537 results about "Real time visualization" patented technology

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention discloses an engineering project supervision real-time visual decision-making platform based on multi-source heterogeneous data fusion, and relates to the technical field of engineering supervision. The visual decision platform is in communication connection with a data acquisition module, a project risk feature module, a risk identification model construction module, a real-time data stream processing module, a risk early warning module and a decision support module. According to the invention, the risk in the engineering project is accurately identified in combination with historical multi-source heterogeneous data and project risk characteristics, and meanwhile, the real-time data stream processing module can analyze data in real time, identify potential risks and problems and generate early warning information, so that the accuracy and timeliness of risk identification are greatly improved, and the risk identification efficiency is improved. According to the invention, powerful decision support is provided for supervisors, and through the preset early warning level and the early warning signal, managers can rapidly understand the risk condition and take corresponding measures, thereby effectively reducing the project risk.

The invention discloses an intelligent auxiliary acupuncture system based on the AR technology, and the system comprises the following modules: a multi-modal sensing module which is used for collecting a multi-modal sensing data set containing the surface information of a patient; the visual SLAM guiding module is used for outputting a relative pose between the camera and the skin of the patient; the three-dimensional reconstruction module is used for generating a three-dimensional skin grid model; the candidate acupoint generation module is used for generating a candidate acupoint set under the condition that the density threshold value and the minimum spacing are met; the stable matching module is used for outputting a stable matching result set; the trajectory planning module is used for binding the needle insertion trajectory to the three-dimensional skin grid model to form a needle insertion guide set; and the augmented reality rendering module is used for carrying out real-time visual rendering on the virtual acupuncture point target and the needle inserting track through an augmented reality display terminal. The invention constructs an intelligent acupuncture guide system which is oriented to a real clinical scene and has a real-time correction capability and a medical significance interpretation capability.

The invention discloses a solar cell defect detection method based on a multi-mode sensing technology. The system comprises the following steps: 1, multi-modal data acquisition: acquiring multi-modal data in real time by using four sensing technologies of near-infrared laser, area array visible light, PL and EL; 2, multi-modal data processing: carrying out denoising, edge detection and feature extraction on the acquired near-infrared, visible light, PL and EL images by adopting a mixed image processing algorithm to obtain key defect features; 3, multi-modal data fusion: combining a multi-modal data fusion method with a decision-making level strategy, synthesizing different technical data, improving the detection precision, and classifying, positioning and labeling the defects of cracks, broken gates, scratches, depressions, poor welding and hot spots by using a convolutional neural network to ensure accurate distinguishing of surface and internal defects; and 4, defect marking and production line adjustment: marking defects and adjusting the production line through a feedback and automatic control module, and feeding back a real-time visual result to a management layer.

The invention discloses an industrial equipment intelligent operation and maintenance management system and method based on 5G-MOM, and belongs to the technical field of industrial internet and intelligent manufacturing. The system comprises a multi-source heterogeneous data acquisition layer deployed in industrial equipment, an edge computing node cluster based on 5G, a cloud intelligent analysis platform and a man-machine collaborative operation and maintenance terminal. The method comprises the following steps of collecting equipment vibration, temperature and current multi-dimensional working condition data in real time through a 5G network; performing data cleaning and feature extraction by using edge computing nodes, and constructing an equipment operation digital twin model; a cloud deep neural network is adopted to carry out fusion analysis on the multi-dimensional time series data, and self-adaptive diagnosis and residual life prediction of a fault mode are realized; a dynamic maintenance strategy is generated based on an MOM system, and field personnel are guided to execute precise maintenance through an AR terminal. According to the invention, 5G ultra-low time delay communication and an industrial mechanism model are creatively combined, and real-time visual management and predictive maintenance decision optimization of the equipment health state are realized.

An AI-powered system for optimizing industrial workflows and predictive maintenance, which includes the following: a network of sensors configured to collect real-time data from industrial plants, including temperature, pressure, vibration, and energy consumption metrics; a central control unit configured to process and analyze the collected data using machine learning algorithms that continuously improve by learning from both historical and real-time data to increase prediction accuracy; a predictive maintenance module configured to analyze data to predict potential equipment failures, schedule proactive maintenance tasks, and prioritize maintenance activities based on the predicted impact of the failure and the criticality of the equipment; a workflow optimization module configured to dynamically adjust operating parameters to optimize the process flow, including the detection of inefficiencies and the suggestion of improvements in real time; an integration module configured to connect and interact with existing industrial control systems and ERP (Enterprise Resource Planning) software to ensure a seamless flow of data between the platforms; a user interface configured to visualize data in real time, provide alerts and customizable dashboards, and is accessible via desktop and mobile devices for remote monitoring and management; an alarm and notification module configured to deliver critical alarms and maintenance notifications via email, SMS and in-app notifications to ensure a timely response; a cloud-based architecture configured to facilitate scalable data storage, remote access, and management across multiple industrial sites; a reporting module configured to generate comprehensive reports on plant performance, maintenance history, and workflow efficiency for analysis and decision-making; A security protocol to protect data integrity and ensure secure communication between all system components.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention provides a road traffic space-time service platform and method based on multi-source heterogeneous data fusion. The platform comprises a data acquisition module; a data fusion management module; the base map making module is used for generating a road traffic space-time service base map taking a road intersection as a key node; the traffic situation analysis module is used for forming a traffic situation prediction map; and the decision support and personalized service module is used for providing real-time and visual traffic situation display, traffic guidance strategies and personalized travel services for traffic managers and traffic participating users based on the traffic situation prediction map. According to the invention, real-time and visual decision support is provided for traffic managers, and personalized travel schemes can be customized for traffic participating users, so that comprehensive perception and accurate regulation and control of road traffic operation states are realized, and the intelligent level of traffic operation management is greatly improved.

An artificial intelligence-driven sentiment analysis system for social media monitoring, comprising: a data collection module configured to retrieve user-generated content from at least one social media platform via application programming interfaces (APIs); a preprocessing module configured to process and clean the collected data by removing irrelevant content, tokenizing the text, removing stop words, and assigning sentiment indicators to emojis, slang, and abbreviations; a sentiment analysis engine comprising one or more deep learning models, wherein the deep learning models include recurrent neural networks (RNNs), long-short-term memory networks (LSTM), and transformer-based models configured to analyze the preprocessed data and classify the content into one or more sentiment categories; an emotion recognition module configured to detect emotional tones within the content and classify them into predefined emotional categories; a real-time data processing unit configured to continuously monitor social media content and provide real-time insights into moods and emotions; a feedback loop module configured to update and refine the deep learning models through reinforcement learning based on new data inputs and sentiment classification results; and a user interface module configured to visualize sentiment and emotion analysis results in real time, including heatmaps, sentiment trend analysis, and emotional breakdowns.

The invention provides a software defect collaborative detection method and system based on multi-agent dynamic adaptation. According to the method, static characteristics and dynamic operation data of software codes are collected, a multi-agent system driven by a hierarchical reinforcement learning model is called based on the collected data, and an optimal detection strategy is dynamically generated, so that detection resources are accurately distributed to a high-risk code area. And then, a cross-modal data fusion technology is adopted to carry out association analysis on heterogeneous detection results, and unified fusion defect features are generated. And finally, presenting the defects in forms of thermodynamic diagrams, structured reports and the like through a real-time visualization engine, and providing intelligent repair suggestions. The problems that a traditional software defect collaborative detection method is low in efficiency, insufficient in accuracy and low in intelligent degree are solved.

A device for time-based tracking and cost optimization in construction projects, the device comprising the following: a robust housing suitable for use on construction sites; a processing unit located inside the housing, configured to perform real-time time-stamping, data acquisition and preprocessing tasks; a multimodal sensor unit that is operationally coupled with the processing unit, wherein the sensor unit comprises at least a motion sensor, an RFID reader, sensors for environmental conditions and a vision module with optical character recognition; a real-time clock module that is operationally connected to the processing unit to provide time synchronization for all sensor data streams; a wireless communication module that supports the Wi-Fi, LoRa and LTE protocols and is configured for transmitting time-stamped data to a central project server; a storage module that is operationally coupled with the processing unit to locally buffer time series data of construction activity during offline operation; a housing-mounted, touchscreen-based human-machine interface configured to allow site personnel to enter activity updates and confirm the status of construction tasks; a cost optimization engine running on the central server, the engine being configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique that incorporates the principles of the critical path and the power value; furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis.

The invention relates to an indoor lighting rate simulation system for indoor design. The system comprises a dynamic meteorological simulation module which generates a dynamically changing sky model based on real-time meteorological data; the building attribute analysis module analyzes building optical attribute parameters by using the building information model data and the hyperspectral imaging data; the light ray tracing processing module performs light ray tracing based on the dynamic sky model and the building optical attribute parameters to obtain indoor glare distribution data; a reverse parameter optimization module generates window form parameters and sunshade device layout parameters according to the target illumination interval and the indoor glare distribution data; and the real-time visual verification module carries out visual rendering on the window form parameters and the sunshade device layout parameters to obtain an updated lighting rate simulation result for verifying the lighting effect of the design scheme. According to the system, through dynamic meteorological fusion, non-uniform material modeling and real-time optimization feedback, the precision and adaptability of indoor lighting simulation can be improved.

The invention discloses a method and a system for measuring a load force at a bridge steel structure connection position, and belongs to the field of bridge maintenance, the method comprises the following steps: constructing a bridge overall digital twin model, and carrying out refined modeling on a steel structure at the bridge steel structure connection position; a sensor is arranged at the steel structure joint of the bridge for real-time data acquisition, and it is ensured that the steel structure digital twinborn model can be dynamically updated; when new data collected by the sensor detection network are received, the digital twinborn platform adjusts parameters of the corresponding steel structure digital twinborn model according to the unique identifier of the sensor; carrying out load force analysis and dynamic prediction according to parameters in the steel structure digital twinborn model in combination with comprehensive data; and integrating to form a real-time visual platform, and displaying the stress condition of the connecting part through the real-time visual platform. According to the invention, the load force change at the joint of the bridge steel structure can be monitored in real time, and the safety of the bridge structure is improved.

The invention discloses a large-span truss hoisting and splitting system and method based on digital twinning, and belongs to the technical field of building construction, and the system comprises a physical entity unit, a data acquisition and transmission unit, a digital twinning model unit, a function service and data analysis unit and an application and interaction unit. According to the invention, through real-time sensing of a physical entity state, multi-source data fusion transmission, high-precision digital twinning modeling, intelligent simulation analysis and decision optimization, and fusion of multi-modal man-machine interaction, a virtual control system which is in whole-course synchronous mapping and bidirectional interaction with a physical entity is constructed; according to the method, real-time visual monitoring, trend prediction and risk early warning of the hoisting and splitting process can be achieved, scheme preview and dynamic optimization are supported, virtual-real fusion operation guidance is achieved through the AR technology, and the safety, precision and efficiency of hoisting and splitting operation of the large-span truss are improved.

The invention relates to the technical field of data processing, and discloses a micro-front-end real-time visualization automatic rendering method and system, and the method comprises the steps: obtaining a data stream, carrying out the windowing processing, abnormal value elimination and feature extraction of the data stream, and generating a standardized data block; sending the standardized data block into a containerized model service for reasoning to obtain a reasoning result; determining a target visual component based on the reasoning result; packaging the meta-information of the target visual component into a standardized instruction, and pushing the standardized instruction to a front end for rendering to obtain a first rendering result; and capturing an interaction event of a user on the target visual component, and refreshing the first rendering result based on the interaction event to obtain a second rendering result. According to the method, the problems that a traditional scheme has the defects of poor expansibility, lack of automation capability and lack of a component hot plug mechanism, so that components cannot be matched according to data characteristics or model types, and different visual components cannot be flexibly replaced or updated are solved.

The invention discloses a foundation settlement early warning method and system based on an intelligent algorithm, and relates to the technical field of settlement early warning. During operation of the system, through combined use of various sensors, foundation settlement related data from multiple sources are collected and processed and fused in real time, foundation settlement is predicted through the intelligent algorithm, and the early warning effect is achieved. The settlement risk of different areas is evaluated by combining a space-time big data analysis technology, a settlement risk index GSI is calculated, an early warning grade is output, early warning decision making is carried out according to a prediction result and a set dynamic threshold value, the severity of the settlement risk is judged and classified, a system automatically generates an early warning report, and once early warning is triggered, the early warning is automatically carried out. The system automatically starts an emergency response scheme, dynamically adjusts reinforcement and intervention strategies according to the actual change of foundation settlement, provides a real-time visual interface of settlement data and early warning information, and displays the settlement state, the early warning level and historical data.

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent network connection vehicle driving risk assessment method and a visualization system based on multi-source data fusion. According to the method, multi-source data such as vehicle perception, communication and maps are fused, an artificial potential field theory is combined, a gravitational force risk potential field function, a repulsive force risk potential field function and a road boundary repulsive force potential field function are constructed respectively, and a unified total risk potential field TPF is formed by a gravitational force risk potential field, a repulsive force risk potential field and a road boundary repulsive force potential field; according to the method, the driving risk of the intelligent networked vehicle is obtained, risk grade division and real-time visual display are realized through clustering analysis, the accuracy and dynamic response capability of risk assessment are improved, the method is suitable for safety decision support of advanced driving assistance and automatic driving systems, real-time assessment of the driving risk can be realized more accurately and efficiently, and the risk assessment efficiency is improved. And the risk assessment precision and the dynamic response capability are improved.

The invention discloses an anesthesia nerve precise positioning method and system based on ultrasonic image auxiliary processing, and the method comprises the following steps: obtaining a real-time dynamic ultrasonic image sequence of a target region through an ultrasonic image data collection device, carrying out the multi-modal data fusion processing, and generating an enhanced ultrasonic image data set; performing three-dimensional semantic segmentation on the enhanced ultrasonic image data set based on a preset anesthesia nerve region segmentation model, and extracting morphological characteristics and spatial distribution information of a target nerve tissue; generating a three-dimensional navigation path grid based on the extracted morphological characteristics and spatial distribution information, and constructing a real-time visual guide interface; and dynamically optimizing a navigation path and a risk early warning strategy based on real-time operation feedback. The method has the following advantages and effects: a precise positioning method capable of fusing multi-scale neural features and dynamically optimizing a navigation path is provided, so that the key technical bottleneck of nerve ending visualization and real-time obstacle avoidance is solved.

The invention discloses a multi-precision dynamic loading and performance optimization rendering method for a hydroelectric generating set model. The method comprises three stages of multi-precision model construction, multi-precision model dynamic loading and rejection, and adaptive rendering optimization. And a multi-precision model construction stage: performing component extraction on the original model file to generate high, medium and low resolution multi-precision models, and converting the models into a GLTF format to improve the loading efficiency. And a dynamic loading and rejecting stage: reading the multi-precision GLTF file, generating a geometric model by applying the conversion matrix, constructing an LOD model according to the sight distance, and rejecting only the visible part in the loading visual field through a view cone to reduce the rendering calculation amount. And an adaptive rendering optimization stage: monitoring the frame rate in real time, dynamically adjusting the resolution when the frame rate is relatively low, simplifying material illumination, forbidding special effects such as shadow and the like, ensuring stable rendering performance, and finally outputting an optimized three-dimensional scene. According to the method, the rendering speed and fluency of the water turbine model are remarkably improved, and the method is suitable for the real-time visualization requirement of a complex three-dimensional scene.

The percutaneous pericardium puncture assembly comprises a puncture tube and a puncture piece, and the puncture tube comprises a tube body and a puncture section arranged at the far end of the tube body. A puncture channel, a negative pressure suction channel and an imaging channel are formed in the tube body and communicated with a working opening in the far end of the puncture section. The imaging module is arranged in the imaging channel in a penetrating mode, and real-time visualization of in-vivo puncture operation is achieved. The near end of the negative pressure suction channel can be in butt joint with a negative pressure suction device to provide negative pressure for the working port so as to adsorb the pericardium wall layer; the puncture piece penetrates through the puncture channel and can puncture the adsorbed pericardium wall layer. According to the embodiment, the vacuum suction channel is formed, the pericardium wall layer can be captured before puncture of the puncture piece, in this way, even if only a small amount of pericardial effusion exists between the pericardium wall layer and the pericardium dirty layer, medical staff can easily complete puncture of the pericardium wall layer under the condition that the pericardium dirty layer is not damaged, and the puncture efficiency of the pericardium wall layer is improved. The operation is simple, and the safety and the success rate of percutaneous pericardium paracentesis under full indications are greatly improved.

The invention discloses a flood risk map result data coupling and management method, and relates to the technical field of hydrological information processing and geographic information systems, and the method comprises the following steps: obtaining hydrological data and topographic data required by flood risk assessment, including water level time sequence data, submerging depth data and TIN topographic data, and carrying out standardized conversion on data in different formats, and carrying out unified alignment based on a coordinate reference system. According to the method, the coupling precision of the hydrological data and the topographic data is improved through the adaptive Delaunay triangulation algorithm, and simulation distortion caused by time-space mismatching is avoided; the storage is optimized by adopting a topological optimization GeoJSON data structure, so that the query and management efficiency is improved; gPU acceleration rendering and LOD layering technologies are combined, the real-time visualization capability of flood simulation is improved, and efficient and accurate data support is provided for flood prevention emergency management and water conservancy decision making.

The invention belongs to the technical field of acupuncture and moxibustion, and discloses a needle body tracking type intelligent acupuncture and moxibustion system which comprises a data collecting and processing module, a needle body positioning module, a 3D model registration and dynamic updating module, a real-time rendering display module and an interaction module. Three-dimensional coordinates, puncture depth and angle information of a needle body are obtained in real time with high precision through a multi-modal composite positioning technology, and real-time visual association of the needle body with key anatomical structures such as bones, blood vessels and nerves in the in-vivo penetrating process is achieved by combining a dynamically-registered 3D anatomical model and the actual body position of a patient. The needle inserting precision and the operation safety are obviously improved; meanwhile, through multi-plane fusion view presentation, a dynamic breathing compensation mechanism and a layered rendering updating strategy, it is ensured that a physician can visually master the real-time space relation between a needle body and surrounding tissue, and important structures are effectively prevented from being accidentally injured; and diversified touch interaction and instant operation feedback are supplemented, so that the operation complexity is greatly reduced.

The invention discloses a transcranial stimulation magnetic therapy coil navigation method, a transcranial stimulation magnetic therapy coil navigation system, a storage medium and equipment. The method comprises the following steps: acquiring three-dimensional structure information of the head of a target object, magnetic resonance imaging (MRI) data of an individual target object, or three-dimensional model data obtained by matching from a general MRI template library; real-time surface geometric information of the head of the target object is collected through an infrared optical tracking system; performing registration fusion on the three-dimensional structure information and the real-time surface geometric information, and generating an individualized three-dimensional brain model synchronized with a real space coordinate system on a navigation interface; the spatial position and posture of the optical marker fixed on the magnetic therapy coil are tracked in real time through an infrared optical tracking system and correspondingly mapped to the individualized three-dimensional brain model, and visual navigation of the magnetic therapy coil relative to the brain anatomical structure is achieved. Through the individualized three-dimensional navigation and real-time visualization technology, the problems that a traditional TMS is low in positioning precision, invisible in operation and poor in treatment repeatability are solved.

The invention discloses a goaf coal spontaneous combustion early warning system and method based on a digital twinning technology, temperature, gas concentration (CO, O2 and the like) and humidity data are collected in real time through a multi-source sensor, a goaf three-dimensional model is constructed in combination with ultrasonic wave and seismic wave exploration, and a spontaneous combustion process is simulated by coupling an Arrhenius equation and a Fick law through COMSOL. And performing Z-Score standardization and machine learning (SVM, random forest) analysis on the data to generate a dynamic thermodynamic diagram early warning risk. The system realizes low-delay transmission through MQTT and 5G, realizes three-dimensional platform real-time visualization, and triggers multi-stage early warning (temperature / CO threshold) and automatic intervention (nitrogen injection and ventilation adjustment). Compared with the prior art, the early warning accuracy is improved by 35%, the response time is shortened to 15 minutes, and the risk of spontaneous combustion accidents is remarkably reduced.

The invention belongs to the technical field of computer point cloud data processing, and discloses a 2D shape point cloud optimization method based on Gaussian interpolation, comprising the following steps: (1) receiving 2D shape contour data by an input module, and obtaining the data through calibration; (2) generating initial point cloud data; (3) smoothing the initial point cloud; (4) expanding a virtual grid layer and filling a zero weight value; (5) removing abnormal points, and generating an optimized target point cloud; and (6) an output module converts the final point cloud into a standardized format, and evaluates shape fidelity and uniformity indexes. The invention discloses a 2D shape point cloud optimization system based on Gaussian interpolation. The 2D shape point cloud optimization system comprises a preprocessing unit; a self-adaptive grid generation unit; and a real-time visualization interaction unit. According to the method, the points of the non-target area are automatically hidden, the point cloud quality is improved, the calculation complexity is reduced, and the method is suitable for real-time rendering and analysis.

The invention provides an intelligent supervision method and system for state-owned assets based on digital twinning, and relates to the technical field of asset management. The method comprises the steps of establishing a digital twinborn model of state-owned assets, analyzing a potential causal relationship among variables in an asset behavior evolution process according to the digital twinborn model, constructing a causal graph, constructing a reinforcement learning scheduling strategy model through the causal graph and the digital twinborn model, and outputting an optimal scheduling strategy. And a supervisor performs remote control and management operation on the state-owned assets through the intelligent terminal according to the optimal scheduling strategy, and an operation instruction is processed by the data processing center and then is fed back to the corresponding Internet of Things sensor and execution equipment, so that intelligent management and control on the state-owned assets are realized. According to the invention, real-time visual supervision and intelligent management and control of the state-owned assets are realized, the supervision efficiency and accuracy are improved, the management cost and risk are reduced, and the operation benefit and safety of the state-owned assets are effectively improved.

The embodiment of the invention provides a pipeline inner wall spraying simulation control method and device based on digital twinning, and the method comprises the steps: importing a guide rail model and a motion mechanism model into offline programming software, configuring a kinematic pair, and constructing a virtual scene corresponding to an actual pipeline inner wall spraying production line based on a digital twinning technology; importing a pipeline three-dimensional model, configuring workpiece parameters, and calibrating coordinate data; identifying the boundary of a spraying area on the inner wall of the pipeline, and determining the spraying range; an initial spraying track is generated, parameters are adjusted according to boundary conditions, and the track is optimized; configuring spray gun parameters, importing a flange plate and a spray gun model, and completing basic tool configuration; a tool model is imported, tool parameters are configured, spraying parameters are configured based on the sampling data, and a spraying tool is obtained; according to the embodiment of the invention, simulation is carried out based on the virtual scene, the optimized track and the spraying tool; and presenting the simulation effect in a three-dimensional visualization manner in real time. Spraying track optimization, parameter dynamic matching and real-time visualization of the spraying effect are achieved.

The invention discloses a natural resource big data off-line comprehensive analysis, annotation and proof system and method, and belongs to the technical field of natural resource informatization and geographic information processing. The system comprises the following components: a spatial data fusion module, which is used for realizing unified management and efficient processing of heterogeneous spatial data; the spatial index optimization module is used for realizing quick retrieval and high-performance query optimization of spatial data; the intelligent annotation and arrangement module is used for realizing low-code rapid configuration and business process automatic arrangement of different natural resource investigation scenes; the off-line analysis module is used for realizing buffer area analysis, space superposition and terrain analysis in an off-line environment; the real-time visualization module is used for realizing multi-level map display, track recording, vector element editing and multimedia evidence collection; and the data quality management module is used for realizing automatic quality inspection of geometry, topology and attributes and statistical analysis, version control and export management of annotation results.