62 results about "Modal coupling" patented technology

The invention discloses a rubber product quality detection method and system, and relates to the related technical field of rubber quality detection.The method comprises the steps that rubber products are conveyed to a target detection table, and data acquisition of the rubber products is executed; inputting the wave impedance distribution data and the surface heat distribution gradient field into a defect causal atlas channel to execute phase inversion fusion processing, establishing a cross-modal coupling phase tensor, and identifying deep defect response characteristics by solving a phase difference matrix; activating a pressure spectrum sensing unit to apply control pressure, reading a local mechanical response spectrum, sending the local mechanical response spectrum to a cross-modal calibration layer, and executing joint error authentication; and performing quality detection report according to a self-calibration defect mapping result. The technical problems that in the prior art, multi-modal sensing data fusion efficiency is low, deep defects are difficult to comprehensively recognize, and consequently the quality detection precision and reliability of rubber products are insufficient are solved, and the technical effects that cross-modal cooperative detection is achieved, and the precision and reliability of defect recognition results are improved are achieved.

The invention discloses a spatio-temporal cross-scale dimension reduction characterization method, system and device for a multi-modal physiological signal. The method comprises the steps of multi-modal signal time sequence alignment, sliding variable scale segmentation, single-modal spatio-temporal feature pre-training, cross-modal coupling alignment and model migration application. In the single-mode spatial-temporal feature pre-training, single-mode signal fragments under different time processes or spatial arrangements are reconstructed through a codec, and multi-level spatial-temporal modeling and analysis of a signal source are realized; in cross-modal coupling alignment, spatial-temporal characteristics of physiological signals of other modals are reconstructed through a codec, and a coupling relationship among different modals of a physiological system is learned; the model after cross-modal coupling alignment can be migrated to a specific task for fine tuning, and the comprehensive characterization capability of physiological signals in a few modals, specified space or limited time collected by a backward device on a physiological system is enhanced. According to the method, through bottom-layer dimension reduction and migration, effective characterization of the whole complex physiological state by the easily obtained signals is achieved.

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

The invention relates to a multi-modal oscillation suppression method and system for a network construction type power supply grid-connected system, belongs to the technical field of power system control, and solves the problem that multi-modal oscillation of a large-scale inversion type power supply grid-connected system cannot be cooperatively and rapidly suppressed. The method comprises the following steps: when a system generates broadband oscillation, collecting grid-connected point voltage and current signals of each power station to extract a dominant oscillation mode of each power station; constructing a modal coupling matrix based on the dominant oscillation mode to extract modal coupling strength; identifying a key path of oscillation propagation in the dominant oscillation mode, and extracting a path weight and a power station participation degree; calculating the comprehensive power regulation and control weight of each power generation station in multiple modes so as to determine the power regulation quantity; and distributing the power regulation quantity to an energy storage device, a static var generator (SVG) and each power generation station according to oscillation characteristics of different frequency bands so as to cooperatively suppress multi-modal oscillation. The active / reactive oscillation energy is cooperatively adjusted, so that the oscillation energy of each frequency band is effectively suppressed.

The invention discloses an elderly accompanying robot system based on multi-mode emotion cognition reinforcement learning. The system comprises a robot body, a multi-source data acquisition and feature extraction unit, a multi-data-source dynamic feature fusion unit, an emotional state estimation and modeling unit and a personalized interaction strategy generation unit. The multi-source data acquisition and feature extraction unit is used for extracting voice signals, physiological signals and behavior signals; the multi-data-source dynamic feature fusion unit is used for realizing cross-modal coupling through a learnable attention mechanism; the emotional state estimation and modeling unit is used for constructing a three-dimensional emotional vector space, predicting an emotional state through a mode of fusing time sequence characteristics and reinforcement learning, and converting a multi-modal signal into a quantifiable emotional index; and the personalized interaction strategy generation unit is used for generating a personalized interaction strategy for controlling the robot body.

The invention relates to the technical field of risk assessment, in particular to a multi-modal information coupled chemical safety risk assessment system which comprises a multi-modal data fusion module, a dynamic knowledge graph construction module, a risk assessment model training module, a risk assessment and judgment module, an operation specification mapping module and a control instruction generation module. Fusing the equipment process and operation data of chemical production with preset safety expert knowledge to generate multi-modal coupling information; constructing a dynamic knowledge graph representing a flow node dependency relationship and a risk evolution path, and cooperatively training and updating a whole-flow risk evaluation model; coupling information is input to complete risk assessment and grade judgment, a result is mapped to an operation specification knowledge base and then cooperatively encoded with a distributed control operation instruction, and a standardized control instruction is output; according to the invention, the efficiency of chemical safety risk assessment based on multi-modal information coupling can be improved.

The invention provides a multi-mode coupling flood forecasting method and system based on a gated cross attention mechanism, and relates to the technical field of flood forecasting. The method comprises the following steps: acquiring high-precision rainfall monitoring information, numerical forecast rainfall information and hydrological station session flood excerpt information of a predetermined drainage basin; constructing training, verification and test samples; a spatial-temporal feature coding module based on a convolutional neural network and Transform is constructed, and rainfall information is fully mined; a time sequence feature coding module based on a time sequence convolutional network is constructed, and runoff information is fully mined; constructing a multi-modal feature fusion module based on a gated cross attention mechanism, and fusing different modal features mined by the time sequence and spatial-temporal feature coding modules; and constructing a flood prediction module based on a self-attention mechanism to perform flood prediction. According to the invention, the accuracy and reliability of flood forecasting are improved based on the multi-modal feature fusion technology.

The invention relates to the technical field of artificial intelligence and multimedia, in particular to an audio guidance visual segmentation method and device based on multi-granularity cross-modal coupling, and the method comprises the steps: extracting multi-level visual features and audio Mel spectrum features of a target video frame; performing intra-modal enhancement on the multi-level visual features and the audio Mel spectrum features to obtain enhanced multi-level visual features and enhanced audio Mel spectrum features; performing cross-modal fusion on the enhanced multi-level visual features and the enhanced audio Mel spectrum features to generate a semantic enhancement query vector; and training a pre-constructed Transform attention decoder by using the semantic enhancement query vector to generate a pixel-level segmentation mask, and fusing the pixel-level segmentation mask with the multi-level visual features to obtain a mask prediction result. Therefore, the problems of intra-modal noise interference, insufficient audio guidance, multi-source sound entanglement and the like in an existing audio-visual segmentation method are solved.

The invention discloses a robot environment identification method based on multi-modal fusion, and the method comprises the following steps: collecting image data, point cloud data and acoustic data in a robot operation environment, and carrying out the preprocessing; inputting the structured multi-modal input sample into a modal perception type quantum encoder of a multi-modal quantum graph neural network; constructing a modal coupling quantum diagram based on the modal quantum state representation set; inputting the modal coupling quantum diagram into an entangled modal propagation unit, and performing cross-modal information propagation of nodes of the modal coupling quantum diagram through a parameterized quantum circuit; quantum measurement operation is executed on the fusion quantum graph embedded representation, and an environment recognition result is output; and inputting an environment identification result into a robot control module, and driving the robot to execute path adjustment, obstacle avoidance or action response. According to the method, the multi-mode quantum graph neural network is adopted, and autonomous recognition of the robot in a complex environment is achieved.

The invention relates to the technical field of remote sensing intelligent interpretation, in particular to a multi-modal remote sensing change detection method and system based on multi-granularity alignment and frequency adaptive fusion, and the method comprises the steps: obtaining optical remote sensing images and SAR remote sensing images of the same geographic region and different time phases, and constructing a multi-modal image data pair; respectively extracting multi-scale features of the optical remote sensing image and the SAR remote sensing image by adopting non-weight sharing double encoders; a multi-modal feature alignment module is used for carrying out feature distribution alignment on multi-scale optical features and SAR features on different spatial scales through KL divergence constraint, in the alignment process, a change area is eliminated through a binary change mask, and alignment constraint is only carried out on an unchanged area; constructing a unified cross-modal embedding space by comparing and learning related semantic alignment modules; and designing a modal coupling attention mechanism, and performing frequency adaptive fusion on the multi-modal features in a frequency domain. According to the method, accurate cross-modal alignment and complementary feature fusion are realized, and the accuracy and reliability of change detection are remarkably improved.

The invention relates to state prediction of a lithium ion battery, in particular to a lithium battery SOH prediction method fusing multi-modal characteristics and an attention mechanism, which is used for solving the problem that the aging process of the lithium battery shows the characteristics of nonlinearity, multi-scale, multi-modal coupling and the like under the actual complex working condition. And a traditional single-structure model is difficult to accurately describe the degradation characteristic. According to the scheme, local time sequence changes in original sensor data such as voltage, current and temperature are mined, meanwhile, long-term dependence information is effectively utilized, optimal fusion of multi-source feature information is achieved through channel weight re-calibration, the lithium battery SOH prediction precision and stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for battery health monitoring and management in various practical application scenes.

The invention relates to an RGB-T target tracking method and system based on spatio-temporal state evolution, and the method comprises the steps: constructing a double-branch RGB-T target tracking model, enabling RGB and TIR branches to share the weight of a ViT encoder, employing an iterative processing frame, and transmitting spatio-temporal context information between frames through an updatable context memory Tokens. In each iteration, after RGB and TIR modal features are extracted respectively, cross-modal time sequence context modeling is carried out through a modal perception time sequence Mamba module, and the module realizes long-time target perception representation learning through a cross-modal coupling state transition mechanism and a prompt guide strategy; and carrying out intra-modal and inter-modal feature fusion in a spatial dimension through a cross-modal Mama aggregation module, and finally outputting a target position through a prediction head. According to the method, lasting cross-modal state evolution can be realized with linear complexity, the spatial-temporal characteristics of visible light and infrared light are effectively fused, and the tracking robustness and accuracy are improved in complex scenes such as rapid target movement, shielding or modal degradation.

The invention provides a robot touch and vision fused double-loop coupling self-adaptive grabbing control method, which relates to the technical field of robot grabbing control, and comprises the following steps of: acquiring surface characteristic data through a light field camera array, acquiring dynamic contact information and extracting stability characteristics by a sensing array, and acquiring the surface characteristic data through a touch sensor array; cross-modal coupling is constructed based on Tucker decomposition, tactile and visual information is fused, mapping parameters are established, an adaptive time window is constructed to optimize grabbing parameters, and the grabbing posture and the clamping force are adjusted in combination with a Lyapunov stability criterion and a dynamic compensation mechanism. According to the method, the grabbing stability and robustness of the robot in a complex environment can be improved.

The invention belongs to the field of autonomous high-throughput characterization of semiconductor materials, and discloses a cross-modal coupling method and system for semiconductor multi-modal data, and the method comprises the steps: firstly, respectively extracting the features of an image, an electricity and a process modal through a multi-modal perception encoder; secondly, reconstructing missing features based on heterogeneous data relevance by using a modal missing compensation module constructed by a graph neural network; dynamic interaction and fusion among the features are realized through a multi-modal cross attention mechanism; inputting the fusion features into a multi-scale output module to generate point location-level, block-level and wafer-level features; and finally, optimizing cross-modal coupling by adopting progressive three-stage training. By adopting the method, material and device characteristics can be comprehensively modeled, cross-modal potential association can be deeply captured, data missing can be effectively compensated, structural adaptation of a semiconductor specific task can be realized, and fusion expression capability and robustness can be remarkably improved.

The invention belongs to the technical field of bridge vibration reduction, and discloses a TMD parameter design method for modal dense bridge multi-modal vortex vibration control. Modal analysis is carried out on the target bridge, and modal frequency, vibration mode and modal mass where vortex vibration possibly occurs are extracted; then based on a modal shape amplitude threshold, screening significant extreme points of each order of modal, calculating the number of the significant extreme points, and determining the priority of TMD design according to the number from small to large; for a mode with the number of extreme points being 1, the TMD is installed at the unique position; for a mode with the number of extreme points exceeding 1, the TMD mass is split and arranged at symmetrical positions meeting the mode decoupling condition, and therefore mode coupling caused by improper installation positions is avoided. And finally, determining the frequency ratio and the damping ratio of each TMD based on a Den Hartog empirical formula. The method does not need a complex optimization algorithm, is simple in design process, and is suitable for engineering practice; and modal coupling can be effectively inhibited, and the multi-modal vortex vibration control efficiency is improved.

The application discloses a multi-modal coupling perception method for target recognition and region segmentation in a confined space, and relates to the technical field of environment perception in a confined space. The method comprises the following steps: firstly, constructing a 3D point cloud data processing network, a 2D point cloud data processing network and an image data processing network; then, constructing a feature fusioner to output an overview map coupled with multi-sensor information; then, constructing a multi-scale feature fusion extraction network and a network output head to output target recognition and region segmentation results, and designing a loss function to train network weights; finally, inputting 3D point cloud data, 2D point cloud data and camera image data into the network model to infer target recognition and region segmentation prediction results, and visualizing and rendering the prediction results. The application constructs a multi-modal coupling perception network according to the characteristics of a millimeter wave radar, can deeply mine effective information in massive point cloud data of the millimeter wave radar, and can effectively improve the detection accuracy of target recognition.

The invention relates to the technical field of electrical equipment detection, in particular to a power plant electrical equipment abnormal state intelligent detection and early warning method, which comprises the following steps of synchronously acquiring electromagnetic radiation pulse signals, vibration acceleration waveforms and surface temperature gradient signals of an electrical equipment body and a connecting component thereof; constructing a time aligned multi-modal sensing data stream; time delay locking characteristics and cross-modal coupling strength indexes among the signals are extracted; and inputting the time delay locking feature and the cross-modal coupling strength index into a composite decision model, and triggering positioning early warning through a double-threshold cross validation mechanism. According to the method, a full-process closed-loop judgment system covering instantaneous interference suppression, trend anomaly tracking and redundancy verification is formed, and the early warning intelligence level and response adaptability of electrical equipment in a complex scene are improved.

The invention relates to the technical field of wind power blade fatigue tests, and discloses a blade dynamic loading method based on double-shaft coupling resonance, which comprises the steps of forced loading in the shimmy direction, pulse type follow-up loading in the flapping direction and phase synchronization, and is characterized in that the loading frequency in the shimmy direction is set to be always close to the current resonant frequency in the flapping direction; the amplitude of the exciting force in the shimmy direction is dynamically adjusted according to the vibration response in the flapping direction, in the thrust loading stage, when the displacement in the flapping direction reaches a peak value, a thrust pulse is applied to the flapping direction, the magnitude of the thrust pulse is decreased progressively along with attenuation of the vibration amplitude, energy needed by active excitation is reduced through the modal coupling effect, and the active excitation effect is improved. Pulse thrust is only applied in a displacement peak value interval in the flapping direction, free attenuation is achieved in other time, invalid energy consumption is avoided, the amplitude is adjusted in a self-adaptive mode, so that shimmy excitation force is dynamically optimized along with flapping vibration response, overload or insufficient excitation is avoided, and rigidity degradation in the blade testing process is adapted.

The invention discloses a cooling and heating integrated heating table control system and method and a multi-mode coupling field actuator. The multi-mode coupling field actuator comprises a heating unit, a cooling unit, an adsorption layer and a thermal control layer. The heating unit comprises a near-infrared laser and a scanning galvanometer and irradiates a high-absorptivity photo-thermal coating arranged on the back face of the substrate, and rapid and local heating is achieved. A microporous electrostatic adsorption film with high insulativity and a microporous structure is arranged on the outer surface of the base plate, and the micro thermoelectric refrigerating unit is integrated below the base plate to jointly construct a solid execution surface capable of realizing zoned temperature control; the space-time field information fusion sensing module integrates a distributed fiber grating temperature sensor and a thermal imaging sensor, and realizes synchronous, global and undisturbed sensing of physical temperature and optical effect; and the adaptive prediction controller establishes a real-time digital twinborn model of a controlled object, maps set temperature distribution into actuator driving parameters through a field domain inverse problem solving algorithm, and introduces a closed-loop multi-mode coupling field actuator for online correction.

The application discloses a kind of based on multi-modal data fusion fermentation environment control method, comprising: collection multi-modal fermentation data and executes pre-processing, forms multivariate time series dataset;Improved MTAD-GAT model is input, calculates prediction error reconstruction error and consistency score, generates dynamic reliable weight;Coupling relationship matrix is constructed across mode, coupling residual is calculated and iterative correction data, reconstructs state vector and uncertainty;Coupling stability index is calculated, and state anchoring is executed, obtains stable reference state parameter;Based on state vector and reference state, construct metabolic feasible region, form control constraint interval;Self-scaling robust controller is constructed, dynamic scaling constraint and rolling optimization calculation, output fermentation control instruction.The application realizes stable identification to fermentation process state and adaptive control to fermentation environment parameter by multi-modal data fusion, coupling relationship analysis and self-scaling robust predictive control.

The application discloses a method for constructing a rapeseed salt-tolerant trait prediction model based on deep learning, comprising collecting multi-modal data of a preset rapeseed area, and preprocessing the multi-modal data; constructing a three-modal coupling encoder with biological prior constraints according to the multi-modal data, and constructing a cross-species migration mechanism based on gene homologous mapping to obtain a mapping library for a rapeseed small sample scene; constructing a strategy optimization mechanism with cooperation of outer meta-learning and inner reinforcement learning, and constructing a rapeseed salt-tolerant trait prediction model according to the three-modal coupling encoder, the mapping library and the strategy optimization mechanism; inputting to-be-predicted data into the rapeseed salt-tolerant trait prediction model to obtain prediction data, obtaining a prediction variance according to the prediction data, triggering an active learning sampling mechanism when the prediction variance is greater than a variance threshold, and adding a current sample into a training support set to output a target model.

The application provides a robot tactile and visual fusion double-ring coupling adaptive grasping control method, relates to the technical field of robot grasping control, and comprises the following steps: acquiring surface characteristic data through a light field camera array, collecting dynamic contact information and extracting stability features by a sensor array, constructing cross-modal coupling and fusing tactile and visual information based on Tucker decomposition, establishing mapping parameters and constructing an adaptive time window to optimize grasping parameters, and adjusting grasping posture and clamping force in combination with Lyapunov stability criterion and a dynamic compensation mechanism. The method can improve the stability and robustness of robot grasping in a complex environment.

The invention discloses a photovoltaic cleaning robot anti-drifting turning control system and method based on multi-mode coupling, and the system comprises a counterweight device which is used for carrying out counterweight when a photovoltaic cleaning robot turns and cleans; the detection module is used for detecting operation parameters of the photovoltaic cleaning robot and parameters of a photovoltaic panel cleaned by the photovoltaic cleaning robot; the driving equipment is used for driving the photovoltaic cleaning robot, so that the crawler belt of the photovoltaic cleaning robot is subjected to differential speed regulation; the electrostatic adsorption crawler belt is arranged on a crawler belt chassis of the photovoltaic cleaning robot; and the control module is used for controlling the counterweight module to carry out counterweight at different counterweight speeds according to different working conditions of the photovoltaic robot according to a carried feedforward prediction algorithm and a carried feedback PID fuzzy control algorithm, and the control module is also used for controlling the voltage of the electrostatic adsorption crawler belt, so that the electrostatic adsorption crawler belt has different adsorption capacities. The turning stability of the photovoltaic cleaning robot can be improved.

A system comprising a diaphragm having a first face and a second face, a roll surround disposed around a perimeter of the first face of the diaphragm, a modal coupling apparatus comprising a plurality of connecting arms and a central body, the modal coupling apparatus coupled to the second face of the diaphragm using the plurality of connecting arms, the central body having a shaped first end for receiving a voice coil former, a voice coil wound upon and secured to an outer surface on a first end of the voice coil former, a second end of the voice coil former placed within the shaped first end of the central body, and a positive electrical terminal and a negative electrical terminal, each terminal coupled to the voice coil for transmitting an electrical current through the voice coil upon receiving electrical power.

The invention provides a multi-mode coupling acoustic resonator, a filter and radio frequency equipment, and relates to the technical field of acoustic resonators. According to the multi-mode coupling acoustic wave resonator, the material of a piezoelectric layer is selected, and the value range of an in-plane Euler angle is set, so that piezoelectric coefficients of the piezoelectric layer comprise an e11 component, an e13 component and an e34 component in a piezoelectric coefficient matrix; by designing the ratio of the thickness of the piezoelectric layer to the periodic wavelength of the interdigital electrode, a transverse electric field and a longitudinal electric field exist in the piezoelectric layer at the same time when voltage is applied to the interdigital electrode; and by designing the ratio of the thickness of the interdigital electrode to the thickness of the piezoelectric layer, the integral of the dot product of the electric field and the alternating stress field in the piezoelectric layer in the thickness direction of the piezoelectric layer is not zero, so that the transverse electric field and the longitudinal electric field excite a multi-mode coupling acoustic wave at the same time, and the multi-mode coupling acoustic wave resonator has the advantages of ultrahigh working frequency, low cost and the like. And the material has a large electromechanical coupling coefficient, and can meet the large bandwidth requirement of centimeter wave communication frequency bands.

The application relates to the field of audio transmission technology and discloses an FD-CAN bus audio composite data stream transmission method and system. Three types of real-time multi-modal parameters, including audio content features, bus physical features and service quality constraints, are acquired, an audio dynamic complexity coefficient is calculated according to audio instantaneous energy and spectral flatness, a bus transmission quality coefficient is calculated according to bus instantaneous load rate and signal-to-noise ratio, the maximum transmission delay allowed is obtained according to service quality and signal-to-noise ratio, a dynamic scheduling priority coefficient is acquired in combination with the bus transmission quality coefficient, a time-frequency-space multi-modal coupling matrix is constructed based on the three types of coefficients, a principal coupling feature vector is extracted through principal component analysis and a dynamic transmission strategy index value is calculated, a frame format, a modulation mode and error correction coding parameters are adaptively selected according to the index value, and the audio is sent after being packaged into an FD-CAN frame. The application solves the problems of bandwidth waste and transmission jitter caused by traditional static scheduling through multi-dimensional parameter coupling and dynamic strategy adaptation.

The invention relates to the technical field of monitoring analysis, in particular to an online monitoring method for beef and mutton processing. The method comprises the following steps: acquiring three-mode data in a beef and mutton processing stage, and preprocessing the three-mode data to obtain preprocessed three-mode data; nonlinear cross-modal coupling mapping transformation is introduced, the preprocessed three-modal data is mapped to a unified scoring space, and a scoring latent variable is obtained; a grading manifold compression mechanism is introduced, the grading latent variables are compressed, and grading compression values are obtained; and a score recursive smooth iteration mechanism is introduced, iteration processing is carried out on the score compression value, a final score result is obtained, and online monitoring is realized. The technical problem that a traditional meat quality detection method is insufficient in real-time performance, continuity and accuracy in online monitoring of beef and mutton processing is solved.

This invention discloses a dual-core PCF-SPR ultrawide-range high refractive index sensor based on multi-field coupling synergistic effects. The invention uses a multi-layer air-hole array arranged in a triangular lattice as the cladding, with the air holes having a uniform diameter. Partial air holes are removed at symmetrical positions within the cladding to form a dual-core symmetrical structure. Symmetrical polishing is performed on both sides along the x-axis to construct an open external analyte channel. A large through-hole is introduced at the center of the optical fiber as a built-in sensing channel. Self-driven fluid communication between the built-in sensing channel and the external analyte channel is achieved through a vertical microchannel. A metal film is deposited on the side polished surfaces and the inner wall of the central large hole as a plasma excitation layer, forming a dual-channel SPP excitation interface. Through the dual-channel SPR excitation mechanism and dual-core modal coupling enhancement, the sensor increases the upper limit of refractive index detection to 1.66 and extends the detection range to 1.40–1.66.

This invention discloses a high-sensitivity displacement detection method, comprising the following steps: establishing a vibration displacement model of mode A when a resonator in a displacement sensor couples with mode A and mode B; exciting modes A and B of the resonator and coupling them together to obtain the first frequency of the current mode B; using mode A of the resonator to sense external displacement information, causing a shift in the resonant frequency of mode B, and obtaining the second frequency of the current mode B; obtaining the frequency change of mode B based on the first and second frequencies, and thus obtaining the displacement of mode A. This invention is applied to the field of displacement detection, utilizing the mechanism of modal coupling effect and the influence law of one mode on the vibration response of another, to achieve high-sensitivity detection of the vibration displacement of coupled modes by detecting the frequency shift of the modes. This is of great significance for exploring and developing high-sensitivity micro-displacement detection technology.

The invention relates to the technical field of ecological environment monitoring and forestry remote sensing, in particular to a method for detecting a mikania micrantha prevention and control effect based on aerial photography of an unmanned aerial vehicle, which comprises the following steps of: S100, acquiring aerial photography images of a mikania micrantha prevention and control area in a first time phase before prevention and control and a second time phase after prevention and control by using the unmanned aerial vehicle; and respectively constructing a digital orthoimage DOM and a digital surface model DSM corresponding to the first time phase and the second time phase through a photogrammetry technology, and remarkably improving the accuracy and robustness of mikania micrantha prevention and treatment effect evaluation by constructing a feature analysis framework based on physical-physiological multi-modal coupling. The method has the main advantages that firstly, aiming at a systematic elevation error which is difficult to eliminate in unmanned aerial vehicle multi-temporal monitoring, a self-adaptive correction algorithm based on PIFs (Pseudo Invariant Feature Regions) is initiated, and centimeter-level vegetation net collapse features are successfully extracted under the background of meter-level flight errors;