6 results about "Online strategy" patented technology

The application relates to the technical field of power system automation, and particularly discloses an intelligent substation automation management system. The system comprises a digital twin synchronous management module, a multi-engine collaborative deduction module, an online strategy optimization and simulation module, and an instruction safety checking and feedback module. The system builds a digital twin synchronized with a physical station as a unified analysis environment; multiple professional analysis engines interacting through a shared cognitive state library are run in the twin, interwoven evaluation and joint deduction of equipment state, protection logic and network reliability are realized; based on the deduction result, online simulation iteration is used to search for a multi-step optimization control plan in the twin environment; and after safety checking of the plan instruction, the instruction is executed, and feedback data is collected to drive the closed-loop evolution of the model and the strategy. The application realizes multi-source data fusion, dynamic collaboration of cross-professional analysis and adaptive optimization of control strategies, and improves the integrity and autonomy of intelligent substation management.

The present application relates to a kind of visual language slow thinking reasoning-oriented semi-offline strategy reinforcement learning method, to solve the problem of insufficient reasoning ability of current large-scale visual language model (LVLM) in complex multimodal task, the present application relates to scalable semi-offline strategy reinforcement learning (SOPHIA) framework, including the construction of semi-offline strategy behavior model combining online strategy visual understanding and offline strategy reasoning, the mechanism of visual and reasoning reward is designed back and distribution, and three parts of offline strategy optimization method based on visual and reasoning reward.Compared with the prior art, the visual slow thinking reasoning ability of LVLM is systematically improved, and the deficiencies of existing methods in visual understanding consistency and reasoning generalization ability are overcome, and the visual slow thinking reasoning ability of LVLM is improved.

This invention discloses an intelligent financial risk control system based on ABTest technology, belonging to the field of financial risk control technology. It includes a process configuration module, a strategy management module, an ABTest management module, a decision execution engine, a verification and analysis module, a machine learning model integration module, and a real-time monitoring and early warning module. The system allows for the configuration of risk control decision-making processes in a visual manner and supports the flexible integration of various strategy tools and machine learning models. Utilizing ABTest and a champion-challenger (Champion Challenger) mechanism, it conducts parallel testing and scientific evaluation of online strategies and challenger strategies. Based on statistical indicators and model performance indicators, it automatically analyzes and recommends the optimal strategy, achieving intelligent switching and iteration of strategies. This invention features high flexibility, scalability, and intelligence, improving the accuracy, efficiency, and system stability of risk control decisions. It is suitable for the risk management and business optimization needs of financial institutions in rapidly changing business environments.

The application provides a reinforcement learning method suitable for inhibiting performance fluctuation, a reinforcement learning intelligent agent containing an online policy network and a target policy network queue is constructed, a main experience pool and an evaluation experience pool are combined, and stable evolution and efficient convergence of a policy are realized. In an environment interaction process, the online policy network and the optimal policy in the target policy network queue are alternately sampled and obtain environment interaction information, and the environment interaction information is stored in the main experience pool. In a preset evaluation period, a parallel multi-environment rolling evaluation mechanism is used to obtain policy evaluation rewards, and the target policy network queue is dynamically updated or a policy rollback is performed based on the evaluation results. A double evaluation network is introduced, and an adversarial evaluation training mechanism based on double Q mean value and small double Q random sampling is used to inhibit value function overestimation and policy over-conservatism. Interaction data generated in the evaluation stage is stored in the evaluation experience pool, and the interaction data is used in combination with main experience pool data in the training process, so that the stability, robustness and convergence performance of the reinforcement learning training are improved.

The application relates to the field of browser usability auxiliary technology in artificial intelligence, and discloses a webpage self-adaptive method and system based on multi-modal emotion perception. The method collects multi-source initial signals of a user, performs synchronization and pretreatment, carries out multi-modal fusion reasoning in a local edge model, obtains an emotion vector and a confidence degree, selects a strategy package S from a strategy library in combination with page context classification and individualized threshold value, executes webpage rendering adjustment on DOM, CSS and ARIA attributes through a browser content script, dynamically changes information density, readability of layout, dynamic effect and interaction complexity, records user feedback, and adopts online strategy updating with constraints on the local side; adaptive degradation is executed when resources are limited; only intermediate features are saved under privacy and permission management, and original audio and video are not externally transmitted. The application realizes real-time closed loop and individualized adaptation of emotion-strategy-page, and significantly improves the online experience of special groups such as people with attention disorders and people with anxiety.