101 results about "Coherence analysis" patented technology

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.

The noise online monitoring system comprises a multi-source sensing module, a positioning analysis module, a baseline generation module, a state association module and an execution control module. The multi-source sensing module synchronously collects target area parameter signals and generates noise identification signals through coherence analysis; the positioning analysis module receives the noise identification signal and generates a positioning signal containing a noise source position and a motion trail; the baseline generation module calls historical noise spectrum data of a corresponding scene according to a noise source position in the positioning signal to generate an adaptive noise baseline threshold signal; the state association module carries out space-time alignment on the baseline threshold signal and the equipment working condition data; the execution control module receives the positioning signal and the correlation degree signal. The noise on-line monitoring system can solve the problems that a traditional noise monitoring system is inaccurate in positioning, poor in base line adaptability and disjunction between noise and the equipment state.

The invention relates to the technical field of document processing, in particular to a knowledge graph-based document intelligent writing and analysis system, which comprises a graph dynamic updating module, an entity recognition and mapping module, a context connection analysis module, a semantic structure rearrangement module and a semantic coherence verification module. According to the method, the real-time data source is adopted, the knowledge graph is dynamically updated and expanded, synchronization of document content and the current information trend is ensured, noun phrases and entity distribution in a text are accurately analyzed through intelligent entity recognition, the accuracy of information extraction is improved, the coincidence degree of keywords between paragraphs is calculated, and synonymous entities are intelligently inserted, so that the information extraction efficiency is improved. According to the method, the context connection quality of the document is improved, the document structure is automatically rearranged according to the correlation of the content, the logic presentation of the information is optimized, the reading experience is enhanced, and the overall semantic consistency of the document is ensured and the interpretation ambiguity is reduced through complex semantic coherence verification.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention discloses an ultrasonic endoscope navigation system and method based on deep learning, and relates to the field of medical image analysis, and the system comprises a parallel encoder module which is used for extracting local features of an ultrasonic endoscope image through a CNN branch, and capturing global context information through a Transform branch; the channel attention fusion module is used for carrying out adaptive weighted fusion on the extracted features; a decoder module that generates an anatomical structure segmentation mask based on the fused feature; the time sequence processing module is used for receiving the frame-by-frame segmentation result output by the decoder module and realizing time sequence coherence analysis of the ultrasonic endoscope video through a bidirectional LSTM network; and the multi-modal fusion module is used for carrying out registration and fusion on the ultrasonic endoscope images. According to the scheme, a high-reliability artificial intelligence auxiliary tool can be provided for early screening of pancreatic cancer, meanwhile, the learning threshold and clinical application cost of the ultrasonic endoscope technology are reduced, and popularization of the ultrasonic endoscope technology in basic medical institutions is promoted.

The invention provides a fresh food quality degradation pre-judgment method and system based on multi-modal dynamic coupling, and aims to solve the problem of prediction lag caused by data isolation and dynamic coupling deficiency in the traditional storage and transportation process. Specifically, sensory layer data, environment layer data and operation layer data are synchronously collected through a multi-source sensor, chromatic aberration quantification, chemical bond feature extraction and spatial chemical fusion processing are carried out on the sensory data, and a quality degradation feature tensor is generated; based on a graph network model and frequency domain analysis, constructing a dynamic coupling parameter set of quality degradation characteristics and environmental parameters; fusing the loading and unloading pulse trajectory tensor, the cold chain stability distribution and the coupling parameter set, and generating a degradation constraint distribution matrix through multivariable weight mapping; and finally, combining acoustic resonance mode coherence analysis and constraint matrix evolution, and outputting a four-stage quality label. According to the method, through multi-dimensional real-time sensing, dynamic coupling modeling and hierarchical management and control, the fresh food storage and transportation loss is remarkably reduced, and the cold chain management efficiency is improved.

The invention discloses a BIM (Building Information Modeling)-based large-span post-tensioned bonded prestressed beam construction method. The BIM-based large-span post-tensioned bonded prestressed beam construction method comprises the following steps: collecting a vibration signal of a working rib and a matt rib reference signal of a matt rib; inputting the dummy rib reference signal into an adaptive filtering algorithm to which phase preserving constraint is applied, and performing noise cancellation processing on the working rib vibration signal to generate a purified pressure wave signal; performing multi-scale decomposition on the purified signal, extracting propagation time delay of the direct wave based on coherence analysis among components, and generating a phase-time delay characteristic matrix; and based on pre-configured geometric parameters of the BIM three-dimensional model, a real-time stress distribution field is calculated through inversion according to the phase-time delay characteristic matrix, and the real-time stress distribution field is synchronously updated to the BIM three-dimensional model. According to the method, the phase distortion problem under noise interference is solved through a physical reference channel and phase preserving filtering, the accuracy of time delay extraction is improved through a multi-path separation technology, and high-precision and high-robustness real-time monitoring of the prestress is achieved.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention relates to the technical field of bioinformatics and clinical medicine, and discloses a multi-omics data association analysis-based tumor early screening and typing early warning system, which comprises a data acquisition and preprocessing module for integrating standardized longitudinal multi-omics data; the dynamics and topology analysis module is used for generating topology fingerprints representing dynamic behaviors of the system through state space reconstruction and persistent coherence analysis; the causal inference and risk assessment module is used for calculating critical moderation indexes in parallel to synthesize risk indexes and constructing a dynamic causal network; and a collaborative diagnosis and report generation module. According to the system, risk indexes derived by critical moderation, topological fingerprints and a dynamic causal network are creatively combined, multi-modal information fusion is carried out through a collaborative diagnosis unit, and finally a comprehensive early warning report is generated. According to the invention, the accuracy and reliability of early risk early warning of tumors can be obviously improved, and a mechanism-level traceability basis is provided for clinical intervention.

The invention relates to an HPLC and HRF dual-mode communication fault detection method and system. The method comprises the following steps that original receiving signals of an HPLC channel and an HRF channel of an HPLC and HRF dual-mode communication module are collected and preprocessed; a composite coherence analysis model is constructed, and the composite coherence of the two channels is calculated through the preprocessed original receiving signals; constructing a composite channel difference model, and calculating the channel difference of the two channels through the preprocessed original received signals; constructing an instantaneous phase difference measurement model, and calculating the instantaneous phase difference of the two channels through the preprocessed original receiving signals; and integrating the composite coherence, the channel difference and the instantaneous phase difference of the two channels to obtain a fault index of the HPLC and HRF dual-mode communication module, presetting a fault index threshold, and when the fault index reaches the preset fault index threshold, judging that the HPLC and HRF dual-mode communication module has a fault.

The invention discloses a frozen soil temperature feature recognition algorithm and a moisture and electric field state prediction model, and the method comprises the steps: synchronously collecting temperature, moisture, resistivity and meteorological data through arranging a multi-depth sensor array, and constructing a multi-source data set with time-space alignment; utilizing continuous wavelet transform and cross-modal coherence analysis, and combining a hidden semi-Markov model to extract a depth-periodic characteristic profile; a time domain fusion Transform model embedded with physical constraints is adopted to realize frozen soil multi-parameter physical consistency prediction and uncertainty quantification; and finally, outputting a prediction result through a visual interface and setting an automatic alarm. The problem of cycle identification caused by weak and non-stable deep frozen soil signals is solved, the prediction accuracy and the physical rationality are remarkably improved, an end-to-end solution from data quality control to early warning is formed, and the reliability and the practicability of frozen soil engineering monitoring are greatly enhanced.

The invention discloses an AI-based gene data analysis system. The system comprises a plurality of omics data matrixes; local association pattern mining is performed on the multi-omics data matrix through a 1D-CNN one-dimensional convolutional neural network, a topological structure of a gene network is identified through continuous coherence analysis, dynamic weights are allocated to sequence features and a topological feature matrix by using a dynamic attention mechanism, and weighted multi-scale feature vectors are output; establishing a multi-modal fusion model based on a Transform architecture to fuse the multi-scale feature vectors, performing fine adjustment on the adaptive disease data set by using the general genome feature of a pre-training model, and outputting a fused feature vector; and inputting the fusion feature vector into an MLP multilayer perceptron for disease risk prediction, generating a disease risk prediction index in combination with an SHAP algorithm, and generating an auxiliary decision scheme according to the prediction index. And the accuracy and generalization ability of disease risk classification are effectively improved.

The invention discloses a method for predicting the residual life of an existing asphalt pavement structure, particularly relates to the technical field of computer simulation and data processing, and is used for solving the problem that the existing prediction method cannot efficiently represent the non-uniformity of an existing pavement material and the dynamic environment load influence on the premise of ensuring the precision. Existing damage data of a road surface are acquired to construct a simulation model reflecting a real initial state, material fatigue parameters are acquired in combination with cross-scale analysis, a dynamic environment boundary is driven by utilizing climate time sequence data, and then time-frequency coherence analysis is performed on a damage mode and a climate load to identify a high-threat working condition, so that the high-threat working condition is identified. And finally, preferentially operating high-fidelity thermal-mechanical coupling numerical simulation aiming at a high-threat working condition, and mapping damage evolution data output by simulation to a macroscopic performance degradation process so as to determine the residual fatigue life.

The invention discloses a fuzzy theory fused tongue diagnosis characteristic quantitative analysis method and system, and relates to the technical field of traditional Chinese medicine tongue diagnos.The method comprises the steps that a micro piezoelectric sensor array and a hyperspectral imaging module are integrated, tongue surface Young modulus distribution data and hyperspectral reflectivity data are synchronously collected, and a multi-mode tongue picture fusion data set is generated; further performing crack region segmentation and super-resolution reconstruction through a neural network model, outputting a crack network diagram and a tongue papilla form entropy, generating topological fingerprints in combination with continuous homology analysis, identifying a mechanical gradient mutation region and extracting a metabolic index, constructing a mechanical-metabolic associated feature set, and obtaining a mechanical-metabolic associated feature set; topological fingerprints and mechanical-metabolic characteristics are fused in a three-dimensional space-time coordinate system, a dynamic intuitionistic fuzzy decision field is constructed, finally, tongue picture pathology evolution risk indexes and a visual early warning report are output through a pathology prediction model, and the system provides quantifiable decision support for'prevention before illness' in traditional Chinese medicine.

The invention discloses a method and a system for quantifying water level and water quality unsteady-state response of a Tongjiang lake. The system comprises a time sequence multi-scale decomposition module, an interannual scale unsteady-state response quantification module, a water level change seasonal item grouping module and a seasonal scale unsteady-state response quantification module. The time sequence multi-scale decomposition module is used for decomposing a water level monthly scale time sequence into a trend term, a season term and an error term based on a seasonal trend decomposition method; the interannual scale unsteady state response quantification module calculates the time-varying dryness of the water level and the water quality in the interannual scale by using wavelet coherence analysis based on the trend term of the interannual scale, generates a time-frequency coherence map, and reveals the unsteady state response coherence intensity between variables; the water level change seasonal item grouping module groups the long-time sequences according to water level change seasonal items; and the seasonal scale unsteady state response quantification module performs wavelet cross transform on the grouped seasonal items, calculates a cross wavelet spectrum between the water level and the water quality, extracts an instantaneous phase difference of the seasonal scale, and quantitatively reveals a leading-lagging response rule between seasonal scale variables. According to the method, the complex interaction between the water level and the water quality can be effectively clarified, and the unsteady state response relation between the water level and the water quality is quantitatively analyzed through seasonal trend analysis, wavelet coherence analysis and wavelet cross transformation.

The invention discloses a defect repeated alarm screening method and system based on a twin network and topology analysis, and relates to the technical field of intelligent substations. The method comprises the steps of extracting image pair features through a twin network and generating matching potential energy; constructing an anti-environmental interference dynamic reference curved surface by using historical data; constructing a weighted coupling graph based on the potential energy residual error and the spatio-temporal context, and screening potential repeated alarm clusters through belief propagation; performing time sequence alignment and multi-dimensional consistency evaluation on intra-cluster alarms, and eliminating low-contribution noise nodes in combination with Shapley value game analysis; and finally, identifying a stable defect chain through topology persistent coherence analysis, selecting main alarms and screening out repeated alarms. According to the method, the problem of misjudgment caused by illumination change, equipment aging and complex interference is effectively solved, the recognition accuracy and the automation level are greatly improved, and the operation and maintenance rechecking workload is remarkably reduced.

The invention relates to the technical field of medical data visualization, in particular to an enteral nutrition data visualization method and system.The enteral nutrition data visualization method comprises the following steps that time periods are divided based on enteral nutrition records, an energy curve and a liquid track are extracted, synchronous abnormity is recognized, energy and liquid direction matching is analyzed, and nutrition distribution and frequency characteristics are extracted; determining an abnormal linkage set, evaluating a deviation degree, calibrating an abnormal level, adjusting a response list, screening a time period needing to be adjusted, and outputting a linkage instruction set. According to the method, the abnormal section is extracted according to the synchronous fluctuation of the energy distribution and the liquid input track in the time period, the abnormal trend identification precision can be improved, the abnormal capture is enhanced by combining directivity and boundary coherence analysis, and the nutrition imbalance area is screened through the combination of the component matrix and the frequency difference. Risk grades are divided based on the standard curve deviation degree, a response list is matched to generate an adjustment instruction set in a linkage mode, response efficiency is improved, and errors are reduced.

The invention discloses a high-precision identification method and system for a noise source of an asynchronous motor, and relates to the technical field of asynchronous motor detection, and the method comprises the following steps: constructing a finite element model of a to-be-analyzed asynchronous motor, dividing the surface into a plurality of detection sub-regions, and carrying out the operation simulation of the finite element motor, analyzing vibration speed and sound pressure signal data of each sub-region, synchronously acquiring actual signals, calculating relative differences with simulation data, evaluating detection priorities by combining historical fault times, forming a detection sequence according to the priorities, and sequentially performing sound-vibration coherence analysis and coherence function calculation to obtain a sound-vibration coherence detection result; according to the method, noise frequency bands are classified into structural noise and air noise, order tracking analysis is performed on the structural noise obtained through classification, an amplitude stability index and an amplitude change gradient are calculated, automatic classification and identification of electromagnetic noise and mechanical noise are realized based on a preset threshold value, and the efficiency and accuracy of fault processing are remarkably improved.

The invention discloses a federated learning-based cross-regional flood risk prediction management system and method, and relates to the technical field of flood risk prediction, and the system comprises a cross-regional data collection module which collects multi-source flood observation data in a distributed manner and adds a credibility label; the coherence analysis clustering module is used for calculating coherence correlation coefficients among the monitoring stations based on the weighted Pearson's correlation coefficients and carrying out topological consistent clustering by combining physical connectivity and relation stability; the joint prediction analysis module is used for training a flood risk prediction model by adopting a federal learning mode based on a clustering result, fusing a statistical channel and a physical channel to carry out joint prediction, and judging and optimizing simulation input through a dominant boundary; and the decision write-back module is used for generating multi-level early warning and targeted scheduling suggestions according to the predicted value and writing back execution data to realize closed-loop optimization. The problem of insufficient flood prediction precision under cross-regional data isomerism and privacy protection is solved, and accurate, efficient and safe cross-regional flood risk prediction management is realized.

The invention belongs to the field of intelligent supervision, and discloses an intelligent engineering supervision method and system based on artificial intelligence, and the method comprises the steps: extracting three-dimensional field frequency features based on a multi-modal data package and a coordinate mapping table, carrying out the fusion through combining with a multi-modal graph neural network, and generating multi-modal fusion features; a VR complex is constructed based on the multi-modal fusion features and the coordinate mapping table, and a three-dimensional topological feature matrix is generated through continuous homology analysis; constructing a defect detection model, and obtaining defect positioning coordinates and engineering structure risk scores according to the generated three-dimensional topological feature matrix in combination with the multi-modal fusion features and the coordinate mapping table; and dynamically adjusting construction parameters according to the engineering structure risk score, and generating an engineering supervision report. According to the method, the VR complex is constructed by reducing the sampling point cloud, and the continuous homology analysis is performed, so that the topological invariant quantitative evaluation of the engineering structure microdefects is realized, and the supervision process has stronger real-time response capability.

The invention discloses a transformer winding vibration abnormity diagnosis and early warning method based on lightweight edge calculation, and relates to the technical field of transformer monitoring. According to the method, vibration signals and magnetic flux signals of a transformer winding are collected in real time at edge nodes, and a basic data set is obtained after preprocessing; vibration and magnetic flux features are extracted to construct a basic diagnosis feature vector, a three-dimensional tensor is constructed through directional coherence analysis, a boundary enhancement structure map is generated, and a structure enhancement fusion feature vector is formed after fusion. The method comprises the following steps of: realizing abnormal grade and trend prediction by a lightweight Transform diagnosis model based on knowledge distillation and parameter compression optimization, and triggering model adaptive updating by combining risk index modeling and a dynamic risk threshold value. And finally, calculating an abnormal level posterior probability through a Bayesian updating rule, and outputting multi-level early warning. The method has the advantages of high precision, low calculation overhead and self-adaptive capability, and is suitable for online monitoring and intelligent operation and maintenance of the state of the transformer.

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.

The invention belongs to the technical field of millimeter-wave radar target detection and signal processing, and particularly discloses a millimeter-wave radar multipath target suppression method in a tunnel environment, and the method comprises the steps: building a geometric position incidence relation between a real target and each-order multipath target in a distance-Doppler graph through employing a millimeter-wave radar echo signal in a tunnel; on the basis, coherence analysis and energy consistency verification are sequentially carried out on candidate signal point sets meeting geometric constraint conditions, so that real target echoes and multi-path interference signals are effectively distinguished, and the judgment accuracy of the multi-path signals is remarkably improved. Meanwhile, after a real target and a multipath signal are distinguished, a multipath signal space distribution estimation graph is constructed by utilizing the identified space position and amplitude information of a multipath signal point, and multipath signal components are eliminated from an original distance-Doppler graph according to the graph and a subtraction proportion, so that multipath interference is directly suppressed in a signal processing stage.

The invention relates to the technical field of bridge health monitoring, and discloses a real-time anomaly prediction and fault diagnosis method and system for a bridge component, and the method comprises the steps: collecting the real-time data and historical data of the bridge component, carrying out the modal parameter recognition of the real-time data, so as to obtain a modal parameter set of the bridge component, constructing a three-dimensional feature tensor of the bridge component based on the real-time data; and performing multi-head self-attention mechanism operation on the three-dimensional feature tensor to obtain a space-time correlation feature tensor of the three-dimensional feature tensor, performing correlation analysis on the space-time correlation feature tensor based on the historical data to obtain a real-time space-time feature tensor of the three-dimensional feature tensor, and storing the real-time space-time feature tensor of the three-dimensional feature tensor. And performing frequency domain spectral coherence analysis on the real-time spatial-temporal feature tensor and the modal parameter set to obtain a spectral coherence coefficient of the bridge component, and the method can perform accurate prediction and diagnosis on the abnormal event of the bridge component.

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 invention discloses a wavelet packet space coherence-based evaluation method for the curative effect of microacupotomy treatment on upper limb spasm. The method comprises the following steps of S1, collecting and preprocessing electroencephalogram signals and electromyographic signals of a post-stroke upper limb spasm testee; s2, performing time decomposition and frequency decomposition on the preprocessed electroencephalogram signal and the preprocessed electromyographic signal by utilizing wavelet packet transformation, and respectively generating an electroencephalogram time-frequency sub-band signal and an electromyographic time-frequency sub-band signal; s3, based on spatial regularization coherence analysis, establishing linear and nonlinear relationships between the electroencephalogram time-frequency sub-band signals and the myoelectricity time-frequency sub-band signals; s4, constructing a WPCOH algorithm in combination with wavelet packet transformation and spatial regular coherence analysis, and capturing a dynamic coupling relationship between the electroencephalogram time-frequency sub-band signal and the myoelectricity time-frequency sub-band signal at different time points and frequency bands; s5, extracting key features of the WPCOH algorithm according to the dynamic coupling relationship; the treatment effect is evaluated by comparing the changes of the key features before and after the microacupotomy treatment.

The invention relates to the technical field of artificial intelligence, and particularly provides a sentence-level illusion detection method, system and equipment for a long text and a storage medium, and the method comprises the following steps: carrying out data cleaning and standardization on the input long text, dynamically determining a sentence boundary based on semantic coherence analysis, and dividing the sentence boundary into a sentence sequence; a hierarchical context encoder is then used to generate a context enhanced semantic representation for each sentence fusing local and global information. On the basis of the representation, feature vectors are extracted from three dimensions of semantic anomaly in sentences, context coherence mutation between sentences and overall topic path deviation. And finally, inputting the feature vectors into a classifier to obtain the illusion probability of each sentence, and calculating a confidence score. According to the method, the defects of weak context modeling and insufficient coherence analysis in a long text in a traditional method are overcome, and the credibility and practicability of the generated content are remarkably improved.

The invention relates to the technical field of signal processing, and discloses an anti-electromagnetic interference processing method and system for a sensor, and the method comprises the steps: carrying out the multi-stage electromagnetic shielding processing of an original mixed signal, and obtaining an initial anti-interference signal; based on the high-order statistical characteristics of the initial anti-interference signal, performing adaptive blind source separation on the initial anti-interference signal to obtain a vibration response signal and an electromagnetic interference reference signal; performing time-frequency domain transformation on the vibration response signal, and identifying a dominant vibration mode component from the transformed vibration response signal; performing coherence analysis on the dominant vibration mode component and the electromagnetic interference reference signal to obtain a coherence coefficient matrix; based on the coherence coefficient matrix, performing cancellation filtering on the dominant vibration mode component and the electromagnetic interference reference signal to obtain an anti-interference vibration signal; performing phase delay compensation on the anti-interference vibration signal to obtain a final anti-interference vibration signal; the anti-electromagnetic interference efficiency of the sensor can be improved.

The invention relates to the technical field of communication, discloses an electromagnetic spectrum abnormal signal monitoring method and system based on double acquisition and comparison, and aims to solve the problems of asynchronous double-channel sampling and high abnormal signal omission ratio and false alarm rate caused by clock drift and physical path delay in the prior art. According to the method, a hardware-level self-adaptive clock synchronization compensation circuit is constructed, clock path delay is quantized and offset in real time before analog-to-digital conversion, and a first sampling clock and a second sampling clock which are accurately synchronized are generated; a dual-channel ADC is used for synchronously collecting the same radio frequency signal, and abnormal signals are judged and identified through spectrum coherence analysis and differential spectrum threshold. The system comprises a main clock unit, a self-adaptive synchronous compensation circuit, a dual-channel ADC module and a data processing comparison module. The picosecond-level synchronization precision is achieved, external time service is not needed, the missing detection and false alarm rate is remarkably reduced, and the method is suitable for high-reliability frequency spectrum monitoring in the complex electromagnetic environment.

The invention relates to the technical field of big data analysis, in particular to a financial risk early warning system driven by big data. The system comprises a data acquisition module, a feature topology construction module, a continuous coherence analysis module, a topology risk quantification module and a risk early warning module. The data acquisition module acquires time series financial data from a plurality of enterprise data sources; the feature topology construction module maps financial indexes into a high-dimensional space point cloud and constructs a simple complex; the continuous coherence analysis module generates a continuous coherence bar code through multi-scale coherence group calculation; the topological risk quantification module calculates the weighted life cycle of the bar code and converts the weighted life cycle into a topological invariant; and the risk early warning module generates an early warning signal by comparing the difference between the current topology invariant and the historical mode. According to the method, deep structure features in financial data can be effectively captured, and the accuracy and timeliness of risk early warning are improved.