5 results about "Rough set" patented technology

This invention provides a method and system for fire emergency response plan simulation and verification based on case-based reasoning. The method includes establishing a structured historical fire case database, calculating the mixed similarity between the current scenario and historical cases based on rough set theory and cloud models, and selecting similar source cases; adapting the handling strategies of the source cases to the model by constraining the model to generate an initial plan to be verified; establishing a generalized stochastic Petri net model representing the evolution of the disaster-affected state and the interaction of rescue resources, mapping the initial plan to be verified to the transition rate and initial identifier in the database within the model, performing concurrent conflict detection and temporal logic deduction, and evaluating transient performance indicators; if the target rescue success indicator does not reach a preset threshold, iteratively correcting the resource scheduling parameters of the plan based on the sensitivity analysis results of key transitions, and repeating the deduction steps until the termination condition is met, outputting the target fire emergency response plan.

This application relates to a method and system for multi-source information fusion in an air-to-ground unmanned system for coastline inspection. The method includes: spatiotemporal synchronization and granular computation of raw data from multiple sensors; filtering high-confidence information granules to generate filtered data; multimodal feature extraction and attention-weighted fusion of the filtered data to extract global environmental features; constructing a factor graph model, using the filtered data as factor edge constraints, calculating the residual Mahalanobis distance of each factor edge, performing fault detection and compensation based on this distance, dynamically generating fusion weights using rough set decision-making to obtain optimal pose estimation, and performing motion distortion correction, dynamic target removal, and map construction on the updated filtered data to achieve target recognition and behavior analysis, generating anomaly event reports. This method can significantly improve the reliability of multi-source data, the robustness of pose estimation, and the accuracy of anomaly target recognition in complex coastline scenarios.

The application belongs to the technical field of fault diagnosis of marine diesel engines. A fault diagnosis method based on a deep space-time convolutional network ST-NN comprises: acquiring original oil spectrum and / or ferrography data and original vibration signals; performing dimension reduction on the original oil spectrum and / or ferrography data by using a rough set attribute reduction method to remove redundant features and obtaining a reduced oil and / or ferrography feature set; performing time-frequency conversion on the original vibration signals by using a continuous wavelet transform to obtain a time-frequency graph sequence; performing three-dimensional convolutional neural network fusion on the reduced oil and / or ferrography feature set as an additional feature channel and the time-frequency graph sequence, and introducing a self-attention mechanism to dynamically weight time steps to obtain a health index sequence; and performing fault trend prediction on the health index sequence by using a Gaussian process regression model to obtain a fault prediction result. The method effectively identifies the operating state of a diesel engine and improves the accuracy and reliability of fault diagnosis.