1338 results about "Variational mode decomposition" patented technology

The invention provides a petrochemical production process anomaly diagnosis and optimization method and system integrated with a knowledge graph, and relates to the technical field of petrochemical production, and the method comprises the steps: collecting technological parameters and equipment state data, carrying out the preprocessing and feature extraction through adaptive wavelet threshold denoising and depth variational mode decomposition, and carrying out the feature extraction; a heterogeneous graph neural network is constructed, a causal relationship graph is constructed in combination with a neural causal discovery network, the causal relationship graph is optimized through knowledge distillation, missing relationships are complemented to obtain a knowledge graph, real-time features are compared with normal feature distribution of a pre-trained contrast diffusion model, and after anomaly detection is triggered, the real-time features are extracted. A causal inference engine is combined with a knowledge graph to analyze an anomaly propagation path, an anomaly root and a prediction trend are determined based on multi-agent reinforcement learning, a multi-objective optimization problem is constructed according to the knowledge graph, an optimization strategy is generated and dynamically adjusted, and an optimization result is evaluated by combining a knowledge distillation hybrid model. And finally, progressive optimization is realized and the knowledge graph is updated.

A method for diagnosing rotor rub-impact in a rotating machinery based on vibration signal deconstruction and frequency modulation characteristic anti-noise enhancement, including: converting, based on two-stage integration transformation and high-pass filtering, vibratory acceleration signals from a rotating machinery device into vibratory displacement signals; performing targeted extraction on a rotating frequency component therein based on an improved variational mode decomposition method; calculating and estimating instantaneous fluctuation characteristics of a fundamental frequency of the extracted rotating frequency component using a quadrature-derivative-based normalized Hilbert transform to obtain an instantaneous frequency; inputting a calculated instantaneous frequency sequence into an optimum stochastic resonance system for anti-noise enhancement of intrawave frequency modulation characteristics; and processing FFT on an instantaneous frequency of intrawave frequency modulation characteristics after anti-noise enhancement, and diagnosing and identifying a rotor rub-impact fault of the rotating machinery based on a distribution characteristic of harmonic amplitudes related to a rotor rotating frequency.

The invention relates to the technical field of wind turbine generator fault monitoring. The invention provides a wind turbine generator fault monitoring method and system. The method comprises the following steps: synchronously acquiring gearbox and environment temperature and humidity data, and generating a time-frequency energy fusion matrix through adaptive wavelet packet transformation; adopting mutual information entropy weighted improved variational mode decomposition to screen out an intrinsic mode component set related to a fault mode; constructing a space-time double-flow residual network based on the intrinsic mode component set, and fusing two branch outputs of the space-time double-flow residual network through a dynamic feature gating mechanism to obtain a multi-dimensional feature vector; and inputting a multi-dimensional feature vector obtained by fusion into a lightweight fault classifier, and outputting a real-time fault probability and a component health degree evaluation index based on a sliding window mechanism. The problems of low efficiency, high false alarm rate, missing detection of early faults, reduction of prediction precision, incapability of mining multivariable coupling relations, need of massive annotation data, and high delay caused by insufficient edge side computing power existing in an existing wind turbine generator fault monitoring mode are solved.

The invention discloses a guardrail collision warning method and system, and the method comprises the steps: carrying out the nonlinear phase alignment of a multi-source heterogeneous signal through an adaptive variational mode decomposition algorithm according to the vibration acceleration, strain tensor and acoustic emission signals collected in real time through a multi-mode sensor array disposed at a guardrail key node, generating a three-dimensional dynamic strain field distribution vector; inputting the three-dimensional dynamic strain field distribution vector into a nonlinear dynamics reconstruction module, and extracting a chaotic feature fingerprint spectrum of the collision event; performing collision intensity grading processing on the chaos feature fingerprint spectrum, and outputting a quantitative evaluation matrix including a collision grade, a damage radius and a residual intensity prediction value; and triggering a multi-mode alarm protocol according to the quantitative evaluation matrix, and synchronously transmitting the multi-mode alarm protocol to a traffic management center and an adjacent vehicle OBU terminal. According to the embodiment of the invention, the state of the guardrail can be monitored in real time, and a collision event can be accurately evaluated and warned.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention discloses a power supply real-time compensation technology and application based on digital signal processing, and relates to the technical field of power electronics, and the power supply real-time compensation technology comprises a split-phase parallel processing architecture: a three-phase independently configured DSP core, each core integration comprises a real-time harmonic detection module, a dynamic sliding window FFT-RT algorithm is adopted, and the window length is adjustable in a set range; the bandwidth of the self-adaptive variable parameter filter can be dynamically adjusted in a set range; the FPGA coprocessor is specially used for PWM generation; an input stage of the harmonic prediction and decomposition module is synchronously acquired by a multi-source sensor; the processing layer comprises an improved VMD decomposition unit and a variational mode decomposition order; the edge prediction network is used for deploying a lightweight LSTM model, embedding a DSP core and predicting a harmonic spectrum in a future short period; and the multi-target dynamic optimization layer is used for setting a dynamic weight distributor and performing real-time adjustment according to the load sudden change rate. According to the invention, through split-phase parallel architecture, harmonic prediction and dynamic optimization, the response speed, multi-target cooperation and reliability improvement in the field of power supply real-time compensation are realized.

The invention discloses a short-term load prediction method and system based on improved GWO-VMD-LSTM, and the method comprises the steps: carrying out the parameter optimization of a mode decomposition number and a penalty factor which need to be determined before VMD feature extraction, carrying out the variational mode decomposition of a load feature time sequence signal, decomposing the signal into a series of intrinsic mode function components, and carrying out the VMD-LSTM. An improved wavelet packet soft threshold function is used, a lugish-greed mechanism is provided to improve the function, denoising processing is performed on IMF component signals, feature coefficients and modal kurtosis values of all components are extracted, and the denoising effect is evaluated; a network structure of a long short-term neural network is improved, a parameter adaptive adjustment optimization algorithm is provided, the learning rate of the LSTM is dynamically optimized, and data is input into the network for training and prediction. The accuracy of short-term load prediction and the precision of classification can be improved, and short-term accurate prediction of the power load is facilitated.

The invention relates to the technical field of hydrogeological monitoring, in particular to a hydrogeological dynamic monitoring and analysis system and method based on big data. The method specifically comprises the steps that hydrogeological data are collected in real time, edge calculation is combined for data denoising, space-time calibration and error correction, variational mode decomposition, geologic model constraint and wavelet transform are adopted for eliminating noise and drift errors, storage and transmission efficiency are optimized through incremental coding and Huffman coding, and quadratic form calibration codes are generated to ensure data reliability; a hydrological evolution trend is predicted by fusing a space-time deep learning model and a self-attention mechanism, and adaptive anomaly detection is realized by combining DBSCAN clustering and prediction error analysis; and optimizing a water resource allocation strategy through reinforcement learning, hierarchically evaluating a risk level and generating a management scheme, and finally visually displaying monitoring data, an abnormal score and a decision result. According to the method, multi-source data fusion, space-time modeling and intelligent decision making are integrated, and hydrological monitoring precision and response efficiency are improved.

The invention relates to the field of energy storage frequency modulation prediction, and discloses an energy storage frequency modulation instruction prediction method and system based on a two-stage method, and the method comprises the steps: obtaining an energy storage frequency modulation instruction, and carrying out the variational mode decomposition; judging mutual attraction or mutual repulsion of the subsequences according to a variation mode decomposition result; if it is judged that the two subsequences attract each other, sequence fusion operation is carried out, and if it is judged that the two subsequences repel each other, sequence generation operation is carried out; obtaining a new sub-sequence set through sequence fusion operation and sequence generation operation; and respectively inputting the sequences in the new sub-sequence set into the recurrent neural network to obtain a final prediction result of the energy storage frequency modulation instruction. By optimizing the frequency modulation instruction in advance, the energy storage system can respond to the frequency change requirement more quickly and accurately, the frequency modulation efficiency and economic benefits of the power system are remarkably improved, and meanwhile the stability of the system and the operation efficiency of the power plant are enhanced.

The invention discloses a drilling multi-source sensing signal denoising method based on dynamic noise decoupling and a self-adaptive mode, and belongs to the technical field of underground processing. The method comprises the following steps of: separating common noise of a noise energy distribution matrix of a sensor and separating specific noise; carrying out adaptive noise set empirical mode decomposition on the separated signals, carrying out variational mode decomposition on residual signals in the signals, and screening effective modes through a kurtosis-entropy joint criterion to obtain signals with effective characteristic components reserved; high-frequency fluctuation of the signal is punished through total variation regularization, an improved alternating direction multiplier method algorithm is used for solving, short-time Fourier transform is carried out on the solved signal, low-frequency and high-frequency features are extracted through a multi-scale convolutional network, and a final denoised signal is output through gating weight fusion. According to the method, the signal denoising precision in a complex noise environment is remarkably improved, the processing time is shortened, and the resource consumption is reduced.

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 discloses a power distribution network state panoramic intelligent sensing method, relates to the technical field of power distribution network monitoring management, and is used for solving the problem of poor power distribution network abnormity monitoring. According to the method, multi-scale decomposition and baseline construction of the signals are realized through accurate acquisition and preprocessing of the node signals of the power distribution network, steady-state and abnormal dynamic characteristics of the power distribution network are effectively extracted, a variational mode decomposition technology is used, and a low-frequency mode is screened through a center frequency to construct a baseline, so that the reliability of the power distribution network is improved. Constructing a robust residual error and an abnormal energy index by combining the difference between a baseline and an original signal, accurately determining an abnormal event, switching a power distribution network node to an ultrahigh frequency sampling mode after abnormal triggering, extracting a local disturbance characteristic by using time domain difference, and adaptively adjusting a filtering parameter according to a local edge ratio through robust filtering noise reduction, so as to achieve the purpose of reducing noise. Therefore, the abnormal edge information is fully reserved, the real-time and high-precision perception of the abnormal state of the power distribution network is realized, and the response speed and accuracy of the panoramic perception of the power distribution network are improved.

The invention provides a tunnel excavation ground surface settlement prediction method and system based on a machine learning hybrid model, and relates to the technical field of tunnel engineering and machine learning crossing, and the method comprises the steps: obtaining the multi-source heterogeneous information of a target tunnel, and constructing a ground surface settlement data set; a Transform-BiLSTM hybrid model is constructed, the robustness of the algorithm in a noise environment is enhanced based on a VMD (variational mode decomposition) algorithm, hyper-parameters are adaptively adjusted and optimized by using a PSO (particle swarm optimization) algorithm based on a ground surface settlement data set, the model prediction precision is maximized, and a ground surface settlement prediction model is obtained; and analyzing decision logic of the ground surface settlement prediction model through the SHAP value, and outputting interpretable engineering guidance suggestions. By constructing a machine learning hybrid model, high-precision and real-time prediction of ground surface settlement in the whole process of tunnel excavation is realized. The precision and generalization ability of the model are improved, the characterization ability of complex spatial-temporal characteristics is enhanced, and overfitting is avoided; and the interpretability is optimized, and the influence of key parameters on a prediction result is quantified, so that construction parameter adjustment is guided.

The invention provides a fiber-optic gyroscope drift compensation method and system fused with LMS self-adaptive filtering, and relates to the technical field of LMS self-adaptive filtering, the method comprises the following steps: synchronously collecting multi-point temperature data, temperature change rate, axial and radial gradient information and original output signals of a gyroscope shell, and calculating the multi-point temperature data, the temperature change rate, the axial and radial gradient information and the original output signals of the gyroscope shell; the original signal is separated into a temperature drift component and a non-temperature noise component through variational mode decomposition, and noise is filtered out; establishing a nonlinear mapping relation by combining the temperature field data and the drift component to generate a drift predicted value, and inputting the predicted value and the residual error of the actual drift component into an LMS adaptive filter for dynamic error compensation; and finally, superposing the compensation result and the predicted value, and outputting a corrected gyro signal. According to the invention, the drift compensation precision and stability of the fiber-optic gyroscope in a complex variable-temperature environment are improved.

The invention provides a bearing fault diagnosis method and system, a storage medium and a computer, and the method comprises the steps: obtaining a bearing vibration signal, and extracting time-frequency features in the bearing vibration signal according to variational mode decomposition and fast Fourier transform; performing convolution operation on the time-frequency features, and processing a convolution output result by adopting an ECA attention mechanism to extract frequency domain features in the time-frequency features; capturing a global time sequence dependency relationship in the time-frequency features based on a Transform model to extract long-distance dependent time domain features in the time-frequency features; and fusing the extracted frequency domain features and time domain features through a bidirectional cross attention mechanism, transmitting the fused features into a full connection layer for classification, outputting fault categories of the bearing, converting a classification result into probability distribution, and selecting the category with the highest probability as a diagnosis result. The bearing fault diagnosis method provided by the invention is high in fault diagnosis accuracy.

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 battery life intelligent prediction method fusing a time convolution network and a Mama state space model. The method comprises the following steps: a TCN branch extracts local time sequence characteristics in a battery degradation process by utilizing multi-layer expansion causal convolution, and nonlinear degradation phenomena such as capacity regeneration and the like are effectively identified; the Mama branch is based on a selective state space mechanism, dynamically models a long-period dependency relationship of a battery aging track, and adaptively focuses a key degradation node through weight adjustment of context sensing. In order to enhance model robustness, a variational mode decomposition unit is integrated to carry out noise suppression and mode separation on an original capacity sequence, and the generalization ability of a cross-battery chemical system is improved in combination with normalized constraint of a hidden state matrix. The accuracy of life prediction is remarkably improved, and key features in a complex degradation mode are effectively captured; the method has excellent noise suppression capability and cross-model generalization, is suitable for multiple types of battery systems, and realizes real-time aging state evaluation.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

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 relates to a hydroelectric power generation prediction method and system based on a VMD-LSTM-KAN model. The method comprises the following steps that historical power generation data and historical meteorological data of a target hydropower station are collected and preprocessed; carrying out VMD variational mode decomposition on the preprocessed historical power generation data, and decomposing the historical power generation data into a plurality of mode components from time sequence data; combining the plurality of modal components and historical power generation data into a multi-dimensional output matrix; fusing the preprocessed historical meteorological data with the multi-dimensional output matrix to generate an input data set; constructing a prediction model based on the LSTM network, performing nonlinear optimization on an output layer of the LSTM network in combination with a KAN network, and training the constructed prediction model through an input data set; and predicting a modal component prediction value and a power generation prediction value of the target hydropower station in a future time period through the trained prediction model.

The invention belongs to the technical field of bridge health monitoring, and aims at solving the problem that a traditional real-time cable force calculation method is low in recognition precision. The invention provides an inhaul cable real-time cable force identification method and device based on deep learning. The method comprises the following steps that vibration image information of a target inhaul cable is acquired; obtaining a target image sequence based on the vibration image information; adopting an optical flow estimation algorithm based on deep learning to obtain vibration displacement of a target inhaul cable in the target image sequence; variational mode decomposition transformation is conducted on the vibration displacement, and the mode component and the real-time frequency of the target inhaul cable are obtained; performing fast Fourier transform on the modal component, and determining the inherent frequency of each order and the corresponding order of the target inhaul cable; and calculating the real-time cable force of the target cable based on the real-time frequency and the order of the inherent frequency corresponding to the real-time frequency. According to the method, the vibration displacement identification precision of the target inhaul cable is improved, and the robustness of real-time cable force identification is 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 invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention relates to a multi-element load combined prediction method fusing a decomposition reconstruction technology and multi-task learning, and the method comprises the steps: firstly obtaining a historical load sequence of an electric load, a cold load and a heat load and a data sequence of influence factor characteristics, and forming a historical multi-element load sequence library and an influence factor characteristic library; performing preliminary decomposition on data in the historical multivariate load sequence library through a variational mode decomposition method, performing reconstruction by using a mixed characteristic evaluation method, and updating the historical multivariate load sequence library; the historical multivariate load sequence library and the influence factor feature library are remodeled into three-dimensional feature tensors, and a CNN-ECA-MMoE multi-task learning model is constructed and used for outputting feature sharing and feature extraction results; and constructing a CNN-ECA-MMoE-LSTM multi-task learning model, carrying out model training by using the three-dimensional feature tensor, and outputting prediction results of an electric load, a cold load and a thermal load through the trained model. According to the method, the prediction precision of the multi-element load is improved.

The invention provides a multivariable multi-step runoff prediction method based on dynamic variational mode decomposition, and belongs to the technical field of drainage basin runoff prediction.The method comprises the steps that various hydrometeorological data of a research drainage basin are collected; decomposing an original runoff sequence by adopting a dynamic variational mode decomposition strategy VMD, performing sliding window slicing processing on drainage basin underlying surface and meteorological data and upstream hydrologic station runoff data, and combining target hydrologic station runoff data subsequences obtained by decomposition into a first characteristic variable; meteorological factor screening is carried out on the meteorological station data, a screening result is used as a second characteristic variable, and the first characteristic variable and the second characteristic variable are combined together to form an input characteristic variable; a sparrow search algorithm SSA is adopted to optimize parameters of a long short-term memory network LSTM to construct the long short-term memory network LSTM, and future runoff is predicted. According to the method, the defect that future forecast information is introduced by binding decomposition is effectively overcome, and the applicability of the decomposition-integration forecast model in actual engineering is enhanced.

The invention belongs to the technical field of time series prediction, and more specifically relates to a time series data prediction method based on a deep integrated learning model and high and low frequency separation. The method comprises the following steps: collecting time sequence data in a system, and preprocessing the obtained data; vMD variational mode decomposition is carried out on original time sequence data, and a time sequence is decomposed into K mode components with limited bandwidth; combining a maximum information coefficient method and a reconstruction error analysis method to determine an optimal decomposition mode number K; dividing high and low frequency components for all decomposed modes by using a zero-crossing rate and a center frequency; appropriate prediction models are established for the high-frequency component and the low-frequency component respectively; and superposing all modal prediction results to obtain a final time sequence prediction result. According to the method, multiple deep learning models are combined, so that the prediction method has higher flexibility and adaptability, and proper models can be selected for components with different characteristics for prediction, thereby improving the overall performance.

The invention relates to the technical field of energy storage frequency modulation, in particular to an energy storage frequency modulation instruction prediction method and system, and the method comprises the steps: decomposing a frequency modulation instruction sequence into a plurality of subsequences; determining the sub-sequence with the maximum frequency change in all the sub-sequences as a target sub-sequence, and generating a target virtual sequence to replace the target sub-sequence; predicting the target virtual sequence and other subsequences by adopting a GRU network to obtain a plurality of corresponding prediction results; and superposing all the prediction results to obtain a target frequency modulation instruction prediction result. The method has the beneficial effects that the frequency modulation instruction sequence is decomposed into a plurality of subsequences by using the variational mode decomposition algorithm, and then the subsequences are processed and predicted through the frequency method and the virtual sequence method, so that the frequency modulation instruction is predicted in advance, an energy storage system can respond more quickly, the response time difference is reduced, and the frequency modulation performance index is improved; and higher economic benefits are created for a power plant.

The invention relates to the technical field of electric power system disturbance identification, in particular to an electric power system disturbance identification method based on a fusion neural network of parameter optimization, which comprises the following steps: acquiring eight types of disturbance frequency data of an electric power system, and optimizing penalty factors and modal feature vector quantity based on variational modal method decomposition by adopting an improved sparrow search algorithm to obtain eight types of disturbance frequency data of the electric power system; obtaining a target parameter group, obtaining modal feature components selected based on a fitness function, optimizing hyper-parameters of a fusion neural network by adopting a Lundao search algorithm, and distributing feature weights of a second time domain index in combination with a multi-head attention mechanism to obtain a time sequence feature signal; and S400, performing disturbance identification on the time sequence characteristic signal in the step S400 by adopting a convolutional neural network-bidirectional gating cycle unit classifier to obtain a disturbance identification result, and remarkably improving the accuracy of disturbance identification through the method.

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.