7053results about "Using wave/particle radiation means" patented technology

Disclosed is a three-dimensional deformation field modeling method fusing global navigation satellite system (GNSS) / synthetic aperture radar interferometry (InSAR) observation data. The three-dimensional deformation field modeling method includes: five parts of solving an InSAR line-of-sight velocity field, unifying a spatial reference datum of the GNSS data and the InSAR data, verifying precision of a result of the InSAR data, solving a three-dimensional deformation field having high precision based on a least square method, and analyzing and verifying a result. According to the method, the InSAR data is processed according to a high-precision time series method, a long-wavelength error of the InSAR data is corrected on the basis of a difference of two data spatial reference datums, and the accuracy of the result is ensured through verification of internal coincidence precision.

The invention provides a ground-based radar landslide monitoring and early warning method and system, and relates to the technical field of landslide monitoring, and the method comprises the steps: collecting a monitoring signal, and carrying out the filtering processing; extracting an instantaneous phase and performing wavelet packet decomposition; enhanced features are obtained through morphological filtering; constructing a monitoring feature vector; carrying out nonlinear dimension reduction on the characteristic time sequence matrix; obtaining an evolution trajectory by using a fractional order adaptive neural network; constructing a hierarchical risk reasoning framework; a risk metric value is obtained through multi-source information fusion; and carrying out dynamic evaluation by adopting a fuzzy evolution algorithm and determining an early warning level. The method can improve the accuracy and real-time performance of landslide monitoring and early warning.

The invention provides a color steel plate coating flatness evaluation method and system based on artificial intelligence. According to the method, the three-dimensional point cloud data is generated by collecting the interference fringe image, and the surface fluctuation characteristics are quantified; capturing a multi-dimensional vibration spectrum of the transmission roller shaft, generating a servo motor compensation control signal, driving a multispectral scanning head to perform reverse displacement compensation, and generating real-time compensation data; inputting the surface topography features in the three-dimensional point cloud and the real-time compensation data into a lightweight convolutional neural network, and outputting fusion features; and dynamically classifying and identifying surface defects and uneven areas based on the fusion result, adjusting a classification threshold in combination with the speed of the production line, outputting a flatness evaluation result, and synchronizing the flatness evaluation result to a speed regulation system of the production line to realize closed-loop optimization. According to the method, laser interference, vibration compensation and lightweight AI technologies are fused, dynamic high-precision evaluation of the surface flatness of the color steel plate of the high-speed production line is achieved, and the problems of defect misjudgment and measurement distortion caused by vibration interference are solved.

The invention provides a slope deformation monitoring method and device based on multi-source data fusion, and relates to the technical field of dam slope safety monitoring. The method comprises the steps that a multi-source monitoring system composed of a global satellite signal receiver, a measuring robot and an interference synthetic aperture radar is arranged in a dam slope area; according to the collected slope deformation disturbance parameters, performing monitoring area selection division on a dam slope area; determining a data fusion weight corresponding to each data fusion observation area; collecting multi-source monitoring data in each data fusion observation area; and based on the determined data fusion weight, performing weighted fusion processing on the multi-source monitoring data in the selected area to generate a slope deformation monitoring result. According to the technical scheme, fusion of multi-source monitoring data is realized through region division, the reliability, comprehensiveness and environment adaptability of slope deformation monitoring data are improved, and the stability, accuracy and precision of a slope deformation monitoring result are improved.

Disclosed is a complex scene deformation monitoring and classifying method based on interferometric synthetic aperture radar (InSAR) and a deep learning self-attention model, including: generating a deformation interferogram by using an InSAR extraction technology combining persistent scatterer (PS) points and distributed scatter (DS) points and relying on a shuttle radar topography mission digital elevation model (SRTM DEM) image; constructing a Delaunay triangulation network and an annular zone, obtaining a time series deformation data of PS points and DS points through adaptive arc densification and omni-directional point expansion, and extracting settlement points on a bridge; decomposing a time series by an SAR-self-attention model, decomposing InSAR time series data into a trend component and a seasonal component, and interpreting and analyzing deformation of the sea-crossing bridge; and describing time series dynamics and a seasonal pattern by comparing a curve fitting method with a seasonal and trend decomposition method.

The invention discloses a geotechnical engineering stability early warning system and method combining side slope deformation monitoring and numerical simulation, and relates to the technical field of geotechnical engineering safety monitoring and disaster early warning. Side slope deformation monitoring and numerical simulation are combined, and multi-source real-time monitoring data of side slope deformation and numerical simulation are fused; constructing a dynamically updated slope deformation evolution analysis system; performing dynamic partition identification on the slope deformation evolution process according to the multi-source real-time monitoring data, and identifying a slope deformation threshold value and abnormal evolution characteristics of rock and soil; and setting an automatic triggering grading early warning signal of a geotechnical engineering early warning index, and completing slope risk dynamic assessment and early warning output of the rock and soil. According to the invention, the automatic triggering of the grading early warning signal is realized, the response speed, the judgment accuracy and the risk assessment scientificity of the geotechnical engineering early warning system are obviously improved, and the intelligent, dynamic and systematic technical support is provided for the safety prevention and control of the side slope.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

A disclosed airborne vehicle includes split-ring resonators (split ring resonators), which may be embedded within a material. Each split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. Each split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, each may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.

A method for monitoring an infrastructure of interest, the method comprising: providing a distributed acoustic sensor, an analysis module, and optical fibre cable adjacent to the infrastructure wherein the optical fibre cable is divided into one or more sections, and the distributed acoustic sensor generates DAS sensor data from the one or more sections; providing a processing module comprising one or more processors and a memory, wherein the memory comprises at least two or more processing algorithms, wherein the one or more processors are configured to execute the processing algorithms with the DAS sensor data as input, wherein each processing algorithm is configured to provide an output state for each section of the optical fibre cable adjacent to the infrastructure of interest.

The invention discloses a landslide susceptibility evaluation method and system based on multi-source data and deep learning. The landslide susceptibility evaluation method combines an SBAS-InSAR technology, remote sensing image analysis, a deep learning algorithm, a TRIIGRS-SCOOPS3D coupling model and MASSFLOW simulation. The method is used for high-precision landslide prone area identification, potential landslide position prediction and three-dimensional landslide process simulation, and is especially suitable for landslide disaster risk management and emergency response in mountainous areas.

A transmission tower deformation monitoring device based on a phased array radar technology relates to the field of irregular contour metering, and comprises a map construction module used for constructing a regional digital map; the boundary determination module is used for near-field region boundaries; the signal acquisition module is used for acquiring a near-field echo signal of a near-field region through a narrow beam and acquiring a far-field echo signal of a far-field region through a wide beam; the calibration table construction module is used for constructing a signal intensity comparison table; the data processing module is used for determining effective monitoring data; the error calculation module is used for performing error calculation on the effective monitoring data to obtain a phase error and an amplitude error; and the parameter determination module is used for determining iron tower deformation parameters. According to the invention, the accuracy of transmission tower deformation monitoring can be improved.

The invention belongs to the technical field of microwave measurement, and discloses an outer wall hollowing microwave reflection detection method based on multi-modal fusion, which comprises the following steps: carrying out multi-modal scanning on a building outer wall to be detected, obtaining visible light image data and infrared temperature distribution data of the outer wall surface, and carrying out space registration and coordinate mapping; a unified multi-modal fusion data set is formed; performing anomaly screening on the multi-modal fusion data set, and identifying a thermal anomaly region by analyzing infrared temperature distribution data; detecting a bump or crack area in combination with texture and morphology anomaly features of the visible light image data; performing information fusion on the thermal anomaly region and the bump or crack region, and extracting candidate detection regions of suspected hollowing; high-precision recognition and quantitative evaluation of the outer wall hollowing are achieved, and the precision and stability of outer wall hollowing detection are improved.

The invention provides a deep and large foundation pit monitoring method and system, a program product and a storage medium, and relates to the technical field of foundation pit detection. The GPS positioning device collects foundation pit slope crest displacement data, the monitoring sensor network collects deep horizontal displacement data, the two data types complement each other, comprehensive information of surface and internal deformation of the foundation pit is provided, and the generated deep horizontal displacement curve chart visually displays the displacement conditions of the supporting structure at all depths. A potential weak area can be identified, a foundation pit excavation response data acquisition device is arranged at the maximum stress part, response data of a key position is obtained, and the quantitative index of overall deformation is provided by measuring and monitoring the position change of a central point of a sensor network and calculating the deformation amount of the foundation pit. According to the multi-dimensional and multi-type data acquisition and integration method, the description comprehensiveness of the foundation pit environment is improved, potential risks can be judged more accurately, and limitation caused by a single data type is overcome.

The invention relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method, and the method comprises the steps: multi-dimensional data collection: synchronously obtaining data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring region; data space-time calibration processing: performing space-time reference unification processing on the acquired data to generate a multi-source displacement data set after space-time calibration; cold door parameter extraction: generating an environment coupling parameter matrix based on ground temperature gradient field data and underground water flow velocity field data in multi-dimensional data acquisition; modeling a three-dimensional deformation field: generating an initial three-dimensional deformation field model; dynamic model correction: performing iterative correction on the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; and visual early warning output: generating an early warning signal with a risk level identifier. According to the method, a multi-source heterogeneous data fusion system is constructed, and a cold geological parameter dynamic compensation mechanism is combined, so that high-precision three-dimensional deformation field modeling and early warning are realized.

A sensor as a service ecosystem is disclosed herein. In use, the system includes functionality for receiving first sensor data at a sensor as a service platform, where the first sensor data corresponds to a first capability level of a first sensor. The system also receives a selection of a sensor upgrade for the first sensor, and configures enhanced sensor capabilities for the sensor upgrade based on the selection. Further, the system sends a sensor update with the enhanced sensor capability from the sensor-as-a-service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensor as a service platform, where the second sensor data corresponds to a second capability level of the first sensor.

A disclosed water droplet sensing system and methods using split-ring resonators, which may be embedded within a material. In use, a component includes at least one split-ring resonator (SRR) which may be embedded within a material of the component. The at least one SRR may be formed from a composite material. Additionally, the at least one SRR may be configured to form a signal that is correlated with a concentration of water proximate to the at least one SRR. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the material may be based on physical characteristics of the material (including fluid accumulation on the material).

The invention provides a bridge structure monitoring method and device based on a microwave deformation radar, and relates to the technical field of bridge structure monitoring, and the method comprises the steps: obtaining the multi-point three-dimensional displacement data of a bridge structure through the microwave deformation radar, carrying out the thermal expansion pseudo displacement compensation and multi-point space smoothing through combining with temperature information, and obtaining a displacement field after environment correction; secondly, extracting a vertical component and separating the vertical component into a static deformation component and a dynamic vibration component by adopting variational mode decomposition; further analyzing and identifying a decoupling region through a time window coherence coefficient, performing recursive quantitative analysis, bispectrum analysis and energy distribution entropy calculation on a dynamic signal of the region, and constructing a high-order damage sensitive feature set; and finally, the dynamic characteristics and the static curvature change are fused to form a comprehensive degradation degree index, the deviation degree is judged according to working condition classification and the mahalanobis distance, and multi-dimensional and cross-working-condition degradation identification and risk early warning of the bridge structure are achieved.

The invention relates to a surface collapse monitoring system based on multi-parameter distributed optical fiber sensing, and the system comprises a monitoring module which comprises a plurality of fully distributed optical fiber sensors laid in a monitoring area, and an optical signal transmission and demodulation module which comprises a continuous spectrum laser, an optical switch, an optical amplifier, an optical filter, and a demodulation module. The data processing and analysis center comprises a data acquisition module and a collapse model modeling module, and the data acquisition module is used for performing acquisition and digital conversion on the optical signal demodulated by the demodulation module; the collapse model modeling module constructs a model according to the monitoring data and can predict the development trend of surface collapse; and the early warning and communication module communicates with a remote monitoring center. The monitoring system provided by the invention has the advantages of multi-parameter monitoring, high monitoring precision, suitability for large-scale laying, real-time response and the like, and can comprehensively and accurately reflect the real condition of surface collapse.

The invention discloses a self-adaptive InSAR-GNSS high-precision three-dimensional deformation resolving method with regularization introduced twice, and the method comprises the steps: obtaining InSAR deformation data and GNSS three-dimensional deformation data of a to-be-monitored region, and building an InSAR error correction model considering spatial correlation based on the InSAR deformation data and the GNSS three-dimensional deformation data; performing system error correction on the InSAR deformation data by using the corrected error correction model to obtain preliminary correction data; grouping the preliminary correction data according to data precision characteristics and determining an initial weight; iteratively calculating until unit weight variances of each group of observation data are approximately equal to obtain a final weight; and obtaining a high-precision earth surface three-dimensional deformation rate based on the final weight. According to the method, Tikhonov regularization is introduced twice, the weight is dynamically adjusted in combination with the IGGIII equivalent weight function, and various error influences are effectively reduced.

The embodiment of the invention provides an unmanned aerial vehicle surveying and mapping adaptive control method and device based on image analysis, and belongs to the technical field of laser surveying and mapping. The method comprises the following steps: controlling an unmanned aerial vehicle carrying a laser scanning distance measurement module to fly to a target area and stably hover, and obtaining three-dimensional point cloud data of a target; analyzing target features based on the acquired three-dimensional point cloud data, and determining at least one target measuring point needing fine displacement measurement; acquiring image data of the target measurement point, and judging whether the measurement point meets a laser interference measurement condition or not based on image processing; collecting a laser interference signal of the target measuring point when image judgment meets a measuring condition, and calculating vibration or displacement data of the measuring point; and performing fusion processing on the three-dimensional point cloud data and the vibration / displacement data of the measurement point, and outputting the geometric dimension and displacement measurement result of the target. According to the scheme, the stability and pertinence of infinitesimal displacement measurement in the surveying and mapping task of the unmanned aerial vehicle are effectively improved.

The invention provides a ground surface settlement monitoring method and system fusing Beidou and InSAR data. The method comprises the following steps: receiving positioning data from a Beidou satellite system and earth surface deformation data acquired by a synthetic aperture radar interference measurement system; based on the positioning data and the earth surface deformation data, correcting the earth surface deformation data according to the environmental factors through a space-time fusion model to obtain corrected earth surface deformation data; by means of the corrected surface deformation data, a multi-stage analysis technology is adopted, the development trend of surface subsidence is determined, a surface subsidence distribution diagram is generated, and the surface subsidence distribution diagram is used for displaying spatial distribution characteristics of surface subsidence; and according to the ground surface settlement distribution diagram, the future ground surface settlement condition is monitored by using the space-time fusion model, and a ground surface settlement monitoring report can provide a scientific basis for early warning and prevention of geological disasters. The ground surface settlement monitoring efficiency and monitoring effect are improved.

The invention provides a vortex aircraft detection signal processing method and system based on optical fiber sensing. The method comprises the following steps: firstly, acquiring an original micro-vibration signal; secondly, performing environment self-adaptive cooperative processing on the original micro-vibration signal to generate a self-adaptive micro-vibration signal; converting the generated self-adaptive micro-vibration signal into a spatio-temporal evolution sequence based on eddy current physical characteristics; then analyzing the spatio-temporal evolution sequence to output a type discrimination result of the vortex aircraft; and finally, generating a customized detection report based on the type discrimination result. According to the technical scheme provided by the invention, the detection and identification capability of a low-altitude low-speed small target in a complex environment is improved, and reliable detection and identification of the vortex aircraft in the complex environment are also realized.

The invention discloses a tower foundation landslide monitoring method, system and device and a medium. The method comprises the steps that InSAR data and GNSS data are fused to conduct wide-area deformation monitoring, a quadratic fitting model is established to conduct error dynamic correction on InSAR observation values, and a potential landslide hidden danger area is recognized; the method comprises the following steps: performing multi-dimensional cooperative monitoring on a potential landslide hidden danger area through an unmanned aerial vehicle carrying a sensor, and fusing multi-source data by utilizing edge computing equipment to generate a landslide risk thermodynamic diagram; an active waveguide acoustic emission system is adopted to capture landslide deep deformation signals, and early warning is achieved through ringing counting and acoustic emissivity parameters. According to the invention, the InSAR and GNSS technologies are fused, and the Kalman filtering algorithm is combined, so that the precision of surface deformation monitoring is improved; the InSAR observation value is constrained and corrected through the GNSS observation value, so that the deformation monitoring precision is improved; more accurate data support is provided for landslide monitoring through the unmanned aerial vehicle carrying sensor equipment; powerful support is provided for early warning of landslide disasters through an active waveguide acoustic emission technology.

A disclosed airborne vehicle includes split-ring resonators (split ring resonators), which may be embedded within a material. Each split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. Each split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, each may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.

The invention relates to the technical field of geological monitoring, and discloses a self-adaptive phase unwrapping foundation SAR deformation monitoring method and system. The method comprises the steps of collecting ground-based SAR echo data, extracting phase gradient features and noise distribution, constructing a model based on a compressed sensing theory to solve a phase unwrapping result, establishing a deformation field dynamic evolution model fusing multiple factors, and generating a deformation monitoring value through an adaptive weighted fusion algorithm. And an abnormal deformation detection model is also constructed to realize adaptive re-calibration of model parameters, and a multi-sensor collaborative calibration network is constructed to improve the monitoring precision. The problem of phase unwrapping is effectively solved, multiple influence factors are comprehensively considered, surface deformation can be accurately monitored in real time, abnormity can be timely found, the model can be calibrated, multi-sensor data can be fused, and the method has important application value in the fields of geological disaster early warning, engineering structure safety assessment and the like.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

A disclosed apparatus includes sensors incorporated into adhesive material. In use, an apparatus may comprise an adhesive material and at least one split-ring resonator (SRR) disposed on or in the adhesive material. Additionally, the at least one SRR is formed from a carbon-containing material, and the adhesive material is a non-elastomeric material or a semi-rigid material. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the adhesive material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the adhesive material is in a second state. A resonant frequency of the adhesive material may be based on physical characteristics of the adhesive material.

The invention discloses a face recognition system based on computer vision and an application method thereof, and relates to the technical field of face recognition. The system comprises a fine-grained shielding classification module, a multi-modal feature fusion module, a cross-modal confrontation identification module and a compensation verification module. By analyzing the depth data and the polarized light parameters, an occlusion thermodynamic diagram is generated, and an identifiable area and an occlusion area are marked. And according to the occlusion label of the thermodynamic diagram, activating infrared and sonic sensor data, extracting the relevance between the temperature change and the vibration frequency, and generating a facial occlusion area feature map. And calculating the similarity between the sheltered region feature map and the non-sheltered region feature, and detecting a forgery attack. And analyzing the difference rate of adjacent frames through a sliding window, marking compensation distortion, rejecting low-credibility data in combination with confidence and attack marks, and outputting a complete facial feature vector and a risk level. And the recognition accuracy and the system security in a complex environment are remarkably improved.

A disclosed leaky coaxial resonant sensor system and methods. In use, the system includes at least one split-ring resonator (SRR) embedded within a material of the component. The at least one SRR is formed from a composite material. Additionally, the at least one SRR is configured to resonate at a first frequency in response to an interrogation signal from a leaky coaxial cable antenna. In some aspects, each SRR may resonate at a first frequency in response to an electromagnetic ping when the composite material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the composite material is in a second state. A resonant frequency of the composite material may be based on physical characteristics of the composite material.

The invention relates to a method and device for detecting spilled objects on an expressway surface, electronic equipment and a medium, and belongs to the technical field of traffic detection.The method comprises the steps that vibration signals of all measuring points on the expressway surface can be obtained based on a grating array vibration sensor, and then the vibration signals of all the measuring points can be monitored in real time; the timeliness of detecting the thrown objects on the expressway is improved; furthermore, peak value extraction can be carried out according to the vibration signal, a peak value point corresponding to each measuring point is determined, and all the peak value points are judged and fitted to obtain a vibration characteristic line set; when the vibration characteristic line set meets a preset condition, it is determined that an object throwing event exists at the current moment; the preset condition is that a plurality of initial characteristic lines meeting the characteristics of the throwing object exist; according to the plurality of initial feature lines, the drop point position of the thrown object in the thrown object event can be obtained, and the positioning precision of the highway thrown object is improved.