114 results about "Fuzzy logic inference" patented technology

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

The invention belongs to the technical field of power systems, and particularly relates to an audit data early warning method and system, and the method comprises the steps: collecting the node voltage and current of a power supply network and the charging and discharging efficiency data of an energy storage system in real time through a distributed sensor network, and generating an original data flow; carrying out localized cleaning and standardized format conversion through an edge computing node; generating a dynamic risk assessment result and a scheduling scheme based on fuzzy logic reasoning and digital twinborn simulation; and analyzing and predicting deviation through meta-learning, and realizing closed-loop optimization by adopting a model distillation technology. The system comprises a distributed sensor network, an edge computing node, a multi-source data fusion module and the like. According to the method, the anomaly detection real-time performance, the risk assessment accuracy and the energy storage scheduling economy of the power system are improved.

The invention discloses a fault prediction and health management system for a belt conveyor, relates to the technical field of intelligent monitoring and management of mechanical equipment, and aims to solve the problems of fault recognition lagging and high misjudgment rate caused by insufficient consideration of real-time performance and high reliability required by industrial equipment. Motor current fluctuation, roller temperature change rate, belt tension distribution and vibration spectrum data are acquired in real time through a data acquisition unit; the fault feature extraction unit extracts abnormal features based on wavelet transform, and calculates an operation stability index through a principal component analysis method; the risk assessment unit combines the abnormal features, the abrasion loss and the stability index, and adopts fuzzy logic reasoning to judge the fault risk level; and the intelligent regulation and control unit dynamically regulates and controls the operation state or gives an alarm according to grading processing, so that accurate early warning and intelligent response of early faults of the equipment are realized.

The invention discloses a ruins protection area tourist diversion system and method based on dynamic bearing capacity. The diversion system comprises a data acquisition module, a bearing capacity calculation module, a diversion strategy optimization module, a tourist guide module and a feedback adjustment module. Real-time tourist data and environment information including tourist flow, residence time, meteorological conditions, site physical conditions and the like are collected; calculating the dynamic bearing capacity of the current ruin area, and comprehensively considering factors such as tourist load and environmental influence; a fuzzy logic reasoning method is adopted to evaluate tourist density and define a congestion degree grade: an IPSO algorithm is adopted to optimize tourist shunting paths, the shunting paths are generated and tourists are guided, and accurate shunting is carried out by adopting modes of intelligent identification, dynamic indication boards, mobile applications and the like; based on historical data and a real-time monitoring result, error correction is carried out on bearing capacity prediction, and the prediction accuracy is improved.

The invention discloses an electric vehicle charging and discharging method, device and equipment based on fuzzy logic reasoning and price excitation demand response and a medium. The method comprises the steps that modeling is conducted on loss of an energy storage battery of an electric vehicle; in combination with a membership function and a fuzzy rule, based on a Mamdani fuzzy logic algorithm, according to the charging time anxiety degree, the charging cost reduction degree and the residual SOC of the user to which the electric vehicle belongs, determining the intention of the user to participate in charging and discharging scheduling; and in consideration of the intention that the user participates in charge and discharge scheduling, combining a price demand response mode and an excitation demand response mode of the electric vehicle to optimize the operation state of a power grid which is used for charging the electric vehicle. The invention belongs to the field of electric vehicle charging and discharging. According to the method, a corresponding charging and discharging strategy scheme can be accurately formulated according to the requirements of an electric vehicle user, the peak-valley difference of a power grid is reduced while the charging cost and the charging time comprehensive cost of the user are reduced, and the load fluctuation is reduced.

The invention provides an indoor positioning method and system based on dynamic mode switching and data fusion, and relates to the technical field of visible light indoor positioning, and the method comprises the following steps: deploying a VLC transmitting device and an auxiliary positioning point, constructing a VLC fingerprint database, carrying out dynamic signal detection and mode conversion, carrying out VLC fingerprint positioning, carrying out IMU data processing, and carrying out main positioning mode data fusion. VLC signal intensity distance estimation, ultrasonic ranging, IMU relative displacement estimation and standby positioning mode data fusion are carried out. According to the invention, by deploying the visible light communication transmitting device and the auxiliary positioning point, the strength and the stability of the VLC signal are monitored in real time, and dynamic positioning mode switching is realized through fuzzy logic reasoning. In the main positioning mode, VLC fingerprint matching is combined with IMU data, and accurate positioning is carried out through a Kalman filtering algorithm; and when the VLC signal is weak or unstable, a standby positioning mode is switched, and accurate positioning is carried out by fusing data through a particle filter algorithm according to VLC signal intensity distance estimation, ultrasonic ranging and I MU data.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention discloses a cardiovascular disease risk assessment system based on big data analysis, which relates to the technical field of medical big data and quantum and comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a risk assessment module and a result display module. Data acquisition integrates multi-source heterogeneous data, quantum encryption is used to guarantee security, and preprocessing is carried out by deep reinforcement learning cleaning and adaptive normalization; feature extraction is combined with quantum principal component analysis and an auto-encoder, and a quantum attention mechanism is introduced; the model training adopts quantum neural network ensemble learning of quantum annealing optimization; introducing fuzzy logic reasoning to correct probability and layering in risk assessment; results are displayed through virtual reality and augmented reality technologies, and report suggestions are automatically generated. The method has the advantages of prominent advantages, comprehensive multi-modal data acquisition, accurate advanced technology preprocessing and feature extraction, efficient and accurate quantum optimization model training, and combination of real-time monitoring and a knowledge graph, provides a basis for prevention and treatment of cardiovascular diseases, and promotes medical intellectualization and precision.

The invention discloses a hydraulic engineering data intelligent monitoring method based on digital twinning, and particularly relates to the technical field of hydraulic engineering intelligent monitoring, and the method comprises the following steps: obtaining control instruction data and entity hydraulic engineering real-time monitoring data, analyzing the time sequence characteristics and the change trend, and obtaining the real-time monitoring data of the control instruction data and the entity hydraulic engineering real-time monitoring data; detecting whether the control instruction data is returned and participates in system calculation, and identifying a data echo effect; after detection, calculating a short-term influence index and a long-term influence index based on the prediction error convergence degree, respectively evaluating the influence of the echo effect on the current operation state and the future scheduling precision, judging the level of the echo effect to be a low level, a middle level or a high level according to the influence degree, and processing by adopting a corresponding optimization strategy; according to the method, dynamic evaluation and grading judgment of the data echo effect are realized, and the early warning and adaptive capacity of the system is improved; fuzzy logic reasoning is adopted to optimize decisions, and regulation and control intelligence is enhanced; and through a differentiation strategy and a credible data source reconstruction model, the operation stability and the scheduling precision of the water conservancy project are guaranteed.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention relates to the technical field of injection mold monitoring, and discloses an injection mold cavity state monitoring method, system, equipment and medium, the injection mold cavity state monitoring method comprises the following steps: multiple sensors collect mold state data in real time, and the mold state data are preprocessed and fused to generate a comprehensive data set; training a machine learning model based on historical samples, and updating model parameters in real time through an incremental learning algorithm; analyzing current data by adopting dynamic fuzzy logic reasoning, and outputting a state evaluation result; real-time data and results are uploaded to the cloud for deep analysis and distributed storage; a user feedback mechanism is integrated, and model parameters and a fuzzy inference rule base are optimized. According to the method, model parameters are updated in real time through an incremental learning algorithm, a multi-parameter nonlinear coupling relation is analyzed in combination with dynamic fuzzy logic reasoning, and based on cloud collaboration and a user feedback closed-loop mechanism, self-adaptive monitoring and continuous optimization of the injection mold state are achieved, and the anomaly detection precision and the system robustness are improved.

The invention discloses an operation risk analysis method and system for a virtual power plant, relates to the field of risk analysis, and solves the problem of insufficient operation stability of an existing virtual power plant, and the method comprises the steps: S1, collecting the operation data of various devices of the virtual power plant; s2, acquiring historical fault data, and constructing a fault rule base; performing fuzzy logic reasoning based on historical fault data to obtain membership values of the abnormal parameters; real-time equipment parameters are collected, calculation is carried out, and a fault judgment value is obtained; fault equipment is judged; s3, calculating the fluctuation condition of the equipment parameters, constructing a fluctuation interval, analyzing and calculating a fault risk value according to the fluctuation interval, judging pre-fault equipment, counting the fault equipment and the pre-fault equipment, and constructing a maintenance equipment list; s4, adjusting the energy supply of the equipment according to the energy supply plan in combination with the maintenance equipment list; the stability of the virtual power plant can be effectively improved.

The invention discloses a greenhouse CO2 concentration partition regulation and control method based on fuzzy logic reasoning. The method comprises the following steps: S1, acquiring an environment parameter data set corresponding to each partition in real time through a sensor network deployed in a greenhouse; s2, performing data preprocessing on the acquired environment parameter data set of each partition; s3, determining priority weights of the partitions; s4, based on a greenhouse partition priority model and the preprocessed environment parameter data set of each partition; s5, calculating the target CO2 concentration range and the regulation and control amount of each subarea; s6, converting the target CO2 concentration range and the regulation and control quantity of each subarea into executable control signals, and executing CO2 concentration subarea regulation and control; and S7, monitoring the environmental parameter data of each partition in real time through the sensor network until the target range is stable. The adaptability to the crop growth environment is improved.

The invention discloses a personalized co-driving type intelligent vehicle man-machine sharing control method and system, a medium and equipment, and relates to the field of intelligent auxiliary driving, and the method comprises the steps: obtaining a risk tolerance coefficient and a sensitivity zooming coefficient of a driver; acquiring a personalized driving risk field according to the risk tolerance coefficient and the sensitivity zoom coefficient of the driver; using the personalized driving risk field to calculate driving risks of different types of drivers; according to the driving risks of the different types of drivers, the driver control weights of the different types of drivers are obtained through fuzzy logic speculation; and establishing a man-machine non-cooperative game model, and realizing personalized man-machine sharing control based on the driver control weights of different types of drivers and the man-machine non-cooperative game model. When the man-machine conflict exists, the driving requirements of different types of drivers can be met while the vehicle driving safety is guaranteed, and personalized man-machine sharing control is achieved.

The invention discloses a personalized precise fitness scheme intelligent pushing method and system, and relates to the technical field of intelligent fitness, and the method comprises the steps: collecting motion data of a user in real time, and carrying out the preprocessing; the method comprises the following steps: collecting a fitness target of a user, converting the fitness target into a target vector, activating a task sub-network in a dendritic neural network according to a matching degree between motion data of the user and the target vector, dynamically adjusting weights and importance of different tasks in the task sub-network through an attention mechanism, and generating and optimizing a preliminary fitness scheme through multiple agents. The most relevant sub-networks are activated in real time, task priorities are adjusted through an attention mechanism to adapt to environment changes and user states, the exercise intensity is optimized through fuzzy logic reasoning, training tasks are automatically adjusted according to real-time feedback of users, the balance between the tasks is optimized through co-evolution between agents, and the training efficiency is improved. The personalized fitness scheme can be continuously optimized, and more accurate exercise recommendation is provided under the dynamic environment and user demand changes.

The invention discloses a charging pile power management system based on the Internet of Things, and particularly relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the real-time operation data of a charging pile through a sensor, and forms a data set; the data processing module is used for processing the data according to a preset or optimization strategy and generating a processed data set; the scene analysis module performs analysis based on the processing data set and divides the operation scene of the charging pile; the data feature extraction module is used for extracting and analyzing features when an optimization mechanism is triggered; the logic judgment module is used for formulating an optimization strategy through fuzzy logic reasoning and sending the optimization strategy to the data processing module; the power energy efficiency management module is used for adaptively adjusting power distribution and improving the energy efficiency of the system; the data transmission delay can be reduced, the response speed can be improved, a user can obtain smoother experience when using the charging pile, transmission of redundant information is reduced through an optimization strategy, and efficient power management is achieved.

The invention relates to the technical field of agricultural Internet of Things, and discloses an edible mushroom growth environment regulation and control method and system based on multi-source sensing data, and the method comprises the steps: deploying a multi-source sensor network, unifying time synchronization, and carrying out event-driven space-time alignment and three-dimensional mapping. Carrying out multi-dimensional reliability evaluation and environment correction, and obtaining an environment parameter fusion data set by adopting a fuzzy logic reasoning method; performing space-time Kriging interpolation, and obtaining a heterogeneous microenvironment feature database by adopting a multi-source data collaborative correction method; carrying out cross-modal attention fusion; performing multi-scale convolution and time sequence attention classification; carrying out deep reinforcement learning and multi-objective optimization; carrying out online incremental learning optimization to obtain a self-adaptive optimized partition collaborative regulation and control strategy; according to the method, the technical problems of insufficient multi-source data fusion precision, lack of environment spatial and temporal distribution modeling, inaccurate parameter coupling prediction, lack of self-adaptability of regulation and control strategies and the like are solved.

The invention provides a quality detection method and system for magnesium ingot production. The method comprises the following steps: acquiring a visible light image, three-dimensional point cloud data and an infrared thermal image of a magnesium ingot to be detected; constructing a three-dimensional voxel model representing surface geometry and color information of the magnesium ingot; identifying and outputting the type, three-dimensional position and size information of the surface defect of the magnesium ingot; taking an infrared thermal image as a surface temperature boundary condition of a heat conduction model, and taking a defect position and a defect size as a heat conductivity disturbance source in the model; obtaining an equivalent heat conductivity distribution field in the magnesium ingot; on the basis of the spatial non-uniformity of the equivalent thermal conductivity distribution field, calculating to obtain an index for representing internal density; calculating contour conformity parameters; and inputting the surface defect severity parameter, the contour conformity parameter and the index of the internal density into a fuzzy logic reasoning system, performing reasoning according to a preset fuzzy rule library matched with a magnesium ingot downstream application process, and outputting the magnesium ingot quality grade.

The invention provides a steering-by-wire control method and system based on particle swarm-sliding mode control and a fuzzy radial basis function, and the method comprises the steps: carrying out the adaptive approximation of an uncertain item and unknown disturbance of a state-space equation based on a radial basis function neural network in combination with an adaptive law, and obtaining the real-time estimation values of the uncertain item and the unknown disturbance; a sliding mode surface is obtained based on the wheel rotation angle error, the sliding mode surface is corrected and compensated based on the real-time estimation value, parameters of the sliding mode surface are optimized in combination with a particle swarm algorithm, and an optimized sliding mode surface is obtained; on the basis of a fuzzy logic controller, the optimized sliding mode surface and the change rate of the optimized sliding mode surface are converted into membership degrees of a fuzzy set for fuzzy logic reasoning, and the fuzzy set of control behaviors is obtained; obtaining a control signal based on the fuzzy set of control behaviors; and completing steering-by-wire control of the vehicle based on the control signal. According to the technical scheme, the response speed and the anti-interference capability of the steer-by-wire system can be improved.

The invention relates to the technical field of robot intelligent control, and discloses an inspection robot task scheduling and state switching method for quantifying a state machine, which comprises the following steps: acquiring state machine operation configuration data; performing health degree evaluation on the sensor, navigation, communication and battery systems in an initial state; in the idle state, a segmented charging strategy is adopted, and task priorities are calculated through a neural network; a breakpoint resume and event-driven architecture is adopted to execute tasks in the inspection state; in a warehouse returning state, a deep neural network is adopted to predict return flight energy consumption, and a path is re-planned through multi-objective optimization when the electric quantity is insufficient; processing a control instruction by adopting speed limitation in a manual or mapping state, and starting an SLAM module in the mapping state; and in an abnormal state, a fuzzy logic reasoning system is adopted to calculate an abnormal grade, and recovery waiting, degradation protection or alarm is executed according to the grade. According to the invention, autonomous operation and intelligent decision making of the inspection robot in a complex dynamic environment can be realized.

The invention discloses an industrial robot adaptive control system fused with a situation prediction mechanism, and particularly relates to the field of situation prediction based on a feedforward network and fuzzy logic, comprising the following steps: in the execution process of an industrial robot, obtaining state input data through continuous sampling; and inputting the state input data into a feedforward neural network with a time sequence feature mapping capability to obtain a prediction output result of the task trend of the robot. A dynamic entropy deviation cooperation mechanism between a feedforward neural network and a fuzzy logic reasoning model is constructed to realize modal attribution conflict identification and control right coordination of a critical state of a multi-modal task, so that continuity of a task response path and stable operation of a control system in a fuzzy transition interval are ensured; therefore, the problems of modal dislocation and out-of-control execution caused by lack of entropy weight coupling of feedforward prediction output and fuzzy reasoning judgment in a control right decision domain are solved.

The invention relates to the technical field of unmanned aerial vehicle flight control, in particular to a track tracking control method for a tandem dual-rotor unmanned helicopter, which comprehensively considers factors such as aerodynamic force and inertia force and is completed through structural parameter measurement, aerodynamic force analysis, kinetic equation derivation and model verification and correction. Designing a self-adaptive sliding mode controller, defining a sliding mode surface, designing a control law and a self-adaptive law, ensuring the robustness of the system, and optimizing a control strategy; in combination with model prediction control, establishing a prediction model, defining a target function, improving the track tracking precision through rolling optimization and feedback correction, and correcting prediction errors and interference influences through sliding mode control; a multi-task switching control strategy is designed, task stages are divided, parameters are set, switching conditions are determined, and a fuzzy logic reasoning or neural network algorithm is adopted to realize rapid adjustment of the control strategy. The technology can accurately track the track, enhance the robustness, improve the real-time performance, flexibly adapt to multiple tasks and have a good application prospect.

The invention belongs to the technical field of airport boundary security and protection monitoring, and discloses an airport boundary grading early warning linkage method and system based on fuzzy logic reasoning. According to the method, an airport boundary is subjected to panoramic scanning to generate an infrared and visible light panoramic image, intrusion target detection and identification are performed on a boundary defense area in the infrared and visible light panoramic image through a multi-mode target detection algorithm based on deep learning, intrusion target early warning grade division is performed through a fuzzy logic algorithm, and an intrusion target early warning result is obtained. And carrying out early warning level priority judgment on the intrusion target, carrying out track labeling on a plurality of coordinate points closest to the intrusion target in the surrounding panoramic base map by using an OPENCV algorithm library, displaying the motion track of the current intrusion target, and judging the intention of the intrusion target. According to the invention, different airport environments are adapted through the rule base self-optimization capability, the manual patrol load is reduced while the high reliability is ensured, and the intelligent level and the safety prevention and control efficiency of the enclosure security and protection system are enhanced.

The invention discloses a visual scene graph generation method based on differentiable fuzzy logic reasoning, and solves the technical problems of semantic fuzziness, logic common sense deficiency and the like during long tail relation processing in the prior art. The method comprises the following steps: inputting a training image into a target detection module, and respectively outputting corresponding high-dimensional geometric embedding vectors to a relation classifier and a fuzzy mapping layer; outputting a visual prediction branch by the relation classifier; meanwhile, the fuzzy mapping layer outputs a fuzzy membership degree vector; the logic tensor reasoning module is combined with a common sense rule in an external knowledge base, simulates a logic reasoning process by utilizing a differentiable logic operator, and outputs a logic reasoning branch of which the relation of each pair of objects meets a preset logic rule in the training image; a gating residual fusion module performs weighted fusion on the visual prediction branch and the logical reasoning branch to generate a scene graph triple of the training image; and finally, training the network model, inputting a test image into the trained model, and outputting a corresponding visual scene graph.

This invention belongs to the field of industrial network performance optimization technology, specifically providing a DDS-5G network collaborative optimization method based on fuzzy logic. By setting static mapping rules, the QoS Policy parameters of the DDS are mapped to the QoS flow identifiers of the QoS flows in the 5G network, thereby achieving initial DDS-5G collaboration. Simultaneously, by establishing a fuzzy logic inference system, using the QoS Policy parameters and real-time network state parameters as input variables and 5QI as the output variable, and by optimizing and adjusting the QoS Policy parameters of the DDS through DQN and updating the fuzzy logic parameters of the fuzzy logic inference system, further DDS-5G network collaborative optimization is achieved. Through a cross-layer adaptation mechanism, efficient collaboration between the application layer and the 5G network is realized, making it suitable for scenarios with high requirements for low latency and high reliability, such as industrial automation and intelligent manufacturing.

This invention relates to the field of speed control technology, specifically to an intelligent lamination and tension control system and method for multi-ribbed belt production lines in washing machines. The system includes: a multi-dimensional state information extraction module, a real-time collaborative control module, a control performance evaluation module, and a fuzzy rule self-tuning module. In this invention, by calculating tension error and lamination position deviation, a basis for dynamic adjustment is provided, eliminating reliance on preset fixed parameters. By invoking a fuzzy logic inference engine, tension error, error change rate, and position deviation are fused and processed to achieve synchronous and precise control of the transmission roller speed and the position of the correction mechanism. This effectively replaces the slow-responding mechanical tension device, solving the problems of tension fluctuation and lamination deviation. Furthermore, a long-term comprehensive performance index is introduced to evaluate the control effect. When performance deteriorates, a gradient descent algorithm is immediately triggered to self-tune the fuzzy rule parameters, continuously optimizing the control strategy and ensuring the system maintains high stability and high accuracy.

The application discloses a charging pile power management system based on Internet of Things and concretely relates to the technical field of Internet of Things, and comprises a data acquisition module for collecting real-time operation data of the charging pile through a sensor to form a data set; a data processing module for processing the data according to a preset or optimized strategy to generate a processed data set; a scene analysis module for analyzing the processed data set and dividing charging pile operation scenes; a data feature extraction module for extracting and analyzing features when the optimized mechanism is triggered; a logic judgment module for formulating an optimized strategy through fuzzy logic reasoning and sending the optimized strategy to the data processing module; and a power energy efficiency management module for adaptively adjusting power distribution and improving system energy efficiency; the application can reduce data transmission delay and improve response speed, so that users can obtain a smoother experience when using the charging pile, and the transmission of redundant information is reduced and efficient power management is realized through the optimized strategy.

The invention belongs to the field of computer system resource allocation based on a specific calculation model, and discloses a display resource allocation system based on fuzzy logic, which comprises the following steps that: the system acquires an acceleration vector of user interaction, and performs authenticity discrimination based on direction consistency of the acceleration vector to generate weighted interaction acceleration; the fuzzy logic reasoning engine maps the weighted acceleration into a resource allocation priority, and meanwhile, the operation cycle of the engine is dynamically adjusted by the core temperature of the system so as to compensate calculation delay; according to the method, resource scheduling is changed from an application state to a user physical intention subjected to authenticity verification, deep synchronization of system response and user attention is realized, delay and lagging of key interaction are avoided, and smooth and harmonious qualitative change of man-machine interaction experience is achieved.