Urban three-dimensional geological intelligent modeling method and system based on distributed cloud computing

Abstract

The application provides a kind of city three-dimensional geological intelligent modeling method and system based on distributed cloud computing, the system includes web end for data interaction, server end for data processing modeling, the steps of data processing modeling of server end include, receiving the geological data transmitted by web end and determining the city large area range to be modeled;The large area range is dynamically divided into several small areas by horizontal and vertical staggered traffic axes;And the intersection of traffic axis and the node on the axis are extracted as topological nodes, and the topological nodes are connected in turn as topological boundaries to construct city sub-area;Based on the drill hole data obtained near the boundary line of city sub-area, the geological profile on the boundary line of each sub-area is generated;A distributed processing framework hadoop2.0 cluster is constructed, each computing node in the cluster parses the sub-area drill hole data and the geological profile to perform stratum modeling, and the modeling result is returned to the web end for rendering and display through the interface;The application improves the modeling efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban geology informatization and cloud computing, and particularly relates to a method and system for urban three-dimensional geological intelligent modeling based on distributed cloud computing. BACKGROUND

[0002] Three-dimensional geological modeling of urban areas is a three-dimensional visualization expression of the spatial position, shape and topological relationship of the geological interface and geological body in urban areas, which provides an intuitive and virtual geological space for in-depth study of geological bodies, comprehensive analysis of geological phenomena, scientific utilization of geological resources and effective prevention and control of geological environmental problems. However, urban geological data is diverse in sources, including drilling data, geophysical data and remote sensing data, and is greatly different in formats and inconsistent in space-time scales. Traditional methods usually use single computers for processing, which cannot effectively process urban-level massive geological data modeling.

[0003] The patent with the application number CN201610948017.9, a stratum model automatic modeling method based on drilling data, and the patent with the application number CN201810899016.9, a three-dimensional automatic modeling method for urban area geology based on topological partition, solve the problems of point-shaped engineering, line-shaped engineering and three-dimensional geological modeling of urban areas. However, the original data of urban three-dimensional geological modeling is multi-source and heterogeneous, and the geological conditions are complex and changeable. The amount of modeling original data in a large-scale range is often very large, and the limited memory space of single computer configuration generally cannot meet the computing needs of modeling, resulting in low computing efficiency.

[0004] In the prior art, such as the "Three-dimensional geological multi-body modeling based on mesh containing topological profile" disclosed in the "Journal of Geotechnical Engineering", the modeling area is divided into several mesh partitions, each partition is independently modeled, and finally the models of each partition are assembled into a whole model of the whole area. In the patent "Three-dimensional stratum parallel modeling method of partition constraint coupling" with application number CN201710347899.8, the modeling area is determined; the survey data is obtained to form an original sample point set S; the modeling area is planarly partitioned to generate a plurality of sub-areas, and the area topological relationship is recorded; the original sample point set is used to differentially constrain the triangular mesh partitioning of each sub-area; the sample points in each sub-area are expanded according to the area topological relationship; the stratum sequence of the modeling area is obtained, the grid nodes on the common boundary of each sub-area are spatially interpolated to obtain an interpolated sample point set; the three-dimensional stratum model of each sub-area is constructed in parallel according to the stratum sequence; and the constructed three-dimensional stratum models of each sub-area are stitched. This method improves the modeling efficiency of large areas to some extent. However, as a whole, for city and marine area geological modeling, the dynamic partitioning method is a common approach in existing research results. However, most of the processes are complex and prone to errors, and it is difficult to meet the requirements of large-area modeling accuracy and modeling efficiency. At the same time, the final implementation of the existing modeling process needs to rely on a professional software platform, which has a high cost. SUMMARY

[0005] Based on the above background, the purpose of the present application is to provide a city three-dimensional geological intelligent modeling method and system based on distributed cloud computing, which solves the core problems of low data processing efficiency and insufficient utilization of computing resources in traditional city-level massive data processing.

[0006] To achieve the above purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a city three-dimensional geological intelligent modeling method based on distributed cloud computing, which is executed based on a city three-dimensional geological intelligent modeling system based on distributed cloud computing, the system including a web end for data interaction and a server end for data processing modeling. The data processing modeling steps of the server end include,

[0008] Receiving the geological survey data transmitted by the web end and drawing a borehole model, and determining the city large area range to be modeled according to the spatial position of the borehole model;

[0009] The large area range is dynamically divided into a plurality of small areas through the horizontal and vertical intersecting traffic axes; and the intersection points of the horizontal and vertical intersecting traffic axes and the nodes on the axes are extracted as topological nodes, the topological nodes are connected in turn as topological boundaries, and the polygon range enclosed by the topological boundaries is defined as a city sub-area;

[0010] Based on the obtained drilling data near the city sub-region boundary, an intelligent connection algorithm is called to connect the position points on the drilling which expose the same stratum to form a continuous geological line, the geological line is given a geological attribute, thereby automatically generating the geological profile on each sub-region boundary;

[0011] A distributed processing framework hadoop2.0 cluster is constructed, the server end submits the drilling data and geological profile data in different modeling regions and regions to the distributed processing cluster, each computing node in the cluster analyzes the sub-region drilling data and geological profile data to automatically perform stratum modeling, after the modeling of each computing node is completed, the city large region modeling result is returned to the web end through an interface for rendering and display.

[0012] In the second aspect, the application provides a city three-dimensional geological intelligent modeling system based on distributed cloud computing, comprising,

[0013] The web end is used for providing a user interaction interface and obtaining user input data, and the data includes geological survey data;

[0014] The server end is used for receiving data and interaction requests transmitted by the web end and establishing a city three-dimensional geological model, and comprises,

[0015] The city large region range establishing module is used for receiving geological survey data and drawing a drilling model, and determining a city large region range to be modeled according to the spatial position of the drilling model;

[0016] The city sub-region division module is used for dynamically dividing the large region range into a plurality of small regions through horizontal and vertical intersecting traffic axes, and extracting the intersection points of the horizontal and vertical intersecting traffic axes and the nodes on the axes as topological nodes, the topological nodes are sequentially connected as topological boundaries, and the polygon range enclosed by the topological boundaries is defined as a city sub-region;

[0017] The sub-region boundary geological profile generating module is used for calling an intelligent connection algorithm based on the obtained drilling data near the city sub-region boundary, connecting the position points on the drilling which expose the same stratum to form a continuous geological line, the geological line is given a geological attribute, thereby automatically generating the geological profile on each sub-region boundary;

[0018] The distributed modeling module is used for constructing a distributed processing framework hadoop2.0 cluster, submitting the drilling data and geological profile data in different modeling regions and regions to the distributed processing cluster, each computing node in the cluster analyzes the sub-region drilling data and geological profile data to automatically perform stratum modeling, after the modeling of each computing node is completed, the city large region modeling result is returned to the web end through an interface for rendering and display.

[0019] The application has the following beneficial effects:

[0020] This method combines intelligent interpretation of geological exploration data, fusion of multi-source heterogeneous data, distributed cloud computing technology, and Web-based online modeling technology to construct efficient, dynamic, and high-precision 3D geological models for urban underground space development. Through a cloud-based modeling architecture, it enables real-time processing and model optimization of massive amounts of geological data, overcoming the bottlenecks of traditional modeling methods in terms of computational efficiency, multi-scale modeling accuracy, and dynamic update capabilities, while also eliminating reliance on client-side BIM platforms. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A schematic diagram of the process of the intelligent urban 3D geological modeling method based on distributed cloud computing provided in an embodiment of the present invention. Detailed Implementation

[0023] To further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, and not for limiting the scope of the claims of the present invention.

[0024] Example 1

[0025] This embodiment uses a 3D geological modeling of a city area as an example. All borehole data is stored in an SQL Server database. This embodiment of the invention provides a city 3D geological intelligent modeling method based on distributed cloud computing. This method is executed by a distributed cloud computing city 3D geological intelligent modeling system. The system includes a web client for data interaction and a server client for data processing and modeling. The steps of data processing and modeling on the server client are as follows: Figure 1 As shown, it includes:

[0026] S1, Data preparation: Receive geological survey data transmitted from the web terminal and draw borehole models. Based on the spatial location of the borehole models, determine the large urban area S to be modeled.

[0027] S2, Urban Large Area Topological Partitioning: The large area is dynamically divided into several small areas by crisscrossing traffic axes; the intersections of the crisscrossing traffic axes and the nodes on the axes are extracted as topological nodes, and the topological nodes are connected in sequence to form the topological boundary. The polygon range formed by the closed topological boundary is defined as the urban sub-region.

[0028] S3, city sub-region interface geological profile generation: based on the obtained city sub-region boundary near the geological survey data, call intelligent connection algorithm, connect the position points of the same stratum exposed on the drill hole, form a continuous geological line, and the geological line is given a geological attribute, thereby automatically generating a geological profile on each sub-region boundary;

[0029] S4, city large area three-dimensional geological distributed modeling: build a distributed processing framework hadoop2.0 cluster, submit drill hole data and geological profile data in different modeling areas and regions to the distributed processing cluster, and each computing node in the cluster parses the sub-region drill hole data and geological profile data to perform stratum modeling;

[0030] S5, Cesium engine rendering: after each computing node modeling is completed, the modeling result is returned to the web end through the interface for rendering and display.

[0031] Specifically,

[0032] In step S1, the web end transmits the geological survey data and draws the drill hole model, and according to the spatial position of the drill hole model, the city large area range S to be modeled is determined, including

[0033] S101: Collect city large area geological survey data, including drill hole data, and perform stratum standardization and drill hole formatting to form an original modeling drill hole set A. The data format in the original modeling drill hole set A is suitable for the format required by three-dimensional geological automatic modeling. The original modeling drill hole set includes stratum unit layer data of all drill holes, and the stratum unit layer data includes drill hole number, drill hole segment serial number and stratum code.

[0034] Wherein, the stratum standardization is to establish the total sequence of strata of the city large area according to the drill hole data, that is, the total stratum age order of all exposed strata in the city large area. The drill hole formatting is to design a drill hole segment data structure according to the drill hole table in the database, which includes the start and end point coordinates of the drill hole segment, the exposed stratum code and its unique stratum serial number, and the color attribute used to distinguish different stratum lithology; a single drill hole is a set composed of a plurality of drill hole segments arranged in sequence.

[0035] S102: Draw a drill hole model consistent with the real geographical position in the Cesium engine according to the original modeling drill hole set A. The drill hole model is a cylindrical model, which is created according to the start point, end point coordinates, stratum code, color and other attributes of each drill hole segment in the drill hole set. Specifically, the drill hole engineering coordinates are converted to the wgs84 coordinate system, and then a plurality of drill hole cylindrical models are created in the Cesium engine according to the drill hole mouth elevation and hole depth, and the drill hole model radius is 0.5m by default;

[0036] S103: According to the spatial position of the borehole model, determine the urban large area range S to be modeled, and express it by a self-closed polygonal line.

[0037] There are two ways to determine the urban large area range S to be modeled: for the case where the user actively provides the borehole data coverage range, the closed polygon submitted by the user will be directly used as the modeling reference boundary; when the user does not specify a specific range, the program will automatically construct the modeling area through spatial analysis algorithm, and the specific process includes:

[0038] (1) Extract the planar coordinate data of all valid boreholes in the original modeling borehole set A, and construct a discrete point set P={p1, p2,..., pn};

[0039] (2) Use the Graham scan convex hull algorithm to perform envelope analysis on the discrete point set to generate the minimum convex polygon boundary. The Graham scan convex hull algorithm is a classic computational geometry algorithm, which sorts and scans the discrete points to finally find all the vertices of the convex hull, and sorts them in counterclockwise order to form the minimum convex polygon boundary;

[0040] (3) The polygon obtained by expanding the minimum convex polygon boundary by 1 times the average borehole spacing is used as the modeling reference boundary.

[0041] The topological partitioning of the urban large area in step S2 includes,

[0042] S201: According to the actual engineering needs, divide the large area into several small areas by using the horizontal and vertical intersecting traffic axes, that is, divide the spatial position of the borehole model into several sub-areas, ensure that each sub-area has adjacent sub-areas, and close without overlapping.

[0043] S202: Extract the intersection points of the horizontal and vertical intersecting traffic axes and the nodes on the axes as topological nodes, connect the topological nodes in turn as the topological boundary, and the polygon range formed by the closed topological boundary is defined as the urban sub-area Si. The urban sub-area must satisfy the following compatibility conditions in the topological structure: ① All urban sub-areas should not exist independently of adjacent urban sub-areas, and should have a common boundary with adjacent urban sub-areas; ② The outer boundary of all urban sub-areas should be self-closed.

[0044] The generation of the urban sub-area interface geological profile in step S3 includes,

[0045] The generated geological section is automatically generated according to the borehole data near the city sub-regional boundary line, the borehole data reveals the stratum distribution near the city sub-regional boundary line, an intelligent connection algorithm is called, position points of the same stratum exposed on the borehole are connected, a continuous geological line is formed, the geological line is given a geological attribute, and the given geological attribute is the standardized stratum information in step S1, thereby automatically generating the geological section on each sub-regional boundary line. Based on the topological relationship of each sub-region, adjacent sub-regions share a geological section Pi.

[0046] In order to realize the automatic connection of the stratum boundary between the boreholes, four types of stratum spatial distribution, including continuous stratum distribution, stratum lens, stratum discontinuity and stratum pinch-out, are abstracted from various stratum structures, and different connection rules are used for connection processing. These four common stratum phenomena reflect the comprehensive results of sedimentation, tectonic movement and erosion.

[0047] (1) The connection rule of continuous stratum distribution is to construct a series of parallel or nearly parallel, laterally continuous horizon interfaces. The algorithm checks whether the adjacent stratum interfaces maintain an approximately parallel or gradual change relationship, and whether there is a lack of obvious lateral discontinuity, such as steep faults or missing. If the interface model extends smoothly in the horizontal direction and has a consistent trend, the algorithm will identify it as a continuously distributed stratum;

[0048] (2) The connection rule of stratum lens is to identify and model the local rock mass that gradually thickens and then thins in the horizontal direction. In the continuous stratum background, some areas are found where the thickness of the rock increases significantly, and the thickness of the rock in the surrounding or both sides of the borehole decreases rapidly or even disappears;

[0049] (3) The connection rule of stratum discontinuity mainly focuses on identifying and modeling unconformity, that is, the interface where the stratum record is interrupted. If the algorithm detects a non-normal, significant jump in the geological era between adjacent stratum interfaces, it is determined that there is a discontinuity;

[0050] (4) The connection rule of stratum pinch-out is to identify and model the geometric feature that the thickness of the stratum gradually decreases in the horizontal direction until it disappears. The algorithm analyzes the thickness trend of the stratum unit between different boreholes, and based on the thickness trend, predicts the area where the thickness of the stratum tends to zero, thereby defining a "pinch-out line" or a "pinch-out zone".

[0051] The intelligent connection algorithm generates a B-spline curve by connecting the position points of the same stratum exposed on the borehole. The B-spline curve has good smoothness and local controllability, and can fit the general trend and morphology of the stratum. According to the stratum pinch-out condition, the curves with intersections are broken, a two-dimensional geological section line is generated, and then through the mapping from two-dimensional to three-dimensional space, the algorithm finally forms a three-dimensional geological section line, completing the construction of the geological section in three-dimensional space.

[0052] The step S4 of constructing the distributed processing framework hadoop2.0 cluster for the city large-area distributed modeling comprises,

[0053] The distributed processing framework hadoop2.0 cluster is constructed by two or more servers, mainly comprising a distributed file system HDFS, a resource management framework YARN, a distributed computing framework MapReduce and the like components. The HDFS, the YARN and the MapReduce are three core components of the Hadoop ecosystem, which are responsible for distributed storage, resource scheduling and parallel computing respectively. Specifically,

[0054] The HDFS is a kind of distributed file system, which connects the storage spaces in the cluster to form a unified and massive distributed storage space. In the embodiment, the operator service is stored on the HDFS, then the operator service is remotely submitted and called, and the parameters to be processed are preprocessed and stored on the HDFS and specified with a specific location, and the operator service acquires the corresponding parameters; the result file is preset on the HDFS, which is used to store the modeling result file.

[0055] The YARN is a kind of resource management framework, which can effectively manage the cluster resources such as CPU, memory, storage and network bandwidth when processing data in a large distributed system, and can distribute the computing tasks to the corresponding resources for execution. The YARN comprises the following main components: a Resource Manager (RM), a Node Manager (NM) and an Application Master (AM). The RM is a master node, which is responsible for managing the states of all slave nodes, and performs resource management and task scheduling. The NM is a slave node, which is a computing node responsible for executing the task program, and it applies the machine resources to process the tasks according to the allocation of the RM. Each program has an AM process, which is managed by the YARN to run the program, and is responsible for starting, supervising and feeding back the running state of the program. Specifically, the user submits an application program to the resource management framework YARN, a task is generated, the RM allocates the first container to start the AM for the application program, the AM registers with the RM after being started, the AM sends a resource request to the RM, the scheduler allocates resources according to the strategy, the AM starts the task in the allocated container, the AM monitors the task state and reports to the RM, and the AM is unregistered and the resources are released after the application is completed.

[0056] MapReduce is a distributed computing framework, mainly responsible for resource management, task scheduling and MapReduce algorithm implementation. In this embodiment, based on the sub-regional division result of the city, the Map task program performs the three-dimensional geological modeling process of the sub-region Si; all nodes executing the Map task program use the same modeling method to synchronously complete the construction of the geological three-dimensional model of other sub-regions of the city. The Reduce task program performs the reduction operation, and sequentially splices all the obtained city sub-regional geological three-dimensional models, so that the common edge geological surface is spliced into a geological surface, and the common plane geological entity is spliced into a geological entity, the geological properties of the spliced geological surface and geological entity are kept unchanged, and a complete city large-area geological three-dimensional model is obtained.

[0057] The three-dimensional geological automatic modeling process of the city sub-region in the Map task program specifically includes,

[0058] Step a, first, the geological lines on the geological profile drawn in step S3 are processed by equal-interval encryption according to the set grid spacing, and based on the encryption points of the same coordinates and different heights and the geological properties of the corresponding geological lines, a virtual drill hole of the geological profile is generated, and all stratigraphic unit stratification data in the drill hole data and the virtual drill hole data of the geological profile are extracted, including at least drill hole number, drill hole section serial number, stratigraphic code;

[0059] Step b, then, all stratigraphic units exposed in all drill holes are found by traversing in the order of new and old of all strata, the set of stratigraphic units between the bottoms of these stratigraphic units is defined as a complete layer, and the bottom position data, the bottom stratigraphic code, the starting drill hole section serial number set, and the ending drill hole section serial number set of the complete layer are extracted as complete layer data;

[0060] Step c, secondly, search for stratigraphic units not exposed in all drill holes in the geological line and drill hole data complete layer segmentation, then define these stratigraphic units as incomplete layers, and extract the bottom position data and stratigraphic code of the incomplete layers in turn according to the geological line and drill hole section from top to bottom and the order of new and old of the strata; the incomplete layer data extraction is a traversal of the complete layer segmentation of the stratigraphic unit, starting from the topological structure of the geological profile and the starting drill hole section of each drill hole in the complete layer segmentation of the stratigraphic unit, finding the drill hole section with the smallest stratigraphic code, searching all drill holes and the drill hole section with the same stratigraphic code on the topological structure of the geological profile, and defining the part of the city sub-regional interface geological profile with the same geological property geological line, the endpoint coordinates and stratigraphic code of the current drill hole section of the part of the drill hole as the incomplete layer data;

[0061] Step d: Finally, generate a complete layer interface based on the complete layer data, and use the complete layer interface to cut the terrain volume in sequence to generate a complete layer entity; generate a non-complete layer interface based on the non-complete layer data by interpolation, and use the non-complete layer interface within the complete layer entity to cut the complete layer entity in sequence to generate a non-complete layer entity.

[0062] The creation of stratigraphic interfaces involves using stratigraphic unit data and the Kriging interpolation algorithm to fit and generate relatively smooth geological surfaces, including intact and incomplete layer interfaces. An intact layer interface refers to the boundary line of a stratum exposed by all boreholes (including virtual boreholes), and it is created based on intact layer data. An incomplete layer interface refers to the stratigraphic boundary line partially exposed by all boreholes (including virtual boreholes). For boreholes with missing stratigraphic points, virtual exposure points are introduced to supplement the incomplete layer data. These virtual exposure points are positioned above the missing stratigraphic points in the borehole, with an elevation distance equal to the cumulative virtual thickness. This constrains the spatial morphology of the incomplete layer interface, ensuring that the incomplete stratigraphic entity pinches out reasonably between boreholes.

[0063] Segmenting complete layer entities involves dividing the original terrain volume using established complete layer interfaces, resulting in one or more complete layer entities. Segmenting incomplete layer entities involves further dividing the complete layer entities using incomplete layer interfaces within them. Finally, all stratigraphic entities are assigned stratigraphic and color attributes, thus completing the automatic construction of a 3D geological model of the urban sub-region based on borehole data and topological geological profiles.

[0064] Step e: Combine the complete layer entities and the incomplete layer entities to obtain a three-dimensional geological model of the sub-region.

[0065] Furthermore, in this embodiment, the specific process for large-area distributed modeling of a city based on the constructed distributed processing framework Hadoop 2.0 cluster includes:

[0066] S401: Users submit applications to the YARN resource management framework, including AM programs, commands to start AM programs, Map task programs, Reduce task programs, etc.

[0067] The AM program is a process running in the cluster, responsible for negotiating resources with the RM of YARN, and monitoring, scheduling and managing the execution of specific tasks such as Map and Reduce within the application. When the NM of YARN allocates a Container to run the AM, it needs to execute a specific command to start the AM process, which usually contains the path of the AM program and necessary running parameters. The Container is the smallest unit of resource allocation in YARN, which is a logical concept and contains a certain amount of CPU, memory and other resources, and is used to run specific computing tasks such as Map tasks and Reduce tasks.

[0068] S402: The RM allocates the first Container for the application and communicates with the corresponding NM to require the AM of the application to be started in the Container;

[0069] The RM is responsible for allocating resources. It allocates the first Container for the submitted application. The Container is specially used to run the AM process of the application. After the Container is allocated, the RM needs to let the agent NM running on the node where the Container is located to execute the task of starting the AM. Therefore, the RM will communicate with the corresponding NM corresponding to the Container allocated to the Container. The NM receives the instruction of the RM and is responsible for creating and starting the Container on the specified node. The Container is essentially an execution environment for resource isolation. The NM executes the command provided by the user when submitting the application program in the Container to run the AM process, thereby running the AM process.

[0070] S403: The AM first registers with the RM, directly checks the running status of the application through the RM, then applies for resources for each task and monitors the running status until the end of the running;

[0071] S404: The AM applies for and collects resources from the RM in a polling manner through the RPC protocol;

[0072] S405: Once the AM applies for resources, it communicates with the corresponding NM to require it to start the task;

[0073] S406: After the NM automatically sets up the running environment (including environment variables, JAR packages, binary programs, etc.) for the task, it writes the task starting command into a script and starts the task by running the script;

[0074] S407: Each task reports the task state and progress to the AM through a certain RPC protocol, so that the AM can grasp the running state of each task at any time, thereby restarting the task when the task fails; the priority of the Map task is higher than that of the reduce task, and when all the map tasks are completed, the sort is performed, and finally the reduce task is performed;

[0075] The above-mentioned sort is a process of preparing input data for the subsequent reduce task, and the output of the Map task is a series of (key, value) intermediate result pairs, and the same key can be generated by different Map tasks. The input of the Reduce task needs to be grouped according to the key, that is, each Reduce task receives all value sets of a certain key or a few specific keys. After the Map task is completed, the data is transmitted from the node where the Map task is generated to the node responsible for executing the Reduce task, and in this transmission and preparation process, the core work is the sort. The data belonging to the Reduce task grabbed by the sort on the node where the Reduce task is located is merged and sorted according to the key. The purpose of sorting is to aggregate all intermediate values with the same key together, so that the Reduce task can conveniently iterate all values of a certain key.

[0076] S408: After the application program is completed, the AM requests to log off and close to the RM. When all the application programs are executed, the final output is a complete city large-area three-dimensional geological model.

[0077] The modeling result in step S5 is returned to the web end through an interface for rendering and display, including,

[0078] The geometric information and attribute information of each stratum of the city large-area three-dimensional geological model obtained in step S4, such as the geometric information of the grid vertex coordinate set, the grid vertex index set, and the vertex normal set, and the attribute information of the stratum name and the stratum color, are transmitted to the web end through an interface;

[0079] The Cesium engine of the web end constructs a Geometry object from the geometric information and attribute information, and adds it to the scene to realize rendering.

[0080] Embodiment 2

[0081] The embodiment provides a city three-dimensional geological intelligent modeling system based on distributed cloud computing, including,

[0082] A web end configured to provide a user interaction interface and obtain user input data, the data including geological survey data;

[0083] The server end is used for receiving data and interaction requests transmitted by the web end and establishing a three-dimensional geological model of a city, comprising,

[0084] The city large-area establishing module is used for receiving geological survey data and drawing a borehole model, and determining a city large-area to be modeled according to a spatial position of the borehole model;

[0085] The city sub-area dividing module is used for dynamically dividing the large-area into a plurality of small areas through horizontal and vertical intersecting traffic axes, and extracting intersection points of the horizontal and vertical intersecting traffic axes and nodes on the axes as topological nodes, the topological nodes being connected in sequence as topological boundaries, and a polygon range enclosed by the topological boundaries being defined as a city sub-area.

[0086] The sub-area boundary line geological profile generating module is used for calling an intelligent connection algorithm based on borehole data near a city sub-area boundary line, connecting position points on the borehole that expose the same stratum to form a continuous geological line, and assigning a geological attribute to the geological line, thereby automatically generating a geological profile on the sub-area boundary line.

[0087] The distributed modeling module is used for constructing a distributed processing framework hadoop2.0 cluster, submitting different modeling areas and borehole data and geological profile data in the areas to the distributed processing cluster, and automatically performing stratum modeling on the sub-area borehole data and the geological profile data by each computing node in the cluster, and returning a city large-area modeling result to the web end through an interface for rendering and display after modeling of each computing node is completed.

[0088] The above description of the embodiments is only used to help understand the method of the present application and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

[0089] 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 application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0090] It should be noted that some of the example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the processes as sequential processes, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps can be re-arranged. The processes can be terminated when their operations are completed, but can also have additional steps not included in the figure, which can also be performed after the operations of the processes are completed. The processes can correspond to methods, functions, procedures, subroutines, subprograms, etc.

Claims

1. A method for intelligent urban 3D geological modeling based on distributed cloud computing, wherein the method is executed based on an intelligent urban 3D geological modeling system based on distributed cloud computing, the system comprising a web interface for data interaction and a server interface for data processing and modeling, characterized in that, The steps for data processing and modeling on the server side include: Receive geological survey data transmitted from the web client and draw borehole models. Based on the spatial location of the borehole models, determine the large urban area to be modeled. The large area is dynamically divided into several smaller areas by crisscrossing traffic axes; the intersections of the crisscrossing traffic axes and the nodes on the axes are extracted as topological nodes, and the topological nodes are connected in sequence to form topological boundaries. The polygonal range formed by the closed topological boundaries is defined as the urban sub-region. Based on the borehole data obtained near the boundaries of urban sub-regions, an intelligent connection algorithm is invoked to connect the locations of the same strata exposed on the boreholes, forming continuous geological lines. These geological lines are assigned geological attributes, thereby automatically generating geological profiles on the boundaries of each sub-region. Four types of stratigraphic spatial distribution are abstracted from various stratigraphic structures: continuous stratigraphic distribution, stratigraphic lenses, stratigraphic discontinuities, and stratigraphic pinch-outs. These types are then connected using different connection rules. The intelligent connection algorithm generates B-spline curves by connecting the locations of the same strata exposed on the boreholes. Based on stratigraphic pinch-out conditions, intersecting curves are broken to generate two-dimensional geological profile lines. Finally, through mapping from two-dimensional to three-dimensional space, three-dimensional geological profile lines are formed, completing the construction of the geological profiles in three-dimensional space. A Hadoop 2.0 distributed processing framework cluster is built using two or more servers. Cluster components include the HDFS distributed file system, the YARN resource management framework, and the MapReduce distributed computing framework. Different modeling regions, along with borehole data and geological profile data within those regions, are submitted to the distributed processing cluster. Each computing node in the cluster parses the sub-regional borehole data and geological profile data to automatically perform stratigraphic modeling. After each computing node completes its modeling, the modeling results for the large urban area are returned to the web interface for rendering and display via an API. The MapReduce distributed computing framework is responsible for resource management, task scheduling, and the implementation of the MapReduce algorithm. The MapReduce algorithm implementation includes Map task program implementation and Reduce task program implementation. Based on the sub-region division results of the city, the Map task program performs the three-dimensional geological modeling process of the sub-region. All nodes executing the Map task program use the same modeling method to synchronously complete the geological three-dimensional model construction of other city sub-regions. The Reduce task program performs reduction operation, sequentially merging all the obtained city sub-region geological three-dimensional models, so that geological surfaces with common edges are merged into a single geological surface, and geological entities with common surfaces are merged into a single geological entity, keeping the geological properties of the merged geological surfaces and geological entities unchanged, to obtain a complete geological three-dimensional model of the large city area.

2. The urban 3D geological intelligent modeling method based on distributed cloud computing according to claim 1, characterized in that, The process involves receiving geological survey data transmitted from the web terminal and drawing borehole models. Based on the spatial location of the borehole models, the scope of the large urban area to be modeled is determined. Receive geological survey data transmitted from the web terminal. The geological survey data is borehole data. The borehole data is standardized by stratigraphy and formatted by borehole to form an original set of modeled boreholes. Draw a borehole model that conforms to the actual geographical location based on the original modeling borehole set; Based on the spatial location of the borehole model, the large urban area to be modeled is determined, and the large area is represented by a self-enclosed polyline.

3. The urban 3D geological intelligent modeling method based on distributed cloud computing according to claim 2, characterized in that, The process of determining the large urban area to be modeled based on the spatial location of the borehole model includes: Extract the planar coordinate data of all valid boreholes from the original modeling borehole set, and construct a discrete point set P={p1,p2,...,pn}; Graham's convex hull scanning algorithm is used to perform envelope analysis on a discrete point set to generate the minimum convex polygon boundary. The polygon obtained by expanding the boundary of the smallest convex polygon outward by one time the average borehole spacing is used as the modeling baseline boundary for the large urban area to be modeled.

4. The urban 3D geological intelligent modeling method based on distributed cloud computing according to claim 1, characterized in that, The HDFS distributed file system connects the storage spaces in the cluster to form a unified distributed storage space; operator services are stored on HDFS, and remote submissions are made to call operator services; parameters to be processed are preprocessed and stored on HDFS with a specified location so that operator services can retrieve the corresponding parameters; a result file is pre-defined on HDFS to store the modeled result file. YARN, as a resource management framework, manages cluster resources when processing data in large-scale distributed systems and allocates computing tasks to corresponding resources for execution; its components include ResourceManager, NodeManager, and ApplicationMaster.

5. The urban 3D geological intelligent modeling method based on distributed cloud computing according to claim 4, characterized in that, The construction of the distributed processing framework Hadoop 2.0 cluster involves submitting borehole data and geological profile data from different modeling regions to the distributed processing cluster. Each computing node in the cluster parses the borehole data and geological profile data of the sub-region to perform stratigraphic modeling. Users submit applications to the YARN resource management framework, which includes the ApplicationMaster program, the command to start the ApplicationMaster, the Map task program, and the Reduce task program. The ResourceManager allocates the first Container for the application and communicates with the corresponding NodeManager to request that the ApplicationMaster of the application be started in the corresponding Container; The ApplicationMaster first registers with the ResourceManager, which then checks the application's running status. The ApplicationMaster will request resources for each task and monitor its running status until it finishes running. ApplicationMaster uses a polling method to request and retrieve resources from ResourceManager via the RPC protocol; After the ApplicationMaster obtains the resources, it communicates with the corresponding NodeManager to request it to start the task; After NodeManager automatically sets up the runtime environment for the task, it writes the task startup command into a script and starts the task by running the script. Each task reports its status and progress to the ApplicationMaster via a certain RPC protocol, so that the ApplicationMaster can keep track of the running status of each task and restart the task if it fails. Map tasks have higher priority than reduce tasks, and reduce tasks are performed after all map tasks have been completed. After the application finishes running, the ApplicationMaster deregisters with the ResourceManager and shuts itself down. Once all applications have finished executing, the final output is a complete 3D geological model of the large urban area.

6. A city 3D geological intelligent modeling system based on distributed cloud computing, used to implement the method as described in any one of claims 1-5, characterized in that, include, The web-based interface is used to provide a user interaction interface and obtain user input data, including geological survey data. The server-side is used to receive data and interactive requests transmitted from the web client and to build a 3D geological model of the city, including... The large urban area establishment module is used to receive geological survey data and draw borehole models. Based on the spatial location of the borehole models, the large urban area to be modeled is determined. The urban sub-region division module is used to dynamically divide a large area into several smaller areas through crisscrossing traffic axes; and to extract the intersections of the crisscrossing traffic axes and the nodes on the axes as topological nodes. The topological nodes are connected in sequence to form the topological boundary. The polygon range formed by the closed topological boundary is defined as the urban sub-region. The sub-region boundary geological profile generation module is used to connect the locations of the same strata exposed on the boreholes based on the acquired borehole data near the urban sub-region boundary, forming a continuous geological line. The geological line is given geological attributes, thereby automatically generating the geological profiles on each sub-region boundary. The distributed modeling module is used to build a distributed processing framework Hadoop 2.0 cluster. It submits different modeling regions and borehole data and geological profile data within those regions to the distributed processing cluster. Each computing node in the cluster parses the borehole data and geological profile data of the sub-region and automatically performs stratigraphic modeling. After the modeling of each computing node is completed, the modeling results of the large urban area are returned to the web client for rendering and display through an interface.

Images