451 results about "Wavelet packet decomposition" patented technology

The invention relates to the technical field of Internet of Things, in particular to an electric energy meter with a wireless communication module, which comprises a parameter acquisition module, a communication optimization module, an interference analysis module, a fluctuation evaluation response module and a load classification module. According to the invention, a mutual inductor captures a single-phase circuit signal, analog-to-digital conversion is combined to realize parameter quantization, interference is eliminated based on an active power identification algorithm, a structured parameter set is integrated, a wavelet packet decomposition parameter and a quantization threshold are dynamically adjusted according to a load rate, and spread spectrum factor configuration is adopted to improve channel efficiency. The method comprises the following steps: extracting key features by using wavelet packet characteristics; continuously monitoring voltage fluctuation by a sliding window; detecting and identifying an instantaneous interference event by a break variable; inhibiting noise by Kalman filtering and predicting a trend; and the performances of the system in the aspects of acquisition precision, transmission efficiency, interference immunity and detection sensitivity are enhanced.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

The invention relates to the technical field of nondestructive testing, and particularly discloses an automatic detection system and method based on a titanium plate welding part, and the method comprises the steps: obtaining an original physical field signal of a to-be-detected part under the excitation of a single energy field, and extracting a space energy attenuation gradient and time phase lag distribution through wavelet packet decomposition and Hilbert transform; constructing a dynamic propagation model for describing a signal propagation path evolution rule, and performing space-time registration and vector difference calculation with a preset ideal reference model to generate a difference evolution graph; high-dimensional topological feature mapping, density clustering and multi-scale persistence analysis are carried out on the atlas, and a stable abnormal mode caused by defects is identified and confirmed; and backtracking a dynamic evolution path of an abnormal mode, extracting defect core parameters, and completing three-dimensional positioning, type classification and security level evaluation in combination with process information.

The invention provides a network information security adaptive threat intelligence analysis and response method and system. The method comprises the following steps: acquiring an encrypted traffic load byte stream, and segmenting the encrypted traffic load byte stream into a time sequence traffic matrix according to a time window; and analyzing the communication metadata, matching the features of the threat intelligence library, and generating a dynamic threat fingerprint based on protocol compliance and behavior abnormality. And performing multi-stage wavelet packet decomposition on the matrix, extracting the energy spectrum density, the information entropy and the time-frequency variable coefficient of the high-frequency component, and performing weighted fusion to generate a frequency-domain composite abnormal index representing heartbeat signal characteristics. And identifying hidden heartbeat periodicity and protocol violation modes through a multi-mode fusion mechanism in combination with the frequency domain index and the threat fingerprint, and outputting a threat score. And if the attack exceeds the threshold value, intercepting the flow, verifying a new attack feature, and then reversely updating the threat intelligence library to form a defense self-evolution closed loop. According to the method, automatic interception and closed-loop updating of the threat intelligence library are realized, and the defense adaptability is remarkably improved.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

The invention provides a multi-sensor dynamic calibration method in rotary geosteering drilling, and relates to the technical field of petroleum engineering and drilling, and the method comprises the following steps: collecting original sensing data in real time through a multi-source sensor array deployed on a rotary geosteering drilling tool, wavelet packet decomposition is combined with an improved sliding window algorithm to carry out online denoising processing on original data, and a three-dimensional feature matrix containing environmental interference factors is established; and based on the three-dimensional feature matrix, constructing a dynamic calibration model based on depth time sequence association, respectively processing sensor body feature flow and environment interference feature flow by using a dual-channel LSTM network, performing dynamic weight distribution and fusion on dual-channel features by means of a gating attention mechanism, and outputting a dynamic deviation compensation coefficient of each sensor. The parameter set is generated and reconstructed through the three-dimensional feature matrix, the two-channel network and cooperative calibration, the drilling efficiency and precision are improved, errors are reduced, and intelligent development is promoted.

The invention belongs to the technical field of semiconductor testing, and discloses a novel automatic detection system and method for the electrostatic protection performance of a power device. A distributed parasitic parameter network model is constructed by acquiring packaging structure parameters of a to-be-tested power device and physical layout data of a test fixture, key parasitic parameter distribution is identified in combination with a time domain reflection measurement technology, a waveform distortion coefficient is calculated, and a spatio-temporal evolution model of electrostatic pulse propagation is constructed. Based on the model, a compensatory predistortion waveform is generated to counteract the parasitic effect of a transmission path, a device port response signal is collected in real time, an energy dissipation characteristic spectrum is extracted through wavelet packet decomposition, and quantitative evaluation of the electrostatic protection performance is achieved. According to the invention, the test precision and repeatability are improved, and the method is suitable for electrostatic protection tests of various novel power devices, especially high-frequency wide-band gap devices.

The invention relates to the technical field of multi-dimensional force sensing measurement, and discloses an anti-interference multi-dimensional force sensing measurement method and system, and the method comprises the steps: optimizing the layout of a heterogeneous sensor array based on a stress field, generating an anti-interference signal through cross-band wavelet packet decomposition and dynamic weight fusion, separating a multi-dimensional force component in combination with a hybrid decoupling algorithm, and carrying out the multi-dimensional force sensing measurement. The weight and the sampling rate of the sensor are dynamically adjusted by adopting fuzzy logic control, and a high-reliability measurement result is finally output through rigid mechanical verification and GAN reconstruction iterative correction; the system comprises a heterogeneous sensor array module, a signal preprocessing module, a dynamic decoupling module, an environment self-adaption module, a redundancy verification module and a data integration module. Through cross-band fusion, a hybrid decoupling algorithm and environment adaptive closed-loop feedback, GAN reconstruction and physical verification are combined, the anti-interference capability and the decoupling precision are improved, and the reliability of abnormal signals and the multi-scene adaptability of the system are ensured.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention discloses a transformer bushing high-frequency partial discharge signal on-line monitoring method and system, and relates to the technical field of electrical equipment insulation state monitoring, and the method comprises the steps: synchronously collecting signals based on a high-frequency current sensor and an ultrahigh-frequency sensor; performing mixed noise reduction and feature extraction according to the collected signals; and carrying out discharge type identification on the processed data by constructing a lightweight convolutional neural network, and carrying out early warning and positioning in combination with a dynamic alarm threshold. The method can significantly improve the signal-to-noise ratio and enhance the weak discharge signal detection capability by combining the dual-mode sensor with a hybrid noise reduction strategy of wavelet packet decomposition and adaptive filtering, adopts the lightweight convolutional neural network, supports edge equipment to implement reasoning, realizes intelligent identification of the discharge type, and improves the detection accuracy of the weak discharge signal. The insulation state of the transformer bushing is monitored online in real time, off-line dependence is reduced, the service life of equipment is prolonged, and the intelligent level of operation and maintenance of a power system is improved.

The invention relates to the technical field of magnetic bias monitoring, in particular to a real-time monitoring method and system for direct-current magnetic bias of a transformer. The method comprises the following steps: acquiring a primary side current signal and an iron core vibration signal of the transformer; analyzing the zero-flux closed-loop characteristic of the primary side current signal of the transformer and performing temperature drift elimination on the primary side current signal of the transformer to generate a primary side optimization signal of the transformer; performing magnetostrictive vibration noise separation on the iron core vibration signal to generate an iron core vibration separation signal; dynamically filtering a power frequency fundamental wave of the primary side optimization signal of the transformer to extract a pure direct current component; and performing wavelet packet decomposition on the iron core vibration separation signal, and performing magnetostriction characteristic spectrum extraction on the decomposed iron core vibration separation signal to obtain an iron core magnetostriction characteristic spectrum. According to the invention, through multi-source signal fusion and material characteristic modeling, the accuracy, real-time performance and graded protection response capability of transformer DC magnetic bias monitoring are improved.

The invention discloses a collision identification method and system for a window cleaning robot, and relates to the technical field of robot environment perception, and the method comprises the steps: carrying out the time difference compensation and space synchronization based on a glass material type and collision detection parameters through a distributed perception collaborative analysis method, and generating a three-dimensional space vibration information matrix; enhancing the three-dimensional space vibration information matrix through a dynamic wavelet packet decomposition algorithm and a sliding window analysis method according to the type of a glass material; according to the enhanced three-dimensional space vibration information matrix, collision coordinates are calculated through a time difference positioning algorithm, the characteristic frequency of the vibration waveform is analyzed, and collision characteristic information is obtained; based on the collision feature information, combining safety parameters of glass material types, adopting a hybrid decision algorithm to analyze the collision feature information, and generating and executing a robot obstacle avoidance instruction; according to the method, the high-resolution three-dimensional vibration field is constructed through space-time collaborative modeling, and the collision coordinate calculation precision and the anti-interference capability are improved.

The invention relates to the technical field of crane line fault diagnosis, in particular to a crane line fault diagnosis system and method based on multi-source data fusion, which comprises a data acquisition and processing unit, a mechanical and electrical coupling characteristic unit and a characteristic fusion and fault quantification unit, the three-axis vibration acceleration and the three-phase current waveform of the track are obtained through the data collecting and processing unit, the mechanical and electrical coupling characteristic unit conducts three-dimensional vector synthesis and wavelet packet decomposition on vibration data, and a time-space incidence matrix of harmonic distortion and vibration is constructed. And the feature fusion and fault quantification unit outputs coupling factors by using a bidirectional long-short-term memory network and an attention mechanism, and outputs a fault probability value through a dynamic time warping matching algorithm after time-frequency domain analysis and sample entropy judgment, so that time-space correlation modeling and dynamic fault matching of multi-source data are realized. And the fault positioning precision and the diagnosis accuracy are improved.

The invention relates to an intelligent CNC (computer numerical control) machining method which comprises the following steps: dynamically monitoring a tool-workpiece interaction interface in a CNC machining process through a multi-dimensional torque sensor array to obtain a machining process feature data set, and analyzing a tool motion track in real time to obtain a tool motion state feature quantity; secondly, performing multi-scale analysis by adopting a wavelet packet decomposition technology, and determining processing quality evaluation data; and if the data does not reach the preset standard, performing microstructure reconstruction on the surface of the workpiece based on the data, and generating a workpiece processing state characteristic spectrum. Then, comprehensively analyzing the atlas by utilizing a multi-objective optimization technology to obtain a processing parameter optimization sequence; and finally, implementing closed-loop feedback control on the numerical control system according to the optimized sequence, and generating a real-time processing control instruction. The key problem of how to improve the automation level and the machining precision in the CNC machining process through an intelligent means is solved.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention relates to the technical field of automobile active safety, in particular to an intelligent brake pad abrasion measuring method and system based on multi-sensor fusion, and the method comprises the steps that S1, multi-source sensing signals are synchronously collected, and wheel speed pulse signals and brake pressure signals from a vehicle CAN bus are received; s2, multi-modal signal feature extraction: carrying out time domain filtering processing on the original thickness signal to form a thickness change feature vector, carrying out space partition calculation on the temperature distribution signal to form a temperature gradient feature matrix, and carrying out wavelet packet decomposition on the braking vibration signal to form a frequency domain energy feature set; s3, dynamically fusing decisions, outputting a joint feature vector, and generating a wear loss estimated value and a confidence index; and S4, performing Kalman filtering correction on the wear loss estimated value according to the confidence index to form final wear thickness data. According to the brake pad wear intelligent measurement method and system based on multi-sensor fusion, the problem that brake pad wear cannot be accurately monitored and intelligently diagnosed online in real time can be solved.

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention discloses an electric vehicle battery health assessment method and system based on multi-source data fusion, and relates to the technical field of new energy vehicle battery application, and the method comprises the steps: collecting a Nyquist impedance curve, terminal voltage relaxation time sequence data and equalization current time sequence data based on a constant current stage cut-off event of a battery pack of an electric vehicle; extracting an impedance form embedding vector corresponding to the Nyquist impedance curve based on an auto-encoder; performing wavelet packet decomposition on the terminal voltage relaxation time sequence data to obtain a corresponding multi-scale voltage relaxation energy feature vector; calculating a dynamic equalization imbalance index based on the terminal voltage relaxation time sequence data and the equalization current time sequence data; and inputting the impedance form embedded vector, the multi-scale voltage relaxation energy feature vector and the dynamic equilibrium imbalance index into a battery SOH prediction model to output an SOH estimated value of the battery pack. Therefore, the accuracy of the SOH evaluation result is improved through the joint modeling of the coupling characteristics of the multi-physical process of the battery.

The invention discloses a hydraulic rammer tamping construction parameter adjusting method based on sensing parameter real-time feedback, and relates to the technical field of hydraulic rammer tamping construction.The hydraulic rammer tamping construction parameter adjusting method comprises the steps that a multi-mode sensing monitoring network is constructed to collect full-amount construction data, a wavelet packet decomposition algorithm is adopted for noise layered suppression, and a multi-mode sensing monitoring network is established; constructing a working condition associated data set in combination with the construction stage labels; based on the working condition associated data set, establishing a dynamic tamping effect evaluation model, and outputting a deviation index moment of time-space distribution; training a parameter adjustment intelligent model based on the deviation index matrix and a transfer learning mechanism, generating a multi-parameter collaborative adjustment strategy, and carrying out working condition adaptation degree scoring and adjustment risk early warning on strategy output; and adjusting the intelligent model based on incremental learning and model distillation technology optimization parameters. According to the method, the multi-modal sensing network, the geological dynamic quantitative model, the improved entropy weight method, the migration and reinforcement learning and the lightweight deployment technology are fused, so that full-chain intelligent dynamic optimization and safe controllable execution of hydraulic rammer construction parameters are realized.

The invention relates to the technical field of civil engineering structure health monitoring, in particular to a concrete bridge crack abnormity intelligent monitoring and early warning method based on GAF image classification and GRU prediction.The method comprises the steps that an intelligent monitoring framework integrating GAF image coding, CNN classification and recognition and GRU time sequence predication is constructed, high-frequency noise is removed through wavelet packet decomposition, and then the GRU time sequence predication is carried out; smoothing the crack-temperature coupling time sequence data; encoding the image into a two-dimensional image through a GAF method, and enabling the CNN to recognize an abnormal mode; and training a GRU model based on the high-quality data set after abnormity elimination, and realizing accurate modeling of crack width evolution under temperature driving. According to the method, residual error approximate normal distribution is predicted, the crack width early warning decision coefficient (R) is stabilized to be more than 0.93, a + / -3 sigma dynamic residual error threshold early warning mechanism is combined, structural damage trends under different disturbance scenes can be identified in a graded mode, and the method is suitable for online monitoring and maintenance decision support of bridge crack diseases in actual engineering.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention relates to the technical field of fault prediction and health management, in particular to a primary and secondary fusion complete ring main unit fault diagnosis method and system. Comprising the following steps: acquiring three-phase instantaneous voltage and current signals in real time, and converting the signals into digital transient data; performing time window preprocessing on the digital transient data, and executing wavelet packet decomposition to generate a transient feature vector; calculating and analyzing the transient feature vector through a transient zero-sequence power direction method and a support vector machine model to generate a local diagnosis result; when the local diagnosis result is that cooperative positioning needs to be started, a transient current similarity coefficient and a transient waveform intensity difference coefficient are calculated based on the digital transient data, and a comprehensive positioning result is generated; determining a fault line according to the comprehensive positioning result, and generating a remote control command; and executing a fault isolation operation based on the remote control command, and executing a PHM process. According to the method, the double local transient diagnosis and the cross-terminal cooperative positioning are deeply fused, so that the high-precision determination of the boundary of the fault section is realized.

The invention discloses a data interaction self-adaptive method for a photovoltaic protocol converter, and particularly relates to the technical field of power grid dispatching cooperative control. The method comprises the following steps: monitoring and calculating a voltage instantaneous change rate of a grid-connected point of a power grid in real time, and comparing the voltage instantaneous change rate with a threshold value; when a threshold value is exceeded, synchronously collecting a current phase and generating a voltage and current phase difference change direction; extracting a voltage high-frequency component, analyzing frequency band energy distribution through wavelet packet decomposition, and calculating a Shannon entropy representing an energy concentration ratio; when the phase difference change direction accords with a preset power grid short-circuit fault feature and the Shannon entropy value is lower than a preset entropy threshold value, determining that a power grid transient event occurs; at the moment, a data caching mechanism of the protocol converter is stopped immediately, and a real-time stream transmission channel is activated; an emergency control instruction issued by a power grid dispatching system is directly transmitted to the photovoltaic inverter through the channel; and when the voltage instantaneous change rate is continuously lower than the preset threshold value for a preset duration, recovering the data caching mechanism of the protocol converter.

The invention discloses an electroencephalogram fatigue recognition method based on multi-scale convolution and a double attention mechanism, and belongs to the technical field of electroencephalogram signal analysis and fatigue detection. The method comprises the following steps: carrying out preprocessing and data integration on multi-channel electroencephalogram signals, extracting sub-band energy by adopting wavelet packet decomposition, and mapping the sub-band energy to four frequency bands of delta, theta, alpha and beta; constructing a data matrix of channel * frequency band * time step, and inputting the data matrix into a multi-scale convolutional neural network to extract time domain features; a space attention mechanism is introduced, and key channel response is enhanced; time sequence characteristics are modeled through an LSTM network, a frequency attention mechanism is added, and a fatigue related frequency band is highlighted. According to the method provided by the invention, the modeling capability of time dependence and spatial frequency band correlation of the electroencephalogram signals is remarkably enhanced, and deep mining of fatigue state characteristics is realized. The model can realize efficient and accurate fatigue recognition without priori knowledge, and is suitable for an electroencephalogram intelligent monitoring scene in a complex environment.

The invention relates to the technical field of data processing, and discloses a building construction quality real-time monitoring method and system based on a sensor network. The method comprises the steps of constructing a sensor grid through hydration heat gradient mapping, recognizing welding defects based on acoustic emission spectrum texture and wavelet packet decomposition, obtaining a quality situation by adopting maintenance age weight time-varying fusion, performing multi-scale anomaly detection by applying a residual attention mechanism, and dynamically adjusting a threshold value to generate an intervention strategy in combination with a working condition switching trigger. And intelligent construction quality monitoring is realized. Through the multi-scale feature extraction and cross-modal data fusion technology, the accuracy and real-time performance of construction quality monitoring are remarkably improved.

The invention discloses a distributed photovoltaic power generation anomaly positioning optimization method, and particularly relates to the technical field of power generation anomaly positioning, and the method comprises the steps: carrying out the space-time alignment of the static information, environmental parameters and dynamic power generation data of multiple stations in a target region, carrying out the spatial density clustering based on the three-dimensional distance of the stations, and dividing spatial sub-clusters; recognizing a space-time hot spot region in combination with a decoupling model of a historical loss rate and irradiance; constructing a space loss gradient field for the hot spot region, and reversely tracing to a normal threshold boundary point to realize pollution propagation path reconstruction; according to the method, wavelet packet decomposition, variational mode decomposition and short-time Fourier transform are adopted for a non-hotspot area to extract multi-scale attenuation and noise features, artifacts are removed through neighborhood slope difference, macroscopic error shielding and microcosmic anomaly positioning of meteorological and component differences are achieved, and the checking accuracy and the operation and maintenance efficiency are improved.

The invention relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.

The invention relates to the technical field of crane equipment health monitoring, in particular to a crane running state health monitoring system and method. A sensing data acquisition unit is used for acquiring a winding drum vibration harmonic component, a steel wire rope leakage magnetic field gradient and a pulley block real-time load; the data analysis unit performs rotating speed synchronous variational mode decomposition on the vibration component to extract resonance characteristics, performs temperature and stress double-compensation correction on a leakage magnetic field gradient, quantifies an energy entropy attenuation rate through wavelet packet decomposition, constructs a phase difference model of a load and vibration to output a slip risk phase offset, calculates a load spectrum damage cumulant, and calculates a load spectrum damage cumulant; and after the four heterogeneous features are fused, a residual life coefficient is output through a dual-channel convolution-long and short-term memory hybrid neural network, and an execution unit triggers crane speed reduction control when the residual life coefficient is lower than a threshold value, so that the problems of insufficient multi-source data fusion and lack of dynamic compensation in the traditional technology are solved, and the fault early warning accuracy is improved.