5results about How to "Improve decision speed" patented technology

The application provides an electronic chart system, method and equipment with water depth change prediction capability, relates to the technical field of marine electronic information technology and marine geographic information, and the system comprises a data acquisition module, a water depth change analysis and prediction module and a risk visualization module. The application superimposes and displays predictive water depth risk information in a standard ECDIS interface through the fusion of official electronic chart data and a dynamic water depth model, so that the existing operation process of the driver can be changed, and enhanced navigation support reflecting the recent seabed change trend can be provided in the case that the chart updating cycle is long.

This invention relates to the field of robot control technology, specifically to an end-to-end closed-loop control method and system for robots based on multimodal perception fusion. It includes: a perception access layer for standardized access to data from multiple sensor types; a fusion processing layer for spatiotemporal alignment and multimodal fusion processing of the standardized access data; a decision control layer for generating execution instructions based on the fusion processing results; and an execution feedback layer for feeding back the execution status to the fusion processing layer to form closed-loop control. This technical solution can solve the problems of standardized access and real-time fusion of multi-source heterogeneous data, and construct a closed-loop control architecture with high fault tolerance and low latency.

E-commerce has become a mainstream lifestyle. Users primarily rely on platform-provided content recommendation algorithms to find the goods or services they need. Recommendations are generated based on VR / AR data (CN118552273A) using virtual digital avatars, and inferred from user social connections (CN118411232A). Existing recommendation algorithms lack personalization, over-rely on user behavior and sales data, and are susceptible to data pollution from fraudulent activities like fake orders, leading to results that don't meet user expectations, causing confusion, and ultimately affecting user decision-making and purchasing decisions. To address these issues, this application provides an intelligent method for recommending goods and services. It innovatively modifies existing recommendation algorithms by introducing advanced technologies such as large-scale artificial intelligence models and computer image recognition algorithms. This method effectively improves the personalization of user search results, creating scenario-specific, personalized search results. This allows search results to help users make faster decisions and increase their willingness to purchase products and services.

The application provides an emergency demand response load side flexibility resource scheduling optimization method and system, and the method comprises the following steps: constructing a resource scheduling control architecture based on regional flexibility resource adjustment capacity; the resource scheduling control architecture comprises a cloud server, a control terminal and a regional control unit (RCU) which is respectively in communication connection with the cloud server and the control terminal; an emergency demand response scheduling model is established by comprehensively considering timeliness, economic benefits and user satisfaction; and an emergency demand response scheduling decision under the resource scheduling control architecture is calculated by using a RAFT distributed algorithm based on the emergency demand response scheduling model. Through the application, an emergency demand response architecture and a scheduling decision optimization method based on a distributed algorithm are provided, rapid decision and optimal benefits are considered, the dynamic change of flexible resources is adapted, various complex power grid operation environments can be coped with, and dynamic evaluation and optimal distribution of flexible resource adjustment potential are realized.

The application relates to a daily regulation type hydropower station economic operation model and an optimization method and system based on a proximal policy optimization algorithm, wherein the method comprises the following steps: obtaining unit parameters and hydrological parameters of a daily regulation type hydropower station to determine a target function, decision variables and constraint conditions, and establishing an optimization operation model of the daily regulation type hydropower station; collecting load instruction data of the daily regulation type hydropower station at each period, monitoring reservoir inflow, an upstream water level and a unit operation state to obtain operation data of the daily regulation type hydropower station; inputting the operation data into the optimization operation model, and solving the optimization operation model based on the proximal policy optimization algorithm to convert the optimization operation model into a Markov decision process, and outputting a daily load distribution scheme. Therefore, the problems in the related art that, due to the fact that the calculation time of a dynamic programming algorithm is multiplied by the scale, and the performance of deep reinforcement learning is greatly different under different conditions, it is difficult to obtain a reliable daily regulation type hydropower station optimization operation scheme are solved.