Oil and gas field three-dimensional visualization system based on digital twinning

Abstract

The invention discloses an oil and gas field three-dimensional visualization system based on digital twinning, and relates to the technical field of digital twinning, and the system comprises the following steps: S1, integrating GIS, BIM, IoT and GD multi-source data, and constructing a unified ground object entity data set; s2, carrying out sensor, camera and positioning beacon data fusion by using the ground object entity data set; s3, carrying out business system data integration and three-dimensional scene modeling by using the ground object dynamic state calibration set; s4, performing multifunctional application system development by using the three-dimensional scene business linkage model; s5, realizing efficient cloud management and redundant backup in combination with a SpringBoot distributed architecture; by setting a ground object dynamic state calibration model, a service system linkage model and a three-dimensional entity management model, multi-dimensional fusion modeling can be performed on geometric features, state parameters and service features of all ground objects in an oil and gas field, and the real-time performance and service adaptation capability of a three-dimensional visualization system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular to a three-dimensional visualization system for oil and gas fields based on digital twins. Background Art

[0002] In oil and gas field production and safety management, 3D visualization systems have become a critical decision-making support and real-time monitoring tool. Most current systems are based on the integration of geographic information systems (GIS), sensor networks, and video surveillance systems, focusing on static terrain rendering, visual management of production factors, and risk area indication. However, faced with the rapid dynamic changes, high business coupling, and complex operating environments of oil and gas fields, traditional static 3D systems lack the ability to dynamically integrate and express the physical state of land features, making it difficult to support the demands of high precision, real-time performance, and coordinated business response. In recent years, the development of digital twin technology has provided a new direction for 3D modeling and intelligent operation and maintenance of oil and gas fields: by constructing synchronized physical and virtual models, it can track and simulate the spatiotemporal evolution of actual land features.

[0003] Existing technologies generally rely on static mapping of underlying sensor data for dynamic updates of 3D scenes. These technologies lack the ability to integrate and model multimodal inputs (such as location beacon offsets, image recognition features, and business data streams), making it impossible to achieve high-precision dynamic calibration of terrain features. This is particularly problematic in complex scenes and heterogeneous data, where response delays and semantic mismatches are common. Consequently, existing 3D modeling technologies struggle to support the deep integration requirements of oil and gas field digital twin systems, a major limitation to their effectiveness. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a three-dimensional visualization system of oil and gas fields based on digital twins to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a three-dimensional visualization system for oil and gas fields based on digital twins, comprising the following steps: S1. Integrate GIS, BIM, IoT, and GD multi-source data and build a unified ground object entity dataset; S2. Use the ground object entity dataset to fuse sensor, camera, and positioning beacon data to obtain the ground object dynamic state calibration set; S3. Use the ground feature dynamic state calibration set to perform business system data integration and 3D scene modeling to obtain a 3D scene business linkage model; S4. Develop a multifunctional application system using the 3D scenario business linkage model to obtain a 3D visualization application set for oil and gas field production and safety; S5. Use the oil and gas field production and safety 3D visualization application set, combined with the SpringBoot distributed architecture to achieve efficient cloud management and redundant backup.

[0006] Further optimize this technical solution, in step S1, input GIS geographic information system data, BIM building information model data, IoT Internet of Things perception data, GD geographic surveying and real-time dynamic geographic data, and output a unified ground object entity data set ,in , that is, The static spatial feature vector of a ground feature, is the three-dimensional position coordinate of the feature; is the orientation angle; For size; is the terrain curvature index; The identification code for the feature material or material category.

[0007] To further optimize this technical solution, step S2 includes calibrating the ground feature dynamic state model: ; in, : No. Features in time Dynamic state calibration value at the moment; : No. The static spatial feature vector of each feature; : In time Time The dynamic feature offset vector corresponding to each feature; : No. Features in time The attribute feature vector obtained by the sensor when : No. Features in time The visual feature vector extracted by the camera when : spatial position offset correction function; : function for generating comprehensive characteristics of ground feature status; : Position-state fusion mapping function.

[0008] Further optimizing this technical solution, the dynamic feature offset vector in step S2

[0009] ; : The offset of the feature’s three-dimensional position in space; : change in the object's orientation angle; : Change in feature size; : change in terrain curvature; : Change in ground feature material or material state.

[0010] To further optimize this technical solution, the attribute feature vector obtained by the sensor in step S2 is: ; : temperature characteristic value after linear normalization; : pressure characteristic value after linear normalization; : Amplitude eigenvalue after linear normalization.

[0011] To further optimize this technical solution, the visual feature vector extracted by the camera in step S2 is: ; Each is the visual feature component obtained through visual analysis, is the dimension of the visual feature vector, where .

[0012] To further optimize this technical solution, the three-dimensional scene business linkage model function in step S3 is: ; in, : No. A feature at a time The output vector of the three-dimensional scene business linkage model; : Indicates the vector-level cascade operation of the same feature in the dimensions of dynamic state information and business attributes; :The static spatial feature vector of the ground object in step S1 Mapping to standard three-dimensional model morphological control factors; : Represents the transformation function of the model morphology control factor in 3D visual modeling, realizing the injection of geometry and texture parameters.

[0013] To further optimize this technical solution, the three-dimensional scene business linkage model function in step S3 includes: By calibrating the dynamic state vector and business system vector The combination of "status + business" can realize the two-way feature fusion of ground objects; Then use the mapping function Combine static structural features and dynamic business status into a standard 3D scene to construct a composite object containing morphology, status, and business semantic information; income Vectors can be used in real-time rendering, linked response control, and state display, achieving linkage from state modeling to visual output. Obtained by this formula It is a three-dimensional scene business linkage model after the fusion of the four elements of "ground object-state-business-3D form", and is the core data entity that supports S4 to develop three-dimensional visualization applications.

[0014] To further optimize this technical solution, step S4 is performed by: Scenario function decomposition and requirements mapping; Linkage model structure analysis and module binding; Dynamic presentation driven by spatial state; 3D scene integration and interactive development; Multifunctional subsystem integration and output; The final output is a 3D visualization application set for oil and gas fields for production management, safety warning, and equipment operation and maintenance application scenarios. ,in Indicates the Features in time The three-dimensional business status on the

[0015] To further optimize this technical solution, step S5 implements efficient cloud management and redundant backup through cloud management and redundant scheduling functions.

[0016] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a three-dimensional visualization system for oil and gas fields based on digital twins as described in the first aspect of the present invention are implemented.

[0017] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of a three-dimensional visualization system for oil and gas fields based on digital twins as described in the first aspect of the present invention are implemented.

[0018] Compared with the existing technology, the present invention provides a 3D visualization system for oil and gas fields based on digital twins, which has the following beneficial effects: This digital twin-based 3D oil and gas field visualization system utilizes a dynamic feature calibration model, a business system linkage model, and a 3D entity management model to perform multi-dimensional fusion modeling of the geometric features, state parameters, and business characteristics of features within the oil and gas field, significantly improving the system's real-time performance and business adaptability. By constructing a dynamic feature calibration formula model, this system overcomes the existing problem of state error accumulation caused by a single data driver. The system introduces a business data integration method based on a 3D business model, making the scenario model scalable and interconnected. In cloud-based deployment, system resource mapping functions, fault detection vectors, and an adaptive scheduling mechanism are designed to ensure high data management reliability and system stability in complex oil and gas field environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a flow chart of a digital twin-based three-dimensional visualization system for oil and gas fields proposed by the present invention; Figure 2 This is a schematic diagram of the development process of a multifunctional application system for a digital twin-based three-dimensional visualization system for oil and gas fields proposed in the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments. Example 1:

[0024] Reference Figures 1 and 2, which is the first embodiment of the present invention, provides a three-dimensional visualization system for oil and gas fields based on digital twins, including the following steps: S1. Integrate GIS, BIM, IoT, and GD multi-source data and build a unified ground object entity dataset: In step S1, GIS geographic information system data, BIM building information model data, IoT perception data, GD geographic surveying and real-time dynamic geographic data are input and processed as follows: Standard identification: Use mature OGC standards to uniformly identify the data formats of GIS data and GD data; Use IFC standards to parse BIM data and ensure the standardization of 3D model information; Use the LWM2M protocol to access IoT static description data and unify the initial attributes of the perception objects; Coordinate system and spatial datum are unified: All input data are transformed into coordinate systems using the mature PROJ coordinate transformation library; Multi-source data entity recognition and spatial registration: Through mature spatial buffer analysis and nearest neighbor matching algorithm, spatial object registration of the same ground object entity is completed; Attribute field standardization and merging: Use mature attribute mapping and standardized ETL tools to define attribute mapping tables and normalize object attributes from different sources to a unified standard; Object ID and dynamic expansion capability assignment: Generate a unique OID for each object using a mature UUID (Universally Unique Identifier) generation mechanism; Schema-free data structure modeling is adopted to enable the dynamic attributes of feature objects to be flexibly expanded in the future.

[0025] Step S1 finally outputs a unified ground object entity dataset through the above processing ,in , that is, The static spatial feature vector of a ground feature, is the three-dimensional position coordinate of the feature; is the orientation angle; For size; is the terrain curvature index; The identification code for the feature material or material category.

[0026] S2. Use the ground object entity dataset to fuse sensor, camera, and positioning beacon data to obtain the ground object dynamic state calibration set; Step S2 includes the ground feature dynamic state calibration model: ; in, : No. Features in time Dynamic state calibration value at the moment; : No. The static spatial feature vector of each feature; : In time Time The dynamic feature offset vector corresponding to each feature; ; : The offset of the feature’s three-dimensional position in space; : change in the object's orientation angle; : Change in feature size; : change in terrain curvature; : Change in ground feature material or material state; Static spatial feature vector and dynamic feature offset vector and 、 and 、 and 、 and All have been normalized. and All are converted to one-hot encoding; : No. Features in time The attribute feature vector obtained by the sensor at the time includes temperature, pressure, and amplitude after linear normalization. , : Temperature characteristic value after linear normalization, : pressure characteristic value after linear normalization, : Amplitude eigenvalue after linear normalization; : No. Features in time The visual feature vector extracted by the camera when , each is the visual feature component obtained through visual analysis, is the dimension of the visual feature vector, where ; : spatial position offset correction function; ; : function for generating comprehensive characteristics of ground feature status; ; : Position-state fusion mapping function, .

[0027] In step S2, firstly, the ground object entity dataset output in step S1 is used As a basis, combined with the dynamic feature offset vector , through the function The static spatial features and dynamic offset features are linearly superimposed at the component level to obtain the dynamically updated spatial feature vector of the ground feature. and the visual feature vector extracted by the camera As input, use the function The physical and visual attributes are combined to form a fused physical-visual joint feature vector. Finally, the function The dynamically updated spatial features of the ground objects are integrated with the physical-visual joint features to output the Features in time Dynamic state calibration vector at , used to construct the dynamic state calibration set of ground objects , This process achieves the precise fusion of multi-source heterogeneous data at the temporal and spatial attribute levels, providing a unified input for the dynamic expression of the state of land features in the subsequent three-dimensional visualization system.

[0028] Step S2 adopts a step-by-step fusion mechanism for static features, dynamic offset features and physical visual features of ground objects, and 、 、 The function implements direct linear superposition and feature concatenation within a unified vector space. Unlike existing mature technologies that process various data types independently, this method achieves the integrated fusion of heterogeneous data at the data level, eliminating the need for multiple cross-domain mappings or complex synchronization processing on the visualization platform. This significantly improves data consistency, response efficiency, and system integration, while also providing a standardized dynamic state data foundation for subsequent applications such as ground feature behavior prediction and intelligent monitoring.

[0029] S3: Use the dynamic state calibration set of the ground feature to integrate business system data and perform 3D scene modeling to obtain a 3D scene business linkage model; The business system data in step S3 (including task progress, equipment status, environmental status, sensor data, visual data, and task priority) usually comes from existing monitoring systems, sensor networks, production management systems, etc. Existing mature technologies obtain these data and perform standardization and normalization through sensor data collection, real-time data transmission, data processing, and storage systems to form business data sets. ,in For the The ground features come from the standardized feature vector set of the business system at time , the standardized business vector is expressed as , and provide it to the subsequent integration and modeling process.

[0030] The three-dimensional scene business linkage model function in step S3 is: ; in, : No. A feature at a time The output vector of the three-dimensional scene business linkage model; : represents the vector-level cascade operation of the same feature in the dynamic state information and business attribute dimensions, used to calibrate the dynamic state vector obtained in step S2 and the standardized feature vector obtained from the business system in step S3 Merge, the merged unified feature vector is used for 3D modeling and business linkage; ; : ; : combined feature vector; :The static spatial feature vector of the ground object in step S1 Mapping is done to the standard 3D model morphological control factors, and the static spatial characteristics of the objects are mapped to the standard 3D model morphological control factors. Converted into standard morphological control factors; ; in, Geometric parameters required for modeling, including 3D coordinate offset, orientation angle, size, and material; : Represents the transformation function of the model morphology control factor in 3D visual modeling, realizing the injection of geometry and texture parameters; ; in, : Three-dimensional modeling function, specifically: ; is the transformation matrix; : No. Features in time 3D modeling expression vector on .

[0031] The three-dimensional scene business linkage model function in step S3 includes: By calibrating the dynamic state vector and business system vector The combination of "status + business" can realize the two-way feature fusion of ground objects; Then use the mapping function Combine static structural features and dynamic business status into a standard 3D scene to construct a composite object containing morphology, status, and business semantic information; income Vectors can be subsequently used for real-time rendering, linked response control, and state display, achieving linkage from state modeling to visual output.

[0032] Obtained by this formula It is a three-dimensional scene business linkage model after the fusion of the four elements of "ground object-state-business-3D form", and is the core data entity that supports S4 to develop three-dimensional visualization applications.

[0033] Step S3 introduces the “ground feature dynamic state calibration vector” into the 3D modeling and "Business System Standardized Feature Vector" The dual fusion mechanism and the static spatial feature mapping function Explicitly injecting geometric morphological control factors into the model generation process achieves deep coupling and dynamic mapping between the three domains of "state-business-morphology" of land features, breaking through the limitations of traditional methods that rely solely on static business attributes or predefined model templates for modeling, and enabling three-dimensional scene models to have variable state response capabilities and real-time business semantic expression capabilities.

[0034] S4. Develop a multifunctional application system using the 3D scenario business linkage model to obtain a 3D visualization application set for oil and gas field production and safety; Step S4 includes the following processing flow: Scenario function decomposition and requirements mapping By utilizing mature scenario function modeling technology (BPMN-based business process modeling), core businesses such as oil and gas field production, safety inspections, emergency response, and equipment monitoring are divided into multiple application sub-modules in the visualization system.

[0035] Linkage model structure analysis and module binding The three-dimensional scene business linkage model output in step S3 Perform structural analysis and use mature data parsing and component identification technology (based on the OGC standard CityGML) to extract the visual components and business attribute labels of each feature in its current state.

[0036] Through the module mapping strategy, the specific business module functions are bound to the corresponding business feature tags. unit.

[0037] Dynamic presentation driven by spatial state Through the existing state-driven modeling technology (space-time visualization modeling system), The real-time status in the scene is used as a driving condition to control the presentation mode (color change, geometric deformation, transparency adjustment) and behavioral response (alarm, linkage prompt, information pop-up window) of each component in the 3D scene.

[0038] 3D scene integration and interactive development The above functions are integrated with the 3D terrain model using mature 3D visualization development technology.

[0039] Combined with real-time data communication mechanisms such as IoT middleware or WebSocket, the system has the ability to link data in real time, realizing the integrated presentation of production monitoring, alarm warning, business scheduling and other functions.

[0040] Multifunctional subsystem integration and output Finally, for different application goals such as production operations, safety management, and equipment maintenance, through functional combination and scenario customization, multiple non-interfering and data-consistent 3D visualization application subsets are output to form a complete oil and gas field 3D visualization application system cluster.

[0041] Step S4, through the above analysis and processing, finally outputs the oil and gas field 3D visualization application set for production management, safety warning, and equipment operation and maintenance application scenarios. ,in Indicates the Features in time The three-dimensional business status on the

[0042] S5. Use the oil and gas field production and safety 3D visualization application set, combined with the SpringBoot distributed architecture to achieve efficient cloud management and redundant backup; The cloud management and redundancy scheduling function in step S5 is: ; in, :The system is at time Cloud scheduling and redundancy control status on the server; : System resource mapping function, at time Reflect system node availability and load at all times; ; : No. The resource state vector of each cloud node; : CPU availability (percentage); : Memory availability; : Bandwidth availability; : Storage space availability; : The number of cloud nodes currently participating in distributed management; : System fault detection vector, used to identify operational anomalies and fault tolerance requirements of cloud components; ; : No. The fault state vector of each node; : Is there a hardware failure? : Check whether there is any communication abnormality; : Whether there is a task execution timeout; "1" indicates a fault has occurred, and "0" indicates normal operation.

[0043] : Scheduling function, controlling task distribution, redundant writing, and high availability construction; ; :Indicates the 3D service status unit Is it in the Schedule deployment on nodes; when When Scheduled to a node ; The internal strategy is based on: Resource optimization principle ( maximum); Fault Avoidance ); Task data locality (nearby scheduling); Redundancy level control (set each Deployed at least available nodes).

[0044] During the use of the cloud management and redundancy scheduling function in step S5: when When a node's storage failure risk is detected, Immediately read The corresponding key business entities , combined with resource status , transfer the node task to the node with the best load; at the same time, A backup scheduling command will be generated. The latest status is synchronized to multiple available nodes to ensure data consistency.

[0045] Step S5 includes: Data introduction phase: Loading the 3D scene business linkage model generated in step S4 ; Resource and risk monitoring stage: Call the distributed monitoring module to obtain and ; Build a resource redundancy model for key business nodes; Scheduling and control phase: Applying the Scheduling Function , combined with 、 、 Perform optimal strategy calculations; Generate scheduling output , control cloud resource allocation and redundant deployment; Feedback update phase: The scheduling results are written into the system scheduling status center; Feedback is provided to the business logic module to continuously optimize subsequent visualization updates.

[0046] In the process of realizing cloud management and redundant backup of oil and gas field 3D visualization application set, step S5 is different from existing mature technologies in that it introduces a dynamic scheduling mechanism with adaptive perception capability: traditional cloud backup and task scheduling are usually based on static resource configuration and fixed strategy, while this step builds a system resource mapping function to realize cloud management and redundant backup of oil and gas field 3D visualization application set. , fault detection vector and the scheduling function , realizing the linkage perception and response scheduling of real-time resource status and fault status, taking into account system load balancing and node fault tolerance while ensuring business continuity, making the data redundancy deployment of the three-dimensional business model more flexible and intelligent, and suitable for complex and dynamically changing oil and gas field production environments.

[0047] Embodiment 2: This embodiment also provides a computer device, which is suitable for a three-dimensional visualization system of oil and gas fields based on digital twins, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a three-dimensional visualization system of oil and gas fields based on digital twins as proposed in the above embodiment.

[0048] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a three-dimensional visualization system for oil and gas fields based on digital twins as proposed in the above embodiment is implemented.

[0049] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0050] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0051] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0052] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0053] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A three-dimensional visualization system for oil and gas fields based on digital twins, characterized by: The following steps are involved: S1. Integrate GIS, BIM, IoT, and GD multi-source data and build a unified ground object entity dataset; S2. Use the ground object entity dataset to fuse sensor, camera, and positioning beacon data to obtain the ground object dynamic state calibration set; S3. Use the ground feature dynamic state calibration set to perform business system data integration and 3D scene modeling to obtain a 3D scene business linkage model; S4. Develop a multifunctional application system using the 3D scenario business linkage model to obtain a 3D visualization application set for oil and gas field production and safety; S5. Use the oil and gas field production and safety 3D visualization application set, combined with the SpringBoot distributed architecture to achieve efficient cloud management and redundant backup.

2. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 1, characterized in that: In step S1, GIS geographic information system data, BIM building information model data, IoT perception data, GD geographic surveying and real-time dynamic geographic data are input to output a unified ground object entity data set. ,in , that is, The static spatial feature vector of a ground feature, is the three-dimensional position coordinate of the feature; is the orientation angle; For size; is the terrain curvature index; The identification code for the feature material or material category.

3. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 1, characterized in that: The step S2 includes calibrating the ground object dynamic state model: ； in, : No. Features in time Dynamic state calibration value at the moment; : No. The static spatial feature vector of each feature; : In time Time The dynamic feature offset vector corresponding to each feature; : No. Features in time The attribute feature vector obtained by the sensor when : No. Features in time The visual feature vector extracted by the camera when : spatial position offset correction function; : function for generating comprehensive characteristics of ground feature state; : Position-state fusion mapping function.

4. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 3 is characterized in that: The dynamic feature offset in step S2 is ; : The offset of the feature’s three-dimensional position in space; : change in the object's orientation angle; : Change in ground feature size; : change in terrain curvature; : Change in ground feature material or material state.

5. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 3 is characterized in that: The attribute feature vector obtained by the sensor in step S2 is: ； : temperature characteristic value after linear normalization; : pressure characteristic value after linear normalization; : Amplitude eigenvalue after linear normalization.

6. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 3, characterized in that: The visual feature vector extracted by the camera in step S2 is: ； Each is the visual feature component obtained through visual analysis, is the dimension of the visual feature vector, where .

7. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 1, characterized in that: The three-dimensional scene business linkage model function in step S3 is: ； in, : No. A feature at a time The output vector of the three-dimensional scene business linkage model; : Indicates the vector-level cascade operation of the same feature in the dimensions of dynamic state information and business attributes; :The static spatial feature vector of the ground object in step S1 Mapping to standard three-dimensional model morphological control factors; : Represents the transformation function of the model morphology control factor in 3D visual modeling, realizing the injection of geometry and texture parameters.

8. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 7, characterized in that: The three-dimensional scene business linkage model function in step S3 includes: By calibrating the dynamic state vector and business system vector The combination of "status + business" realizes the two-way feature fusion of ground objects; Then use the mapping function Combine static structural features and dynamic business status into a standard 3D scene to construct a composite object containing morphology, status, and business semantic information; income Vectors can be used in real-time rendering, linked response control, and state display, achieving linkage from state modeling to visual output. Obtained by this formula It is a three-dimensional scene business linkage model after the fusion of the four elements of "ground object-state-business-3D form", and is the core data entity that supports S4 for the development of three-dimensional visualization applications.

9. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 1, characterized in that: The step S4 is as follows: Scenario function decomposition and requirements mapping; Linkage model structure analysis and module binding; Dynamic presentation driven by spatial state; 3D scene integration and interactive development; Multifunctional subsystem integration and output; The final output is a 3D visualization application set for oil and gas fields for production management, safety warning, and equipment operation and maintenance application scenarios. ,in Indicates the Features in time The three-dimensional business status on the 10. The digital twin-based three-dimensional visualization system for oil and gas fields according to claim 1, characterized in that: The step S5 implements efficient cloud management and redundant backup through cloud management and redundant scheduling functions.

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