144 results about "Topological consistency" patented technology

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention discloses a question and answer method and system based on a knowledge graph, and the method comprises the steps: outputting a structured query graph of which nodes comprise entities, relationships and semantic weights according to a natural language question input by a user; on the basis of the structured query graph, outputting candidate entity sub-graphs of which the ambiguity is eliminated; outputting a reasoning path set with the highest probability according to the candidate entity subgraph; based on the reasoning path set, outputting candidate answers which conform to logic and are coherent in grammar; and according to the topological consistency of the candidate answers and the knowledge graph, calculating an answer credibility score by using a causal reasoning model, and outputting a final answer and an interpretability verification report by analyzing a logic causal chain implied in the answers and verifying with the knowledge graph. By utilizing the embodiment of the invention, deep integration and value mining of park multi-source data can be realized, and powerful support is provided for fine management and intelligent decision-making of the smart park.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention discloses an aviation equipment detection and maintenance method and equipment based on target detection and a medium, and relates to the technical field of aviation equipment detection, and the method comprises the steps: inputting a correction image set into a pre-trained target detection network, carrying out the multi-scale feature analysis and edge texture recognition, and generating a part detection result; establishing a component topological relation according to a component detection result, and comparing the component topological relation with a standard configuration by using a graph structure analysis method to generate topological consistency information; performing defect positioning and quantitative analysis on the topological consistency information, extracting a defect position, a defect type and a defect quantitative index, tracking an extension track of the defect quantitative index along with time through continuous time frame difference, and generating a defect parameter; and performing risk assessment on the defect parameters, generating and executing a maintenance scheme, verifying the maintenance effect through real-time image detection, and forming a detection and maintenance closed loop. According to the invention, the accuracy and integrity of aviation equipment detection are finally improved.

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention discloses an automatic retinal vessel segmentation method fusing large-kernel multi-scale cavity convolution and an attention mechanism, which is suitable for accurate extraction of a fine-grained vessel structure in a fundus image. According to the method, an improved deep neural network (LKD-UNet) is constructed, a collaborative receptive field module is embedded in each layer of an encoder, and large kernel convolution and multi-scale cavity convolution are combined to enlarge the receptive field and enhance the texture analysis capability; an efficient Transform module is introduced into a bottleneck layer, a cross-regional semantic relationship is modeled through a multi-head self-attention mechanism, and the topological consistency of microvessels is improved; the decoder adopts a shallow symmetric structure and a kernel scale scheduling strategy, and realizes high-resolution reduction in combination with jump connection. In addition, a four-category pixel-level error visualization mechanism is introduced and is used for assisting segmentation error diagnosis and result interpretation. Experimental results show that the method is superior to an existing mainstream model in multiple public data sets, has remarkable advantages in the aspects of segmentation precision, structural coherence and deployment efficiency, and is suitable for remote fundus screening and blood vessel extraction tasks in embedded medical equipment.

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 field of image processing, and discloses a video image segmentation method which is used for improving the precision, robustness and real-time performance of video image segmentation in a complex dynamic scene. The video image segmentation method comprises the following steps: generating an entropy generation rate map through weighted combination of light flow divergence and rotation, and quantifying motion irreversibility; a double-virtual-form prime concentration field is constructed, next-frame texture prediction is realized through iterative evolution, and the dynamic background adaptability is enhanced; fusing the projection entropy generation rate graph and the enhanced texture residual graph, dynamically distributing motion and appearance weights, and generating a high-precision boundary response graph; and extracting and persistent filtering are carried out, topological consistency maintenance of segmentation masks is realized, multi-scale collaborative segmentation and dynamic computing resource scheduling are supported, and efficiency and precision are balanced. The segmentation precision of the method is obviously superior to that of a traditional method in complex scenes such as illumination variation and rapid motion, and the method is suitable for the fields with high real-time requirements such as monitoring, medical treatment and automatic driving.

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 spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

The invention relates to the field of CIMs, and discloses a method and system for automatically degrading a CIM6-level three-dimensional model to CIM5 based on a semantic rule engine, and the method comprises the steps: obtaining CIM6-level three-dimensional model data, and constructing a scene hierarchical tree based on the three-dimensional model data; identifying the category, the attribute, the service label and the importance constant of each object in the scene hierarchical tree; calculating geometric complexity, semantic importance and topological dependency of each object, and calculating a comprehensive weight; matching the interval of the comprehensive weight and executing a corresponding processing operation based on the matched interval; performing grid merging and texture baking operation on the reserved object; performing grid instantiation on the proxy object after proxy replacement; collision holes in the model are detected through an octree in combination with a GJK algorithm to complete topological consistency verification, a lightweight CIM5-level model data packet is generated, and a difference report is output. According to the method, the consistency of the key semantic information and the topology is kept while the data volume of the CIM6 model is reduced.

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 provides a multi-modal fusion road topology reasoning method and system, and the method comprises the steps: converting an extracted lane mask into a semantic seed point in a historical point cloud, and carrying out the pseudo-label setting based on the semantic seed point. And performing model training based on the historical image with the pseudo tag and the historical point cloud to obtain a lane detection model. Obtaining lane line information from the real-time image and the real-time point cloud by using a lane detection model, mapping the lane line information to a bird's-eye view feature map, calculating a topological relation between lane lines based on the lane line information in the bird's-eye view feature map, obtaining a topological reasoning matrix, and obtaining a topological reasoning result; and obtaining a corresponding road topology reasoning result based on the graph neural network according to the topology reasoning matrix. According to the scheme, on the basis of a pseudo label generation mechanism, dependence on manual marking is reduced, data preparation cost is reduced, effective fusion of a lane line representation method based on bird's-eye view space unified mapping and lane topology reasoning is introduced, and a lane topology reasoning result with high precision and good topology consistency can be obtained.

The invention relates to a knowledge tracking model research method integrating difficulty perception and memory enhancement. According to the method, a graph attention network coding exercise-knowledge point topological relation is constructed, and a difficulty embedding layer and a gating fusion mechanism are combined to realize joint characterization of difficulty features and a topological structure. The difficulty coefficient is innovatively introduced as a dynamic regulation factor of attention weight in memory network updating, the memory intensity of high-difficulty exercises is enhanced, and the discrimination and topological consistency of knowledge representation are improved through a multi-task optimization framework integrating graph structure loss and contrast loss. According to the method, challenges of exercise difficulty perception and knowledge point topological relation modeling are effectively solved, experimental results show that the AUC of the model reaches 0.93 (improved by 7.8% compared with traditional BKT) on an ASSIST2009 data set, the AUC of the model reaches 0.90 (improved by 0.21 compared with KSGAN) on an EdNet data set, objective indexes and teaching scene verification show that the method has remarkable advantages in the aspects of knowledge point correlation modeling and knowledge tracking, and the method is suitable for popularization and application. The method provides an innovative solution for personalized education, and has technical breakthrough and industrial application values.

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 relates to the technical field of data processing, in particular to a topological consistency risk analysis and monitoring early warning method for a tower inclination state, and the method carries out the time alignment and quality control of multi-modal data, such as an inclination angle, a wind speed and direction, temperature and humidity, positioning and an equipment state. Constructing a time-varying adjacency matrix based on line topology, geographical adjacency and wind field elements, and normalizing the time-varying adjacency matrix to form a graph model; the inclination angle or the trend of the inclination angle serves as a graph signal, space consistency measurement is calculated, and linkage with sliding window trend detection of a single rod and EWMA and ADWIN drift detection is carried out; identifying segment-level or region aggregation anomalies by synthesizing image smoothing energy, adjacent rod difference and spatial self-correlation; and outputting early warning grades under wind direction, wind speed, duration time window and data quality gating, and adaptively adjusting a threshold value, a time window and a model weight during drifting. The method can reduce false alarm and missing alarm, improves early warning accuracy and interpretability, and is suitable for centralization or edge cloud collaborative deployment.

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.

The invention provides a motor current interpretable diagnosis method based on a dual-channel collaborative optimization deep convolutional neural network model. Firstly, time-frequency domain features of current signals are extracted through multi-stage wavelet decomposition and sideband energy analysis, and it is ensured that the physical significance of fault features is clear; secondly, a dual-output network structure is constructed, the dual-output network structure comprises a physical feature head and a classification head, and the physical feature head restrains the matching degree of hidden layer features and theoretical physical parameters through a spectrum consistency loss function; physical significance projection of a high-dimensional feature space is realized based on t-SNE, and topological consistency of feature distribution and a fault mode is verified through spectral clustering; and establishing a physical parameter inversion channel, carrying out error convergence analysis on a physical feature vector output by a network hidden layer and a theoretically calculated feature frequency, and ensuring that a diagnosis result simultaneously meets a data driving criterion and a physical mechanism constraint. According to the method, the advantage of high precision of deep learning is maintained, and meanwhile, the model decision interpretability and the reliability of a diagnosis result are improved.

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.