16results about How to "Efficient decision-making" patented technology

The application discloses a kind of robot rule real-time computing method and device based on cloud edge computing, comprising: visualization module is used to provide the editing function of rule and event, and edited rule data and event data are stored in storage module, also for showing calculation result and event sending result;Storage module is used to store rule data and event data;Calculation module is used to read rule data from storage module, and rule calculation is carried out by real-time acquisition feature data to obtain calculation result;Event module is used to read event data from storage module, and determine the sending channel of event, when obtaining rule calculation completion and reaching the condition of sending event, event is sent according to the sending channel of event by calculation module trigger, and the event carries rule calculation result.The method and device can realize the efficient transmission and sending of robot overall system, realize the monitoring of real-time robot running state and rule, realize the efficient information processing of robot.

The application relates to the technical field of artificial intelligence, and discloses a cross-modal intelligent agent base deployment system and method based on an AI large model, which comprises the following: a data layer comprising an externally connected multi-source heterogeneous database and a multi-modal dynamic knowledge graph, a cross-modal knowledge base is constructed through unified coding and joint retrieval; a processing layer comprising a multi-modal feature parser, an intelligent routing decision engine and a three-level dynamic model matrix, the multi-modal feature parser is used for analyzing data, the intelligent routing decision engine selects a target model from the three-level dynamic model matrix for processing to generate a routing decision; and an optimization layer comprising a hierarchical retrieval enhancement module and a dynamic feedback optimization module, which optimizes the data layer and the processing layer, the application effectively fuses different modal data, selects a model for processing through the intelligent routing decision engine, improves the response speed without losing accuracy, the optimization layer improves the overall performance, the cross-modal intelligent agent base efficiently processes multi-modal data, and intelligent decision-making is realized.

The application belongs to the technical field of water industry, and relates to a multi-source data semantic interaction method and system for the water industry, comprising: analyzing and processing multi-source monitoring data collected in real time to obtain a structured semantic data set containing multiple parameters; based on a preset early warning threshold, performing abnormality judgment on a target parameter; the target parameter is a specific monitoring parameter selected in the structured semantic data set; when the target parameter is abnormal, based on a pre-constructed correlation retrieval fusion model and the structured semantic data set, calculating the causal correlation strength of a first-level correlation parameter directly correlated with the abnormal target parameter; and according to the causal correlation strength of the first-level correlation parameter, the structured semantic data set and a parameter correlation rule, dynamically reasoning the abnormal reason of the abnormal target parameter. The application breaks through the semantic barrier of multi-source monitoring data, improves the accuracy of alarm, can accurately locate the abnormal reason, realizes efficient decision-making, and adapts to the multi-scene requirements of the water industry.

This invention discloses an active distribution network integrated optimization scheduling method and system based on SVM-L2O, belonging to the field of distribution network optimization scheduling technology. The method includes: solving a multi-timescale integrated scheduling model based on acquired historical wind power output and load data to obtain unit start-up and shutdown strategies and continuous output decision variables, and constructing a training dataset; training a support vector machine for the start-up and shutdown strategies of each unit in each time period to obtain a surrogate model predicting the unit start-up and shutdown strategies; removing start-up and shutdown related constraints from the model based on the generated start-up and shutdown strategies to obtain a simplified model; constructing an L2O neural network, using Lagrange multipliers to incorporate the constraints of the simplified model as penalty terms into the loss function, training the L2O neural network to obtain a surrogate model predicting continuous output decision variables; acquiring wind power output and load data in real time, generating optimal scheduling decisions through the two surrogate models, and realizing integrated and efficient solution of multi-timescale distribution network scheduling.

This invention relates to the field of process control technology, specifically to an intelligent control system for thin-film lithium niobate etching processes based on machine learning. The system includes: a process sensing and acquisition module that collects thin-film lithium niobate etching process parameters, etching effect detection data, process environment data, and etching equipment status data in real time; a machine learning processing center that integrates and analyzes the data, using machine learning algorithms to uncover potential patterns, correlations, and anomalies; a process visualization and interactive platform that presents a real-time heatmap of etching parameter distribution, etching defect location, and etching effect trends, automatically switching interface layouts according to different process scenarios; an intelligent process decision engine that dynamically generates etching parameter adjustment strategies and process emergency response plans; and an etching parameter adjustment unit that adjusts the process parameters of the thin-film lithium niobate etching equipment and updates the control status. This solves the problems of poor adjustment response and low etching yield in existing technologies.

The application relates to an automatic driving multitask coordination decision-making method based on hierarchical rolling optimization. The automatic driving vehicle environment state information is used as the input of a reinforcement learning decision-making framework, the driving target is planned as the connection of multiple driving tasks, the coordination among the multiple tasks is realized, each driving task is specifically realized as a control action, and only the control action of the first driving task is executed. Then, rolling forward to the next time step, based on the updated automatic driving vehicle environment state information, planning and control action execution are carried out again. The planning is repeatedly carried out, and rolling optimization decision-making is realized. The method can realize multitask coordination and is suitable for long-term decision-making of automatic driving in a complex driving scene.

The application provides an anisotropic rock mass evaluation method and system based on a directional structure index, and belongs to the technical field of geological engineering, and comprises the following steps: obtaining internal strength factor data, structure strength factor data and boundary condition factor data of a rock mass to be evaluated; calculating internal strength factor rating Ri; constructing a directional weight function W(θ); calculating structure surface comprehensive condition rating Jc, and directionally correcting the rock quality designation RQD value of a drill hole to obtain a directional structure strength index DSSI(θ) through coupling calculation; calculating boundary condition factor rating Bc; calculating a directional rock mass quality comprehensive index D-RMR(θ) and calibrating the original value of D-RMR(θ); generating a visual representation map by taking the calibrated D-RMR(θ) value corresponding to different direction angles θ; and evaluating the quality grade of the rock mass to be evaluated. The application can accurately and quantitatively reveal the directional characteristics of anisotropic rock mass, effectively avoid distortion of key parameters, and has the advantages of high accuracy, strong practicability and intuitive results.

The invention discloses a cluster networking method based on an unmanned aerial vehicle task networking database. The cluster networking method specifically comprises the steps of performing stage division of various types of tasks based on task scenes; constructing a basic task scene networking database based on historical experiences; based on the divided task stages, performing similarity search with task scenes in a networking database; and carrying out consistent networking strategy transfer on the cluster based on a networking database similarity search result. According to the method, historical task stages are classified and analyzed through machine learning based on the constructed task scene networking database, reusable experience is formed, a new task directly calls a verified optimization strategy through scene matching, and particularly, when a large-scale unmanned aerial vehicle cluster executes a dynamic multi-stage task, the task efficiency is greatly improved. The optimal networking strategy matched with the current task scene can be quickly, adaptively, consistently and reliably selected and switched, so that the overall collaborative efficiency and robustness of the cluster in a complex task environment are improved.

The invention discloses a honeycomb-free large-scale MIMO unmanned aerial vehicle associated power control method and equipment based on reinforcement learning, and a medium, and relates to the technical field of wireless communication. The method comprises the following steps: acquiring parameters of a communication system containing multiple unmanned aerial vehicles and access points; a cellular-free large-scale MIMO transmission model is constructed; the method comprises the following steps: determining a state space, an action space and a reward function based on a cellular-free large-scale MIMO transmission model, and respectively constructing intelligent agents for an uplink power control coefficient optimization problem of an unmanned aerial vehicle in a system and an access point unmanned aerial vehicle clustering optimization problem; training the intelligent agent by using a depth deterministic strategy gradient reinforcement learning algorithm; and obtaining an uplink power control coefficient of the unmanned aerial vehicle in the system and an unmanned aerial vehicle clustering result of each access point by using the intelligent agent based on an optimized uplink power distribution strategy and an access point unmanned aerial vehicle clustering strategy. According to the method, efficient joint optimization of unmanned aerial vehicle access point association and uplink power control can be realized under a rapidly changing channel condition, and the method has relatively high practical practicability under a low-altitude economic background.

The invention belongs to the technical field of computational fluid mechanics and artificial intelligence crossing, and particularly relates to a supersonic speed tail flame flow field rapid calculation method based on a physical information neural network, and the method comprises the following steps: S1, constructing a profile spectrum parameter data set and a tail flame flow field sample data set of a rocket engine; constructing a physical information neural network model; constructing a total loss function of a physical information neural network model, taking the profile spectrum parameter data set as an input data set of the neural network model, and normalizing the tail flame flow field data to serve as an output data set of the neural network model; training and testing the neural network model to obtain a trained neural network model; obtaining profile spectrum parameters of a rocket engine to be measured, inputting the profile spectrum parameters into the trained neural network model, and outputting a corresponding tail flame flow field distribution prediction result; the problems that an existing rocket engine tail flame flow field calculation method is high in calculation cost, physical information is lost, and large-scale efficient calculation requirements are difficult to support are solved.

The invention relates to the technical field of oil and gas exploitation, in particular to an oil and gas exploitation process intelligent linkage method which comprises the following steps: S1, data acquisition and monitoring: acquiring temperature, pressure and flow data in an oil refining process in real time through data acquisition equipment; s2, model optimization and prediction; s3, intelligent control and adjustment; according to the method, the production process is optimized through data analysis, the mining efficiency is improved, the model is utilized, the operation state of the oil refining process is known, adjustment is made according to needs, efficient decision making is achieved, all units in the oil refining process are automatically controlled, the operation process is more accurate and stable, and the oil refining efficiency is improved. Model prediction can be used to accurately analyze the correlation between data so as to accurately predict the future trend, and is not influenced by subjective consciousness and personal experience, the prediction result is relatively reliable, the human input and subjective interference in the prediction process are reduced, excessive bandwidth and computing power are not consumed, and the prediction efficiency is improved. And seamless connection and sharing of data can be realized.

The invention discloses a wood product dust risk prediction method and system, and relates to the technical field of industrial safety and disaster prevention and control. The method comprises the following steps: acquiring original data of each process parameter and historical dust concentration of a wooden product in real time; a multi-step preprocessing process including data cleaning, feature extraction, normalization processing, feature dimension reduction based on correlation analysis and the like is adopted; constructing a deep learning model based on an LSTM neural network to perform dust concentration prediction, performing automatic optimization on hyper-parameters of the neural network by using Bayesian optimization, and calculating dust risk factors such as dust intake, dust concentration forward change rate, average dust concentration and the like based on prediction data; and calculating a comprehensive risk value of each sample through combined weighting of an entropy weight method and an analytic hierarchy process, and finally outputting different risk levels corresponding to each sample and corresponding early warning strategies. According to the invention, the dust concentration of the workshop is accurately predicted, the dust harm is reduced, and the health of workers is guaranteed.

The application discloses a sarcopenia risk assessment method based on a knowledge graph, relates to the field of medical large models, collects full-dimension multi-source data of a patient and carries out pretreatment; a six-tuple entity relationship model is defined, a dynamic entity life cycle and a heterogeneous relationship type system are introduced, multi-modal feature fusion is realized through modal adaptive weight learning, knowledge conflict resolution and entity evolution embedding; based on an enhanced spatiotemporal graph convolution network, through heterogeneous relationship attention space convolution, spatiotemporal adaptive time convolution and triple contrast learning tasks, enhanced entity embedding is output, and the enhanced entity embedding comprises a historical state, a current state, an evolution rate and a future trend. Through multi-modal spatiotemporal alignment and enhanced knowledge graph construction, the application realizes deep fusion of multi-source heterogeneous data and spatiotemporal dynamic relationship modeling, and significantly improves the comprehensiveness and precision of sarcopenia risk assessment.

The invention discloses an intelligent monitoring feeding decision-making system and method, and belongs to the technical field of aquaculture intelligent management and cloud computing cooperative control. The system comprises a sensing layer, an execution layer, a computing platform and an application layer, wherein the sensing layer collects fish behaviors and environment data in real time through an intelligent material platform integrating an underwater camera and a multi-parameter water quality sensor; the execution layer is composed of an intelligent feeding ship with precise feeding and navigation functions; the computing platform adopts an edge-cloud collaborative architecture, a lightweight YOLOv5 model is operated on the edge side to realize real-time image processing, and a multi-objective optimization and reinforcement learning algorithm is integrated on the cloud side to realize generation and optimization of a dynamic feeding strategy; and the application layer provides a visual interface. Intelligent closed loop of fish feeding behavior recognition, water quality monitoring and feeding control is achieved, the bait utilization rate is remarkably increased, the water quality is improved, the labor cost is reduced, and the method is suitable for large-scale aquaculture scenes.

This invention discloses a multi-UAV task allocation and fusion method based on improved reinforcement learning, comprising: determining an initial parameter set for multi-UAV task allocation; fragmenting and decomposing multi-task objectives to determine cluster centers; using an adaptive clustering strategy based on center points to screen candidate clustering strategies and determine the optimal cluster set; determining a dynamic decay gradient strategy for the improved Q-learning network based on the action value function of basic reinforcement learning; determining a spiral cross-assignment mechanism for task redistribution based on a task redistribution mechanism with weighted coefficients; and determining a multi-UAV task allocation strategy based on the optimal cluster set of task points, the dynamic decay gradient strategy of the improved Q-learning network, and the task redistribution mechanism to complete the multi-UAV task allocation. This invention can effectively avoid unnecessary redundant allocation, improve the time consumption and range of task allocation, and increase the efficiency of task allocation.

This invention proposes a hierarchical task offloading and resource allocation method for large-scale model tasks in the Industrial Internet, belonging to the technical fields of Industrial Internet and mobile edge computing. The invention includes: establishing a cloud-edge-device collaborative reasoning architecture for large-scale models in Industrial Internet scenarios, and modeling the large language model reasoning task as a phased resource consumption model including a pre-filling stage and a decoding stage; modeling the large-scale model task offloading and resource allocation problem as a hierarchical Markov decision process, constructing a composite reward function; and utilizing the proposed hierarchical multi-agent large-scale model task offloading and resource allocation algorithm based on deep reinforcement learning, adopting a two-layer collaborative architecture of distributed offloading at the bottom layer and centralized allocation at the top layer to achieve task offloading and resource allocation. This invention significantly improves memory security and service stability during the large-scale model reasoning process; greatly reduces the action space dimension, and achieves rapid convergence and efficient decision-making in large-scale scenarios.