5results about How to "Adequate response time" patented technology

The invention relates to a heat supply prediction method and system based on spatial-temporal feature fusion deep learning, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-source heterogeneous data, and constructing an integrated data set; s2, preprocessing the data; s3, constructing a graph structure model of the heat supply system, and constructing a weighted undirected graph; s4, constructing and executing forward calculation of the space-time double-flow deep network; s5, designing a composite loss function including mean square error loss and physical constraint loss, and performing joint optimization training on the space-time double-flow deep network; and S6, performing multi-step heat supply load prediction by using the trained model, outputting a heat supply load curve of each heat exchange station in a specified time period in the future, and integrating a prediction result with a heat supply scheduling system. The method has the advantages that the prediction precision is improved compared with that of a traditional machine learning model by capturing the spatial-temporal characteristics at the same time, and the advantages are more remarkable in extreme weather.

The invention discloses a wave pitching motion control method and system for an offshore floating type wind turbine generator, and belongs to the technical field of offshore wind power equipment control. The method comprises the following steps: signal acquisition: acquiring unit operation state, marine environment and pitching motion state parameters in real time; wave load prediction: predicting a future wave load based on the improved LSTM neural network model; evaluating a pitching motion state, pre-judging a motion trend through a kinetic model, and judging whether the motion trend exceeds a safety threshold value or not; cooperative control instruction generation: generating cooperative control instructions for fine tuning of a variable pitch angle, a generator torque and a cabin yaw angle based on an MPC algorithm; and control execution and feedback correction: driving an execution mechanism to act and dynamically correcting a control instruction. By accurately predicting the wave load, cooperatively adjusting multiple controllable components and dynamically correcting, efficient and accurate suppression of pitching motion is achieved, and the unit operation stability, the wind energy capturing efficiency and the equipment safety are improved.

The application discloses a mine area closed goaf pollution release early warning method based on water quality and water level monitoring, and belongs to the underground water monitoring field. The method comprises the following steps: collecting water level data and representative water samples of water-filled rock layers, performing water chemical and isotope analysis on the water samples, analyzing the direct contribution water volume of each water-filled rock layer, inputting a data set containing dynamic monitoring parameters and future expected parameters into a pre-trained water level prediction model to obtain a predicted water level, calculating the expected exchange flow between the closed goaf and the water-filled rock layer by using the Darcy seepage formula based on the predicted water level and the goaf shape, and calculating the expected pollutant discharge amount in the prediction period. If the expected pollutant discharge amount exceeds a preset safety threshold, a warning for the corresponding closed goaf is generated, so that the early warning of the pollutant discharge of the closed goaf is realized, and timely and scientific decision-making basis is provided for the active prevention and control and precise management and control of the mine area underground water pollution risk.

The invention discloses a complex wind regime identification method based on deep learning, and particularly relates to the technical field of wind regime identification, and the method comprises the five steps: collecting the original data of a three-dimensional wind field through an X-band laser wind measurement radar; data preprocessing is completed through signal processing and edge calculation; constructing a composite model of a convolutional neural network and a long-short-term memory network, and training and optimizing in combination with transfer learning; outputting identification results of the type, the strength and the trend of the wind regime; and triggering a three-level grading safety protection strategy. The complex wind regime recognition method based on deep learning has the specific effects that data acquisition is accurate and comprehensive, processing is efficient and low in delay, the model recognition accuracy is larger than or equal to 95%, prediction can be carried out 30 minutes in advance, safety and efficiency balance is achieved through graded protection, and the method is suitable for complex wind fields on the sea, high altitudes and the like; operation safety of the fan can be effectively guaranteed, and the service life is prolonged.

This invention discloses a self-sensing flange gasket for structural health monitoring of pipeline systems. The flange gasket consists of functional components and signal transmission circuit components. The functional components have a three-layer concentric ring contact structure, including a sealing inner ring layer, a leakage detection middle ring layer, and a sealing reinforcement outer ring layer, used to achieve the sealing and condition monitoring functions of the flange gasket. The signal transmission circuit components include an inner ring edge electrode layer and a middle ring planar interdigitated electrode layer, used to acquire and transmit electrical signals from the flange gasket. All components use flexible PDMS as the base material to ensure the elastic deformation sealing capability of the flange gasket, while the self-adhesive properties of PDMS are used to achieve seamless assembly between the layers. This invention, through a multi-functional layer modular design, utilizes multi-dimensional electrical signal changes caused by different physical parameters to achieve simultaneous detection of fastening pressure and leakage components, thus meeting the engineering requirements for condition monitoring of pipeline systems.