121 results about "Topological consistency" patented technology

The invention discloses a pipeline topological relation construction method based on a graph neural network. The method comprises the following steps: collecting and preprocessing original spatial data of a pipeline system; utilizing feature extraction to generate a node feature matrix and an edge feature matrix; constructing a pipeline diagram structure; index alignment and size verification are executed, and a pipeline diagram input matrix is generated; inputting to an improved EvolveGCN model, and introducing a graph attention control evolution mechanism to obtain a time sequence embedding feature matrix of each pipeline node object; generating a predicted pipeline topology connection set; carrying out topological consistency verification to obtain a final pipeline topological relation structure; a pipeline topological relation model file is formed, automatic modeling and optimization of a pipeline system are achieved, and the recognition precision and modeling efficiency of a pipeline topological structure are improved.

The invention relates to the technical field of electrical digital data processing, and discloses an AI semantic enhanced unstructured manufacturing document data structuring method, which comprises the following steps: acquiring a character recognition stream of a target document to extract a semantic anchor point and topological coordinates thereof, and vectorizing a to-be-structured field to generate a to-be-processed vector; querying an engineering logic mapping table to determine association intensity, and calculating a logic gravitational field intensity value of the to-be-processed vector relative to the semantic anchor point according to the association intensity; associating the to-be-processed vector to a target semantic anchor point according to the field intensity value, starting a logic polarization program when the to-be-processed vector is identified to be interfered by the homogeneous semantic anchor point in the association stage, extracting bias characteristics to generate a polarization vector, and modulating the native gravitational field intensity; according to the method, the attribution ambiguity of similar semantic entities in a dense distribution scene is solved by introducing the logic gravitation constraint, the structured conflict caused by spatial distribution and engineering logic decoupling is eliminated, and the topology consistency in the data conversion process is ensured.

The invention discloses a virtual space multi-person interaction synchronous control method oriented to an end-cloud collaborative architecture. The invention relates to a computer graphics and three-dimensional modeling technology, and discloses a cross-format lightweight and geometric consistency maintenance method based on a three-dimensional model. Through format-independent geometric representation and a self-adaptive lightweight strategy, efficient compression and precision maintenance of three-dimensional model cross-format conversion are realized. The method specifically comprises the following steps: performing format analysis and geometric feature extraction on an input model, and establishing a unified internal representation; adaptively selecting a multi-level LOD lightweight strategy based on the complexity of the model; the accuracy of key information is ensured through geometric feature keeping and topology consistency detection; the geometric consistency is dynamically maintained by combining error monitoring and an iterative correction mechanism; and generating a target format lightweight model and carrying out quality verification. According to the method, adaptive precision control, multi-level consistency maintenance and format irrelevant processing are combined, the model size and conversion errors are effectively reduced, and cross-platform compatibility and geometric fidelity are improved. The method can be widely applied to the fields of industrial design, game development, virtual reality and the like.

The invention provides an energy storage device damage diagnosis and interaction system based on penetration vision and a large model, and relates to the technical field of energy storage device damage diagnos.The energy storage device damage diagnosis and interaction system comprises a penetration type 3D sensing module, a periodic topology analysis module, a feature mapping and retrieval module and a large model reasoning and interaction module, and the energy storage device is scanned and reconstructed; the method comprises the following steps: constructing a time-space decoupling periodic Transform network, introducing a periodic mask matrix to force the network to pay attention to a repeatability rule of an internal structure of a battery, and identifying internal tiny deformation and structural damage by calculating topological consistency under the condition that a large amount of negative sample training is not needed; visual defect features are mapped into text embedding by utilizing a feature projection technology, a diagnostic report containing physical cause analysis and maintenance suggestions is generated by combining a retrieval enhancement generation technology and a large language model, and a user is supported to perform interactive questions and answers in a natural language. The method can solve the problems that in the prior art, three-dimensional deformation is difficult to quantify, small samples are difficult to train, and intelligent decision-making ability is lacked.

The invention discloses a monorail crane multi-vehicle cooperative scheduling control method based on edge calculation, which comprises the following steps: carrying out topological modeling on a track, and constructing a section-time slice space-time resource model; acquiring real-time operation data of each vehicle at an edge computing node, generating a vehicle state set and executing time-space conflict prediction; when a multi-vehicle potential conflict is detected, carrying out topology consistency check on a conflict section; calculating a priority value of each vehicle in the corrected candidate traffic relation; performing section occupation authorization and shadow time slot distribution through a double-threshold mechanism; the node issues an operation instruction to the authorized vehicle; when abnormal events such as communication interruption, retention or task insertion are detected, the node triggers local path replanning and occupancy right redistribution. According to the method, a multi-factor priority scheduling mechanism is introduced into the edge computing node, so that real-time conflict avoidance and self-adaptive cooperative control of multiple monorail cranes is realized.

The invention discloses a real-time positioning method, system and device supporting multi-frequency Beidou signals and a medium. The method comprises the steps that multi-frequency observation is collected in parallel, and quality features are generated; performing distribution alignment of risk perception based on the quality features to obtain equivalent observation; generating a causal weight by adopting anti-fact evaluation based on influence approximation; in the cross-frequency fusion model, taking multiple paths as potential disturbance quantities, only implementing group stripping on suspicious stars, frequencies or time periods, and solving and outputting a positioning result in combination with robust constraint and increment; a confidence radius is generated on line, the confidence radius is used as a main control signal to execute risk-driven self-adaptive control, and the risk-driven self-adaptive control comprises weight adjustment, measurement subset selection and front-end loop and frequency chain working state adjustment; and implementing topological consistency goalkeeping on a multi-frequency phase level, and triggering time domain virtual array suppression and feeding back to the fusion model when abnormity occurs. The system can significantly improve the precision, robustness and integrity of Beidou multi-frequency real-time positioning.

The invention relates to the technical field of computer vision and image processing, in particular to a cross-day-and-night boundary target thermo-optic joint detection method, which comprises the following steps of: firstly, acquiring a visible light image and an infrared image and unifying the visible light image and the infrared image to an image plane reference system; extracting topological features and multi-scale energy features from the input, fusing the topological features and the multi-scale energy features to generate a feature map, and outputting a central heat map, target size regression and sub-pixel offset by adopting anchor-frame-free detection; implementing optimal transmission correction according to an evidence field obtained by normalization of a cross reconstruction residual field to obtain a correction heat map and an initial candidate; triggering a fixation area by using the shape correction heat map and the initial candidate, executing super-resolution and secondary detection in the area, and performing affine reprojection and primary detection fusion to form an updated heat map and a fusion candidate; and in combination with uncertainty and topological consistency, a final detection set is output by using a conditional random field and non-maximum suppression. The method is stable in low-contrast, small-target and strong-interference scenes.

The invention provides an urban digital twinning modeling method and system based on multi-source heterogeneous data fusion, and relates to the technical field of digital twinning, and the method comprises the steps: obtaining a registration image and a registration point cloud of a target region, respectively extracting an image boundary candidate and a point cloud boundary candidate, generating a boundary uncertain band for each boundary candidate, and carrying out the boundary uncertain band extraction; determining an uncertain band width and an offset direction identifier; boundary matching pairs are formed according to the spatial intersection relation, and intersection sections with opposite bias direction identifications are determined as a confrontation boundary section set; for each confrontation boundary section, determining an image confidence weight and a point cloud confidence weight, and generating a boundary judgment result according to the image confidence weight and the point cloud confidence weight; and performing topological consistency verification on a boundary judgment result, and determining a unique fusion boundary. According to the method, under the condition that local physical characteristic difference and systematic geometric deviation exist in multi-source data, the geometric edge fusion precision and topology reliability can be improved, and the stability and availability of the urban digital twin model are enhanced.

The invention discloses an awareness perception quantum gravitational nerve acceleration unified computing platform and method based on formalized verification, and belongs to the field of high-performance computing, medical AI and quantum information processing. A quantum gravitational coupling neural processing unit (QGCNPU); a consciousness information integral calculation core (phi ICC); formalizing a receipt chain verifier (FRCV); and the mirror reflection symmetry correction module (MRSCM) is used for eliminating the continuous projection mistake and the discrete Boolean mistake through topological equivalence verification of a local observer and a global GodCam visual angle. The system adopts an FPGA + ASIC + quantum processor three-layer heterogeneous architecture, and supports real-time pathological modeling (precision gt; gt) of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ALS; 99.7%), drug target discovery (acceleration by 50 times) and consciousness state monitoring (time resolution lt; and meanwhile, the mathematical proving performance and the clinical auditing performance of the calculation process are ensured, and the requirements of the global 152 billion dollar neural science and technology market and the 89 billion dollar quantum calculation industry are met.

The invention discloses a microstructure mapping modeling and mechanical simulation method suitable for a two-phase structure alloy, and the method comprises the steps: carrying out the self-adaptive median filtering denoising and Retinex enhancement of a metallographic diagram, so as to improve the gray scale comparison of a matrix and a second phase; training a U-Net + + segmentation model by using the enhanced image to complete pixel-level phase region extraction; a two-phase geometric model is established according to the segmentation result, a finite element feature data set is obtained through multi-physics field coupling calculation, lossless fusion and topological consistency verification are conducted on the finite element feature data set and a prior model, and a fusion model is obtained; simulation parameters are set based on actual working conditions, mechanical simulation is executed, and mechanical property parameters and response curves are output. The method can accurately reflect the mechanical behaviors of the two-phase structure alloy under different working conditions, provides a scientific basis for material design and performance optimization, has high universality, and can be widely applied to performance analysis and evaluation of various types of alloys.

The invention discloses a collaborative planning method for multi-unmanned aerial vehicle dynamic target positioning, and belongs to the field of unmanned aerial vehicle trajectory planning and visual perception, and the method comprises the steps: obtaining the position information of each unmanned aerial vehicle and a dynamic target in real time; modeling a multi-unmanned aerial vehicle system into a pseudo-rigid body structure, taking the geometric center of the structure as a path search node, and performing multi-unmanned aerial vehicle cooperative path search on the basis of the position information under the condition of meeting obstacle avoidance constraint and topological consistency constraint to obtain an initial trajectory of each unmanned aerial vehicle; multi-unmanned aerial vehicle combined continuous time-space trajectory optimization is carried out on the initial trajectory, the optimization target is to minimize control cost and flight time cost, and dynamic feasibility constraint, environment obstacle avoidance constraint, cluster mutual avoidance constraint and public view field constraint are met. By introducing the topological consistency constraint and the public view field constraint, mutual visibility among multiple machines is effectively maintained in a complex obstacle environment, it is guaranteed that a public view field area is not shielded, and the robustness of a dynamic target observation system is improved.

The invention provides a binocular stereo matching method and system, and the method comprises the steps: obtaining a left and right stereo image pair captured by a binocular camera, and carrying out the feature extraction and enhancement through a depth separable convolution basic network and an ECANet attention model; carrying out topological structure transformation by using a TopoAug feature enhancement strategy, constructing a topological consistency loss function and carrying out adaptive feature fusion; a large neighborhood search strategy and a hybrid node-destructor model are adopted to construct an initial cost body, and cost aggregation is carried out through capacity routing; and constructing an M uniform loss grid and a grid motion model to carry out parallax estimation, and obtaining a final parallax map through unsupervised consistency optimization. Through the technologies of lightweight network design, topology perception feature enhancement, hybrid node optimization cost body construction, unsupervised consistency optimization and the like, the calculation complexity is reduced while high precision is kept, and the method is particularly suitable for real-time monitoring scenes in resource-constrained environments such as substations and the like.

The invention discloses a multi-source geographic vector data matching method based on topological relevance, which comprises the following steps: collecting multi-source basic geographic vector data to form a preprocessed data set; constructing multi-index data quality evaluation based on local topological features; extracting and classifying spatial topological relation edges; constructing a weighted adjacent matrix of the target map; a neighborhood topology consistency measurement index is calculated, and neighborhood topology consistency quantization and adaptive weight fusion are carried out; establishing a global optimization objective function by taking the confidence coefficient matrix as input, solving a maximum weight bipartite graph matching problem with topology constraints by taking the topology retention rate as a hard constraint condition, and outputting a global optimal matching scheme; and constructing a quantitative evaluation index topology deviation measure based on topology invariant difference, judging whether a matching scheme has a significant topology conflict, and performing hierarchical repair according to the conflict condition. According to the method, the reliability and logic self-consistency of multi-source vector data fusion are improved, and the key problem of topological contradiction in the prior art is solved.

The invention discloses a water supply network safety monitoring system based on a hydraulic model, and belongs to the technical field of water supply network monitoring. The Caputo fractional order operator introduces historical memory characteristics through an integral term, explicit modeling is carried out on disturbance such as user water consumption fluctuation and sensor noise, and a clear disturbance compensation object can be provided for subsequent multi-source data fusion verification and self-adaptive correction; the pipeline-node coupling relation is embedded into the model through the topological incidence matrix, and structured input can be provided for a subsequent topological consistency verification algorithm; in combination with historical memory characteristics of a Caputo operator, the observer has robust estimation capability on slowly changing leakage disturbance and sudden water impact; the time-varying scaling item decays along with the time index, the gain of the observer is dynamically reduced, and quantization noise and electromagnetic interference of the SCADA sensor can be effectively filtered out in the steady-state stage; and through the positive error convergence index and the negative error convergence index, respectively optimizing the convergence speeds of the positive and negative pressure fluctuations.

This invention discloses a method for generating art images for the creation of traditional Chinese style content. The method first constructs a multimodal atlas of traditional Chinese style art based on independent copyright. Next, a parallel dual-stream perceptual feature extraction network is constructed to extract the topological geometric features (such as paper-cutting connectivity) and texture features (such as mural pigment texture) of the image. During the generation stage, the feature fusion weights are dynamically adjusted through a physically-aware cue word adapter and an adaptive gated cross-attention mechanism. A LoRA strategy and ControlNet are combined for joint fine-tuning, and a topological consistency loss function and a traditional Chinese style aesthetics scoring loss function are introduced to strongly constrain the generation process, effectively solving physical defects such as broken lines and structural inconsistencies. Finally, physically-rendered (PBR) technology is used for post-processing to reshape the microscopic material texture and historical weathering feel, generating professional traditional Chinese style art images with museum-level visual realism and copyright protection.

The invention belongs to the technical field of electrical engineering, and particularly relates to a power distribution network simulation deduction and load regulation and control method based on Internet of Things data, which comprises the steps of acquiring multi-source Internet of Things data and performing timestamp checking, topology consistency deduction and cross-source error reforming to form a standardized power grid state data set; constructing a cross-scale operation feature based on the data set, generating a panoramic operation situation through topology and time sequence alignment, inputting the panoramic operation situation into a reinforcement learning agent, executing exploration in a simulation environment to generate an extreme compound fault scene, extracting risk severity from the scene, constructing a risk evolution path, and obtaining a risk evolution result; the method comprises the steps of identifying key induction nodes and easy-to-diffuse links, constructing a defensive load regulation and control strategy oriented to a causal chain, performing multi-working-condition simulation verification on the strategy, constructing an optimized regulation and control strategy set based on simulation deviation, and driving a power distribution network control system to execute load regulation and control actions so as to improve system stability and risk suppression capability.

The application discloses a rice disease image segmentation method based on frequency domain modulation and topological constraint, which comprises the following steps: 1, frequency domain decomposition is carried out on rice disease image data to obtain amplitude spectrum and phase spectrum features; 2, a frequency domain adaptive modulation module is constructed to selectively enhance the frequency components of the structural key information; 3, the topological invariants of the image are extracted through a topological constraint module, and the reference topological structure is obtained by down-sampling the label to construct a topological consistency loss; 4, the topological consistency loss and the rice disease image segmentation loss are jointly optimized, the model training under multi-level structure constraint is realized, and a rice disease image segmentation model is constructed. The sensitivity of the model to the structure features of the rice disease lesion is enhanced through frequency domain modulation, and the morphological consistency of the segmentation result is maintained through topological constraint, so that the robustness of the model under natural conditions such as light change and background complexity difference is effectively improved, and the performance of the disease image segmentation in an open farmland scene is improved.

The application discloses a kind of spatial intelligence three-dimensional reconstruction method and system based on cross-scale topological consistency, it is related to spatial intelligence field, solve the problem that existing spatial intelligence three-dimensional reconstruction technology exists three-dimensional reconstruction effect is not good, including steps S1: acquisition area plan image is carried out to modeling region space marking, obtain modeling region plan image, to target modeling region preliminary creation three-dimensional model, obtain target region preliminary three-dimensional model, step S2: target region preliminary three-dimensional model is verified with target modeling region scene geometry topological, according to the topological element repair of target region preliminary three-dimensional model according to verification result, obtain target region repair three-dimensional model, step S3: according to the real-time color data of target modeling region corresponding to target region preliminary three-dimensional model is carried out real-time color restoration, obtain spatial region three-dimensional reconstruction model, the application improves the structural integrity and geometric accuracy of three-dimensional model under complex scene.

This invention discloses a method and system for extracting and analyzing key points of legal documents based on a large model, relating to the field of natural language processing technology. The invention generates a semantic parsing candidate graph based on legal semantic nodes and semantic relationships, calculates the matching scores of candidate paths in the semantic parsing candidate graph, summarizes the matching scores of all candidate paths in the semantic parsing candidate graph, and calculates the topological consistency index of the semantic parsing candidate graph. It identifies semantic conflict units and calculates initial conflict weights, and calculates the overall conflict impact of the semantic parsing candidate graph. Based on the topological consistency index, the basic semantic confidence of the semantic parsing candidate graph, and the overall conflict impact, it calculates a comprehensive score for the semantic parsing candidate graph and selects the optimal result, comprehensively optimizing and dynamically adjusting the legal semantic parsing results, significantly improving the accuracy, reliability, and intelligence level of legal text parsing.

The application relates to the technical field of intelligent temperature control, and discloses a pressure roller heating temperature control method and system of a PE packaging line and a storage medium, the method comprising the following steps: taking process variables and environmental variables in high-dimensional time sequence data as nodes and taking dynamic causal relationships as edges to construct a dynamic causal structure diagram of the packaging line; taking the pressure roller temperature as a target node, screening a causal influence relationship to obtain a local network, tracing a key path to obtain a precursor variable; mapping a dynamic behavior mode of the precursor variable to generate a pressure roller temperature feedforward compensation strategy; analyzing the feedforward compensation strategy and a real-time state vector of the precursor variable to obtain a predictive compensation amount; based on the predictive compensation amount, a pressure roller temperature control instruction is cooperatively decided, and a comprehensive control instruction is generated; according to the execution effect of the comprehensive control instruction and topological consistency, the strategy applicability is judged, and a reconstruction trigger signal is obtained; and the application can improve the pressure roller temperature control operation efficiency of the PE packaging line.

The invention relates to the technical field of medical image calculation, in particular to a heart three-dimensional polygon mesh construction method and system, and the method comprises the steps: firstly obtaining and registering an image, generating an inductor density and a signed distance field based on boundary features, and fitting anatomic anchor points; determining a patient specific three-dimensional domain through mass conservation transmission and homeomorphic deformation; constructing pull-back measurement in the domain, solving three groups of harmonic functions, extracting contour surfaces according to common area constraint, and intersecting to form a network; deformation and harmonic functions are taken as joint unknown quantities, synchronous optimization is carried out in combination with pixel consistency, physical consistency and topological consistency, and the density of the inducer is re-weighted by layer area deviation feedback until convergence. According to the method, the surface net with consistent anatomy, controlled topology and balanced layer is output, and the method is adaptive to diagnosis, treatment and simulation application.

This application provides a method and system for modeling multi-class lung airway tree models based on shape-aware sampling. The method includes: acquiring three-dimensional medical image data containing multiple types of tracheal anatomical annotations and determining a training set; determining sampling patches for the images in the training set based on a fractal dimension-driven sampling patch adjustment strategy; determining a skeleton-weighted loss term based on the training set using a shortest path cost skeletonization method; determining a total loss function based on the skeleton-weighted loss term and volume overlap loss; training a pre-defined neural network for multi-class lung airway tree modeling based on the sampling patches and total loss function of the images in the training set; and modeling the three-dimensional medical image of the lung to be modeled using the trained multi-class lung airway tree modeling model. This application effectively improves the volume overlap accuracy and topological consistency of multi-class tracheal model reconstruction, alleviating branch breakage and topological errors in discrete voxel space.

This invention relates to the field of computer vision technology, specifically disclosing a disparity detection method based on synchronous filtering. The method comprises the following steps: S1: feature extraction and cross-view point pair construction; S2: constructing a disjoint-set data structure with dynamic member tracking capabilities; S3: constructing a synchronous filtering sequence based on global ranking; S4: dynamic topological consistency analysis and disparity suspect point marking; S5: filtering to obtain highly robust final disparity feature points and outputting them. The technical solution of this invention can accurately identify and retain disparity feature points that have physical consistency but violate the global geometric model in scenarios with large disparity and complex depth, without pre-setting a global geometric model or relying on fixed parameters.

The present application relates to the technical field of power equipment state monitoring and centralized alarm, in particular to a distributed disconnecting switch operation state centralized alarm system. The system receives state events and operation command records uploaded by the distributed disconnecting switch through the centralized alarm processing end, generates aligned state sequences of the same operation process according to the event time, message sequence and the last trusted state; obtains state transition residuals through the software state machine containing stable split position, stable closed position and transition state, and generates topological consistency residuals in combination with the one-time system topological constraint relationship, and forms time consistency residuals according to the command issuing time, the first variable position time and the final stable state time. The system can distinguish the real disconnecting switch operation abnormality from the data abnormality caused by message disorder, delay and jitter, and improve the reliability of centralized alarm judgment and the consistency of alarm object positioning.

The invention discloses a space intelligent three-dimensional reconstruction method and system based on cross-scale topological consistency, relates to the field of space intelligence, and solves the problem that the existing space intelligent three-dimensional reconstruction technology is poor in three-dimensional reconstruction effect. S2, performing scene geometric topology verification on the initial three-dimensional model of the target area and the target modeling area, performing topology element repair on the initial three-dimensional model of the target area according to a verification result, and performing three-dimensional model construction on the initial three-dimensional model of the target area to obtain an initial three-dimensional model of the target area; s3, performing real-time color restoration on the initial three-dimensional model of the target area according to the real-time color data corresponding to the target modeling area to obtain a three-dimensional reconstruction model of the space area, and improving the structural integrity and geometric accuracy of the three-dimensional model in a complex scene.

The application discloses a kind of unmanned vehicle road accessory facilities breakage intelligent identification method and system for complex road conditions, comprising: acquisition obtains multi-view image and constructs original image dataset;Visible light and infrared image are sequentially modal consistency enhancement, cross-modal bidirectional isomerization collaborative compensation, topological consistency constraint and robustness enhancement of antagonistic sample, respectively output enhancement feature, compensation feature, maintenance feature and robust feature;The four kinds of features are fused, and the semantic segmentation network of multi-constraint joint optimization is constructed, and the pixel level recognition and segmentation of breakage area are carried out;Connected domain analysis and skeleton extraction are carried out to segmentation result, according to breakage geometric feature classification and breakage area or length calculation, output quantitative evaluation result. Through modal consistency enhancement and cross-modal bidirectional isomerization collaborative compensation, the feature difference and sample imbalance problem of visible light and infrared image are solved, and the precision and robustness of breakage identification under complex road conditions are improved.

The invention provides a component type roadway modeling and splicing method and system based on a total station, which are applied to the technical field of three-dimensional modeling, and the method comprises the following steps: respectively collecting three-dimensional coordinates and surface point cloud data of key feature points of a roadway through the total station and laser scanning equipment; after time synchronization and space registration, a unified three-dimensional point cloud is generated through fusion; extracting geometric features of the roadway based on the fused data, dividing the roadway into three types of basic geometric components including straight line segments, curves and intersections, and performing parameterized expression; performing sequential splicing on the components by adopting splicing constraints of coarse, medium and fine levels, and realizing high-precision model construction through endpoint matching, seam geometric error optimization and local fitting; and carrying out topological consistency check and global optimization on the spliced model, and outputting the topological consistency check and global optimization. According to the method, high precision, automation and structuralization of roadway modeling are realized, and the problems of large splicing error and inconsistent topology of a three-dimensional model in a complex roadway environment are effectively solved.

The present application relates to a kind of DSA coronary vessel segmentation deep learning method of edge and topological feature, comprising the following steps: obtaining DSA coronary angiography image dataset and corresponding coronary vessel label dataset;Data is input into deep learning network, the deep learning network is end-to-end U type deep learning network, include improved bidirectional Transformer module and multi-scale semantic fusion module, and encoder and decoder are connected by jump connection and multi-scale semantic fusion module;Coronary vessel segmentation model is trained in three stages using the composite loss function comprising Dice loss, Focal loss, edge loss and topological loss, the segmentation model of the training completion can be carried out complete segmentation to the coronary vessel in DSA coronary angiography picture;Using composite evaluation index, the overall segmentation accuracy of segmentation model, edge segmentation accuracy and the ability of keeping topological consistency are comprehensively evaluated.

The invention provides a female age prediction system and method fusing facial images and biochemical indexes, and belongs to the field of artificial intelligence and health management, and the system comprises a data collection and preprocessing module, a dual-channel feature extraction module, a multi-modal feature topology fusion module, an age prediction and health evaluation module, and a personalized health intervention module. According to the method, a biochemical feature topological space and a facial feature topological space are innovatively constructed, feature fusion is realized through a cross-modal attention interaction network and a topological consistency keeping mechanism, the system predicts the biological age and generates a three-dimensional health risk score based on the fused features, and dynamic calibration is carried out in combination with female physiological cycle data, so that the health risk of the female is evaluated. According to the method, the technical contradictions that the traditional detection method is high in cost due to high precision and obvious in error due to low cost are solved, and accurate and reliable female physiological age evaluation is provided.

The invention discloses a lightweight accelerated calculation method for a large assembly model, which is used for performing parallel calculation on quadratic error measurement QEM of an industrial three-dimensional assembly and an approximate curvature optimization simplification algorithm of the industrial three-dimensional assembly based on an OpenMP parallel calculation framework, and solves the problems of low simplification efficiency, poor real-time performance and difficulty in considering precision and efficiency of a large-scale industrial three-dimensional model in the prior art. Through an OpenMP parallel framework, a QEM algorithm and an approximate curvature optimization algorithm are subjected to thread-level acceleration, the model simplification efficiency is remarkably improved, the average speed-up ratio reaches 4.5 times, meanwhile, the geometric accuracy and topological consistency of the model are effectively reserved, and the method is suitable for resource sensitive scenes such as mobile terminal loading, real-time rendering and virtual reality; the invention further comprises a computer system for implementing the light-lining accelerated calculation method for the large assembly model.