124 results about "Source localization" patented technology

The invention discloses an anomaly detection and intelligent diagnosis method, system and device based on digital power grid multi-source data and a medium, and belongs to the technical field of anomaly detection, and the method comprises the steps: obtaining multi-source operation data from a power grid operation process, carrying out the preprocessing, and generating a standardized data set; time sequence features are extracted based on historical data, a power grid state reference model is constructed, and normal operation states in different load scenes are represented; on the basis of deviation calculation of the standardized data set and the power grid state reference model, abnormal candidate signals are detected, and high-confidence-coefficient abnormal signals are screened and generated; determining an abnormal source based on the high-confidence abnormal signal in combination with a power grid topological structure, and performing analysis to obtain fault type information; and generating a control instruction according to the fault type information and issuing the control instruction to a power grid control system. According to the method, a complete technical scheme of multi-dimensional data fusion, dynamic deviation detection, high-confidence anomaly screening, anomaly source accurate positioning and fault type rapid diagnosis is realized.

A wide-band oscillation wide-area monitoring system and a wide-band oscillation source localization method, which relate to the technical field of power system stability. The wide-band oscillation wide-area monitoring system comprises: a plurality of signal sensing modules, configured to acquire electrical signals from measured points; a plurality of wide-band measurement devices connected to the signal sensing modules, each wide-band measurement device being configured to perform spectrum analysis on the basis of electrical signals acquired by each signal sensing module, and generate oscillation alarm information on the basis of the spectrum analysis result, and the wide-band measurement devices being further configured to: in response to a waveform recording instruction, start to carry out waveform recording to generate waveform recording files for a same time section, and upload the waveform recording files and the spectrum analysis results; and a wide-band measurement master station in communication connection with each wide-band measurement device, the wide-band measurement master station being configured to: on the basis of the oscillation alarm information uploaded by each wide-band measurement device, determine whether a wide-band oscillation risk is present for a power system, and if so, send the waveform recording instruction to all of the wide-band measurement devices, and on the basis of the spectrum analysis result fed back by each wide-band measurement device, perform oscillation source localization.

The invention relates to the field of trajectory tracking, and discloses a gun trajectory tracking system and method fused with multi-source localization, the system comprises a satellite positioning module, an RFID node network, an inertial measurement unit (IMU) and a processing unit, the processing unit determines an open area, a semi-shielded area or a full-shielded area in real time through an environment classification sub-module, based on a dynamic weight distribution strategy, the fusion proportion of the satellite data, the RFID data and the I MU data is adjusted; synthesizing multi-source positioning data by adopting a weighted fusion algorithm, and outputting a real-time gun trajectory; and when the RFID node is located in a full-shielding area, the topological optimization calibration sub-module is combined with the RFID node topological relation to correct the accumulated error of the I MU, and weight distribution is dynamically fed back and adjusted based on the calibration error. According to the method, the problems of track interruption and drifting caused by failure of a single positioning source or inertial navigation accumulative errors in a complex shielding scene are solved, and high-robustness continuous tracking of gun tracks in multiple scenes such as cities, fields and indoors is realized.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

An autonomous system for detecting, localizing, and potentially deactivating chemical threats or emissions using multiple sensing modalities and reinforcement learning techniques. The system includes visual sensors (e.g., RGB, RGBD, LIDAR), non-visual sensors (e.g., gas concentration, airflow, GPS, RADAR), a neural network architecture and processor to fuse information from different sensors, a module based on deep reinforcement learning for decision making, and a robotic interface for executing actions. The neural network extracts relevant information from sensor streams and encodes them into a joint embedding space. The module considers the current observations, historical data, and previous actions to determine the optimal action for threat localization under partially observable conditions. The system is trained in simulated environments to minimize source localization time while accounting for various constraints. The autonomous system enables effective chemical threat detection and source localization in complex, dynamic environments without endangering human operators.

The invention provides a high-precision source positioning method suitable for a cross-scale complex rock mass medium. The high-precision source positioning method comprises the following steps of construction of a homogenized reference frame, generation of a linear equation set, dynamic weight estimation, matrix truncation decomposition for enhancing stability, and output of sound source coordinates, wave velocity and trigger time. According to the method, error transmission of a single sensor is eliminated through a homogenized reference frame, noise interference is suppressed through dynamic weight estimation, the problem of ill-conditioned matrix inversion is solved in combination with matrix truncation decomposition, geometric limitation of a sensor array is broken through, and finally high-precision and high-stability cross-scale acoustic emission source positioning is achieved. Reliable technical support is provided for complex engineering environments such as a dynamic velocity field, random sensor layout and multi-scale monitoring requirements.

The invention provides an atmospheric particulate high-value pre-judgment method and equipment based on deep learning. A pollution condition dynamic graph is generated based on multi-source data fusion, a dynamic transmission model and a visualization technology, the time evolution of a pollution propagation path is displayed, the coverage area is dynamically adjusted along with the change of a meteorological field, and the possible PM2.5 high-value time, place and intensity within 1-24 hours in the future can be effectively predicted. The method breaks through the obvious limitations of pollution source positioning and early warning in space coverage and time resolution, achieves the quick and accurate positioning of the pollution source, and meets the business demands and daily management demands of the environmental protection supervision fields of counties, villages, streets and the like.

The invention discloses a multi-snapshot Newton orthogonal matching pursuit sound source localization method under related Gaussian noise, and the method employs an autoregression model to estimate the inversion of a noise covariance matrix, carries out the pre-whitening processing of a received signal, and remarkably improves the robustness of a system under the background of related Gaussian noise. Secondly, obtaining initial incident angle estimation by utilizing coarse-grained search, and refining an angle estimation value through Newton iteration so as to improve estimation precision; and further eliminating the optimized angle and the corresponding signal component by using an orthogonal matching pursuit principle, and calculating a residual error. And the iteration stop opportunity is determined according to the self-adaptive termination criterion based on the overestimation probability threshold value, the finally detected angle number is the corresponding actual sound source number, and the limitation that the sound source number needs to be preset or the threshold value is set unreasonably in a traditional method is avoided. According to the method, the multi-source positioning precision under a complex noise background is remarkably improved on the premise of keeping relatively low calculation complexity, and the method has a wide engineering application prospect.

The invention provides a sound emission source positioning method and system based on a hybrid model, and relates to the technical field of sound source localization, and the method comprises the steps: calibrating the angle range and sound velocity value of each sound velocity partition; calculating a theoretical receiving time difference between the reference acoustic emission receiver probe and other acoustic emission receiver probes for receiving acoustic emission sources at different grid points; acquiring an actual receiving time difference; calculating a plurality of error values between the actual receiving time difference and theoretical receiving time differences corresponding to different grid point acoustic emission sources; the preset proportion error value is reserved, the reserved error value is clustered, and the position of a first prediction acoustic emission source of the target to be detected is positioned; inputting the current actual receiving time difference into the random forest model, and outputting a second predicted acoustic emission source position of the to-be-detected target; and verifying the comprehensive confidence of the first predicted acoustic emission source position, weighting the first predicted acoustic emission source position and the second predicted acoustic emission source position, and outputting the target predicted acoustic emission source position of the target to be detected.

The invention relates to the technical field of communication interference detection, and particularly discloses an interference detection method based on an OFDM communication system, and the method comprises the steps: S1, signal preprocessing, S2, time-frequency joint analysis, S3, deep learning model construction, S4, multi-interference source positioning, S5, intelligent interference suppression, and S6, interference information feedback. According to the method, Gaussian noise and non-Gaussian interference can be effectively distinguished through a third-order cumulant and other high-order spectrum analysis method, nonlinear features of complex signals can be captured, and the detection accuracy of complex interference is remarkably improved; the combination of the convolutional neural network and the long short-term memory network can automatically extract complex features in interference signals, provide more accurate interference type classification, utilize a beam forming algorithm and a multi-antenna array technology to accurately position a plurality of interference sources, and through a space-time joint estimation method, under the condition of facing the plurality of interference sources, improve the accuracy of the interference sources. And the positioning precision is obviously improved.

The invention relates to the field of hydraulic structures, in particular to a three-dimensional damage rapid positioning method for a complex aqueduct structure. Acoustic emission or microseismic signals in the aqueduct structure are collected, and the first arrival time of the signals is picked up through an STA / LTA long and short time window energy ratio method; constructing a curved surface structure model composed of a groove surface and a side wall; parameterizing the cross section, and constructing a groove surface propagation path set according to the position relationship between the damage point and the sensor in a parameter space; for each pair of damage points and sensors, selecting a path with the minimum groove surface path length as a shortest propagation path, and calculating theoretical arrival time according to the shortest propagation path; establishing a residual function between the theoretical arrival time and the actually measured arrival time, and constructing a jacobian matrix of arrival time change; and solving the coordinates and occurrence time of the damage source. According to the method, the parameterized model of the aqueduct section is established, traditional ray tracing is replaced with the curved surface shortest path thought, and the numerical difference and rapid iteration methods are combined, so that efficient and robust damage source positioning is achieved.

The invention discloses a radiation source positioning method based on multi-agent deep reinforcement learning, and belongs to the field of unmanned aerial vehicle sensing positioning. The method comprises the following steps: high-precision sensing and positioning of a radiation source are realized through an independent decision-making mechanism and a multi-unmanned aerial vehicle cooperative positioning method; according to the method, an intelligent agent and environment interaction mechanism is designed, a distributed Actor and a'center + local 'type Critic network architecture are adopted, the emergency degree, the energy state and the environment complexity of a target are comprehensively evaluated in combination with a dynamic weighting function, and dynamic optimization of decisions is achieved; a ternary loss structure is introduced through an improved CLIP mechanism, and violent fluctuation of a decision is prevented; task-specific knowledge is integrated in the reward function design, and the unmanned aerial vehicle is guided to explore an abnormal radiation source; by designing an RSSI-based multi-dimensional reward and punishment mechanism and a three-point positioning cooperation strategy and through organic combination of a distance dynamic reward function and a three-point positioning reward mechanism, the continuous tracking capability of the unmanned aerial vehicle to a target is ensured, and accurate positioning is realized.

The invention discloses a wide-area oscillation source positioning method and system based on physical feature embedding and a graph attention network, and the method comprises the steps: obtaining synchronous phasor data of a power system, carrying out the modal parameter identification of subsynchronous oscillation and / or super-synchronous oscillation based on the synchronous phasor data, constructing a topological graph of the power system, and carrying out the positioning of a wide-area oscillation source. And constructing the oscillation amplitude, the attenuation factor and the phase of each node obtained by identification into a feature vector of each node in a topological graph, inputting the topological graph and the feature vector into a graph attention network, and outputting an oscillation source positioning result, so that high-frequency recording data after an accident does not need to be waited, and the positioning accuracy of the oscillation source is improved only on the basis of synchronous phasor data. The identified modal parameters are used as physical characteristics to be embedded into the graph attention network, and the interpretability of a physical mechanism and the graph structure mining capability of deep learning are fused, so that subsynchronous oscillation and super-synchronous oscillation are accurately positioned.

The present disclosure provides an interference source localization method and apparatus, a storage medium, and a program product. The interference source localization method comprises: sending a sensing reception configuration to a first network node; receiving interference measurement information fed back by the first network node and determined on the basis of the sensing reception configuration; and determining, on the basis of the interference measurement information, whether an interference source is present, and localizing the interference source.

The invention provides a multi-sensor fusion positioning method and device of a vehicle, equipment and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring different and independent multi-source positioning information through a plurality of sensor sub-modules; sorting the multi-source positioning information according to the size of the observation timestamps, and screening the multi-source positioning information based on a preset time delay window; performing filtering fusion calculation on the reserved multi-source positioning information to obtain first positioning state data after time delay; performing time adjustment on the time-delayed first positioning state data by using the motion observation information to obtain second positioning state data at the current system moment; and post-processing the second positioning state data to obtain stable and smooth third positioning state data.

The invention discloses a point source high-risk gas leakage rapid and accurate traceability positioning method and system, and relates to the technical field of gas leakage source localization, and the method comprises the steps: determining a mesoscale turbulence diffusion model according to on-site geographical environment information and high-risk gas characteristics, the position of the virtual monitoring point is optimized by verifying the validity of the virtual monitoring point in an actual scene; and then batch numerical simulation is carried out to obtain an associated data set, a neural network model is trained according to the associated data set, a leakage source positioning model is obtained, the predicted actual leakage source position is output according to the gas concentration collected by each actual monitoring point, and accurate traceability of the leakage source is realized. According to the scheme, the diffusion phenomenon is accurately simulated through the optimized diffusion model, so that the obtained data is more practical, and the traceability precision is indirectly improved; and based on a machine learning method of an artificial neural network, the correlation between the concentration distribution and the leakage source position is established, and accurate and rapid traceability positioning of the leakage source is realized.

The invention relates to an optimized iterative shrinkage threshold generalized inverse beam forming noise source positioning method, belongs to the technical field of underwater detection, and solves the problems of wide main lobe and poor positioning precision of underwater target noise source positioning based on a sound source sparse problem and a generalized inverse beam forming method at present. The method comprises the following steps: constructing an L1 norm constraint generalized inverse beam forming objective function according to the sparsity of a noise source, and solving the objective function by using an iterative shrinkage threshold algorithm; constructing a regularization matrix according to a result obtained by the first iteration of the iterative shrinkage threshold algorithm in the previous step; and using the regularization matrix obtained in the previous step to construct a new objective function of L1 norm constraint generalized inverse beam forming, and solving the new objective function by using an iterative shrinkage threshold algorithm to obtain a positioning result of the noise source. According to the method, high-precision positioning of the underwater target noise source can be realized, the noise source identification precision and the spatial resolution are improved, and the method has a good engineering application prospect.

The invention discloses a hydrophone array multi-sound-source positioning method based on a frost ice optimization algorithm, and belongs to the field of sound source positioning. According to the method, a linear hydrophone array is constructed, an underwater sound source positioning scene is set, and a sound source signal is collected and processed to obtain a receiving signal; a MUSIC algorithm is used to process the received signal, a spatial spectrum is generated to preliminarily estimate information such as an azimuth angle and a pitch angle of a sound source, normalization processing is carried out on the spatial spectrum, a gravitational force area and a repulsive force area are divided, and a gravitational force-repulsive force type artificial potential energy field is constructed; an independent sub-population is initialized for each sound source by adopting a frost ice RIME optimization algorithm, individual positions are updated in combination with an artificial potential energy field and a frost puncture mechanism in an iterative updating process, and particles with the highest fitness are selected as final estimation positions through iterative search. The method overcomes the limitation of a traditional optimization algorithm, remarkably improves the precision and reliability of sound source positioning, and is particularly suitable for a multi-sound-source positioning scene in a complex underwater sound environment.

This invention discloses a GNSS interference source localization method based on ADS-B data. The method involves acquiring ADS-B data, saving data points affected by GNSS interference, and, if no NIC is available, constructing ADS-B data with a NIC. The message data is then sorted by ICAO and timestamp. A diagonal matrix W is calculated based on the ICAO, timestamp, NIC, a table showing the correspondence between NIC and received interference power, and the hyperparameter value τ. A residual vector R is calculated based on the message data and the optimization vector, and the optimized vector is then output. This invention analyzes large-scale aviation ADS-B data, sorts and classifies the original message data to determine whether flights are subject to GNSS interference, and abstracts the interference source localization problem into a least-squares problem. An optimization function is constructed, and an iterative optimization algorithm is used to calculate the possible GNSS interference source locations and transmission power, thus more efficiently locating GNSS interference sources.

The invention relates to the technical field of electroencephalogram signal monitoring and processing, in particular to a user category determination method based on an auditory brainstem reaction normal form and source localization, and the method comprises the following steps: obtaining auditory brainstem reaction data and electroencephalogram data when a target user is stimulated by external sound; based on a feature extraction module, feature extraction is carried out on source estimation data corresponding to the auditory brainstem response data and the electroencephalogram data; performing feature fusion on the basis of the features extracted from the auditory brainstem reaction data and the features extracted from the source estimation data, and determining fusion features; and determining the category of the target user based on the fusion features and a user category prediction model. According to the scheme for carrying out early diagnosis and screening on the autism infants based on the auditory brainstem reaction data and the electroencephalogram data, early autism (ASD) screening of the infants can be finally achieved under the support of the electroencephalogram equipment and auditory brainstem reaction testing.

The present invention discloses a method and system for tracing the source of broadband power grid oscillations based on a modal dynamic modulation graph network. The method of the present invention includes inputting the oscillation data sequence of the power grid and the power system graph into the modal dynamic modulation graph network to obtain the oscillation source localization result based on the power system graph. The processing of each level of the modal dynamic modulation graph module in the modal dynamic modulation graph network includes: modulating the oscillation data sequence through a modal modulator and then performing eigenmode decomposition; extracting key features through convolutional layers and pooling layers; performing dynamic modulation based on a Prony dynamic modulator and segmenting feature blocks; extracting key features through convolutional layers and pooling layers; extracting node features through a graph network; and using a classifier for the node features to achieve the tracing of the broadband power grid oscillations. The purpose of the present invention is to reduce the interference of other information on the broadband oscillation information, dynamically analyze the data from both the time and space dimensions, and quickly and accurately achieve the localization of the broadband oscillation source.

The present application discloses an adaptive sound pickup method and system based on multi-source localization. The method includes: extracting features from the collected multi-source sound generation data to obtain a comprehensive feature vector set; inputting the comprehensive feature vector set into a preset sound source localization model for processing to obtain target sound source features; according to the target sound source features, obtaining the optimal sound pickup device parameters in the current environment through a preset sound pickup strategy prediction model; and setting the adjustable parameters of the sound pickup device according to the optimal sound pickup device parameters to perform sound pickup. The present application accurately locates the target sound source when multiple sound sources are generating sound simultaneously, and sets the relevant parameters of the sound pickup device according to the information of the target sound source, so as to achieve more accurate and rapid sound pickup.

This invention discloses a broadband oscillation disturbance source localization method based on compressed sensing and graph convolutional neural networks (GCNs), comprising two stages: offline training and online localization. In the offline training stage, electrical quantities under subsynchronous / supersynchronous broadband oscillation modes are obtained using measurement data from actual systems or simulation examples, constructing a broadband oscillation offline sample library. Compressed sensing is used to encode and compress the electrical quantities in the offline sample library, obtaining a training set and a test set. The constructed GCN localization model is trained using the training set until the test set reaches the target localization accuracy, resulting in a broadband oscillation localization model. In the online localization stage, a substation collects electrical quantity data and compresses and encodes it using compressed sensing technology; the substation data is uploaded to the master station; at the master station, the feature matrix and system adjacency matrix are input into the trained GCN localization model, and the oscillation source location is output. This invention's method is applicable to broadband oscillation disturbance source localization under various operating conditions.

The present invention discloses a method for positioning a spaceborne radar radiation source considering ionospheric correction. An observation equation of a reference station considering only the ionospheric influence is established. The double-difference equation is obtained by pairwise subtraction of the reference station observation equations, thereby eliminating the receiver clock error of the reference station and the receiver clock error of the navigation spaceborne transceiver, and obtaining the double-difference ionospheric delay corresponding to the corresponding sub-reference station. The double-difference ionospheric delays of each sub-reference station are combined with the approximate coordinates of the virtual observation station to obtain the double-difference ionospheric delays of each sub-navigation spaceborne transceiver corresponding to the virtual observation station. Then, short baseline solution is carried out in combination with the coordinates of the radiation source target without ionospheric correction to complete differential positioning and obtain the coordinates of the radiation source target after eliminating the ionospheric delay. The present invention uses the VRS method model to eliminate the influence of the ionosphere, and the positioning result has higher accuracy.

The invention discloses a deep learning brain power supply positioning method and system based on residual error iteration. The method comprises the following steps: S1, acquiring a current electromagnetic signal; s2, acquiring a preliminary source positioning result based on the current electromagnetic signal; s3, obtaining a sparse source positioning result based on the initial source positioning result; s4, obtaining a maximum intensity activation source based on a sparse source positioning result; and S5, updating the current electromagnetic signal, repeating the steps S2-S5 until a preset condition is met, and obtaining a final source positioning result. The sLORTA is used for preliminary source positioning, and a rough brain source positioning result is provided. Then, a neural network is used for repairing a sLORTA result to recover a sparse source at an accurate position, and the intensity of an activation source is recovered as much as possible. According to the method, the rapid calculation and preliminary positioning capability of the sLORTA and the high-precision repair capability of the neural network are integrated, so that a more accurate and more stable result is realized in source positioning.

This invention discloses a non-cooperative signal localization method based on radiation source decoupling, belonging to the field of wireless signal processing and spatial positioning. The method includes: transforming multi-source joint localization into multiple independent single-source localization problems by constructing a source-level decoupling theoretical framework. First, the method utilizes multiple receiving nodes to acquire mixed signals; then, it uses a mask-based separation network (MS-Net) to perform blind source separation on the mixed signals, obtaining the decoupled components of each radiation source. This network does not require prior information such as pilot signals; finally, for each decoupled component, a broadband Doppler model considering time-scale effects is used for direct single-source localization. This invention significantly reduces computational complexity, improves model adaptability and localization accuracy in high-speed motion scenarios, and has good engineering application prospects.

The application discloses a multi-modal deep learning source tracing method and system combining magnetoencephalogram and electroencephalogram, relates to the field of artificial intelligence and neuroimaging technology, and inputs real magnetoencephalogram signals and electroencephalogram signals into a source tracing model after preprocessing, predicts the probability of the occurrence of corresponding regional sources, combines an imported source partition distance matrix, and obtains a final source tracing result; the training process of the source tracing model is as follows: a generative adversarial network is constructed to generate a multi-modal neuroelectrophysiological data set; the multi-modal neuroelectrophysiological data set is input into a double-branch structure residual network to perform stage hierarchical extraction and decoupling on the magnetoencephalogram signals and the electroencephalogram signals respectively; a multi-scale convolution module is used to extract features at different stages, the extracted features are input into a classifier after being fused, and a loss function is defined to update trainable parameters of the source tracing model; and the source tracing method improves the accuracy and generalization ability of source positioning.

The application discloses a kind of deep learning brain electromagnetic source positioning method and system based on residual iteration, comprising: S1, current electromagnetic signal is obtained;S2, based on current electromagnetic signal, obtain preliminary source positioning result;S3, based on preliminary source positioning result, obtain sparse source positioning result;S4, based on sparse source positioning result, obtain maximum intensity activation source;S5, update current electromagnetic signal, repeat S2-S5 until reach preset condition, obtain final source positioning result. Carry out preliminary source positioning using sLORETA, provide a more rough brain source positioning result. Then the result of sLORETA is repaired using neural network, to restore the sparse source of accurate position, and as far as possible restore the intensity of activation source. The application comprehensively sLORETA's fast calculation and preliminary positioning ability, and the high-precision repair capability of neural network, so as to realize more accurate, more stable result in source positioning.

The present invention discloses a method for locating a forced oscillation source based on a wavelet dissipation energy spectrum, which includes: performing wavelet transform on the electrical variation quantities of a generator to obtain a wavelet coefficient matrix of each electrical variation quantity; calculating the wavelet dissipation energy spectra of each generator at each wavelet coefficient matrix, and positioning the key scale wavelet coefficients strongly related to forced oscillation according to the variation trend of the wavelet dissipation energy spectra at each wavelet scale coefficient; extracting the wavelet dissipation energy spectra corresponding to each generator at the key scale wavelet coefficients; arranging the wavelet dissipation energy spectra of n generators in a columnar manner, and locating the oscillation source according to the proposed criterion for locating a forced oscillation source in a power system based on the wavelet dissipation energy spectrum; and outputting the result of locating the forced oscillation source. The present invention not only inherits the characteristic of the traditional DEF that it can locate the forced oscillation source without a detailed system model and component parameters, but also avoids the need for signal reconstruction processing of the required electrical quantities in the traditional time-domain DEF, thereby improving the efficiency of locating the forced oscillation source.