12 results about "Coherence analysis" patented technology

This invention belongs to the field of wind power equipment condition monitoring and fault diagnosis technology, specifically relating to an intelligent diagnostic device and method for blade icing based on acoustic-vibration feature fusion. It includes: a multimodal sensing unit for acquiring vibration response signals and aeroacoustic signals; a signal conditioning and synchronous acquisition unit connected to the multimodal sensing unit for conditioning and synchronous analog-to-digital conversion of the vibration response signals and aeroacoustic signals to obtain synchronized digital vibration and digital sound signals; and an edge computing and diagnostic unit connected to the signal conditioning and synchronous acquisition unit. This invention synchronously acquires and deeply fuses vibration signals reflecting the blade's structural state with specific frequency band sound signals reflecting the aerodynamic state of the blade surface, constructing an intelligent diagnostic system based on acoustic-vibration coherence analysis and machine learning models. This achieves high-precision, robust online diagnosis and early warning of blade icing conditions.

This invention discloses a data communication scheduling system for intelligent manufacturing production lines based on edge computing, belonging to the field of industrial IoT edge computing data scheduling technology. The system synchronously acquires the real-time load current discrete sequence of the target device node and the real-time signal-to-noise ratio sequence of the main communication path, extracting the second-order derivative zero-crossing discrete component and the first-order attenuation discrete component of the envelope. A cross-correlation algorithm is used to perform coherence analysis to derive a cross-domain healthy coupling index. Using this index as the independent variable, a weighted mapping of the real-time packet loss rate is performed through an exponential gain function to generate a virtual impedance factor. It determines whether the index exceeds a threshold to trigger backup gateway context pre-synchronization and calculates the positive offset. Finally, a normalization function is used to calculate the traffic allocation weight and configure path scheduling labels to achieve dynamic scheduling of multi-path traffic splitting actuators. This solution achieves a priori prediction of industrial communication link quality and refined multi-path traffic splitting scheduling.

The application discloses a kind of tensor field direction constraint weighted fitting fracture prediction method and system.It includes: the original seismic data is carried out dip orientation filter processing, obtains filtered seismic data body;The filtered seismic data body is carried out eigenvalue coherence analysis, obtains coherence volume, and the coherence volume is carried out coherence enhancement processing, obtains enhanced coherence volume;Based on enhanced coherence volume, using tensor direction constraint vector ant colony tracking algorithm, automatically track fracture strike, obtain fracture tracking result;Based on fracture tracking result, construct gradient structure tensor field, use the direction information of gradient structure tensor field to carry out anisotropic Gaussian weighted fitting, obtain the quantitative description result of fracture.The application is through with tensor field direction information as unified constraint throughout fracture prediction whole process, realizes the quantitative description of small scale, low density fracture, and the coincidence rate of prediction result and disclosed fracture area reaches more than 80%, provides reliable basis for mine safety exploitation and reservoir evaluation.

This invention belongs to the field of power system monitoring technology and discloses a method, system, equipment, and medium for analyzing the icing state of transmission lines based on point cloud modeling and deep learning, thereby overcoming the limitations of existing methods in time series processing. The method includes: dividing the time series point cloud of the transmission line into multiple time subsequences; generating a topological persistence graph for each subsequence through voxelization and persistent coherence analysis; calculating the dynamic topological distance between subsequences based on the persistence graph; extracting topological features to construct a feature matrix, and fusing topological distance, feature space, and feature distribution three views to construct a similarity matrix; using multi-view joint learning to obtain a consensus subspace and performing subsequence clustering within this space to obtain cluster labels; combining physical context information and predefined rules to assign physical pattern labels to the clustering results, generating an icing state classification report and visualizing it. This invention achieves temporal topological quantitative analysis and highly robust state identification of icing morphology.

This invention discloses a knowledge graph-based financial data analysis method, comprising the following steps: collecting and preprocessing multi-source financial data; performing semantic unification and hierarchical element extraction on standardized financial datasets; constructing a set of financial semantic subgraphs based on the hierarchical financial semantic element set; determining target cross-layer connection relationships and constructing a hierarchical financial knowledge graph based on the set of financial semantic subgraphs; comprehensively analyzing cross-layer association paths between different semantic layers based on the hierarchical financial knowledge graph; constructing a filtering sequence and performing continuous coherence analysis to extract hierarchical graph structural features and generate a set of hierarchical graph structural features; determining potential association relationships, association strength, and risk transmission relationships between financial entities, and generating financial data analysis results. This invention employs knowledge graphs and cross-layer semantic analysis methods to achieve multi-source financial data association modeling, possessing the advantages of accurate association identification and comprehensive risk analysis.

The application provides a power transmission tower modal parameter identification method and system for automatic inspection, converts acceleration response data of each measuring point of a structure to be identified into natural vibration frequencies of the structure to be detected based on a dynamic differential equation, obtains a power spectrum of the structure to be identified, and obtains initial natural vibration frequencies of each measuring point of the structure to be identified; coherence analysis is performed on the initial natural vibration frequencies of any two measuring points, and a plurality of coherent initial natural vibration frequency peak points between the coherence function of 0.8-1 are obtained; cluster analysis is performed on the plurality of coherent initial natural vibration frequency peak points, and the coherent initial natural vibration frequency peak points within the cluster identification range are selected as real natural vibration frequency peak points, and the frequency corresponding to the real natural vibration frequency peak points is the real natural vibration frequency; the real natural vibration frequency is selected in the power spectrum of the structure to be identified for modal identification analysis, and the subjectivity and noise interference problems existing in the selection of the real natural vibration frequency are solved.

This invention discloses a method for predicting the remaining life of existing asphalt pavement structures, specifically relating to the fields of computer simulation and data processing technology. It addresses the problem that existing prediction methods cannot efficiently characterize the non-uniformity of existing pavement materials and the influence of dynamic environmental loads while ensuring accuracy. The method involves acquiring existing pavement damage data to construct a simulation model reflecting the true initial state, combining this with cross-scale analysis to obtain material fatigue parameters, and using climate time-series data to drive dynamic environmental boundaries. Furthermore, it performs time-frequency coherence analysis on damage modes and climate loads to identify high-threat conditions. Finally, it prioritizes high-threat conditions by running high-fidelity thermo-mechanical coupled numerical simulations, mapping the damage evolution data output from the simulation to the macroscopic performance degradation process, thereby determining the remaining fatigue life.

This invention discloses a regional groundwater environmental risk assessment system and method considering the vulnerability of risk receptors, relating to the field of hydrogeology. The system includes the following steps: S100, establishing a cross-timescale monitoring baseline, collecting groundwater recharge intensity sequences and hydrochemical sequences, extracting sedimentary pollution fingerprints through multidimensional correlation, and identifying germination areas with re-release characteristics; S200, under the constraint of the cross-timescale monitoring baseline, calculating the temporal coherence and phase residuals between the recharge disturbance sequence and the sedimentary pollution fingerprint, identifying key spatiotemporal locations triggering pollution release, and constructing a pollution release causal residual spectrum. This invention extracts pollution fingerprints through cross-timescale monitoring, identifies high-risk release areas, reconstructs pollution pathways by combining coherence analysis and phase inversion, sets threshold surfaces and precursor scores, generates pre-suppression parameters, and achieves pollution limiting and closed-loop control through frequency shifting and time inversion, thereby improving groundwater risk response and management capabilities.

The application discloses a kind of based on QGNNs and PH's extra-high voltage converter transformer fault location method and related equipment, it is related to power equipment fault diagnosis technical field. Including the acquisition of converter transformer multi-source heterogeneous monitoring data, and according to its physical structure graph model is constructed, sensor measuring point or monitored component is mapped as node;The sustained coherence analysis of the multidimensional time series data of each node is carried out, and topological invariant feature is extracted, and original node feature is fused to form enhanced node feature to construct enhanced graph model;Quantum-classical hybrid graph neural network is constructed and trained on the enhanced graph model, extracts multi-scale feature, outputs the fault type and corresponding fault location of converter transformer. It can be stable in strong noise and complex working condition Characterization of the structural characteristics of multi-source signal, make full use of the physical topological relationship of converter transformer internal components, realize the high-precision identification and component-level positioning of composite fault and early latent fault, improve the accuracy and reliability of fault diagnosis.

An active noise control system, an active noise control method and an engineering vehicle. The active noise control method is used for noise control in a noise suppression area of an engineering vehicle, and comprises: obtaining at least two sets of original noise data of different sources of an engineering vehicle on the basis of the current working mode of the engineering vehicle; performing partial coherence analysis on each set of original noise data on the basis of the at least two sets of original noise data, so as to obtain respective sets of decoupled noise data with the influence of other sets of original noise data eliminated; generating active noise reduction signals corresponding to the respective sets of decoupled noise data; generating a sound-emitting device drive signal on the basis of the active noise reduction signals corresponding to the respective sets of decoupled noise data; and on the basis of the current working mode, sending the sound-emitting device drive signal to a noise-suppression sound-emitting device located in or near a noise suppression region, such that the noise-suppression sound-emitting device emits a sound wave for suppressing noise in the noise suppression region.

This invention discloses a robust method and related equipment for extracting human vital signs information. The method includes: acquiring raw radio frequency data collected by radar; preprocessing the raw radio frequency data to obtain a target data matrix; identifying multipath channels based on the target data matrix; performing parallel phase demodulation on each identified multipath channel to obtain a phase sequence for each multipath channel; performing coherence analysis on the signal components of all phase sequences; adaptively weighting and fusing the phase sequences based on the results of the coherence analysis to obtain a fused vital sign signal; and performing vital sign analysis based on the fused vital sign signal to obtain the target vital sign information. This invention effectively solves the key technical problem of weak vital sign signals being submerged in complex environments through the collaborative processing of multipath signals, enabling high-precision and highly robust non-contact vital sign monitoring, and can be widely applied in the field of radar vital sign monitoring technology.