1312 results about "Energy distribution" patented technology

The invention provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault positioning method based on traveling wave detection, and the method comprises the following steps: S1, distributed detection network construction and signal collection: collecting a three-phase current traveling wave signal in real time; s2, time-frequency joint analysis and feature extraction: extracting time-frequency energy distribution features; s3, intelligent screening of effective fault wave heads: screening out the effective fault wave heads; s4, initial arrival time parameter determination: determining the initial arrival time of each detection point; s5, according to topology self-adaptive error correction, transmission path wave velocity deviation is corrected through line parameter dynamic compensation, and clock errors of the asynchronous detection device are eliminated through adjacent node clock difference estimation; and S6, multi-source data fusion and fault positioning: resolving space coordinates of fault points. According to the invention, the method improves the recognition accuracy of fault features, and solves the problems of poor anti-interference capability and high misjudgment rate of a single feature in a conventional method.

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

The invention provides a slope quality monitoring and early warning method and system based on seismic oscillation data, and the method comprises the steps: firstly obtaining a seismic oscillation monitoring data set of a target slope region, covering seismic oscillation waveform data and environment correlation parameters of a plurality of monitoring nodes in a preset time period, and then carrying out the dynamic preprocessing of the seismic oscillation monitoring data set, comprising data alignment, noise filtering and dimension unification, a standardized data set is obtained, then a dynamic association feature set containing seismic oscillation energy distribution, slope response frequency and environment coupling fluctuation features is extracted based on the standardized data, and then a slope quality analysis model is called to carry out state prediction on the feature set. And generating a slope quality evaluation result according to the difference degree between the stable state characteristics and the abnormal fluctuation characteristics, and finally determining an early warning strategy according to the slope quality evaluation result and triggering early warning signal output, thereby realizing effective monitoring and early warning of the slope quality.

The invention provides a high-precision five-axis rotary table cooperative control method and system based on multi-closed-loop feedback, and the method comprises the steps: obtaining a real-time acceleration signal of a five-axis rotary table, carrying out the spectrum analysis of the acceleration signal, determining a current resonant frequency drift range and a main frequency component, and carrying out the frequency spectrum analysis; dynamically simulating the turntable structure by adopting a pre-established finite element model, and predicting the vibration mode change trend of the structure by comparing the deviation between the modal frequency obtained by simulation and the actually measured signal spectrum; according to the vibration mode change trend, matching similar vibration mode modes from a pre-established rotary table vibration mode database, obtaining corresponding vibration energy transmission path distribution, and determining an energy transmission abnormal region by analyzing the distribution of energy on each transmission path to obtain vibration energy transmission characteristics; according to an optimized motion instruction of a five-axis turntable, the dynamic response of the turntable is estimated, vibration frequency domain characteristics are extracted, and frequency domain compensation parameters are generated in combination with a preset frequency domain error matrix.

The invention relates to the technical field of quartz sand detection, in particular to a quartz sand impurity detection method which comprises the following steps: acquiring spectral data of a quartz sand sample, and obtaining spectral line interference degrees of characteristic peaks according to energy distribution conditions of the characteristic peaks of the spectral data; based on the peak shape variation condition of each characteristic peak and the offset condition of each characteristic peak and a matrix characteristic peak, the matrix interference degree of each characteristic peak is obtained, and the interference intensity of each characteristic peak is obtained by combining the anti-interference degree of each characteristic peak so as to optimize the chaos regulation factor when the chaos mapping population is initialized; and finally, combining with an optimization algorithm to obtain a characteristic wave band with minimum interference, and detecting the purity and impurity content of the quartz sand based on the characteristic wave band. The quartz sand impurity detection precision can be improved.

The invention provides a thin film surface defect detection method and system, and relates to the technical field of defect detection.According to the thin film surface defect detection method and system, an intelligent secondary verification link is constructed by introducing a defect confidence evaluation mechanism based on form and energy distribution, so that the detection performance is fundamentally improved; according to the mechanism, real physical defects with regular forms and concentrated energy and pseudo defects caused by electromagnetic interference, instantaneous film wrinkles and the like can be accurately distinguished, and the problem of high false alarm rate caused by dependence on single signal strength in the prior art is effectively solved while the high detection rate of low-contrast defects is reserved; besides, the judgment model based on physical characteristics has natural robustness for background noise generated in high-speed motion, and an adjustable confidence threshold value endows the system with extremely high practical flexibility, so that the system can adapt to complex and changeable industrial environments and different quality control standards, and the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the intelligent level of the whole detection system are obviously enhanced.

The invention provides a dissimilar material crystallization welding control method based on microwave energy distribution, which comprises the following steps: acquiring a high-density temperature map of a to-be-welded workpiece through a fiber grating array, and acquiring a real-time temperature value from a dissimilar material interface edge to a central area of the to-be-welded workpiece to obtain initial temperature distribution; calculating a temperature difference value between the edge and the central area, identifying a local overheat point position by comparing a temperature gradient or setting a temperature difference threshold value, and comparing the overheat point temperature with a preset material overheat threshold value to judge whether an overheat area exists or not; if the temperature difference value exceeds a temperature difference threshold value or a local overheating area exists, extracting space coordinates of a low-temperature area and a high-temperature area from the high-density temperature map, and identifying an energy distribution target area needing to be adjusted; and extracting temperature distribution from the updated energy distribution diagram, analyzing a temperature gradient to identify a heating power concentration region, and calculating to obtain a target stress distribution state in combination with the heating power concentration region.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention discloses a glass ceramic thickness detection method and a related device, and the method comprises the steps: obtaining a vibration signal generated in a glass ceramic thinning and grinding process, and extracting a vibration dominant frequency, a frequency spectrum centroid frequency and a frequency band energy distribution ratio as thickness characteristic parameters through fast Fourier transform processing; three-dimensional position coordinates of the grinding head are obtained, space mapping and weighted average processing are conducted on the thickness characteristic parameters, and space distribution of the thickness characteristic parameters is obtained; performing process interference compensation and sliding window time sequence smoothing processing according to the grinding process parameters to obtain stable thickness characteristic distribution; based on the stable thickness feature distribution and the current grinding process, final thickness distribution is predicted, and a thickness qualification pre-judgment result is obtained; and extracting a time sequence change characteristic of the thickness characteristic parameter as a vibration evolution index, and performing comprehensive quality evaluation in combination with a thickness eligibility pre-judgment result. According to the scheme, the thickness can be detected in real time in the thinning and grinding process of the microcrystalline glass, and the production efficiency is improved.

The invention relates to the technical field of ship power systems and energy management. The invention provides an energy optimization management method and system for an extended-range hybrid power ship. The method comprises the following steps: collecting ship multi-source heterogeneous data in real time, and constructing a multi-source heterogeneous data set; based on the multi-source heterogeneous data set, constructing a load prediction model based on a time sequence convolutional network and long and short term memory fusion, and dynamically outputting propulsive power demand probability distribution in a future navigation period; a multi-objective optimization model is established, and an improved multi-objective genetic algorithm is adopted to generate a Pareto frontier solution set; and on the basis of a rolling time domain optimization framework, in combination with real-time navigation situation awareness data, carrying out online correction on the Pareto solution set, and generating and executing an optimal energy distribution instruction set. The problems of an existing extended-range hybrid power ship energy management technology in the aspects of working condition adaptability, multi-energy coupling efficiency, real-time state sensing and predicting capacity and emission and economical efficiency balance are solved.

The invention provides an automatic waste heat recovery method which comprises the steps that waste heat pressure parameters, component media and flow characteristic data are obtained, waste heat data are classified, characteristic vectors of different types of waste heat are obtained, and the characteristic vectors of different types of preheating are stored in a waste heat characteristic database; analyzing and predicting randomness and intermittency rules of waste heat generation and a waste heat concentration area, and dynamically adjusting a cold absorption strategy or a heat absorption strategy; corresponding cold absorption or heat absorption operation parameters are called from the waste heat characteristic database, and cold absorption or heat absorption equipment is controlled to set a self-adaptive adjustment operation state according to the parameters; the cold and heat storage energy in the recovery process is monitored in real time, and the energy loss value of waste heat recovery is analyzed; and constructing a photovoltaic power storage energy optimization scheduling model, solving to obtain an energy distribution strategy, dynamically adjusting a use strategy of cold and heat storage energy, optimizing a distribution proportion and a time sequence of cold and heat energy, and realizing collaborative optimization control of photovoltaic power storage and waste heat recovery.

The invention relates to the technical field of big data, and discloses an audio and video monitoring and early warning method and system based on multi-modal model driving, and the method comprises the steps: S1, collecting audio and video signals in a monitoring scene, and extracting the energy distribution characteristics of the audio and video signals, wherein the energy distribution characteristics at least comprise the frequency energy distribution of the audio signal and the brightness and color change rate of the video signal; s2, multi-modal feature extraction of energy distribution features is carried out based on audio and video signals, and environment background feature vectors are constructed in combination with environment background information; s3, dynamically adjusting anomaly detection thresholds of the audio signal and the video signal based on the environmental background feature vector; s4, comparing the audio and video signal energy distribution characteristics extracted in real time with an anomaly detection threshold value, and when the audio and video signal energy distribution characteristics exceed the range of the anomaly detection threshold value, determining an abnormal event and obtaining early warning information; and S5, sending the early warning information from the edge equipment to a monitoring center through a low-bandwidth communication protocol.

The invention provides an elevator energy recovery application method and system based on a super capacitor, and relates to the technical field of energy recovery, and the method comprises the steps: collecting elevator operation data and super capacitor state data, constructing a state space and an action space, and constructing a reward function according to peak-valley electricity price economic benefits, super capacitor life loss and energy utilization efficiency; training a deep reinforcement learning model based on the double-Q network to obtain an energy recovery optimization strategy; when the elevator is braked, regenerative braking energy is stored in the super capacitor; when the elevator is driven, the stored energy is released for traction; the optimal storage allocation scheme of the multiple elevator rooms is calculated through the virtual energy storage unit, hierarchical control is implemented based on an optimization strategy and the allocation scheme, the upper layer adopts fuzzy self-adaptive weight to determine the energy storage priority, and the lower layer adopts a distributed algorithm to calculate the energy allocation coefficient and the target power; and the charge-discharge rate is updated according to the health state of the supercapacitor.

The invention discloses a DC voltage divider parameter anomaly detection method based on harmonic characteristics, and the method comprises the steps: deploying an isolation type voltage sensor at a bus of a DC power transmission system, carrying out the voltage sampling through combining with an ADC, carrying out the data buffering and preprocessing, extracting a fundamental wave component and a target harmonic component through the fast Fourier transform, and calculating the harmonic distortion rate. And constructing a low-voltage arm capacitance and harmonic distortion rate model based on a voltage divider secondary circuit equivalent circuit and an energy distribution principle, and calibrating the low-voltage arm capacitance and harmonic distortion rate model. And designing a multi-objective optimization scheduling algorithm based on energy storage capacity and capacitance characteristics, and realizing dynamic power distribution and harmonic suppression of the system. And after abnormal triggering, the system automatically loads a capacitance compensation coefficient, adaptively adjusts algorithm parameters in combination with a measurement loop PI control link, reduces a harmonic amplification effect, and ensures that a measurement error is within an acceptable range. Harmonic monitoring precision and capacitance compensation optimization capability of the direct-current power transmission system are effectively improved, and stable operation of the system is guaranteed.

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 provides a welding equipment state monitoring method and device based on artificial intelligence, relates to the technical field of artificial intelligence and data processing, and obtains a vibration data flow corresponding to target welding equipment by collecting vibration signals generated in the operation process of the target welding equipment in real time. And time-frequency energy distribution corresponding to the vibration data flow is determined, and energy changes of the signals in two dimensions of time and frequency are revealed, so that the signal-to-noise ratio of the signals is enhanced, non-correlation frequency band interference is reduced, early fault signals are enhanced, and the sensitivity of the model to abnormal events is improved. Rapid change features and stable structure features in the to-be-identified data are extracted by adopting a preset dynamic gating attention mechanism through a deep residual neural network, so that the rapid change features and the stable structure features can be distinguished and weighted, and the sensitivity of the model to abnormal events (such as early fault impact) is improved; therefore, accurate equipment state analysis is carried out on the welding equipment.

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 the technical field of noise maps, in particular to a noise map dynamic deduction method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: carrying out the space-time alignment of a multi-cluster low-dimension feature vector in an edge calculation node, and obtaining a multi-cluster low-dimension feature vector; generating a three-dimensional sound field reconstruction parameter of the first target data sequence and a spectrum energy distribution parameter of the second target data sequence; based on the three-dimensional sound field reconstruction parameter and the spectrum energy distribution parameter, dynamically controlling the sensor cluster to sleep and generating a space-time difference parameter; hierarchical encryption is carried out on the time-space difference parameters and the two types of data sequences in the dormancy period, and a basic ciphertext sequence and a feature ciphertext sequence for verification are generated; according to the method, the noise deduction map with space-time continuity is generated, high-precision dynamic deduction and energy efficiency optimization of the noise map are achieved, the evolution rule of noise along with time and space is effectively reflected, the real-time performance and accuracy of noise data are improved, and the actual situation of the noise can be reflected more truly, efficiently and in real time.

The invention discloses a quantum dot enhanced dynamic power consumption management method, a quantum dot enhanced dynamic power consumption management system and related equipment. Aiming at the problem of insufficient optimization caused by difficulty in comprehensively, accurately and dynamically sensing the internal energy distribution and conversion process of a chip by a traditional method, the method comprises the following steps of: acquiring the quantum state of the chip in real time by utilizing a quantum dot sensing array, and constructing an adaptive power consumption prediction model by combining multi-source data fusion and feature extraction; and an optimal management strategy is generated based on a multi-objective optimization framework, an execution feedback and self-learning mechanism is introduced, and finally a complete closed-loop control system is formed. The method can be widely applied to various processor chips in the fields of high-performance calculation, the Internet of Things, artificial intelligence and the like, dynamic power consumption management of the chips is achieved through the quantum dot enhancement technology, the performance is guaranteed, meanwhile, energy consumption is minimized, the battery endurance time is prolonged, the heat dissipation cost is reduced, and the energy efficiency of the chips can be remarkably improved.

The invention relates to the technical field of signal analysis, and discloses a primary frequency modulation large disturbance signal peak value analysis method, which comprises the following steps: collecting a primary frequency modulation large disturbance signal and carrying out filtering processing of multi-band noise separation; performing multi-scale decomposition on the filtered disturbance signal, extracting a dynamic entropy of a multi-scale signal sequence, and performing dynamic entropy denoising on the disturbance signal to obtain a denoised disturbance signal; and calculating a potential peak position of the denoised disturbance signal, and carrying out multi-condition verification on the potential peak position to obtain peak information for frequency modulation disturbance analysis. Based on spectrum energy distribution, specific wavelet bases are selected for different noise frequency bands for noise reduction, the wavelet noise reduction performance is improved, the energy distribution complexity and the structure complexity of the disturbance signals on different scales are extracted, the dynamic characteristics of the disturbance signals are captured more comprehensively, the noise reduction precision is improved, and the noise reduction efficiency is improved. And the accuracy of wind power integration fault identification and equipment life monitoring in the peak value analysis process is improved.

The invention discloses an online fault detection method and system for a charging pile, and relates to the technical field of intelligent monitoring of the charging pile, and the system monitors the filtering performance data of the charging pile in real time through an installed monitoring device, and transmits the data to a central control system for preprocessing through the Internet. The method comprises the following steps: calculating a ripple dynamic evolution index ahf, carrying out filter health assessment, calculating a higher harmonic comprehensive energy index xnl and a high-frequency energy distribution response index xpb when the filter health assessment shows that an aging sign exists, then comprehensively calculating a comprehensive health evolution index FHI to carry out comprehensive filter performance assessment, and when the filter has a failure risk, carrying out comprehensive filter performance assessment. And stopping the operation of the charging pile and notifying related personnel to carry out maintenance. And finally, storing the data through the cloud platform, and generating a broken line graph and a pie chart report. The system can efficiently and accurately monitor and evaluate the health state of the filter of the charging pile, and ensures stable operation and timely maintenance of the charging pile.

The invention relates to the technical field of fatigue evaluation, and discloses an offshore wind turbine blade frequency domain fatigue evaluation method and system based on an MIMO nonlinear dynamic system, and the method comprises the steps: collecting the load data of an offshore wind turbine blade; preprocessing the load data; an MIMO nonlinear dynamic system is established; establishing a dynamic mapping relation between the preprocessed load data and the frequency domain response by using an MIMO nonlinear dynamic system; decomposing the time domain dynamic response data by using a frequency domain decomposition method, and quantifying the influence weight of each frequency band load on the blade stress based on a frequency domain energy distribution matrix to obtain a frequency domain energy distribution function of the total stress; and calculating the fatigue damage of the blade through a frequency domain fitting optimization method based on the frequency domain energy distribution function of the total stress. The method solves the problems that fatigue damage evaluation confidence is insufficient under the multi-load coupling effect and actual engineering requirements are difficult to meet in the prior art, and has the characteristic that the coupling effect of the wind load and the wave load can be accurately processed.

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 relates to the technical field of data monitoring analysis, and discloses a centrifugal machine data remote monitoring analysis method and system based on a cloud computer, and the method comprises the following steps: collecting centrifugal machine operation index data, carrying out time stamp synchronization, carrying out the recognition and elimination of abnormal values in the data, and carrying out the data integration through data normalization processing, and generating synchronous monitoring data. According to the invention, the data frequency domain analysis of the centrifugal machine is implemented on the cloud platform, the vibration signal frequency spectrum and energy distribution are accurately identified, the efficient marking and trend analysis of abnormal data are realized, the detailed degree and response speed of abnormal detection are enhanced, and the monitoring of the running state of the centrifugal machine is more accurate; by combining real-time trend analysis and historical fault data, the accuracy of fault prediction is improved, the classification of fault modes is more scientific, the situations of false alarm and missing alarm are effectively reduced, the timeliness and pertinence of preventive maintenance measures are enhanced, and the running efficiency and safety of the centrifugal machine are remarkably improved.

The invention discloses an integrated energy distribution network frame reconstruction multi-objective optimization method based on a BAS algorithm, and relates to the technical field of distribution network optimization, and the method comprises the steps: introducing a fault prediction model based on a convolutional neural network, training fault data under different topological structures, carrying out the hyper-parameter adjustment and optimization through the combination of cross validation and grid search, and carrying out the reconstruction of a network frame of an integrated energy distribution network. The fault type can be accurately predicted; according to the method, active power loss minimization, voltage deviation index minimization and load balancing are taken as optimization objectives, and a comprehensive objective function is constructed based on a plurality of optimization objectives; by introducing a switch state constraint equation, the feasibility of an optimization scheme in actual operation is ensured, in addition, a BAS algorithm is adopted for solving, the optimization scheme is converted into a specific control instruction, the specific control instruction is issued to a power distribution network control system, switch operation and reactive compensation operation are executed, dynamic reconstruction optimization of the power distribution network is achieved, and the power distribution network optimization efficiency is improved. And the optimization efficiency and reliability are improved.

The invention discloses a distributed photovoltaic access power distribution network relay protection analysis method and system, and belongs to the technical field of photovoltaic grid connection, and the method specifically comprises the steps: injecting a dynamic resonance detection signal at the AC side of a current converter of a photovoltaic grid-connected system, and enabling the frequency to be matched with the natural resonance frequency of a target section; analyzing the energy focusing effect of the dynamic resonance detection signal in the power distribution network, and generating a resonance energy distribution map; when the energy density of a certain section in the resonance energy distribution map exceeds a preset threshold value, the photovoltaic output current waveform is adjusted to be a discontinuous waveform containing periodic intermittent cutoff; based on the reflection delay difference of the waveform in the fault section, constructing a space-time mapping model of a fault point, and analyzing and determining a fault position; according to an output result of the space-time mapping model, controlling a photovoltaic side circuit breaker and a power grid side relay to execute a selective tripping action based on waveform phase synchronization; according to the invention, the sensitivity and reliability of distributed photovoltaic access power distribution network relay protection are improved.