485 results about "Pattern generation" patented technology

The invention discloses an abnormal behavior detection method and system based on a multi-source log and electronic equipment, and the method comprises the steps: collecting log data, carrying out alignment processing and data preprocessing, and generating normalized log data; carrying out attribute screening on the standardized log data set, and packaging the screened logs into a JSON format; extracting features of the log data, converting the features into data points, clustering the data points, and retaining a log cluster according to a clustering result; mining a hidden association mode between log attribute items in the log cluster by using an improved Apriori algorithm, generating an association rule, and constructing a knowledge graph based on the association rule; and constructing a time sequence of the log data by using a self-adaptive time window, and inputting the time sequence and the tag sequence of the log data into the LSTM neural network by combining context information in the knowledge graph and a tag sequence generated by an association rule to obtain an abnormal behavior detection result. According to the method, the log data format is unified, correlation analysis is enhanced, and the accuracy of an anomaly detection result is obviously improved.

The pattern generation method based on diffusion model fine tuning comprises the following steps: constructing a data set; the generation model comprises an edge detection model ControlNet and a stable diffusion model, the stable diffusion model comprises a text encoder CLIP, a diffusion model U-Net and an image decoder VAE, the CLIP text encoder converts an input natural language into word vector features and inputs the word vector features into a U-Net network in a submerged space, and the diffusion model U-Net and the image decoder VAE are connected with the edge detection model ControlNet and the diffusion model U-Net; the U-Net network is controlled to carry out iterative denoising on pure noise in a low dimension to generate a compressed image, and finally the compressed image is restored to an original pixel image through a decoder VAE, so that text content control image generation is realized; carrying out fine tuning training on the generative model by adopting a LoRA parameter efficient fine tuning technology; and intelligently generating a batik style pattern. The method has the advantages that the texture precision of the generated image can be improved, and the personalized style image can be generated.

Systems and methods are provided for generating a flat garment pattern and / or 3D mesh representation of a garment from one or more images depicting the garment laid flat or hung up. A system may obtain both a front image depicting a front view of a garment and a back image depicting a back view of the garment. A front and back silhouette of the garment may then be generated, which may include segmenting the garment depiction from background image content. A parametric representation of the garment may then be generated based on the front and back silhouettes, which may be implemented by iteratively optimizing, using differentiable rendering techniques, a garment representation within a parametric garment space previously learned for the particular garment type. A 3D mesh garment representation may then be generated based on the parametric representation, from which a flat sewing pattern may subsequently be generated if desired.

The invention relates to a two-dimensional garment making layout generation method and device and computer equipment, and the method comprises the steps: carrying out the three-dimensional reconstruction of an input image of a target garment, and obtaining a three-dimensional model of the target garment; identifying the three-dimensional model to obtain a plurality of three-dimensional curved surface cutting pieces in the three-dimensional model; mapping each three-dimensional curved surface cutting piece into a corresponding two-dimensional tiled cutting piece, and determining cutting piece parameter information of each two-dimensional tiled cutting piece; and based on each two-dimensional tiled cutting piece and the cutting piece parameter information of each two-dimensional tiled cutting piece, generating a two-dimensional layout of the target garment. According to the method and the device, the problems of low generation efficiency of the layout and insufficient precision of the layout are solved, and the generation efficiency of the layout and the precision of the layout are improved.

An offload server includes a code analysis section for analyzing whether function blocks executable by an accelerator is included, a processing pattern generation section for generating patterns for offloading the function blocks, and a performance measurement section for deploying each pattern for offloading the function blocks to a verification machine to measure performance. When the performance measured by the performance measurement section satisfies a desired level, the processing pattern generation section generates patterns for offloading remaining loop statements except the function blocks, and the performance measurement section deploys each pattern to a verification environment to measure performance. When the performance measured by the performance measurement section fails to satisfy the desired level, the processing pattern generation section generates patterns for offloading loop statements, and the performance measurement section deploys each pattern to the verification environment to measure performance.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

The invention discloses a garment pattern personalized customization system and method based on an improved Stable Diffusion model, the system comprises an input module, a core processing module and an output module, the input module receives multi-modal user input and fuses the multi-modal user input; the core processing module generates an initial pattern by using an improved Stable Diffusion model, realizes accurate adaptation and parameterization control of the pattern and a clothing model through a TPS algorithm, CLIP semantic analysis, Perlin noise and the like, and reduces feedback delay in combination with 3D real-time rendering; the method comprises the steps of multi-modal pattern generation, dynamic adaptation and parameter control and production file output, the full-link bottleneck of'design-adaptation-production 'of garment customization is broken through, normal form transition from manual leading to AI driving is achieved, and the production efficiency is improved. And personalized and industrial production standards are considered.

The invention relates to the technical field of textile, in particular to a textile pattern generation style migration method. Comprising the following steps: adopting a textile pattern structure and style separation coding mechanism; introducing a pattern rule structure field PSF, and introducing a style representation mapping domain SFD to perform high-dimensional deconstruction on color distribution density and texture frequency response extraction; a joint orthogonal deembedding network is utilized to maximize the independence of the structure and the style in the submerged space; adopting style component reconfigurable mapping; further refining the style characteristics into five types of core components; an inductive weight bias function is introduced, so that the system understands aesthetic tendency to make migration strategy adjustment; constructing a structure constraint graph matching mechanism; keeping the similarity of the pattern structure field PSF in the submerged space; introducing a topology order-preserving loss function, and keeping the original topology continuity for the repetitive unit arrangement; deformation of the structure caused by style replacement is limited through a structure and style cooperative adjustment curve; and generating a network by adopting dynamic adaptation of a style migration pattern.

The invention belongs to the technical field of intelligent clothing pattern making, and relates to a text-driven intelligent clothing pattern generation method, which specifically comprises the following steps of: inputting a user-defined clothing pattern feature description text into a trained intelligent clothing pattern generation model to generate clothing pattern data; jointly inputting the generated garment template data and a user-defined action feature description text into a trained learning-based three-dimensional virtual fitting model, and setting parameters of the inelastic fabric to obtain garment template data of the inelastic fabric; the real elastic fabric parameters are adopted to optimize the garment template data of the inelastic fabric, and garment template data of the real elastic fabric are generated; and finely adjusting and storing the garment template data of the real elastic fabric according to individual requirements. According to the method, the generation efficiency and quality of the garment template are remarkably improved, the fitness and comfort of the garment in a dynamic scene are effectively improved, and an innovative solution is provided for intelligent garment pattern making.

The invention discloses an Internet of Things equipment topological relation identification method based on a graph neural network, and the method comprises the steps: capturing a time sequence evolution mode of historical connection and communication states of equipment through fusion of a time sequence attention mechanism and a self-adaptive time sequence attenuation factor, and constructing an initial equipment relation graph; carrying out incremental learning by adopting a quantum heuristic optimization algorithm, and dynamically updating the topological structure of the equipment; extracting a cross-protocol communication mode by using a meta-path guided graph convolution operation, and generating a graph structure fingerprint irrelevant to a protocol; a zero sample learning framework is combined to map into a standardized embedded vector; and deploying a hierarchical federated learning framework to execute topological reasoning on edge equipment, and dynamically outputting an equipment relation confidence score. According to the method, the real-time performance and compatibility of topological relation identification are improved, and the method is suitable for a heterogeneous Internet of Things environment.

The invention discloses an AI diffusion model pattern generation platform and method, the generation platform comprises a visual interaction module, an image generation module and a pattern mapping module, and the image generation module comprises a training sub-module and a reasoning sub-module; the generation method comprises the following steps: preprocessing a text input by a user and an optional structure image, and outputting standardized data; a semantic embedding vector is generated through text coding; carrying out LoRA fine tuning to obtain a style weight module; a ControlNet structure condition is injected; performing diffusion sampling to generate candidate patterns; performing CLIP scoring to screen an optimal pattern; and outputting the optimal pattern and previewing the carrier map. According to the platform and the method provided by the invention, the LoRA low-rank fine tuning, the ControlNet structure condition control and the CLIP similarity evaluation algorithm are fused, so that the effects of customizing the special style pattern with a small number of samples at low cost and accurately controlling the structure and style of the pattern are realized.

The invention provides an intelligent agent automatic generation method and system based on a large language model. The method comprises the steps that a page is created according to a component requirement and a page requirement input by a user, a component is generated, and configuration parameters are set; analyzing the interface document through a large language model, generating page components according to the component knowledge base, and binding and combining the page components, configuration information of the components and interaction behaviors through a translator; initializing a configuration interaction behavior; configuring behavior conditions of the interaction behaviors, selecting data sources of the components, and modularly rendering a system page according to configuration information of the page components; generating a page layout according to the generation mode of the combined page of the business requirements and the input natural language and the page generation mode, and binding the interactive behavior information to generate a system page; and customizing the storage mode of the intelligent agent. The method depends on a memory unit mechanism, is driven by a large model, assembles personalized application pages according to requirements and automatically executes task requirements, and has a higher-level automatic generation technology.

The invention relates to the technical field of data processing, and discloses a method for automatically generating a patchwork pattern based on an intelligent algorithm, and the method comprises the following steps: S1, user demand analysis: collecting and analyzing design demands, including color preference, style requirements, pattern complexity and other parameters, input by a user; s2, preparing a patchwork element data constructing the patchwork element database, wherein the patchwork element database comprises geometrical shapes, textures, color matching schemes and historical design cases; s3, generating a patchwork pattern: adopting a GAN deep learning model to generate a candidate patchwork pattern; s4, optimizing the pattern of the patchwork: optimizing and generating the pattern in combination with reinforcement learning; s5, performing image segmentation and optimization algorithm: through the image segmentation and optimization algorithm, ensuring seamless connection when the cloth splicing blocks are spliced, and adapting to actual cloth processing requirements; s6, editing a patchwork design scheme: generating a patchwork design scheme in an editable format for a user to preview and modify; and S7, outputting the patchwork pattern: outputting the final patchwork pattern.

The invention relates to the technical field of building energy saving, and discloses a building energy-saving control system and method.The method comprises the steps that firstly, multi-source environment data inside and outside a building is collected, and a time-space fusion data set is generated through timestamp synchronization and data alignment; dynamic energy consumption characteristics are extracted, and the future energy consumption state of each region is predicted by using a hidden Markov model; decomposing a historical energy consumption mode according to a prediction result and a non-negative matrix factorization algorithm to generate a multi-objective optimization control strategy; and finally, adjusting an equipment operation sequence through a dynamic priority scheduling algorithm and outputting an instruction. The system comprises a multi-source data acquisition module, a feature extraction module, a state prediction module, a strategy generation module, an equipment scheduling module and a feedback correction module. According to the method, energy consumption can be accurately predicted, multi-target optimization control, dynamic equipment scheduling and online strategy correction are realized, the building energy utilization efficiency is effectively improved, energy consumption is reduced, the comfort level is guaranteed, and equipment loss is reduced.

The invention discloses an unmanned aerial vehicle performance pattern generation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting historical unmanned aerial vehicle performance patterns, and carrying out the text marking of the historical unmanned aerial vehicle performance patterns, so as to construct a training data set; training a text graph model and a corresponding control model by using the training data set so as to construct a pattern generation model; and generating a corresponding unmanned aerial vehicle performance pattern by using the pattern generation model based on a set pattern generation requirement. According to the method, historical unmanned aerial vehicle performance patterns are collected and text marking is carried out to construct a training data set, the data set is utilized to train a text graph model and a control model thereof, a pattern generation model is constructed, and finally patterns are generated through the model pattern generation model according to set pattern generation requirements. In this way, the process of manual direct design or repeated adjustment can be reduced, the generation period of the unmanned aerial vehicle performance pattern is remarkably shortened through automatic model generation, and the generation efficiency is improved.

The invention discloses a large model query generation system and method combining graph structure analysis and execution, and the system comprises a graph mode standardization module which converts a description file into a unified structured representation; the large language model module is used for screening out nodes, edges and sub-graph modes required by query statements according to the natural language problem and the structured representation of the user; the graph mode detection module is used for detecting and modifying the screened nodes, edges and sub-graph modes through scripts; the large language model module generates an initial query statement according to a natural language problem of a user and detected and modified nodes, edges and subgraph modes; the query statement detection module is used for detecting the initial query statement through a script and generating an error report; and the large language model module generates a modified query statement. According to the large model query generation system and method combining graph structure analysis and execution, the accuracy of graph mode selection is remarkably improved through the thinking ability of the large language model and the script error detection ability, and therefore the quality of graph database query statement generation is improved.

Efficient generation of multi-platform compatible source code and test code with consistency and quality in software development SOLUTION: The schema-driven development system includes a user input unit, a schema generation unit, and a code generation unit, wherein the user input unit acquires data in various formats such as a natural language and a CSV file, the schema generation unit converts the acquired data into a schema, and the code generation unit generates a source code and a test code based on the generated schema.SELECTED DRAWING: Figure 1

The invention relates to the technical field of automatic pattern generation and execution, and provides a beverage garland track generation method based on a specific pattern, which can be widely applied to the fields of beverage garland, food decoration, artistic creation and the like, and comprises the following steps: S1, receiving pattern input; s2, analyzing the pattern input to obtain control parameters; s3, generating an execution path based on the control parameters; and S4, generating a control track according to the execution path and executing the control track. Through the basic geometric primitive combination, the pattern formula system and the user input conversion pipeline, the preset garland pattern and user-defined input are adapted, accurate and stable reproduction and flexible expansion of the garland pattern are realized, and the personalized creation capability of the automatic coffee machine is improved. The system is compatible with multiple types of executing mechanisms such as 4-7-axis mechanical arms, breaks through traditional input limitation, supports multi-mode interaction such as natural languages and gestures, can autonomously learn and evolve, constructs open creative ecology, and has four innovation points.

The invention discloses a traditional building micro-reconstruction performance improving system based on machine learning. According to the method, the inherent contradiction between traditional building protection and performance improvement is effectively broken through an intelligent multi-dimensional data fusion and generative design technology. A holographic information model constructed by the multi-source data acquisition module completely retains historical genes and space passwords of a building, and provides an accurate space-time reference for subsequent transformation; a two-channel analysis mechanism of the performance evaluation module brings physical structure safety and cultural value continuity into a unified evaluation system, and ensures that a transformation scheme not only meets modern use requirements but also inherits and builds wisdom. The pattern generation and space reconstruction technology of the module is creatively designed, the limitation of traditional craftsman experience inheritance is broken through, contemporary expression of traditional building languages is achieved in the digital twinning environment, and historical style protection is changed from passive limitation to active innovation.

This application is directed to automated graph schema generation. A schema generation method includes receiving a graph schema having vertices and edges associated with a plurality of data tables. The method also includes automatically generating, based on the plurality of data tables and the graph schema, one or more suggestions using a computational model. The one or more suggestions identify one or more vertices and / or one or more edges. The one or more suggestions include a respective suggestion that identifies a respective subset of the one or more vertices and / or the one or more edges. The method further includes receiving a first user input that is a selection of the respective suggestion from the one or more suggestions. The method further includes updating, based on the first user input, the graph schema. The method further includes outputting the updated graph schema.

A computer-implemented method and system standardize code patterns within enterprise software environments. A code modernization system utilizes a Large Language Model (LLM) and a specialized prompt library. The library includes pattern recognition prompts to guide the LLM in identifying specific code patterns within selected software code, potentially using enterprise-specific context. It also includes standardized solution prompts to guide the LLM in generating replacement code conforming to predefined enterprise standards for the identified patterns. The system orchestrates communication, transmitting code and relevant prompts to the LLM and receiving identified patterns and subsequently the generated standardized replacement code. This automated approach facilitates improved code quality, consistency, maintainability, and can support code translation efforts within the enterprise.

The invention relates to a dynamic wireless key frequency hopping communication anti-interference system and a method thereof. The system comprises a transmitting end and a receiving end, the transmitting end comprises an information source scrambling module, a Turbo coding module, a spread spectrum module, a framing module, a frequency hopping pattern generation module and a forming filtering module; and the receiving end comprises a digital front-end processing module, a frequency hopping synchronization module, a signal synchronization module and a demodulation module. The frequency hopping pattern based on the chaotic system has high randomness and predictability, and tracking interference and eavesdropping are effectively resisted.

The invention discloses a method and device for improving printing feathering uniformity and a storage medium, and the method comprises the following steps: S1, inputting a to-be-printed picture through an image input module, analyzing each pixel point in the picture, and determining the position of the pixel point in a coordinate system and a corresponding color gray value; s2, according to printing requirements, a feathering template gray curve and a dot pattern generation algorithm are selected or generated; s3, the printing control module determines the printing mode of the printer according to the resolution and feathering degree requirements of the printed picture; (S4); in the printing process, the feathering template generation module dynamically overlaps a feathering template generation algorithm according to the content of each position of a picture scanned by the swath, and generates an independent feathering template for each swath, so that the feathering effect is dynamically adjusted according to the real-time change of the printing content; according to the method, the feathering effect can be dynamically adjusted according to the real-time change of the printing content, and the finer and more natural printing effect is achieved by partitioning the picture and matching the feathering templates with different precisions.

The present disclosure discloses a master pattern generation method which is a major pattern in a repeated cycle by analyzing programmable logic controller (PLC) logic, and a method for training a model that may analyze an error of a cycle using the generated master pattern.The master pattern generation method and the training method for a cycle analysis model according to the present disclosure are different from the related art in that the methods are a technology of processing a machine control language (low-level language) that is difficult for humans to analyze and converting the machine control language into an analyzable language (high-level language), i.e., a machine language processing (MLP)-based technology that analyzes the executed machine language (a language that controls a machine) with a computer and can be understood by humans.

The invention is applicable to the field of near-infrared spectrum modeling analysis, and provides a near-infrared spectrum modeling characteristic mode generation and collaborative optimization method, which comprises the following steps: acquiring sample near-infrared spectrum data, performing preprocessing operation, and converting one-dimensional near-infrared spectrum data into two-dimensional structure data by using a conversion method; on the basis of the existing two-dimensional structure data, generating more two-dimensional structure data by utilizing a generation technology; for different feature modes, extracting depth features by using different neural networks, and constructing an optimal minimum feature mode set by using a cross adaptive attention mechanism and a feature collaborative construction method; and building a model according to the optimal minimum characteristic mode set, optimizing the model by using a collaborative optimization loss function, and finally realizing efficient modeling and accurate analysis of the near infrared spectrum data. According to the method, the accuracy of near infrared spectrum modeling in substance component content analysis is remarkably improved.

To provide a travel pattern generation device, a vehicle, and a travel pattern generation method, capable of ensuring high safety and regularity and even margin time.SOLUTION: A travel pattern generation device comprises: a danger-degree evaluation and countermeasure examination unit that calculates, for each of detection performance information representing detection performance for an obstacle, a section characteristic map representing a type of a travel section and characteristics of a surrounding object, standard speed limit information, vehicle position information, standard driving curve information, and a plurality of countermeasure candidates defined by the standard speed limit information or the detection performance information, a danger degree which is an index taking into consideration the occurrence frequency and the damage degree of both or one of a human damage and a vehicle damage at the time of collision in a travel section using the section characteristic map and the countermeasure candidates for each travel section and margin time, selects a countermeasure on the basis of the danger degree and the margin time, and calculates a countermeasure speed limit; and a travel pattern generation unit that calculates a travel pattern up to a next station on the basis of countermeasure speed limit information regarding the countermeasure speed limit, the standard driving curve information, and the vehicle position information.SELECTED DRAWING: Figure 2

The invention provides a three-dimensional to two-dimensional combined garment pattern rapid generation method, and belongs to the technical field of garment customization. Scanning to obtain three-dimensional scanning data of the to-be-customized object, and obtaining a three-dimensional model of the to-be-customized object; converting the three-dimensional model into a three-dimensional virtual model through virtual fitting software; a 3D clothing basic model is obtained through the three-dimensional virtual model; establishing a parameterized structure model; and generating a 2D clothing pattern. According to the method, rapid generation of three-dimensional to two-dimensional clothing patterns is studied, and personalized patterns which most accord with human body characteristics can be rapidly obtained according to the body type of a person; the paper pattern generation method provided by the invention is stable, rapid and good in effect; virtual fitting and pressure simulation results show that the sample plate generated by the parameters can meet the requirements of different fit degrees, the comfort is good, a new parameter paper pattern generation method is provided for fit clothes, and generation and manufacturing of fit clothes paper patterns can be completed more efficiently and intelligently.

A method for processing of a data stream using a graph-based data model includes receiving a data stream including data messages; disassembling the data messages data elements and metadata; generating a structured data model comprising the set of data elements based on the type of the data elements and the pattern of the data messages; instantiating a workflow to process the structured data model; configuring a CPS-G model sub-graph to add to the CPS-G model based on the type of the data elements and the pattern of the data messages; adding the CPS-G model sub-graph to the CPS-G model to form the CPS-G dataset; and storing the CPS-G dataset including the CPS-G model sub-graph in a data store for further processing by streaming computation and machine learning algorithms.

The invention provides a mask pattern generation method, a mask, a semiconductor structure and a manufacturing method thereof. The mask pattern generation method comprises the following steps: acquiring a mask pattern template comprising a cutting channel region and a reference mark pattern comprising a graphic pattern; the reference mark pattern is divided into a plurality of reference mark areas; extracting contours of partial graphic patterns in each reference mark area by using the auxiliary graphs to obtain a plurality of auxiliary mark patterns, and arranging the auxiliary mark patterns in the cutting channel area; and respectively intercepting a part of the graphic pattern in each reference mark area to obtain a plurality of local mark patterns, and arranging the local mark patterns at the positions of the corresponding auxiliary mark patterns in the cutting channel area to obtain a target mask pattern, the plurality of local mark patterns transferred to the semiconductor structure are combined into an alignment mark pattern matched with the graphic pattern in the cutting channel of the semiconductor structure. According to the embodiment of the invention, the alignment precision in the processing process of the packaging technology is improved.

The present invention provides a method and system for automatically generating a textile lace pattern in the technical field of textile lace pattern design, the method comprising: step S1, creating a textile lace pattern generation model based on a feature extraction module, an encoding module, a pattern generation module, a fine-tuning module, and an identification module; step S2, setting a loss function and a hyperparameter; step S3, acquiring a large number of historical textile images and historical description texts to construct a data set; step S4, training the textile lace pattern generation model through the data set until the loss function is less than a preset loss threshold, and continuously optimizing the hyperparameters during the training process; step S5, inputting an input reference image or a real-time description text into the trained textile lace pattern generation model to generate a textile lace pattern; step S6, fine-tuning the textile lace pattern based on input user feedback. The present invention has the advantages of greatly improving the quality and efficiency of textile lace pattern generation.