22 results about "Computational topology" patented technology

This invention relates to the field of architectural planning technology, specifically to a method and system for urban and rural planning based on digital twins. The method includes acquiring building information models and geographic information system (GIS) data of the area to be planned; constructing a three-dimensional digital twin basic scene through spatiotemporal registration and semantic alignment; identifying planning entities and multidimensional constraints to construct a topological hypergraph model; calculating topological centrality and constraint coupling weights to generate a non-uniform computational potential energy field; constructing a hierarchical scheduling strategy based on the potential energy field gradient; loading a LOD3 level fine model to extract topological parameters for high-potential-energy sensitive areas; loading a LOD1 level block model to extract filling data for low-potential-energy background areas; inputting a generative layout algorithm to generate intermediate data; detecting and repairing geometric overlaps; and outputting a planning scheme. This invention, by constructing a non-uniform computational potential energy field and a hierarchical scheduling strategy, achieves on-demand loading and differentiated processing of large-scale scene data, effectively solving the computational bottleneck problem of processing massive heterogeneous data.

The present application relates to the technical field of cell image analysis and medical artificial intelligence, and discloses a cell segmentation and adaptive cascade reasoning method and system based on prior frame guidance. The method comprises: acquiring a multi-modal cell image with a detection frame; performing adaptive histogram equalization preprocessing on the image and executing global preliminary screening segmentation; calculating a multi-dimensional weighted score of the mask and the detection frame, if the score is lower than a first matching threshold, extracting a local image block to dynamically adjust a flow field threshold and an estimated diameter for cascade retry; if the score after retrying is lower than a second threshold, triggering a label consistency protection mechanism to discard or back up poor samples; finally, performing connected domain purification on the retained mask, and calculating a topological solidity, and performing convex hull reconstruction on the mask with low solidity. Through two-stage reasoning, double-track quality filtering and topological constraint, the present application effectively improves the segmentation effect of dense and irregular cells and improves the generalization performance of downstream analysis.

The application discloses a kind of based on topological analysis's graphic image splicing fusion method and system, belong to image processing field, the method includes: obtaining first image and second image, generate first binary image and second binary image;Respectively topological feature analysis is carried out, and the topological skeleton of image foreground area is extracted and topological invariants is calculated, and initial matching point pair set is generated;After alignment, the first image is generated;Image splicing seam is solved in the cost matrix by dynamic programming algorithm, and the target image after splicing fusion is output.The present application prevents tearing and overlapping distortion phenomenon in image alignment process.

The application relates to a spatial transcriptome spatial domain identification method and system based on double contrast learning. The method comprises the following steps: performing Gaussian smoothing processing on gene expression data to obtain smoothed gene expression data; constructing an initial adjacency matrix based on spatial coordinate data, performing edge pruning to obtain an optimized adjacency matrix; inputting the smoothed gene expression data and the optimized adjacency matrix into a graph convolution network encoder to obtain low-dimensional latent features and reconstruction features, and calculating a reconstruction loss; inputting the low-dimensional latent features into a multi-branch multi-layer perception fusion module to obtain enhanced features; constructing a regional contrast learning task, calculating a regional contrast loss, constructing a topological contrast learning task, and calculating a topological contrast loss; optimizing model parameters through a total loss, using an optimized model to perform spatial domain identification, and obtaining a spatial domain identification result. The spatial domain identification accuracy can be improved and the cost can be reduced.

The present application relates to the technical field of data processing, more specifically, the present application relates to a power project demand reserve overall balance method based on intelligent algorithm, comprising: obtaining the declaration text data and electrical scene image data of the power project to be checked; detecting the equipment entity node by using the Grounding DINO model; constructing a spatial interaction matrix based on the spatial distribution characteristics of the equipment entity node; fusing the text semantic features, visual feature vectors and spatial interaction intensity, deducing the electrical topology connection probability and generating the project topology graph; and calculating the topology structure similarity by using the subgraph isomorphism algorithm to determine the duplicate declaration project. The present application realizes the identification of electrical connection relationship by evaluating the spatial relationship and fusing multi-modal information, solves the problem that the traditional method cannot distinguish the same equipment in different topology positions, and improves the accuracy of the power project demand reserve overall balance.

The application relates to the technical field of fault prediction, and discloses a water level monitoring equipment fault prediction method based on multi-source data fusion, which comprises the following steps: performing stationary processing on multi-source time series data to obtain weakly stationary multivariate sequences; constructing a dynamic coupling relationship network through a dynamic conditional covariance model and a Granger causality test; extracting a topological feature vector therefrom, establishing a deviation degree reference model based on the topological feature vector of a historical normal period, and calculating the deviation degree of the topological feature vector to obtain an equipment coupling health degree index; and generating a fault prediction warning based on the evolution trend of the index and the matching degree of the index with a pre-stored fault mode; the application can improve the accuracy of a water level monitoring equipment fault prediction based on multi-source data fusion.

This invention discloses an adaptive carbon footprint data processing method and system based on the coupling of risk perception feedback and large model inference constraints. The method includes: parsing the metadata of the original data object and calculating a data risk vector containing time deviation and physical conservation deviation; constructing an initial computational topology and transforming data requirements into a constraint satisfaction problem; if no solution is found, triggering topology adaptive reconstruction based on semantic reasoning; dynamically adjusting the inference hyperparameters of the generative model based on the data risk vector, and updating the mapping weights between risk and hyperparameters online using the logical deviation scores generated by subsequent source tracing audits. This invention solves the technical problem of inference illusion caused by high-uncertainty data by establishing a closed-loop coupled control mechanism between data risk and model behavior; and solves the technical problem of static workflows being unable to cope with process interruptions caused by missing data through runtime semantic reconstruction of the computational topology.

The application discloses a carbon emission total amount acquisition method, device and equipment and a storage medium, and belongs to the technical fields of Internet and computers. The method comprises the following steps: displaying a configuration interface related to carbon emission calculation, the configuration interface being used for configuring a calculation topology of the carbon emission total amount; acquiring topology configuration information provided in the configuration interface, the topology configuration information being used for determining the calculation topology of the carbon emission total amount of a target object, the calculation topology comprising emission classes, emission items and calculation formulas arranged according to levels; and displaying a calculation result of the carbon emission total amount of the target object in a target period, which is obtained by calculating source data of the target object based on the calculation topology, the source data comprising data required for calculating the carbon emission total amount of the target object in the target period. According to the application, the object is provided with a calculation topology of the carbon emission total amount which can be configured by the object itself, so that the object can determine the structure of the calculation topology according to actual conditions, and the method for calculating the carbon emission total amount is more flexible.

This invention provides a statistical method and system for the digital integration degree of water pipeline network operation and maintenance information. The method defines the statistical boundaries of the entire operation and maintenance process, extracts key information nodes to construct a process node mapping table, constructs a multi-dimensional indicator system covering digital coverage, integration connectivity, and value creation, and uses a subjective and objective combined weighting algorithm to determine the dynamic comprehensive weight of the indicators. It collects underlying business data, constructs a directed graph of data flow and a digital elevation space model, calculates topology and spatiotemporal correction coefficients based on graph theory topology anomaly pruning diagnosis and spatial gradient penetration algorithms, and then corrects the basic score to obtain the comprehensive integration degree. Finally, it classifies the levels and outputs a directional optimization sequence including key speed-limiting nodes. This invention deeply integrates the physical spatial characteristics of the pipeline network with the underlying graph theory topology of the system, effectively filtering out pseudo-integration and redundant data transmission, accurately quantifying the true interconnection level of the entire business process, and providing a scientific diagnostic basis for the optimization of the water digital architecture.

The application discloses a coal rock dynamic permeability detection method and system, comprising the following steps: collecting seepage response data in the process of stress disturbance or loading evolution of a coal rock sample, and performing pretreatment; constructing a pressure difference sequence, generating a seepage response state set; constructing a seepage path node set, establishing a path connection relationship set; calculating the path transmission contribution value of each seepage path, forming a seepage path probability field; constructing a path topological structure matrix according to the seepage path probability field, and calculating a path concentration index sequence; calculating a topological entropy sequence and a topological entropy change rate sequence; inputting the topological entropy sequence, the topological entropy change rate sequence and the path concentration index sequence into a permeability structure mapping model to generate a coal rock dynamic permeability detection result. The application adopts a path structure probability modeling method, realizes coal rock dynamic permeability detection, and has the advantages of strong structure sensitivity and high detection precision.

The application provides a gas turbine operation and maintenance large model and small model collaborative diagnosis method, and belongs to the technical field of gas turbine operation and maintenance.The application collects vibration acceleration signals, acoustic emission signals and thermal image sequences of the gas turbine, combines a multi-source monitoring feature matrix and performs persistent homology analysis to calculate a topological feature vector, inputs the multi-source monitoring feature matrix and the topological feature vector into a topological coupling diagnosis model to output a global anomaly confidence, and when the global anomaly confidence is greater than 0.75, the global anomaly confidence is transmitted to a hierarchical attention diagnosis model to perform local feature extraction and calculate a local anomaly score, a comprehensive diagnosis index is obtained by weighted fusion to determine a fault state, and the operation parameters of the gas turbine are regarded as a set of trajectory points on a Riemann manifold, a manifold distance is calculated by using a geodesic distance to identify an abnormal working condition mode, and the technical problem that a gas turbine fault diagnosis method cannot accurately identify a global coupling fault mode and a local transient anomaly is solved.

This invention discloses a method, system, equipment, and medium for predicting the annual horizontal load of 10kV medium-voltage lines, belonging to the field of power system planning technology. Addressing the problems of existing load prediction methods neglecting distribution network topology, load characteristic differences, and the difficulty in effectively utilizing automation data, this invention proposes a multi-factor adaptive model that integrates topology correction factors, load type benchmark coefficients, and automation data. First, a capacity-current benchmark coefficient library for different load types is established through cluster analysis; second, topology correction factors are calculated based on the number of line segments and tie points; then, basic predictions are performed by combining the annual natural growth rate and the annual horizontal installed capacity; finally, segmented load predictions can be achieved by integrating historical data from automated switches. Verification using actual data from the Shagang Tangtou area of ​​Foshan shows that the average prediction error of this method is less than 3.5%, reducing it by more than 60% compared to traditional methods, and it can quickly respond to adjustments in planning schemes, significantly improving the scientific nature and efficiency of distribution network planning.

The application discloses a power transmission line robot automatic icing identification and deicing method, the method comprises the following steps: obtaining ice layer dielectric properties and meteorological data, calculating ice energy level index; after successful connection, based on quantum heuristic algorithm, point cloud matching is performed on the ice layer and an ideal power transmission line spiral stranded model, and a topological singular point density value is calculated to dynamically generate an adaptive fractal rolling path; in the moving rolling process, the Hausdorff dimension change of ice cracks and the nonlinear distortion component of the roller motor are fused, and a brittle-plastic phase transition critical factor is calculated; for the ice crushing removal effect, the sound force coupling dissipation rate is calculated through the Hilbert spectrum of the acoustic emission signal, and a model predictive control framework is constructed to realize efficient centrifugal throwing of ice chips; after the operation section is finished, a global residual risk potential field is generated based on the sound force coupling dissipation rate, and secondary fine polishing or intelligent unhooking return is performed according to the cleanliness convergence value. The application improves the deicing efficiency while ensuring the safety of the power transmission line body.

This application relates to the technical field of smart blackboards and discloses an interactive resource scheduling and utilization optimization system for smart blackboards. The system includes a task module that instantiates multimodal interactions into task nodes through intent parsing and associates them with resource nodes to form a dynamic interactive topology; an edge weight module that dynamically calculates the weights of the topology edges based on task demand intensity, resource load, and energy consumption; a decision module that transforms task nodes into agents and, through weight-based multi-round local policy interactions and conflict negotiation, enables the system to converge to a Nash equilibrium state in resource allocation; and a resource control module that issues precise control commands based on the equilibrium matching relationship. This system solves the problems of rigid resource allocation, insufficient real-time performance, and low energy efficiency in smart blackboards under concurrent multi-task interaction, achieving adaptive, high real-time, and low-energy resource scheduling based on interactive intents.

The application relates to the technical field of data processing, and discloses a multi-source heterogeneous data-oriented big data cleaning and fusion method, which comprises the following steps: writing asynchronous observation values into a time attenuation buffer matrix, combining a dynamic attenuation rate constant to calculate state weights; calculating physical conduction delay, updating a field dependence matrix and updating the dynamic attenuation rate constant; calculating conditional information entropy based on the updated matrix and state weights, mapping the conditional information entropy into confidence weight to complete data cleaning; performing topological edge pruning on the updated matrix based on the average value of the confidence weight, calculating topological survival rate and a regularization coefficient; and performing weighted least square fusion on the observation values by using the confidence weight and the regularization coefficient, and outputting a state vector. The application retains time sequence characteristics to improve the accuracy of data cleaning, avoids divergence by regularization punishment, and realizes stable big data cleaning and fusion for multi-source heterogeneous data.

This invention relates to the field of electronic digital data processing technology and discloses an AI-based intelligent computing operating system for energy and carbon data. The system includes an energy and carbon tensor management module, a bypass scheduling module, and a mapping processing module. The energy and carbon tensor management module allocates a non-paged, continuous physical space in memory to store time-series energy consumption data streams. The bypass scheduling module acquires the server's memory access topology graph and identifies the local memory nodes corresponding to different cores. The mapping processing module extracts topology boundary nodes based on the computational topology correlation matrix and generates kernel-state topology boundary cut sets. The operating system implements physical mirror multi-projection mapping based on the topology graph and cut sets, establishing redirection paths to convert remote memory access into local addressing access. This invention eliminates hardware cache consistency probe signals through a mirroring mechanism, solving the memory access latency bottleneck in large-scale graph computation and ensuring computational determinism.

The application relates to the technical field of pipe network data processing, and discloses a pipeline network spatial topological relation analysis method and system based on a graph neural network, which comprises the following steps: collecting pipe section objects, pipeline nodes and constraint objects. A spatial topological index structure is constructed, which comprises a spatial block index, a topological adjacency index and a standard constraint index. A constraint template is selected according to a pipeline category and a topological relation type to determine candidate object pairs and calculate topological indexes, and finally, a preliminary topological relation classification result is obtained. The relation classification input feature is input into a relation classification model, feature transmission and aggregation are carried out on a graph structure formed by the pipe section objects, the pipeline nodes and the constraint objects, and a topological relation classification result is output. Version identifiers are set for spatial blocks and candidate object pairs, and a spatial topological relation analysis result of the pipeline network is determined according to pipeline network data update conditions. The application improves the accuracy of topological rule violation identification and reduces the consumption of computing resources.

The invention discloses a transformer shielding fault intelligent diagnosis method based on multi-modal data and a transformer, and the method comprises the steps: collecting the multi-modal data and topological structure data of a shielding layer, calculating a time sequence entropy change sequence, and constructing a three-dimensional coupling data set; processing the three-dimensional coupling data set by adopting an entropy change perception graph attention network, outputting a three-dimensional coupling feature vector and topological connectivity, and calculating modal feature anomaly based on the three-dimensional coupling feature vector; outputting a topological distortion position and an entropy change abrupt change point through a combined self-encoder; respectively calculating a topology distortion degree and an entropy change standard exceeding degree; inputting the modal feature anomaly degree, the topological distortion degree and the entropy change overproof degree into an improved fuzzy Bayesian inference model, and outputting a fault diagnosis result by the improved fuzzy Bayesian inference model; by collecting the multi-modal data and combining the topological structure data and the entropy change characteristics, the fault of the shielding layer can be accurately judged, so that the shielding layer can be processed in time, and stable operation of the high-frequency transformer is guaranteed.

The application relates to the field of artificial intelligence medicine, in particular to a chronic precancerous lesion recognition method based on a topological multi-scale graph neural network, which first collects a multi-spectral endoscope image under a gastroscope and carries out pretreatment to construct a multi-channel feature map; subsequently, instance segmentation is carried out on glandular structures to form a gland point cloud set, a topological persistent graph is constructed by using a persistent homology theory, and topological invariant features are calculated; on this basis, a multi-scale gland topological graph is constructed, feature aggregation and updating are carried out through a graph attention mechanism; finally, related features are fused to input a classifier, and a lesion risk grade and positioning result are output, and a loss function is used for optimization. By fusing the persistent homology theory in algebraic topology and the multi-scale graph neural network, a gastroscope auxiliary diagnosis algorithm conforming to biological pathological features is constructed, and accurate recognition and risk grading of chronic precancerous lesions are realized.

This invention relates to the field of internet security service technology, specifically to a method for identifying graded protection risks based on log behavior analysis. The method includes the following steps: collecting identifiers of subjects and accessed resources, as well as semantic vectors of security level construction behavior; constructing a current and baseline log topology according to generation time; calculating the difference in semantic vectors of topology elements to construct a mapping relationship set; extracting non-overlapping edges from the relationship set to calculate the security level risk ratio and generate a baseline result; and extracting the last value of the continuous window mapping change trajectory and weighting and adjusting the baseline result to generate a graded protection risk identification result. In this invention, by fusing multi-dimensional identifiers and security level construction semantic vectors and establishing a dynamic topology according to time sequence, mapping log connectivity differences to uncover node evolution, this method overcomes the limitations of single-point static analysis. It extracts deviation trajectories to calculate risk ratios and tracks continuous window mapping change sequences, performs weighted deviation calibration on abnormally active subject trajectories, restores concealed dangerous intentions, and improves the sensitivity of multi-step attack identification.

The application discloses an internet topology reinforcement detection method based on decision optimization, and belongs to the field of internet topology detection. The method firstly constructs a topology detection knowledge graph to represent prior knowledge related to topology detection, then selects an optimal topology detection decision based on the knowledge graph by constructing heuristic rules and a strategy network, then trains and optimizes the strategy network by adaptive reinforcement learning to update the topology detection strategy by calculating the gain reward of the topology detection, and finally triggers a detection strategy for a multi-path by identifying and judging path stability. Through heuristic screening, reinforcement strategy decision and iterative update of detection feedback, the detection process can automatically explore high-value areas, reduce repeated detection, improve topology coverage and effectiveness, and guarantee the authenticity, integrity and dynamicity of internet topology detection in a large-scale internet environment with strong dynamics and strong heterogeneity.

The application relates to the field of artificial intelligence and computer image generation technology, in particular to a multi-dimensional feature extraction and generation system for an animation character based on deep learning; the system comprises a multi-dimensional feature decoupling and graph construction unit, an implicit space topology and physical constraint unit, a semantic and style coupling generation unit, and an entity consistency evaluation and interception unit; multi-modal input including at least target semantic labels and target visual angle parameters is received, apparent features, structural features and semantic variable features are extracted and a feature dependency graph is constructed, a pseudo-three-dimensional coordinate system is established in a latent space and visual angle conversion and physical correction are performed, a candidate image is generated based on a diffusion model, a topology deviation risk value and a style deviation risk value are calculated, a redrawing instruction is generated for local redrawing when the values exceed a threshold, and a final image and a consistency evaluation report are output when the values meet the threshold; the application can realize identity consistency control of the same animation character under multi-view and multi-semantic conditions.