3027results about "Drawing from basic elements" patented technology

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.

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.

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.

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.

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 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 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.

The present disclosure provides a visual data analysis method and device used for visual analysis for multiple types of data sources. By establishing connection relationships with various types of data sources, multiple types of data sources can be obtained in real time, and various types of data sources are combined and analyze in real time. The method includes: obtaining multiple types of data sources, and establishing a connection with each type of data source, wherein the type of data source is used to represent a source from which data is obtained; displaying, through a visual page, each piece of table information contained in each type of data source with which the connection is made.

A system for generating a projection structure using a graphical user interface, wherein the system comprises: a display device configured to display a graphical user interface (GUI); a computing device comprising a memory; and a processor, wherein the memory contains instructions configuring the processor to: receive a plurality of datasets, wherein each dataset comprises a plurality of parameters; generate a first projection structure as a function of the datasets; display the first projection structure through the GUI; receive natural language query data corresponding to the first projection structure using a chatbot interface; generate response data as a function of the query data; map, as a function of a look-up table, each categorization to a database entry index; obtain response data from the database; adjust the parameters as a function of the response data; generate a second projection structure; and display it in the GUI.

The invention discloses an intelligent war game deduction method based on reinforcement learning, and the method comprises the steps: constructing a dynamic battlefield model: constructing an adjustable battlefield simulation platform, and defining the landform, resources and army distribution elements in a battlefield; a reinforcement learning strategy is generated and optimized, an intelligent strategy generation algorithm based on deep reinforcement learning is designed, and an efficient combat strategy is generated in multiple rounds of training by constructing a state space and an action space and combining a situation reward function; a double-agent chess playing training mechanism is introduced, black parties and white parties are modeled into reinforcement learning agents, red parties and blue parties are modeled into reinforcement learning agents, and a real battlefield game is simulated through multiple rounds of alternate training; result visualization deduction: a dynamic decision visualization function is provided, and battlefield situation, troop dynamics and a strategy execution process can be displayed in real time; according to the method, the problems of rule solidification, limited strategy generation capability, insufficient antagonism and the like in the prior art are solved.

The invention discloses a structured data self-learning method based on a graph neural network, and the method comprises the following steps: S1, analyzing structured data, extracting entity fields and relation fields, and constructing a structure candidate graph; s2, generating a node embedding feature matrix, and initializing and recording the adjacency relation of candidate edges; s3, constructing a graph neural network model, inputting node features and an adjacent matrix, and defining a task loss function; s4, evaluating the gradient contribution degree of edge connection by adopting a gradient sensitive sparse adjacency self-learning algorithm, and updating the graph structure representation; s5, introducing an embedded interpretability gradient backtracking mechanism, correcting an edge connection relation and enhancing interpretability; s6, training the graph neural network by using the corrected structure, and updating the node embedding and graph structure; and S7, outputting a final graph structure and an interpretability index, and generating a graph modeling visualization result. According to the method, efficient modeling and explanatory analysis of structured data are realized through a dynamic graph structure learning and gradient backtracking mechanism.

The invention discloses a corn germination image segmentation method based on an elite adaptive rime algorithm. Relates to the technical field of agricultural seed detection and image processing, in particular to the technical field of corn germination image segmentation based on an elite adaptive rime algorithm. According to the method, a dual-adaptive weight mechanism and an elite reselection strategy are introduced into a rime optimization algorithm, the convergence capability of the algorithm is enhanced, and multi-threshold segmentation is carried out on the corn kernel germination image in combination with the Kapur entropy. And the image segmentation precision is effectively improved. The method comprises the following steps: acquiring a corn germination image data set; drawing a two-dimensional histogram, and inputting the two-dimensional histogram into a Kapur entropy function to obtain an objective function fobj; an elite solution module is initialized; a dual-adaptive weight mechanism is added; a soft rime strategy and a hard rime strategy are improved; updating the elite solution after the rime search strategy module; and the target function fobj is input into a rime improvement algorithm, and an optimal threshold value is obtained.

The invention provides an intelligent self-adaptive video editing method and system based on deep learning, and relates to the technical field of video processing.The method comprises the steps that firstly, a semantic mapping relation between a to-be-edited video material and a preset editing requirement is established, and an editing requirement mapping result is generated, the preset editing demand comprises a content style and a rhythm control demand, and then semantic feature association processing is carried out based on the mapping result to obtain a semantic association feature set comprising lens unit content semantic features and rhythm association features; then calling a pre-trained editing decision model (including a semantic matching module and a rhythm adjusting module) to carry out editing strategy matching on the set, generating a preliminary editing strategy set, generating an initial video editing scheme according to the preliminary editing strategy set, carrying out parameter adjustment on the initial scheme according to a strategy optimization suggestion output by the model, and carrying out video editing on the initial scheme; and a final video editing scheme is obtained, and intelligent self-adaptive editing of the video is realized.

The invention relates to the technical field of education, in particular to a visual chart system for analyzing test question quality and test object ability, which comprises a dynamic data preprocessing module, a sliding window adaptive adjustment module, a multi-axis fluctuation intensity analysis module, a group trend disassembly module and an abnormal test question judgment module. According to the method, a difference absolute value algorithm is adopted to construct an adjacent test question physical correlation model, a test question serial number is mapped as a time axis variable to generate a thermodynamic diagram, the time-space continuity of score fluctuation is captured, a sliding window algorithm is adopted to establish dynamic correlation between window density and fluctuation amplitude, and normalized parameter difference degree is matched with sampling frequency and data features. A three-axis direction change rate is defined to construct a three-dimensional radar coordinate system, Kriging interpolation is combined to generate a distribution cloud picture, group trend departure and standard deviation distribution difference are analyzed, spatial autocorrelation modeling is used to enhance the anomaly recognition capability, and time sequence modeling, dynamic feedback and spatial vector analysis are used to cooperatively improve the detection and evaluation precision and reduce the misjudgment risk.

The invention discloses a landslide risk assessment method based on an extreme rainfall and geology coupling model, and relates to the technical field of geological disasters. Comprising the following steps: S1, constructing a three-dimensional probability density field of a fracture network and a non-Gaussian random field model of a permeability coefficient tensor; s2, setting a physical kernel layer according to the non-Gaussian random field model, setting a data driving layer through space-time Transform coding, and constructing a graph attention network model; s3, generating an adversarial network through physical information, constructing extreme rainfall coupling data, and updating the non-Gaussian permeability coefficient random field model according to the graph attention network model; and S4, acquiring an entropy generation rate according to the mechanical field data, the seepage field data and the temperature field data, and determining a risk level. Physical interpretability grading early warning of landslide risks is realized, and meanwhile, risk space distribution can be visually displayed through a sliding surface probability cloud picture, so that accurate decision support is provided for disaster prevention and control.

The invention belongs to the field of damage assessment, and relates to a typical engineering target rapid damage assessment method based on an image recognition technology, and the method comprises the steps: building different types of image damage feature damage assessment criteria: obtaining damage pictures and anti-explosion damage data, carrying out the classification of damage grades, drawing a P-I curve graph, defining the damage grade and the critical state of damage, and carrying out the calculation of the damage assessment criteria; adopting a cyclic interpolation method to obtain a failure critical condition, a critical progressive impulse and a critical progressive overpressure, substituting into a classical P-I curve expression for fitting to obtain a failure critical curve expression, and further simulating to obtain a failure critical area; establishing a corresponding relationship between the damage assessment criterion-damage critical area and the damage level of the image damage feature under the engineering target category; a damage picture is shot, the damage area is extracted, the damage area is compared with the image damage feature damage evaluation criterion, and the damage level is rapidly determined. According to the method, the mapping relation between the image damage characteristics and the structural mechanical response is established, so that the damage level is quickly researched and judged.

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 discloses a deep learning-based cold resistance evaluation system for medicago sativa L., and the system comprises a data generation module which is used for generating a phenotypic image and corresponding physiological data of medicago sativa L. under low-temperature stress through a diffusion model embedded with plant low-temperature response physical constraints; the evaluation model module is used for extracting cold resistance characteristics from the image and physiological data by adopting a causal-driven dual-channel adaptive network; and the decision module comprises a hierarchical model distillation unit and a federal reinforcement learning unit, and the hierarchical model distillation unit and the federal reinforcement learning unit realize joint training of model compression and decision strategy optimization through an edge-cloud collaborative architecture, output a cold-resistant decision and realize visualization through an augmented reality interface. The method can effectively solve the core problems of traditional medicago sativa cold resistance assessment in the aspects of data generation, model generalization, decision-making efficiency and the like.

The invention provides a ship trajectory optimization method and system based on a visual language model and multi-modal collaborative decision, and relates to the field of ship trajectory prediction.The method comprises the steps that a trajectory prediction model is established, and a ship trajectory optimization model is established based on the visual language model; establishing a ship trajectory optimization cue word template based on chain thinking; first multi-modal data is obtained, and the first multi-modal data at least comprises a track classification label; generating a basic trajectory prediction result according to the first multi-modal data through a trajectory prediction model; generating second multi-modal data according to the basic trajectory prediction result and the first multi-modal data; and through the ship trajectory optimization model, according to the ship trajectory optimization cue word template based on chain thinking and the second multi-modal data, an optimized trajectory prediction result is generated, and the ship trajectory prediction method has the advantage of improving the accuracy of ship trajectory prediction.

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

The invention relates to the technical field of deformation prediction, in particular to a lateral deformation prediction system and method based on a deep foundation pit wall body, and the system comprises a deformation data collection module which collects a ground surface settlement monitoring value and a lateral displacement monitoring value of a corresponding measuring point of the deep foundation pit wall body, carries out the time sequence alignment processing of data points, removes an abnormal monitoring value, and obtains a deformation data collection module; and establishing calibration deformation sequence data. According to the invention, through real-time acquisition and strict execution of data time sequence alignment processing, the consistency of ground surface settlement data and wall lateral displacement data is ensured, abnormal monitoring data is effectively eliminated, and the data accuracy and consistency are improved; in the spatial interpolation process, width measurement of a settlement area is introduced, the width characteristic of a settlement groove is determined, and the earth surface deformation trend is captured according to a deformation index; and extracting the maximum lateral displacement of each time section of the monitoring section, establishing a dynamic corresponding relation between the maximum displacement and the width of the settling tank, and identifying the regular influence of the change of the construction process on the lateral deformation.

Technology is disclosed herein for the integration of spreadsheet environments with LLM services. In an implementation, an application service receives a natural language input from a user associated with a spreadsheet hosted by a spreadsheet application. The application service generates a prompt based on the natural language input which includes asking a large language model (LLM) service to classify a statement in the input as referring to one of multiple capabilities of the spreadsheet application. The application service inputs the prompt to the LLM service and receives an output from the LLM service which identifies a determined one of the multiple capabilities. The application service generates a revised prompt based on the input and the determined one of the multiple capabilities and inputs the revised prompt to the LLM service.

The invention discloses a current transformer distortion current reconstruction method based on a U-I trajectory curve, and the method comprises the following steps: carrying out the synchronous sampling of a voltage value and a current value at a measurement point, and carrying out the per-unit processing; constructing a coordinate system taking the current value after per unit processing as a horizontal axis and the voltage value as a longitudinal axis in the two-dimensional coordinate, and drawing a U-I track curve; analyzing U-I track curve characteristics of the current transformer in a normal operation state, a transient state and a steady state; linear transmission sections of transient saturation and steady saturation are identified; and taking sampling points in the linear transmission section as input values, restoring a U-I trajectory curve by using an ellipse fitting algorithm, extracting current values corresponding to abscissas on the restored U-I trajectory curve, and arranging the current values according to a time sequence to realize current reconstruction. According to the method, the reconstruction of the CT secondary distortion current is realized through a least square ellipse fitting method by utilizing the similarity between a U-I trajectory curve and an elliptic curve during normal linear transmission. The problem that the differential protection adaptability is reduced or even fails due to distortion current is effectively prevented, and the protection stability is effectively improved.