the unknown coefficient, i =1,2,..., n , n is the number of monitoring points where the pollutant monitoring data is collected, To adjust the empirical parameters of the surface curvature, r i Interpolate the target point (x, y, z) to the i-th monitoring data point The Euclidean distance between The expression is: （2） described 、 、 、 、 F i By solving formula (3), we can obtain: （3） Where c j is the weighted value of the smoothness of the surface, c j =16 D / k j , D is the bending stiffness, k j is the elastic constant about point j, is the Euclidean distance from point j to point i; Let c j =0, so that the obtained three-dimensional hypersurface spline function is consistent with the pollutant monitoring data after integrated management at the known points. The matrix expression of formula (3) is: （4） Where, (5) （6） Where T represents the matrix transpose.

4. The multi-factor volumetric visualization method for spatial distribution of organic pollutants in soil and groundwater according to claim 3 is characterized by: The preset conversion function in the ray casting algorithm in step S6 is specifically as follows: （7） （8） （9） （10） in, is the sampling value of the light, is the scalar value of volume V, is the opacity, s is the total number of images in the mosaic, is the color space, For a given The color is defined by the transfer function, is the light factor, is the color of any pixel in the k-th step of the light, is the alpha component of the pixel in ray step k that is set to 1 at the end of the rendering process.

5. A multi-factor volumetric visualization technology device for spatial distribution of organic pollutants in soil and groundwater, characterized by: The device comprises: The acquisition module is used to collect geological structure data and pollutant monitoring data of the target site; Integrated management module, used to integrate and manage geological structure data and pollutant monitoring data using the GBIM cloud data management system; The first conversion module is used to process the pollutant monitoring data after integrated management according to the three-dimensional spatial interpolation method to obtain continuous spatial data, and convert the continuous spatial data into the data format required by the organic pollutant spatial distribution modeling and model visualization platform; A construction module is used to construct a multi-factor model of the spatial distribution of organic pollutants in soil and groundwater based on continuous spatial data, using the Internet Geographic Information System (WebGIS) and three-dimensional online modeling technology; Establish a module for dividing each geological unit grid into voxels based on the multi-factor model of spatial distribution of organic pollutants in soil and groundwater, and assigning corresponding attribute values ​​to establish a three-dimensional volume data model; The second conversion module is used to assign color and opacity to each voxel of the 3D volume data model according to a preset conversion function in the ray casting algorithm based on a dynamic volume visualization method based on the web graphics library WebGL. This converts the 3D volume data model into a 3D volume visualization image, thereby realizing a volume visualization expression of multiple elements of spatial information of soil and groundwater organic pollutants. Based on continuous spatial data, the Internet Geographic Information System (WebGIS) and three-dimensional online modeling technology are used to construct a multi-factor model of the spatial distribution of organic pollutants in soil and groundwater, including: Based on the 3D GIS map in WebGIS, the site model boundary is selected and the points within the boundary are encrypted to obtain multiple encrypted points; Based on the site model boundary, multiple encryption points and borehole data, grid generation is performed to obtain the basic network structure; Match and fuse the stratigraphic layer file with the basic network structure to generate a three-dimensional stratigraphic model corresponding to the stratigraphic layer of the pollutant migration model; Select characteristic pollutants corresponding to solute transport analysis based on the actual pollution data of the site; Set the initial conditions of the pollutant transport model and uniformly add and manage the material parameters in the model; The geological unit grid of the three-dimensional stratigraphic model is partitioned and assigned values, point boundary conditions and surface boundary conditions of the pollutant transport simulation model are added centrally, and multiple encrypted points are used as pollutant leakage points; The pollutant migration model is solved according to the preset calculation parameters to obtain simulation result data, and a multi-factor model of the spatial distribution of soil-groundwater organic pollutants is constructed based on the simulation result data.

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