823 results about "Energy distribution" patented technology

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

The invention 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 provides an electrical equipment remote maintenance guidance method and system. The method comprises the following steps: capturing an aperiodic torque waveform and a magnetic field gradient abnormal signal by deploying a torque fluctuation sensor and a magnetoresistive sensor; performing multi-scale frequency band division on the non-periodic torque waveform, screening out a transient high-frequency component in a start-stop stage of equipment, and separating out a magnetic field polarity reversal characteristic from a magnetic field gradient abnormal signal; performing cross-domain matching on the occurrence time of the transient high-frequency component and the spatial orientation of the magnetic field polarity reversal feature to generate a mixed feature mark; calculating a frequency band overlapping degree based on the time-frequency energy distribution marked by the mixed features and generating a coherence map; and matching a fault historical case library according to the coherence map, and outputting a maintenance strategy set aiming at the rotating shaft dynamic unbalance and electromagnetic interference superposition fault. According to the invention, through fusion of torque fluctuation and magnetic field distortion collaborative analysis, the precision of mechanical and electromagnetic composite fault diagnosis of the rotating shaft is improved.

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 invention provides a power load prediction method and device, and belongs to the technical field of power load prediction.The method comprises the steps that current waveform data are obtained, and fundamental wave and harmonic components in the current waveform data are extracted; carrying out waveform spatial form geometric analysis to obtain a real-time load characteristic sequence, and then carrying out segmentation processing; the current effective value sequence of each time window is converted into a time-frequency domain energy distribution vector, and then a three-level feature library is constructed; constructing a three-dimensional tensor model through equipment start-stop event identification, inputting the three-dimensional tensor model into a multi-target optimizer to evolve feature weights, and filtering abnormal samples to obtain a feature cluster; performing random masking processing on the time sequence data of the feature cluster to generate a mask sequence, inputting the mask sequence into an encoder to reconstruct masking data, comparing, learning and judging abnormal output correction data, and inputting the corrected data into a prediction network to generate a feedback signal flow; and analyzing the feedback signal flow to update the prediction network weight. Based on the method, the invention also provides power load prediction equipment. According to the invention, the precision of power load prediction is obviously improved.

The invention relates to the technical field of medical equipment control, in particular to a control system and a control method of cornea crosslinking equipment. The control system comprises a multi-modal data fusion module which is configured to integrate a preoperative corneal topographic map curvature matrix, OCT layering thickness data and biomechanical parameters to generate a four-channel fusion tensor; the virtual cornea modeling module is configured to input a conditional generative adversarial network to generate a virtual cornea model under the condition of the four-channel fusion tensor; the dynamic energy planning module is configured to generate a thermodynamic diagram of energy distribution based on mechanical response prediction of the virtual cornea model; and an illumination control module configured to perform patterned illumination based on the thermodynamic diagram. According to the method, the virtual cornea model is constructed, the energy distribution thermodynamic diagram is generated, accurate patterning reference can be provided for irradiation treatment, and a better treatment effect is achieved.

The invention relates to the technical field of distributed energy storage system control optimization, and discloses a cooperative control and energy optimization distribution system of a distributed energy storage system, and the system comprises an energy storage node data sensing module which obtains a basic sensing unit and a state data collection interval through hierarchical division of an energy storage node operation environment; the multi-source information fusion processing module collects multi-type energy storage operation information according to a preset period, and generates time sequence fusion information; a cooperative control strategy modeling module constructs a state abstract model according to the state abstract model and extracts multi-dimensional control features; the energy distribution state evaluation module identifies a local distribution state and determines an overall distribution state; the dynamic optimization strategy generation module dynamically adjusts energy distribution parameters, and optimizes an energy storage operation environment in real time to obtain a real-time distribution state. According to the system, accurate cooperative control and efficient energy distribution of the distributed energy storage system are realized, and the operation performance of the system is improved.

The invention discloses a DC fault arc identification method and device based on a wavelet transform algorithm, and the method comprises the steps: collecting a DC line current signal through a high-bandwidth current sensor, carrying out the digitalization of a signal conditioning circuit and a high-speed ADC, and sequentially carrying out the preprocessing of the elimination of baseline drift through differential filtering and the suppression of specific frequency interference through adaptive notch; performing 3-8-layer multi-scale decomposition on the pure signal by adopting a Daubechies wavelet basis, preferably selecting high-frequency detail coefficients from the first layer to the third layer, and creatively fusing a multi-scale energy entropy representing energy distribution randomness and a time domain kurtosis representing transient impact to form a composite characteristic quantity; a threshold value (threshold value = mean value + adaptive coefficient * standard deviation) is dynamically generated based on sliding time window historical data, the adaptive coefficient is dynamically adjusted according to the load type, the resistive load is 1.8-2.2, and the inductive / capacitive load is 2.5-3.5; and when the composite characteristic quantity exceeds a threshold value and the sample proportion is greater than or equal to 85% within the duration of 0.5-5ms, a tripping execution mechanism is triggered.

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 an excitation load grounding fault detection method, system and device based on EEMD and Hilbert spectrum analysis and a medium, and belongs to the technical field of power system energy storage equipment, and the method comprises the steps: collecting and preprocessing an electric signal, and constructing a discretization time domain data sequence; performing iterative decomposition on the sequence to obtain multiple groups of components; time domain structure analysis is carried out, and target components containing fault mutation features are screened; frequency domain transformation is carried out on the target component to construct spectrum distribution, and normalization processing is carried out; and then time-frequency joint transformation is carried out, energy distribution and energy entropy indexes are calculated, and diagnosis is completed. According to the method, modal aliasing is suppressed by using EEMD adaptive noise injection and a set average strategy, accurate separation of high-frequency transient disturbance and low-frequency harmonic waves is realized, multi-dimensional feature combined diagnosis is formed by combining frequency domain screening and time-frequency analysis, and the fault detection precision and reliability are improved.

The invention discloses a reclosing coordination control method and system based on electrical parameter measurement, particularly relates to the technical field of intelligent power grid fault processing and security defense, and is used for solving the problem that the reliability of fault judgment is reduced due to the fact that high-proportion distributed photovoltaic is connected to a power distribution network. A harmonic detection signal is injected during a reclosing waiting period, a response signal is measured, harmonic subspace energy distribution is extracted, an energy distribution matrix is constructed to calculate a nuclear norm change rate and form similarity, a system harmonic impedance stable state is judged through double verification, and finally a reclosing decision is executed according to a fault judgment parameter. The method can accurately identify the convergence characteristic of the dynamic process of the system, effectively distinguish the instantaneous fault from the permanent fault, and significantly improve the reclosing success rate and the power supply reliability of the distributed photovoltaic power distribution network.

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 discloses a digital twinborn intelligent travel management system and method, and relates to the technical field of travel interaction, and the method comprises the following steps: building a multi-projection unified phase baseline and energy distribution auditing model, carrying out the nanosecond-level synchronous sampling of the phase difference and brightness change curve of a plurality of projection light waves in a virtual-real superposition region, and carrying out the nanosecond-level synchronous sampling of the phase difference and brightness change curve of the multi-path projection light waves in the virtual-real superposition region; generating a corresponding initial light field energy coupling matrix; and constructing an interference trajectory inversion network based on the initial light field energy coupling matrix, and replaying the light wave superposition sequence by using a time inversion algorithm. According to the invention, accurate monitoring and active regulation and control of light field energy are realized through unifying a phase baseline and energy auditing, interference focusing and brightness mutation are effectively eliminated, and visual stability and immersion experience are improved; and energy self-adaptive balance and dynamic closed-loop control are realized through phase conjugate driving and spectrum inverse diffusion gating, so that safe operation of a light field and soft and stable brightness are ensured, and a comfortable and healthy digital twinborn travel light environment is created.

The invention discloses an adjustable flat plate type ozone generating device and an adjusting method thereof. The adjustable flat plate type ozone generating device comprises an ozone generating assembly, a radial magnetic field adjusting assembly and an axial magnetic field adjusting assembly, the radial magnetic field adjusting assembly comprises a radial rail base and a spiral line base disc, a plurality of radial rails are arranged on the surface of the radial rail base at equal intervals in the circumferential direction, and magnets are arranged in the radial rails in a sliding mode. The axial magnetic field adjusting assembly comprises a base and an axial track, the radial track base is installed on the base, and the base is driven by a ball screw mechanism to move on the axial track in a reciprocating mode. By arranging the radial and axial adjustable magnetic fields, the motion trail and energy distribution of high-energy electrons in the discharge channel are optimized, the activity of ozone generation reaction is effectively enhanced, and the device can maintain high ozone output efficiency for a long time in actual operation due to the establishment of a magnetoelectric cooperative discharge environment; meanwhile, through a mechanical adjustment structure of the electrode spacing and area, discharge parameters can be optimized in real time according to different working condition changes, the fluctuation of ozone yield is remarkably reduced, and the continuity and stability of system operation are ensured.

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 discloses a geological disaster monitoring and early warning system based on a rock-soil body micro-seismic sensing network, particularly relates to the technical field of geological disaster early warning, and is used for solving the problems of insufficient positioning precision and poor early warning reliability caused by neglecting rock stratum heterogeneity in an existing seismic source positioning method. The method comprises the following steps: constructing a three-dimensional heterogeneous rock stratum model containing fractures and faults by acquiring geological exploration data of a monitored area, simulating a distortion propagation path of micro-seismic waves in an anisotropic medium, and generating a waveform feature library; extracting time-frequency domain energy distribution of an actual signal, matching the time-frequency domain energy distribution with the feature library, and dynamically correcting equivalent wave velocity field parameters; calculating a propagation time difference matrix in combination with sensor coordinates and a corrected wave velocity field, and constructing a nonlinear equation set taking wave velocity deviation as a constraint to solve seismic source space coordinates; and finally, generating a dynamic early warning signal based on the distribution relationship between the seismic source and the rock stratum model. And the seismic source positioning precision and the early warning accuracy in a complex rock mass environment are remarkably 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 data management and control, and discloses a smart park energy data dynamic management and control system. The energy data acquisition module of the system acquires and preprocesses the operation data flow in the park in real time. The energy feature analysis module performs multi-dimensional feature extraction on the data stream, and identifies feature vectors such as peak load and steady-state operation. The event trigger engine generates an energy management event signal based on conditions such as feature vector and threshold comparison, timing pattern matching and the like. The parameter node identification module analyzes an event signal time sequence and positions key nodes such as equipment start and stop and load sudden change. The offset calculation module calculates the offset of the energy parameter relative to the historical reference for the key node. The similar parameter derivation module derives a migratable parameter set containing general control parameters and scene adaptive parameters according to the offset and a historical database. And the dynamic regulation and control module performs real-time adjustment such as power distribution and equipment scheduling on park energy distribution by using the set.

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.

A regional building cooling and heating load artificial neural network energy distribution optimization method disclosed by the present invention comprises the steps of obtaining inter-regional topological relation data and load-related data, constructing a graph network model to capture spatial dependency, fusing meteorological data and historical load records to generate a multivariable dynamic data set, and obtaining the optimal energy distribution of the cooling and heating load artificial neural network. Processing the multivariable dynamic data set by adopting a graph convolutional neural network to obtain an initial prediction result, adjusting the initial prediction result through an artificial neural network to generate adjusted load distribution data, and analyzing by combining a heat transfer rule and a time sequence to obtain spatial and temporal distribution characteristics; and integrating equipment efficiency constraint and cost fluctuation data by adopting a multi-objective optimization algorithm to obtain an optimized objective function value, adjusting equipment dynamic parameters through deep reinforcement learning to obtain a parameter adjustment scheme, and optimizing an energy distribution proportion by adopting a linear programming algorithm to obtain a final energy distribution scheme.

The invention discloses a power transmission line galloping risk early warning and system based on multi-physics field fusion, and the method comprises the steps: firstly obtaining a multi-physics field data set of a power transmission line galloping process, and generating a dynamic three-dimensional point cloud sequence through space-time alignment and three-dimensional attitude reconstruction; galloping energy distribution features, frequency spectrum features and space trajectory features are extracted and fused to obtain a fused feature vector; inputting the vector into a physical constraint enhanced bidirectional long-short-term memory network, predicting a galloping evolution trend and outputting a joint prediction matrix of galloping amplitude and frequency; inputting the matrix into a pre-trained risk propagation reasoning model, and calculating a multi-level risk quantitative index; and finally, generating a risk early warning prompt according to the index and sending the risk early warning prompt to corresponding risk management equipment so as to realize power transmission line galloping risk early warning. The power transmission line galloping evolution trend can be accurately predicted, so that the power transmission line galloping risk can be accurately early warned.

The invention provides a construction project quality detecting and monitoring method and system, and relates to the technical field of data processing.The method comprises the steps that spatial positioning sensors are deployed at steel structure beam column welding joints, a concrete core shear wall root pouring area and a curtain wall top cantilever connecting end; welding acoustic emission frequency domain signals of beam-column joints, concrete vibration energy distribution waveforms of the root of the shear wall and dynamic load stress spectrums of the cantilever connecting ends are collected respectively, and a spatial heterogeneous original data set is generated; acquiring three-dimensional coordinates of positioning sensors on a steel structure beam column welding node, a concrete core shear wall root pouring area and a curtain wall top cantilever connecting end so as to construct a dynamic space reference frame, and calculating a deformation feature vector of the dynamic space reference frame; the deformation feature vector comprises a reference frame area change gradient value, a frame side vector fluctuation coefficient and a space included angle offset entropy value. According to the invention, the real-time performance of construction project quality monitoring is improved.

The invention discloses an energy digital management method and system, and the method comprises the steps: receiving a multi-source heterogeneous energy data collection signal, and generating an initial energy characteristic spectrum based on a space-time correlation feature extraction algorithm; performing graph neural network coding on the initial energy feature graph, constructing an energy knowledge graph vector set, and decomposing a high-dimensional vector in the knowledge graph vector set into a plurality of subspace vectors; performing real-time scheduling decision on the subspace vectors by using an energy flow optimization model driven by reinforcement learning to generate an energy distribution strategy set; a digital twinborn verification algorithm is applied to the energy distribution strategy set, physical-digital space consistency verification is carried out, and a trusted energy scheduling scheme is generated; and performing block chain evidence storage and intelligent contract execution on the credible energy scheduling scheme to generate a non-tampering energy transaction record. By utilizing the embodiment of the invention, the scheduling precision and the response speed of the energy system can be improved, and full-link credible management from decision-making to execution is realized.

The present application relates to the technical field of data processing, and particularly relates to a power monitoring method and system for remote electric energy meter, the method comprising: constructing a time series factor graph model with voltage and current phasor as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence to generate a time-frequency energy distribution graph and obtain a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a deep auto-encoder pre-trained on normal power consumption working condition data to calculate a reconstruction error, simultaneously calculating a Lyapunov index of the sequence within a preset time window, and obtaining a negative log-likelihood probability according to a pre-established Gaussian mixture model describing the distribution of the index under normal working condition; and weighting and fusing the reconstruction error and the negative log-likelihood probability to generate a comprehensive abnormality index for judging power consumption events. The present application can realize high-precision and low-false-alarm-rate detection of power consumption events.