1020results about "CAD network environment" patented technology

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention discloses a cooperative monitoring device and method for a large deep foundation pit complex supporting system, and relates to the technical field of foundation pit supporting. The device comprises a multi-dimensional sensor network module, a BIM-GIS digital twin platform module, an intelligent analysis and early warning module, a construction collaborative decision module, a data backup and recovery module and a remote monitoring and management module. The method comprises the following steps: step 1, carrying out multi-source data space-time registration; step 2, dynamically constructing a digital twinborn model; step 3, coupling risk assessment; step 4, construction collaborative optimization; according to the technical scheme, the method comprises the following steps of data processing, data quality control and system performance evaluation, through three innovations of deep coupling of multi-source data, dynamic model correction and intelligent collaborative decision making, a'monitoring-analysis-decision-execution 'full closed loop is constructed, technical breakthroughs are achieved in complex working condition adaptability, early warning real-time performance and construction safety, and the method has remarkable engineering application value.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention relates to the technical field of model construction, in particular to a method and a system for establishing a parameterized tunnel model based on cloud computing. The method comprises the following steps: acquiring a tunnel section type, and extracting structural semantics to obtain a section feature description set; constructing a parameter dictionary of the tunnel section based on the section feature description set to obtain a dynamic parameter dictionary data set; and modeling a tunnel section geometric structure based on the dynamic parameter dictionary data set to obtain an initial three-dimensional tunnel geometric model. Through dynamic parameter management, a self-correction mechanism and a Latin hypercube sampling technology, efficient automatic modeling and precise simulation optimization of the tunnel section three-dimensional geometric model are achieved, meanwhile, through unified packaging and authority control, safe and efficient management and collaborative sharing of cloud model resources are achieved, and the method is suitable for large-scale popularization and application. And the intelligent level and the engineering efficiency of tunnel model construction and application are comprehensively improved.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention provides an intelligent collaborative management method and system for network security operation and maintenance work, and relates to the technical field of network security, and the method comprises the steps: obtaining operation and maintenance work order information containing a work order priority and a processing time limit, and converting the operation and maintenance work order information into a work order feature vector; inputting the skill score, the historical completion rate and the workload of the operation and maintenance personnel into a multi-objective optimization model, and calculating a work order matching degree score and an emergency degree score; performing state coding on the work order feature vector, the personnel feature and the constraint condition by adopting a multi-layer perceptron, extracting spatial correlation by utilizing a double Q network, and calculating a target Q value; and generating a work order distribution scheme based on a Pareto optimal solution algorithm, monitoring a processing state in real time through a task collaborative scheduling model, and generating a collaborative scheduling strategy. The intelligent level of work order distribution is improved, collaborative management of operation and maintenance tasks is realized, and the operation and maintenance efficiency is improved.

The invention provides a dynamic fusion system for digital twin energy management BIM modeling and a method thereof, which are used for improving the intelligent level of energy management and optimizing energy scheduling. The system comprises a data sensing node used for collecting heterogeneous operation data of energy facilities; the local twin processor is used for carrying out real-time digital twin mapping on the data and generating a dynamic feature vector; region twinborn nodes perform local optimization based on edge calculation, and realize decentralized collaboration through a block chain or a knowledge graph; the cloud BIM fusion engine constructs a digital twin BIM model, generates a self-optimization energy scheduling scheme and predicts a long-term trend; and the operation control terminal is used for presenting a real-time view and providing a regulation and control instruction. According to the method, technologies such as multi-source data fusion, streaming aggregation optimization and adaptive twin mapping are combined, and the accuracy and dynamic response capability of energy management are improved.

The invention discloses a comprehensive transportation junction emergency scene simulation deduction system based on digital twinning, which relates to the technical field of digital simulation and comprises a data processing unit, a simulation deduction unit and a visual display unit, the data processing unit is used for acquiring emergency scene basic data of the target digital twin comprehensive transportation hub, performing multi-source integration on the basic data and constructing a model data environment; the simulation deduction unit calls a simulation deduction algorithm to construct a simulation deduction algorithm model according to the emergency scene model data, and carries out emergency situation deduction and scheduling processing based on the simulation deduction algorithm model; and the visual display unit is used for carrying out distribution management on user permissions, carrying out deduction data plotting based on GIS map basic information, and carrying out query and visual display on the deduction data according to a query instruction of a user. According to the method, a digital twinborn platform is built based on a simulation deduction algorithm model and dynamic data, so that the deduction process is more accurate, and visual display is realized in combination with GIS map data.

The invention discloses a transistor high-power electromagnetic heat model optimization system based on deep learning, and relates to the technical field of electromagnetic heat. Comprising an input module, a deep learning module, a global optimization module, an online adaptive module and a real-time simulation module. The input module obtains various key parameters and constructs a high-dimensional input space; the deep learning module constructs a deep learning model by embedding a physical constraint and uncertainty quantization mechanism; the global optimization module is used for carrying out global search on design parameters, rapidly evaluating a target function by utilizing an agent model, and searching a Pareto optimal solution in a design parameter space; the online adaptive module is used for drift detection and updating; the real-time simulation module simulates an electromagnetic field and a thermal field in real time, and data exchange ensures timely synchronization of boundary data through dynamic scheduling and load balancing; all the modules cooperate with one another through data and feedback, and a complete, closed-loop and continuously self-adaptive updating optimization system is achieved.

The embodiment of the invention discloses a cloud architecture CAD (Computer Aided Design) and CAE (Computer Aided Engineering) integrated collaboration method, equipment and a medium, relates to the technical field of collaboration design, and is used for solving the problems of low collaboration efficiency, insufficient knowledge reuse rate and the like in the prior art. The method is applied to a CAD and CAE integrated cooperative system built on a cloud architecture, the system comprises a client and a server, and the method comprises the steps that modeling processing is conducted on a model creating instruction of the client through a preset geometric modeling engine of the server, and a current CAD model file is obtained; inputting the data into a preset CAE (Computer Aided Engineering) pre-processing and post-processing module for processing to generate a finite element model; performing simulation solution on the finite element model based on a preset multi-solver in a CAE simulation module to obtain a finite element result; and according to a preset data conversion service module of the CAE simulation module, performing data analysis processing on the finite element result, and organizing an analysis result into visual data to display so as to feed back and optimize CAD modeling.

The invention discloses an intelligent industrial wastewater treatment method based on edge calculation and multi-modal data fusion, which comprises the following steps: synchronously acquiring various data of an industrial wastewater treatment site through edge nodes, automatically adjusting and aligning the generation time of each data, and ensuring that the time error of different data is less than a set threshold value; organically fusing the image features, the chemical parameters and the component analysis report to generate pollutant description features; constructing a simplified simulation model on the edge equipment, and predicting the diffusion trend of the wastewater pollutants by using the simulation model based on the pollutant description characteristics; and when the network is interrupted, the edge equipment is automatically switched to a local mode, and the operation is continued by using the stored model and the wastewater data acquired in real time. According to the method, the simulation model is deployed through the edge equipment, so that the calculation amount can be greatly reduced, the diffusion trend of wastewater pollutants can be accurately predicted, effective treatment measures can be taken in time, and the stability and reliability of an industrial wastewater treatment system are improved.

The invention relates to the technical field of mine intelligent ventilation and poison filtration, and discloses a mine intelligent ventilation and poison filtration dynamic regulation and control method and system.The mine intelligent ventilation and poison filtration dynamic regulation and control method comprises the steps that a neural physical hybrid model is constructed, physical constraint conditions are combined with a deep neural network, and mine airflow field distribution is predicted; decomposing the neural physical hybrid model into three hierarchical sub-models, and deploying the three hierarchical sub-models to corresponding computing nodes to realize a hierarchical computing architecture; establishing a multi-level cooperation mechanism based on federal learning, and realizing distributed model training and optimization; constructing a task allocation decision model, and dynamically determining an optimal execution hierarchy of an airflow field calculation task according to task characteristics, resource states and real-time requirements; dynamically adjusting the precision and performance balance of airflow field calculation; according to the method, the key technical problems in the mine ventilation and poison filtration system are solved through the neural physical hybrid model, the multi-level distributed computing architecture and the federal learning collaboration mechanism.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention relates to the technical field of artificial electromagnetic structures, and particularly discloses a multi-physics field coupling driven electromagnetic structure topology collaborative optimization method and device and a storage medium, and the method comprises the steps: building an electromagnetic-thermal-stress multi-physics field coupling model, and quantifying the performance attenuation law of a topological structure under a complex working condition; based on an electromagnetic-thermal-stress multi-physics field coupling model, constructing a gradient optimization framework based on topological sensitivity analysis, and identifying a high-sensitivity area as a preferential optimization area; the integrated generative adversarial network generates an anti-disturbance topological configuration; and deploying a multi-physics field collaborative optimization algorithm and implementing dynamic iteration. The problems that a traditional single physical field optimization method in the prior art is large in limitation, low in calculation efficiency and insufficient in robustness are solved.

The invention discloses a hydrological data prediction method based on a convolutional neural network and a physical information neural network. Comprising the following steps: 1) utilizing an HEC-RAS hydrological model to generate cross sectional area, flow and water level elevation data of a key section of a target river channel in a preset time period as training reference truth values; 2) constructing space-time coordinates corresponding to the data as an input data set; 3) space-time input is processed through a physical information neural network model based on a convolutional neural network, the convolutional neural network extracts local heterogeneity features of a spatial position of a river channel through one-dimensional convolution, and a continuity equation and a momentum equation of a Saint-Venant equation set are embedded into a training process in a residual form through physical information constraint; and 4) finally, directly outputting a hydrological parameter prediction result of the target river channel. Therefore, under the environment that the observation data is deficient or missing, the hydrological parameter prediction precision and the model generalization ability are remarkably improved by fusing the simulation data and the physical mechanism constraint.

The invention discloses a power distribution network-virtual power plant non-iterative collaborative optimization scheduling method and a related device, and relates to the field of power system optimization scheduling calculation, and the method comprises the steps: building a virtual power plant optimal economic scheduling model according to the characteristics of access resources in a virtual power plant; according to the optimal economic dispatching model of the virtual power plant, determining a variation range constraint of the power of the tie line of the virtual power plant based on a rapid approximate projection method of a feasible region of operation of the virtual power plant; constructing a non-iterative collaborative optimization scheduling model of the power distribution network and the virtual power plant based on variation range constraints according to a scheduling decision target of the power distribution network, a scheduling target of the virtual power plant and characteristics of access resources; according to the collaborative optimization scheduling model, solving a power distribution network scheduling result and a virtual power plant tie line power scheduling result; and inputting the tie line power scheduling result into the optimal economic scheduling model of the virtual power plant to obtain an optimal scheduling result of internal resources of the virtual power plant. According to the invention, the decision-making efficiency of collaborative optimization scheduling can be improved.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

The invention discloses a photovoltaic digital twin operation and maintenance platform oriented to multi-source collaborative diagnosis, and particularly relates to the technical field of photovoltaic operation and maintenance, normalization, time-frequency feature extraction and adaptive filtering are performed on component health degree and environmental parameters through edge nodes, and a lightweight encrypted performance abstract is generated, and is subjected to differential compression and synchronized to a cloud end; the cloud executes security federal fusion and dynamic reputation weighting on the abstract, and outputs a robust global performance vector; the collaborative optimization engine calls reinforcement learning based on the vector and a historical strategy to generate an original power distribution strategy, and the original power distribution strategy is issued after being corrected through a projection gradient descent mapping function; an edge actuator loads an environment snapshot to perform parallel simulation verification and risk assessment in a digital twin environment, so that the problems of multi-source data isolation and privacy disclosure are effectively solved; a self-adaptive multi-resolution grid, heterogeneous acceleration parallel and distributed message scheduling are provided, and high efficiency and real-time performance of large-scale multi-physics coupling simulation are achieved.

The invention relates to the technical field of electrolytic hydrogen production, and discloses a PEM electrolytic cell energy-economy collaborative optimization method based on digital twinning, which comprises the following steps: S1, calibrating parameters of a membrane electrode mathematical model by using a standard test for a single cell of a PEM electrolytic cell, and establishing a high-precision physical simulation model; s2, constructing a high-precision digital twin system based on the high-precision physical simulation model; s3, inputting multi-dimensional data of electricity price fluctuation, hydrogen demand prediction and equipment state, and performing collaborative optimization analysis through an optimization decision algorithm; s4, forming a PEM electrolytic cell control optimization instruction; and S5, the physical entity PEM electrolytic bath executes the instruction. According to the method, the'electricity price-demand-equipment 'dynamic relation is planned as a whole from the global perspective, the process steps cover data-driven modeling, multi-target decision, digital twinning cooperation and closed-loop feedback, balance needs to be achieved among algorithm efficiency, model precision and engineering feasibility, and energy-economy-life multi-target cooperation is achieved.

A computer-implemented method for simulating an aircraft system in a closed-loop functional simulation environment is disclosed. The computer-implemented method may include: receiving, at a computer system, one or more control inputs from an interface; processing, by a control system model, the one or more control inputs to generate one or more system command signals; applying the one or more system command signals to one or more virtual system models; simulating a physical response of the aircraft based on output generated by the one or more virtual system models in response to application of the one or more system command signals; generating, responsive to monitoring the output generated by the one or more virtual system models, sensor data; transmitting the sensor data to the control system model; and outputting simulation results, wherein the simulation results at least comprise an indication of the physical response of the aircraft.

The invention discloses an integrated energy system micro-grid group optimization method and system and related equipment. The method comprises the following steps: collecting observation data of an operator agent and a micro-grid agent; on the basis of the collected observation data of the operator intelligent agent and each micro-grid intelligent agent, alternately training an action network and an evaluation network of a double-layer intelligent agent, and repeatedly iterating to obtain a stably converged operator intelligent agent and a plurality of micro-grid intelligent agents; on the basis of the one-master multi-slave double-layer game model, a micro-grid group autonomous collaborative optimization model considering the multi-agent game is constructed; based on the state space, the action space, the environment and the reward function and the micro-grid group autonomous collaborative optimization model considering the multi-agent game, a micro-grid group autonomous collaborative optimization model based on deep reinforcement learning under the multi-agent environment is constructed; and solving the micro-grid group autonomous collaborative optimization model based on multi-agent deep reinforcement learning to obtain a micro-grid group optimization scheme. The method is beneficial to protecting the privacy of each micro-grid.

According to the new energy automobile flexible assembly line multi-target dynamic intelligent scheduling optimization method coping with the uncertain disturbance influence, the flexibility of the assembly manufacturing unit and the flexibility of the assembly manufacturing process sequence are comprehensively considered, and a dynamic intelligent scheduling optimization scheme is generated for multiple optimization targets. The method comprises the following steps: firstly, constructing a multi-target dynamic intelligent scheduling optimization problem mathematical model of an automobile flexible final assembly line; furthermore, the optimization problem is converted into a Markov decision process, and states, actions, multi-target reward components and reward component aggregation thereof are defined. And then, carrying out a multi-target deep reinforcement learning training process oriented to a new energy automobile flexible final assembly line, and constructing and forming a scheduling agent. And finally, realizing self-adaptive decision making of the automobile flexible assembly line under an uncertain disturbance condition based on the scheduling intelligent agent. According to the method, the robustness and the execution efficiency of the new energy automobile flexible assembly line in a dynamic environment can be remarkably improved, and the method has good practical value and popularization prospect.

The invention discloses a multi-stage collaborative protection simulation verification method for a subway power supply system, and the method comprises the steps: building an integrated simulation model which comprises an automatic reclosing switch, a spare power automatic switching switch and an automatic change-over switch based on an MATLAB / Simulink platform, and especially carrying out the precise modeling of an advanced compensation function of ATSE. The model is verified by a power-off test, a simulation result and actually measured data are highly consistent in voltage recovery time, an error of advanced compensation action time of ATSE is less than 2ms, and dynamic characteristics of an actual system are accurately reproduced. Model simulation shows that the advanced compensation strategy of the ATSE can effectively control the voltage sag amplitude within 15%, the power supply reliability of the system is remarkably improved, and through strict'simulation-actual measurement 'double-track verification, the model accurately reproduces the dynamic characteristics of the actual system, and shows remarkable advantages in the aspect of improving the power supply reliability.

The invention provides an automatic agent model construction method for a product CAD (computer-aided design) model, which comprises the following steps of: defining a part CAD model of a product form and a size and a dependency relationship of a logic relationship and a constraint between different parts; according to a mapping rule for converting an assembling CAD model into Sysml-based model elements based on a plurality of parts and relative positions and connection modes among the plurality of parts, realizing standardized proxy model expression of the product CAD model; and realizing automatic conversion from a native model to a Sysm l-based proxy model based on ATL, and realizing automatic construction of the whole process of the proxy model. A mechanical arm product CAD model serves as an example, an agent model of the mechanical arm product CAD model is constructed, and the abstraction and expression process of the product CAD model is displayed. According to the method, the CAD model of the product can be effectively supported to be incorporated into a digital main line, rapid transmission and feedback of information in the whole life cycle process of the product are promoted, and information cross-domain integration and collaboration are promoted.

Techniques for evaluating use of one or more construction products in a construction project, the techniques involving executing a construction software platform to perform: (A) obtaining an initial construction project model for the construction project; (B) obtaining a selection of a first construction product for evaluating its use in the construction project, the first construction product being in a first construction project category; (C) identifying, using the first construction product category, a first mapping software program for mapping the first construction product to the initial construction project model; (D) executing the first mapping software program to map the first construction product to the initial construction project model to obtain an updated construction project model; and (E) outputting the updated construction project model.

The invention provides a wheel center positioning method and system of a railway vehicle. Wherein the first detection unit and the second detection unit are mounted on two sides of the wheel, and the chord length of the wheel is measured through the first detection unit. When the second unit triggers detection, the second unit is controlled to move for a semi-chord distance in the same direction, and initial positioning of the wheel center is determined. Dynamic contact spot deformation and rim degradation data are fused, a collaborative analysis model is constructed, the nonlinear relation between the abrasion gradient and contact spot offset is extracted through deep learning, and dynamic correction is generated. And millimeter-level dynamic compensation is carried out on the positioning center based on the dynamic correction, stability control over the wheel-rail contact center in the transverse direction, the vertical direction and the rotating dimension is achieved, and stress concentration caused by rim abrasion is restrained. According to the technical scheme provided by the invention, the wheel-rail contact center positioning precision and stability are improved.

Methods and apparatus for spatial annotation of design plans in an AI-powered collaborative environment. It includes a controller to receive a static representation of a building's design plan and an AI engine to analyze raster images to identify design elements such as architectural aspects, walls, and rooms. An interactive user interface generates polygons and lines representing these elements, allowing users to select elements for annotation. The apparatus determines positional coordinates of selected elements, links annotations to these coordinates, and updates the interface in real-time for multiple users. Additional features include automated annotation suggestions, user interaction options like commenting and approval, and learning from user interactions to enhance future annotations. The apparatus can detect physical changes in design elements using cameras and update the interface accordingly. It supports dynamic collaboration and enhances the accuracy and efficiency of design plan annotations.

The invention discloses a parameter-controlled intelligent oil field shaft digital twinning modeling method, which belongs to the field of petroleum engineering and comprises the following steps of: generating well track data by adopting a multi-source data fusion mode, and optimizing a multi-source data fusion weight coefficient by adopting a genetic algorithm when a measurement depth is generated; well track curvature optimization is conducted based on the minimum curvature principle, a minimum curvature second derivative serves as a target function, and geological factors are introduced to dynamically adjust the smooth weight; a second-order Taylor expansion compensation item is provided for curvature factor dynamic correction, and three-dimensional coordinate data of the well track are generated; and based on the three-dimensional coordinate data of the wellbore track, parameterized-driven wellbore geometric modeling is carried out, and parameterized programmable control over the wellbore radius, the wellbore wall thickness and the section inclination angle is achieved. The invention provides an efficient and accurate wellbore three-dimensional modeling solution, and the wellbore modeling efficiency is greatly improved.