10results about How to "Improve decision speed" patented technology

The invention provides a building robot obstacle avoidance path planning method and related equipment, and the method comprises the steps: fusing the state information of a target building robot with environment data through Bayesian filtering, and obtaining an occupied grid map; inputting the occupied grid map into the trained lightweight convolutional neural network for feature extraction, and generating environment feature data; the environment feature data and the current state of the target building robot are input into a trained decision model for decision making, an action instruction is obtained, the target building robot reaches the target position within the shortest time based on the action instruction, and the path from the target building robot to the target position is the obstacle avoidance path of the target building robot; by improving the accuracy and robustness of environment perception, efficient obstacle avoidance and path planning in a complex environment are realized.

The invention relates to a design method and a control system and method of an unmanned aerial vehicle command and control large model, and belongs to the technical field of command and control. In order to solve the problem that an existing command and control system depends on a traditional rule-based or manual programming method and is limited when processing complex tasks, the method for controlling the unmanned aerial vehicle through the unmanned aerial vehicle command and control system comprises the following steps that task texts are input into an unmanned aerial vehicle command and control large model, and a MavLink format instruction is generated; according to the method, unsafe instructions are corrected, executable unmanned aerial vehicle tasks are planned into a behavior tree, flight and task execution of the unmanned aerial vehicle are controlled, the powerful language understanding and generation capability of LLama3 is utilized, the intelligent level and performance of an unmanned aerial vehicle command and control system are improved, and the task execution efficiency and success rate of the unmanned aerial vehicle are improved.

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.

The invention provides an event assessment and auxiliary decision-making method and system for an offshore operation platform, and the method comprises the steps: taking platform data in different complex operation scenes as input, employing different key data preprocessing methods, and obtaining event assessment key factors and relationships of the whole offshore platform; an evaluation analysis model is established to analyze and evaluate the whole platform event space, auxiliary decision support data is improved, visual display is carried out with different granularities or different abstract levels, and the function of accurately evaluating and deciding platform events is achieved. According to the method, the decision-making speed is increased by analyzing, concluding and integrating complicated data accumulated by subsystem equipment of the maritime satellite launching and recycling platform and carrying out classified analysis on evaluation data by utilizing an intelligent optimization genetic algorithm; an event evaluation model constructed based on a fusion algorithm is applied to assist the platform and personnel in performing remote diagnosis and decision-making on the operation of the offshore operation platform, so that risk threats caused by emergencies are reduced, and the operation efficiency of the platform is improved.

The invention relates to the technical field of MOS transistor monitoring, and discloses a high-power MOS transistor monitoring method and system and a storage medium, and the method comprises the steps: S1, collecting key parameter data in a working process of a high-power MOS transistor; s2, performing gradient calculation on all parameter data in the key parameter data to obtain a parameter gradient feature set; s3, performing abnormal behavior analysis on each parameter gradient feature in the parameter gradient feature set to obtain abnormal behavior data, constructing a behavior analysis rule, and inputting the behavior data into the behavior analysis rule to obtain a risk behavior mode of the abnormal behavior data; s4, performing behavior score calculation based on the risk behavior mode to obtain a behavior score, and judging whether the behavior score is smaller than a set state intervention threshold value or not; by calculating the behavior score of each segment and comparing the behavior score with the threshold value, the behavior abnormity of the MOS transistor can be detected in the early stage, so that measures can be taken in time to avoid potential problems.

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 invention discloses a rule-based scene library setting automatic driving decision-making system and method and a vehicle, and belongs to the technical field of automatic driving, and the system comprises a sensor module which is used for collecting environment information around the vehicle; the data processing module is connected with the sensor module, and the data processing module is used for processing and analyzing the environment information collected by the sensor module so as to identify an obstacle and extract feature information of the obstacle; the decision module is used for making an avoidance decision according to the feature information extracted by the data processing module on the basis of a predetermined rule; and the execution module is used for controlling the vehicle to execute the avoidance decision generated by the decision module. According to the invention, various obstacles and moving states thereof can be accurately identified in a complex urban traffic scene, sudden occurrence of the obstacles can be rapidly judged and processed, and decision rules are continuously optimized through real-time learning and experience accumulation so as to adapt to different urban environments and obstacle arrangement.

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.