3results about How to "Increase information entropy" patented technology

This invention discloses a reconfigurable job shop scheduling method based on cooperative multi-agent deep reinforcement learning, belonging to the field of intelligent manufacturing and job shop scheduling technology. Addressing the reconfigurable job shop scheduling problem under constraints of limited auxiliary modules and limited extended processing functions, this invention constructs a cooperative multi-agent deep reinforcement learning framework, decoupling job scheduling and equipment function reconfiguration into two sub-problems and setting independent agents. Collaborative optimization is achieved through a prior knowledge interaction mechanism. A heterogeneous graph embedding model is designed to extract state features; a rule mining method based on gene expression programming is introduced to initialize the experience replay pool; a reward-based decision space reduction mechanism is proposed to reduce computational complexity; and a target network soft update and local search restart mechanism are adopted to improve training stability and global search capability. Experiments show that the average RPD of this invention is significantly better than existing methods on 100 test cases, and it is suitable for multi-variety, small-batch reconfigurable intelligent manufacturing scenarios such as automotive parts and aerospace structural components.

The invention discloses an infrared polarization multi-dimensional fusion imaging method based on nonlinear mapping, and belongs to the technical field of photoelectric detection and computational imaging. Performing Stokes vector solution on the input detection data to obtain a total intensity component, a polarization degree component and a polarization angle component; performing logarithmic domain mapping and gradient calculation on the total intensity component, and performing nonlinear enhancement based on local statistical characteristics on the polarization degree component to obtain an enhanced polarization degree; in an HSV color space, constructing a brightness component according to a logarithm domain mapping result, constructing a saturation component by combining the enhanced polarization degree and gradient information, constructing a hue component according to polarization angle information, and performing threshold constraint on the hue component based on the enhanced polarization degree; and a multi-dimensional fusion image is generated through color space inverse transformation from HSV to RGB. According to the method, background clutters are effectively suppressed, the image information entropy and the average gradient are remarkably improved, and high-contrast clear imaging of a weak and small target in a complex scene is realized.

This invention discloses a two-dimensional heterojunction array sensing method and device for high-precision monitoring of carbon pollution. The two-dimensional heterojunction array sensing method includes: emitting light signals with dynamically changing wavelength, power, and modulation frequency to a two-dimensional heterojunction array using a tunable light source according to a preset switching sequence; acquiring the electrical signals generated by the two-dimensional heterojunction array under the combined action of the target gas and the light signals, and converting the electrical signals into digital signals in multi-dimensional matrix form; and using a hybrid model integrating convolutional neural network branches and recurrent neural network branches to extract spatial pattern features and dynamic temporal features, ultimately outputting the type and concentration of the gas. This invention effectively solves the cross-sensitivity problem in gas identification by exciting the two-dimensional heterojunction array with optical sequence modulation and combining it with a lightweight neural network model, thus improving the identification accuracy and response speed in complex environments.