16 results about "Structural decomposition" patented technology

The invention relates to the technical field of data compression, in particular to a ZSTD compression algorithm optimization method based on joint coding and ternary decomposition, and the method comprises the following steps: S1, building a joint frequency table based on the joint distribution of the literal length and the matching length of a previous section, setting a frequency deviation criterion, and dynamically adjusting a low-frequency combination proportion; s2, decomposing the offset value structure into a leading segment, a trailing segment and a middle segment, performing bit width-entropy ratio analysis on the middle segment, and selecting a symbol index or bit stream coding; and S3, entropy discriminant coding is performed on the joint symbol and the offset symbol respectively, and a structure position is mapped synchronously to generate a structured compressed output stream. According to the method, collaborative optimization of frequency self-adaption, redundancy minimization and structured mapping is realized through joint symbol dynamic modeling and offset ternary structure entropy discriminant coding, so that the ZSTD compression efficiency and the coding stability are remarkably improved.

The invention provides a structural decomposition and information identification method of multi-modal data, a medium and equipment, and the method comprises the steps: obtaining to-be-processed multi-modal literature data, the multi-modal literature data being documents or pictures, the types of the documents including word documents and PDF (Portable Document Format) documents; the method comprises the following steps: converting to-be-processed multi-modal literature data into to-be-processed literature data in an image form, preprocessing the to-be-processed literature data to obtain input image data, inputting the input image data into a field fine-tuning DETR model, identifying a logic region category of the input image data through the field fine-tuning DETR model, obtaining a region category identification result, and outputting the region category identification result. The logic region category comprises a title region, an author region, an abstract region, a text region, an illustration region, a table region, a formula region, a footer region and a reference region; and carrying out differential information extraction on each region category identification result to obtain information corresponding to each region category identification result so as to realize accurate identification of information in the multi-modal data.

The invention belongs to the technical field of weapon target distribution, and particularly relates to a firepower distribution method and system based on a double-layer decoupling and multistage penalty genetic algorithm. Constructing a firepower distribution primary decoupling model comprising a threat matrix and a firepower distribution membership degree, constructing a firepower distribution secondary optimization model oriented to an objective function, and fusing hard constraint, soft constraint, a multi-stage penalty mechanism and a self-adaptive penalty factor; and adopting an MLP-GA optimization method fusing a multilayer perceptron and a genetic algorithm to iteratively solve the secondary firepower distribution model. The firepower distribution method based on the optimization mechanism of double-layer decoupling and multi-stage penalty has the capabilities of structural decomposition, intelligent optimization and environmental adaptability, solves the technical problems that an existing firepower distribution method is slow in response and difficult to meet the requirements of a complex dynamic battlefield, and improves the real-time performance and robustness of firepower resource allocation in the complex battlefield.

The application discloses a kind of object grid generation method and system based on three-dimensional model, the method includes: obtaining the three-dimensional model of object;The segmentation boundary is determined by carrying out segmentation operation to three-dimensional model;Based on the segmentation boundary, cut three-dimensional model into multiple segmentation regions;Boundary zone is generated between adjacent segmentation regions;The grid region of the boundary zone is executed to realize boundary reconstruction by remeshing operation, and the reconstructed segmentation boundary is obtained;Based on the reconstructed segmentation boundary, generate the update grid of object;And between detachable component and fixed component separated by segmentation boundary, build multiple connection structures.The application realizes structure decomposition and boundary reconstruction optimization based on the three-dimensional model of object, improves the structure compatibility, assembly stability and connection transition smoothness in additive manufacturing process, improves the rationality and manufacturing adaptation ability of reconstructed morphology.

The application discloses a high-robustness reconstruction method for a power tower based on airborne point clouds, which comprises the following steps: S1, point cloud redirection; S2, point cloud data amplification, coordinates of the redirected point cloud are amplified by n times; S3, point cloud preprocessing, specifically, a symmetry neighborhood denoising method, adaptive adjustment of point cloud distribution density and symmetry enhancement of point cloud data; S4, structure decomposition, the tower is divided into an inverted triangular pyramid structure, a quadrangular prism structure and a complex structure from bottom to top; S5, inverted triangular pyramid structure reconstruction; and S6, quadrangular prism structure reconstruction, the external frame and the internal structure of the quadrangular prism are reconstructed respectively. According to the high-robustness reconstruction method for the power tower based on the airborne point clouds, the point cloud noise in the interior and the boundary of the tower can be effectively removed through the symmetry neighborhood denoising, the interference of the noise on the reconstruction result is avoided, the modeling precision is higher, the adaptive neighborhood point cloud density adjustment can solve the problem that the modeling parameters are not fixed due to the point cloud sparsity difference, and the modeling effect is more stable.

The application provides a railway system operation behavior modeling method and system based on behavior primitives and hierarchical state machines, and belongs to the technical field of railway system modeling and simulation operation. The application abstracts the railway system operation process into behavior primitive units, classifies and expresses them according to event perception, cognitive decision and intervention execution, realizes the structural decomposition and unified modeling of complex operation behaviors, upgrades the behavior expression from 'process description' to 'computable behavior unit', enhances the resolvability and modeling consistency of operation behaviors, constructs a hierarchical state machine system, realizes the cross-level state collaborative expression, establishes system-level, equipment-level and component-level hierarchical state machine models, introduces state mapping relationships, realizes the collaborative evolution expression of different granularity operation states, takes the behavior primitives as a unified triggering mechanism, realizes the cross-level state synchronous update and consistency mapping, and improves the multi-scale operation behavior modeling capability.

The application discloses a kind of visual language data quality evaluation methods based on structure decomposition theory belongs to data quality evaluation method field, this method is by sharing embedding module and is projected to uniform representation space with picture-text data, based on structure decomposition theory, the evaluation process is divided into integrity, richness and consistency three dimensions and respectively constructs sub-evaluation module, using the strategy based on stability perception fuses each dimension score, finally gives overall quality evaluation result.The application is based on structure decomposition theory modeling evaluation dimension, enhances the interpretability of method;Using logical consistency detection and automated scoring process, improves the evaluation efficiency;Using stability perception fusion strategy, reduces the error influence of single evaluator, improves the robustness of evaluation result;Combined with heuristic method and the reasoning ability of large language model, improve the evaluation accuracy of method, reduce the consumption of computing resources.

This invention relates to the field of hydrological model parameter calibration technology, specifically to an automatic parameter calibration method and system for a surface water-groundwater coupled model. The method includes: acquiring multi-dimensional measured and model simulation output sequences of the surface water and groundwater subsystems; fusing and constructing coupled hydrological data entities containing time-varying and spatial distribution characteristics; separating unstable response features based on spatiotemporal deviations after applying perturbation; filtering out these features to form a de-perturbed steady-state data entity and matching it with a multi-mode parameter response template library; generating a hierarchical model parameter feature cluster set through adaptive structural decomposition; constructing a dynamic constraint network for surface water and groundwater parameters; obtaining a preliminary adjustment scheme through synchronous optimization and cross-validation; locating key transmission and boundary interaction parameter nodes; optimizing the scheme path; and generating the final parameter calibration instruction sequence. This method eliminates non-steady-state data interference, adapts to the parameter correlation characteristics of the coupled system, and achieves adaptive and precise execution of parameter calibration.

The invention discloses a method for automatically generating a beam and a plate based on column structure decomposition and axis net connection. According to the method, geometric information of columns is extracted from a two-dimensional structure planar graph, through the processes of axis net decomposition, adjacent coupling beam pairing, degree sealing and surface domain tracking plate forming, a beam net frame is automatically generated according to the positions of the columns, a floor slab surface is finally generated, and therefore the complete topological relation among the columns, the beams and the slabs is established. According to the method, generation of wrong beam segments or wrong connections can be effectively avoided through a pairing strategy of pseudo interval filtering and outer contour priority. Therefore, the stable automatic modeling process of automatically generating the beam from the column and automatically generating the plate from the beam is realized. The method is used in a two-dimensional building structure plane graph, the arrangement of beams is automatically generated according to the positions and shapes of columns, and the floor outline is formed through closing. According to the method, manual participation can be reduced, manual omissions and errors are avoided, the modeling efficiency is greatly improved, and an accurate data basis is provided for engineering quantity calculation and cost analysis.

The invention discloses a holographic interaction three-dimensional animation design model display device, particularly relates to the field of computer 3D modeling, and is used for solving the problems of large calculated amount and insufficient real-time performance of existing holographic display under dynamic interaction. Performing voxelization processing on the triangular mesh data through a structure decomposition module, and representing the triangular mesh data by a sparse voxel octree structure; an interaction receiving module collects an interaction instruction of a user to the logic component, and an interaction analysis module locates a changed space region in the model; carrying out incremental updating on the voxel data of the changed region through a local updating module and multiplexing the data of the unchanged region; generating light wave phase and amplitude by a complex amplitude calculation module in combination with material, transparency, normal and space coordinate information, and synthesizing the light wave phase and amplitude with historical wavefront to obtain hologram complex amplitude distribution; and finally, the display driving module maps the synthesized data to a spatial light modulator to realize real-time holographic interactive display of the three-dimensional animation model.

The invention relates to the field of green buildings, and discloses a green building full-life-cycle multi-professional intelligent collaboration method and system, and the method comprises the steps: obtaining an initial data identifier and a corresponding coding rule from a source system, and carrying out the structural decomposition of the initial data identifier, and obtaining a rule assembly set; comparing the rule component set with a target system standard to obtain a data difference element, and generating a temporary storage data identification version; performing association processing on the temporary storage data identifier version and the target coding rule to obtain a mapped data identifier prototype; extracting a continuity identifier from the mapped data identification prototype, and if the continuity identifier is matched with a preset continuity rule in the source system, injecting traceability data into the mapped data identification prototype to generate an enhanced data identification entity; and transmitting the enhanced data identification entity to the cooperation platform to obtain the cross-platform consistent data identification. According to the technical scheme, the problem that the identification cannot be automatically adapted due to cross-system coding isomerism is solved.

The invention discloses a competing product comparison method based on a knowledge graph, and the method comprises the following steps: obtaining competing product comparison multi-source structural data, and constructing a competing product comparison knowledge graph and a competing product entity pair input structure; in combination with a CompGCN model, constructing a competing product alignment graph reasoning framework and a relation combination operator, and generating a propagation record; performing structural decomposition and recombination on the relation combination operator, constructing dimension convolution configuration, and generating a dimension competing product representation result; generating a difference propagation path set based on the dimensionalized competitive product representation result and the propagation record; mapping the difference propagation path set into a comparison feedback signal, and modulating a competing product alignment graph reasoning framework; in the modulated competing product alignment graph reasoning framework, feedback alignment convolution propagation is executed, and a competing product comparison representation result is generated; and generating a competitive product comparison conclusion based on the competitive product comparison representation result. According to the invention, traceable modeling and stable comparative analysis of the competitive product difference forming process are realized.

The invention relates to the technical field of artificial intelligence, and provides a model training method and a related device, and the method comprises the steps: carrying out the structure decomposition of an initial model based on a model structure of a reference model, and determining a parameter updating structure network in the initial model and a parameter inheriting structure network which is the same as the model structure of the reference model; inheriting model parameters of the reference model by using the parameter inheriting structure network, performing knowledge distillation on the initial model based on the reference model to obtain updated parameters of the parameter updating structure network, and performing parameter updating on the parameter updating structure network in the knowledge distillation process; generating target model parameters according to the model parameters of the reference model and the updated parameters, and updating the model parameters of the initial model based on the target model parameters to obtain a target model. Through parameter inheritance, the parameter integrity of the reference model is ensured, and rapid transmission of knowledge is realized in combination with knowledge distillation, so that the model training speed and the model performance are improved.

The application relates to the technical field of bridge parameterized modeling, and discloses a curve bridge parameterized modeling method based on BIM and GIS technology, which comprises the following steps: performing structural decomposition on a target curve bridge to obtain the characteristic cross sections of the profile components of different bridge sections and corresponding constraint conditions; performing parameterized modeling on the bridge sections according to the characteristic cross sections and the constraint conditions to obtain bridge section models; assembling the bridge section models to obtain a curve bridge BIM splicing model; constructing a spline curve model and generating a bridge overall model according to the curve bridge BIM splicing model and the spline curve model; and performing model loading matching based on the bridge overall model to generate a bridge overall parameterized model scheme. The application can realize parameterized driving and data linkage updating of a complex bridge structure.

The application relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to the power distribution network, carrying out structural decomposition and preprocessing, so as to construct a time-space double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the time-space double graph structure, carrying out multi-scale topology feature extraction, and generating a time-space separated feature vector set; deeply coupling and fusing the time-space separated feature vector set, generating a unified topology feature representation containing an abnormal topology; constructing a dynamic topology state prediction model based on the unified topology feature representation, optimizing a time-space joint loss function, and outputting corresponding real-time topology connection relationships and device state change trends, simultaneously carrying out dynamic topology reconstruction, so as to generate a current time reliable topology graph containing potential branch disconnection and temporary tripping. The application can improve the topology analysis accuracy of the power distribution network.

The invention discloses a micro-grid load prediction method based on deep learning, and the method comprises the following steps: S1, collecting micro-grid load data, and generating a load time sequence; s2, executing load structure decomposition to generate load subsequences; s3, executing load operation scene identification, and generating a load operation scene label and a load structure subsequence; s4, inputting the load structure sub-sequence into an improved SCINet model, introducing a load structure perception guide mechanism, and outputting a load prediction sequence; s5, executing consistency constraint processing, and generating a consistency load prediction sequence; s6, generating prediction deviation data; and S7, executing adaptive updating, and outputting an updated load prediction sequence. According to the method, high-precision prediction and continuous adaptive updating of the micro-grid load time sequence under the conditions of multiple operation scenes and multiple time scales are realized, and the stability and reliability of a load prediction result are effectively improved.