9 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".

This invention discloses a parameter estimation method, system, device, and storage medium for a nonlinear diffusion model. The parameter estimation method includes: constructing a spatial basis function based on system data snapshots for a nonlinear Fisher-type diffusion system applied to the heating and cooling process of an iron rod, and separating spatiotemporal variables using an orthogonal decomposition method; sparsely sampling the nonlinear terms using a discrete empirical interpolation method to obtain the optimal low-order approximation of the high-order system; initializing a particle swarm to obtain n particles corresponding to the m-dimensional solution vector of the low-order time series model, and calculating the fitness of each particle according to the calculation formula of the parameters to be identified and the objective function; iterating and optimizing each particle, and outputting the position of the particle with the global optimum value; adding a non-Gaussian Levy process to the traditional particle swarm algorithm, and avoiding premature concentration of the particle swarm in the same direction through the random jump of the Levy process, thereby increasing the mutual learning ability between particles.

The application discloses a kind of bill image content identification method and system, method includes: using nonlinear diffusion filter to the source bill image obtained with standard bill image is constructed to obtain nonlinear diffusion scale space;Using image block strategy removes the first layer scale space in nonlinear diffusion scale space, then carries out grid unit division to nonlinear diffusion scale space and obtains stable uniform point feature for feature point extraction;According to point feature, gradient position direction histogram algorithm is used to construct feature point descriptor;Using bilateral fast approximation nearest neighbor algorithm, the feature points expressed by feature point descriptor are matched, and the point pairs of false matching are removed by using random sample consensus algorithm to obtain matching point pair, and the bill image of successful matching is obtained;The bill image is preprocessed, target text is detected, and specific field detection is carried out to obtain the text image labeled with specific field and input into the text recognition model to recognize the text content and obtain the field information.

The invention relates to the technical field of environmental monitoring, in particular to a gas diffusion imaging processing method considering path confidence correction, which comprises the following steps of: calculating link input, establishing a relationship between observation and concentration by adopting a nonlinear diffusion imaging model, and improving inversion stability by adopting path length estimation and confidence correction. A reliable concentration field is obtained through energy functional optimization; based on the concentration sequence and the image sequence, combining brightness consistency and conservation error to estimate a wind speed field, and then using physical stepping and learning residual errors to obtain an evolution operator with physical and data constraints to predict the concentration of a plurality of steps in the future; a prediction module is embedded into system dynamics, the influence of equipment actions on the concentration is described, a risk measurement design and an optimization control strategy are configured, and a key area is controlled to keep the safe concentration under various disturbances. According to the scheme, the problems of unstable imaging error and diffusion prediction, uncontrollable intervention and the like in the traditional technology are solved.

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.

The present application relates to the technical field of environmental monitoring, and more particularly to a gas diffusion imaging processing method considering path confidence correction, comprising: calculating link input, establishing the relationship between observation and concentration by using a nonlinear diffusion imaging model, improving inversion stability by using path length estimation and confidence correction, and obtaining reliable concentration field through energy functional optimization; based on concentration sequence and image sequence, combining brightness consistency and conservation error to estimate wind speed field, and then using physical stepping and learning residual to obtain evolution operator prediction of future concentration with physical and data constraints; embedding the prediction module into system dynamics to describe the influence of device action on concentration, configuring risk measurement design and optimization control strategy, and controlling the key area to maintain safe concentration under various disturbances. The present application solves the problems of imaging error, unstable diffusion prediction and uncontrollable intervention in the prior art.