42 results about "Hierarchical modeling" patented technology

This invention provides a semi-automatic method for constructing and iterating an ontology in the financial and tax domain, comprising the following steps: S1, system initialization and configuration; S2, data acquisition and preprocessing; S3, based on a few-shot learning model and an expert rule base, automatically extracting core concepts from structured financial and tax data and inferring relationships between concepts, performing hierarchical modeling according to financial and tax business logic, and generating a draft ontology model; S4, multi-dimensional automatic verification and manual optimization of the draft; S5, ontology output and version management; S6, dynamic iterative update of the ontology; S7, iterative automatic verification of the ontology in multiple dimensions. This invention achieves four core advantages: improved efficiency, quality assurance, scenario adaptation, and dynamic updates, solving key technical challenges in the construction and application of ontology in the financial and tax domain, providing core support for the efficient operation of financial and tax intelligent agents, and possessing broad application prospects and commercial value.

The application discloses a kind of 6G network multidimensional resource AI model dynamic deployment optimization methods based on category theory, belongs to intelligent collaborative optimization technical field;Method is: the cross-layer consistency dependency of end-to-end AI reasoning service is formalized by functor form;Establish the joint optimization model with long-term average end-to-end delay minimization as target, while being constrained by multidimensional resource and service quality;Convert long-term random optimization problem into time-slot online decision problem;Get AI model dynamic deployment and task scheduling result.The application realizes cross-layer consistency description to task scheduling and model deployment through category theory unified modeling and functor composite mechanism, reduces the inconsistency and redundant constraint caused by hierarchical modeling, improves the structured degree and explainability of joint decision;Under the constraint of multidimensional resources such as calculation, memory, storage and bandwidth, dynamic adaptive optimization is realized, node resource over-limit and load imbalance are effectively avoided, and congestion and queuing delay are reduced.

This invention discloses a multi-scale method for identifying and controlling the propagation paths of airworthiness risks for autonomous vessels. The method includes: hierarchically modeling multi-source airworthiness risk factors based on ontology to construct a structured knowledge graph; establishing logical relationships between risk factors using structural equation modeling and converting them into a Bayesian network; dynamically evaluating input vessel operation data and identifying multi-scale airworthiness risk propagation paths and key risk nodes using graph theory topology analysis; and finally, generating a hierarchical control strategy based on the identification results. This invention enables dynamic and probabilistic assessment of the initial and ongoing airworthiness of autonomous vessels, effectively identifying risk propagation mechanisms and key links, and providing support for the safe design, operation, and supervision of autonomous vessels.

This invention provides a zero-shot anomaly detection method and system based on bidirectional alignment enhancement. The method includes: achieving direct interaction between global and local features through the cross-attention module CAGL; extracting fine-grained local features through the region token alignment module RegionAlign; and projecting global visual semantics onto the text embedding space through the text embedding enhancement module TextAug to generate domain-aware cue words. This invention solves the technical problems of poor generalization ability, neglect of local spatial details, and difficulty in hierarchical modeling of multi-scale visual anomalies.

A SAR image change detection method based on a dual-stream hierarchical fusion engine network includes the following steps: A dual-stream hierarchical fusion encoder is constructed to collaboratively extract global and local features from SAR images at different time phases. The enhanced features are then used in a hierarchically aware U-shaped decoder to achieve hierarchical modeling and refined representation of the difference features. A frequency-domain channel attention mechanism with fused spatial weights is designed to enhance channel selectivity in the frequency domain and strengthen the response to change regions in the spatial dimension. A noise-resistant weighted loss function based on the difference map ablation coefficient is constructed to adaptively adjust the loss weights of each region, achieving noise region suppression and change region enhancement, effectively mitigating gradient bias caused by class imbalance and speckle noise. Training includes a dual-stream hierarchical fusion encoder and a U-shaped decoder network; SAR images from different time phases are input into the trained dual-stream hierarchical fusion engine network, which outputs a change detection binary map.

This invention relates to the field of new energy power generation technology and discloses a method and system for determining the equipment status of new energy generator sets. The method includes: acquiring the operating parameters of each piece of equipment in a new energy power station to obtain standardized time-series data of the equipment; virtualizing the relationships between the equipment based on the standardized time-series data to generate virtual point data; performing hierarchical modeling on the standardized time-series data of the equipment and configuring equipment status determination rules, which are a two-level status determination rule system including a primary status category and a secondary status detail; based on the configured equipment status determination rules, performing status determination on the operating data of all measuring points of each piece of equipment to obtain the equipment status determined by the status determination rules; and determining the current status of the equipment based on the equipment status determined by the status determination rules and a preset default equipment status. This application achieves the effect of accurately and efficiently determining the equipment status of new energy generator sets.

This application relates to the field of soil analysis technology and discloses a multispectral image-driven intelligent soil quality assessment method and system. By introducing a working condition parameter recording and explicit modeling mechanism, it effectively optimizes the consistency problem faced by multispectral remote sensing data in long-term application and cross-regional promotion. Traditional soil quality remote sensing assessment methods often use single-temporal data for modeling, neglecting the systematic impact of changes in observation conditions on spectral data, resulting in a significant performance degradation of the model when applied in different times and regions. This method eliminates external interference factors through spectral correction and state decoupling techniques, enhances the stability of feature expression through multi-scale feature construction constrained by mechanisms, balances the model's universality and specificity through a hierarchical modeling architecture, ensures performance for cross-regional application and long-term operation through regional adaptation and drift detection techniques, and maintains the model's continuous effectiveness through a continuous learning mechanism.

This invention relates to the field of converter protection technology. It discloses a method, system, equipment, and medium for calculating protection principle settings for multiple types of converters. The method includes: acquiring multi-dimensional electrical signals from multiple types of converters; identifying fault characteristics and classifying levels using a multi-level converter fault hierarchy model; determining the boundary conditions of a valve group short-circuit current attenuation prediction model; obtaining dynamic characteristic parameters of short-circuit current attenuation through the prediction model; performing distributed collaborative optimization of protection parameters using a distributed intelligent collaborative protection algorithm; conducting multi-scenario simulation tests using a converter fault simulation calculation platform; and correcting setting parameters based on test data to form a protection setting value system adapted to multiple types of converters. This invention integrates hierarchical modeling, dynamic prediction, collaborative optimization, and simulation verification technologies, adapting to the topological differences and operating conditions of multiple types of converters, and improving the universality, accuracy, and reliability of protection setting calculations.

The application provides a high-precision rainfall prediction method based on SFDL sequence feature decomposition and a dendritic learning model, through SFDL sequence feature decomposition technology, original rainfall time series data is accurately disassembled into a trend item, a seasonal item and a residual item, long-term change law, periodic fluctuation and irregular disturbance characteristics are captured respectively, hierarchical modeling of complex sequences is realized, for the residual item after decomposition, an improved dendritic learning model DNM is introduced, the bionic structure and multiplication unit characteristics of the model can efficiently fit nonlinear relationships, meanwhile, model parameters are optimized by combining a BP algorithm, the prediction ability for chaotic data is improved, the SFDL decomposition accuracy is optimized by local weighted regression LOESS, the original feature correlation of data is reserved, and the high-dimensional mapping effect of time series data is improved by adopting phase space reconstruction PSR preprocessing; through modular design, a decomposition-prediction-fusion process is integrated, the model structure is simplified, the calculation complexity is reduced, and the real-time prediction demand and the engineering deployment feasibility are considered.

The application discloses a transaction strategy simulation evaluation system based on scenario generation, and belongs to the technical field of financial transactions. The system comprises a parameter setting module, a data acquisition module, an economic scenario generation module and a strategy evaluation module. The economic scenario generation module constructs macro, meso and micro models through hierarchical modeling, couples each model by using a joint correlation matrix, and generates multiple future market scenario paths based on Monte Carlo simulation. The strategy evaluation module executes the strategy on each path and calculates the evaluation index. The system realizes joint dynamic simulation of multi-dimensional economic variables and asset yield, solves the technical problem that traditional models are difficult to depict cross-level statistical correlation, and improves the consistency and reliability of the simulation path. Through end-to-end automatic integration, manual intervention and system interaction overhead are significantly reduced, efficient strategy simulation testing of large scale and multiple scenarios is supported, and system computing throughput and testing efficiency are improved.

The application relates to the technical field of bioinformatics, and discloses a bacteriophage DNA sequence generation method and device based on hierarchical modeling, which comprises the following steps: obtaining an elementized characterization result of a bacteriophage DNA sequence to obtain an element sequence composed of elements arranged in sequence; converting the element sequence into a digital sequence composed of digital identifiers based on a preset mapping relationship; inputting the digital sequence into a trained hierarchical generation model, cooperatively modeling global dependency relationships and local context information in the digital sequence through the hierarchical generation model, and generating a target digital sequence; decoding the target digital sequence into a corresponding target element sequence based on the preset mapping relationship, and splicing each element in the target element sequence one by one according to a preset correspondence relationship between elements and DNA fragments to obtain a complete bacteriophage DNA sequence. The application can effectively generate a complete bacteriophage DNA sequence meeting characteristics, and improve the generation quality and integrity of the bacteriophage DNA sequence.

This invention relates to the field of artificial intelligence technology, specifically to a script semantic-driven multi-digital human collaborative generation method. The method includes script input, script semantic feature encoding, script semantic dual-graph construction, dynamic-static graph joint optimization based on a constraint-driven mechanism, script temporal semantic hierarchical modeling, multi-digital human role behavior strategy generation, retrieval-enhanced digital human speech generation processing, triple consistency constraint fusion, and multimodal scene generation. By constructing a script semantic dual-graph structure containing role nodes, scene nodes, and event nodes, and introducing a joint modeling and optimization mechanism of a global static relationship graph and a local dynamic interaction graph, the accuracy and expressive power of complex script semantic modeling are improved. Furthermore, by introducing a multi-digital human behavior generation mechanism based on role relationship constraints, and combining retrieval-enhanced speech generation with semantic, emotional, and temporal triple consistency constraints, the consistency and naturalness of multi-digital human collaborative expression are enhanced.

The application discloses a phase change heat library operation monitoring method and device based on digital twinning, and a medium, and relates to thermal process monitoring, comprising: hierarchical modeling of the phase change heat library and establishment of a digital twinning model, equivalent of a shell outer surface heat exchange boundary condition to a segmented boundary parameter, completion of initial calibration and solidification of a new segmented trigger threshold initialization rule; collection and preprocessing of monitoring data, generation of a cleaning input sequence, a cleaning observation sequence and effective data points; driving of the digital twinning model by the effective data points to output a prediction sequence, combination of observation to calculate a residual sequence; determination of a variable point and event segmentation based on recursive statistics, maintenance of thermal state continuity and resetting of a baseline and a threshold in a new segment; calibration of the segmented boundary parameter based on a segmented index and joint constraint of temperature consistency and energy consistency to write back the model, and playback of deduction to generate a segmented record. The application enhances model prediction accuracy and guarantees reliability of thermal state evolution representation.

The present application is in the field of intelligent regulation of cut tobacco, and particularly relates to a multi-brand sheet cut tobacco non-steady state intelligent regulation method based on a hierarchical probabilistic generation model, the specific steps being: collecting and preprocessing key data in the multi-brand sheet cut tobacco process; constructing a hierarchical probabilistic generation model containing "three layers" based on the processed data; obtaining a joint probability distribution function based on the hierarchical probabilistic generation model; establishing a log-likelihood function of the model using the maximum likelihood estimation method; solving and updating the parameters of the log-likelihood function using the gradient descent method; using the trained model to identify the non-steady state of the cut tobacco process in real time; determining the optimal regulation parameters according to the inference prediction results combined with the cut tobacco quality requirements; uploading and updating the final determination results in real time. This method accurately depicts the complex relationship of the multi-brand cut tobacco process through hierarchical modeling, can effectively identify the non-steady state and optimize the regulation parameters, and improves the stability and consistency of the multi-brand sheet cut tobacco quality.

This invention discloses a data processing method and system based on digital twins, belonging to the field of information technology. It includes: multi-source heterogeneous data acquisition and preprocessing, constructing a static spatial database for water plants and real-time access to dynamically sensed data; multi-precision hierarchical modeling, establishing a digital twin model covering geometric topology and semantic attributes; high-fidelity virtual scene construction and optimization, coupling water quality parameters into a water body rendering model to achieve physically driven visual representation; virtual-real fusion interaction and dynamic mapping, achieving real-time binding of sensor data and 3D model attributes through a data platform; and multi-dimensional business logic processing and decision analysis, constructing business modules such as energy efficiency optimization and pipe burst location. This invention solves the data silo problem through semantic fusion and achieves a balance between high-fidelity rendering and real-time interaction through hierarchical modeling and dynamic scheduling, significantly improving the operational efficiency and decision support capabilities of the water affairs digital twin system.

This invention discloses a target tracking test method for wireless detection devices based on multi-dimensional dynamic scenarios, belonging to the interdisciplinary field of target detection and artificial intelligence. The method includes: Step 1, generating a high-fidelity target tracking dynamic scenario through hierarchical modeling; Step 2, generating structured test data based on a parameterized space; Step 3, performing multi-dimensional performance evaluation on a verification platform: inputting the generated I / Q data into the algorithm under test deployed on the platform, and calculating five types of indicators in real time; Step 4, dynamically optimizing the test scenario based on the evaluation results. This invention addresses the shortcomings of existing technologies in dynamic scenario reproduction, algorithm evaluation dimensions, and hardware-in-the-loop verification by constructing a configurable electromagnetic environment model, a multi-dimensional evaluation indicator system, and a hardware-in-the-loop verification architecture, thereby quantitatively evaluating the real-world capabilities of algorithms in complex dynamic environments.

The application provides a system-level electronic product accelerated degradation evaluation method based on confirmed reliability modeling, and belongs to the technical field of reliability. The method comprises the following steps: obtaining a component degradation equation through a reliability scientific experiment, adopting a hierarchical modeling method to substitute the component degradation equation into a performance equation of a subsequent layer from bottom to top, and obtaining a system-level degradation equation; then deducing a system-level acceleration model, obtaining an acceleration factor, and determining an acceleration experiment scheme; correcting a system performance degradation model based on a residual error fitting of a theoretical value and an experimental value, obtaining an optimized system performance degradation model, and finally realizing confirmed reliability evaluation. The application solves the problem that a generalized Arrhenius form failure physical model cannot reflect nonlinear interaction between different failure mechanisms and cannot reflect the influence of the failure mechanism on product function performance when the generalized Arrhenius form failure physical model is used as a system-level acceleration model in an accelerated degradation experiment, and the reliability of the model is insufficient.

The application discloses a kind of user emotion evaluation method, device and related medium based on multi-modal.It is described as follows.The method includes in the topic level emotion score prediction stage, for the user answer sample of single question, obtain multi-modal data and respectively carry out feature extraction and retain emotion score prediction ability, then generate each modality weight by modality weight routing mechanism and carry out weighted fusion, output corresponding question emotion prediction score and emotional feature representation;In the sample level risk assessment stage, the emotional feature representation and / or emotion prediction score of multiple questions are input, and the weight parameters of each question are generated, the prediction results of multiple questions are weighted and fused under the premise of retaining the prediction ability of single question, and finally the emotion evaluation result is generated.The application realizes hierarchical modeling and fusion of multi-modal emotional information by introducing weighting mechanism at modal level, question level and sample level, and improves the accuracy and robustness of overall user emotion evaluation.

The application provides a harmful video detection method and device based on cross-frame timing semantic understanding. Based on the feature that harmful semantics in a video is often jointly constituted by entity attributes, entity relationship and timing evolution process, a hierarchical analysis and joint reasoning strategy for complex semantic structure is proposed. A hierarchical modeling and joint decision technology is adopted to perform harmful semantic analysis from three aspects of entity attribute and emotion modeling, entity timing relationship modeling and global semantic graph reasoning, and finally determine through semantic fusion, so as to improve the recognition ability of implicit harmful content and rule violation semantics in complex context.

The application provides a general visual unsupervised defect detection method based on hierarchical reconstruction, which firstly processes input images through a self-attention mechanism and a hierarchical skip connection reconstruction module; the reconstruction module performs hierarchical modeling and reconstruction on foreground, background and masks of the images to generate multi-dimensional reconstruction representations of the original images. This design can realize structural decoupling, enabling the model to accurately learn and reconstruct complex background textures; then, a defect discrimination module is used to discriminate the feature distribution of the original image and the reconstructed image to determine whether the input sample has defects. The method combines global semantic information and multi-level structural features, effectively improving the robustness, accuracy and real-time performance of defect detection, and is suitable for complex and variable industrial visual detection scenes, and has good application prospects.

This invention discloses a method for smoothing clothing edges based on hierarchical modeling and dual-domain optimization. The method includes: separating the clothing region from the background region of the input image; dividing the clothing region into a main region for maintaining overall deformation and texture consistency, and an edge band for handling wrinkles, seams, and contour transitions; extracting the overall structural features of the main region and the local detail features of the edge band using a dual-branch neural network; and optimizing the clothing edges for dual-domain consistency based on the overall structural features and local detail features using image domain constraints and frequency domain constraints. This invention can be used for clothing edge trimming in image generation and editing, effectively solving the problem of irregular fluctuations or wrinkles at clothing edges, achieving a natural and smooth visual effect. This enables high-quality visual effects in applications such as virtual try-on, beauty enhancement, film and television special effects, and virtual human generation.

The present application belongs to the technical field of tunnel construction intelligent analysis and management, and relates to a tunnel large machine supporting operation control analysis method based on multi-source data fusion. First, the process duration data of continuous multiple construction cycles of a tunnel operation surface is obtained, and a process tree structure containing longitudinal membership and horizontal connection is constructed. Then, the deviation duration of each component unit in the process tree structure relative to the historical cycle is calculated, and the abnormal source node is identified according to the threshold value performance. Subsequently, the data is processed according to the abnormal source level selection resolution dimension, the abnormal type and confidence are traced back, and the optimization suggestion is generated and pushed according to the rules. Finally, the adoption state is detected in the subsequent cycle and closed-loop feedback is performed. The present application improves the accuracy and interpretability of abnormal source tracing through hierarchical modeling, quantifies the abnormal reasons through multi-dimensional analysis, realizes the iteration of optimization strategies relying on closed-loop verification, and improves the tunnel construction diagnosis efficiency and collaborative operation level.

The application belongs to the field of software development application data processing, and particularly relates to a software development application data processing method based on an AI large model, which comprises the following steps: adopting a unified collection and text standardization processing method for first-line feedback and work order data, cleaning and labeling description information from each terminal; performing semantic analysis through an AI large model to extract elements such as equipment, process, function and problem type; adopting a hierarchical modeling and relationship extraction method for equipment, process and function information to construct an equipment-process-function relationship network; performing path traversal and weighted aggregation on text associated nodes to calculate the influence score of each software function; performing task template filling and historical case retrieval on high-score functions; and performing effect evaluation and parameter updating on the running results of the online development task; the application can uniformly process multi-source application data, quantify the influence of software functions and automatically generate development tasks, and realizes fine research and development decision-making.

This invention discloses a layered modeling method for transient thermal field analysis of high-voltage three-core submarine cables under fluctuating loads. The method simplifies the three-core cable into a coaxial single-core structure based on the principle of equal thermal resistance. Then, based on this simplified equivalent structure, the cable and its external laying environment are layered to determine the heat source intensity, thermal resistance, and heat capacity parameters, establishing a trapezoidal thermal circuit model. The method then simulates the transient temperature rise characteristics of each layer of the cable under fluctuating loads. This method can be applied to any single-core or multi-core submarine cable structure with different insulation types. It can be applied to AC or DC cables, performing transient thermal field analysis under different fluctuating current load waveforms and varying ambient temperatures to accurately determine the steady-state and transient allowable load capacity of the cable.

This invention provides a rapid detection method for total soil salinity based on conductivity conversion and multi-parameter coupled correction. The method includes: obtaining an initial feature set based on the target area to be detected; introducing nonlinear functions and interaction terms for standardized preprocessing and multi-level environmental compensation to obtain a compensated feature set; introducing probabilistic models and rule functions for soil salinity type identification and conductivity response category classification to obtain a salinity type label set; performing multi-level correction for salinity classification, conductivity response, and regional compensation to construct a multi-parameter correction feature set; constructing a hierarchical conversion model for total salinity to output an initial predicted value of total soil salinity; performing anomaly assessment and heterogeneity correction to obtain accurate detection results for total salinity; and performing regional adaptive conversion on the model based on laboratory test data. This invention achieves high-precision, robust, and adaptive rapid detection of total soil salinity through multi-parameter coupled correction and hierarchical modeling.

The application discloses a kind of human action content generation method and system based on text, belong to computer graphics and artificial intelligence field.The technical solution points include: the ability of MambaTrans hybrid backbone network fusion Mamba model processing long sequence data and the ability of Transformer model capturing global dependence relationship;Introduce layered adaptive feature enhancement mechanism, including adaptive frame weighting module dynamically allocating frame weight and multi-scale feature fusion module fusing different time scale features;By residual vector quantization autoencoder, the action is decomposed into layered tokens, and the basic token is generated using the mask Transformer, and the residual token is generated layer by layer using MambaTrans.The application realizes higher generation quality, stronger layered modeling capability and faster inference speed, and is suitable for virtual reality, augmented reality, film animation production and game character control.

The application discloses a kind of based on hierarchical modeling and hybrid machine learning's grain pile temperature prediction method and system, method includes: acquisition temperature time series, and pre-processing;Temperature time series includes grain pile surface layer temperature time series and grain pile main body temperature time series;For the grain pile surface layer temperature time series with strong nonlinearity, multiple time scale characteristics, obtain the grain pile surface layer temperature prediction sequence;For the temperature inhomogeneity and spatial heterogeneity of grain pile main body area, obtain the temperature prediction sequence of grain pile main body area;Based on timestamp alignment strategy, grain pile surface layer temperature prediction sequence and the temperature prediction sequence of grain pile main body area are fused, and global grain pile temperature prediction result is obtained.The application fuses spatial modeling and time series prediction, can both depict the complex spatio-temporal structure of grain pile internal temperature, and can enhance the accuracy, generalization and engineering deployability of model, to support the construction and application of future intelligent grain condition management system.