46 results about "Nonlinear modulation" patented technology

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention provides a power transmission network equipment insulation aging state evaluation and life prediction method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: obtaining multi-dimensional operation monitoring data, extracting features through multi-scale time-frequency decomposition, and carrying out the orthogonal transformation to obtain a decoupling feature vector; calculating a mahalanobis distance to obtain an aging state deviation metric value; constructing a stress accumulation damage factor to perform nonlinear modulation on the degradation rate; an aging state trajectory is predicted based on the modified degradation rate and a remaining life is determined. The method can accurately evaluate the insulation aging state and predict the residual life of the equipment, and improves the evaluation precision and reliability.

The invention relates to the technical field of refrigeration equipment state monitoring and data analysis, and discloses a freezer remote monitoring and fault prediction system, which comprises the steps of intercepting operation data of a freezer in a door closing silence period, calculating an environment dew point temperature sequence and extracting a unit temperature difference load fluctuation ratio sequence of a refrigeration system; based on the time change rate of the environment dew point temperature sequence and the unit temperature difference load fluctuation ratio sequence, an instantaneous cross bispectrum coupling potential energy sequence representing the nonlinear modulation relation of the time change rate and the unit temperature difference load fluctuation ratio sequence is constructed; then, a two-dimensional phase space hysteresis track is constructed with the environment dew point temperature sequence as the horizontal axis and the instantaneous cross bispectrum coupling potential energy sequence as the longitudinal axis, and the cross bispectrum hysteresis entropy of the dew point driving type load volatility is calculated based on the probability density distribution of the track and the directed circulation area; and finally, judging an operation state and outputting a fault prediction result by verifying whether the entropy value is within a historical reference interval.

The invention discloses an underground pipeline intelligent leakage detection method and device, and relates to the field of intelligent leakage detection.The method comprises the steps that a high-frequency pressure sequence of a target pipe section is collected, and based on the Boltzmann superposition principle, the high-frequency pressure sequence is used for constructing a neural lag operator network by taking the environment temperature as a heat rheological regulation factor; taking the full historical stress-strain memory field tensor as input, carrying out non-local memory characteristic and hysteresis loop analysis, carrying out point-by-point differential processing on a high-frequency pressure sequence and the intrinsic nonlinear hysteresis response signal of the pipe, stripping non-stationary background fluctuation, avoiding modulation interference of material nonlinearity on a fluid signal, and obtaining a non-linear hysteresis response signal of the pipe. The method comprises the following steps: extracting the abnormal residual error of the fidelity fluid dynamics, constructing a spatio-temporal evolution Poincare section, inputting the spatio-temporal evolution Poincare section into a convolutional neural network, judging the real leakage negative pressure wave and the random drift of the sensor through the morphological difference of attractors on the spatio-temporal evolution Poincare section, and eliminating the periodic false alarm caused by the rheological property of the material.

The invention discloses a method for predicting the residual fatigue life of a titanium alloy component based on frequency mixing nonlinear ultrasound. The method comprises the following steps: firstly, building a detection system; the detection system is controlled to generate low-frequency f1 and high-frequency f2 sinusoidal signals to carry out frequency mixing excitation on the titanium alloy component, and ultrasonic response signals are collected; performing frequency domain analysis on the ultrasonic response signal, extracting a characteristic amplitude and calculating a nonlinear modulation parameter beta m; then, a Nazarov-Sutin equation and an Elber equation are combined, and a theoretical prediction model of the nonlinear modulation parameter beta m changing along with the fatigue life percentage f is constructed; finally, actual measurement parameters are substituted into the model to reversely derive the consumed fatigue cycle number N, and the residual fatigue life Nr is predicted by combining the critical failure total cycle number Nt of the component. According to the method, the quantitative mapping relation between the nonlinear acoustic parameters and fatigue damage accumulation is revealed by establishing the physical model, and accurate quantitative prediction of the residual fatigue life of the titanium alloy component is achieved.

This invention relates to a circuit and method for suppressing drift error and background light in a SPAD lidar. The SPAD array and readout circuit unit acquire optical signals; a target recognition algorithm unit measures the distance to targets within the effective field of view based on the optical signals to obtain distance measurement information; a laser power adjustment drive unit dynamically adjusts the output power of the laser emitting unit based on the distance measurement information; a nonlinearly modulated gated high-voltage drive circuit unit adjusts the drive voltage of the SPAD array and readout circuit unit based on the distance measurement information; and the laser emitting unit, acting as a pulsed laser source, emits laser light from its output end to an optical lens, which then illuminates the surface of the target within the effective field of view. This invention effectively suppresses SPAD drift error and improves the dynamic range, measurement accuracy, and background light suppression capability of the SPAD lidar.

This invention provides a production monitoring system for mirror cabinet processing equipment based on multi-source data, relating to the field of process control. The system acquires multi-source data from the mirror cabinet through an acquisition module and constructs a discrete mesh model; a deviation topology construction module generates directional selective edge weights and a nonlinear modulation function; a propagation control coupling module achieves synchronous iterative convergence of deviation states and control components; a topology constraint module identifies structural boundaries and divides control sub-regions; a region control module generates region control vectors, which are then decomposed into processing adjustment quantities and coupled in by the instruction module; and a feedback reconstruction module optimizes parameters based on the processing results. This invention enables deviation propagation to match the processing path, achieving bidirectional coupling of state and control, thus improving accuracy and control stability in the processing of complex curved surfaces and multi-material mirror cabinets.

A method (100) for determining a power dissipation value of an electronic power circuit during an electrical period of a modulated control signal is disclosed, comprising: determining a linear mean switching energy value (110) of the electronic power circuit based on a modulated control signal with linear modulation; determining a nonlinear mean switching energy value (120) of the electronic power circuit based on a modulated control signal with nonlinear modulation, in particular overmodulation; determining a scaling factor value (130) based on the linear mean switching energy value and the nonlinear mean switching energy value; and determining the power dissipation value (140) based on the linear mean switching energy value and the scaling factor value.Furthermore, a computing unit designed and equipped to execute the procedure (100) is disclosed.

This invention discloses a method, system, equipment, and medium for predicting the wear of sliding liners in wind turbine generators. The invention discretizes the continuous yaw process and calculates the dynamic equivalent contact pressure of the liner using real-time aerodynamic overturning moment to characterize the nonlinear modulation effect of wind load fluctuations on the normal pressure of the liner. Simultaneously, it combines a frictional heat generation model to calculate a transient thermal correction factor, quantifying the degradation of material wear resistance caused by temperature rise. Based on this, the actual physical yaw angle is corrected to a wear equivalent angle and historical data is accumulated. By comparing this with the material's ultimate wear threshold, the remaining life prediction result of the sliding liner is output. This invention overcomes the shortcomings of traditional linear estimation methods, significantly improving the physical realism and accuracy of liner wear prediction under complex variable operating conditions, and can effectively guide condition-based maintenance and proactive health management of the generator unit.

The application relates to the technical field of remote sensing information processing and geological disaster monitoring, and discloses a mountainous area landslide intelligent identification method and system fusing multiple remote sensing sources, which comprises the following steps: inputting an optical image and a digital elevation model into an experience mapping function to generate a static coherence expectation value; extracting a soil moisture stress adjustment factor based on a precipitation sequence, and generating a dynamic coherence background matrix through a nonlinear modulation model; performing interference coherence estimation on polarized radar complex data to extract a coherence residual matrix; screening first-level abnormal image elements with continuously increasing residuals, calling a polarized target decomposition algorithm to extract polarized scattering power components, and determining second-level abnormal image elements based on a physical mechanism partial differential equation set; generating a catchment network based on an elevation model, and performing micro-geomorphology spatial topological filtering on abnormal image element aggregation patches. The application excludes environmental and hydro-meteorological interference, and effectively reduces the false alarm rate of landslide identification in complex mountainous areas.

The invention discloses a strain sensor calibration device with a limiting structure and a calibration method, and belongs to the technical field of measuring instruments. The calibration device comprises a vibration isolation platform, a clamping support beam fixed on the vibration isolation platform, an upper limiter and a lower limiter which are symmetrically arranged above and below a cantilever beam, and the cantilever beam of which one end is connected with the piezoelectric ceramic exciter through threads and the middle part is clamped by the limiters. And the piezoelectric ceramic exciter penetrates through the clamping support beam and applies displacement excitation with preset amplitude and frequency to excite the cantilever beam to vibrate. When the deflection of the short beam section of the cantilever beam exceeds a set gap, the limiting stopper makes contact with the short beam section, subharmonic vibration and chaotic motion of the short beam section are restrained through the nonlinear modulation effect, and therefore the main beam section outputs stable periodic deflection response. According to the invention, the problems of unstable signals and insufficient precision in dynamic calibration are solved, and high-precision dynamic calibration of the strain sensor under the conditions of low frequency and large deflection is realized.

Provided in the present invention is an all-optical nonlinear activator based on dual-light-beam cavity field modulation, which can be applied to the technical field of optical computing. The activator comprises: an input end, an optical microcavity and an output end, wherein the input end is used for providing an input light beam; the optical microcavity is used for generating resonance, so as to excite and enhance an optical nonlinear effect; and the output end is used for outputting a nonlinearly modulated optical signal. Nonlinear modulation is performed on an optical field in the optical microcavity or on a transmission spectrum of the optical field in the optical microcavity by means of two coherent light beams having a phase difference, thereby forming nonlinear mapping between the light intensity of input light and the light intensity of output light, i.e., an all‑optical self-modulation process. An effective nonlinear operator is provided for all-optical computing, thereby accelerating the convergence of network training and inference, and improving the recognition accuracy of a model in different applications. Further provided in the present invention is an implementation method for the all-optical nonlinear activator based on dual-light-beam cavity field modulation.

The application discloses a kind of complex terrain area air pollution multi-source heterogeneous data fusion and space-time alignment method.For the problem that multi-source data is inconsistent in time scale, spatial scale is not matched and alignment error is large under complex terrain, first, the application obtains multi-source data and pre-processes;Then realize time alignment by using the method based on time distance and double weight of data quality;In spatial alignment, introduce the terrain factor of elevation, slope, aspect for nonlinear modulation;Then get collaborative expression by terrain constraint fusion and multi-source feature fusion;Finally, construct ensemble learning error correction model to correct space-time deviation, output unified dataset.The application significantly improves the spatial alignment accuracy of complex terrain area, reduces space-time alignment error, and provides reliable data basis for air pollution mechanism analysis and prediction.

The invention relates to the field of converter service life evaluation, and particularly discloses a multi-scale-based wind turbine generator converter service life evaluation method and system. High-frequency instantaneous junction temperature data and long-period average junction temperature data of a wind turbine generator converter are synchronously processed, and a dual-channel depth time sequence coding architecture is introduced; and carrying out deep deconstruction on the junction temperature sequences of two different time scales. And through a cross-scale interactive fusion mechanism, a nonlinear modulation effect of long-scale average junction temperature on short-scale damage accumulation is captured and quantified to obtain a deep fusion vector. And finally, based on the deep fusion vector driving life prediction model, precise evaluation of the life consumption rate under the current working condition is realized, dynamic iteration is carried out in combination with historical damage, and online prediction of the residual service life of the converter is completed. Therefore, the limitation of a traditional linear accumulation criterion can be effectively overcome, and the accuracy and reliability of wind turbine generator converter life prediction are remarkably improved.

The application discloses a strain sensor calibration device with a limiting structure and a calibration method, and belongs to the technical field of measuring instruments. The calibration device comprises a vibration isolation platform, a clamping support beam fixed on the vibration isolation platform, upper and lower limiters symmetrically arranged above and below a cantilever beam, and the cantilever beam with one end connected with a piezoelectric ceramic exciter through a thread and the middle part clamped by the limiter. The piezoelectric ceramic exciter penetrates through the clamping support beam and applies displacement excitation with a preset amplitude and frequency to excite the cantilever beam to vibrate. When the deflection of the short beam section of the cantilever beam exceeds the set gap, the limiter is in contact with the short beam section, the nonlinear modulation effect is used to suppress the subharmonic vibration and chaotic motion of the short beam section, so that the main beam section outputs stable periodic deflection response. The application solves the problems of unstable signal and insufficient precision in dynamic calibration, and realizes high-precision dynamic calibration of the strain sensor under the condition of low frequency and large deflection.

The application provides an electromagnetic electrostatic double-drive Coppe filter chip and a preparation method thereof, and relates to the fields of optoelectronic chips and semiconductor technologies.The filter chip comprises a movable mirror body, a fixed mirror body, a permanent magnet, a PCB circuit board and a spacing layer.A first reflecting mirror, a first detection electrode, a second detection electrode, a first drive electrode and a second drive electrode are arranged on the end face of the movable mirror body.The first detection electrode and the first drive electrode are arranged on one side of the first reflecting mirror, and the second detection electrode and the second drive electrode are arranged on the other side of the first reflecting mirror.A permanent magnet slot for embedding the permanent magnet is arranged on the other end face of the movable mirror body.The application can overcome the problems of the single-drive Coppe filter chip, such as nonlinear modulation, limited tuning range and poor monochromaticity.

The invention relates to the technical field of photoelectric tracking and confrontation, and discloses a high-precision laser dazzling closed-loop control system based on a deep learning vision algorithm, which comprises a laser dazzling denial unit and a high-precision servo tracking device, and adopts a double-rate asynchronous closed-loop architecture. The deep learning visual processing unit outputs target observation data with an absolute timestamp in a low-speed loop; the high-precision servo controller executes high-frequency deduction on the basis of inertial data in a fast loop, measurement updating of space-time alignment is carried out by utilizing a historical state buffer queue and a forward re-integration algorithm, and visual delay is eliminated; the system also establishes a self-adaptive region-of-interest adjustment mechanism based on prediction covariance matrix interaction, and integrates inverted PWM trigger exposure control, dynamic geofence verification and power nonlinear modulation functions. According to the method, the problem of tracking lag caused by visual delay under the high dynamic condition is effectively solved, and the tracking precision, robustness and operation safety of the system are improved.

The application provides a silicon-based light-controlled nonlinear activation unit, method and photonic neural network, relates to the technical field of silicon-based photonics, and comprises a photodetector, an electro-optic intensity modulator and a bias / interface circuit. A control signal light is converted into a driving electrical signal by the photodetector, directly drives a silicon-based PIN phase modulation arm, and performs intensity domain nonlinear modulation on independent data signal light. The electro-optic intensity modulator combines segmented electrodes and programmable bias to form a single-threshold or double-threshold nonlinear mapping relationship. The bias circuit and the temperature control module ensure that the device maintains a stable operating point under environmental disturbance. The activation unit can be deployed in a cascaded manner in a multi-layer photonic neural network, and a power management module is introduced between layers to dynamically match the control light and data signal light power, thereby ensuring the consistency of the cross-layer nonlinear response. The scheme is compatible with the CMOS process, has the characteristics of low power consumption, scalability and easy programming, and is suitable for on-chip deep photonic neural networks and efficient edge intelligent computing.

The invention relates to the technical field of visual perception, discloses a biologically inspired collision detection system and robot for atopic impending perception recognition, and constructs a brand-new collision detection model for atopic impending perception recognition. According to the technical scheme, through a six-layer structure (P-> E / I-> A-> S-> G-> LGMD), exponential attenuation, nonlinear modulation and a spatial clustering mechanism are combined, and accurate distinguishing and real-time collision early warning functions of approaching and leaving motion states are jointly achieved from the three aspects of structure, function and biological simulation; the method has the advantages of high direction selectivity, high robustness and low power consumption calculation efficiency, and is particularly suitable for rapid motion sensing tasks in embedded robots and low-calculation-power visual platforms.

The invention provides a silicon-based light-operated nonlinear activation unit, a silicon-based light-operated nonlinear activation method and a photonic neural network, and relates to the technical field of silicon-based photons.The silicon-based light-operated nonlinear activation unit comprises a photoelectric detector, an electro-optic intensity modulator and a bias / interface circuit, control signal light is converted into a driving electric signal through the photoelectric detector, a silicon-based PIN phase modulation arm is directly driven, and the electro-optic intensity modulator and the bias / interface circuit are electrically connected; and performing intensity domain nonlinear modulation on the independent data signal light. The electro-optic intensity modulator is combined with the segmented electrodes and the programmable bias to form a single-threshold or double-threshold nonlinear mapping relation. And the biasing circuit and the temperature control module ensure that the device keeps a stable working point under environmental disturbance. The activation unit can be deployed in a multi-layer photon neural network in a cascade manner, and a power management module is introduced between layers to dynamically match control light and data signal light power, so that cross-layer nonlinear response consistency is ensured. The scheme is compatible with a CMOS (Complementary Metal Oxide Semiconductor) process, has the characteristics of low power consumption, expandability, easiness in programming and the like, and is suitable for an on-chip deep photon neural network and efficient edge intelligent calculation.

The application provides an optimization method for an injector of an energy recovery type linear accelerator, comprising the following steps: estimating the beam group parameters at the outlet of an injection part and the distance from the outlet to the inlet of a main accelerator through an existing design; determining the transformation of the caused phase space nonlinear modulation amount in the unit length caused by the transverse space charge force; simultaneously, estimating the phase space nonlinear modulation amount caused by the transverse space charge force in the matching section and the merging section of the injector; adding an emittance optimization function in the original constraint condition of the injection part in the process of optimizing the injection part of the injector device; and then, making the emittance optimization function tend to 0. The optimization method of the application optimizes or screens the beam group of the injection part of the injector device on the basis of considering the transverse space charge effect of the subsequent low-energy region, reduces the influence of the transverse charge force on the beam group in the subsequent transmission, and can effectively improve the power of the subsequent radiation section.

This invention relates to the field of fault diagnosis and monitoring technology, and particularly to a method and system for diagnosing equipment faults. The method includes: acquiring electrical signals from both high-voltage and low-voltage sides of a transformer, extracting the fundamental frequency, instantaneous phase, and amplitude envelope; real-time monitoring of phase fluctuation variance, and when exceeding a preset threshold, extracting a complete sequence containing the steady-state segment before the disturbance and the attenuation segment after the disturbance; constructing a virtual zero-phase reference and performing differential preprocessing on the sequence; performing second-order rate-of-change operation on the processed sequence to obtain phase acceleration characteristics, and combining this with the amplitude envelope for nonlinear modulation mapping to construct instantaneous phase-change energy flow characteristics; classifying internal winding deformation faults and external power grid disturbances by calculating the time-frequency correlation index of the energy flow characteristics on both sides; extracting the sub-band energy distribution components of the internal fault signal, and using a fault fingerprint database to match and identify winding deformation patterns. This invention significantly improves the sensitivity of transformer winding micro-deformation diagnosis and the anti-interference capability of online monitoring.

This invention discloses a method and system for providing cold chain traceability data for chilled meat. By deploying IoT nodes within refrigerated trucks to collect environmental and image data in real time, and preprocessing the collected data to obtain standard cold chain traceability data, the system extracts time-cumulative features using GRU gated recurrent units. It then extracts spatial fluctuation features using a GCN graph convolutional network. A cross-attention mechanism is used to calculate the nonlinear modulation effect of the time-cumulative features on the spatial fluctuation features, and weight adjustments are made. The fused feature vector is input into a physical information neural network, combined with biochemical dynamics equations for prediction and correction, outputting the current comprehensive quality index of the chilled meat, which is then mapped and displayed on the user's end. This enables differentiated display across multiple user terminals, meeting the service needs of multiple stakeholders, including operators, regulators, and consumers.

The application relates to the technical field of semiconductor optical chips, in particular to a semiconductor optical nonlinear operation device, a modulation method and an operation device, which comprise a waveguide, a P-type semiconductor and an N-type semiconductor, wherein the P-type semiconductor and the N-type semiconductor are respectively doped on two sides of the waveguide to form a PN junction; when a reverse bias voltage value applied on the PN junction is greater than a preset threshold value, the waveguide is in an avalanche critical state, the waveguide absorbs incident light to generate a photoelectric current to trigger avalanche, plasma is generated in the waveguide, and the refractive index of the waveguide is changed based on nonlinear effects such as plasma dispersion effect or thermal effect, so that nonlinear modulation of light intensity is realized. Therefore, the problems of low nonlinear coefficient, poor scalability, high power consumption, small application range and weak light nonlinear in the related art are solved.

This invention discloses an optical quantum neural network computing system based on an optical non-Hermitian evolutionary system architecture, belonging to the field of optical quantum neural network computing technology. It includes an optical quantum processing chip system and a control circuit. The optical quantum processing chip system comprises a quantum light source, an integrated optical quantum computing chip, and a photoelectric processing system. An optical non-Hermitian evolutionary system architecture is integrated on the integrated optical quantum computing chip. This architecture includes a linear unitary transform region, a linear non-unitary transform region, and a nonlinear modulation region. The quantum light source generates quantum state light signals and inputs them to the integrated optical quantum computing chip. The signals are sequentially processed through the linear unitary transform region, the linear non-unitary transform region, and the nonlinear modulation region. The output light signal is read by the photoelectric processing system and returned to the control circuit. This invention achieves a highly efficient optical quantum neural network chip architecture, overcoming the bottleneck of traditional optical quantum neural networks' difficulty in cascading probabilistic gate circuits and nonlinear functions, and possesses the computational advantages of large-scale and deep networks.

The application discloses a human body kinetic energy collection device based on nonlinear magnetic response and belongs to the technical field of energy collection and wearable devices. The human body kinetic energy collection device comprises an upper stator disc, a lower stator disc, a rotor magnet disc, an eccentric mass block and a nonlinear modulation magnet. The upper stator disc, the rotor magnet disc and the lower stator disc are coaxially stacked to form a containing cavity for the rotation of the rotor. The eccentric mass block is fixed to the outer edge of the rotor magnet disc and provides an eccentric driving torque by using the gravity component when the human body arm swings. The nonlinear modulation magnet is fixed to the gap of the trapezoidal coil on the stator disc, and the periodic interference between the alternating magnetic poles of the rotor magnet disc and the rotor magnet disc is generated when the rotor rotates, thereby constructing a single-dual stable nonlinear potential energy field. The nonlinear electromagnetic micro energy capture device is used for efficiently collecting the low-frequency and random arm swing kinetic energy of the human body, and the limitation of the narrow resonant frequency band and high starting threshold of the conventional single stable linear system is broken through.

The invention provides a pump frequency model prediction regulation and control method for a membrane bioreactor, and aims to solve the problems of prediction misalignment, energy consumption increase and inaccurate regulation and control caused by the phenomena of sudden flow change and severe pump frequency fluctuation of pump station regulation and control in a membrane bioreactor system of an urban sewage plant. The method comprises the steps that firstly, a flow prediction model based on self-adaptive state perception is established, the length of a prediction domain is adjusted through a nonlinear modulation function, and rapid perception and response of a pump station to sudden working conditions are achieved; and secondly, designing an intelligent model prediction controller, and carrying out online solving on the composite objective function by using a gradient projection method under engineering constraints. And finally, in combination with real-time operation data and an intelligent controller, optimal regulation and control of the frequency combination of the lifting pump are realized. Results show that the method can quickly adjust the frequency of the lift pump under the sudden working condition caused by flow abrupt change, flow tracking and energy consumption suppression are considered at the same time, and the stability and energy efficiency of a pump station are remarkably improved.

The application discloses a building concrete internal defect ultrasonic detection signal processing method and system, belongs to the material nondestructive testing technical field, and the method comprises the following steps: configuring a full matrix capture array to collect multi-channel original time domain data; performing a time domain flip operation on the data and mapping to the full matrix capture array to perform a synchronous retransmission algorithm; extracting sum frequency and difference frequency amplitudes of specific frequency components in the matrix, and constructing a nonlinear amplitude coordinate mapping table; performing statistical analysis on a full-field energy focusing distribution matrix, performing a difference operation with the sum frequency component amplitudes to generate a defect response coordinate set; and feeding back the coordinate set to the full matrix capture array to trigger supplementary scanning and update the matrix. The application adopts dual-frequency nonlinear modulation and time reversal focusing technology, can effectively suppress aggregate scattering noise interference, and improves the three-dimensional imaging precision and detection signal-to-noise ratio of concrete micro defects.

The invention relates to a weather-driven rural highway risk prediction method, which comprises the following steps: constructing a road-weather double-graph structure based on input road network information and weather grid point information, and fusing road and weather information based on a cross-domain attention propagation mechanism to generate a road-weather fusion feature vector; introducing a road physical attribute vector based on the generated road-weather fusion feature vector, and performing feature interaction fusion and nonlinear modulation by fusing feature modulation of physical prior to obtain a feature vector after physical constraint modulation; the modulated feature vectors are input into an edge-center collaborative reasoning framework for risk prediction, and joint prediction of local quick response and global fine optimization is realized through collaborative calculation of edge nodes and center nodes; and based on a risk prediction result, performing model parameter updating through a prediction-inspection hierarchical feedback mechanism, and generating a visual risk map and a management strategy. According to the method, rural highway risk prediction with high precision, high real-time performance and long-term adaptive capability can be realized.

The invention discloses an underground water level observation method and system fusing multi-dimensional sensing information, and the method comprises the steps: firstly obtaining a coarse-resolution dynamic remote sensing image and a high-resolution static geographic data set, and constructing multi-dimensional input through space-time reference alignment and multi-scale feature stacking; on the basis, a subgrid heterogeneity feature extraction mechanism is utilized to explicitly quantify terrain and geological complexity in a coarse grid, and a physical constraint relation of high-resolution geographic features to low-resolution remote sensing signals is established through an adaptive modulation module, so that the model can simulate a nonlinear modulation effect of a local environment to hydrological signals. And finally, performing end-to-end nonlinear mapping and super-resolution reconstruction on the fused multi-dimensional features by using a U-Net architecture comprising an encoder, a decoder and jump connection, and recovering fine underground water level spatial distribution details while keeping a remote sensing macroscopic trend.