551 results about "Energy distribution" patented technology

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention discloses a high-voltage cable operation state on-line monitoring, intelligent early warning and fault positioning system, particularly relates to the technical field of power equipment insulation monitoring, and is used for solving the problems that transient discharge signals generated in the cable insulation degradation process are difficult to effectively capture and accurate fault positioning cannot be realized in the prior art. The method comprises the following steps: collecting transient leakage current signals and traveling wave propagation characteristic parameters, performing phase correlation analysis on leakage current pulses and power frequency voltage to identify discharge types, and performing variational mode decomposition and Hilbert-Huang transform on the signals to respectively extract complexity characteristics and energy distribution characteristics; signal logic conflicts are judged by analyzing similarity and statistical distance among characteristics of a plurality of monitoring points, and collaborative diagnosis is started or traveling wave distance measurement is directly utilized to carry out insulation state evaluation and fault location. And finally, differential early warning levels are generated according to the diagnosis result, and fault line selection and positioning information is output to realize real-time monitoring, intelligent early warning and accurate positioning of the operation state of the high-voltage cable.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention discloses a monitoring equipment fault rapid positioning method based on causal diagram reasoning, and the method comprises the following steps: building a thermal diffusion model composed of equipment nodes and links through collecting the topological structure and operation data of a monitoring system; normalizing and fusing the operation deviation information into a node abnormal energy value, and establishing an abnormal energy propagation equation; when an alarm is triggered, collecting an energy change sequence of each node to generate a space-time energy distribution matrix, and reversely solving a thermal diffusion equation to calculate a disturbance source item; performing clustering backtracking on disturbance source items, determining fault root cause nodes, and comprehensively evaluating confidence; and when multi-source abnormity exists, thermal field orthogonal decomposition is executed, and model parameters are adaptively updated according to field results. According to the method, abnormal propagation of monitoring equipment is modeled into a thermal diffusion process, and reverse solution and adaptive calibration are combined, so that rapid, explainable and high-precision positioning of fault root causes in a complex monitoring network is realized.

The invention discloses a wind turbine generator blade damage judgment method and detection system based on voiceprint detection, and belongs to the technical field of wind turbine generator blade detection. The damage judgment method comprises the following steps: acquiring environmental parameters of a wind power plant, and establishing a sound velocity correction model based on the environmental parameters; acquiring a blade resonance sound signal, and correcting the blade resonance sound signal based on the sound velocity correction model to obtain a blade resonance sound signal with a high signal-to-noise ratio; performing FFT spectrum analysis on the blade resonance sound signal with the high signal-to-noise ratio, identifying the peak position of the first three-order natural resonance frequency of the blade, and performing analysis to obtain the current modal frequency, damping ratio and sub-band energy distribution entropy value of the blade; historical baselines of the modal frequency, the damping ratio and the sub-band energy distribution entropy of the blade in the healthy state are obtained, and the modal frequency offset, the damping ratio change rate and the acoustic impedance spectrum entropy of the blade are obtained; and wind turbine generator blade damage determination is carried out by using a high-precision time difference detection technology.

The invention discloses a ship equipment vibration test method and system based on frequency domain kurtosis mapping and non-Gaussian signal generation. The ship equipment vibration test method comprises the following steps: collecting an actual ship impact vibration signal and extracting a target power spectral density curve; the method comprises the following steps: identifying an impact energy concentration frequency band based on spectrum energy distribution, carrying out frequency band division by adopting an elliptical filter bank with a second-order section structure, and calculating an envelope kurtosis value of each frequency band to establish a kurtosis-frequency mapping model; after the initial Gaussian driving signal is generated, a non-Gaussian driving signal is generated through a phase randomization and amplitude dynamic scaling algorithm based on a mapping model; a multi-target optimization controller based on Pareto leading edge search is adopted, a power spectral density error and a kurtosis error are taken as optimization targets, synchronous optimization is carried out through fast non-dominated sorting and an adaptive hybrid variation strategy, and a driving signal is output. According to the invention, the broadband and non-Gaussian impact vibration environment of the ship equipment in the actual severe sea condition can be reproduced in a high-fidelity manner, and the authenticity of the test and the fault mode coverage capability are remarkably improved.

The invention discloses a muck treatment capacity monitoring method and a muck treatment capacity monitoring system, and relates to the technical field of shield construction and muck treatment capacity prediction treatment, and the method comprises the following steps: collecting operation parameters of a shield tunneling machine deslagging and conveying link, calculating a muck flow coupling index, evaluating a deslagging link coupling state, and carrying out optimization; the load coupling index of the shield tunneling machine is calculated, the energy distribution balance is judged, and dynamic adjustment is conducted according to the unbalance state; calculating the energy consumption deslagging propulsion efficiency, evaluating the matching degree, and optimizing the power and conveying parameters of each link; a CNN and LSTM combined multi-scale modeling method is adopted to predict the future muck handling capacity, and online correction is carried out through a sudden change adaptive network; and finally, fusing the standardized data set with the actual operation parameters to generate a unit time muck handling capacity sequence. By means of the method, the deslagging and propelling matching degree of the shield tunneling machine can be improved, the energy utilization efficiency is optimized, and high-precision prediction and real-time regulation and control of the muck handling capacity are achieved.

The invention relates to the technical field of image data processing, in particular to a water-soluble fertilizer dissolution rate visual measurement method and system, and the method comprises the steps: obtaining a sequence image of a dissolution process; performing multi-scale frequency domain analysis on the image, and calculating a scale convergence index by using the deviation between a local phase and a weighted average phase; a spectral sharpness factor is calculated according to the ratio of the high-frequency energy response to the low-frequency energy response; fusing the scale convergence index and the spectral sharpness factor to obtain a solid boundary probability; segmenting the particle region based on the probability and converting the particle region into physical mass, and differentiating a mass change sequence to obtain a dissolution rate. By fusing the phase consistency and the frequency domain energy distribution characteristics, the physical entity edge and the optical refraction halo are effectively distinguished, the refraction interference problem during high-concentration dissolution is solved, and the dissolution rate measurement accuracy is improved.

The invention relates to the technical field of electrical engineering and intelligent fault diagnosis, and discloses a water pump motor fault diagnosis method and system based on current harmonic characteristics, and the method comprises the steps: collecting stator current, rotating speed and load signals; generating an alpha-beta current component through Clark transformation, constructing an analytic signal, extracting an instantaneous phase, and calculating an instantaneous frequency; a mapping relation is established in combination with the rotating speed, and the current signals are resampled to eliminate frequency dispersion; obtaining a high-resolution harmonic energy distribution diagram by adopting multi-scale synchronous compression wavelet transform; and extracting an energy concentration ratio, a peak offset and a bandwidth expansion coefficient in the characteristic frequency band, inputting the energy concentration ratio, the peak offset and the bandwidth expansion coefficient into the pre-trained multi-layer perceptron classifier, and outputting a fault type. According to the method, through high-precision time-frequency focusing and multi-dimensional feature fusion, the fault recognition accuracy and robustness are remarkably improved, and online diagnosis of multiple typical faults can be achieved only through current signals.

The invention discloses a deep rock mass fracture imaging method and system based on micro-seismic monitoring, and belongs to the technical field of geophysical exploration, and the method comprises the steps: collecting multi-channel micro-seismic event waveform data, obtaining a main seismic phase direct waveform and a scattering tail waveform through wave field separation, and carrying out the imaging of a deep rock mass fracture. Determining a three-dimensional space coordinate and an energy distribution form of the seismic source through joint inversion, carrying out clustering analysis, and constructing an initial three-dimensional space distribution form; each seismic source is regarded as an active excitation source, a scattering tail wave waveform is regarded as a reflection signal, scatterer three-dimensional point cloud data is generated through reverse time migration processing, analysis is carried out in combination with an initial three-dimensional space distribution form, and an enhanced fracture structure is formed; and finally, through topology analysis and orientation clustering, discrete scatterers are eliminated, and a fracture development map is generated. According to the method, a technical path of combining a main seismic phase direct wave inversion seismic source and a scattering tail wave inversion scatterer is adopted, high-precision and high-fidelity imaging of a deep rock mass movable fracture can be realized, and physical activity is revealed.

The invention relates to the technical field of energy management, and provides a park light storage and charging energy distribution control method and system, and the method comprises the steps: transmitting an instruction to an energy storage system in an emergency, enabling the energy storage system to release an electric energy pulse to a micro-grid at the maximum available power within a preset time, and enabling the micro-grid to be in the maximum available power; providing time buffer for the power reduction process of the load units in the park; after the power reduction instruction is executed, identifying response characteristics of each load unit in the park to the power reduction instruction, and grading each load unit according to the response characteristics to obtain grading information; during the time buffering period, according to the grading information of the load units, a grading decreasing load reduction plan is formulated and executed, and a reduction instruction is issued in a peak shifting manner, so that the overall load is smoothly reduced; and continuously monitoring the power quality of the micro-grid, and finely adjusting the output of the energy storage system according to the power quality of the micro-grid. The method has the effect of improving the stability and reliability of light storage and charging in the park.

The invention relates to the technical field of harmonic intelligent positioning, and discloses a coupling harmonic intelligent positioning method and system based on multi-modal fusion, and the method comprises the steps: obtaining the voltage amplitude time sequence data of each observation point, constructing a frequency distribution characteristic curve, and extracting a frequency center and a broadening parameter; parameter updating is realized in combination with priori information of power grid impedance, and an adaptive frequency analysis window is constructed on this basis to extract energy distribution characteristic quantity; the reliability weight of an observation point and the time sequence weight of harmonic source equipment are further introduced, and the dynamic characteristics of the space credibility and the operation state are comprehensively reflected; and finally, taking the two types of weights as weighting factors, carrying out weighted calculation on the harmonic propagation model deviation of the candidate nodes to obtain a comprehensive residual value, and positioning the node with the minimum comprehensive residual value as a coupling harmonic source node, thereby remarkably improving the accuracy of harmonic source positioning in a complex power grid environment.

The invention relates to the technical field of signal processing, in particular to a radar antenna signal processing method and system, and the method comprises the following steps: collecting an antenna time domain echo extraction channel energy curve, calculating the initial phase attenuation curvature of a period length energy peak value, and building an interference characteristic parameter set; matching real-time echoes, judging homology, eliminating interference, extracting main echoes, extracting main peak position spacing through frequency spectrum transformation, calculating a judgment threshold, comparing, and judging and executing decoupling operation to obtain target frequency spectrum data. According to the method, by extracting Doppler channel coherent energy distribution and establishing a periodic interference characteristic parameter set, the intrinsic characteristics of periodic interference can be accurately described, rapid judgment and effective stripping of interference source homology are achieved, and by analyzing the Doppler center frequency change trend and generating a pulse delay compensation quantized value, the interference source homology can be quickly judged and effectively stripped. The stable target identification and frequency decoupling operation under the interference background is realized, and the target identification and frequency control level in the variable environment is obviously enhanced.

The invention discloses a micro-grid energy coordination control method considering communication topology dynamic change, which belongs to the technical field of electric energy control and management, and comprises the following steps: monitoring a communication connection state between control nodes, and generating a topology change event; evaluating energy supply capability and communication reliability and adjusting influence in energy coordination to generate adaptive weights; in combination with real-time sensor information and historical operation data, short-term energy prediction data is generated, in combination with adaptive weight, a global energy distribution consensus is achieved, and then an energy distribution decision is generated; in the execution process, the topology change event and the deviation between the short-term energy prediction data and the real-time energy data are monitored, and if a deviation threshold value is exceeded, an event signal is triggered to drive the control strategy to be updated. According to the method, the adaptive weight is generated by monitoring the communication topology and the energy state, and distributed negotiation and event triggering updating are performed in combination with short-term prediction, so that the energy coordination robustness and the operation stability of the micro-grid in a dynamic environment are improved.

The invention provides a TMD lockout control method for motion suppression of a semi-submersible platform, and the method comprises the steps: carrying out the frequency domain decomposition of a random wave predicted by a target sea condition, obtaining the energy distribution of each frequency component, obtaining the equivalent amplitude of each frequency component according to the energy distribution, considering the amplitude response of the platform under the action of waves of different frequencies, and achieving the motion suppression of the semi-submersible platform. The method comprises the following steps of: calculating a weight ratio of platform motion response caused by each frequency component under the action of random waves by integrating three types of information, namely amplitude, amplitude response and motion suppression effect, and finally carrying out weighted combination on the locking time corresponding to each frequency component according to the weight ratio, so as to obtain the motion suppression effect of the platform. And the optimal locking control time suitable for the current random wave is obtained. According to the method, a high-complexity non-causal control command does not need to be deduced, control parameters can be updated in real time according to short-term sea condition changes, pitching response of a platform is effectively reduced, and comprehensive optimization control over multi-frequency waves is achieved.

The invention relates to the technical field of intelligent sensing systems, in particular to an algorithm and system for avoiding water surface floating objects and improving monitoring precision based on radar water level monitoring, and the method comprises the following steps: collecting radar echoes, constructing spatial-temporal characteristics based on waveform asymmetry and coherent attenuation gradient, and calculating the spatial-temporal characteristics; according to the method, the central moments of the front edge and the rear edge of the echo are calculated, the waveform asymmetry is quantified, the dynamic stability is analyzed in combination with the attenuation gradient of the multi-time-lag coherence coefficient, the floating object interference is recognized from the double dimensions of the spatial form and the time memorability, and the actual water level is inverted. An effective scattering trailing interval is positioned, nonlinear approximation operation is performed on trailing data in combination with an index physical model, a shielded water surface energy distribution curve is reconstructed, a derivative zero point is solved, distance measurement deviation caused by floating objects is corrected, the real water level height is effectively restored, and the monitoring anti-interference capability and the data accuracy in a complex hydrological environment are improved.

The invention relates to the technical field of insulation degradation detection, and discloses an on-line detection method and system for insulation degradation of electric control equipment, and the method comprises the steps: obtaining and decomposing broadband dielectric response data of the electric control equipment, and obtaining a feature sequence of a plurality of frequency components; according to the energy distribution of the high-frequency component in the feature sequence, determining a potential degradation area of the electric control equipment and extracting a degradation feature component, applying pulse excitation to the degradation feature component by adopting a time domain reflection algorithm, and obtaining a time difference sequence of a reflected signal; comparing the time difference sequence with a preset delay reference, determining the initial point position of degradation invasion, and analyzing the continuous change of the time difference sequence to obtain the boundary contour information of the insulation degradation area of the electric control equipment; three-dimensional coordinate mapping of the insulation degradation path of the electric control equipment is generated based on the boundary point set, the three-dimensional coordinate mapping is optimized, a final path map of insulation degradation of the electric control equipment is generated, and the detection precision of insulation degradation of the electric control equipment is improved.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The invention relates to the technical field of intelligent power grid monitoring, in particular to a measuring and testing method for an intelligent low-voltage distribution box line, which comprises the following steps of: acquiring and decomposing an original signal of a reconstructed line to form a characteristic image; steady-state and outlier feature clusters are autonomously emerged through unsupervised clustering, and anomaly recognition without a preset threshold value is achieved. And parameterizing the steady-state features into a dynamic template for real-time comparison and marking of feature deviation events. For a deviation event, an original signal is deeply deconstructed to extract energy distribution and phase synchronization distortion characteristics, and the characteristics, a switching event and a load record are subjected to time sequence alignment and coupling analysis, so that the logic relevance of an abnormal source is locked. Based on the relevance, the sensing device is driven to adaptively adjust a sampling strategy, a new round of verification measurement is started, and a closed loop from sensing, diagnosis to optimization tracking is formed. According to the method, unknown abnormity can be adaptively identified, and automatic analysis of abnormity reasons and closed-loop optimization of a monitoring strategy are realized.

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

The invention discloses a data management system and method based on artificial intelligence, and relates to the technical field of data intelligent processing, and the method comprises the steps: carrying out the frequency spectrum transformation of an encrypted feature vector, calculating the energy density of each frequency spectrum coefficient, constructing a cumulative distribution function, determining an effective frequency spectrum interval based on the energy distribution function, and constructing a projection matrix. Compressing the spectrum vector to a low-dimensional space by using a projection matrix to generate a compressed feature vector; and constructing a neural network model to calculate an abnormal score, setting a detection threshold, performing label detection on the abnormal score, forming a label vector, performing homomorphic decryption on the label vector, and generating a plaintext feature vector. According to the method, a frequency spectrum energy distribution function is introduced on the basis of homomorphic encryption, safe dimensionality reduction and rearrangement of feature vectors are realized by combining periodic mapping and an integer mechanism, the structural identifiability of the feature vectors in a finite field is enhanced, an offset period is optimized through a fluctuation potential function, and the sensitivity to abnormal changes is improved.

The invention discloses a mine blasting design optimization method and device based on digital twinning and a medium, and relates to the technical field of digital twinning, and the method comprises the steps: collecting terrain three-dimensional point cloud, geological drilling records, joint and fracture distribution and rock mass mechanical parameters of a physical mine, and constructing an initial digital twinning body; through a deep reinforcement learning algorithm of a multi-target reward function, performing multiple rounds of interactive training in a digital twinborn simulation environment to obtain a deep reinforcement learning agent; implementing an optimal blasting scheme in the physical mine, acquiring blasting vibration signals, and inputting the blasting vibration signals into the vibration inversion neural network to obtain rock mass parameter correction values; the updated digital twinborn body is corrected based on the rock mass parameter correction value, a blasting simulation scene is reconstructed, blasting energy distribution, fragment granularity and slope stability are evaluated, and a blasting design optimization feedback data set is generated; a high-fidelity initial digital twinborn body fused with multi-source geological information is constructed, and the safety of mine blasting operation is effectively improved.

The invention discloses a multi-sensor fusion target scoring system high-precision positioning method, and relates to the technical field of target scoring positioning, and the method comprises the following steps: building a transient interference recognition channel based on the electromagnetic response of a shock wave sensor, obtaining the electromagnetic disturbance of the bullet hit front edge, and generating an interference time sequence; performing delay triggering on the image sensor according to the interference time sequence, and avoiding an interference peak value to form a safe exposure time period; performing charge pre-release on the image acquisition chain before safe exposure; carrying out synchronous compensation on the shock wave signal based on energy distribution of charge pre-release, so that the shock wave signal and image energy response are symmetrical and mutually stable in a time domain; and dynamic shielding control is carried out on a space coupling path between the two types of sensors by utilizing an energy balance region formed in the mutual stable state. Through energy symmetric compensation and dynamic shielding control of shock waves and image signals, time-space separation and synchronous mutual stability of the signals are achieved, and high precision and stability of target scoring positioning under high-energy shooting are ensured.

The invention discloses a power grid big data multi-dimensional risk perception method and system, and belongs to the technical field of power grid big data risk prediction.The power grid big data multi-dimensional risk perception method comprises the steps that multi-source perception data is collected, time-space synchronization and normalization processing are conducted, and a multi-dimensional feature representation matrix is constructed; constructing a feature coordinate system based on the matrix, mapping the feature coordinate system to a risk topology network, and generating a key node connection weight map; a multi-dimensional disturbance response entropy model is further constructed, and nonlinear situation modeling of power grid state disturbance is achieved; generating a risk energy distribution diagram by using the prediction model, extracting a high-risk path, and generating a microcosmic situation compensation curve in combination with a historical trajectory for adjusting an abnormal threshold; and finally, outputting a risk level evaluation result, and triggering a safety control and early warning response mechanism in a linkage manner. The method has the advantages of being high in multi-source data fusion degree, high in risk prediction accuracy and intelligent in response control, and the risk perception and active defense capability of a power grid in a complex environment is effectively improved.

The invention provides a tunnel blasting vibration monitoring method and system based on image recognition, and relates to the technical field of tunnel safety monitoring, and the method comprises the steps: obtaining a geological parameter set of a tunnel target region and an image sequence of a blasting moment; carrying out fluctuation analysis on geological parameters to obtain vibration characteristic data reflecting rock mass non-uniform characteristics, synchronously carrying out optical flow analysis and region identification processing on an image sequence, respectively generating a displacement vector field and an identification deformation region, carrying out spatial association on the vector field and a micro-deformation region to form vibration field data with rock mass structure characteristics, and carrying out vibration field identification processing on the vibration field data; and coupling with the vibration characteristic data to reconstruct a three-dimensional propagation path, and finally identifying a damage risk area of the tunnel target area based on an energy distribution pattern on the path. According to the method, the damage risk identification precision of the tunnel rock mass under blasting vibration is improved.

The invention relates to the technical field of image processing, in particular to a device and method for fish behavior monitoring, and the device comprises a fish body grid construction module, a vertex optical flow tracking module, a compression potential energy calculation module, an energy field thermodynamic mapping module and a swimming mode identification module. According to the method, a triangular topological grid covering the surface of an organism is constructed, a time sequence vertex light flow track is tracked, an affine transformation matrix is used for analyzing the area scaling ratio of a local grid unit in the movement process, and geometric deformation of a visual layer is mapped into a physical potential energy index representing muscle contraction strength; a micro deformation difference is amplified in combination with nonlinear exponential operation, a visual energy distribution thermodynamic map is generated through Gaussian smoothing processing, conduction characteristics of a body high-energy connected region in a space-time dimension are identified, and crossing from pure track observation to biomechanical function evaluation is realized. And the energy consumption efficiency and explosion starting characteristics in different tail swinging modes can be accurately identified.

The invention relates to a dynamic light beam shaping system and method based on a spatial light modulator. The system comprises a laser light source module, a beam expanding collimation light path, a light beam modulator, a focusing optical assembly, a detection and feedback module and a control and optimization module. According to the method, the uniformity of the output flat-topped beam can be corrected by virtue of a random parallel gradient descent (SPGD) algorithm and combining a Zernike polynomial as a control variable. Meanwhile, a composite performance index comprehensively considering energy distribution and shape features is constructed, the correction process of the flat-topped beam is accelerated by adopting a mode of gradually improving Zernike polynomial orders in multiple stages, the convergence trend of the performance index is used as a judgment basis of stage switching, stage independent variable dimension expansion is implemented, and the correction precision of the flat-topped beam is improved. And the convergence speed is obviously improved. Besides, adaptive selection of disturbance amplitude and learning rate parameters in the SPGD algorithm is realized by using noise estimation and a local linearity index, and the robustness of the system to noise and environmental disturbance is enhanced.

The invention discloses a source load multi-body dynamic clustering and regulation method based on a double-layer coupling local subgraph, and belongs to the technical field of source load regulation. The method comprises the following steps: firstly, constructing a local sub-graph model with coupled topology layer and physical layer, fusing node power, energy storage state and load characteristics, and realizing accurate description of high-order interaction relationship between source and load; secondly, providing a multi-scale feature extraction and incremental random walk clustering algorithm, and realizing dynamic updating and feature adaptive adjustment of node clustering in a local subgraph; and finally, constructing a layered autonomous regulation and control and cross-sub-graph cooperation mechanism, realizing local power balance in a sub-graph layer, realizing energy storage coordination and optimal energy distribution in a global layer, and realizing continuous updating and closed-loop optimization of a regulation and control scheme in combination with an incremental feedback actuator. According to the method, an integrated framework of coupling modeling-dynamic clustering-distributed regulation and control is formed, and the stability and toughness of multi-main-body cooperative operation of the power system are remarkably improved.