2195 results about "Reinforcement learning algorithm" patented technology

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a temperature control and noise reduction method for an analog optical module, and relates to the technical field of data processing. The method comprises the following steps: collecting and preprocessing an original temperature signal, environment temperature time sequence data and an optical wavelength original detection value of an optical module; improving the LSTM model, constructing a junction temperature dynamic prediction model, and outputting prediction data; a pre-trained BP neural network is utilized to calculate the temperature compensation amount according to the real-time wavelength offset, and a PPO reinforcement learning algorithm is adopted to optimize with the temperature stability, the wavelength offset and the TEC power consumption as multiple targets to obtain an optimal PID parameter mapping table; and finally, predicting, compensating and optimizing parameters are fused, and control output for driving the TEC is generated through a multi-mode intelligent decision. The problems of insufficient control precision and response lag are effectively solved, cooperative high-precision control over the junction temperature and the emission wavelength of the laser is achieved, transmission noise is remarkably reduced, and meanwhile system energy consumption and the self-adaptive capacity are considered.

The invention discloses a quadruped robot motion control method based on adaptive deep reinforcement learning. The method comprises the following steps: S1, determining a network model, a composite reward function, a state space and a bionic action generation mechanism of a simulation training environment; the state space provides environment information input, the network model processes the input information and generates a decision, the bionic action mechanism executes a specific decision behavior, and the composite reward function evaluates a behavior effect and optimizes a decision direction; s2, constructing a simulation training environment of the quadruped robot, wherein the simulation environment comprises quadruped robot model information and simulation environment information; s3, training the network model by using a deep reinforcement learning algorithm based on robot model information and simulated environment information to obtain a trained motion control strategy; and S4, verifying the feasibility of utilizing the trained motion control strategy by controlling the motion of the quadruped robot in a real environment. According to the invention, the self-adaptive capability to a complex environment is obviously improved.

The invention discloses a smart mine mining method and device based on an industrial cloud platform, and a medium, and relates to the technical field of smart mining, and the method comprises the steps: enabling a digital twinborn analysis module to communicate with a data governance fusion module through a data service bus, and receiving mine comprehensive state data outputted by the data governance fusion module, constructing a three-dimensional geologic model, carrying out abnormal behavior detection through a deep learning algorithm, and outputting an abnormal detection result; and the intelligent decision optimization module is associated with the digital twinborn analysis module through an algorithm cooperation interface, and is used for establishing a disaster prediction model and performing disaster risk assessment based on an abnormal detection result and mine comprehensive state data, and optimizing a mining strategy by using a reinforcement learning algorithm to obtain an optimal mining scheme. And the data is transmitted back to the data acquisition upper cloud module through a feedback control link to guide field acquisition and scheduling. And the overall safety, the operation efficiency and the intelligent level of a mine system are improved.

The invention discloses an edge computing collaborative distributed computing power scheduling method and device, and relates to the technical field of distributed computing and edge computing. The method comprises the following steps: collecting real-time operation state data of each edge node in a distributed edge node group; processing the real-time operation state data through a preset time sequence analysis operation, and predicting a predicted user load of each edge node in a preset future time period; combining the real-time operation state data with the predicted user load, and constructing a joint state vector; inputting the joint state vector into a preset reinforcement learning algorithm, and outputting a GPU resource dynamic allocation strategy; and when an AI reasoning request input by a user is received, determining a target edge node for the AI reasoning request from the distributed edge node group according to the GPU resource dynamic allocation strategy, and assigning the AI reasoning request to the target edge node. By implementing the technical scheme provided by the invention, the real-time performance and the stability of the distributed computing power system in a high-concurrency scene are improved.

The invention belongs to the field of router protection, and particularly provides an energy router protection system and method based on deep reinforcement learning and virtual impedance collaboration, and the method comprises the steps that a high-speed sensing and monitoring unit collects and preprocesses the data of each power branch; the main control unit operates deep Q network fault reasoning, and a fault criterion threshold value is finely adjusted in combination with online Q-learning; calling a virtual impedance injection module, and superposing compensation voltage on the PWM reference signal to realize injection; a topology self-reconfiguration module is triggered synchronously, a fault branch is isolated, and a standby branch is switched to maintain power supply of a key load; the thermal management regulation and control module deploys load shedding through PID load shedding control and a temperature-current permission curve based on the thermal RC equivalent model and the real-time temperature; and synchronously issuing a related instruction by means of the CAN-FD bus. According to the method, the deep reinforcement learning algorithm and the virtual impedance technology are combined, so that adaptive protection and topological optimization of the energy router under complex working conditions are realized.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention relates to the technical field of automatic spraying, and particularly discloses a spraying system based on multi-modal vision and artificial intelligence self-correction, comprising a multi-modal vision acquisition module for acquiring color image information, depth information and infrared feature information; the control module is used for generating a spraying sensing model according to the information of the multi-modal visual acquisition module; the control module carries out spraying area identification, track planning and spraying parameter decision making based on the spraying sensing model; the spraying execution module is used for spraying; and the feedback self-correction module iterates the spraying parameter decision in the control module based on a reinforcement learning algorithm according to the difference between the actual coating state information and the expected state. The control method based on multi-modal vision and artificial intelligence self-correction is applied to a spraying system based on multi-modal vision and artificial intelligence self-correction. The scheme is used for solving the problems that the spraying quality of complex workpieces is not high due to the single sensing dimension of an existing spraying system, and the production flexibility is poor due to the rigid control mode.

The invention relates to the technical field of automatic control, and discloses a pressure closed-loop self-calibration method for an ultrahigh-speed jet injection process, and the method comprises the steps: initializing a system, and building a pressure and temperature combined compensation model; a high-precision sensor is used for collecting real-time pressure and temperature data; by calculating a pressure deviation intelligent switching control strategy, PID is combined with feed-forward compensation in a steady state, and a deep reinforcement learning algorithm is switched to realize quick response in a transient state; automatically triggering closed-loop self-calibration according to operation time or a deviation threshold value, executing zero point and full scale calibration and updating compensation parameters; and carrying out residual analysis by using a digital twinborn model to realize fault diagnosis and early warning. According to the invention, the problems of low control precision and poor long-term stability of an ultra-high-speed jet flow process under nonlinear and large-lag working conditions are solved, and high-precision stable control and system self-adaptive calibration under a wide speed range are realized.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention relates to the technical field of rail transit intelligence, and discloses a rail transit intelligent scheduling management method and system, and the method comprises the steps: collecting the entering and exiting data of passengers, the number of people in a waiting area, the train load factor and the platform congestion degree through an automatic fare collection system, a video monitor and a sensor of each station of rail transit; the collected passenger flow data are preprocessed, a box plot about passenger flow distribution is constructed, and sudden passenger flow fluctuation areas are identified in different time windows; a K-means clustering algorithm is adopted to classify passenger flow modes in peak periods, and the distribution type of passenger flow fluctuation is analyzed; a time sequence prediction model is constructed by adopting a Transform model in combination with weather, holidays and festivals and emergencies, the future short-term and medium-and-long-term passenger flow trend is predicted, and the train departure interval is optimized; and based on the predicted passenger flow distribution, a scheduling optimization objective function is constructed, and a reinforcement learning algorithm is combined. The method has the advantage of improving the passenger flow prediction precision in the peak period.

The invention relates to the technical field of power systems, and provides a power distribution network multi-dimensional regulation and control decision method, system and device and a medium, and the method comprises the steps: inputting the preprocessed multi-source operation data into a preset state perception model, and obtaining a multi-dimensional state vector representing the operation state of a power distribution network; a multi-dimensional state vector is used as a state space, regulation and control operation is used as an action space, a composite reward function is established according to a power distribution network operation target, and modeling is carried out to obtain a Markov decision process framework; interacting with a power distribution network simulation environment by adopting a deep reinforcement learning algorithm, obtaining a current state from a state space, selecting and executing regulation and control operation in an action space according to a strategy network, updating strategy network parameters based on feedback of a composite reward function until an optimal regulation and control strategy network is obtained, and obtaining a deep reinforcement learning strategy model; and performing strategy rolling updating based on the real-time monitoring data to obtain a target regulation and control strategy. According to the invention, comprehensive optimal regulation and control of a complex operation scene can be realized.

The invention provides an intelligent warehouse goods posture recognition and automatic sorting method and system, and the method comprises the steps: S2, extracting the edge contour and surface features of goods from point cloud data for a preliminary value set, calculating the direction vector and shape distribution characteristics of the goods through a principal component analysis method, and obtaining a quantitative result of shape feature extraction; s6, historical sorting data and real-time sensor data are extracted from the warehousing system database according to the classification basis adapted to the complex scene, the dynamic posture change trend of the goods is predicted through a time sequence analysis method, and optimization parameters of real-time processing are obtained; and S7, the movement track and the grabbing angle of the sorting mechanical arm are adjusted through the optimization parameters subjected to real-time processing, a deep reinforcement learning algorithm is adopted to conduct iterative optimization on the sorting action sequence, and an execution scheme of sorting accuracy is determined. According to the method, the accuracy of goods posture recognition and the automatic sorting efficiency in the complex storage environment are remarkably improved.

The invention discloses an oil product pipeline leakage monitoring method, device and equipment and a medium, and relates to the technical field of pipeline monitoring, the method comprises the steps that a graph model of a pipeline monitoring network is constructed, and each node pipeline in the graph model is provided with a measuring point; collecting pipeline data of each measuring point in real time, wherein the pipeline data comprises temperature, pressure and flow data; performing feature extraction on the pipeline data to obtain various feature data; determining a pipeline working condition for the various characteristic data corresponding to each measuring point, and adjusting a fusion weight for fusing the various characteristic data in real time by adopting a reinforcement learning algorithm based on the pipeline working condition; according to each fusion weight, performing feature fusion on the multiple feature data to obtain a multi-source fusion feature; inputting the multi-source fusion feature of each measuring point into a sensor fault recognition model, and outputting a sensor fault mark; and inputting the multi-source fusion feature of each node carrying the sensor fault mark into a leakage identification and positioning model, and outputting the leakage probability of each node. The prediction accuracy can be improved.

The invention relates to the technical field of artificial intelligence and judicial informatization, and particularly discloses a multi-format document intelligent retrieval and semantic association system driven by a large model. Comprising a multi-modal document intelligent analysis module, an intention-driven semantic retrieval module, a knowledge graph enhanced association recommendation module, a retrieval result visualization and interaction module, a reinforcement learning-driven system optimization module and a multi-format document data storage module. According to the method, the multi-format judicial document is intelligently analyzed through a large model technology; the query intention of the user is accurately understood by means of a semantic retrieval technology Semantic association among the documents is deeply mined through the knowledge graph technology; a retrieval result is visually displayed through a visualization and interaction interface; and the retrieval strategy and model performance are continuously optimized according to user feedback by relying on a reinforcement learning algorithm, so that the retrieval efficiency and semantic association capability of the multi-format document in the judicial field are effectively improved, and the development of judicial informatization is promoted.

The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twin and AI algorithms, and belongs to the field of thermal power plant optimization control. The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twinborn and AI algorithms. The system comprises a data fusion processing module, a digital twinborn body construction module, a collaborative optimization and decision module, a strategy decomposition and execution module and an online learning and updating module. According to the method, the problems that the existing thermal power plant optimization control lacks global collaboration and is difficult to adapt to dynamic complex working conditions, and online self-evolution of a model and a strategy cannot be realized are solved; and a deep reinforcement learning algorithm is utilized to carry out multi-target collaborative optimization on the whole plant level, so that a global optimal control strategy which comprehensively considers the operation cost, the energy efficiency, the equipment service life and the environmental protection constraint can be dynamically generated, and the limitation of traditional decentralized control and static optimization is effectively overcome.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

The invention provides a coordination control method and device of a photovoltaic energy storage inverter for automobile charging based on artificial intelligence, and belongs to the technical field of automobile photovoltaic energy storage coordination control, and the method comprises the steps: monitoring the output power of a photovoltaic array, the state of charge (SOC) of an energy storage system, the state of a power grid and the power demand of a load in real time; a reinforcement learning algorithm is introduced, weather forecast and historical load data are combined, photovoltaic output and load demands in a future time period are predicted, and a power distribution instruction is generated; deciding a current operation mode and generating a mode switching instruction; seamless switching between grid connection and grid disconnection is controlled, and after switching is completed, the charging and discharging proportion of the lithium battery and the super capacitor is coordinated; integrating the energy storage real-time state and the power distribution instruction, and optimizing and adjusting the control parameters of the inverter in real time; continuously monitoring a running state and a switching process, and starting a standby mode when a fault is detected; and the data is uploaded to a monitoring platform. The system operation is efficiently optimized, the service life is prolonged, and stability is guaranteed.

The invention discloses an industrial robot multi-machine cooperative interaction method and system based on artificial intelligence. The method comprises the following steps: S1, collecting and preprocessing interaction data of an industrial robot; s2, constructing a cooperative relation graph formed by industrial robot nodes and interaction edges; s3, disassembling the target task by adopting a priority mechanism and generating an execution weight matrix; s4, in combination with the interaction data and the weight matrix, generating a cooperative action vector through a multi-target reinforcement learning algorithm; s5, action conflict detection is executed, path overlapping and resource conflicts are eliminated, and a candidate action sequence is obtained; s6, generating a path and a control instruction, and forming a distributed execution instruction stream; and S7, driving the robot to execute the task, collecting feedback, updating the atlas, and circularly executing until the task is completed. According to the invention, task cooperation, path obstacle avoidance and dynamic optimization control among multiple industrial robots are realized, and the scheduling efficiency, the execution stability and the system intelligence level are effectively improved.

The invention discloses a special-shaped pipe forming compensation method and system based on digital twinning, and belongs to the technical field of computer-aided manufacturing and intelligent control. The method comprises the steps that multi-source sensing data in the special-shaped pipe forming process is obtained, and time-space fusion processing is executed; generating a time-space aligned multi-source sensing data set, carrying out dynamic calibration on the parameterized virtual digital twin, generating a calibrated virtual digital twin, carrying out simulation calculation on the forming process of the special pipe in a future time window, outputting predicted deviation data, and combining with a preset compensation strategy to generate a compensation instruction through logical judgment and calculation; and obtaining an adjustment result after the compensation instruction is applied, and updating the compensation strategy by using a reinforcement learning algorithm. According to the method, prospective simulation prediction is carried out by constructing the dynamically calibrated virtual digital twins, closed-loop optimization is carried out on a compensation strategy by utilizing a reinforcement learning algorithm, and active, high-precision and self-adaptive compensation control on the special pipe forming process can be realized.

The invention discloses a photovoltaic cluster flexible grid-connected regulation and control method and system, and the method comprises the steps: collecting the state data of a power distribution network node and a photovoltaic cluster, carrying out the data cleaning and timestamp alignment preprocessing, and obtaining a system state comprehensive data flow; constructing a multi-target optimization model containing power grid friendliness and photovoltaic power generation benefits based on the system state comprehensive data flow, and solving by utilizing a reinforcement learning algorithm to obtain a control strategy of a photovoltaic inverter and an energy storage unit; according to the control strategy, active power output and reactive power output of the photovoltaic inverter and charging and discharging power of the energy storage unit are controlled in a coordinated mode, and flexible grid connection of the photovoltaic cluster is achieved; and based on system response data, optimizing a control strategy by adopting an optimal decision tree algorithm, updating the reinforcement learning model, and establishing an optimized experience knowledge base. According to the invention, efficient flexible grid-connected regulation and control of the photovoltaic cluster are realized, and the problems of voltage out-of-limit, reverse power transmission and the like when large-scale photovoltaic access to the power distribution network are solved.

The invention relates to the technical field of low-altitude equipment, in particular to a low-altitude resource intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting the real-time state and network load data of a low-altitude flight equipment group, and constructing a dynamic operation data set; generating an operation mode feature set through multi-dimensional airspace situation awareness and analysis, and performing sparse clustering division based on the feature set to form a network resource demand priority mapping table; traversing the mapping table to dynamically calculate the resource demand, determining a multi-dimensional weight coefficient, and performing high-dimensional feature dimension reduction and optimization through a mixed integer nonlinear programming solver to obtain a resource demand feature vector; constructing a resource scheduling strategy optimization model by adopting a deep reinforcement learning algorithm based on the vector; inputting real-time data into the model to execute a resource scheduling decision, and outputting a dynamic allocation strategy; simulation deduction and compliance verification are carried out on the strategy in the digital twin simulation platform, and cooperative intelligent scheduling of communication, calculation and spectrum resources is achieved.

The invention relates to a distributed data unified management and intelligent scheduling method based on data braiding, and the method comprises the steps: collecting the real-time state data of a node, and generating a node capability portrait; constructing a dynamic incidence matrix of the metadata and the node capability portraits; establishing a multi-dimensional scheduling evaluation model, inputting a business data dependency relationship, performance data in the node capability portrait and a node future load predicted by the LSTM model, and outputting an initial scheduling scheme; a static threshold value and a dynamic prediction threshold value are preset to serve as scheduling optimization triggering conditions, the initial scheme is optimized through a reinforcement learning algorithm, and an optimized scheduling decision is obtained; and sending a scheduling instruction containing a priority identifier to a corresponding node, collecting feedback data such as response delay and an error rate in real time, updating the association strength of the dynamic association matrix according to the feedback data, and optimizing the parameters of the multi-dimensional scheduling evaluation model. Unified management of distributed data is achieved, scheduling intelligence and accuracy are improved, node state changes can be dynamically adapted, and data processing efficiency and reliability are effectively guaranteed.

The invention discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention belongs to the technical field of computing power distribution, and particularly relates to a dynamic computing power distribution method and system based on reinforcement learning, and the method comprises the following specific steps: S1, covering cloud, edge and end full-node scenes, and collecting computing power resource states, task demand features and cross-domain network condition data in real time; s2, on the basis of standardized data output by a cross-domain computing power sensing module, by constructing a state space fusing computing power, tasks and a network, defining an action space of computing power scheduling direction and proportion, and designing a multi-target reward function for balancing the resource utilization rate, the task satisfaction rate and long-term conflict avoidance; and realizing self-learning and self-iteration scheduling strategy generation based on a reinforcement learning algorithm. According to the invention, the reinforcement learning agent autonomously learns the computing power demand of the emergency scene and the new type of task, the rule does not need to be manually preset and modified, and the method has the advantage of realizing dynamic adaptation of computing power distribution to complex and changeable scenes.

The invention discloses an adaptive evaluation method for the health degree of an electrolytic cell, and the method comprises the following steps: collecting the multi-dimensional operation parameters of the electrolytic cell in real time, and carrying out the preprocessing of the collected time series data, so as to construct a training sample with a time window; extracting a multi-scale time sequence feature from the training sample to form a feature vector; inputting the feature vector into a weight adjustment network, and outputting a dynamic weight vector; weighting the feature vector by using the dynamic weight vector to generate a weighted feature vector; inputting the weighted feature vector into a performance prediction model, and outputting a short-term performance prediction value of the electrolytic cell at a future moment; after the corresponding real performance value is obtained, calculating a prediction error of the short-term performance prediction value, and constructing a reinforcement learning reward signal based on the prediction error; updating a strategy of the weight adjustment network through a reinforcement learning algorithm by utilizing a reward signal, thereby optimizing dynamic weight vector generation at a subsequent moment; based on the dynamic weight vector and the feature vector at the current moment, a comprehensive health degree index of the electrolytic bath is obtained through calculation; according to the method, main factors influencing the equipment health degree in different stages are intuitively revealed, and a basis is provided for operation and maintenance decision making.