1159 results about "Root mean square" patented technology

The invention relates to the technical field of fault prediction, in particular to a centrifuge fault prediction system based on machine learning, which comprises a data channel synchronization module, a multi-dimensional feature extraction module, a state evolution index construction module, a trend aggregation trajectory recognition module and a fault section recognition module. According to the method, different types of data are synchronously aligned by a multi-channel signal segmentation processing mechanism based on a periodic state, a state evolution sequence is constructed in combination with a unified sampling structure based on a time scale, a state characteristic track is established through a multi-dimensional parameter set, a trend change index is constructed by means of a difference root-mean-square between adjacent states, and the state evolution sequence is analyzed. According to the method, the aggregation section is recognized and the trajectory deviation frequency is counted by utilizing continuous trend mutation, so that dynamic migration of the trajectory boundary and intelligent recognition of the fault section are realized, the boundary failure problem caused by static preset conditions is avoided, and the continuous prediction stability of long-period equipment and the application range under a non-standard working condition are effectively enhanced.

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention relates to the technical field of electrical performance testing, in particular to a water supply equipment electrical performance detection system based on artificial intelligence, which comprises an electrical parameter acquisition module, an energy efficiency analysis module, an insulation performance evaluation module and a comprehensive diagnosis module. According to the invention, a three-phase voltage and current root-mean-square value is dynamically calculated through a broadband current transformer and a sliding window integration method, a 50Hz fundamental component amplitude is extracted to separate power frequency and high frequency harmonic waves, a continuous period energy efficiency ratio standard deviation triggers an insulation detection threshold value, and a parameter fluctuation and insulation degradation response mechanism is established. Harmonic interference correction synchronous processing energy efficiency ratio and leakage current integration, the detection error being lower than 5%, temperature rise rate linear regression and bearing current threshold value cooperative determination, winding aging cross validation, fault misjudgment rate reduction to 26%, closed-loop detection fusion transient capture, dynamic threshold value triggering and multi-parameter diagnosis, early warning success rate reaching 92%, and positioning response being reduced to 3 seconds.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention discloses a low-altitude atmosphere three-dimensional wind field inversion method based on wind profile radar vertical observation, relates to the technical field of three-dimensional wind field inversion, and aims to solve the problem of poor inversion effect caused by inaccurate model parameters in the wind field inversion process. The difference between simulation data and actual observation data is evaluated from multiple angles, a comprehensive optimization basis is provided, parameter calculation optimization is performed by adopting a particle swarm optimization method, the method has the characteristics of high convergence speed and high global search capability, is suitable for optimization of complex problems, and is used for confirming model requirements according to a wind field inversion rule. The model is selected by combining the evaluation result of the model library, it is ensured that the selected model has high pertinence and applicability, the prediction precision and reliability of the model are improved, model initialization is conducted through wind field fusion data, data information of multiple sources is fully fused, and the comprehensiveness and accuracy of the data are improved.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention relates to the technical field of trajectory tracking control of piezoelectric motion tables, and discloses a trajectory tracking control method and device for a piezoelectric console, and the method comprises the steps: constructing an initial dynamic model of a piezoelectric motion table based on an asymmetric Bouc-Wen hysteresis model, and obtaining a to-be-identified parameter set; based on the root-mean-square error between the actual output displacement of the piezoelectric motion platform at the plurality of sampling time points and the predicted output displacement of the initial dynamical model, constructing a self-adaptation function of the parameter set to be identified; identifying and acquiring a target model parameter set by using a multi-modal Bayesian gradient optimization algorithm and taking the value convergence of the self-adaption function as an optimization target to obtain a target dynamic model; based on the target dynamic model, constructing a hysteresis state observer, and obtaining feed-forward compensation voltage; constructing a self-adaptive fuzzy PID feedback controller, and obtaining a feedback control voltage; and summing the feedforward compensation voltage and the feedback control voltage to generate a driving voltage so as to drive the piezoelectric motion table to perform trajectory tracking.

The invention relates to the technical field of belt conveyor carrier roller fault monitoring, and discloses a belt conveyor carrier roller fault distributed optical fiber monitoring system which comprises a double-light-source DAS system, optical fiber information acquisition, filtering preprocessing, time domain analysis, time-frequency domain analysis, phase analysis, multi-mode feature fusion, fault classification and fault position and grade output. By building a dual-light-source polarization diversity DAS system and combining an adaptive filtering preprocessing technology, high signal-to-noise ratio acquisition and noise reduction processing of vibration signals are realized, dual-light-source orthogonal polarization state transmission effectively suppresses polarization fading, wavelet packet denoising and variable-step LMS filtering collaboratively filter environmental noise and pulse interference, and the noise reduction performance of the vibration signals is improved. Meanwhile, the application of the dynamic weighted root-mean-square value and the continuous wavelet transform ensures the accurate capture of the time domain energy characteristic and the frequency domain time-varying characteristic, and lays a reliable foundation for the multi-modal characteristic fusion.

The invention provides a sea surface height abnormal data downscaling method based on deep learning, and relates to the technical field of data processing, and the method specifically comprises the following steps: preprocessing data including AVISO data, SWOT data and key auxiliary variables; an enhanced super-resolution generative adversarial network ESRGAN is constructed, a high-resolution feature map is generated, and the ESRGAN comprises a generative network and a discrimination network; using a plurality of loss function combinations to optimize the performance of the generative network and the discriminant network, including adversarial loss, perception loss and pixel loss; and evaluating the pixel-level error and the spatial detail consistency output by the ESRGAN by adopting a root-mean-square error and a structural similarity index respectively. According to the technical scheme, the problems that in the prior art, a data downscaling method is limited in applicability and generalization ability, and is difficult to adapt to fusion requirements of different regions and multi-source data are solved.

The invention discloses a visible light and infrared fusion target detection method and system for an all-time unmanned aerial vehicle scene, and belongs to the technical field of unmanned aerial vehicle aviation and intelligent image processing. The method comprises the following steps: analyzing an original visible light image to calculate average brightness and root-mean-square contrast, and quantifying scene illumination conditions; distortion correction and registration are carried out on the original image; a parallel backbone network is adopted to extract visible light and infrared twinborn feature maps, the number of channels allocated to two modal features is dynamically adjusted through 1 * 1 convolution according to illumination condition parameters, and then a dual-light fusion network containing a gated convolution block is utilized to perform weighted fusion; and target prediction is carried out through the progressive feature pyramid network. Through an illumination adaptive fusion strategy and targeted data enhancement, the precision and robustness of target detection in a complex all-day scene are significantly improved, and the method is suitable for real-time target detection tasks of the unmanned aerial vehicle.

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

The invention relates to a rainfall nowcasting method based on U-KAN grading loss weighting and frequency self-adaption, which comprises the following steps: (1) carrying out quality control and screening on a radar puzzle, and establishing a data set; (2) dividing a training set, a verification set and a test set, and standardizing; (3) constructing a U-KAN model, selecting training parameters and inputting data: combining a traditional Unet structure with a KAN network to construct the U-KAN model; then performing model training to obtain a prediction result; the prediction result is restored to the original magnitude through destandardization; (4) introducing a loss function based on a root-mean-square error and grade weighting in a model training stage, and performing post-processing on model output by adopting a frequency deviation correction method; (5) integrating and averaging the forecast products processed by the two complementary strategies, and recording the forecast products as U-KANE; and (6) predicting a rainfall result in the next three hours by using radar echo data in the past one hour, and outputting a rainfall short-term and imminent forecast result by the U-KANE.

The invention provides a slope displacement monitoring method and system based on reinforcement learning and enhanced Kalman filtering, and the method comprises the steps: carrying out the preprocessing of displacement data collected by Beidou, and carrying out the abnormal value elimination, missing value interpolation and time consistency inspection; establishing a Kalman filtering model containing displacement and speed state vectors, and initializing a process noise covariance matrix Q and an observation noise covariance matrix R as initial filtering parameters; q and R matrixes are dynamically optimized through a PPO reinforcement learning algorithm, and parameter self-adaptive adjustment is achieved; carrying out displacement trend analysis on the filtered output data, marking abnormal trend data by adopting a statistical test and trend inflection point recognition algorithm, and feeding back a root-mean-square error of the abnormal trend data to a PPO algorithm to carry out parameter readjustment; data stage changes are analyzed based on a sliding window technology, independent experience playback buffer areas are set for data in different stages in PPO, and associated updating of filtering parameters is achieved. According to the invention, the precision and reliability of slope displacement monitoring are improved.

The invention relates to the technical field of electroencephalogram signal processing, and provides a brain-computer AI glasses electroencephalogram signal multi-dimensional quality dynamic detection method, which comprises the following steps: preprocessing collected electroencephalogram signals; performing task frequency band component analysis on the channel signals subjected to the matrix rank analysis, and calculating a power proportion of a target frequency band; performing maximum gradient analysis on the channel signal, and calculating the maximum gradient value of the channel signal; performing root-mean-square analysis on the channel signal, and calculating a root-mean-square value of the channel signal; performing fluctuation amplitude analysis on the channel signal, and calculating a peak-to-peak value of the channel signal; performing brain hemisphere symmetry analysis on the left and right homologous channel signals, calculating the correlation and mean square error of the channel signals, and setting a dynamic threshold according to the frequency band of the channel signals; obtaining an electrode impedance value, and mapping the electrode impedance value into an impedance score through a preset exponential attenuation function; the final quality score of the channel is obtained, and dynamic detection and real-time score updating of the electroencephalogram signal quality are achieved.

The invention relates to the technical field of motor control, in particular to a control and parameter intelligent optimization method for an anti-impact high-precision PMSM (permanent magnet synchronous motor) feeding servo system, which comprises the following steps of: S1, establishing a physical structure and a dynamic model of a PMSM driving feeding system; s2, designing a variable gain fractional order super-spiral sliding mode controller VGFSTSMC, realizing finite time convergence of position tracking errors by dynamically adjusting and controlling gain coefficients, and inhibiting system jitter; s3, designing an adaptive sliding mode disturbance observer ASMDO, and estimating and compensating unknown disturbance in real time through adaptive gain adjustment and an integral sliding mode surface; s4, identifying a system Stribeck friction model based on a least square method, and inputting the system Stribeck friction model as a friction feedforward compensation FFC to a current loop to reduce the disturbance uncertainty of the system; s5, dynamically adjusting and optimizing control parameters of the VGFSTSMC and the ASMDO by adopting an optimization algorithm so as to minimize a root-mean-square value RMSE of a position tracking error and a maximum instantaneous error; and S6, VGFSTSMC, ASMDO and FFC are combined with an optimization algorithm, and the PMSM is driven to realize high-precision position control.

A method for removing tube wave interference from optical fiber acoustic wave sensing seismic data, including: acquiring seismic wavefield data which contains a tube wave and is collected by an optical fiber acoustic wave sensing instrument; calculating a root-mean-square amplitude of the waveform data cut on the seismic trace as an amplitude normalization factor; performing normalization processing on the amplitude value; performing de-tail mean filtering processing on the normalized amplitude value along the travel time of the tube wave, to obtain a predicted amplitude value; performing tube wave interference removal processing on each seismic trace, and performing inverse normalization processing to obtain the seismic wavefield data without tube wave interference. The method effectively suppresses the tube wave interference in the optical fiber acoustic wave sensing seismic data. An apparatus for removing tube wave interference from optical fiber acoustic wave sensing seismic data, and a computer device are further provided.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention provides a rolling bearing digital twinning dynamic evolution method and system based on continuous learning, and belongs to the technical field of bearing life prediction. The method comprises the following steps: processing a bearing monitoring signal through short-time Fourier transform to generate standardized time-frequency data; constructing health indexes based on the index degeneration function and dividing health levels; training a life prediction model by using the extended LSTM network and taking the time-frequency data as input; real-time data is collected through a fixed time window to predict the life, and when the root-mean-square error of a predicted value and an actual value exceeds the limit, the edge device is triggered to upload new data; evaluating parameter importance in combination with a Fisher information matrix, and dynamically adjusting a regularization intensity updating model; and monitoring the data standard deviation in real time, triggering shutdown when the data standard deviation exceeds the limit, otherwise, predicting the remaining life by updating the model, and stopping when the remaining life reaches the threshold value. According to the method, dynamic evolution of the digital twin model is realized through continuous learning, and the industrial equipment state monitoring and predictive maintenance capability is effectively improved.

The invention provides a vibration reduction buffering and posture self-adaptive adjusting method in wind turbine generator cabin transportation, and relates to the technical field of wind turbine generator transportation, which comprises the following steps: collecting transportation vibration time sequence data, after wavelet denoising and normalization processing, adopting synchronous compression decomposition and nonlinear cross-scale feature mapping to extract a dominant frequency component, and then extracting the dominant frequency component; synchronously calculating time domain parameters (a root mean square value, a peak factor and a waveform factor) and frequency domain parameters (a frequency centroid and bandwidth energy distribution); inputting the multi-dimensional features into a graph convolutional neural network to realize vibration mode classification, constructing an attitude optimization model based on a classification result, performing cooperative solution through a multi-agent depth deterministic strategy gradient algorithm, and outputting an optimal attitude adjustment parameter; after the adjustment is implemented, new vibration data are collected to update network parameters, and closed-loop control of vibration feature recognition and attitude optimization is formed. Transportation vibration impact is effectively reduced, and the transportation stability and safety of wind power equipment are improved.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention discloses a turning signal noise reduction method based on an improved wavelet threshold, and the method comprises the steps: carrying out the multi-layer wavelet decomposition of a noise-containing turning signal, obtaining the low-frequency and high-frequency wavelet coefficients of each layer, and processing the high-frequency wavelet coefficients of each layer through an improved threshold function, thereby achieving the noise reduction of the turning signal. And reconstructing a signal through wavelet inverse transformation of the processed wavelet coefficient to realize denoising. The optimal number of decomposition layers required by a sampling signal is determined according to noise power, and finally, an optimal wavelet basis is determined by taking a signal-to-noise ratio, a mean square error and the like as evaluation indexes. By means of the improved wavelet threshold function, on one hand, the signal denoising effect can be achieved, and on the other hand, the situation that details are damaged due to excessive noise reduction can be avoided. Experimental data show that the improved threshold function effectively suppresses random errors, the signal-to-noise ratio is improved, the root-mean-square error is reduced, and the denoising effect is good.

The invention provides a bearing fault diagnosis method based on a one-dimensional local binary pattern and a Hankel matrix. The bearing fault diagnosis method comprises the following steps: acquiring a discrete vibration signal; performing first-order differential operation on the discrete vibration signal to obtain a differential signal; performing inherent time scale decomposition on the differential signal to obtain an inherent rotation component signal; performing quantization and signal reconstruction on each inherent rotation component signal by taking a root mean square as a quantization criterion of a one-dimensional local binary mode method to obtain a decimal feature signal; constructing a Hankel matrix of the decimal characteristic signal and performing signal reconstruction according to a covariance matrix of the Hankel matrix; performing spectral analysis on the reconstructed signal, calculating the fault characteristic frequency of the bearing, and then judging the state and the fault type of the bearing through a frequency component obtained through spectral analysis and the fault characteristic frequency of the bearing obtained through calculation. According to the bearing fault diagnosis method, noise can be effectively suppressed, the bearing fault feature information can be effectively extracted, and the bearing state and the fault type can be accurately identified.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The invention discloses a high-tower wind turbine generator fault early warning and diagnosis method and system. The method comprises the following steps: preprocessing SCADA data of the high-tower wind turbine generator; decomposing the preprocessed SCADA data through an improved EEMD algorithm, extracting feature information of at least two time scales, and splicing I MF components of at least two time points into a feature matrix; inputting the feature matrix into an enhanced vision Transformer (Vi T) model to obtain a prediction result of the real-time state of the wind turbine generator; and calculating a residual matrix based on the prediction result and the SCADA data, and when a root mean square error of the residual matrix exceeds a preset early warning threshold, triggering fault early warning. According to the technical scheme, by improving the EEMD algorithm and enhancing the visual Transform model, the accuracy and real-time performance of fault early warning and diagnosis of the high-tower wind turbine generator system are improved, the intelligent level of the system is enhanced, and the operation and maintenance cost is reduced.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

The invention relates to the technical field of signal processing, and discloses a power equipment partial discharge signal denoising method and system, and the method comprises the steps: S1, generating an exponential decay type pulse signal simulating partial discharge, and superposing white noise and narrow-band interference into the exponential decay type pulse signal to generate a noisy signal; s2, decomposing the noisy signal into a plurality of intrinsic mode components through an ensemble empirical mode decomposition method, calculating a correlation coefficient and a kurtosis index of each intrinsic mode component, and identifying a noise component and a signal component based on the correlation coefficient and the kurtosis index; s3, wavelet threshold denoising is carried out on the noise components, and a wavelet threshold is dynamically adjusted for each noise component through a particle swarm optimization algorithm; and S4, reconstructing the de-noised noise component and the reserved signal component into a final de-noised signal, and evaluating the de-noising performance based on the signal-to-noise ratio, the root mean square error, the peak signal-to-noise ratio and the correlation coefficient.

The invention relates to a server-side satellite precision product anomaly detection method and system and a terminal, specifically, a previous time window of a current epoch is obtained, the time window comprises a plurality of epochs, the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated, and the residual mean value and the root-mean-square of each satellite in each epoch in the time window are calculated; normalizing the residual mean value and the root-mean-square to obtain the characteristics of the satellite in epochs; acquiring features of all the satellites in all epochs in the previous time window to form a feature set, performing outlier detection on the feature set by adopting an LOF method, and determining the LOF score of the satellite at the current epoch moment by utilizing the features of the current epoch of the satellite and the feature set after removing the satellite of which the LOF score is greater than a preset value from the feature set; the LOF scores of the satellites are sent to the PPP user side, when the PPP user side carries out ambiguity fixing, all the satellites are tried to be fixed firstly, if fixing fails, the satellite with the maximum LOF value is removed from the ambiguity set, fixing is tried again, and continuous circulation is carried out till the ambiguity is fixed successfully.

The invention discloses a key parameter long-time-sequence prediction method for a complex process industry, and the method comprises the steps: collecting multivariable sensor data in the process industry, and constructing a high-dimensional long-time-sequence prediction data set; constructing a PatchConvRNN prediction model by combining time slice embedding, dimension decoupling convolution, depth separable convolution and a recurrent neural network based on a sequence-to-sequence normal form; a point value-statistical mixed loss function is adopted, the point prediction precision, the sequence mean value and the standard deviation consistency are optimized at the same time, a prediction model is trained in combination with an optimization algorithm, and network model parameters are adjusted; and comprehensively evaluating the prediction model through a root-mean-square error, an average absolute percentage error and a standard deviation average absolute error. According to the method, high-precision prediction and fluctuation maintenance of the key time sequence variables under the complex working condition of the industrial process are achieved, and powerful support is provided for quality control and predictive maintenance of the production process.

The invention relates to the technical field of attitude determination of a photoelectric pod of an unmanned aerial vehicle, in particular to an MEMS-IMU dual-mode correction attitude determination method for the photoelectric pod of the unmanned aerial vehicle. Comprising the following steps: establishing a damping system second-order transfer function model, and outputting an attenuation coefficient matrix; based on MEMS-IMU three-axis motion parameters, distinguishing a large maneuver turning state and a linear flight state; respectively compensating a roll angle, a pitch angle and a course angle of the MEMS-IMU by using the main inertial navigation according to different states; converting the corrected attitude angle into a quaternion, and optimizing and outputting a fused quaternion through a gradient descent method; taking the fused quaternion as an input, constructing a seven-dimensional state space model, and outputting an error compensation amount through Kalman filtering; dynamically adjusting the main inertial navigation weight according to the accelerated speed root mean square; and outputting a final attitude angle in combination with the error compensation amount and the weight, and verifying the final attitude angle through GNSS (Global Navigation Satellite System) position inversion. The method has the advantages that cross interference is avoided; therefore, the method is suitable for the photoelectric pod scene of the unmanned aerial vehicle.