780 results about "Variational mode decomposition" patented technology

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

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 belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention provides a wind turbine generator voiceprint fault recognition method, and relates to the technical field of wind turbine generator state monitoring and fault diagnosis, and the method comprises the steps: carrying out the noise reduction of an original audio signal through variational mode decomposition, screening a target mode of which the frequency, energy and kurtosis accord with features, and reconstructing the signal; extracting a Mel frequency cepstrum coefficient and a sensing noise robust coefficient, and generating multi-dimensional voiceprint data in combination with statistical characteristics such as a frequency spectrum gravity center, a spectrum entropy, energy, kurtosis and a zero-crossing rate; constructing a support set based on the prototype network, realizing small sample fault classification by calculating the Euclidean distance between the feature vector and the prototype vector, and outputting a preliminary result; judging whether the voiceprint is abnormal according to a preset threshold value, if so, storing the voiceprint into a dynamic abnormal voiceprint knowledge base; frequently occurring abnormal samples are manually labeled and added into a support set, the prototype network is retrained to update the model, and continuous optimization of the fault recognition capability is achieved.

The present invention relates to the technical field of power source power allocation. Disclosed are a direct current power source power allocation method and system for a generator status monitoring apparatus. The method comprises the following steps: by means of system data, constructing a variational problem model; solving the constructed variational problem model, and using a particle swarm optimization algorithm to optimize computing parameters of the variational problem model; and, on the basis of a computing result of the variational problem model, allocating the total output power of a hybrid energy storage system to an energy-type storage device and a power-type storage device according to a ratio. By means of using the convergence-guaranteed particle swarm optimization algorithm, the present invention not only excels in the solving speed but also shows significant advantages in computational accuracy; furthermore, by means of solving the variational problem model, more accurate allocation ratios are obtained; variational mode decomposition can achieve adaptive matching of the optimal center frequency and bandwidth for each mode, thus effectively separating intrinsic mode components and achieving frequency domain partitioning of signals.

The invention discloses a slope displacement prediction method, device and equipment and a storage medium, and the method comprises the steps: collecting displacement data and multi-source environment factor data of a to-be-monitored slope region, and carrying out the preprocessing of the displacement data and the multi-source environment factor data; performing time-frequency decoupling on displacement data by adopting a variational mode decomposition method to obtain a plurality of mode components, and merging the mode components with the multi-source environment factor data to obtain an input matrix; introducing a supervision loss function into the constructed initial prediction neural network model, and training based on an error feedback mechanism and the input matrix to obtain a time sequence prediction neural network model; and inputting monitoring data of a slope area to be monitored into the time sequence prediction neural network model, and generating a complete future displacement prediction sequence through inverse mode reconstruction. According to the method, the problems in the prior art are solved from data acquisition and processing, feature analysis, model training and prediction output, and the accuracy and reliability of slope displacement prediction are improved.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably 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 relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention provides a semiconductor chip defect detection positioning method and system, and relates to the technical field of semiconductor chip detection, and the method comprises the steps: obtaining an original image and electrical test data; forming a defect candidate area based on local density analysis; variational mode decomposition is carried out on the electrical data, and electrical characteristic abnormal points are marked; mapping the image features to an electrical characteristic space and calculating mutual information values to determine defect points; and calculating the distribution and path of a diffusion field and determining the severity of the defect. According to the invention, accurate positioning and severity evaluation of chip defects are realized, and the detection efficiency and accuracy are improved.

The invention discloses a high-reliability planetary gearbox signal denoising method, system, medium and equipment, and the method comprises the steps: obtaining original vibration signals of a nuclear power circulating pump planetary gearbox in different health states, and carrying out the detrending and demean preprocessing of the signals; performing modal decomposition on the original vibration signal by adopting an empirical mode decomposition (EMD) algorithm, an ensemble empirical mode decomposition (EEMD) algorithm and a variational mode decomposition (VMD) algorithm to obtain a plurality of different modal components; iteratively optimizing a hyper-parameter value in the variational mode decomposition algorithm VMD by adopting a sparrow search algorithm SSA so as to realize the self-adaptive decomposition of the variational mode decomposition algorithm VMD on the signal; and carrying out modal decomposition on the gearbox vibration signal by adopting a variational modal decomposition algorithm VMD after iterative optimization, removing noise components in modal components, and reconstructing the signal to realize gearbox vibration signal denoising.

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 belongs to the technical field of vibration analysis and testing of bearings, and particularly relates to a cam driven bearing wear degree monitoring method and system, and the method comprises the steps: carrying out the equal-angle resampling processing of a vibration signal through a rotating speed signal, decomposing an obtained angular domain vibration signal into a plurality of mode components through a variational mode decomposition algorithm, and carrying out the measurement of the vibration signal; according to the kurtosis value of each modal component and the correlation coefficient of each modal component and the original vibration signal, evaluating the impact saliency weight of each modal component, and performing weighted summation on the energy of each modal component to obtain comprehensive impact energy; calculating to obtain a speed decoupling wear index without the influence of the rotating speed by utilizing the comprehensive impact energy and the vibration energy calculated by the physical mapping model; and the speed decoupling wear index is compared with a preset self-adaptive alarm threshold value, and the wear state of the cam driven bearing is judged according to a comparison result. According to the invention, the problems of false alarm and missing alarm under the variable-speed working condition are solved.

The invention discloses a broadband random power quality adaptive monitoring system, which relates to the field of power grid monitoring and comprises a broadband signal acquisition module, a synchronous phasor measurement module, an edge calculation module, a cloud edge collaborative analysis module and a local traceability module. The broadband signal acquisition module performs high-frequency sampling on a power grid signal and ensures integrity; the synchronous phasor measurement module is used for realizing time synchronization of multiple monitoring points and generating data with time marks; the edge calculation module is used for studying and judging disturbance and early warning through deep learning, and uploading data to the cloud; the cloud edge collaborative platform aggregates data, deduces regional power quality and issues an optimized AI model; the local traceability module quantifies harmonic responsibility and positions a broadband disturbance source. The system innovatively adopts an ensemble empirical mode decomposition and variational mode decomposition parallel strategy, combines a spectrum kurtosis maximization algorithm to determine the number of modes and accurately separate transient and steady components, solves the problem of feature confusion of a traditional method, and realizes the crossing from single analysis to multi-mode fusion research and judgment.

The invention discloses a distribution network fault positioning method, system and device based on variational mode decomposition and a medium, and relates to the technical field of power system fault positioning, and the method comprises the steps: collecting a traveling wave signal of a power distribution network, and carrying out the preprocessing of the collected traveling wave signal; performing modal decomposition on the preprocessed traveling wave signal, and outputting a plurality of modal components; based on the mode component obtained through decomposition, wave head arrival time is extracted; performing correction compensation based on the wave head arrival time difference, the phase difference and the propagation characteristics, and calculating the relative distance between the fault point and the known node; calculating the position coordinates of the fault point in the distribution network according to the geographic coordinates and the direction vectors of the nodes in combination with the relative distance; and constructing a visual interface, displaying the position of a fault point, and storing the traveling wave signal data and a fault positioning result into a database. According to the method, the adaptive correction function and the time difference compensation model are constructed, so that time difference errors caused by factors such as frequency change and phase nonlinearity can be corrected, and the accuracy of fault positioning is improved.

The invention provides a prediction method for a sudden change type signal of gas dissolved in oil. The prediction method comprises the following steps: firstly, acquiring a time sequence signal of transformer gas monitoring; processing the time sequence signal according to a variational mode decomposition algorithm to obtain a plurality of mode components; according to the variational mode decomposition algorithm, the sum of confusion entropies of all mode components is used as an objective function of parameter optimization; and finally, according to a pre-established prediction model, performing prediction and superposition reconstruction on each modal component to obtain a prediction result of the abrupt change type signal of the gas dissolved in oil. According to the method, the VMD decomposition architecture optimized by the frost ice algorithm is introduced, the non-stationarity of the signal is quantified and obviously reduced through the chaos entropy index, higher prediction precision and robustness of the gas signal in the mutation type oil are realized, and the method has obvious innovativeness and superiority in the field of transformer fault trend prediction.

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.

The invention provides an electroencephalogram signal artifact removing method and device, equipment and a medium, and relates to the technical field of biological signal processing, collected electroencephalogram signal data is processed to obtain an optimal modal number and an optimal bandwidth parameter, and the optimal modal number and the optimal bandwidth parameter are used for conducting self-adaptive variational mode decomposition on the electroencephalogram signal data to obtain a plurality of modal components; then carrying out multi-dimensional feature analysis to obtain a plurality of feature indexes for judging an artifact suspicion mode and an effective mode component; performing short-time Fourier transform on the artifact suspicion mode, and constructing a time-frequency confidence map to guide weighted time-frequency independent component separation on the artifact suspicion mode to obtain an artifact component; suppressing the artifact component to obtain a suppressed independent component, and performing weighted reconstruction and multi-component fusion on the suppressed independent component and the effective modal component based on the time-frequency confidence map to obtain an artifact-removed electroencephalogram signal; and the fidelity, the robustness and the real-time performance of the electroencephalogram signal are improved.

The invention relates to the technical field of battery management, in particular to a battery pack multi-fault diagnosis method and system based on signal decomposition and entropy feature fusion. The method comprises the following steps: acquiring voltage data of each single battery in a battery pack in real time, and performing segmentation processing on voltage time sequence data by adopting a sliding window mechanism based on a Fibonacci sequence; adaptively optimizing key parameters of variational mode decomposition by using a Schrodinger optimization algorithm, and performing adaptive decomposition on the voltage signal in each window to obtain a plurality of intrinsic mode functions; calculating the Shannon entropy value of each intrinsic mode function, preferably selecting a preset number of representative mode components according to the Shannon entropy characteristic contribution degree, and carrying out superposition reconstruction to obtain a reconstructed voltage signal; extracting the Shannon entropy, the spectrum sparsity index and the time domain stability index of the reconstructed voltage signal to form a three-dimensional fault feature vector; and identifying a connection fault, a short circuit fault and a sensor fault through a multi-level threshold judgment strategy based on the three-dimensional fault feature vector.

The invention discloses a multi-scale space-time fusion water quality prediction and anti-counterfeiting method based on a dynamic graph neural network. Comprising the following steps: 1) collecting water quality index hour data of a plurality of monitoring stations in a drainage basin; 2) decomposing the data into a plurality of intrinsic mode functions through variational mode decomposition; 3) constructing a dynamic graph neural network spatial feature extraction module, and generating a discrete dynamic graph structure; 4) constructing a multi-scale time feature extraction module, and synchronously capturing short-term fluctuation and long-term trend; 5) designing a residual fusion mechanism to integrate the spatial-temporal characteristics, and outputting a water quality prediction result through a full connection layer; and 6) calculating a path distance between the input data and a prediction result through a dynamic time warping algorithm, and comparing residual distribution by combining K-S to realize authenticity discrimination of the input data. The method can fully excavate the spatial and temporal characteristics of the basin water quality under the condition that the geographical spatial distribution of the sites is unknown, improves the prediction precision, carries out the authenticity recognition of the water quality data of an unknown source, and prevents the data from being tampered.

The invention provides a multi-source data convolution fusion TBM electric drive system fault diagnosis method, and relates to the technical field of TBM electric drive control. The method comprises the following steps of: filtering and denoising operation data of an electric drive system by using variational mode decomposition (VMD), extracting time domain features, and constructing a multi-mode fusion data set; secondly, extracting short-term, medium-term and long-term time sequence features in parallel through a residual causal convolutional network, and enhancing key features by using a convolutional block attention module (CBAM) to highlight early weak fault signals; furthermore, working condition parameters are embedded into a feature space by adopting working condition adaptive coding, and cross-working-condition feature consistency constraint is realized based on a maximum mean difference (MMD) criterion. According to the method, the weak fault identification capability and the cross-working-condition diagnosis generalization performance are effectively improved, and the TBM operation stability and the tunnel construction safety are guaranteed.

The invention provides a method for monitoring and controlling cable-stayed buckling construction of a concrete-filled steel tube arch bridge based on a digital twin system, and the method comprises the steps: collecting the monitoring data, such as wind speed and wind direction, arch rib stress displacement, tower bottom stress, tower deviation and cable force, through a sensor; establishing a concrete filled steel tube arch bridge construction digital twinning system based on a three-dimensional model, real-time monitoring, data analysis and a visual platform; time sequence data is decomposed into different frequency components through variational mode decomposition (VMD), a bridge construction stage prediction model is established through a long short-term memory network (LSTM), and real-time accurate prediction of the construction progress and state is achieved. And secondly, based on the prediction model and on-site real-time monitoring conditions, an intelligent load adjustment construction control method based on a digital twin system is provided, and high-precision control over automatic load adjustment of cable hoisting and real-time rectification of tower displacement is achieved. The problems that in a traditional monitoring and control method, early warning is not timely, the assembling precision is insufficient, and the arch axis shape control difficulty is large are solved.

The invention relates to the technical field of ocean engineering, and discloses a method for training a sea wave significant wave height prediction model and predicting the sea wave significant wave height, and the method for training the sea wave significant wave height prediction model comprises the following steps: according to the correlation between historical meteorological characteristic time series data and historical sea wave height time series data, calculating the sea wave significant wave height prediction model; selecting a target meteorological feature from the plurality of meteorological features; based on the decomposition number and penalty factor of the variational mode decomposition, optimizing to obtain a target decomposition number and a target penalty factor, and performing variational mode decomposition on the historical sea wave height time sequence data to obtain an intrinsic mode sub-sequence; the sea wave significant wave height prediction model is trained according to the target historical meteorological characteristic time series data and the intrinsic mode subsequences, the correlation between the meteorological data and the historical sea wave height data is analyzed, and the variational mode decomposition parameters are optimized, so that the input accuracy of the sea wave significant wave height prediction model is improved, and the prediction accuracy of the sea wave significant wave height prediction model is improved. Therefore, the precision of the sea wave significant wave height prediction model is improved.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention provides a bridge stay cable video multi-target identification tracking and vibration extraction method based on an unmanned aerial vehicle. The method comprises the following steps: 1, constructing a bridge stay cable refined inclined slender target detection model based on a YOLOv11 model; 2, providing a multi-target tracking algorithm fusing the inclined slender target detection model and a StrongSORT algorithm; step 3, improving a displacement extraction method combining SIFT / ORB feature point matching and a sub-pixel refinement technology; 4, designing an unmanned aerial vehicle motion correction algorithm based on variational mode decomposition and time-frequency domain combined screening; and 5, constructing a joint working modal analysis algorithm for realizing combination of a natural excitation technology and a random subspace recognition algorithm. According to the method, high-precision extraction of the vibration signals of the stay cable and identification of modal parameters of the stay cable are realized, and technical support is provided for health monitoring of the large-span cable-stayed bridge.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

The invention discloses a mixed gas absorption spectrum analysis method and system based on variational mode decomposition, specific laser is injected into an optical resonant cavity unit, and a detector unit continuously monitors the light intensity change and records a light intensity attenuation signal; pre-processing the recorded light intensity attenuation signal; carrying out VMD decomposition on the preprocessed ring-down signal to obtain a plurality of IMF components, respectively introducing CO2 and CO gases with known concentrations into the optical resonance unit, and recording spectral data of each single gas component; calculating the similarity and contribution degree of each IMF component, and setting a weight combination to form a joint score; the gas with the highest joint score is selected, the score is compared with an adaptive threshold value, and when the score is higher than the adaptive threshold value, the IMF component is marked as the characteristic component of the corresponding gas; and finally, gathering and outputting the marked IMF components according to gas types to obtain characteristic signals of the gases, thereby realizing multi-component gas separation in the mixed gas absorption spectrum.

The invention relates to the technical field of energy storage frequency modulation prediction, in particular to an AFGRU-based energy storage frequency modulation instruction prediction method and related equipment. The method comprises the following steps: decomposing an obtained original frequency modulation sequence by using a variational mode decomposition method to obtain a plurality of sub-sequences; fusing the plurality of sub-sequences into a set number of comprehensive components through a multi-scale entropy-dynamic alignment fusion method; and inputting the comprehensive component into the trained AFGRU network model, and predicting a next energy storage frequency modulation instruction to obtain an energy storage frequency modulation instruction prediction result. According to the method, by accurately predicting the energy storage frequency modulation instruction, the stability and reliability of the power grid are improved, and the influence of frequency fluctuation on power equipment and users is reduced.

The invention relates to an LSTM daily runoff prediction method based on MFF and NRBO, and the method comprises the steps: carrying out the variational mode decomposition of an original runoff sequence, and obtaining a plurality of intrinsic mode function components; hydrometeorological characteristics highly related to the runoff are screened through correlation analysis; fusing the intrinsic mode function component with the screened hydro meteorological features, and constructing a multi-dimensional information feature matrix; using a Newton-Raphson optimization algorithm to optimize hyper-parameters of the LSTM model; and taking the multi-dimensional information feature matrix as input, and performing daily runoff prediction by adopting the optimized LSTM model. The method has the beneficial effects that the Newton-Raphson optimization algorithm is applied to hyper-parameter optimization of the runoff prediction model for the first time, and the global search capability is enhanced, so that the prediction precision is improved. Meanwhile, multi-feature fusion is carried out on the intrinsic mode function component and the screened hydro meteorological features, and the multi-feature fusion is combined with hyper-parameter optimization, so that runoff prediction is more efficient and stable.