7results about How to "Strong non-linear mapping ability" patented technology

The invention provides an electric power system chaotic oscillation control method and system based on a reactive power compensation device. The method comprises the following steps: establishing a mathematical model of an interconnected dual-machine electric power system containing an SVC according to a kinetic equation; simplifying the mathematical model to obtain a simplified mathematical model; designing a sliding mode control function; determining a relation function between the coefficient of the exponential reaching law and the sliding hyperplane; a three-layer feedforward neural network is constructed, and training is carried out through an operation data set of the interconnected dual-computer power system containing SVC according to the relation function; the neural network calculates an adjustment value of a sliding mode control parameter and a compensation value for system uncertainty according to the input state variable; finally, the control signal acts on the interconnected dual-machine power system of the SVC, and the system state is guided to converge to a stable state. Through the combination of the neural network and sliding mode control, the resistance of the whole reactive power compensation system to the interference is further enhanced, and the stable operation and control precision of reactive power compensation are ensured.

The present invention proposes a Beidou time synchronization attack detection method based on PSO-GRU under extreme weather conditions, including: constructing and training a PSO-GRU network model for predicting clock states; preprocessing Beidou clock data under the extreme weather conditions based on an adaptive sliding window; predicting and measuring clock states; performing time synchronization attack detection and identifying the type of time synchronization attack, and completing Beidou time synchronization attack detection based on PSO-GRU under extreme weather conditions. The present invention solves the problem that the time synchronization attack detection method based on Kalman filtering is greatly affected by noise and outliers in the data under extreme weather conditions, and it is difficult to estimate the clock state in real time. The present invention has good nonlinear mapping capabilities and a high degree of self-learning capabilities, and can avoid problems such as gradient explosion, gradient disappearance, and overfitting. At the same time, the accuracy and efficiency of TSA detection can be improved to a certain extent.

The invention discloses a control method and system for automatic establishment of a pressure stabilizer steam cavity of a pressurized water reactor nuclear power plant, and the method comprises the steps: building a system model suitable for simulating the process of building the steam cavity of a pressure stabilizer, and obtaining the design priori knowledge of a controller and the simulation data required for training a neural network; designing a voltage stabilizer according to the operation flow and priori knowledge, and establishing a control system architecture and a controller of the steam cavity process; selecting simulation data to carry out offline training on the designed neural network controller; a simulator or a high-precision model is used for simulating the steam cavity building process, online simulation verification is conducted on the voltage stabilizer automatic steam cavity building control system based on the neural network, controller correction and optimization iteration are conducted according to the simulation verification result, and automatic control is achieved. The method can reduce the burden of an operator, reduces the probability of human errors, and improves the starting economy of the nuclear power plant.

The invention discloses a driving subject identification method and system in an auxiliary driving environment. The method comprises the following steps: acquiring driving data of a vehicle; processing the acquired driving data, and screening lane changing time, steering wheel angle and acceleration fragment data; inputting the screened fragment data into a pre-constructed Stacking integrated driving subject recognition model, and recognizing the probabilities of driving subjects corresponding to different time periods in the fragment data; the Stacking integrated driving subject identification model comprises a primary learner and a secondary classifier, the primary learner comprises a lane changing working condition driving subject identification model and a steering working condition driving subject identification model, and the secondary classifier is a variable speed working condition driving subject identification model. According to the invention, the identification accuracy of the driving subject can be improved.

This invention discloses a groundwater model parameter estimation method combining deep learning and local set updating, comprising: constructing a groundwater numerical model; constructing a prior parameter sample set; obtaining the corresponding model output set; acquiring observation data; determining the number and proportion of local sets; randomly selecting local center samples and calculating similarity; forming a local prior sample set; updating all local prior sample sets one by one to a local posterior sample set; randomly selecting posterior samples to obtain the overall posterior sample set; replacing the prior sample set with the posterior sample set, and iteratively outputting the parameter estimation results. By combining a deep learning update mechanism with a local set update strategy, this invention effectively addresses the challenges of high-dimensionality, strong nonlinearity, non-Gaussian parameter distribution, and the "different parameters, same effect" problem in groundwater model parameter estimation, significantly improving the computational efficiency and accuracy of data assimilation algorithms in complex groundwater system parameter estimation tasks.

This invention relates to the field of oil and gas reservoir exploration technology, specifically a method, apparatus, electronic device, and storage medium for predicting the content of multiple minerals. The method includes acquiring input features of the well section to be predicted; inputting these features into a multi-component mineral content prediction model; and outputting the corresponding prediction results for the content of each mineral component. The multi-component mineral content prediction model is trained using multiple samples on a pre-set deep learning model, which employs a strategy of bidirectional multi-scale feature extraction and cross-scale attention fusion. The model constructed by this invention can not only efficiently capture the global long-term trend and local abrupt fluctuations of logging curves, but also automatically learn the mutual constraints between minerals, achieving accurate mapping between multi-scale logging features and specific mineral categories. Therefore, the multi-component mineral content prediction model enables efficient and collaborative prediction of multiple minerals, providing an effective data foundation for oil and gas exploration and development, reservoir evaluation, and production capacity prediction.

The present invention relates to the technical field of hearing aid speech enhancement, and in particular to a speech enhancement method, device and hearing aid equipment based on dual-channel neural network time-frequency masking. The method comprises the following steps: using two microphones to receive speech signals under noise and reverberation conditions, performing preliminary speech enhancement on each microphone signal received through a trained single-channel neural network, and removing noise in the same direction as the target speech; different from traditional positioning that blindly relies on signal energy, an adaptive ratio mask provides a view of the target signal, can accurately identify the speech-dominant time-frequency unit on each microphone channel, and uses the time-frequency unit for sound source positioning, thereby ensuring high positioning accuracy under noise and reverberation conditions; and removing noisy speech in different directions from the target speech and suppressing room reverberation through the calculated weight input WPD beamformer, thereby obtaining enhanced speech with good speech quality and high intelligibility.