8022 results about "Visulation" patented technology

The invention provides a news scene three-dimensional reconstruction and visualization method based on multi-source remote sensing data, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting multi-modal data feature points through employing a spatial position weighted iterative nearest point algorithm and a tensor projection algorithm; after bidirectional attention deep neural network processing and local geometric consistency constraint screening, registration multi-source remote sensing data is generated, data enhancement is carried out through a variational auto-encoder network and a generative adversarial network, the enhanced data is used for depth information generation and texture feature migration, an initial three-dimensional grid model is generated based on a Poisson reconstruction algorithm, and the initial three-dimensional grid model is constructed; and carrying out topological optimization by combining multi-view projection consistency constraint and a grid optimization algorithm to obtain an optimized three-dimensional scene model, setting a space division function, carrying out space division, constructing a grade model and a depth region, generating a scene semantic segmentation map, and updating a visual scene.

The invention provides an intelligent visual management method and system for enterprise big data. The method comprises the following steps: extracting a space-time association rule of an operation and maintenance report fault field and an equipment log error code, and generating a dynamic mapping data stream to drive a three-dimensional visual association topology; the method comprises the following steps: collecting cabinet vibration energy data, and synchronizing a highlight energy sudden increase area and error log entries according to a timestamp; dynamically distributing a vibration energy weight, and generating a risk probability matrix in combination with an error increment; fusing the time sequence characteristics and the physical topology path, constructing an abnormal event timestamp graph, and marking a fault propagation chain; and based on the map density gradient and the causal association strength, superimposing and rendering the penetrating thermodynamic diagram, associating topology, a fault chain and an equipment structure, and adaptively adjusting the color gradation highlighting abnormal region. According to the technical scheme provided by the invention, the multi-source fault correlation analysis efficiency is improved, and the abnormal risk is dynamically, visually and accurately positioned.

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention discloses an intelligent visualization and text association method for a multi-modal knowledge graph, and relates to the technical field of information processing. According to the intelligent visualization and text association method for the multi-modal knowledge graph, multi-modal data in an internal database of an enterprise is obtained, and a multi-modal feature vector is generated by using a data preprocessing and feature extraction technology; the feature vectors are mapped to a unified vector space through a cross-modal embedding algorithm, semantic alignment, association and fusion among multi-modal data are realized, and a complete multi-modal knowledge graph is constructed. Through bidirectional association and multi-modal joint query, the information acquisition efficiency is improved, and visual display of the knowledge graph is realized by using the graph neural network. Meanwhile, the knowledge graph is dynamically updated through a graph reasoning algorithm, and the timeliness and accuracy of the knowledge graph are ensured. A more efficient solution is provided for an enterprise in the aspects of management and application of multi-modal data, and the intelligent process of knowledge management is promoted.

An example operation may include one or more of displaying a report on a user interface of a software application on a user device, listening to a call between a user on the user device and a different user on a second user device that is connected to the user device via a network, executing a generative artificial intelligence (GenAI) model based on content that is heard during the call and content within the report displayed on the user interface to identify content within the displayed report that is discussed during the call, and modifying the displayed report to emphasize the identified content within the displayed report on the user interface.

The invention relates to the field of digital twinborn modeling, and discloses a digital twinborn visual modeling method and system based on a neural network, and the method comprises the steps: collecting the multi-dimensional perception data of a target physical system, carrying out the format unification and normalization processing of the original data of a sensor through a heterogeneous data fusion module, and obtaining a data fusion module; constructing a high-dimensional input feature set for neural network modeling by combining a structured embedding algorithm; carrying out stability pre-evaluation on the constructed input feature set, and screening core features by adopting a disturbance sensitivity analysis mechanism; a dynamic residual feedback mechanism is introduced to carry out enhanced training on the preliminary twin model, and modeling is carried out on the space-time dependency relationship of different components through a graph neural network; tracking a stability index of visual representation in real time in a model training process; and according to a multi-dimensional visualization result output by the final twin model, performing interpretation in combination with an industrial scene semantic rule base. The method has the advantage of improving the practicability of the twin model in the industrial scene.

The invention provides a construction scene prediction method and device fusing an image, a text and a BIM modal, and relates to the technical field of intelligent construction prediction management. According to the method, the BIM semantic graph is constructed by extracting the BIM semantic information of the BIM model, and the target detection recognition of the construction site video is carried out in combination with the YOLO model to obtain the target detection result; performing cross-modal alignment with the CLIP to realize deep fusion of the multi-modal data of the image, the text and the BIM to obtain a multi-modal heterogeneous graph; and inputting the multi-modal heterogeneous graph into a space-time sequence model for prediction, outputting prediction results of construction scenes at a plurality of moments in the future, and dynamically mapping the prediction results to a digital twinborn platform to realize risk early warning and visual display. The construction dynamic change can be captured in real time, the construction progress and risk can be accurately predicted, and the intelligent level of construction management is improved.

The invention relates to a multi-modal AI data fusion processing method, device and equipment and a medium, and the method comprises the steps: firstly extracting visual, auditory and text modal features through a pre-training encoder, executing dimension alignment, and generating a standard data feature set with unified dimensions; a cross-modal semantic graph is constructed based on a cosine similarity algorithm, and the problem of semantic mismatch of heterogeneous data is solved; residual enhancement is carried out on the map nodes, and noise interference is eliminated; fusing the optimized features and the semantic topology in combination with a graph convolutional network to generate aggregation graph representation; the fusion features are mapped to a low-dimensional semantic space through a variational auto-encoder, and cross-modal correlation essence is captured; the key dimension contribution degree is quantified, a visual report is generated, and semantic association rules among modals are disclosed, so that the dimension isomerism limitation of a traditional fusion technology is broken through, quantifiable cross-modal semantic mapping is established, the whole process traceability from feature fusion to decision interpretation is realized, and the method is suitable for popularization and application. And the multi-modal decision black box problem in the fields of medical diagnosis, automatic driving and the like is effectively solved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

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.

The engineering management system based on digital twinning comprises a data acquisition synchronization module which acquires construction site data by using an Internet of Things device and a sensor; a digital twinborn model: constructing a digital twinborn model corresponding to an actual project based on the collected data; the twinborn model management module is used for constructing and maintaining a digital twinborn model of an engineering project; the data digitalization module is used for digitally processing the collected data, and comparing and analyzing the obtained point cloud data with a digital twinborn model to verify the integrating degree; the data analysis and decision-making module is used for evaluating project progress deviation by predicting project risks and generating corresponding decision-making suggestions; and the visual interaction module displays the digital twinborn model through a three-dimensional visual interface, and views an engineering state, historical data and a prediction result in real time through interactive operation, and aims to solve the problems of low data acquisition and processing efficiency, unsmooth information transmission and poor management collaboration in a traditional engineering management mode.

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention discloses an intelligent building design generation system based on multi-source heterogeneous data and an AI large model, and the system comprises a multi-source heterogeneous data collection and processing module which is responsible for automatically collecting the multi-source heterogeneous data related to a project, and carrying out the data processing; the knowledge graph module is used for carrying out structured storage and association on multi-source heterogeneous data by constructing a knowledge network covering upper planning, historical culture, architectural design standards and law and regulation contents; the AI large model fine tuning module is used for performing fine tuning on the AI large model by using data in the special architectural design field; the scheme design module is responsible for generating various design schemes, providing choices of different design styles and function layouts and optimizing the schemes; and the visual interaction and feedback module is used for providing an interaction interface with a user. The method is remarkably superior to the prior art in the aspects of design substantial content processing, multi-source heterogeneous data integration, design generation and optimization, user interaction experience and the like, and a more comprehensive, intelligent and efficient solution is provided for building design.

The technology relates to systems and methods for generating context-aware, dynamic visualizations. In an example, a method includes extracting a dynamic context signal; based on the dynamic context signal, retrieving a first visualization; causing a display of the first visualization as part of an application user interface; at an expiration of a refresh period, extracting an updated dynamic context signal; based on the updated dynamic context signal, retrieving a second visualization; and replacing the first visualization with the second visualization as part of an application user interface.

The invention discloses an intelligent number-asking large-screen display system and method based on a multi-modal large model, belongs to the technical field of artificial intelligence and big data processing, and aims to solve the technical problem of how to flexibly adapt to diversified application scenes, perform optimization adjustment according to specific scene requirements and improve applicability and pertinence. According to the technical scheme, a multi-mode input processing module is used for receiving input of a user and conducting semantic conversion by means of the natural language processing technology; the multi-modal large model analysis engine module is used for performing analysis and aggregation by means of a trained multi-modal and cross-modal deep learning model; the data processing and integrating module is used for processing and integrating data from different sources in real time according to the output requirements of the analysis engine; and the large-screen visualization display module is used for visually displaying the processed data on a large screen in the forms of charts, thermodynamic diagrams and 3D models by utilizing an advanced data visualization technology and interactive design.

A system, method, and device for visualizing network topology are disclosed. The method includes (i) automatically generating a network topology visualization of network assets for a network, and (ii) grouping the network assets into a plurality of groupings based on a set of user selected distinct criteria.

The invention relates to the technical field of knowledge maps, in particular to a knowledge map method and system for multi-source heterogeneous data fusion. The method comprises the following steps: cleaning multi-source data through an auto-encoder, dynamically weighting and standardizing after low-rank decomposition and PCA denoising, aligning GNN entities and authenticating standard approval serial numbers; combining CNN / GNN to extract texts, images and sensor multi-modal features, fusing time sequence information with BiLSTM, and performing weighted aggregation; a BERT-BiLSTM-SelfAttention-CRF is adopted to identify an entity, a GCN inference relationship is adopted, a TransE is embedded into an entity relationship to a low-dimensional space, and the entity relationship is stored to Neo4j to support real-time query; dynamically updating the atlas by incremental learning; and visually displaying the constructed knowledge graph. According to the method, a complete closed loop from data cleaning, multi-modal fusion and graph construction to dynamic optimization is formed, and comprehensiveness, accuracy and expandability of the knowledge graph in a multi-source heterogeneous scene are ensured.

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

The invention provides a method and system for achieving CUDA call tracking based on an eBPF, and relates to the field of GPU call tracking of the eBPF. The method comprises the following steps: dynamically mounting an eBPF program at an API function entry and an API function exit of a user mode CUDA runtime library through an uprobe technology of the eBPF; the eBPF program captures metadata of the CUDA API calling event in a kernel mode and packages the metadata into a predefined structural body; asynchronously transmitting the structural body data to a user mode through an annular buffer area; and calling a standard function library by a user mode program to load an eBPF program, reading data in the annular buffer area in real time, and completing analysis and visual display of a CUDA calling event. According to the method, the calling parameter, the return value and the nanosecond timestamp are effectively captured, and the observability and the debugging efficiency of the GPU application program are improved.

The invention relates to the technical field of digital twinning, and discloses a power plant load visualization control method based on digital twinning, and the method comprises the steps: obtaining power plant entity data, constructing and integrating a power plant appearance three-dimensional model and an internal equipment three-dimensional model, and obtaining a static three-dimensional model; constructing a dynamic digital model based on a mechanism modeling method and a data-driven modeling method, and integrating the static three-dimensional model and the dynamic digital model to obtain a digital twinborn model; processing the real-time operation data and the historical operation data of the power plant based on a reinforcement learning algorithm and a deep learning algorithm to obtain a prediction result; performing abnormal state judgment on the prediction result and an early warning threshold value based on the static three-dimensional model to obtain a judgment result; according to a judgment result, evaluating the severity of the abnormal state by using a rule model so as to formulate countermeasures; and based on the digital twinborn model, a dispatching instruction is calculated by using countermeasures and a PID control algorithm, so that the operation state of the power plant is visually monitored and controlled.

The invention provides a low-code rule engine method and platform supporting dynamic service adjustment, and relates to the technical field of data processing.The method comprises the steps that original rule data are obtained, and the original rule data are subjected to structured conversion and unified format specification; generating a visual rule component, presenting rule logic through a graphical interface, supporting business personnel to edit and modify without coding, and converting a modification result into component rule data; performing real-time analysis and verification on the component rule data to obtain operation rule data; performing version identification on the operation rule data, and storing the operation rule data in a rule warehouse; based on the current service context, matching an applicable version, automatically selecting the operation rule data, and loading the operation rule data to a rule execution engine for execution; the autonomy and accuracy of the low-code rule engine are improved.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

The invention relates to the technical field of visual rendering, and provides a three-dimensional data visual rendering method and a visual rendering engine system.The method comprises the steps that space segmentation processing is conducted on three-dimensional model data to be rendered through the LOD technology to generate block slice data with different geometric detail levels, organizing the block slice data through a tree structure with a spatial relationship, and outputting the tree structure as an index file; the method comprises the following steps: adaptively screening block and slice data of different levels of detail based on a user vision field range, storing the block and slice data into a rendering request queue, querying block and slice data to be rendered from the rendering request queue according to index codes of the block and slice data, and updating a layer browser database; according to the method, to-be-rendered block slice data are downloaded and analyzed in parallel in batches in a separated collaborative operation mode, and a rendering task is executed according to a data analysis result, so that the rendering scheduling frame rate is remarkably improved, and rapid visualization of urban mass three-dimensional slice data at a webpage end is realized.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.