224 results about "Fuzzy logic control" 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 relates to the technical field of abalone peptide production, and discloses an abalone peptide production process optimization and intelligent decision-making method and system, and the method comprises the steps: collecting production data through a multi-source sensor, and extracting features through a convolutional neural network; the genetic algorithm optimization model is combined with an adaptive mechanism and a multi-target fitness function to generate a process parameter optimization scheme; and the multi-objective production optimization model outputs optimal production process parameters by means of an improved particle swarm algorithm. In the hierarchical intelligent decision-making model, the planning layer realizes global resource allocation, the scheduling layer adjusts a process in real time based on a Bayesian network and the like, and the operation layer controls equipment through fuzzy logic and constructs quality closed-loop control. According to the method, the problems that parameter control depends on experience, data processing is difficult, optimization is one-sided, decision making is not intelligent and the like in traditional production are solved, data can be accurately processed, the process is optimized, decision making is intelligent, production efficiency, the raw material utilization rate and product quality are effectively improved, cost is reduced, and the application prospect is wide.

The invention relates to the technical field of photography focusing control, in particular to a photography focusing control method based on machine vision. Comprising the following steps: collecting and preprocessing multi-frame image data; detecting a target area in the image through a machine vision technology and a deep learning algorithm, marking a main body object and dynamically adjusting a focusing priority; extracting multi-dimensional visual features by using a sparse coding algorithm, and constructing a dynamic focusing optimization model based on the features to calculate an optimal focusing parameter; the imaging definition and the focusing effect are evaluated in real time through a fuzzy logic control algorithm, and focusing parameters are adjusted according to feedback; and the photographic equipment is controlled to generate a high-resolution focused image or video data, so that the optimal imaging effect is ensured. According to the method, artificial intelligence and an optimization algorithm are utilized, the intelligent level of the photographic equipment is improved, the focusing capability and the imaging quality of the photographic equipment in a complex scene are improved, convenient and efficient photographing experience is provided for a user, and meanwhile dependence on manual operation is reduced.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention discloses a network fault adaptive detection system based on machine learning, which relates to the technical field of network security and comprises a data acquisition module, a feature extraction and selection module, a model training and updating module, a fault detection module, a fault positioning and diagnosis module and an adaptive optimization module. According to the network fault adaptive detection system based on machine learning, by generating new features, the expression ability of a feature space is significantly enhanced, and the detection precision of a machine learning model is improved; the deep learning model based on the causal relationship is combined with the graph neural network and time sequence analysis, so that the complex causal relationship in the network is effectively processed, and the fault diagnosis is more accurate; through the combination of the multi-objective evolutionary algorithm and the fuzzy logic control algorithm, the network optimization strategy is more flexible and efficient, the network performance is improved, the weight of the optimization objective can be dynamically adjusted according to the actual demand, and the performance of the system in a complex network environment is remarkably improved.

The invention discloses a dredge pump control system and method for river regulation, and relates to the technical field of dredge pump control. The dredge pump control system comprises a sensing module, a centralized control module, an energy-saving execution module and a control parameter recording module; the sensing module is arranged on a multi-element sensor at a key part of a dredge pump for acquiring data, and the data is pre-processed by a microprocessor and then transmitted to the centralized control module at a high speed by a wireless transmission module, so that an accurate basis is provided for system control; according to the invention, by integrating multiple sensors and an advanced control algorithm, the dredging efficiency and accuracy of the dredge pump of the dredger are obviously improved; the sensing module can monitor key parameters of the dredge pump in real time, such as suction pressure, discharge flow, pump shaft temperature and sediment concentration, and it is ensured that the running state of the dredge pump is comprehensively known; and the centralized control module dynamically optimizes a control sequence by using a model predictive control and fuzzy logic control algorithm so as to adapt to variable working conditions.

The invention relates to the technical field of process control, in particular to an intelligent environment monitoring and adjusting system for lip fish culture, which comprises an environment parameter monitoring module, a multivariable linkage calculation module, a self-adaptive adjusting module, a fuzzy logic control module and an abnormity emergency response module. According to the method, the trend value is formed through time-phased grouping analysis and relevance calculation of the water quality parameters, dynamic prediction of the breeding environment change is achieved, the cooperative adjustment capacity among the dissolved oxygen, the water temperature and the ammonia nitrogen concentration is optimized through analysis of the multi-parameter linkage relation, the accuracy of environment control is improved, and the method is suitable for large-scale popularization and application. The cooperation efficiency of oxygen pump frequency and temperature control equipment response is improved through real-time matching calculation of equipment operation parameters, the rapid adaptive capacity to water quality changes is enhanced, abnormal fluctuation monitoring is combined with equipment linkage adjustment, rapid response during water quality sudden change is ensured, and the adverse effect of environment fluctuation on fish growth is reduced.

The invention relates to the technical field of steel production and processing, and discloses an electrical steel strip annealing temperature real-time monitoring and regulation system. The system comprises a temperature data acquisition module which is responsible for acquiring temperature and cooling rate data in an annealing process and acquiring expected parameters; the temperature deviation analysis module is used for calculating a deviation coefficient through a dynamic weighting algorithm to construct a matrix; the dynamic regulation and control module is used for generating regulation and control parameters based on a fuzzy logic control algorithm; the abnormity grading module is used for grading annealing abnormity; and the real-time feedback module transmits the regulation and control parameters and the abnormal identifier to the control end. The system also has the functions of adjusting a rule base according to the characteristics of the steel strip, backtracking historical data, optimizing regulation and control, adaptively updating an abnormal threshold value and the like. Through cooperative work of all the modules, real-time monitoring and accurate regulation and control of the annealing temperature are achieved, the annealing quality and production efficiency of the electrical steel strip are improved, and the cost is reduced.

The invention relates to the technical field of dust collector control, and discloses a multi-motor cooperative speed regulation and energy efficiency optimization control method and device for a dust collector, and the method comprises the steps: monitoring the rotating speed, current, voltage, temperature and other operation parameters of a plurality of motors in real time, and determining the working load state of each motor through a load recognition model. Different cooperative speed regulation strategies are matched according to load states, such as PID control for low loads, fuzzy logic control for medium loads and model prediction control for high loads. A dynamic weight distribution algorithm is used for adjusting the target rotating speed, and an energy efficiency optimization algorithm is used for optimizing power distribution. Meanwhile, environmental parameters are considered, and control parameters are optimized through a self-adaptive parameter adjustment model. And the system also has the functions of adjusting strategies according to load fluctuation and power consumption, motor priority ranking during high load, energy efficiency algorithm parameter optimization, rotating speed adjustment during filter screen blockage, motor state monitoring and prediction and the like, so that accurate speed regulation and efficient energy conservation are realized, and the performance and the intelligent level of the dust collector are improved.

The invention relates to the technical field of nuclear power plant equipment inspection and data transmission management, and provides a nuclear power plant equipment inspection data transmission management method and system. According to an existing nuclear power plant equipment inspection system, reliable transmission and intelligent processing of data are difficult to achieve in complex high-radiation, high-humidity and strong-electromagnetic-interference environments, the data are prone to interference in the inspection process, and the fault detection precision is insufficient. The device state information is dynamically collected in real time through the multi-modal data collection technology, the sampling frequency is adjusted according to the environment change through the fuzzy logic control algorithm, and the timeliness and accuracy of data are improved. After data preprocessing, high-dimensional fusion and feature extraction of multi-modal data are realized through a Bayesian network and a deep neural network (DNN). The system realizes intelligent inspection and data transmission management of nuclear power plant equipment in a high-interference environment, has high reliability and real-time adaptability, and can be widely applied to an intelligent safety inspection system of a nuclear power plant.

The invention relates to the technical field of digital audio processing, in particular to a digital audio processing method and system for sound equipment. The system comprises an audio signal acquisition module, a signal analysis module, an adaptive adjustment module, a howling suppression module and a sound effect optimization feedback module. The audio signal acquisition module acquires data such as frequency spectrum and amplitude of the sound equipment in real time. And the signal analysis module generates a signal analysis report based on a non-negative matrix factorization algorithm and a generative adversarial network algorithm. And the adaptive adjustment module automatically adjusts audio parameters through a fuzzy logic control algorithm. The howling suppression module suppresses howling noise by using an adaptive feedback cancellation algorithm. And the sound effect optimization feedback module adopts a Bezier curve to enhance the sound effect and realize frequency, phase and space positioning optimization. Through cooperative processing of audio acquisition, accurate analysis, adaptive adjustment, howling suppression and spatial sound effect optimization, the audio quality and stability are improved, and the audio spatial effect and user experience are enhanced.

The invention relates to the technical field of molded case circuit breaker control, and discloses an intelligent molded case circuit breaker control method and system based on big data, and the method comprises the steps: collecting the operation data of a circuit breaker and the related data of a circuit system in real time through a plurality of sensors disposed on the molded case circuit breaker, and obtaining multi-source heterogeneous data; constructing a deep generative adversarial network to carry out abnormal data detection on the collected multi-source heterogeneous data, and processing to obtain preprocessed data; constructing a risk assessment model by adopting a CNN-LSTM-Transform neural network, inputting the preprocessed data into the risk assessment model, and outputting a risk assessment result; according to a risk assessment result, combining an operation environment and a load condition of a current circuit, and based on an adaptive threshold decision algorithm and fuzzy logic control, dynamically adjusting an action threshold of the circuit breaker, and formulating a corresponding control strategy; according to the invention, the control intelligence level and reliability of the circuit breaker are improved.

The invention relates to the technical field of air separation plant management, and discloses an air separation plant energy efficiency intelligent management system based on data analysis, which comprises a data acquisition module, an energy efficiency analysis module, an optimization strategy generation module, a real-time adjustment module and a dynamic feedback module. The data acquisition module acquires and stores data in real time in a multi-source heterogeneous manner. The energy efficiency analysis module uses a hybrid convolutional neural network and an adaptive wavelet packet decomposition algorithm to extract features. And the optimization strategy generation module formulates a strategy in combination with deep reinforcement learning and a multi-target particle swarm optimization algorithm. And the real-time adjustment module dynamically adjusts equipment parameters based on fuzzy logic control. And the dynamic feedback module forms closed-loop optimization. The system solves the problem of energy efficiency management of traditional air separation equipment, and can accurately collect and process data, deeply analyze energy efficiency, intelligently generate optimization strategies, accurately adjust parameters in real time and continuously optimize the parameters, improve the energy efficiency of the air separation equipment, reduce energy consumption, enhance the operation stability and reliability of the equipment and assist industrial sustainable development.

The invention discloses a packaging tin material proportioning optimization method based on a particle swarm optimization algorithm, and the method comprises the following steps: initializing a particle swarm, dynamically adjusting the positions and speeds of particles through a fuzzy logic control system, and generating an initial material proportioning scheme; a fitness function is constructed based on a non-Euclidean geometric method, multiple indexes such as strength, toughness, corrosion resistance and cost are comprehensively considered, and fitness values of all the schemes are calculated; gradually optimizing a material proportioning scheme by using a biologically inspired particle swarm optimization algorithm; a dynamic clustering mechanism is introduced, and different material matching schemes are analyzed and optimized; dynamically switching between global search and local search through a hybrid search strategy, and further optimizing a selected scheme; and finally, outputting an optimized optimal material proportioning scheme, and evaluating the reliability of the optimal material proportioning scheme through experimental verification and simulation. According to the invention, the optimization efficiency and performance of the packaging tin material proportioning are improved, and the method is suitable for the requirements of production automation and high efficiency.

The invention relates to the technical field of multi-dimensional force sensing measurement, and discloses an anti-interference multi-dimensional force sensing measurement method and system, and the method comprises the steps: optimizing the layout of a heterogeneous sensor array based on a stress field, generating an anti-interference signal through cross-band wavelet packet decomposition and dynamic weight fusion, separating a multi-dimensional force component in combination with a hybrid decoupling algorithm, and carrying out the multi-dimensional force sensing measurement. The weight and the sampling rate of the sensor are dynamically adjusted by adopting fuzzy logic control, and a high-reliability measurement result is finally output through rigid mechanical verification and GAN reconstruction iterative correction; the system comprises a heterogeneous sensor array module, a signal preprocessing module, a dynamic decoupling module, an environment self-adaption module, a redundancy verification module and a data integration module. Through cross-band fusion, a hybrid decoupling algorithm and environment adaptive closed-loop feedback, GAN reconstruction and physical verification are combined, the anti-interference capability and the decoupling precision are improved, and the reliability of abnormal signals and the multi-scene adaptability of the system are ensured.

The invention relates to the technical field of closed-loop control, in particular to an irrigation area water gate automatic adjusting system based on fuzzy logic control, which comprises a load monitoring module, a gate parameter optimization module, an environment response adjusting module, a water level threshold setting module, a flow prediction module and a gate feedback control module. According to the method, accurate calculation of the inter-regional load difference is performed by integrating the water resource flow and pressure data, the irrigation efficiency and the response speed are remarkably improved, water requirements can be accurately matched through refined regional load analysis, water resource waste is avoided, and the adaptability of the system to environmental changes is enhanced; environment variables are monitored in real time, gate operation is adjusted, the optimal water level is maintained under the constantly changing condition, agricultural requirements and sustainable utilization of water resources are ensured, predictive analysis of flow data enhances preventive regulation and control of the system, the system can respond to future requirement changes in advance, resource allocation is optimized, and the system is suitable for large-scale popularization and application. The water resource utilization rate and the automation level of irrigation area management are effectively improved.

The invention provides a special lithium ion battery system for a UPS (Uninterrupted Power Supply) based on intelligent management, and relates to the technical field of UPSs, the system comprises a battery state monitoring and predicting module, an intelligent charging management module and a fault diagnosis and fault-tolerant control module of a battery system, and the SOH and SOC of a battery are accurately predicted through a support vector machine, a random forest and a particle filter algorithm. A basis is provided for reasonable charging and discharging, the service life of the battery is prolonged, the intelligent charging management module dynamically adjusts charging parameters according to the SOC and the temperature of the battery through a fuzzy logic control algorithm, balance of rapid charging and battery protection is achieved, the intelligent charging management module adapts to different environments, and the fault diagnosis and fault-tolerant control module detects faults in time with the help of a support vector machine algorithm. A model prediction control algorithm optimizes control input when a fault occurs, it is ensured that the UPS system still operates normally under the battery fault, a data acquisition and transmission module and a system monitoring and display module monitor the battery state and provide historical data query analysis, and a user can conveniently master the battery condition and make a decision.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

The invention relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

The invention discloses a machine room energy-saving operation and maintenance management method and system, and particularly relates to the technical field of machine room management. Computing resources are integrated in combination with a virtualization technology, virtual machine allocation is dynamically adjusted, migration flow is optimized by adopting an intelligent network bandwidth scheduling mechanism, and migration efficiency is improved; by combining a temperature and humidity control system and equipment operation temperature anomaly detection, the operation states of the air conditioner and the cooling equipment are dynamically optimized, energy consumption is reduced, early warning is performed based on the equipment operation temperature and the network load condition through a fault prediction model, equipment faults are prevented, and fuzzy logic control is adopted; based on the energy consumption efficiency and the fault prediction result, resource configuration is dynamically adjusted, the energy efficiency of the whole machine room is optimized, the utilization rate of computing resources is effectively improved, the influence of network migration on services is reduced, the energy consumption of the machine room is reduced, the equipment stability is improved, the management cost is optimized, and efficient, energy-saving and intelligent machine room operation and maintenance management is achieved.

The invention discloses a micro-grid power control method, system and device based on fuzzy modulation and a storage medium, and relates to the technical field of intelligent grid integration of a photovoltaic system and a micro-grid, and the method comprises the steps: firstly obtaining the real-time power data of a photovoltaic system, carrying out the control processing through fuzzy logic, and calculating a reference instruction; according to the reference instruction, hysteresis time is dynamically adjusted through modulation hysteresis control, and an inverter switching signal is generated; converting direct current into alternating current based on the switching signal and a power switching device, injecting the alternating current into the micro-grid, calculating active and reactive power reference values, ensuring that the system operates according to a unit power factor, and giving active power to the reference value; according to the method, the power control precision and adaptability of the micro-grid are improved, excessive switching of the inverter is avoided, loss is reduced, the service life is prolonged, power matching and stable transmission are achieved, reactive loss is reduced, bus voltage is stabilized, harmonic waves can be reduced, the electric energy quality is improved, and efficient and stable operation of the micro-grid is guaranteed.

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 provides an improved ESKF-based encoder and IMU fusion indoor positioning method, and aims to improve the positioning precision and control stability of an automatic guided vehicle (AGV) or other mobile robots in a dynamic environment. According to the method, the adaptive characteristic of fuzzy logic and the nonlinear state estimation capability of the ESKF are combined, and uncertainty compensation and dynamic adjustment of multi-sensor data are realized. In the implementation process, firstly, a motion state of a system is modeled by using an encoder and IMU data, and data alignment is realized through a time synchronization mechanism; and then, a fuzzy logic control system is introduced, and a process noise covariance matrix and an observation noise covariance matrix of the ESKF are dynamically adjusted according to the change of the sensor noise, so that the filtering performance is optimized. In the ESKF prediction step, encoder data is used as main input, and the vehicle state is estimated through a kinematic model; in the updating step, state estimation is further corrected in combination with angular velocity and acceleration information provided by the IMU so as to enhance the adaptivity and robustness of the algorithm. Through experimental verification, the method shows the excellent performance of accurate fusion of the AGV motion state in a complex dynamic environment, and effectively improves the positioning precision and the real-time response capability of the system.

The invention provides an air hospital intelligent logistics distribution method based on deep reinforcement learning, and the method comprises the steps: receiving all target tasks, determining the priorities of the tasks through a hierarchical task decision model, sorting all the tasks according to the priorities, and entering a scheduling stage; constructing a task and aircraft resource matching model to perform single-task and single-aircraft matching between tasks and aircrafts, performing multi-aircraft cooperative scheduling task allocation in combination with the cooperation cost of the aircrafts, and performing optimization adjustment on the priority based on real-time monitoring; generating a flight instruction based on the optimized and adjusted priority; an optimized flight instruction is generated by adopting a fuzzy logic control method, and dynamic path adjustment is performed on the total flight path to ensure that the aircraft always keeps the optimal flight path in the task execution process; feedback of the flight instruction for executing optimization is collected, and dynamic optimization is conducted on the flight task.

The invention discloses a power distribution cabinet remote monitoring system based on wireless communication, and relates to the technical field of remote monitoring, and the system comprises a communication scheduling module which is used for receiving an uploaded data packet, carrying out link evaluation through an uploading link optimization method in combination with a real-time network condition, and carrying out link optimization according to an actual link condition and the priority of the uploaded data; selecting an initial uploading link and uploading data; the Bluetooth self-healing module is used for evaluating the link quality according to the link quality data in a data uploading process, dynamically switching an active link and a standby link according to a link quality evaluation result, and carrying out Bluetooth task transmission; the remote control module is used for carrying out data reduction by adopting a multilayer decryption mechanism and a mixed format analysis mechanism based on the encrypted Bluetooth task data, evaluating the operation state of the power distribution cabinet, generating a control instruction through a fuzzy logic control method according to an operation state evaluation result and executing the control instruction; through a dynamic link switching mechanism of the Bluetooth self-healing module, continuous and stable transmission of monitoring data of the power distribution cabinet is ensured.

The invention discloses an energy storage power pack active equalization system and method based on fuzzy control, and the method comprises the steps: obtaining the current state information of each single battery in an energy storage power pack, and the current state information comprises a state of charge (SOC) and a voltage; based on a fuzzy logic control model, taking the current state information as an input variable, and outputting a corresponding equalizing current signal; and generating a corresponding control instruction based on the output equalization current signal, adjusting an equalization circuit of each single battery in the energy storage power pack, and implementing active equalization operation. The energy difference between the single batteries can be effectively reduced, the overall performance of the battery pack is improved, and the service life is prolonged.

The invention discloses a high-altitude falling simulation system based on VR equipment, and relates to the technical field of virtual reality simulation, and the system comprises a data collection module which collects the pupil distance and initial sight line data of a user; the visual disturbance module is used for dynamically adjusting local visual disturbance of a corresponding area in the high-altitude virtual scene based on the gaze data to form a visual disturbance effect; the psychological index calculation module is used for calculating an anxiety index and a focus drift speed by adopting a time sequence convolutional neural network according to the visual disturbance effect and the eye movement behavior of the user to obtain an anxiety index and a focus drift speed index; and the disturbance adjustment module adaptively adjusts the frequency and amplitude of the visual disturbance effect by adopting a method of combining fuzzy logic control and adaptive gain adjustment to obtain disturbance rhythm parameters. According to the method, the personalized matching ability and training effectiveness in psychological behavior intervention and virtual high-altitude adaptation training are improved.

The invention discloses a control system for a cooling fan, and relates to the technical field of cooling fan control systems. The temperature acquisition module is used for monitoring the temperature of a key heating part in real time by adopting a high-precision temperature sensor and converting an analog signal into a digital signal through a data acquisition card; the data processing module is used for receiving the data from the temperature acquisition module and analyzing the data by using an embedded processor; a self-adaptive filtering algorithm is built in the module so as to reduce the influence of environmental noise on temperature reading; and the core of the control algorithm module is a PID (Proportion Integration Differentiation) controller, and a hybrid control strategy is formed by combining fuzzy logic control. Through accurate temperature monitoring and an intelligent control algorithm, the system can quickly respond to temperature change, automatically adjust the rotating speed of the fan, realize efficient heat dissipation and prolong the service life of equipment; the system adopts a variable-frequency speed regulation technology, the operation of the fan is adjusted according to actual requirements, unnecessary energy consumption is avoided, and the energy-saving effect is achieved.

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system and method based on fuzzy logic control. Comprising a renewable energy power generation unit, an electrolytic cell array, a multi-type energy storage device, a power divider and a fuzzy logic controller, the renewable energy power generation unit is connected with the electrolytic cell array and the power divider through a direct-current bus, and the output end of the power divider is connected with the storage battery pack and the super-capacitor bank. The fuzzy logic controller collects the bus voltage fluctuation ratio, the electrolytic cell temperature gradient and the energy storage charge state in real time to serve as input variables, and outputs a multi-target optimization instruction to the power divider. Wherein the system dynamically adjusts the start-stop combination and the energy storage charge-discharge priority of the electrolytic cell array. According to the scheme, the mindset that fluctuation suppression and life management are separated in the prior art can be broken through, and a new paradigm of multi-parameter cooperative control is established.

The invention discloses a temperature control method for low-temperature high-NOx-concentration flue gas denitration in the nuclear industry, and relates to the technical field of flue gas denitration control. According to the method, flue gas temperature time sequence distribution, NOx concentration gradient and environment radiation intensity data are integrated through a space-time alignment algorithm, a synchronous data matrix representing a system coupling state is generated, then a temperature and efficiency response curved surface is established, a high-efficiency temperature area and an inactivation risk area are dynamically divided through fuzzy logic control, and an optimal temperature regulation and control threshold value is recognized. The drift effect of quantitative radiation on the surface temperature of the catalyst is analyzed through regression, a quantitative relation model of radiation dose and temperature zone deviation is established, and real-time correction of anti-radiation compensation parameters is achieved. And finally, optimizing the multi-objective cost function by adopting a non-dominated sorting genetic algorithm, and obtaining dynamic balance between the maximum NOx conversion rate and the minimum temperature fluctuation. And multi-parameter coupling control of a temperature field and a radiation field is realized, and the denitration efficiency stability under a low-temperature condition is remarkably improved.