3511 results about "Closed loop feedback" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention discloses a multi-modal AI-based power equipment anomaly detection method and system, and the method comprises the steps: synchronously collecting electrical, mechanical and thermal modal data of power equipment through an edge computing node, carrying out the load adaptive dynamic preprocessing, and uploading the data to a cloud end; the cloud constructs a multi-modal feature extraction network based on a structural causal model, analyzes a causal path between modals through a Bayesian network and performs weighted fusion on feature vectors; capturing device state mutation by using a gating attention mechanism, and updating the feature vector; executing time-space consistency verification of the equipment group to identify regional group abnormality and suppress single-point misinformation; generating an interpretable report containing an abnormal root cause analysis and priority ranking maintenance strategy; and establishing a closed-loop feedback mechanism to correct the cause and effect probability distribution of the Bayesian network model. The system comprises a multi-modal sensor array, an edge computing node and a cloud analysis platform, wherein the cloud analysis platform is integrated with a causal reasoning engine, a space-time consistency verification module and the like. According to the invention, by analyzing the multi-modal deep causal association, the method adapts to the dynamic change of the equipment, reduces the false alarm rate, generates an interpretable report, supports closed-loop self-optimization, and improves the anomaly detection accuracy and operation and maintenance decision efficiency of the power equipment.

The invention provides an intelligent question and answer method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, the method comprises the following steps: S1, integrating multi-source heterogeneous data to construct an initial knowledge graph; s2, analyzing the questions of the user, executing single-hop query and judging whether a result meets requirements or not; if not, entering S3, performing multi-hop dynamic search based on an adaptive path extension algorithm, and generating a reasoning path and evidence; s4, calling a large language model to combine with an attention mechanism to perform semantic matching on a result or a path, and screening an optimal answer; and S5, correcting the knowledge graph and performing closed-loop feedback to the query process to realize continuous optimization. Through cooperation of the knowledge graph and the large language model and combination of dynamic path search and semantic matching, the problems that a traditional method is insufficient in knowledge coverage, low in reasoning efficiency and poor in answer interpretability are solved, the method has the advantages of being efficient, accurate and self-optimized, and the performance and user experience in a complex scene are remarkably improved.

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

The invention discloses an agent autonomous decision control method based on multi-modal data fusion. The method comprises the following steps: S1, synchronously collecting multi-source heterogeneous data; s2, dynamic weight adaptive fusion is carried out; s3, generating a task-driven decision; and S4, performing autonomous decision closed-loop optimization. According to the method, through dynamic weight distribution and space-time correlation modeling, the problems of heterogeneity and environment adaptation in multi-modal data fusion are solved; furthermore, a risk-sensitive reinforcement learning framework and a closed-loop feedback mechanism are combined, so that full-link cooperative control from data fusion, strategy generation to optimization execution is realized. In the mechanism level, the method breaks through the limitations of static fusion, single-target optimization and offline training, can adapt to a dynamic environment, ensures that the intelligent agent is in a complex scene such as noise interference, illumination abrupt change and task emergency switching, and meets the requirements of decision-making efficiency, safety and environment robustness at the same time.

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention relates to a welding quality detection system which comprises a process monitoring layer configured with a multi-mode sensor array and used for collecting original performance data in the welding process in real time; the intelligent analysis layer is provided with a multi-source data fusion module and a defect prediction module, and the multi-source data fusion module adopts an improved D-S evidence theory algorithm to perform fusion processing on the original performance data collected by the process monitoring layer to obtain multi-dimensional performance data; the defect prediction module analyzes and captures abnormal data according to the multi-dimensional performance data, dynamically predicts a defect development trend and outputs defect prediction information; the decision execution layer is provided with a self-adaptive control module and is used for dynamically adjusting welding process parameters according to the defect prediction information output by the intelligent analysis layer; and the data communication bus is used for realizing real-time data interaction and closed-loop feedback control among the process monitoring layer, the intelligent analysis layer and the decision execution layer. The method has the effect of effectively improving the detection precision and the detection efficiency.

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.

The invention provides a color steel plate coating thickness dynamic monitoring method and system based on artificial intelligence. According to the method, the temperature distribution data of the surface of the color steel plate coating is obtained, the thermal response map is generated in a pulsed eddy current excitation mode, then the temperature distribution data and the thermal response map are subjected to time sequence correlation processing, the heat conduction characteristics are extracted, and then the heat conduction characteristics are synchronously analyzed; color steel plate coating defect types and thickness abnormal grades are distinguished through a dynamic weight distribution mechanism, a detection result containing defect positions, sizes and thickness deviation values is generated, and finally a coating thickness dynamic compensation instruction is generated according to the detection result; according to the technical scheme provided by the invention, the online control precision and the production efficiency of the coating quality are remarkably improved, and a closed-loop feedback system from detection to control is formed.

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention discloses a wharf armor block quality detection method, and belongs to the technical field of concrete structure detection.The method comprises the steps that a surface model is constructed through a movable three-dimensional laser scanning device, and a high-risk defect area is recognized; performing encryption scanning and impact echo detection in the high-risk area by adopting a frequency-adjustable dual-mode flaw detection device to generate a three-dimensional defect distribution diagram; a core sample is drilled in the defect area for compressive strength testing, and the material performance is analyzed in combination with a non-contact infrared spectrum; and establishing a dynamic evaluation model, calculating a safety coefficient by integrating surface defects, internal defects and material discreteness data, and generating a three-dimensional visual report. The problems that a traditional detection method is low in efficiency, insufficient in internal defect positioning precision, one-sided in material performance evaluation and the like are solved, full-dimensional detection from the surface to the interior and from the macroscopic level to the microscopic level is achieved through a multi-source data fusion and closed-loop feedback mechanism, and the reliability of structural safety evaluation and the scientificity of maintenance decision are remarkably improved.

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention discloses an integrated power distribution cabinet energy efficiency dynamic optimization method based on multi-modal data fusion, and relates to the technical field of intelligent power grids. The problems of failure of an energy efficiency optimization model and poor long-term operation stability caused by data time sequence misalignment and error accumulation of a multi-source sensor in the prior art are solved. According to the scheme, the time offset is dynamically corrected through the adaptive time sequence deviation prediction model; compensating missing data by adopting a non-uniform time step reconstruction algorithm and Kalman filtering; detecting an error drift trend through an exponentially weighted moving average model, updating a feature weight, and inhibiting long-term error accumulation; constructing a self-adaptive time sequence attention fusion network model, and fusing physical constraints and a data driving mechanism to generate an optimization decision; bayesian optimization is utilized to quantify parameter uncertainty, closed-loop feedback execution data is carried out, and model parameters are dynamically updated; according to the invention, the precision, long-term stability and equipment safety of energy efficiency optimization of the power distribution cabinet are remarkably improved, and efficient and reliable operation under multi-physics field coupling constraint is ensured.

The invention discloses a power grid dispatching method and system adapting to power system requirements, and relates to the field of industrial big data, and the method comprises the following operation steps: S1, multi-source heterogeneous data collection and edge preprocessing; s2, knowledge graph construction and data fusion; s3, load prediction and renewable energy output prediction based on deep learning; s4, generating a dynamic optimization scheduling strategy; s5, carrying out security and credible execution on the data endowed by the block chain; and S6, real-time monitoring and closed-loop feedback optimization are carried out. According to the power grid scheduling method and system adapting to the power system demand, the scheduling method integrates edge calculation, block chain, deep learning and reinforcement learning, can realize multi-source data real-time processing, dynamic optimization strategy generation and data security and credibility, improves the power grid operation efficiency, stability and renewable energy consumption capability, and improves the power grid scheduling efficiency. And the dynamically optimized scheduling strategy can reduce the operation cost of the power grid, and can reduce carbon emission at the same time.

The invention provides a drainage basin flood runoff forecasting system and forecasting method based on water conservancy big data, relates to the technical field of flood runoff forecasting, and constructs the drainage basin flood runoff forecasting system based on the water conservancy big data by integrating the advantages of a physical model and a data driving model. The adopted bidirectional LSTM network is used for abnormal value interpolation, so that the data quality can be effectively improved; the HEC-HMS model is combined with a terrain correction factor to optimize the accuracy of the runoff process; the LSTM network enhances the data driving capability of flow prediction through the fusion of geographic space features and time series data, the model fusion module introduces an attention mechanism and water conservation constraints, the dynamic weight balance of a physical model and a data driving model is realized, the reliability and consistency of a prediction result are comprehensively improved, and the prediction efficiency is improved. And the model verification module realizes continuous comparison and verification of a forecast result and actually measured data through a closed-loop feedback mechanism of a total error and a dynamic threshold value.

The invention provides an AI-based post competency and recruitment post matching method, and relates to the technical field of big data processing, and the method comprises the steps: obtaining multi-source heterogeneous data, and extracting dominant skill entities and implicit ability entities of candidates; constructing a candidate ability evolution graph; analyzing a post dynamic demand by combining a time sequence prediction model, and forming a dynamic post portrait including a current demand, a hidden demand and a future evolution demand; calculating the structural similarity between a candidate map and a post portrait through a map neural network matching algorithm, and generating and sorting comprehensive matching scores in combination with adaptive dimensions such as core capability difference, future integrating degree and team integration potential; team cooperative effect simulation is introduced, the influence of candidate addition on a team capability structure and a cooperative network is predicted, and the model is optimized through a closed-loop feedback mechanism; the talent recognition accuracy and coverage range are remarkably improved, prospective strategic talent matching is achieved, and the matching efficiency is improved.

The invention discloses a source network load storage system cooperative regulation and control method and system, and the method comprises the steps: employing a high-precision sensor and multi-protocol communication to obtain system multi-dimensional data through global data collection and preprocessing, and carrying out the noise reduction; constructing a dynamic association model based on a graph neural network, and accurately capturing a system node relationship in combination with a multi-head attention mechanism and topological constraints; layered multi-objective decision, reinforcement learning real-time regulation and control, layered control architecture and closed-loop feedback correction are adopted, and economic optimization, safety guarantee and strategy iteration are considered. The system is composed of a global data sensing unit, a system dynamic modeling unit and the like, and all the units are in close cooperation. According to the method and the system, the defects of insufficient data processing, low model precision, single regulation and control and the like in the prior art are effectively overcome, the system fault prediction accuracy can be improved, the carbon emission is reduced, the power fluctuation coping capacity is enhanced, and the operation efficiency and the stability of the source network load storage system are remarkably improved.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

The invention relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

The invention discloses a central air conditioner load self-adaptive regulation and control method based on artificial intelligence, which relates to the technical field of heating ventilation air conditioner automatic regulation and control, and comprises the steps of edge end data preprocessing, deep learning load prediction and self-adaptive updating, intelligent control strategy, closed-loop feedback and continuous optimization and fault diagnosis and remote operation and maintenance. Through edge end denoising and feature labeling, the data transmission pressure is remarkably reduced, and the response speed is increased; the deep learning model is combined with online iteration and reinforcement learning, key parameters such as chilled water temperature and water pump frequency are accurately regulated and controlled, and energy consumption and comfort are considered; according to the method, prediction errors and equipment states are monitored, a model and a control strategy are synchronously updated when abnormity is detected, fault early warning is triggered in combination with health assessment, energy consumption is effectively reduced, the service life of equipment is prolonged, and finally an efficient and stable self-adaptive regulation and control system capable of being operated and maintained online in a long period is constructed.

The invention relates to the technical field of safety monitoring, in particular to a bridge safety monitoring system based on sensor data. The method comprises the steps that a sensor measuring unit monitors key physical parameters of a bridge in real time; the data acquisition and transmission unit reads sensor data and transmits the read sensor data; the data processing and analysis unit extracts key features reflecting the health state of the bridge in the sensor data based on a multi-modal physical perception feature fusion method, identifies an abnormal mode in the sensor data through a support vector machine model, and evaluates the remaining life of the bridge based on a carbonization-corrosion-crack closed-loop feedback control rule; and the information management unit stores the analysis result of the data processing and analysis unit and displays the monitored bridge physical parameters. According to the design of the invention, a multi-modal physical perception feature fusion method is introduced, and adversarial learning, graph wavelet transformation and a three-dimensional convolutional neural network are combined, so that high-precision feature extraction of bridge structure response is realized.

The invention relates to the field of shield tunneling, and discloses a shield tunneling control method and system based on a rock-soil particle motion trail, and the method comprises the steps: fusion deployment of various types of high-precision sensors, intelligent anti-noise data collection and preprocessing, precise physical model construction, efficient numerical simulation optimization, and multi-parameter comprehensive detection and correlation analysis. And real-time data-driven adaptive control model construction, control instruction rapid generation and transmission, closed-loop feedback and continuous optimization are realized. According to the method, multiple links including fusion deployment of multiple types of high-precision sensors, intelligent anti-noise processing, physical model construction and the like are covered, all the links have a synergistic effect, data can be accurately obtained, data quality can be guaranteed, a simulation foundation can be provided, comprehensive evaluation, intelligent control and rapid instruction transmission can be achieved, continuous optimization is achieved through closed-loop feedback, and the method is suitable for large-scale popularization and application. And finally, the construction level in all aspects is improved, and complex engineering requirements are met.

The invention relates to the technical field of wind power generation, in particular to a long flexible blade monitoring method and system based on multi-source data fusion, which adopts a multi-source data fusion technology, collects optical fiber strain, vibration, environment and image data by arranging sensor nodes on a fan blade, and generates comprehensive feature data through preprocessing; and extracting potential feature vectors by a multi-modal auto-encoder, and mapping the potential feature vectors into predicted stress data by using a physical information neural network in combination with offline finite element simulation data and physical constraint conditions. Then, a control decision is generated through the multi-modal deep fusion network and hierarchical reinforcement learning, the blade state is adjusted in real time, and the model is optimized through a closed-loop feedback mechanism; according to the invention, precise monitoring and active regulation and control of special working conditions such as blade pollution and icing are realized.

The invention discloses a mechanical arm dynamic deviation correction method and system based on visual driving and a medium, and relates to the technical field of mechanical arm control. The method comprises the steps that when the tail end of a mechanical arm enters a preset machining space, an integrated 3D visual sensor is triggered to collect 3D point cloud of a workpiece to be machined; after pose recognition is carried out on the point cloud, the offset is recognized according to the teaching pose and the actual pose, and the initial offset is output; calling the multi-dimensional perception data, performing fusion correction, and outputting a correction offset; performing interference correction through an offset compensation model, and outputting a target offset; parameter adjustment and optimization are carried out according to the target offset, and a joint angle adjustment instruction is output; and performing correction closed-loop feedback according to the updated pose data. The technical problems of precision errors and low efficiency caused by deviation in the operation process of the mechanical arm are solved, and the technical effects that through dynamic deviation correction and multi-sensor data fusion, the operation precision and efficiency of the mechanical arm are improved, and stable operation in a complex environment is ensured are achieved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.