159 results about "Learning controller" patented technology

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention relates to the technical field of intelligent control of the welding process, and discloses an online monitoring system and control method for the friction stir welding process, and the system comprises a multi-mode sensor array module which is used for collecting temperature, pressure and displacement data in the welding process; the edge computing node module preprocesses the collected data and extracts spatial-temporal characteristics; the space-time diagram convolutional network module is used for mining a space-time association relationship between the sensors; the hierarchical reinforcement learning controller generates a control instruction according to the associated features; the execution mechanism module receives the control instruction and outputs a welding signal; the digital twinborn verification module performs simulation feedback on the control effect; and the cloud model optimization module fuses the multi-source feedback information to continuously optimize the control strategy. The welding process self-adaptive control technology based on reinforcement learning is adopted, the technical effect of adjusting the welding parameters in real time to optimize the welding quality is achieved, and the problem that in a traditional method, the welding quality is unstable due to working condition changes is solved.

The invention discloses an electrical control circuit fault adaptive diagnosis system and method based on deep learning. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a diagnosis model module, an adaptive optimization module and a visual interaction module. Based on an improved Transform architecture, a time convolution operator is embedded to enhance the local feature perception capability of a self-attention mechanism, high-dimensional time sequence features are extracted, a dual-channel deep residual network is constructed, electrical features and thermal-mechanical features are analyzed respectively, a fault classification result is output after fusion, and model parameters are updated through online incremental learning. And on the basis of a transfer learning controller, new fault sample distribution characteristic transfer pre-training parameters are adapted, and a genetic algorithm is utilized to dynamically optimize the network depth and the convolution kernel size so as to improve the generalization ability. The method has the advantages that high-precision and multi-dimensional information fusion is realized through an improved Transform architecture and a self-adaptive optimization mechanism in combination with time convolution and transfer learning, and the method can quickly adapt to new fault types.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

The invention discloses an expressway hard shoulder dynamic opening method and system based on an NFGD model and a simulation platform. Comprising the following steps: collecting and adopting a dynamic confidence mechanism to carry out weighted fusion on multi-source real-time sensing data to obtain fused traffic state characteristics; a hard road shoulder dynamic control decision is made based on an NFGD model comprising a fuzzy-neural hybrid controller, a multi-target genetic algorithm controller and a reinforcement learning controller; and real-time interaction with a simulation platform is realized, and control instruction issuing, feedback acquisition and online strategy evaluation are realized. Compared with a traditional method based on a fixed empirical threshold value, the fuzzy-neural hybrid controller, the multi-target genetic algorithm controller and the reinforcement learning controller are fused, self-adaptive modeling and open discrimination are achieved, and decision-making precision and scene adaptability are improved. And meanwhile, by combining with a prediction adjustment feedback type step length control structure, simulation time drift is effectively inhibited, the timeliness of a control strategy is enhanced, and the method has relatively high practical application value.

The invention discloses a finite iteration error tracking learning control method for robot accurate trajectory tracking. The method comprises the steps of establishing a kinetic equation of a repeated operation robot system, determining a discrete dynamic model of the robot system, designing a finite iteration error tracking learning controller and carrying out simulation verification. According to the method, under the conditions that the initial position of the robot system is arbitrary and the expected trajectory changes, the number of iterations required for achieving arbitrary precision tracking of the given expected error trajectory by the output error of the robot system can be calculated by designing an error tracking learning controller and only utilizing error information obtained through first iteration. According to the method, the limitation that zero error convergence needs to depend on infinite iterations in a traditional iterative learning control method is effectively overcome; meanwhile, it is ensured that the robot system can still achieve the preset tracking precision with the rapid convergence capacity under the disturbance conditions that the initial position is arbitrary and the expected trajectory changes, and the practicability and the popularization value of iterative learning control in actual engineering are improved.

The invention discloses an agricultural irrigation control method and system, and relates to the technical field of irrigation control, and the method comprises the following steps: constructing a crop water demand model based on the fusion of crop physiological sensing data and a data driving model, and outputting a target irrigation flow; calculating a control error between the target irrigation flow and the actually acquired flow, and generating a control signal through a model prediction control strategy; inputting a static mapping model fitted by a Sigmoid function, and superposing the output of a dynamic correction model driven by state sensing data to generate a final driving signal; feedback data is collected, a driving signal and the feedback data are subjected to closed-loop comparison, and irrigation control strategy parameters are adaptively optimized through a reinforcement learning controller; according to the method, the crop physiological sensing data and the data driving model are fused, the space and time sequence feature fused water demand model is dynamically constructed, and the problems that agricultural irrigation cannot be accurately controlled and the irrigation strategy is rigid are solved in combination with a static and dynamic composite control mapping mechanism.

The invention relates to the technical field of data center thermal management, and discloses a data center liquid cooling precise temperature control method and system, and the method comprises the following steps: pre-analyzing a calculation task to generate a power consumption prediction sequence; combining the power consumption prediction sequence with the real-time state information of the liquid cooling system, calculating the heat cost of the task at each candidate calculation node through a digital twin model, and selecting a target execution node according to the heat cost; inputting the power consumption prediction sequence and real-time state information into a reinforcement learning controller, and generating a composite coordination action containing a flow regulation instruction and a dynamic power consumption upper limit instruction; and cooperatively controlling the micro-fluidic execution array and the chip power consumption management unit on the target execution node according to the composite coordination action. Through prospective power consumption prediction and two-way cooperative control from the heating end and the heat dissipation end, intelligent distribution and accurate temperature control of thermal loads are achieved, temperature overshoot is effectively restrained, and the overall operation energy consumption of the data center is reduced while the system reliability is guaranteed.

The invention relates to the technical field of steel plate hot-dip production, and discloses an intelligent scheduling system for a steel plate hot-dip production process. The system comprises a working condition sensing module which is used for continuously collecting steel belt inlet temperature, zinc liquid component concentration fluctuation data, air knife pressure time sequence parameters and roller system operation state signals of a galvanizing pot area; the feature extraction module receives the working condition data flow, analyzes the steel strip surface temperature field distribution features and the zinc liquid viscosity change rule, and generates a process feature spectrum; the parameter optimization module dynamically adjusts membership function parameters and rule base weight coefficients of the fuzzy reinforcement learning controller according to the process characteristic spectrum, and outputs an optimized plating layer control parameter set; the self-adaptive plating layer control module generates a plating layer thickness control instruction based on the real-time zinc liquid temperature parameter and the optimized plating layer control rule set; and the dynamic roller system scheduling module realizes global cooperative control of the roller system speed based on the instruction. The system can realize intelligent scheduling of the steel plate hot-dip production process.

The invention discloses a reinforcement learning-based spring steel wire drawing control method, which comprises the following steps of: acquiring real-time process parameters to form a process state data sequence; inputting the process state data sequence into the state space model, and constructing a virtual working condition sample; based on the virtual working condition sample, pre-training a reinforcement learning controller and outputting an initial control strategy; inputting the initial control strategy into a lower-layer strategy network, and outputting a wire drawing speed adjusting instruction; the current process state and the wire drawing speed adjusting instruction serve as synchronous input, and an implicit context vector is generated; extracting control strategy characteristics in the edge controller, performing compressed encoding and forming control strategy representation; uploading the control strategy representation to a cloud server, and outputting a unified global control strategy model; and carrying out anomaly detection on the current process state, and if a detected state reconstruction error exceeds an anomaly judgment threshold, triggering safety control logic. The spring steel wire drawing control device realizes spring steel wire drawing control.

The invention discloses a robot motion control optimization method, device, equipment and medium, and the method comprises the steps: inputting task description in a natural language form, robot state information, a historical execution track and environment feedback information into a preset large language model; through the large language model, selecting a target motion strategy of the robot according to the task description; when it is detected that the task of the robot fails or the action of the robot is abnormal, a reward function used for controlling a deep reinforcement learning controller of the robot is reconstructed; and when the state of the robot is abnormal, parameters of the deep reinforcement learning controller are adjusted and optimized, the motion stability, the task completion efficiency and the strategy adaptability of the robot in a high-risk, high-temperature and high-complexity scene are remarkably improved, and the self-adaptive adjusting and optimizing capability and the abnormity recovery capability of the robot in a dynamic environment are enhanced.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.

The invention discloses a water treatment method and equipment based on analogue simulation and deep learning and a medium, and relates to the technical field of urban water supply systems and water treatment. The method comprises the steps of obtaining design data related to water treatment plant facilities, and based on the design data, performing simulation modeling on the water treatment whole-process facilities to obtain a facility simulation model; obtaining process data related to processes of the water treatment plant, and performing reaction simulation on the facility simulation model based on the process data to obtain a water treatment simulation model; the method comprises the following steps: acquiring raw water quality data and equipment state data of a water treatment plant in real time, and inputting the raw water quality data and the equipment state data into a pre-trained deep reinforcement learning controller to generate a facility or process control instruction; and inputting the facility or process control instruction into the water treatment simulation model for execution, and monitoring effluent quality data in real time so as to dynamically adjust a control strategy according to the effluent quality data.

The invention discloses an intelligent acquisition risk identification system based on deep learning, and the system comprises a time sequence sample construction module which is used for building a time sequence sample sequence; the hierarchical attention structural feature coding module is used for carrying out structural feature coding on the sequential sample sequence by utilizing a hierarchical attention network; the time sequence feature extraction module is used for inputting the structural feature vector into an ETSform model to perform time sequence feature extraction; the hierarchical time sequence collaborative attention adaptive fusion module is used for performing bidirectional attention interaction and dynamically generating a hierarchical weight and a time sequence weight through a meta-learning controller; the improved CatBoost risk identification module is used for outputting a risk score and a risk type label; and the system fusion module is used for summarizing and merging the risk scores and the risk type labels. The method and the device are suitable for merchant transaction risk identification in an acquiring business scene.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

A high-speed matching-engine architecture is disclosed that sustains deterministic sub-microsecond latency while processing more than 10 million order messages per second per core on commodity multi-core processors. Orders reside in cache-aligned Data Holder Nodes whose occupancy and price-level boundaries are tracked with constant-time bitmask operations, eliminating pointer-chasing penalties. Per-core huge-page pools, SIMD copy kernels, and lock-free, cache-line-aligned queues further minimize TLB misses and coherence overheads. Overflow is handled by Push Back / Push Forward cascades that relocate the least- or most-prioritized orders between adjoining nodes without violating price-time priority. Node capacities vary monotonically with book depth and are re-tuned online by a lightweight machine-learning controller that maximizes cache-hit probability under changing market micro-structure. The design tightens spreads, raises match-rate revenue, and complies with stringent regulatory latency caps using standard x86-64, Arm, or other architectures.

Systems, methods, software, and devices are disclosed herein related to trajectory computation by way of a memory-based learning (MBL) controller. An MBL controller in various embodiments stores a set of trajectories in memory. The trajectories connect various initial states of a dynamical system with a target state. In addition to the memory, the controller further includes a processor that collects a current state of the dynamical system and determines, using memory-based learning (MBL) on training instances derived from the set of trajectories, a control policy that defines a trajectory connecting the current state of the dynamical system with the target state. The processor controls the dynamical system according to the control policy.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

The invention discloses a memory-based event triggering specified time performance control method applied to an under-actuated ASV, and the method comprises the steps: constructing a preset specified time performance function, obtaining a trajectory tracking constraint according to an initial condition, enabling the function to customize convergence time, and eliminating a constraint problem that an initial error must be within a performance boundary; based on a deep neural network DNN, according to an ASV kinetic model, an optimized ASV kinetic model containing DNN modeling errors is obtained, a modeling error approximation observer is constructed, the DNN modeling errors in the optimized ASV kinetic model are subjected to approximation processing so as to construct a deep learning controller, unknown dynamics of ASV is learned by designing the deep learning controller, and the ASV dynamic model is obtained. The learning accuracy is improved; the interpretability of the DNN is enhanced; based on the constructed memory event triggering mechanism, the method is used for optimizing communication resource utilization, dynamic adjustment can be carried out according to real-time data and historical data, when a control signal changes suddenly, the memory event triggering mechanism preferentially considers the historical data, and the problem that a traditional event triggering mechanism excessively depends on the real-time data is solved.

The invention discloses a robot self-adaptive control method and system based on deep learning, and relates to the field of robot control, and the method comprises the steps: receiving and preprocessing multi-modal data, obtaining the preprocessed multi-modal data, carrying out the multi-dimensional dynamic evaluation and event triggering judgment, and carrying out the multi-dimensional dynamic evaluation and event triggering judgment. Generating a dynamic evaluation result of the control mode sign and the dynamic feature vector; selecting a deep learning controller network structure according to a control mode mark in a dynamic evaluation result, performing calculation based on the preprocessed multi-modal data and the dynamic feature vector, and outputting a preliminary joint torque instruction; and the initial joint torque instruction and the preprocessed multi-modal data are input into a micro safety barrier layer, the initial joint torque instruction is corrected according to preset robot safety constraints, and a safety joint torque instruction is generated. According to the method, the parameters of the controller are updated on line based on data such as actual states and tracking errors, and self-adaptive and high-safety robot control is achieved.

The invention discloses a technological process monitoring method and system for a polyester net, and belongs to the technical field of intelligent control, and the method comprises the steps: obtaining real-time technological parameter data in the production process of the polyester net; inputting the real-time process parameter data into a digital twin model, predicting the mesh forming quality of the next period, and generating predicted quality parameters; collecting a polyester mesh surface image, inputting the polyester mesh surface image into the semantic segmentation neural network, identifying a mesh abnormal region, and generating an actual mesh quality score; on the basis of a deviation value between the predicted quality parameter and the actual mesh quality score and energy consumption per unit yield, constructing a reward signal and inputting the reward signal into a deep reinforcement learning controller; the deep reinforcement learning controller outputs a process parameter adjustment instruction and sends the process parameter adjustment instruction to an execution mechanism; and feeding back the actual mesh quality score to the digital twin model, correcting the error of the prediction quality parameter, and updating the model synchronization precision. The product quality stability is improved.

The invention belongs to the technical field of production line monitoring optimization, and particularly relates to an automatic optimization method and system for can-making stamping parameters, and the system comprises a multi-source data collection module which comprises an industrial camera, a material component sensor and an equipment state sensor; the defect intelligent analysis module is used for identifying stamping part defects based on a deep learning model and associating causes; the parameter dynamic optimization module is used for generating an adjusting instruction according to the defect cause; multi-source data collection is matched with defect intelligent analysis, the defect type and the defect position of the can making stamping part are determined, then dynamic weight analysis is carried out, defect causes are matched according to the defect type, then the matched adjusting quantity is obtained, then the adjusting quantity is optimized and verified according to a compensation execution structure and a virtual verification module, and therefore the accuracy of the can making stamping part is improved. The finally obtained adjusting quantity is used for adjusting production line parameters, in addition, closed-loop optimization is carried out through an incremental learning controller, and the system can systematically optimize the problems of slow defect response, high misjudgment rate and poor adjusting precision in the can making stamping field.

The invention provides an underwater robot multi-task rapid adaptive control method based on meta reinforcement learning. The method comprises the following steps: constructing an underwater robot dynamic model and a thrust distribution strategy matrix; three reinforcement learning controllers, namely, a non-overshoot position controller, an overshoot allowing position controller and a propeller flexible control controller, are respectively designed according to diversified task requirements; introducing a meta-learning mechanism to build a meta-training platform, and training the three reinforcement learning controllers to obtain a group of optimal initialization parameters; and deploying the obtained optimal initialization parameters and the subtask reinforcement learning controller to the underwater robot, and carrying out two-stage training according to different tasks. Finally, when the controller is deployed in engineering practice, the output of the propeller can be intelligently adjusted according to the relative distance and speed information, calculated in real time, between the controller and the target position, and it is ensured that accurate position control can be achieved in various task scenes. The method aims at meeting the requirement for rapid self-adaption of multiple tasks of the underwater robot in the complex and changeable underwater environment, and accurate and flexible response to position control is achieved through the control method based on meta reinforcement learning when the task requirements change.

The invention discloses a self-adaptive optimization power system load prediction method and system, and relates to the technical field of power system load prediction, and the method comprises the steps: collecting and fusing multi-source parameters and operation and maintenance event data, unifying time steps to construct a feature matrix, building a graph structure in combination with a power grid topology, extracting node features, and generating an initial population based on clustering. And iteratively optimizing individuals through a self-learning adjustment differential evolution algorithm, dynamically adjusting control parameters in combination with prediction errors and fitness, fusing individual prediction results by adopting a multi-model integration strategy, and finally realizing population adjustment and convergence judgment based on error trend feedback, and outputting an optimal parameter combination and a prediction result. According to the self-adaptive optimization power system load prediction method provided by the invention, the self-learning controller is introduced to dynamically adjust the variation factor and the crossover rate in the differential evolution algorithm, and the global search capability and the model convergence efficiency can be improved according to the population evolution state and the prediction error trend.

The invention discloses a two-dimensional adaptive iterative learning control method of a magnetic control shape memory alloy actuator, and belongs to the technical field of tracking control of the magnetic control shape memory alloy actuator. According to the method, on-line data of a magnetic control shape memory alloy actuator is directly utilized for data driving modeling, a mathematical model of a system does not need to be known in advance, the system is described as a tight-format dynamic linearization model, and a self-adaptive controller is designed. And then, in order to effectively utilize information on an iteration axis, combining an adaptive controller on a time axis with classical P-type iteration learning control, and designing a two-dimensional adaptive iteration learning controller. According to the controller, the control law is continuously updated on the iteration axis, the defect of open-loop control of the one-dimensional iteration learning controller on the time axis is overcome, and disturbance on the time axis is effectively dealt with.

This invention discloses a method for predicting and protecting the overheating risk of asynchronous motors, specifically relating to the field of motor control and protection technology. Based on an intelligent fusion model, it estimates temperature and thermal stress field, calculates the rate of change of thermal stress, non-uniformity, and hotspot trends, and calculates a dynamic health index using historical data. These parameters are then input into a multi-objective reinforcement learning controller to optimize long-term health and short-term performance, generating a thermal shaping control vector to regulate the motor. This invention combines physical mechanisms with data-driven approaches through an intelligent fusion model, utilizing a graph neural network to learn the structure of the heat conduction graph and verify physical laws, thereby improving the accuracy of thermal state estimation. It achieves a multi-dimensional risk characterization combining transient impact and cumulative effects through the rate of change of thermal stress, non-uniformity, hotspot trends, and dynamic health index. By optimizing long-term health and short-term performance losses, a thermal shaping control vector is generated to achieve regulation from passive protection to active prevention, extending the motor's service life.

The present invention discloses an agricultural irrigation control method and system, which relate to the field of irrigation control technology. The method and system comprise the following steps: constructing a crop water demand model based on the fusion of crop physiological sensor data and a data-driven model to output a target irrigation flow; calculating the control error between the target irrigation flow and the actual collected flow, and generating a control signal through a model prediction control strategy; inputting a static mapping model fitted by a Sigmoid function, superimposing the output of a dynamic correction model driven by state perception data, and generating a final driving signal; collecting feedback data, performing a closed-loop comparison between the driving signal and the feedback data, and adaptively optimizing irrigation control strategy parameters through a reinforcement learning controller; the present invention dynamically constructs a water demand model that integrates spatial and temporal characteristics by fusing crop physiological sensor data with the data-driven model, and combines a static and dynamic composite control mapping mechanism to solve the problems of agricultural irrigation that cannot be precisely controlled and irrigation strategy rigidity.