5 results about "Nonlinear diffusion" patented technology

This invention discloses a method and system for identifying enterprise association risks based on graph computing. The method constructs an association graph of multiple types of entities and their complex relationships, and introduces a risk transmission model based on dynamic edge weights and iterative feedback of node risk states. This model simulates the nonlinear diffusion process of risk in the association network, ultimately outputting an accurate list of risk nodes, quantified scores, and key transmission paths, and providing visualized rendering of the results. By dynamically quantifying the strength and probability of risk transmission along different association links, this invention effectively overcomes the shortcomings of traditional static association analysis, which cannot characterize the dynamic and non-uniform transmission process of risk, significantly improving the accuracy, foresight, and decision support value of risk identification.

This invention discloses an internet cloud security thumbnail preservation encryption method based on chain-based sum-diffusion. The main steps are as follows: Extracting the plaintext image size information and invariant features such as pixel sums for each color channel, hashing and fusing them with the master key to generate a derived key related to the image content; using the derived key to drive a chaotic system to generate a pseudo-random sequence with anti-degradation performance; employing a binary space partitioning strategy to process the three channels of the image into irregular rectangular blocks; within each independent sub-block, globally scrambling the pixel positions according to the pseudo-random sequence; finally, based on a pseudo-random traversal path, performing a chain-based sum-diffusion cyclic shift operation on each scrambled sub-block to achieve non-linear diffusion of pixel values. This invention effectively eliminates cross-channel boundary leakage that may result from regular block partitioning while ensuring the semantic usability of the encrypted image at low resolution, and significantly enhances encryption security through a dual mechanism of "scrambling-chain-diffusion".

The present application provides a pipeline early micro-leakage anomaly detection method based on deep learning, relating to the field of data anomaly detection, specifically comprising a disturbance coupling analysis and embedding module, a disturbance condition propagation module, a residual projection modulation module, and a model fusion and anomaly detection module. First, the monitoring data is encoded into a disturbance state tensor by the disturbance coupling analysis and embedding module, solving the response difference and asymmetric coupling problem. Next, the disturbance condition propagation module is used to model the nonlinear diffusion of the disturbance between time series and features. Then, the residual projection modulation module is used to realize multi-scale cointegration analysis and extract robust features. Finally, the model fusion and anomaly detection module integrates multi-scale information and obtains micro-leakage anomaly detection values.

The application discloses a lithium battery energy storage monitoring method and system, which comprises the following steps: obtaining a historical solid-phase diffusion coefficient sequence and a historical liquid-phase impedance sequence, and establishing a coupling correlation matrix therebetween; obtaining a real-time solid-phase diffusion coefficient and a real-time liquid-phase impedance through online identification; selecting a first candidate period through sliding window correlation analysis; selecting a second candidate period through impedance spectrum shape similarity calculation; calculating a current capacity loss amount by using a nonlinear diffusion-impedance coupling model; determining a current calibration capacity based on an initial calibration capacity and an aging factor; and determining a remaining available capacity based on the current calibration capacity and the current capacity loss amount. By extracting the diffusion-impedance coupling correlation matrix from historical data and deeply embedding the whole process of parameter identification, candidate period screening, capacity loss calculation and remaining capacity evaluation, the accuracy and robustness of the estimation of the remaining available capacity of the lithium battery are significantly improved.

The present application provides a pipeline early micro-leakage anomaly detection method based on deep learning, relating to the field of data anomaly detection, specifically comprising a disturbance coupling analysis and embedding module, a disturbance condition propagation module, a residual projection modulation module, and a model fusion and anomaly detection module. First, the monitoring data is encoded into a disturbance state tensor by the disturbance coupling analysis and embedding module, solving the response difference and asymmetric coupling problem. Next, the disturbance condition propagation module is used to model the nonlinear diffusion of the disturbance between time series and features. Then, the residual projection modulation module is used to realize multi-scale cointegration analysis and extract robust features. Finally, the model fusion and anomaly detection module integrates multi-scale information and obtains micro-leakage anomaly detection values.