7 results about "Evolutionary learning" patented technology

The application provides a spectrum sensing method and system based on multiple agents, a storage medium and a terminal. The method comprises the following steps: constructing a state vector of uplink transmission of multiple primary users; inputting the state vector into M agents for deep reinforcement learning, acquiring M basic spectrum sensing strategies, and M is a natural number greater than 1; performing evolutionary learning on the M basic spectrum sensing strategies to acquire a population of offspring spectrum sensing strategies; performing integrated learning on excellent strategies in the population of offspring spectrum sensing strategies to acquire an optimal spectrum sensing action. The spectrum sensing method and system based on multiple agents, the storage medium and the terminal based on the collaborative design and adaptive optimization of multiple agents can realize high-precision and strong-robust spectrum intelligent sensing.

The application discloses an implementation method and system for self-evolution learning of an intelligent agent, and belongs to the technical field of intelligent agent reinforcement learning, large language models, memory and cognitive intelligence, and transfer learning; the method comprises the following steps: acquiring an environment state; inputting the environment state into a pre-trained strategy to output an optimal action and executing the action; after executing the action, a feedback signal is acquired; the feedback signal comprises an environment response and a task completion degree; and a new strategy is obtained by optimizing the strategy according to the feedback signal. The task performance of the application is continuously upgraded: the intelligent agent can be continuously optimized when facing long-range or repetitive tasks, and the intelligent agent will become more and more skilled in processing cross-platform complex tasks. The application reduces the research and application cost: the intelligent agent can reduce the dependence on manual work, can autonomously discover reinforcement learning rules, does not need to continuously manually annotate data, and relies on environment feedback for iterative optimization.

This invention discloses a hardware autonomous control method and system based on spatial intelligence and self-evolutionary learning, an electronic device, and a computer-readable storage medium. The system includes: a multi-source device discovery module for automatically scanning intelligent hardware devices and establishing a unified device model through multiple communication protocols; a capability reflection module for automatically extracting device control capabilities and parameter constraints from protocol metadata; a skill management module for storing and loading skill packages and establishing a mapping index from device type to skill package; a rule engine module for performing millisecond-level deterministic evaluation of sensor data and generating control commands; an intelligent decision engine module for orchestrating planning nodes and execution nodes based on state diagrams and making context-aware control decisions through a large language model; a command security module; an execution verification module; a multi-layer memory module; a preference learning module; and a multi-layer self-evolutionary engine module.

This invention discloses a method and apparatus for flavor-oriented fermentation regulation of wampee vinegar based on reinforcement learning, relating to the field of artificial intelligence learning. The method includes: constructing a fermentation state-space model containing terpene concentration; setting flavor target encoding and flux-oriented reward function; using a physical information deep Q-network for continuous action decision-making; implementing multi-level intervention execution; and optimizing strategies through self-evolutionary learning and flux feedback. This invention achieves specialized monitoring and regulation of the characteristic aroma of wampee vinegar, solves the problem of non-monotonic coupling control of two microbial communities, reduces training samples by embedding prior knowledge of strains, ensures action safety by embedding physical and biological constraints, and improves batch-to-batch flavor consistency through delay compensation and self-evolutionary mechanisms.

This application relates to the field of oilfield enhanced oil recovery and data-driven utilization of remaining oil, and discloses a method and apparatus for generating a transitional benchmark for remaining oil utilization. The method includes: acquiring and integrating the development dynamics of a first well group and a second well group in a time sequence to obtain a regional state sequence, then segmenting this sequence to obtain multiple segmented state fragments; extracting features from these fragments to obtain a displacement feature sequence, then segmenting and organizing this sequence to obtain a displacement feature sequence input object; performing network encoding and evolutionary learning on this input object to obtain the potential displacement state of the region; extracting this potential displacement state; obtaining the pre-boundary benchmark state and the post-boundary benchmark state; and then performing time-series splicing to obtain a state transition benchmark. This application can avoid excessive smoothing of states across development stage boundaries, establish a unified potential displacement state space, and accurately identify areas where remaining oil is difficult to continue to displace.

The application discloses a line board production whole-process quality regulation system and belongs to the technical field of line board quality control, comprising a data acquisition module, a cause-effect knowledge graph construction module, a cause-effect reasoning and prediction module, a cross-process feedforward compensation module and a self-evolution learning module.The application breaks through the limitations of fragmentation and passive response of the prior art in quality control, realizes a leap change from post-detection to pre-prevention and from single-point control to global coordination through a complete closed loop of data acquisition, cause-effect graph construction, reasoning and prediction, feedforward compensation and self-evolution learning, and realizes synchronous evolution of the cause-effect knowledge graph and the compensation strategy through a self-evolution learning mechanism driven by reinforcement learning, thereby forming a mutually enhanced positive cycle, so that the regulation precision and self-adaptive ability of the system are continuously improved with the extension of the running time, and the system is significantly superior to the static system in the prior art which needs artificial regular maintenance and parameter adjustment.