9 results about "One step prediction" patented technology

This invention discloses a one-step snap-fit ​​prediction method for flexible flat cable based on virtual-real fusion and adaptive gradient. First, in the real operating environment of mobile phone flexible flat cable assembly, real tactile images are acquired using a tactile sensor. Then, based on the real tactile images, the Material Point Method (MPM) is used to simulate the real tactile sensation in a virtual environment, generating a virtual tactile image. Real and virtual tactile sensations together constitute a digital twin environment. By performing adaptive gradient calculation on the virtual and real tactile images in the digital twin environment, a one-step prediction model based on adaptive gradients is established. The model uses a CNN+LSTM network to predict the tactile images, generating predicted tactile images, which are then compared with ideal tactile images. Then, by encoding and decoding the tactile images, a one-step snap-fit ​​guidance strategy is obtained and applied in the real operating environment. The one-step prediction model based on adaptive gradients is trained and iterated in the digital twin environment.

The present application belongs to the technical field of ship working condition prediction, and discloses a rotating speed prediction method for a ship gas-electric hybrid power system. A ship gas-electric hybrid power propeller rotating speed prediction model is constructed based on an adaptive neuro-fuzzy inference system (ANFIS); a difference between the obtained predicted rotating speed and the actual rotating speed is obtained; an improved rotating speed prediction model is constructed using the obtained difference and the used rotating speed information; and future rotating speed prediction is performed through the constructed improved rotating speed prediction model. The present application uses the initially constructed rotating speed prediction model to obtain the first-step predicted rotating speed, and provides the difference between the predicted rotating speed and the actual rotating speed for the improved prediction model; the improved rotating speed prediction model is constructed according to the obtained difference and the initial rotating speed information, so as to improve the rotating speed prediction accuracy and achieve the real-time prediction effect within a given time step.

The application discloses a kind of optimization distributed filtering method of target tracking system under work cycle scheduling strategy, the method is as follows: one, the dynamic model of target tracking system with state constraint and probability quantization is established;Two, distributed filter design is carried out to dynamic model under work cycle scheduling;Three, the upper bound of one-step prediction error covariance matrix is calculated;Four, filter gain matrix is calculated;Five, it is obtained in distributed filter by substituting, whether the total length of sensor network is reached, if, then execute six, if, then end operation;Six, the upper bound of filtering error covariance matrix is calculated;Let, execute two, until meet. The application solves the problem that existing distributed filtering method cannot simultaneously process sensor network with state constraint, probability quantization and work cycle scheduling, leading to the problem of reduced filtering performance.

This invention proposes a multi-step fault prediction method for chemical processes based on a knowledge-enhanced graph Transformer. The steps are as follows: constructing a multivariate time series and processing it with a sliding time window to obtain input samples; constructing a mechanism constraint graph and assigning weights to node pairs to form a prior adjacency matrix; constructing a learnable adjacency matrix based on the input samples and fusing it with the prior adjacency matrix; inputting the node feature matrix into a graph convolutional network to obtain a spatial feature matrix; adding a position encoding vector to the input samples and inputting them into the Transformer module to obtain the temporal dependency feature matrix for each time step; concatenating the inputs into a gating network and performing weighted fusion to obtain a fused feature matrix; using the obtained fused feature matrix to generate the next prediction, and using the prediction results and historical sequences for inference to achieve multi-step prediction. This invention significantly improves the early fault identification and warning capabilities of industrial processes under complex operating conditions while maintaining model interpretability and operational stability.

The application provides a trust-aware security distributed tracking method for an interactive multi-target system and belongs to the technical field of state estimation. The application has the following steps: a nonlinear random state space model is established; a memory attack detection mechanism based on trust awareness is designed, an attack detection function is constructed based on measurement residuals, and a limited memory window is used to realize real-time detection of whether the measurement data is attacked; three states of the measurement data are divided, and a measurement credibility factor is constructed; weighted fusion processing is performed to obtain effective measurement information; a security distributed state estimator is constructed to calculate one-step prediction of the system state; an upper bound of a one-step prediction error covariance matrix of each target state is recursively calculated; an estimator gain matrix of each target is calculated; the estimation of each target state is updated by using the gain matrix, and the upper bound of the estimation error covariance matrix is recursively calculated until a preset termination time. The application can improve the security, robustness and reliability of the system in a complex network environment.

The application discloses a SLAM method and system based on a robust adaptive unscented Kalman filter multi-sensor information fusion, which comprises the following steps: generating symmetric distribution sampling points according to system parameters and calculating corresponding weights, substituting the sampling points into a nonlinear function to complete one-step prediction, obtaining one-step prediction values of states and covariance through weighted summation, generating new sampling points based on the prediction results to perform observation prediction, and synchronously calculating an observation correlation covariance matrix; meanwhile, multi-step sub-processes are performed, including sampling point processing, weight application, operation monitoring and other operations, a dynamic noise estimation and adaptive sensor weight distribution mechanism is designed, a robust adaptive filter framework is combined, multi-source sensor complementary information is fully integrated, and abnormal data interference is effectively resisted, so that the positioning accuracy and robustness of the SLAM system in a complex environment are improved, and the application is suitable for real-time positioning and map construction scenes of autonomous mobile devices.

The application discloses a kind of based on data analysis's commercial building central air conditioning energy consumption prediction system and method, it is related to data analysis technical field, the present method includes the following steps: obtaining the multi-source data related to commercial building central air conditioning energy consumption;Data is preprocessed;Processing time sequence characteristic data, generate time sequence characteristic importance weight, and filter key time sequence characteristic;Processing static characteristic data, generate static characteristic importance weight, and filter key static characteristic;Splice key time sequence characteristic and key static characteristic, construct energy consumption prediction model;Predict key time sequence characteristic data, further predict energy consumption data;Define energy consumption cost and numerical / classification type key time sequence characteristic conversion cost, with total cost minimization as target, combined constraint condition is solved using mixed integer optimization algorithm, output key time sequence characteristic optimal target state.The present application can effectively improve the situation that energy consumption optimization scheme is difficult to effectively utilize influencing factor in prior art.

The application discloses an indirect air cooling system operation regulation method considering both freezing prevention and economy, which comprises the following steps: firstly, preprocessing and DBSCAN clustering analysis are performed on historical operation data, and a corresponding relationship among load, environmental conditions, circulating water flow and back pressure is determined by combining a steam turbine variable working condition calculation model, a condenser heat balance model and an air cooling tower theoretical calculation model; secondly, the best back pressure value in the whole working condition domain is solved according to a best back pressure calculation model; further, the integrated database is normalized, and a best back pressure prediction model and a louver opening degree combination prediction model are constructed based on an integrated learning algorithm of Bayesian optimization; and finally, the best operation back pressure of the indirect air cooling unit is predicted according to real-time data, and the best louver opening degree combination considering both freezing prevention and economy is further predicted. The method can realize the collaborative optimization of the economy and safety of the cold end system, significantly improve the operation efficiency and freezing prevention capacity of the indirect air cooling system, and has important engineering application value.

This invention discloses a U2U beam tracking method and apparatus for ISAC-enabled UAVs under jitter. The method constructs a jitter model, a communication signal model, and a sensing signal model for the UAV, forming an ISAC-enabled U2U communication system model under jitter. Combining this with beamformer characteristics, an optimization problem is constructed. Through interactive multi-model fusion and unscented Kalman filtering, one-step and two-step prediction angles are obtained. Based on the one-step prediction angle, a first sub-problem is constructed, and the optimal half-beamwidths along the Ψ and Θ axes are obtained, adaptively controlling the transmit beam. The UAV-U, using the two-step prediction angle as a priori, constructs and solves a second sub-problem to obtain the optimal jitter compensation angle, optimizing the receive beam pointing. Based on the controlled transmit beam and optimized receive beam, the UAV-B transmits ISAC signals to the UAV-U, achieving beam tracking and data transmission in the U2U communication system under jitter. This invention solves the problems of insufficient tracking accuracy and poor robustness in existing technologies.