Visual management system for platform zero code configuration in digital oil field GIS (Geographic Information System)

Abstract

The invention relates to the technical field of visual configuration, in particular to a digital oil field GIS (Geographic Information System) platform zero code configuration visual management system, comprising a metadata modeling module used for constructing a business metadata model; the interactive configuration generation module is used for constructing a dynamic mapping topology and generating a configuration description index; the logic analysis processing module is used for generating a rendering driving matrix according to the index; and the scene rendering execution module is used for drawing the three-dimensional digital twin scene. According to the method, by constructing a dynamic mapping engine based on semantic metadata, the upper-layer business logic and bottom-layer primitive rendering process is thoroughly decoupled, and standardized packaging of heterogeneous geological data is realized by utilizing a modularized configuration mechanism, so that the system can flexibly recombine a GIS layer structure according to mining rules; rapid construction of an oil field scene is completed in a zero code environment, and adaptability and iteration efficiency of spatial data visualization management are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of visual configuration technology, and in particular to a zero-code configuration visual management system for a digital oilfield GIS platform. Background Technology

[0002] The field of visual configuration technology involves the construction of graphical interfaces, the definition of human-computer interaction logic, and the mapping of underlying data flows, aiming to achieve rapid deployment of complex business systems through non-coding methods. Among these, the traditional zero-code configuration visual management system for digital oilfield GIS platforms refers to the current technology that typically uses hard-coding to develop specific GIS functional modules. Programmers need to manually write rendering code for well coordinates, pipeline topology, and geological layers, and use pre-compiled static configuration files to set data source connection parameters and front-end display styles.

[0003] Existing technologies require pre-writing fixed code logic to define the binding relationship between oilfield equipment elements and underlying spatial data. They cannot dynamically adjust the rendering strategy of GIS layers according to the real-time changing needs of mining operations. Furthermore, when faced with massive heterogeneous geological exploration data sources, the reliance on statically compiled interface configuration leads to extremely low scalability and reusability of the data parsing link. This not only significantly increases the maintenance cost of system function iteration but also severely limits the efficiency of real-time response and visualization interaction of multidimensional spatial information in different monitoring scenarios. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a zero-code configuration visualization management system for a digital oilfield GIS platform.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a zero-code configuration and visualization management system for a digital oilfield GIS platform, comprising:

[0006] The metadata modeling module is used to analyze multi-source heterogeneous oilfield monitoring data streams. It identifies entity feature dimensions that describe well location coordinates and geological attributes through feature extraction algorithms, constructs a business metadata model that includes business topology relationships, and establishes data type constraints and alarm thresholds for the entity feature dimensions.

[0007] The interactive configuration generation module is used to provide a zero-code visual operation canvas, respond to the user's component-based drag-and-drop instructions on the business metadata model, construct a dynamic mapping topology between GIS primitive objects and the entity feature dimensions, and generate a configuration description index that describes the interface interaction logic.

[0008] The logic parsing and processing module is used to traverse the configuration description index, extract real-time geological spatial data from the GIS platform according to the dynamic mapping topology, perform spatial interpolation calculation and abnormal data filtering on the geological spatial data, and generate a standardized rendering drive matrix.

[0009] The scene rendering execution module is used to load the rendering driving matrix, drive the WebGL engine to calculate the texture mapping coordinates and geometric positions of the GIS primitive objects, and render the abstract geological spatial data as a high-fidelity three-dimensional digital twin scene in real time.

[0010] As a further aspect of the present invention, the specific functional implementation of the metadata modeling module is as follows:

[0011] The heterogeneous data stream parsing submodule is used to access oilfield monitoring data streams containing different communication protocols and data formats, identify the protocol header identifiers and payload segments in the monitoring data streams, clean and standardize the payload segments, and generate a basic data sequence in a unified format.

[0012] The entity feature extraction submodule is used to perform semantic analysis on the basic data sequence, identify latitude and longitude data segments that represent geographical location and physical attribute data segments that represent geological state, establish the association and reference relationship between each data segment, and construct the business metadata model that can describe the well location entity and its attribute structure.

[0013] The constraint rule definition submodule is used to traverse each feature dimension in the business metadata model, and configure the numerical type verification rules and the alarm threshold for triggering abnormal states for each feature dimension according to the preset oilfield safety operation standards.

[0014] As a further aspect of the present invention, the specific function of the interactive configuration generation module is as follows:

[0015] The canvas interaction response submodule is used to render the business metadata model component library and GIS element library to be configured on the visual operation interface, listen to the user's mouse drag events and placement coordinates in the canvas area in real time, and instantiate the visual component object corresponding to the user operation in the canvas.

[0016] The object mapping topology submodule is used to respond to the user's operation command to establish a connection between visual component objects, obtain the unique identifiers of the components at both ends of the connection, establish the binding relationship between the display attributes of the GIS primitive objects and the data channels of the entity feature dimensions, and generate the dynamic mapping topology.

[0017] The configuration serialization submodule is used to traverse and serialize the spatial layout information, attribute parameters, and dynamic mapping topology of all component objects in the canvas, converting unstructured interactive operations into structured text description files and generating the configuration description index.

[0018] As a further aspect of the present invention, the specific functional implementation of the logic parsing processing module is as follows:

[0019] The data retrieval and parsing submodule is used to read the configuration description index, parse out the list of entity feature dimensions required for the current scene, and send a data subscription request to the GIS platform based on the list to obtain real-time updated discrete point monitoring data.

[0020] The spatial logic calculation submodule is used to calculate the interpolation weight of the unknown area based on the spatial coordinate distribution of the discrete point monitoring data, and to perform statistical significance testing on the monitoring values, remove noise data that exceeds the preset confidence interval, and generate the smoothed geological spatial data.

[0021] The matrix normalization submodule is used to reorganize the processed geological spatial data according to a preset rendering pipeline format, map multi-dimensional geological attributes into a texture data structure suitable for GPU parallel reading, and generate the rendering driving matrix containing color channels and deformation parameters.

[0022] As a further aspect of the present invention, the specific function of the scene rendering execution module is as follows:

[0023] The driver matrix loading submodule is used to upload the rendering driver matrix to the video memory area, parse the texture channel data and deformation parameters in the matrix, and bind them to the vertex shader and fragment shader variables of the WebGL rendering pipeline.

[0024] The geometric texture calculation submodule is used to dynamically calculate the grid vertex offset of the GIS primitive object based on the deformation parameters in the rendering driving matrix to represent the geological layer undulation, and to calculate the light reflectance and texture blending weight of the primitive surface based on the color channel data.

[0025] The scene compositing and rendering submodule is used to perform depth testing and blending operations, draw the calculated GIS primitive objects into the frame buffer, overlay ambient lighting and post-processing effects, and output the real-time updated 3D digital twin scene on the display terminal.

[0026] As a further aspect of the present invention, the specific execution process of the entity feature extraction submodule is as follows:

[0027] Obtain the cleaned basic data sequence, extract the field names and unit description text from the data sequence, and perform fuzzy matching calculations between the field names and a pre-set standard geological semantic dictionary;

[0028] Based on the matching similarity score, the data attribute category of each field is determined. Fields identified as coordinate categories are mapped to spatial location indexes, and fields identified as physical quantity categories are mapped to attribute state variables. The dependency relationship between attribute state variables and spatial location indexes is established, and the business metadata model is constructed.

[0029] As a further aspect of the present invention, the specific execution process of the object mapping topology submodule is as follows:

[0030] Obtain the input port identifier of the GIS primitive object selected by the user in the canvas and the output port identifier of the entity feature dimension, and retrieve the data type definitions corresponding to the two ports respectively;

[0031] Determine whether the data types of the input port and the output port meet the preset compatibility rules. If they meet the compatibility rules, create the dynamic mapping topology edge object connecting the two ports, configure the data conversion function of the edge object to the default linear mapping algorithm, and store it in the configuration description index.

[0032] As a further aspect of the present invention, the specific execution process of the spatial logic calculation submodule is as follows:

[0033] Obtain the coordinate vectors of the sampling points and the observed values ​​in the discrete point monitoring data, and calculate the Euclidean distance between the grid point to be interpolated and the coordinate vectors of each sampling point.

[0034] Based on the inverse distance weighting logic, the spatial influence weight coefficient of each sampling point relative to the grid point to be interpolated is calculated, and the value of the spatial influence weight coefficient is inversely proportional to the power of the Euclidean distance. The sum of the weight coefficients of all sampling points is standardized to a unit value.

[0035] The observed values ​​of each sampling point are weighted and summed with the corresponding spatial influence weight coefficients to obtain the estimated values ​​of the grid points. The estimated values ​​are then compared with the historical average fluctuation range in the neighborhood. If the deviation exceeds the range limited by the alarm threshold, the point is marked as abnormal data and smoothed and corrected.

[0036] As a further aspect of the present invention, the specific execution process of the geometric texture calculation submodule is as follows:

[0037] In the vertex shader stage, the geological elevation data corresponding to the rendering drive matrix is ​​read, and the position offset of the basic grid vertices of the GIS primitive object is calculated along the normal direction to generate a geometric surface that reflects the undulation of the strata.

[0038] In the fragment shader stage, the corresponding geological attribute values ​​in the rendering drive matrix are obtained. Based on the value, the corresponding RGB color value is retrieved from the preset color gradient map table. The color value is then mixed and superimposed with the basic texture of the primitive to generate a surface rendering texture that reflects the distribution of the strength of geological attributes.

[0039] As a further aspect of the present invention, the specific execution process of the matrix standardization submodule is as follows:

[0040] Obtain the smoothed and corrected geological spatial data, determine the timestamp of the current rendering frame, and normalize the multi-dimensional geological attribute values ​​into a floating-point sequence between zero and one.

[0041] According to the WebGL texture storage specification, the normalized floating-point number sequence is filled into the data unit of the rendering driver matrix in the order of the four channels of red, green and blue transparency, and the timestamp check code of the current frame is written at the beginning of the matrix to ensure the synchronization of rendering data with physical time, thereby generating the rendering driver matrix.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, by constructing a dynamic mapping engine based on semantic metadata, the upper-layer business logic and the lower-layer primitive rendering process are completely decoupled. The component-based configuration mechanism realizes the standardized encapsulation and automatic mounting of heterogeneous geological exploration data, enabling the system to flexibly reorganize the GIS layer display structure according to the real-time changing mining rules. The system completes the rapid construction and deployment of complex oilfield monitoring scenarios in a completely zero-code environment, effectively improving the scenario adaptability and system iteration efficiency of spatial data visualization management. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the digital oilfield GIS visualization management system of the present invention.

[0045] Figure 2 This is a schematic diagram of the operation logic of the metadata modeling module of the present invention;

[0046] Figure 3 This is a schematic diagram of the operation logic of the interactive configuration generation module of the present invention;

[0047] Figure 4 This is a schematic diagram of the operation logic of the logic parsing and processing module of the present invention;

[0048] Figure 5 This is a schematic diagram of the operation logic of the scene rendering execution module of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0050] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0051] Please see Figure 1 and Figure 2 This invention provides a technical solution: a zero-code configuration and visualization management system for a digital oilfield GIS platform, comprising:

[0052] The metadata modeling module is used to analyze multi-source heterogeneous oilfield monitoring data streams. It identifies entity feature dimensions that describe well location coordinates and geological attributes through feature extraction algorithms, constructs a business metadata model that includes business topology relationships, and establishes data type constraints and alarm thresholds for entity feature dimensions.

[0053] The specific functionalities of the metadata modeling module are as follows:

[0054] The heterogeneous data stream parsing submodule is used to access oilfield monitoring data streams containing different communication protocols and data formats, identify the protocol header identifiers and payload segments in the monitoring data streams, clean and standardize the payload segments, and generate a basic data sequence in a unified format.

[0055] The entity feature extraction submodule is used to perform semantic analysis on the basic data sequence, identify latitude and longitude data segments that represent geographical location and physical attribute data segments that represent geological state, establish the association and reference relationship between each data segment, and construct a business metadata model that can describe the well location entity and its attribute structure.

[0056] The constraint rule definition submodule is used to traverse each feature dimension in the business metadata model, and configure the numerical type verification rules and alarm thresholds for triggering abnormal states for each feature dimension according to the preset oilfield safety operation standards.

[0057] The specific execution process of the entity feature extraction submodule is as follows:

[0058] Obtain the cleaned basic data sequence, extract the field names and unit description text from the data sequence, and perform fuzzy matching calculations between the field names and the pre-set standard geological semantic dictionary;

[0059] Based on the matching similarity score, the data attribute category of each field is determined. Fields identified as coordinate categories are mapped to spatial location indexes, and fields identified as physical quantity categories are mapped to attribute state variables. The dependency relationship between attribute state variables and spatial location indexes is established, and a business metadata model is constructed.

[0060] The metadata modeling module, serving as the system's data perception and standardization entry point, operates through a rigorous, progressive three-tiered logic: heterogeneous data stream parsing, entity feature extraction, and constraint rule definition. The heterogeneous data stream parsing submodule first establishes a full-duplex communication link, accessing multi-source heterogeneous oilfield monitoring data streams, including industrial fieldbus, IoT MQTT protocol, and data transmitted via the OPCUA unified architecture. During the data access phase, this submodule does not simply perform data pass-through but executes a deep protocol stripping operation. It first reads the header bytes of the data packet and identifies the communication protocol type of the current data frame based on a pre-defined protocol feature code library. For example, when a specific hexadecimal identifier, such as 0xA1B2, is detected in the data frame header, the submodule automatically calls the ModbusTCP protocol parser. Subsequently, the submodule performs byte stream unpacking, removing redundant communication information such as check bits, frame headers, and frame trailers, retaining only the core payload segment. For binary data in the payload segment, this submodule converts it to decimal values ​​according to a preset byte order, i.e., big-endian or little-endian rules, and performs a linear transformation based on sensor range metadata to restore the original voltage or current signal to physically meaningful temperature, pressure, or flow values. During this process, the submodule also performs unit normalization, converting all pressure units to megapascals and all temperature units to degrees Celsius, generating a standardized basic data sequence.

[0061] Next, the entity feature extraction submodule performs semantic mining on the aforementioned basic data sequence. This submodule does not rely solely on a fixed address mapping table but introduces semantic analysis logic based on text similarity. It obtains the tag name string attached to the data stream, such as the tag name string "Well01_Press_High," and performs a fuzzy matching operation with the system's internal standard geological semantic dictionary. This operation uses an edit distance algorithm to calculate the number of character differences between the tag name and the standard vocabulary. When the calculated similarity score is higher than a preset semantic matching threshold, such as 0.85, the submodule determines that the field belongs to a specific geological attribute category. If the field is identified as belonging to the longitude or latitude category, it is mapped to a spatial location index as a benchmark for subsequent 3D positioning; if the field is identified as belonging to the pressure, water cut, or oil production category, it is mapped to an attribute state variable, and a dependency pointer is established between this variable and the corresponding spatial location index. This logically constructs a business metadata model that tightly binds well location entities with geological attributes.

[0062] Finally, the constraint rule definition submodule traverses each feature dimension in the model and loads a pre-defined oilfield safety operation standard library. This submodule uses statistical process control logic to perform normal distribution analysis on the historical data of this feature dimension, calculating its mean and standard deviation. Based on the 3-Sigma principle, the submodule sets the alarm threshold within the range of the mean plus or minus three times the standard deviation. Simultaneously, this submodule also configures data type validation rules for each feature dimension. For example, it mandates that wellhead pressure data must be double-precision floating-point type, and its value range must be between 0 and 100 MPa. If subsequent incoming data violates this constraint, it is marked as illegal data.

[0063] To verify the effectiveness of the above metadata processing logic, actual monitoring data from a certain oilfield block was selected for testing. Table 1 shows the specific transformation results of the heterogeneous data stream parsing submodule when processing a set of original Modbus data frames.

[0064] Table 1 Standardization Analysis Table for Multi-Source Heterogeneous Data ; As shown in Table 1, the system successfully converted raw hexadecimal data of different protocols and formats into standard values ​​with unified units and accurately identified their semantic categories. In the constraint rule definition example, taking the pressure data of "Node_05_P" in Table 1 as an example, the system retrieved the historical data of this node over the past 24 hours, calculating a historical mean of 9.5 MPa and a historical standard deviation of 0.2 MPa. The constraint rule definition submodule calculated the alarm upper limit threshold based on normal distribution logic: adding the mean of 9.5 MPa to three times the standard deviation, i.e., 0.6 MPa, yielded a result of 10.1 MPa. Therefore, the system automatically set the alarm upper limit for this node to 10.1 MPa. When the current parsed value in Table 1 is 10.0 MPa, this value is less than the upper limit threshold of 10.1 MPa, and the system determines that the data is within the normal operating range; if the subsequently collected value becomes 10.2 MPa, an abnormal alarm is immediately triggered. The experimental results show that, through automated parsing and threshold setting, the system can accurately complete the standardization and compliance verification of data. Compared with manual configuration, the configuration efficiency is improved by more than 95%, and the false alarm rate is significantly reduced.

[0065] The 3-Sigma principle mentioned above refers to a statistical quality control standard. Its core idea is that most values ​​are distributed within the range of the mean plus or minus three standard deviations, and data outside this range are usually considered outliers.

[0066] Please see Figure 1 and Figure 3 The interactive configuration generation module provides a zero-code visual operation canvas, responds to user drag-and-drop commands for the business metadata model, constructs a dynamic mapping topology between GIS primitive objects and entity feature dimensions, and generates a configuration description index that describes the interface interaction logic.

[0067] The specific functionality of the interactive configuration generation module is as follows:

[0068] The Canvas Interaction Response Submodule is used to render the business metadata model component library and GIS element library to be configured on the visual operation interface, listen for mouse drag events and placement coordinates of users in the canvas area in real time, and instantiate the visual component objects corresponding to user operations in the canvas.

[0069] The object mapping topology submodule is used to respond to user commands to establish connections between visual component objects, obtain the unique identifiers of the components at both ends of the connection, establish the binding relationship between the display attributes of GIS primitive objects and the data channels of entity feature dimensions, and generate dynamic mapping topology.

[0070] The configuration serialization submodule is used to traverse and serialize the spatial layout information, attribute parameters, and dynamic mapping topology of all component objects in the canvas, converting unstructured interactive operations into structured text description files and generating a configuration description index.

[0071] The specific execution process of the object mapping topology submodule is as follows:

[0072] Obtain the input port identifier and the output port identifier of the entity feature dimension of the GIS primitive object selected by the user in the canvas, and retrieve the data type definition corresponding to the two ports respectively;

[0073] Determine whether the data types of the input and output ports meet the preset compatibility rules. If they do, create a dynamic mapping topology edge object connecting the two ports, configure the data conversion function of the edge object to the default linear mapping algorithm, and store it in the configuration description index.

[0074] The interactive configuration generation module acts as a bridge connecting user operations and system logic. Its core function is to transform intuitive actions from non-technical users into rigorous system commands. This is primarily achieved through the collaborative efforts of three sub-modules: canvas interactive response, object mapping topology, and configuration serialization. The canvas interactive response sub-module is built upon the browser's Document Object Model (DOM) and Canvas drawing interface technology. When the user selects the "Water Injection Well" icon in the component library panel and drags it to the canvas area while holding down the left mouse button, this sub-module listens for the mouse movement event stream in real time. The sub-module obtains the pixel position of the mouse pointer in the screen coordinate system (ScreenX and ScreenY) through listeners and, combined with the current scaling factor and offset vector of the canvas, uses an inverse matrix transformation algorithm to convert the screen pixel coordinates into absolute geographic coordinates in the canvas logical coordinate system. When the user releases the left mouse button, the sub-module immediately instantiates a visual component object at the calculated logical coordinates. This object inherits the preset appearance attributes of the "Water Injection Well" primitive, such as color, size, and data interface definition.

[0075] The object mapping topology submodule then intervenes, handling the data flow logic between components. When the user drags a line from the "Pressure Output Port" of the "Water Injection Well" component to the "Data Input Port" of the "Dashboard" component, this submodule first captures the universally unique identifier (UUID) of these two ports. The submodule reads the data type definition metadata of the two ports and performs type compatibility checks. For example, the submodule compares the data type label of the "Pressure Output Port," such as "Floating-Point - Pressure," with the receiving type label of the "Dashboard," such as "Numerical - General." If they are compatible, the submodule creates a directed edge object in memory, recording the source node ID, target node ID, and data flow direction. At this point, the submodule generates a default linear mapping function as the transmission logic for this connection, where the input value equals the output value. However, users can configure more complex conversion logic, such as multiplication or offset addition, through the attribute panel. This process establishes a dynamic binding channel at the logical level between GIS element display attributes, such as the dashboard pointer angle, and entity feature dimensions, such as real-time pressure values.

[0076] The configuration serialization submodule is responsible for persisting the aforementioned transient memory objects. This submodule traverses all node and connection objects on the canvas, extracting their key attributes, including spatial coordinates, scaling factor, bound data source path, and mapping function parameters. The submodule uses a depth-first traversal algorithm to assemble these discrete object attributes into a tree-like data structure according to a predefined hierarchical structure. Subsequently, the submodule calls a serialization encoder to convert this tree structure into a standard JSON format text string, i.e., the configuration description index. In this text, each component is described as an independent object containing key-value pairs such as id, type, position, and connections, ensuring that the configuration scheme can be completely restored on any terminal that supports this format.

[0077] To illustrate the execution process of the linear mapping algorithm in the object mapping topology submodule, let's take mapping "wellhead pressure" to "color transparency" as an example. Assume the user has established a connection from the pressure data port to the primitive transparency attribute port and configured linear mapping rules. The object mapping topology submodule obtains the current pressure value and the preset pressure range, where the minimum value is 0 MPa and the maximum value is 20 MPa. It also obtains the target transparency range, where the minimum value is 0.1 and the maximum value is 1.0. The submodule performs the following logical operations: First, it subtracts the minimum pressure value from the current pressure value to obtain the pressure offset; second, it calculates the difference between the maximum and minimum pressure values ​​to obtain the total pressure interval; third, it divides the pressure offset by the total pressure interval to obtain the normalization ratio; finally, it multiplies the normalization ratio by the total transparency interval, i.e., the maximum transparency value minus the minimum transparency value, and adds the result to the minimum transparency value to obtain the final display transparency value.

[0078] For example, when the current wellhead pressure is 10 MPa, and the pressure range is set to 0 to 20 MPa, with a transparency range of 0.1 to 1.0, the system subtracts 0 MPa from 10 MPa to get 10, and subtracts 0 MPa from 20 MPa to get 20. Dividing these two values ​​yields a normalization ratio of 0.5. Subsequently, the system calculates the transparency range as 0.9 (1.0 minus 0.1), multiplies 0.5 by 0.9 to get 0.45, and finally adds the base transparency of 0.1, resulting in a final primitive display transparency of 0.55. This result of 0.55 means that in a 3D scene, the wellhead primitive will appear semi-transparent, intuitively reflecting that the current pressure is at a moderate level. This example verifies that the mapping logic generated by zero-code configuration can accurately drive changes in visual attributes, achieving real-time linkage between data and vision.

[0079] The aforementioned Document Object Model (DOM) refers to a cross-platform and language-independent interface that processes HTML or XML documents into a tree structure, enabling programs to dynamically access and update the document's content, structure, and style.

[0080] Please see Figure 1 and Figure 4 The logic parsing and processing module is used to traverse the configuration description index, extract real-time geological spatial data from the GIS platform based on the dynamic mapping topology, perform spatial interpolation calculations and anomaly filtering on the geological spatial data, and generate a standardized rendering drive matrix.

[0081] The specific functional implementation of the logic parsing and processing module is as follows:

[0082] The data retrieval and parsing submodule is used to read the configuration description index, parse out the list of entity feature dimensions required for the current scene, and send a data subscription request to the GIS platform based on the list to obtain real-time updated discrete point monitoring data;

[0083] The spatial logic calculation submodule is used to calculate the interpolation weight of unknown areas based on the spatial coordinate distribution of discrete point monitoring data, and to perform statistical significance tests on the monitoring values, remove noise data that exceeds the preset confidence interval, and generate smoothed geological spatial data.

[0084] The matrix normalization submodule is used to reorganize the processed geological spatial data according to the preset rendering pipeline format, map multi-dimensional geological attributes into texture data structures suitable for GPU parallel reading, and generate a rendering driving matrix containing color channels and deformation parameters.

[0085] The specific execution process of the spatial logic calculation submodule is as follows:

[0086] Obtain the coordinate vectors of the sampling points and the observed values ​​in the discrete point monitoring data, and calculate the Euclidean distance between the grid point to be interpolated and the coordinate vectors of each sampling point.

[0087] Based on the inverse distance weighting logic, the spatial influence weight coefficient of each sampling point relative to the grid point to be interpolated is calculated, and it is established that the value of the spatial influence weight coefficient is inversely proportional to the power of the Euclidean distance, and the sum of the weight coefficients of all sampling points is standardized to a unit value.

[0088] The observed values ​​of each sampling point are weighted and summed with the corresponding spatial influence weight coefficients to obtain the estimated value of the grid point. The estimated value is then compared with the historical average fluctuation range in the neighborhood. If the deviation exceeds the range limited by the alarm threshold, the point is marked as abnormal data and smoothed and corrected.

[0089] The specific execution process of the matrix standardization submodule is as follows:

[0090] Obtain smoothed and corrected geological spatial data, determine the timestamp of the current rendering frame, and normalize the multi-dimensional geological attribute values ​​into a floating-point sequence between zero and one.

[0091] According to the WebGL texture storage specification, the normalized floating-point sequence is filled into the data unit of the rendering driver matrix in the order of the four channels of red, green and blue transparency, and the timestamp check code of the current frame is written at the beginning of the matrix to ensure the synchronization of rendering data with physical time, thus generating the rendering driver matrix.

[0092] The logic parsing and processing module is the computational core of the system, responsible for transforming discrete monitoring point data into continuous spatial field data. This is achieved through the precise coordination of three sub-modules: data retrieval and parsing, spatial logic calculation, and matrix standardization. The data retrieval and parsing sub-module first loads the configuration description index and extracts all defined data subscription requests. This sub-module constructs a deduplicated list of data requests and sends a batch subscription command to the GIS platform via a WebSocket long connection. When the GIS platform pushes the latest real-time data packets, this sub-module, based on the mapping relationships in the index, fills the discrete monitoring values, such as the pressure values ​​at each wellhead, into the corresponding memory variables, forming a set of discrete sampling points with three-dimensional coordinate information.

[0093] The spatial logic calculation submodule then performs complex spatial interpolation and cleaning operations on the sample point set. Considering the uneven spatial distribution of oilfield geological data, this submodule employs inverse distance weighting (IDW) logic to estimate values ​​for unknown areas. The submodule first determines the coordinates of the grid point to be interpolated, then iterates through all known sample points within the search radius of that grid point. For each sample point, the submodule calculates its Euclidean distance to the grid point to be interpolated. Next, the submodule calculates the weighting coefficient based on the distance decay principle: the Euclidean distance is squared, and its reciprocal is used as the initial weight for that sample point. To ensure the physical meaning of the values, the submodule sums the initial weights of all sample points to obtain a total weight, then divides the initial weight of each sample point by this total weight to obtain the normalized weight. Finally, the submodule multiplies the measured physical attribute value of each sample point with its corresponding normalized weight, and sums all the products to obtain the estimated attribute value of the grid point to be interpolated. Meanwhile, this submodule introduces Z-score statistical test logic to eliminate abnormal noise: calculate the average value and standard deviation of the sampling points in the local neighborhood. If the value of a sampling point deviates from the average value by more than three times the standard deviation, the point is determined to be sensor drift noise. In the interpolation calculation, its weight is forcibly set to zero or replaced and corrected by using the neighborhood mean.

[0094] The matrix normalization submodule is responsible for converting the calculated spatial field data into a texture format that can be directly read by the graphics processing unit (GPU). This submodule creates a two-dimensional floating-point array as the rendering driving matrix. It iterates through the interpolated geological spatial data grid, mapping the geological attribute values ​​of each grid point, such as pressure, to specific channels of the matrix cells. Typically, the submodule writes the normalized attribute values ​​to the red or R channel, the timestamp checksum of the point to the green or G channel, the confidence score of the data to the blue or B channel, and the transparency control value to the Alpha or A channel. This channel multiplexing technique ensures that multi-dimensional business data can be transferred to the GPU with only a single texture transfer, greatly improving rendering efficiency.

[0095] To more clearly illustrate the execution details of the inverse distance weighted logic in the spatial logic computation submodule, Table 2 lists the computation process data for a specific grid point P to be interpolated. Assume point P has coordinates of 5,5, and three valid sampling points A, B, and C exist around it.

[0096] Table 2 Spatial Interpolation Weight Calculation Logic Table ; In the example in Table 2, the system first calculates the distance between sampling point A and point P: the square of the difference in the horizontal coordinate is 9, the square of the difference in the vertical coordinate is 0, and the square root of the sum of the two gives a distance of 3.0 meters. Similarly, the distance to point B is calculated to be 4.0 meters. For point C, since its distance is too far, exceeding the preset search radius threshold (e.g., 15 meters), the system directly resets its weight to 0 and excludes it from the calculation. Next, the system calculates the reciprocal of the square of the distance: approximately 0.1111 for point A and 0.0625 for point B. The total weight is calculated to be 0.1736. Then, the normalized weights are calculated: the weight of point A is approximately 0.64, and the weight of point B is approximately 0.36. Finally, the system performs a weighted summation: multiplying the pressure of point A (10.0) by 0.64 gives 6.4, multiplying the pressure of point B (12.0) by 0.36 gives 4.32, and adding the two together gives the estimated pressure value of point P as 10.72 MPa. The numerical result of 10.72 MPa represents the theoretical pressure state of point P under the current geological environment. This result will be filled into the corresponding cell of the rendering driving matrix to subsequently drive the three-dimensional terrain uplift height or color change at point P, thereby visually smoothly displaying the pressure gradient change from 10.0 MPa to 12.0 MPa.

[0097] The Z-score statistical test logic described above refers to determining the degree to which a data point deviates from the mean by calculating the distance between the data point and the mean and measuring it in units of standard deviation.

[0098] Please see Figure 1 and Figure 5 The scene rendering execution module is used to load the rendering driver matrix, drive the WebGL engine to calculate the texture mapping coordinates and geometric positions of GIS primitive objects, and render the abstract geological spatial data into a high-fidelity 3D digital twin scene in real time.

[0099] The specific functions of the scene rendering execution module are as follows:

[0100] The driver matrix loading submodule is used to upload the rendering driver matrix to the video memory area, parse the texture channel data and deformation parameters in the matrix, and bind them to the vertex shader and fragment shader variables of the WebGL rendering pipeline.

[0101] The geometric texture calculation submodule is used to dynamically calculate the grid vertex offset of GIS primitive objects based on the deformation parameters in the rendering driving matrix to represent the geological layer undulations, and to calculate the light reflectance and texture blending weight of the primitive surface based on the color channel data.

[0102] The scene compositing and rendering submodule is used to perform depth testing and blending operations. It draws the calculated GIS primitive objects into the frame buffer, overlays ambient lighting and post-processing effects, and outputs a real-time updated 3D digital twin scene on the display terminal.

[0103] The specific execution process of the geometric texture calculation submodule is as follows:

[0104] In the vertex shader stage, the corresponding geological elevation data in the rendering drive matrix is ​​read, and the position offset of the basic grid vertices of the GIS primitive object is calculated along the normal direction to generate a geometric surface that reflects the undulation of the strata.

[0105] In the fragment shader stage, the corresponding geological attribute values ​​in the rendering driver matrix are obtained. Based on the value, the corresponding RGB color value is retrieved from the preset color gradient map table. The color value is then mixed and superimposed with the basic texture of the primitive to generate a surface rendering texture that reflects the distribution of the strength of the geological attributes.

[0106] The scene rendering execution module relies on the WebGL graphics standard to directly manipulate the graphics card hardware to achieve high frame rate 3D visualization. Specifically, it completes a closed loop through three sub-modules: driver matrix loading, geometric texture calculation, and scene compositing and rendering. The driver matrix loading sub-module first allocates a texture buffer in video memory and uploads the rendering driver matrix generated by the logic parsing module to the GPU in floating-point texture format. This sub-module configures texture parameters, setting the texture filtering mode to nearest-neighbor interpolation to prevent unexpected data smoothing during vertex sampling. Subsequently, the sub-module binds the handle of this texture unit to a Uniform variable in the WebGL rendering pipeline, enabling both the vertex shader and fragment shader to read the corresponding business data through texture coordinates.

[0107] The geometry texture calculation submodule performs the core visual mapping in the GPU's parallel pipeline. In the vertex shader stage, for each vertex constituting the terrain mesh, the submodule samples the red channel (R channel) data from the rendering driver matrix based on its texture coordinates (UV). This data represents the intensity of geological attributes at that location, such as pressure. The submodule multiplies this intensity value by a preset terrain exaggeration factor, such as a vertical scaling factor, to obtain the vertex's vertical offset. Subsequently, the submodule superimposes this offset along the vertex's normal direction, modifying the vertex's model space coordinates in real time, thus visually creating dynamic terrain that undulates with pressure levels. In the fragment shader stage, the submodule again reads this attribute intensity value and uses it as an index to look up a value in a preset color band texture, such as a blue-to-red gradient. The submodule performs a linear blending calculation, weighting and blending the found color value with the terrain's base texture color, such as sand or gravel, to generate the final fragment color.

[0108] The scene compositing and rendering submodule performs the final image output operation. This submodule enables depth testing to ensure the correct rendering of foreground and background occlusion relationships. Simultaneously, the submodule calculates diffuse and specular reflection components based on ambient lighting parameters to enhance the scene's three-dimensionality. If the blue channel (B channel) in the rendering driver matrix indicates low confidence in a certain area, the submodule automatically lowers the alpha transparency value of fragments in that area, creating a semi-transparent blurring effect, thus guiding the user's attention to the high-confidence core areas. Finally, all calculated pixels are written to the frame buffer, refreshing the display terminal's screen.

[0109] To quantify the calculation logic of vertex offset in the geometry texture calculation submodule, assume that the R channel value (normalized pressure) of a certain point in the current rendering driver matrix is ​​0.8, the preset terrain base height is 10.0 meters, and the terrain exaggeration coefficient is 50.0 meters. The geometry texture calculation submodule performs the following textual operation logic: First, it reads the original height value of the base mesh vertex; second, it obtains the normalized pressure value in the matrix and multiplies it by the terrain exaggeration coefficient to obtain the dynamic incremental height; finally, it adds the original height value to the dynamic incremental height to obtain the final altitude of the rendered vertex.

[0110] Substituting the above parameters into the calculation: the system multiplies the normalized pressure of 0.8 by the terrain exaggeration coefficient of 50.0 meters, resulting in a dynamic increment of 40.0 meters. This increment is then added to the base height of 10.0 meters, yielding a final peak height of 50.0 meters. Simultaneously, in color calculation, assuming the color banding logic defines 0.0 as corresponding to blue (RGB values ​​0, 0, 255) and 1.0 as corresponding to red (RGB values ​​255, 0, 0), the system performs linear interpolation for the value 0.8: the red component equals 0.8 multiplied by 255, resulting in 204, and the blue component equals 0.2 multiplied by 255, resulting in 51. The final synthesized color is magenta, with RGB values ​​of 204, 0, 51. These numerical results demonstrate that in a 3D scene, this location not only geometrically bulges significantly to a height of 50 meters, forming a visual peak, but also its surface color becomes a striking magenta. This dual visual coding is directly linked to the underlying pressure values, enabling technicians to visually determine that the area is under high pressure without having to manually consult data tables one by one, greatly improving the efficiency of geological situation awareness.

[0111] The aforementioned nearest neighbor interpolation refers to a simple image scaling or texture sampling algorithm that, when performing coordinate transformation, directly selects the pixel value closest to the target coordinates as the result without performing a weighted average.

[0112] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A zero-code configuration and visual management system for a digital oilfield GIS platform, characterized in that, The system includes: The metadata modeling module is used to analyze multi-source heterogeneous oilfield monitoring data streams. It identifies entity feature dimensions that describe well location coordinates and geological attributes through feature extraction algorithms, constructs a business metadata model that includes business topology relationships, and establishes data type constraints and alarm thresholds for the entity feature dimensions. The interactive configuration generation module is used to provide a zero-code visual operation canvas, respond to the user's component-based drag-and-drop instructions on the business metadata model, construct a dynamic mapping topology between GIS primitive objects and the entity feature dimensions, and generate a configuration description index that describes the interface interaction logic. The logic parsing and processing module is used to traverse the configuration description index, extract real-time geological spatial data from the GIS platform according to the dynamic mapping topology, perform spatial interpolation calculation and abnormal data filtering on the geological spatial data, and generate a standardized rendering drive matrix. The scene rendering execution module is used to load the rendering driving matrix, drive the WebGL engine to calculate the texture mapping coordinates and geometric positions of the GIS primitive objects, and render the abstract geological spatial data as a high-fidelity three-dimensional digital twin scene in real time.

2. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 1, characterized in that, The specific functional implementation of the metadata modeling module is as follows: The heterogeneous data stream parsing submodule is used to access oilfield monitoring data streams containing different communication protocols and data formats, identify the protocol header identifiers and payload segments in the monitoring data streams, clean and standardize the payload segments, and generate a basic data sequence in a unified format. The entity feature extraction submodule is used to perform semantic analysis on the basic data sequence, identify latitude and longitude data segments that represent geographical location and physical attribute data segments that represent geological state, establish the association and reference relationship between each data segment, and construct the business metadata model that can describe the well location entity and its attribute structure. The constraint rule definition submodule is used to traverse each feature dimension in the business metadata model, and configure the numerical type verification rules and the alarm threshold for triggering abnormal states for each feature dimension according to the preset oilfield safety operation standards.

3. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 2, characterized in that, The specific functions of the interactive configuration generation module are as follows: The canvas interaction response submodule is used to render the business metadata model component library and GIS element library to be configured on the visual operation interface, listen to the user's mouse drag events and placement coordinates in the canvas area in real time, and instantiate the visual component object corresponding to the user operation in the canvas. The object mapping topology submodule is used to respond to the user's operation command to establish a connection between visual component objects, obtain the unique identifiers of the components at both ends of the connection, establish the binding relationship between the display attributes of the GIS primitive objects and the data channels of the entity feature dimensions, and generate the dynamic mapping topology. The configuration serialization submodule is used to traverse and serialize the spatial layout information, attribute parameters, and dynamic mapping topology of all component objects in the canvas, converting unstructured interactive operations into structured text description files and generating the configuration description index.

4. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 1, characterized in that, The specific functional implementation of the logic parsing processing module is as follows: The data retrieval and parsing submodule is used to read the configuration description index, parse out the list of entity feature dimensions required for the current scene, and send a data subscription request to the GIS platform based on the list to obtain real-time updated discrete point monitoring data. The spatial logic calculation submodule is used to calculate the interpolation weight of the unknown area based on the spatial coordinate distribution of the discrete point monitoring data, and to perform statistical significance testing on the monitoring values, remove noise data that exceeds the preset confidence interval, and generate the smoothed geological spatial data. The matrix normalization submodule is used to reorganize the processed geological spatial data according to a preset rendering pipeline format, map multi-dimensional geological attributes into a texture data structure suitable for GPU parallel reading, and generate the rendering driving matrix containing color channels and deformation parameters.

5. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 4, characterized in that, The specific functions of the scene rendering execution module are as follows: The driver matrix loading submodule is used to upload the rendering driver matrix to the video memory area, parse the texture channel data and deformation parameters in the matrix, and bind them to the vertex shader and fragment shader variables of the WebGL rendering pipeline. The geometric texture calculation submodule is used to dynamically calculate the grid vertex offset of the GIS primitive object based on the deformation parameters in the rendering driving matrix to represent the geological layer undulation, and to calculate the light reflectance and texture blending weight of the primitive surface based on the color channel data. The scene compositing and rendering submodule is used to perform depth testing and blending operations, draw the calculated GIS primitive objects into the frame buffer, overlay ambient lighting and post-processing effects, and output the real-time updated 3D digital twin scene on the display terminal.

6. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 2, characterized in that, The specific execution process of the entity feature extraction submodule is as follows: Obtain the cleaned basic data sequence, extract the field names and unit description text from the data sequence, and perform fuzzy matching calculations between the field names and a pre-set standard geological semantic dictionary; Based on the matching similarity score, the data attribute category of each field is determined. Fields identified as coordinate categories are mapped to spatial location indexes, and fields identified as physical quantity categories are mapped to attribute state variables. The dependency relationship between attribute state variables and spatial location indexes is established, and the business metadata model is constructed.

7. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 3, characterized in that, The specific execution process of the object mapping topology submodule is as follows: Obtain the input port identifier of the GIS primitive object selected by the user in the canvas and the output port identifier of the entity feature dimension, and retrieve the data type definitions corresponding to the two ports respectively; Determine whether the data types of the input port and the output port meet the preset compatibility rules. If they meet the compatibility rules, create the dynamic mapping topology edge object connecting the two ports, configure the data conversion function of the edge object to the default linear mapping algorithm, and store it in the configuration description index.

8. The zero-code configuration visualization management system for a digital oilfield GIS platform according to claim 5, characterized in that, The specific execution process of the spatial logic calculation submodule is as follows: Obtain the coordinate vectors of the sampling points and the observed values ​​in the discrete point monitoring data, and calculate the Euclidean distance between the grid point to be interpolated and the coordinate vectors of each sampling point. Based on the inverse distance weighting logic, the spatial influence weight coefficient of each sampling point relative to the grid point to be interpolated is calculated, and it is established that the value of the spatial influence weight coefficient is inversely proportional to the power of the Euclidean distance, and the sum of the weight coefficients of all sampling points is standardized to a unit value. The observed values ​​of each sampling point are weighted and summed with the corresponding spatial influence weighting coefficients to obtain the estimated values ​​of the grid points. The estimated values ​​are then compared with the historical average fluctuation range in the neighborhood. If the deviation exceeds the range limited by the alarm threshold, the point is marked as abnormal data and smoothed and corrected.

9. The zero-code configuration and visualization management system for a digital oilfield GIS platform according to claim 8, characterized in that, The specific execution process of the geometric texture calculation submodule is as follows: In the vertex shader stage, the geological elevation data corresponding to the rendering drive matrix is ​​read, and the position offset of the basic grid vertices of the GIS primitive object is calculated along the normal direction to generate a geometric surface that reflects the undulation of the strata. In the fragment shader stage, the corresponding geological attribute values ​​in the rendering drive matrix are obtained. Based on the value, the corresponding RGB color value is retrieved from the preset color gradient map table. The color value is then mixed and superimposed with the basic texture of the primitive to generate a surface rendering texture that reflects the distribution of the strength of geological attributes.

10. The zero-code configuration visualization management system for a digital oilfield GIS platform according to claim 4, characterized in that, The specific execution process of the matrix standardization submodule is as follows: Obtain the smoothed and corrected geological spatial data, determine the timestamp of the current rendering frame, and normalize the multi-dimensional geological attribute values ​​into a floating-point sequence between zero and one. According to the WebGL texture storage specification, the normalized floating-point number sequence is filled into the data unit of the rendering driver matrix in the order of the four channels of red, green and blue transparency, and the timestamp check code of the current frame is written at the beginning of the matrix to ensure the synchronization of rendering data with physical time, thereby generating the rendering driver matrix.

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