91 results about "Fuzzy logic controller" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses a switch load balancing method based on fuzzy logic control, and the method comprises the following steps: S1, monitoring a network state in real time through quantum sensing equipment, and obtaining related parameters; s2, transmitting the parameters to a fuzzy logic controller for processing; s3, generating a load distribution strategy according to a predefined rule; s4, transmitting the preliminary strategy to each switch node in the block chain network, and recording related data; s5, verifying the data by using a distributed consensus mechanism of the block chain; s6, dynamically evaluating a node trust value based on an enhanced fuzzy trust model, and selecting nodes meeting requirements to participate in a load balancing game decision; s7, carrying out cooperative calculation on the selected nodes by utilizing a game theory cooperation mechanism, and generating a globally optimized load distribution strategy; and S8, optimizing a load distribution strategy through a quantum genetic algorithm, and performing automatic execution through an intelligent contract. According to the method, the advantages of fuzzy logic, the block chain and quantum computing are combined, and the intelligence and safety of load balancing are improved.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

The invention discloses a power system frequency modulation method and system based on flywheel energy storage auxiliary wind power, a flywheel energy storage system is equivalent to a first-order inertia link, an inertia time constant of the flywheel energy storage system is dynamically adjusted, a virtual damping coefficient is optimized in real time in combination with a fuzzy logic controller, and the stability of power grid frequency is remarkably improved. When the frequency of the power grid fluctuates, the flywheel energy storage system can respond quickly, the frequency modulation power close to the rated frequency can be output in an extremely short time, and the steady-state frequency deviation is strictly controlled in an extremely small range. Meanwhile, the fuzzy logic is used for dynamically correcting the power instruction, and the problem of frequency modulation instruction tracking errors caused by wind power output randomness is effectively solved. Besides, after frequency modulation is finished, the damping coefficient is dynamically adjusted through a fuzzy decision mechanism, the flywheel rotating speed recovery time is greatly shortened, secondary frequency disturbance caused by rotating speed lag is avoided, and the recycling efficiency of flywheel energy storage in a continuous frequency modulation scene is ensured.

The invention belongs to the field of electrical variable measurement, and particularly relates to a single battery equalization management method for a lithium battery BMS (Battery Management System) protection board, which comprises the following steps of: acquiring voltage, temperature, total current and cycle index of each single battery in real time; based on a second-order equivalent circuit model and an extended Kalman filtering algorithm, multi-source information is fused, and the health state and the charge state are estimated with high precision; constructing a dynamic equilibrium trigger threshold fusing the charge state standard deviation, the range and the health state attenuation factor; a bidirectional flyback or multi-winding transformer active equalization strategy is selected according to the inconsistent distribution characteristics and the temperature field, and equalization current is accurately controlled through pulse width modulation; and continuously monitoring the efficiency, the temperature rise rate and the convergence in the equalization process, and dynamically optimizing the equalization parameters by using the fuzzy logic controller until the inconsistency reaches the standard. According to the technical scheme, accurate, self-adaptive, high-efficiency and safe single battery equalization management can be realized, the service life of the battery pack is effectively prolonged, and the operation reliability of the system is improved.

The invention discloses a frequency stability control method for accessing new energy to an offshore energy platform, and the method comprises the steps: firstly carrying out the state recognition and parameter extraction of frequency modulation resources, building a frequency-power response model of the frequency modulation resources, carrying out the superposition construction of a system frequency response total model, and carrying out the packaging of a dictionary structure; based on a frequency safety index, disturbance occurrence and grade are judged in real time, and a frequency change trend is dynamically estimated; according to the disturbance level and the response characteristics of various resources, a fast-medium-slow layered frequency modulation control strategy is constructed, different types of resources are scheduled in stages for cooperative adjustment, the frequency state is monitored, and control parameters are dynamically corrected; constructing a fuzzy logic controller to process nonlinear disturbance, and introducing a whale optimization algorithm to set and optimize parameters of the fuzzy controller; and finally, dynamically correcting the control strategy according to the frequency control result and the resource utilization rate index. The method can guarantee the frequency stability, reduces the frequency modulation cost, and improves the frequency control precision, the response speed and the overall operation toughness.

The invention discloses sealing ring trimming equipment and a self-adaptive control system thereof. The sealing ring trimming equipment comprises a combined cutting device, a detection device and the self-adaptive control system. The combined type cutting device integrates a rough cutting assembly and a mechanical fine cutting assembly, and rough cutting and fine trimming of a sealing ring blank can be completed in sequence. The detection device adopts a multispectral confocal sensor, and the self-adaptive control system comprises a rough cutting module which controls a rough cutting assembly to reserve a set flash; the fine cutting control module dynamically generates a tool path correction parameter and a cutting depth compensation value according to the real-time scanning data; the edge feature analysis module extracts key quality indexes such as residual flash contours and surface roughness; and the correction module dynamically adjusts the reserved flash amount and the fine cutting parameters through the fuzzy logic controller. The equipment is further provided with a cutter resistance monitoring module and an abrasion prediction compensation module, closed-loop control from rough cutting to fine cutting is achieved, the edge machining precision and production efficiency of the sealing ring are remarkably improved, and the equipment is suitable for the field of high-precision sealing element manufacturing.

The invention relates to the technical field of medical cosmetology, discloses a laser hair removal method based on multi-parameter feedback, and aims to solve the problems of unstable curative effect and insufficient safety caused by single parameter of traditional laser hair removal. The method comprises the following steps: synchronously acquiring multi-dimensional physiological parameters such as skin type, hair follicle depth and epidermis temperature through multispectral imaging, high-frequency ultrasound and temperature sensing; constructing a personalized treatment parameter model based on a deep neural network, and generating initial energy density, pulse width and cooling parameters; laser output is regulated and controlled in real time by using a fuzzy logic controller in the treatment process, and it is ensured that thermal injury accurately acts on the hair papilla; performing adaptive optimization on the treatment strategy in combination with reinforcement learning, and continuously updating the model through a closed-loop learning mechanism; meanwhile, a treatment history database is established, curative effect prediction and parameter backtracking are supported, precision, individuation and high safety of laser depilation are achieved, depilation efficiency is remarkably improved, and the risk of skin adverse reaction is reduced.

The invention relates to an infusion speed alarm method and system. The infusion speed alarm method comprises the steps that a multi-mode sensing data set is formed; according to the multi-mode sensing data set, whether the conditions of dripping speed abnormity, pressure sudden increase, too large bubbles, pipeline blockage or backflow exist or not is judged, and corresponding abnormal feature information is generated; inputting the abnormal feature information into a prediction model and a fuzzy logic controller of a long short-term memory network, outputting a prediction result of a future infusion speed in an infusion process, and dynamically adjusting an alarm threshold according to the prediction result; executing graded early warning based on the prediction result and the dynamic adjustment alarm threshold value to obtain a graded early warning result of infusion speed alarm; uploading the grading early warning result to a centralized monitoring end of a nurse station through wireless communication to obtain a nursing response result; and performing iterative optimization on the prediction model and the fuzzy logic controller based on reinforcement learning. The accuracy of infusion speed alarm can be improved.

The invention relates to the technical field of electric vehicle charging control, and discloses a charging pile power regulation and control method and device, electronic equipment and a storage medium. The method comprises the following steps of: monitoring multi-dimensional time sequence data such as charging voltage, current and battery temperature in real time, performing state segmentation, and calculating the similarity between a data sequence of each stage and a standard charging template so as to accurately screen out a stable charging state stage. And extracting a power adjustment amount based on the time sequence characteristic and the fluctuation mode of the stage, fusing the adjustment amount of the environment temperature, the equipment load parameter and the charging power, and dynamically generating a power switching rule by using a fuzzy logic controller. And finally, smooth switching and cyclic adjustment of the power levels of the charging piles are realized according to the rule. According to the method, the accuracy and the anti-interference capability of charging state identification are improved, and the adaptability and the collaborative optimization capability of power regulation to a complex environment and a power grid condition are enhanced, so that the charging efficiency and the power grid stability are both considered on the premise of ensuring the safety of the battery.

The invention belongs to the technical field of industrial measurement and control, and discloses a multi-case cascade vibration noise data acquisition clock synchronization method. A clock drift prediction model based on deep learning is constructed by acquiring local clock signals and environment parameters of all cases in real time, and accurate prediction of clock behaviors is achieved. A multi-mode communication strategy combining optical fibers and wireless radio frequency is innovatively adopted, reliable transmission mechanisms which are redundant to each other are formed, and the anti-interference capability is remarkably enhanced. A dynamic compensation mechanism based on the fuzzy logic controller can intelligently adjust compensation parameters according to clock states and environment changes, and accurate correction is achieved. A multi-level verification mechanism including local self-inspection, mutual inspection between cases and global optimization is established, a graph neural network technology is introduced to carry out system-level optimization, and the system has self-learning and self-optimization capabilities. According to the method, the time consistency of the vibration noise data is remarkably improved.

The invention discloses a tippler dynamic unhooking robot control method based on visual identification, and belongs to the field of industrial robots, and the method comprises the steps: deploying a visual camera array in a tippler operation area, collecting tippler-carriage image data, and carrying out the dynamic target identification of the tippler-carriage image data; constructing a compartment claw instance segmentation network to perform segmentation positioning on the dynamic instance target set; acquiring claw relative motion state data to predict claw motion, compensating a claw motion deviation prediction parameter to a vehicle claw connection position, and determining an unhooking operation position parameter; and performing smooth unhooking control on an execution mechanism of the dynamic unhooking robot of the car dumper through the fuzzy logic controller based on the unhooking operation position parameters. The technical problems that in the prior art, unhooking of a car dumper depends on manual assistance or a fixed program, the adaptability to dynamic conditions such as claw vibration and image interference is poor, and unhooking stability and accuracy are caused are solved.

The invention relates to the technical field of vehicle safety, and discloses an integrated motorcycle auxiliary driving system and method. The method comprises the following steps: synchronously acquiring vehicle kinematics parameters and environment three-dimensional point cloud data through a multi-source sensing system of a motorcycle; executing a dynamic risk mapping operation by using the collected data to generate a risk probability distribution diagram; acquiring a color image and a depth image of a road scene through a stereoscopic vision camera, inputting the color image and the depth image into the multi-scale feature extraction network for analysis in combination with the risk probability distribution map, and outputting a comprehensive risk score; performing obstacle trajectory prediction according to the comprehensive risk score to obtain an obstacle prediction trajectory, scanning a target area by using an infrared sensor, processing point cloud data by using a point cloud segmentation algorithm based on the prediction trajectory, and extracting actual obstacle attributes; and inputting the comprehensive risk score and the actual obstacle attribute into a fuzzy logic controller for data fusion, and finally generating an integrated motorcycle aided driving control instruction.

The invention belongs to the technical field of wind power plants, and discloses a wind power plant AGC and primary frequency modulation system coordination control method and device, and the method comprises the steps: S1, system initialization and parameter configuration; s2, real-time data monitoring and preprocessing; s3, dynamic priority allocation execution; s4, power prediction and instruction correction; and S5, switching and recovering the control right. Through multi-dimensional innovation, the stability and economical efficiency of grid-connected operation of the wind power plant are remarkably improved; according to the technology, AGC and primary frequency modulation functions are integrated, parallel control is achieved through a hardware isolation channel, the frequency modulation response speed is smaller than or equal to 20 ms, and the AGC instruction correction period is smaller than or equal to 50 ms. The pitch angle subsection control and the virtual inertia control are cooperated, so that the frequency drop is effectively inhibited; a fuzzy logic controller is adopted to dynamically allocate response weights, the LSTM neural network is combined to predict a power value in the next five seconds, and an AGC instruction is optimized through a PID controller to adjust the step length; the particle swarm algorithm optimizes a frequency modulation dead zone threshold value, the genetic algorithm sets PID parameters, and the frequency recovery time is shortened.

The invention discloses a multi-fuel combustion boiler SCR (Selective Catalytic Reduction) ammonia injection fuzzy PID (Proportion Integration Differentiation) parallel superposition mixing control system and method, and belongs to the field of boiler flue gas pollutant control. The system adopts a hybrid control architecture in which a fixed parameter PID controller and a fuzzy logic controller (FLC) work in parallel. The system calculates the deviation and the change rate of the NOx concentration of an outlet in real time, on one hand, the fixed parameter PID controller outputs the reference ammonia spraying amount, and accurate control under the stable working condition is achieved; on the other hand, the fuzzy logic controller outputs an intelligent compensation control quantity according to the deviation and the change rate, and the intelligent compensation control quantity is specially used for coping with severe working condition changes. And finally, the two outputs are superposed as a total ammonia spraying instruction. According to the method, the adaptive capacity and the control robustness to multi-fuel complex working conditions are improved. The fuzzy controller can perform coarse adjustment quickly when the working condition changes drastically, and the deviation is restrained quickly; and the fixed PID is responsible for fine adjustment after the system is stable, so that ammonia escape is effectively reduced, and the denitration efficiency is ensured.

The invention provides an off-grid optical storage inverter system cooperative control method and system based on a multi-mode intelligent algorithm, and relates to the field of optical storage inversion, and the method comprises the steps: obtaining a hardware configuration demand, collecting multi-source data in real time, and carrying out the precise tracking of a phase through a phase locking algorithm, and obtaining a standardized control signal; photovoltaic voltage and current data, a battery state and illumination abrupt change environment parameters are obtained, photovoltaic tracking is carried out, battery management is carried out in combination with a fuzzy logic controller, and a cooperative control instruction is output; historical load data and a real-time state flag are obtained, a load demand prediction curve is generated through cloud model processing, a working mode program is driven to make a decision, and a seamless switching instruction is output; real-time monitoring data is processed through multi-algorithm fusion, and a closed-loop protection instruction is obtained in combination with convolutional neural network fault diagnosis and voice feedback. The method is used for solving the problems of low energy conversion efficiency, poor dynamic response speed and poor all-working-condition adaptability of off-grid optical storage.

The invention discloses a source, network, load and storage distributed cooperative control method, and relates to the technical field of power grid regulation and control, and the method comprises the steps: collecting the real-time operation data of a source, a network, a load and storage, and carrying out the data preprocessing, and obtaining the standardized multi-source data; carrying out system state modeling based on the standardized multi-source data, constructing a distributed cooperative control model, adjusting a data source weight coefficient through a fuzzy logic controller, carrying out cooperative control strategy prediction, and outputting a cooperative control strategy prediction result; the cooperative control strategy prediction result is converted into a control instruction, the control instruction is distributed to each distributed unit, a distributed optimization algorithm is adopted to adjust the data source weight coefficient again, an optimization control instruction is generated, and each distributed unit executes the optimization control instruction. According to the method, the reliability and the integration of state recognition can be improved, and invalid scheduling triggered by misjudgment is reduced; global coordination of source network load storage multiple subjects is realized, and the stability of a power system is enhanced.

The invention relates to the technical field of digital data processing, in particular to a water gate remote control system based on the internet of things, which comprises a basic parameter initialization module, an initial discrete action sequence construction module, a fluid pressure gradient calculation module, a real-time pressure gradient numerical value calculation module and a gate action self-adaptive shaping module. A fuzzy logic controller is used for calculating an action correction coefficient, an initial discrete action sequence is adjusted to generate a shaped drive control instruction, and a discrete stepping drive execution module is used for sending a control signal and generating a brake locking instruction when the positioning deviation reaches the standard. According to the invention, real-time fluid pressure is collected and gradient is calculated, a fuzzy logic controller is used to calculate a correction coefficient according to pressure gradient deviation, an initial discrete action sequence is subjected to adaptive shaping, and a driving strategy is adjusted during pressure fluctuation so as to smooth mechanical stress, ensure opening and closing stability and realize high-precision position locking. And a water hammer effect and mechanical damage are avoided.

A control method for a turbine speed regulation optimization system based on hybrid intelligent feedforward compensation belongs to the field of turbine speed regulation system control technology. This method simultaneously triggers two feedforward channels—data-driven and knowledge-driven—when a change in head or load is detected: the data-driven channel outputs a first opening compensation amount using a neural network model trained on historical operating data, while the knowledge-driven channel outputs a second opening compensation amount using a fuzzy logic controller built based on the turbine's physical characteristics and expert experience. The severity index of the operating condition change is calculated based on the changes in head and load, and the two compensation amounts are adaptively weighted and fused to generate the final feedforward opening compensation amount. This compensation amount is then superimposed on the PID controller output command to form the main guide vane opening control signal. This invention overcomes the lag of traditional feedback control through a hybrid feedforward mechanism, improving the dynamic response speed and regulation stability of the unit under conditions of large head fluctuations and frequent load changes.

The invention discloses an improved DWA local path planning method based on a guide field and self-adaption. Relates to the technical field of autonomous mobile platform path planning, in particular to an improved DWA local path planning method based on a guide field and self-adaption. A global path guide field is constructed, a multi-dimensional evaluation function is improved, constraints are added into a DWA model, and a fuzzy logic controller is fused to adaptively adjust the weight of the evaluation function. The method comprises the following steps: acquiring a global path, and constructing a global path guide field; constructing a multi-dimensional evaluation function; constructing and improving a DWA model, generating a candidate trajectory set, generating a weight coefficient of each evaluation item of each candidate trajectory, and obtaining a score of each candidate trajectory; judging whether the current position of the autonomous mobile platform is in a circular area which takes the target point as the center and has the radius of 0.5 m or not; if yes, local path planning is stopped, and a smooth path is obtained; and judging whether collision occurs on the smooth path or not.

The invention discloses a mobile robot control method and system based on multi-source fusion dynamic trajectory re-planning. The method comprises the steps that vehicle kinematics data and environment characteristic observation data are acquired in real time; state prediction is carried out based on a kinematic model; performing multi-dimensional feature extraction and confidence evaluation on the environment features; carrying out measurement updating by utilizing extended Kalman filtering; performing time domain differential analysis on the posterior state; when the state jump is detected, generating a smooth dynamic transition track by taking the current posterior state as a starting point; acquiring predicted curvature information of the front path from the global map; inputting the confidence score and the predictive curvature into a fuzzy logic controller, and calculating the adjustment amount of PID control gain; and calculating a motor control instruction based on the dynamic transition trajectory by using an adaptive PID algorithm, and outputting a final control signal. According to the method, high-precision positioning and stable control can be realized only by depending on a low-cost vehicle-mounted sensor, the cost is low, the universality is high, and the method is suitable for various complex indoor scenes.

The invention relates to the technical field of underground structure displacement measurement, and discloses a structure displacement monitoring method and system based on an intelligent sensor. The method comprises the following steps: acquiring an initial inclination angle signal, an acceleration signal and a gyroscope static signal; generating a zero initial depth reference point; constructing a depth time sequence; the fuzzy logic controller FLC executes real-time compensation adjustment; constructing an equal-interval sampling point sequence; and calculating accumulative displacement based on a weight curvature corrected dual-channel displacement fusion mechanism. The technical problem that in the prior art, a displacement estimation method depending on tilt angle sensor integration or acceleration sensor accumulation is difficult to realize high-precision structural displacement measurement especially in a soft soil layer scene with multi-source signal drift and probe speed fluctuation is solved. Due to the fact that a speed increment adjusting mechanism driven by the fuzzy logic controller is introduced, dynamic deviation correction and depth displacement decoupling modeling of multi-sensor data are achieved, and the spatial analysis precision of a displacement monitoring result is improved.

The invention discloses an unmanned aerial vehicle engine load characteristic test method based on fuzzy control. The method comprises the following steps: S1, generating an engine state quantity data stream; s2, inputting the engine state quantity data flow into an improved Type-2 fuzzy logic controller, generating a control instruction and driving a loading execution mechanism; s3, establishing a limit load test variable space; s4, performing online optimization in the limit load test variable space by adopting a Jaya algorithm, and outputting a current iteration optimal limit load test sequence; s5, forming an optimization control coupling closed loop; s6, engine response parameters and a preset safety threshold value are monitored in the execution process of the limit load test sequence, and when any response parameter approaches the safety threshold value, a safety concession strategy is triggered; and S7, generating a limit load dynamic response evaluation result. The effectiveness of the engine safety margin and limit response database is improved.

The invention discloses an energy storage system virtual impedance control parameter optimization method, which combines a fuzzy logic controller and a genetic algorithm to carry out control parameter optimization, takes power grid operation data as the input of the fuzzy logic controller, and utilizes the fuzzy logic controller to calculate the fuzzy adjustment amount of PID control parameters; constructing a fitness function by taking virtual impedance adjustment comprehensive error minimization as an optimization target, generating an optimization rule base by utilizing a genetic algorithm, and extracting association rules of PID control parameters according to the optimization rule base to obtain a parameter relational expression; the PID control parameters are updated by using the parameter relational expression and the fuzzy adjustment amount, the updated PID control parameters are used as the basis for adjusting the virtual impedance, the PID control parameters are adjusted on line in real time by using the fuzzy logic controller according to the state of the power grid, and meanwhile, the rule base and the membership function of the fuzzy logic controller are fuzzy offline by using the genetic algorithm. Therefore, a parameter relational expression is established, and oscillation or overshoot caused by single parameter adjustment is avoided.

The invention relates to the technical field of equipment control, and provides a thermal control method and device for a vehicle air conditioner, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring a reference temperature set by a user and an actual temperature in a compartment; according to the reference temperature and the actual temperature, a temperature difference value is calculated, and before the actual temperature reaches the reference temperature, the temperature variation of the temperature difference value in each sampling period is calculated; the temperature difference value and the temperature variable quantity are input into a preset fuzzy logic controller, dynamic reasoning is conducted by calling a plurality of pre-stored fuzzy control rules, and the target rotating speed of the compressor is obtained; and controlling the compressor to operate according to the target rotating speed to adjust the temperature in the compartment.

The invention relates to the technical field of data processing, and discloses a control method and system for energy conservation of a motor, and the method comprises the steps: firstly calculating effective values of current and voltage, and carrying out the load state monitoring and feature extraction of the motor; power factor calculation is carried out, specifically, active power, apparent power and power factors are calculated, and load rate calculation is carried out; designing a fuzzy logic controller, and performing input / output variable fuzzification; efficiency evaluation and feedback optimization are carried out, specifically, real-time input and output power calculation is carried out; and carrying out algorithm reinforcement learning optimization. The energy-saving effect is remarkable, light load / no load optimization is achieved, by dynamically adjusting the voltage and the frequency, the motor operates in a more efficient low-voltage / low-frequency state when the motor is in the light load state, and iron loss and copper loss are reduced.

The invention discloses a ship cabin healthy environment dynamic regulation and control method and system based on multi-modal data fusion and deep learning. The method comprises the steps that multi-modal environment data and user physiological data (such as heart rate, body movement and skin conductance) are collected through a multi-sensor network; preprocessing the data; extracting space and time features by using a hybrid model of a convolutional neural network and a long-short-term memory network, and generating a fusion feature vector; generating a user health state score based on a deep reinforcement learning framework; environment parameter adjustment amounts (such as temperature, humidity, air purification intensity and illumination adjustment amount) are calculated through a fuzzy logic controller; and realizing closed-loop regulation and control by controlling the environment equipment through the actuator network. The problems of inaccurate health risk assessment and regulation response lag caused by single data and simple processing of an existing environment regulation system are solved, real-time, accurate and adaptive dynamic regulation is realized, and the safety and comfort of the cabin health environment are improved.

This application relates to the field of artificial intelligence and discloses a spatiotemporal graph convolutional method for order demand prediction that integrates functional semantic similarity. The method includes: collecting multi-source heterogeneous data streams containing orders, delivery personnel poses, road conditions, and the environment; constructing a unified grid feature vector representation through spatiotemporal gridding processing; performing order demand prediction using a spatiotemporal graph convolutional network that integrates spatial adjacency edges and functional semantic similarity edges; capturing spatiotemporal correlation features and generating prediction results through a multi-head attention mechanism and gated recurrent units; encoding the prediction results as state vectors and inputting them into a reinforcement learning policy network to output optimal allocation instructions; triggering a pre-scheduling mechanism based on the prediction results to guide idle delivery personnel to move to high-demand grids before orders are generated; performing distributed computing using an edge-cloud collaborative architecture; and dynamically adjusting and optimizing weights and performing anomaly detection and emergency recovery through a fuzzy logic controller.

This application relates to the field of multi-terminal flexible DC transmission system equipment, specifically to a method and system for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system. The method includes: real-time monitoring and acquisition of local electrical quantities; inputting the electrical quantities as input variables to a main fuzzy logic controller; outputting an initial adjustment amount for the droop coefficient; inputting the absolute values ​​of the real-time power margin deviation and DC voltage deviation to the corresponding auxiliary fuzzy logic controllers to obtain a power margin weighting factor and a DC voltage weighting factor, which are then used to weight and correct the initial adjustment amount to obtain the droop coefficient adjustment amount; adding the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and performing amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient; applying the method to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics, thus forming a hierarchical intelligent decision-making system.

The invention relates to the technical field of wireless communication secure transmission, and provides a dynamic secure transmission strategy optimization method and device based on RIS-UAV. According to the method, the gain change caused by the directivity of the RIS antenna is dynamically compensated by introducing the three-dimensional attitude angle of the RIS, and accurate control on a wireless channel is realized, so that the leakage of privacy signals to an eavesdropper is inhibited while the shielding obstacle is overcome. A constrained optimization problem is converted into an unconstrained double-layer optimization problem through a Lagrange duality method, so that the learning complexity of the intelligent agent is reduced; a post-decision state mechanism is introduced into problem solving, so that the intelligent agent can quickly learn and adapt to the change of user channel state information in the dynamic wireless communication system; the fuzzy logic controller can increase exploration when the environment uncertainty is high and enhance the utilization when the strategy is stable, so that more intelligent and efficient exploration-utilization balance is realized, the convergence speed is remarkably accelerated, and the final performance is improved.