9 results about "Differential transformation" patented technology

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

The invention relates to a river flow prediction method, which comprises the following steps of: 1, dividing a daily runoff data set, and performing multi-stage decomposition on a training set by using Haar wavelets to obtain de-noising parameters; 2, multiplexing the verification set and the test set to obtain data; step 3, carrying out first-order differential transformation, and zooming to an interval of [0, 1] by using MinMaxScaler to obtain a normalized value of a differential sequence; 4, mapping the data to a high-dimensional space through a linear embedding layer, and then carrying out position coding to obtain data; step 5, inputting the data obtained in the step 4 into a DTransformer encoder, so that a catastrophe point obtains a higher attention weight so as to strengthen difference characteristics of adjacent time steps and obtain encoder output; 6, inputting the data obtained in the step 5 into an LSTM decoder, and performing feature recombination to obtain encoder output; 7, mapping the data obtained in the step 6 to 7-dimensional output through a full connection layer; and 8, performing inverse normalization and inverse difference transformation on the data obtained in the step 7 to obtain final output.

This invention relates to the field of optical monitoring technology for water environment, and discloses a method for analyzing and characterizing eutrophication components of water bodies based on hyperspectral feature inversion. The method includes: retrieving the intrinsic absorption spectrum sequence of pure water as a physical constraint benchmark; calculating the ratio of the hyperspectral reflectance to be measured to the benchmark to generate a modulation vector; determining the fractional-order differential sequence related to the sampling wavelength; extracting trough features through morphological baseline correction; completing the fractional-order differential transformation by combining the order sequence; extracting the characteristic trough depth and skewness parameters; and outputting the concentrations of chlorophyll a and colored soluble organic matter using an unmixing model. This invention utilizes the benchmark to drive the dynamic evolution of the differential operator, achieving topological separation of the scattering background and trace component absorption characteristics in different bands, suppressing local waveform distortion caused by background scattering heterogeneity, and improving the accuracy of component inversion under complex matrices.

This invention discloses an interference suppression method for a random phase-modulated electromagnetic metasurface, comprising: constructing a mathematical model for the modulated radar echo signal; wherein, a random phase-modulated electromagnetic metasurface is installed on the target surface, the radar transmitted signal is incident on the random phase-modulated electromagnetic metasurface, and the radar echo signal is generated after random time-domain phase modulation; the radar echo signal is subjected to digital down-conversion and delinear frequency modulation, followed by discrete sampling processing, and then self-mixing processing to estimate additional phase information; the radar echo signal after discrete sampling processing is phase-compensated considering noise, and then subjected to time-domain differential transformation and discrete Fourier transform; the phase change time is estimated using a subspace algorithm, and the interference modulation sequence is reconstructed based on the estimation result; the random phase interference is demodulated using the reconstructed interference modulation sequence, and matched filtering processing is performed to obtain the output signal.

The invention provides an electric-thermal integrated energy system dynamic energy flow calculation semi-analytical method and system, and belongs to the technical field of integrated energy system calculation and analysis. The method comprises the following steps: constructing a semi-analytical model of a power network and thermoelectric coupling equipment through one-dimensional generalized differential transformation; constructing a semi-analytical model of the heat supply network through two-dimensional generalized differential transformation; calculating a power series coefficient of the semi-analytical model by adopting a linear recursive non-iterative dynamic energy flow algorithm EFC, and performing hydraulic coefficient recursion, thermal coefficient recursion, thermoelectric coupling calculation and power coefficient recursion; according to the power series coefficient, a time continuous expression of thermoelectric state variables is generated, the thermoelectric state variables comprise bus voltage, electric power, pipeline temperature, mass flow of water in the pipeline and temperature, and dynamic energy flow calculation of the electric-thermal comprehensive energy system is carried out. According to the method, the problems of precision-efficiency selection and multi-time step length matching in dynamic energy flow calculation of the electric-thermal integrated energy system are relieved.

This invention discloses a method and system for predicting bearing degradation using a deep latent variable state-space model. The method uses the Box-Cox transformation function to correct simulated and real bearing degradation data, fusing multiple time-domain degradation features to obtain degradation health indicators. A degradation state characterizing the bearing degradation rate is obtained through differential transformation. Within the state-space model framework, a prediction model based on a deep latent variable state-space model is constructed. Features are extracted, fused, and differencing are performed on the simulated and real data to obtain their respective degradation states. The deep latent variable state-space model is pre-trained using the degradation states obtained from the simulated degradation data, and the model weights are initialized. Then, the prediction model based on the deep latent variable state-space model is used to predict the degradation states obtained from the real data. The predicted degradation values ​​are obtained by accumulating the predicted degradation states. This method solves the problems of existing processes being complex and time-consuming.

The invention relates to the technical field of mechanical arm control, and discloses an approximation damping reciprocal function feature solving method and system for singular point avoidance. The solving method is applied to mechanical arm control equipment, and specifically comprises the following steps that S101, the geometrical configuration and joint parameters of a mechanical arm are obtained, a complete kinematics model frame is established, a forward kinematics equation is established through a Denavit-Hartenberg parameter method, the mapping relation between the pose of an end effector and joint variables is determined, and the pose of the end effector is determined; deriving a speed level Jacobian matrix of the mapping relation by adopting a differential transformation method; s102, performing algebraic analysis and decomposition on the obtained Jacobian matrix, identifying key algebraic factors causing matrix singularity, and establishing corresponding singularity factor libraries for different mechanical arm configurations; according to the adaptive damping reciprocal function constructed based on the composite exponential function, the numerical stability in a singular point neighborhood is ensured by optimizing parameter combination, and the problem of joint velocity kick caused by damping mutation is thoroughly eliminated.

The invention relates to the technical field of time series data classification, discloses a time series data classification method and device based on convolution pooling, and aims at solving the problems that an existing time series data classification method based on a neural network is low in efficiency and poor in precision and interpretability. According to the scheme, the method mainly comprises the steps that preprocessing and first-order and second-order differential transformation are conducted on original time series data so as to highlight the change rate and acceleration characteristics; performing convolution on the original and differential sequences by adopting a convolution kernel with fixed parameters to generate a feature map reflecting the local mode matching degree; multiple pooling operators are applied to extract statistical features representing different dimensions of matching degrees, and the statistical features are spliced into a high-dimensional feature vector; and training a classification model based on the high-dimensional feature vector to realize classification of new time series data. According to the method, the time series data classification efficiency, precision and interpretability are improved, and the method is suitable for multiple fields of industrial monitoring, medical diagnosis and the like.

The application relates to the technical field of petroleum drilling engineering, and particularly discloses a petroleum drilling and production data analysis and prediction system, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data, and forms a time sequence feature set through time sequence alignment and fusion processing; through differential transformation and modal decomposition on the feature set, a transient response characteristic vector is extracted and a risk distribution map is generated; a parameter-risk correlation model is established, mechanical vibration and thermal stress interference are decoupled by solving a physical equation, and a dynamic risk index is obtained; the risk index and process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, information aggregation and reconstruction are realized by adopting a feature fusion grid, and a comprehensive risk assessment index is generated; a dynamic early warning threshold is established according to the assessment index, and drilling parameters are optimized in real time through closed-loop control.