2917 results about "Closed loop feedback" patented technology

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 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 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 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 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 cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention discloses a stamping part forming precision dynamic monitoring system based on digital twinning, and particularly relates to the field of general monitoring and adjusting systems, and the stamping part forming precision dynamic monitoring system comprises a digital twinning modeling module, a twinning simulation prediction module, an intelligent function coupling decision module and a physical execution closed loop feedback module; the digital twinning modeling module collects stamping related data through a multi-source sensor, and constructs and dynamically updates a geometric, physical and behavior three-level digital twinning body after preprocessing; the twinborn body simulation prediction module is based on three-stage twinborn bodies and is combined with parameter initialization, double-source simulation, result fusion and threshold decision to realize pre-judgment of forming precision; the intelligent function coupling decision-making module generates a targeted regulation and control instruction through a basic regulation and control function and a dynamic coupling mechanism according to the precision out-of-tolerance signal and the physical data; and the physical execution closed-loop feedback module completes instruction execution, state perception and model correction through iterative loop, realizes dynamic monitoring of the forming precision of the stamping part, and improves the forming precision stability and the production efficiency of the stamping part.

The invention discloses a mechanical arm motion control method based on multi-agent cooperation, and the method comprises the steps: firstly, receiving an RGB image through a sub-task generation agent, and generating a structured sub-task sequence according to a natural language task instruction of the RGB image; secondly, performing joint modeling on a task text and a scene image through a 3D sensing intelligent body, positioning specific coordinates of a target object in a three-dimensional space, reasoning dynamic characteristics of a current environment based on historical state information of a robot by combining an environment sensor, and generating an environment sensing vector; and finally, the action generation agent performs fusion modeling according to the subtask text, the subtask target coordinates, the current state of the robot and the environment perception vector, generates a continuous action vector, drives a mechanical arm to complete each subtask action, and constructs closed-loop feedback by a controller and a discriminator to realize task execution state judgment and automatic circulation. The precise action control instruction can be effectively generated, and the task execution fineness of the mechanical arm is remarkably improved.

The invention belongs to the technical field of artificial intelligence, and discloses a computer network fault detection system based on an artificial intelligence technology, which comprises a data acquisition module, a feature extraction and preprocessing module and a fault positioning and repairing module. According to the invention, the detection link is accurate and comprehensive, the data acquisition module performs multi-source fusion to obtain rich materials, the feature extraction and preprocessing module generates multi-dimensional vectors to capture complex features, and multi-level fault identification performs comprehensive troubleshooting and reduces misjudgment; the diagnosis process is intelligent and efficient, the hybrid fault diagnosis model fuses supervised and unsupervised learning, processes known faults and detects unknown anomalies, and supervised learning branches are optimized to improve performance; the repair work is timely and reliable, the automatic repair scheme generation method has strategy library matching, dynamic adjustment and rollback mechanisms, quick response and flexible repair can be achieved, and the subsequent diagnosis accuracy is improved through repair verification multi-dimensional evaluation and closed-loop feedback.

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

The invention relates to the technical field of aircraft intelligent detection and maintenance systems, and discloses an aircraft defect intelligent evaluation system and method based on multi-modal fusion, and the system comprises a multi-modal data collection module, a tensor construction and decomposition module, a meta-prototype relation network module, a multi-target game optimization module, and a closed-loop feedback module. The method comprises the steps of constructing a five-order feature tensor through multi-modal data synchronous acquisition and space-time alignment, extracting low-rank features through hypergraph block item decomposition, dynamically generating a defect prototype set in combination with meta-learning, generating a maintenance decision by adopting a Nash equilibrium strategy and fusing multi-constraint conditions, and optimizing system parameters through closed-loop feedback. The whole-process intelligentization of aircraft defect detection and maintenance is realized; according to the method, high-precision defect detection is realized through multi-modal data fusion and hypergraph modeling, an intelligent decision is generated in combination with dynamic prototype learning and multi-target game optimization, and continuous self-optimization is performed by means of a closed-loop feedback mechanism, so that the operation and maintenance efficiency and safety of the aircraft are improved automatically in the whole process.

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 belongs to the technical field of artificial intelligence and intelligent sensing, particularly relates to a multi-source sensor fusion sensing system based on adaptive noise suppression, and aims to solve the problems of noise interference, modal mismatch and insufficient robustness in multi-source sensor fusion in a complex dynamic environment. The system comprises a front-end preprocessing module, an adaptive noise suppression engine, a multi-modal feature alignment unit, a credibility-driven fusion reasoning core and a closed-loop feedback optimization mechanism. Through real-time noise modeling and dynamic weight adjustment, high-precision alignment and fusion of multi-source signals are realized, and the sensing stability and real-time performance in an extreme scene are significantly improved.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

The invention provides a field electrical prospecting data anomaly identification method based on big data analysis, and the method comprises the steps: collecting multi-source heterogeneous data to construct a digital twin model, and generating a virtual electrical prospecting data set in combination with geological physical law constraints; performing multi-device cooperative training through a federated learning framework to obtain a self-evolution anomaly detection model, deploying the model to an edge computing node to obtain an anomaly recognition result, and visually displaying the anomaly recognition result; and iteratively optimizing the geological digital twinning and anomaly detection model to obtain a field electrical prospecting data anomaly recognition result. Space-time synchronization is realized based on related technologies, a three-dimensional geological structure model is constructed in combination with a geological knowledge graph, and geological logic constraints are embedded; real geological scene features are injected through closed-loop feedback of a generative geological simulator and a federated learning framework, and an electrical method response mapping relation is corrected, so that the model adapts to geological structure changes; and mapping an anomaly recognition result to the three-dimensional geologic model for visual display, thereby solving the problem of low matching degree of a traditional method.

The invention discloses a temperature monitoring and control system in a goat breeding house. The system comprises an environmental data sensing unit, an edge data preprocessing unit, an optimized long-short term memory neural network processing unit, an air internal and external circulation regulation and control strategy generation unit, an equipment linkage execution unit and a system operation state monitoring unit. Environmental data in a breeding house is collected in a self-adaptive mode through multiple types of high-precision sensors, after edge processing, temperature spatial-temporal characteristics are extracted deeply through an optimized long-short-term memory neural network, an air circulation regulation and control strategy is generated in combination with a multi-target optimization model, equipment is driven to execute precisely, and system dynamic optimization is achieved through closed-loop feedback. The system overcomes the defects of insufficient monitoring coverage and extensive regulation and control in the prior art, considers the coupling relationship between temperature and air components, realizes multi-factor collaborative accurate regulation and control, effectively reduces energy consumption, creates a stable and comfortable growth environment for goats, and improves the breeding benefit and intelligent management level.

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

The invention discloses an unmanned aerial vehicle non-visual flight management method based on an unmanned area three-dimensional model, and belongs to the technical field of unmanned aerial vehicle air traffic management and autonomous flight control, and the method comprises the steps: fusing multi-source heterogeneous data to generate a probability map of a data confidence field; calculating a space-time risk potential field based on the map and the kinematics state of the unmanned aerial vehicle; performing adaptive perception and route optimization planning according to the risk potential field; and a closed-loop feedback signal is generated according to the flight execution deviation so as to regulate and control the data fusion and risk calculation process, and data assimilation is carried out. According to the technical scheme, environment modeling fusing multi-source data and confidence evaluation is adopted, dynamic risk calculation of kinematics and global closed-loop feedback regulation are combined, and the safety, autonomy and environment adaptability of non-visual flight of an unmanned aerial vehicle in a complex unmanned area can be remarkably improved.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention discloses a region denial method and system for an unmanned aerial vehicle. Precise defense is realized through a four-stage cooperation mechanism. The method comprises the following steps: S1, a multi-source detection stage; s2, an intelligent identification stage; s3, a dynamic response stage; and S4, a closed loop feedback stage. The system architecture comprises a control center, a composite detection array, an intelligent analysis engine and a multi-mode countering unit, and a complete automatic link from situation awareness to effect execution is formed. Through cooperation of soft and hard killing means, detection and recognition integrated design and an intelligent decision engine, the unmanned aerial vehicle interception efficiency in a complex electromagnetic environment is remarkably improved, an echelon defense system covering electron suppression to precise destroy is constructed, and the method is particularly suitable for important place protection, city security and other high-value target protection scenes.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention discloses a QoS guarantee method and system for a communication network, and relates to the technical field of communication networks, and the method comprises the steps: collecting and preprocessing network state data, and forming a standardized data set; dynamically classifying service types based on an improved random forest algorithm, and predicting a future QoS demand trend of each priority service in combination with an LSTM neural network; establishing a mapping model of QoS demands and resource parameters, converting predicted demands into allocable resource indexes, monitoring the resource utilization rate in real time, and setting an elastic reservation mechanism and conflict early warning; when early warning is triggered, selecting an optimal transmission link by adopting a multi-path collaborative algorithm, and implementing differentiated resource allocation according to service priorities; and a closed-loop feedback mechanism is triggered to dynamically adjust resource allocation by monitoring the deviation between the actual QoS and a predicted value in real time. The method has the advantages that through multi-dimensional perception, LSTM prediction, dynamic resource management and multi-path scheduling, QoS requirements of services with different priorities are accurately matched, and dynamic changes of the network are efficiently coped with.

The invention discloses an energy-saving control method and system based on a large refrigeration house, and the method comprises the steps: S1, obtaining cargo attribute information in real time through a cargo label, and inputting a dynamic load prediction model to generate a cooling capacity demand prediction signal in a future time period; s2, generating a multi-device cooperative control signal based on the cooling capacity demand prediction signal in the future period; s3, a shelf-level cooling capacity demand distribution signal is generated in combination with the cooling capacity demand prediction signal; s4, generating a directional cold airflow path signal matched with goods shelf distribution according to the goods shelf level cold capacity demand distribution signal; and S5, closed-loop feedback adjustment is conducted on the compressor frequency, the refrigerant flow and the air valve opening through an edge calculation module, and a dynamic balance control instruction of cooling capacity supply and space distribution is generated. The intelligent air quality monitoring method and system based on sensing data feedback can solve the problems of excessive refrigeration energy consumption waste caused by inaccurate cold capacity demand prediction of a large refrigeration house and extra energy loss caused by low cooperative efficiency of multiple devices.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The invention relates to the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention discloses an urban power grid real-time load collaborative peak regulation method based on multi-energy complementation and AI scheduling. The method comprises the following steps of multi-source data access and high-dimensional feature space construction, embedded entropy calculation and interactive network construction, domain knowledge and data-driven model fusion, hierarchical scheduling and dual-stage optimization, and real-time decision and closed-loop feedback. According to the method, the real-time performance and hierarchical scheduling thought are emphasized, and an organic closed loop is formed on the three aspects of intra-day scheduling, hour-level rolling correction and minute-level or second-level emergency response. Meanwhile, by means of a multi-stage optimizer switching mechanism, the model can complete rapid convergence of high-dimensional parameters in a short time, finer strategy fine adjustment is carried out in the later period, and the accuracy and reliability of a peak regulation scheme are guaranteed; the method can be applied to advanced power grid systems such as intelligent power grid dispatching, a multi-energy collaborative optimization platform and demand side response management, and has the characteristics of high real-time performance, strong adaptability and good expandability.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.