4096results about "Drawing from basic elements" patented technology

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

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.

An attitude display system for use in an aircraft has at least one display unit visible by a pilot within a cockpit. A symbology generator is configured for receiving input from at least one attitude sensor and generating symbology image data representative of a pear-shaped attitude indicator, distorted vertically to represent a pitch of the aircraft and rotated to represent a roll angle of the aircraft. The symbology generator may further be configured for adding a horizontal line that represents a horizon, a regular trapezoid representing a downward gravity vector at a base of the trapezoid, a pitch angle of the aircraft, split wings representing an angle of attack of the aircraft, and a stall warning in a sawtooth crack shape. A tail portion of a T-shape with the curved top edge is itself curved in a direction opposite that of an aircraft spin direction.

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The invention relates to the field of business intelligence and artificial intelligence, and discloses an AI analysis technology-based store site selection accurate data comprehensive measurement and calculation system, which comprises a data acquisition module, a data preprocessing module, an AI analysis and optimization module, a financial prediction and risk assessment module and a report generation module. The data acquisition module acquires geographic information, human traffic, rent cost, competitor information and the like of candidate positions from a multi-dimensional data source; the data preprocessing module preprocesses the data; the AI analysis and optimization module generates a recommendation scheme of a candidate position by using multi-objective optimization, linear programming and a machine learning algorithm; the financial prediction and risk assessment module carries out financial income prediction and risk analysis on each position; and the report generation module displays the analysis result through a visual tool and generates a decision support report. According to the method, the optimal store site selection scheme can be dynamically and accurately recommended, and merchants are helped to make scientific decisions and adapt to market changes.

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.

Systems and methods for generating custom art fonts with consistent style include receiving user input that identifies a base font style for a custom font and includes descriptive text that defies one or more text effects to use for the custom font. Depth maps are selected for characters to be included in the custom font. The depth maps are preprocessed to add noise to the depth maps. A generative model generates custom font images conditioned with the text prompt and the depth maps. The custom font images are then used to render text on a display screen of a computing device.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

Provided are a processing system for a three-dimensional model, a method for obtaining a physical model on the basis of three-dimensional printing of a three-dimensional processing system, a system for generating a three-dimensional model from a virtual reality / augmented reality three-dimensional sketch, a diffusion model training method, and a method for editing a three-dimensional model on the basis of a virtual reality / augmented reality three-dimensional sketch. The method for obtaining a physical model on the basis of three-dimensional printing of a three-dimensional processing system comprises: step one, at an input apparatus, a user using a hand or a handle to draw three-dimensional sketch content in the air, wherein a three-dimensional sketch is stored in the form of a three-dimensional point cloud, and when the user draws a trajectory, a corresponding point cloud is generated in the drawn trajectory and is displayed in a three-dimensional virtual space in a highlighted manner, and displacement and deletion operations of the user for the three-dimensional sketch are overall displacement and deletion operations for points in a region corresponding to the point cloud of the three-dimensional sketch; step two, a computing apparatus converting the three-dimensional sketch content into a three-dimensional model; step three, converting the three-dimensional model into a readable file for an execution apparatus, wherein the conversion is implemented by using a slicing algorithm; and step four, a three-dimensional printer executing the readable file to perform a printing operation, so as to obtain a physical model.

The invention provides a dissimilar material crystallization welding control method based on microwave energy distribution, which comprises the following steps: acquiring a high-density temperature map of a to-be-welded workpiece through a fiber grating array, and acquiring a real-time temperature value from a dissimilar material interface edge to a central area of the to-be-welded workpiece to obtain initial temperature distribution; calculating a temperature difference value between the edge and the central area, identifying a local overheat point position by comparing a temperature gradient or setting a temperature difference threshold value, and comparing the overheat point temperature with a preset material overheat threshold value to judge whether an overheat area exists or not; if the temperature difference value exceeds a temperature difference threshold value or a local overheating area exists, extracting space coordinates of a low-temperature area and a high-temperature area from the high-density temperature map, and identifying an energy distribution target area needing to be adjusted; and extracting temperature distribution from the updated energy distribution diagram, analyzing a temperature gradient to identify a heating power concentration region, and calculating to obtain a target stress distribution state in combination with the heating power concentration region.

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

Various disclosed embodiments are directed to deriving, via a language model, a summary of data by converting or encoding table data into one or more natural language sentences, which are then used as input to the language model for generating the summary. One or more embodiments are additionally or alternatively directed to deriving, via a language model, a response to a user question or command via a chat interface by providing the language model with the generated summary as input. In this way, for example, the language model can use the summary as a prompt or other target context for providing a response.

The invention relates to a stylized font generation method based on a diffusion model, and aims to solve the problems of font stroke structure disorder and style deviation in the prior art and realize accurate and stylized font generation. The method is divided into two stages: a first stage, performing pre-training of a font style classification task, including constructing a font style label, designing a style feature extraction network, and training by using a data set to obtain a pre-trained style encoder so as to extract font style features; and in the second stage, training of the generation model is started, style encoder parameters are frozen in the process, content features, stroke order structure features and style features serve as conditions, model parameters are optimized through mean square loss and contrast loss, and therefore a high-quality generation image is trained and generated. According to the method, the structural accuracy and style expressive force of font generation are remarkably improved, and efficient technical support and application value are provided for font library construction and design.

The invention relates to the technical field of image analysis, in particular to an ETFE film cutting control method and system based on image recognition, and the method comprises the following steps: enhancing an edge through multi-scale Gaussian filtering, extracting a gray level change rate pixel by a Sobel operator, generating a path segment, outputting a coordinate, calculating a gradient direction, constructing a direction continuous sequence, and recording an angle deviation; a dynamic window judges abnormal fluctuation to trigger calibration, a fluctuation segment vector is replaced to update a motion track, and a B spline is fitted to generate a track output cutting instruction. According to the method, multi-scale Gaussian filtering is combined with Sobel operator enhanced edge extraction continuous pixels, the fracture problem caused by a fixed threshold value is solved, vector space projection constructs a direction sequence to quantify angle deviation, dynamic window range judges fluctuation to trigger calibration, and a vector replacement strategy corrects an abnormal section control point direction. A smooth track is generated through B spline interpolation, mechanical vibration is reduced, and the flexible material cutting precision and the equipment cooperation efficiency are improved.

A method, apparatus, non-transitory computer readable medium, apparatus, and system for image generation include obtaining a sketch input depicting an object, processing the sketch input to obtain sketch guidance, and generating a synthesized image based on the sketch guidance using an image generation model, where the synthesized image depicts the object from the sketch input.

The invention provides a multi-modal fusion method for breast cancer prognosis prediction based on deep learning. The multi-modal fusion method comprises the following steps: preprocessing a data set, marking a tissue region of a full-slice pathological image, performing slice processing, extracting multi-scale features, re-sampling a medical image, and extracting features of a tumor region; a two-dimensional region fusion module and a three-dimensional region fusion module are respectively selected to carry out multi-scale feature fusion on the preprocessed full-slice pathological image and the preprocessed medical image; performing multi-region feature fusion on the medical image by using a three-dimensional region fusion module; aligning the multi-modal data, performing cross-modal feature fusion by using self-attention pooling, and capturing cross-modal interaction information; and predicting the disease-free lifetime and the total survival of the patient through the loss function. The problems of limitation of single-mode analysis, unsatisfactory prognosis effect and the like are solved, and more accurate breast cancer prognosis prediction is realized.

Systems and techniques for are described herein. Natural language workflow descriptions are received from expert users, the descriptions are processed using language models to identify action sequences, and directed acyclic graphs (DAGs) are generated that represent workflows by mapping actions to automated agents. These DAGs are stored as composite action recipes in a vector database. End users can then submit natural language queries, which the system matches to stored recipes. Selected recipes are executed in a sandboxed environment, with the system sequentially invoking automated agents according to the DAG structure. The systems and techniques described herein provide real-time feedback during execution and support features such as graphical workflow validation, dynamic parameter collection, role-based access control, and dry run simulations. This approach enables efficient creation and reuse of complex workflows based on natural language inputs.

An online concierge system trains a fine-tuned generative image model for distinct categories of items based on a generative image model that takes a textual query as input and outputs and an associated image. Training of the fine-tuned generative image model is additionally based on a small set of representative images associated with the various categories, as well as textual tokens associated with the categories. Once trained, the fine-tuned generative image model can be used to generate realistic representative images for items in a database of the online concierge system that are lacking associated images. The fine-tuned model permits the generation of different variants of an item, such as different quantities or amounts, different packaging or packing density, and the like.

The invention relates to the technical field of computer vision and industrial automation, in particular to a shoe sole three-dimensional gluing contour extraction system and method based on vision single-line laser, and aims to solve the problems that virtual calibration target spots cannot be accurately generated based on shoe sole geometry, the positions and sizes of the target spots are difficult to determine by combining curvature extreme values and principal component analysis in the prior art, and the production cost is low. The problem that a double-branch deep learning model cannot be adopted to fuse feature prediction transformation, and the re-projection error is increased is solved; a virtual calibration target spot is automatically generated based on sole geometry through a feature fusion calibration module, a grid is generated through point cloud processing and Poisson reconstruction, the position and size of the target spot are determined by combining a curvature extreme value and principal component analysis, a corresponding relation is established by utilizing two-dimensional and three-dimensional feature matching, initial alignment is realized through ICP and re-projection error optimization, and the target spot position and size are determined. A double-branch deep learning model is adopted to be fused with feature prediction transformation, iterative optimization is carried out through space consistency errors, and re-projection errors are reduced.

The present application relates to the field of social management technology, and discloses a social stability risk assessment and prediction software platform, including: a data acquisition and preprocessing module, which is used to collect multimodal data from text, images, videos, audio and structured data, and to normalize, clean and extract features from the data; a feature extraction and multimodal fusion module, which is used to extract features from each modal data and form a unified risk feature representation through multimodal fusion technology; a risk prediction and real-time dynamic adjustment module, which is used to predict social risks based on the fused features and dynamically adjust model parameters in real time. By extracting information from multi-source data, while ensuring that data of different modalities are consistent in a unified embedding space, efficient complementarity between features is achieved, and in conjunction with the self-attention mechanism and comparative learning, the comprehensiveness and accuracy of the platform's risk assessment in complex social situations are greatly improved.

A system and method for automatically extracting data from tables and graphs in scientific literature, particularly in life sciences and healthcare, is presented. The invention employs a hybrid approach combining computer vision, natural language processing (NLP), and a large language model (LLM)-based data extraction module. A neural network enhances accuracy by providing contextual information. The system performs table structure detection, optical character recognition (OCR), Vision Transformers (ViTs) for text recognition, and graph-to- table conversion. An LLM refines the extracted data using advanced prompt engineering. A user interface enables data review, validation, and iterative refinement. The system employs a novel two-stage approach: de-rendering tables and graphs into a machine-readable format, followed by interpretation and user-defined mapping. A knowledge graph enhances extraction by resolving ambiguities and inferring relationships. Designed for scalability and continuous improvement, the invention significantly enhances data extraction efficiency and accuracy, accelerating research and knowledge discovery.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

In various examples, various types of sensor data from multiple ego-machines are used to infer lanes and / or generate lane graphs for use in autonomous systems and applications. In some embodiments, one or more DNNs may be used to infer lane data indicating a representation of a lane shape using sensor data from various vehicles to represent a 3D environment. The inferred lane data may include cross-section indicators that indicate cross-sections of a lane and / or connection indicators that indicate a lane channel connecting two locations (e.g., two lane portions). The inferred lane data may be used to generate a lane graph that represents lanes on a road and, in some cases, lane dividers (e.g., polyline represented as a solid line, a dashed line, a double line, etc.). A lane graph may be used, for example, to model the environment around a vehicle, facilitate localization, provide guidance for autonomous driving, etc.

The invention relates to the technical field of robot navigation and environmental perception positioning, and discloses a laser radar and vision fused positioning and mapping method in a dynamic environment, and the method comprises the steps: obtaining laser point cloud data of a surrounding environment, two-dimensional image data with depth information, and posture information of a robot; preprocessing the laser point cloud data and the two-dimensional image data to obtain the laser point cloud data and the preprocessed two-dimensional image data; feature values of the preprocessed laser point cloud data and the preprocessed two-dimensional image data are extracted, the extracted feature values are matched and fused to obtain fused feature point cloud data containing image feature information at the same time, the feature point cloud data and the attitude information are optimized, and the fusion feature point cloud data is obtained. Obtaining fusion data containing the feature point cloud data and the positioning information; and performing back-end optimization and loopback detection on the fused data to realize map construction and self-positioning, so that the positioning and map construction precision in a dynamic environment can be improved.

The invention discloses a wind resource assessment report generation method based on a large model technology. The method comprises the following steps: processing multi-source data, and constructing a domain knowledge base and a fine tuning database; field adaptive RAG enhancement is carried out, professional literatures, historical cases and industry specifications in a field knowledge base are retrieved by adopting a retrieval enhancement generation technology, and wind resource assessment contents meeting technical standards are generated through a multi-modal fusion word vector technology; performing large model fine tuning, and optimizing the generative large model through a gradient-free optimization algorithm Wind-DFO on the basis of a fine tuning database by adopting a field self-adaptive fine tuning strategy; and automatically generating a report, arranging and matching multiple templates through an AI workflow, embedding a data visualization chart, executing verification of a grammar layer, a logic layer and a compliance layer, and outputting a standardized wind resource assessment report. The accuracy, specialty and efficiency of report generation are remarkably improved, and the problems that a traditional method depends on artificial experience, the data utilization efficiency is low, and standardization is insufficient are solved.

The invention provides an instrument panel type building engineering construction progress display method, system and device and a medium, and relates to the field of building construction management.The method comprises the steps that geographic information system map data and building information model data of an area where a building project is located are obtained and fused, and then a multi-dimensional display platform of the building engineering progress is constructed and formed; actual progress data, historical construction data and environment data of a construction site are collected, risk factors of construction tasks are analyzed, the dependency relationship between the construction tasks is analyzed through topological sorting, and a construction risk task tree is constructed; a construction risk task tree is adopted to identify potential risks of a construction task, weighted calculation is carried out on risk factors according to weight coefficients, a comprehensive risk assessment score of the construction task is generated, a construction progress sequence is optimized, and integration is carried out through an instrument panel to form a unified visual display interface. Dynamic prediction, real-time display and automatic adjustment can be realized, and the accuracy of construction progress monitoring is improved.

In some implementations, a computing device may include one or more processors, a memory for storing instructions that when executed by the one or more processors perform operations may include: accessing a drill plan for a drilling operation; receiving a plurality of drilling parameters for the drilling operation; generating a report including: a graphical depiction of the drilling operation based on the plurality of drilling parameters for the drilling operation, the graphic depiction including an animated or video illustration of the drilling operation within a past predetermined time period; a plurality of metrics associated with the drilling operation as provided by the drill plan; and a summary of a planned activity for a drilling operation for a future predetermined time period. Also, the device may include saving the report on a sever connected to a network. Further, the device may include providing the report to at least one user. Similar systems and methods may be used to automatically generate and distribute or provide access to video reports of completion and / or production activities during a first time interval and planned completion and / or production activities during a future, second time interval.

A device receives a real image (e.g., photograph or video frame) that includes a human. The device creates a synthetic image corresponding to the real image. The synthetic image includes a synthetic environment and a humanoid shape that correspond to the human. The device predicts, using a trained viewpoint neural network and based on the synthetic image, a predicted viewpoint heatmap. The device predicts, using a trained pose neural network and based on the synthetic image, a predicted pose heatmap. The device provides, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap and creates a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment. The device classifies the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.

The invention provides a production operation dynamic supervision method, which comprises the following key steps: S1, data integration: widely collecting data of different production links, transmitting the data to a database through a network interface, and constructing a comprehensive data basis; s2, establishing a visual monitoring platform to integrate data to drive development, vividly displaying a production state by means of a three-dimensional model, a chart and a report, and helping a manager to intuitively insight; s3, intelligent analysis and early warning: deeply mining a data rule trend by using big data and an artificial intelligence algorithm, accurately setting an early warning threshold value, and giving an alarm in time when the threshold value is exceeded, so as to effectively prevent risks; and S4, collaborative management and decision support are carried out, department information barriers are broken, efficient sharing and collaboration are realized, scientific decision support is provided for managers, and production plans, maintenance strategies and emergency plans are optimized. According to the method, through multi-link collaborative operation, the production operation dynamic management and control capability is comprehensively enhanced, and efficient, safe and robust development of enterprises is powerfully promoted.