109 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 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 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 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 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.

The invention discloses an internal combustion engine in-cylinder transient self-adaptive exposure control method based on FPGA acceleration, and belongs to the technical field of internal combustion engine endoscopic visualization. In order to solve the technical problem of image overexposure or underexposure caused by violent illumination change in the combustion process in an internal combustion engine cylinder, a real-time exposure detection unit is fused with a camera built-in sensor and an endoscope added sensor to monitor light intensity data; the adaptive exposure control unit accelerated by the FPGA adopts adaptive PID control and fuzzy logic control to generate a parameter adjustment direction, the adaptive PID dynamically adjusts gain based on a machine learning model, and the fuzzy logic outputs a decision according to a fuzzy rule base of brightness, light intensity and errors; the parameter adjusting unit adjusts the aperture size, the shutter speed and the light sensitivity in a linkage mode according to the target and actual gray scale difference value, and millisecond-level multi-parameter exposure control is achieved. Overexposure of a highlight area and loss of details of a low-light area are effectively avoided, and stable and clear images are obtained in the whole combustion process.

The invention relates to the technical field of ship navigation safety, in particular to a real-time ship trajectory tracking and early warning method, which comprises the steps of multi-source data acquisition and preprocessing, trajectory prediction model construction, collision risk assessment, early warning triggering and the like. By integrating AIS, radar and meteorological data, accurate trajectory prediction is realized by adopting Kalman filtering and extended Kalman filtering algorithms, and the method adapts to a complex environment based on dynamic weight factors and fuzzy logic control. Meanwhile, a multi-level early warning mechanism and redundant communication design are provided, and timely transmission of early warning information is ensured. The collision risk assessment model is optimized by using the deep neural network, and the early warning precision is improved. The real-time performance, accuracy and dynamic adaptability of ship trajectory tracking and early warning can be improved, and the efficient and safe requirements of modern shipping are met.

The invention relates to the technical field of charging switching control, in particular to a dynamic switching system for cooperative work of wireless charging and wired charging, which performs node modeling and edge weight propagation on multiple parameters such as magnetic field intensity, current fluctuation ratio, contact resistance and temperature threshold through a graph neural network. Structured expression and dynamic updating of a nonlinear coupling relation among parameters are achieved, abnormal feature aggregation and channel state characterization are more refined, fuzzy reasoning and weight adjustment are conducted on multiple variables such as power output, the heat growth rate, the voltage drop rate and the energy efficiency ratio through fuzzy logic control, the stability of a channel priority sequence is enhanced, and the stability of the channel priority sequence is improved. Through dynamic detection of power curve intersection points and transition section time sequence calculation, continuous power distribution is converted into a smooth function interval, instantaneous energy abrupt change during switching is reduced, closed-loop correction of multi-period signals is achieved, and the system keeps stable operation and energy efficiency output consistency under high-dimensional parameter disturbance.

The invention relates to the technical field of integrated development debugging, and discloses a multi-protocol debugging communication module initialization method and system, a storage medium and equipment, and the method comprises the steps: scanning all connected debugging target equipment, carrying out the weighted evaluation of the scanning priority of the calculation equipment, and constructing a protocol capability matrix of each equipment; pre-distributing communication channels by adopting an optimization algorithm according to the protocol capability matrix, monitoring network environment indexes, and controlling a dynamic decision protocol switching opportunity through fuzzy logic; hot plug detection and equipment health degree evaluation are implemented, and when connection abnormity is detected, a multi-stage recovery strategy is triggered step by step to process the abnormity; monitoring an interface calling success rate and response time, analyzing and predicting an interface state based on a time sequence of historical data, and executing a self-adaptive repairing action of the interface; according to the method, the hardware capability characteristics of the audio equipment are detected, initialization parameters are optimized based on equipment capability scores, and meanwhile, a DHCP address allocation strategy is dynamically managed, so that the unified management of adaptive initialization of various communication protocols is realized.

The invention provides a multifunctional low-power-consumption power distribution terminal power supply system which comprises a filtering and rectifying unit. The charging and discharging management unit is integrated with an intelligent charging and discharging control algorithm, performs charging and discharging management on the charging process of the capacitor and the battery, and switches to a battery discharging mode to provide a starting power supply when the voltage of the capacitor is insufficient; wherein the intelligent charging and discharging control algorithm is an algorithm based on the combination of a fuzzy logic control algorithm and a dynamic threshold adjustment strategy; the PWM control unit is used for outputting voltage by adopting a pulse width modulation technology and automatically reducing the power to the lowest level in a non-working state; and the CPU control unit is used for controlling the normal operation of the charging and discharging management unit and the PWM control unit, and monitoring the working states of the three-stage rectification topological structure, the grading protection circuit and the high-precision acquisition circuit in real time through a built-in intelligent monitoring algorithm. Stable, reliable, efficient and low-power-consumption power supply support can be provided for the power distribution terminal, and further development of an intelligent power grid is promoted.

The invention discloses a tidal irrigation water and fertilizer integrated system integrating dynamic regulation and control and a dynamic regulation and control method, the system comprises a tidal irrigation module, a control module, a sensor module, a water inlet module, a pesticide mixing module and a drainage module, the control module is connected with the sensor module, the drainage module, the water inlet module and the pesticide mixing module; the tidal irrigation module comprises a tidal irrigation frame and multiple layers of seedbeds, the multiple layers of seedbeds are sequentially arranged on the tidal irrigation frame in the vertical direction, the control module, the water inlet module, the pesticide mixing module and the water drainage module are arranged on the tidal irrigation frame, and the water inlet module and the water drainage module are connected with the seedbeds respectively. And the control module realizes dynamic regulation and control according to data of the sensor module and a fuzzy logic control algorithm. Irrigation and flushing tasks can be efficiently executed through regulation and cooperation of water and fertilizer, and the survival rate and growth efficiency of plants are remarkably improved.

The invention discloses a prefabricated dish automatic ingredient production system and method, and relates to the technical field of food intelligent manufacturing, and the method comprises the steps: extracting a global semantic feature vector of a denoised high-quality image, and generating process sequence constraint intensity through MLP; by taking the global semantic feature vector as a node and the process sequence constraint strength as an edge weight, constructing a transmembrane state time sequence diagram through a dynamic graph convolutional network, optimizing a graph propagation process through a time smooth constraint loss formula, and generating a time sequence confidence coefficient; and based on the time sequence confidence coefficient, a mechanical arm action regulation and control scheme is obtained, a fuzzy logic control mapping method is adopted for mapping, and a mechanical arm control instruction is generated and executed.

The invention provides an autonomous cleaning control method of a photovoltaic panel intelligent cleaning robot, and relates to the technical field of automatic control. The autonomous cleaning control method of the photovoltaic panel intelligent cleaning robot comprises the following steps of S1, based on a photovoltaic panel surface environment, S2, based on an original sensor data set, S3, based on a smudginess index distribution diagram, S4, based on a cleaning demand prediction model, S5, based on an optimized cleaning path plan, S6, based on cleaning execution state feedback, and S7, based on an adjusted cleaning parameter set. Surface images and temperature data of the photovoltaic panel are collected through visual and infrared sensors, noise filtering and standardization processing are carried out, data quality and consistency are improved, reliable input is provided for follow-up analysis, a multi-sensor fusion technology is combined with fuzzy logic control to process environmental uncertainty, unified smudginess evaluation indexes are generated, detection accuracy is enhanced, and the method is suitable for large-scale popularization and application. And misjudgment caused by limitation of a single sensor in a traditional method is avoided.

The invention relates to the technical field of low-grade gold ore recovery, and discloses a photoelectric intelligent sorting-based low-grade gold ore comprehensive recovery method, which comprises the following steps of: firstly, collecting physical characteristic data such as ore particle size distribution, color characteristics and density, calculating a sorting path and parameters, and evaluating a processing demand to generate a target sorting strategy; combining historical sorting data and ore grade fluctuation data, making and optimizing a preliminary sorting plan through clustering analysis, a random forest algorithm and the like, and generating a personalized sorting scheme; according to the final implementation scheme, the ore recovery rate index is monitored in real time through a sensor, the light intensity threshold value and the separation speed are dynamically adjusted through a long-short-term memory network, a fuzzy logic control system and the like, and a comprehensive recovery scheme is generated. According to the method, by means of multiple intelligent algorithms and a real-time feedback mechanism, the sorting precision and the ore recovery rate are improved, and efficient comprehensive recovery of the low-grade gold ore is achieved.

The invention relates to the technical field of hydrogen energy scheduling, in particular to a hydrogen energy full-link scheduling system and method based on big data. The system comprises a hydrogen energy link data acquisition module, a hydrogen energy supply and demand prediction module, a hydrogen production storage and transportation matching module, a hydrogen energy link scheduling optimization module and a safety feedback module. Multi-dimensional operation data covering hydrogen production to terminal utilization is collected through a distributed sensor network, a supply and demand trend is predicted by adopting a stochastic differential equation and a neural random process, and a reinforcement learning optimization model is constructed in combination with link constraints to generate an initial scheduling scheme; and constructing a graph structure path planning model by using an artificial potential field algorithm to optimize full-link scheduling, and finally realizing safety evaluation and closed-loop feedback regulation and control through fuzzy logic control. According to the invention, the digital, intelligent and efficient development of the operation of each link of the hydrogen energy industry chain is promoted, and the full-link operation efficiency and intelligent level of hydrogen energy are comprehensively improved.

The invention relates to the technical field of numerically-controlled machine tool monitoring, in particular to a numerically-controlled machine tool monitoring method and system, terminal equipment and a storage medium, and the numerically-controlled machine tool monitoring method comprises the following steps: based on key parts of a numerically-controlled machine tool, monitoring tiny wear or deformation by adopting a time domain reflection method and a frequency domain reflection method; and the collected ultrasonic signals are analyzed in combination with a convolutional neural network, phase changes and abnormal features in the signals are identified, data fusion processing is carried out, and wear identification indexes are generated. According to the method, the advanced monitoring and analysis technology is adopted, comprehensive optimization of the performance of the numerical control machine tool is achieved, the time domain reflection method and the frequency domain reflection method are combined with convolutional neural network analysis, the detection precision of tiny abrasion or deformation is improved, dynamic optimization is achieved on clamping force adjustment through fuzzy logic control, the method adapts to different machining conditions, and the machining precision of the numerical control machine tool is improved. Workpiece damage is reduced, the machining quality is improved, and the machining precision is improved through deep diagnosis and dynamic compensation strategies of multi-axis synchronous errors.

The invention relates to the technical field of flow metering, in particular to a temperature return control method of an intelligent control valve. The method comprises the following steps: receiving a temperature return control strategy sent by a user and a set value of specified temperature return, and judging an initial control parameter set according to the temperature return control strategy; real-time operation data are collected from the temperature sensing module and the flowmeter according to the sampling period, the return water temperature and flow at the current moment are obtained, and the return temperature deviation is calculated; calculating a control target value of the intelligent control valve by utilizing a model fuzzy logic predictive control algorithm based on the temperature return deviation; and the valve opening degree is adjusted based on the control target value, and the control parameters are dynamically corrected through the temperature return control strategy for self-adaptive temperature return control. According to the method, through a composite algorithm of fusing fuzzy logic control and model predictive control (MPC), on the basis that preliminary control signals are generated through fuzzy reasoning, a nonlinear system model is combined to carry out multi-step prediction on the future return water temperature change trend.

The invention provides an anesthesia auxiliary system supporting personalized administration and risk early warning. A hardware adaptation layer of the anesthesia auxiliary system adopts a general protocol and a customized driving mode; the data layer realizes acquisition, cleaning, storage and sharing of multi-source data, constructs an anesthesia whole-process data pool, and provides data support for algorithm operation; the core function layer utilizes an algorithm to run through the whole process of decision making, early warning and control to realize intelligent anesthesia management; and the interaction layer adopts a professional mode and concise mode double-interface design, so that the use requirements of different scenes are met, and the operation efficiency is improved. A multi-modal data acquisition-decision support-drug infusion control-risk early warning-data tracing full-closed-loop software system is designed, a layered decoupling architecture is adopted, and anesthesia equipment and a hospital information system are in butt joint through a standardized interface. According to the core improvement point, multi-modal data acquisition is supplemented, a data closed loop is perfected, algorithms such as off-line reinforcement learning, LSTM look-ahead early warning and fuzzy logic control are mainly integrated, and data driving and knowledge driving double-wheel decision making is achieved.

The invention discloses a tobacco leaf curing process optimization method and system, and the method comprises the steps: building a dynamic process diagram with multi-dimensional nodes according to the historical data of a tobacco leaf curing process, and enabling the dynamic process diagram to represent the state and the processing process of tobacco leaves at any processing moment; the weight of the processing technology at the moment is calculated according to the tobacco leaf shape modal data and the processing technology at any moment in the real-time dynamic technology diagram, and the processing technology at the moment is adjusted in real time; integrating the plurality of modal data of the real-time tobacco leaf shape and establishing a training model, wherein the training model is used for outputting optimal process parameters; searching an optimal process path globally according to the optimal process parameters output by the training model, inhibiting parameter fluctuation, and regulating and controlling the real-time process parameters through fuzzy logic control; and performing feature extraction on the to-be-baked tobacco leaves to match the optimal process parameters and the optimal process path for baking. The process is intelligently and dynamically optimized, the quality is accurately controlled, the consumption is reduced, and browning is efficiently and stably inhibited.

The application discloses a city air traffic system control architecture switching method and device, relates to the technical field of system engineering and automatic control, and is based on an adaptive architecture switching control model constructed based on adaptive control theory and fuzzy logic control. Based on state variable values and performance index values of the system, the adaptive architecture switching control model is used to switch the type of the city air traffic control system architecture and the corresponding model structure in real time, so that the operation efficiency of the city air traffic system can be effectively improved, and the loss caused by the safety risk can be reduced. The application designs three types of city air traffic control system architectures for the city air traffic system, can meet the command and control requirements of different scenes, and provides a reference for the design of the city air traffic system control architecture.

The invention discloses a temperature acquisition control system and method for an industrial production intelligent temperature control digestion furnace, and relates to the technical field of industrial production process control, and the method comprises the steps: constructing a fuzzy logic control model, processing fused temperature field data, calculating the actual temperature deviation and temperature change rate, and carrying out the fuzzy processing, generating a fuzzy temperature deviation information set; reasoning the fuzzification temperature deviation information set and the fuzzy control rule set to generate a heating power regulation instruction set; the heating power adjusting instruction set is issued to a heating element driver, the heating power is adjusted in real time, the latest temperature is collected, and a real-time temperature feedback data set is generated; and performing fusion analysis on the real-time temperature feedback data set and the fusion temperature field data, updating temperature field distribution, adjusting parameters of the fuzzy logic control model, and generating an optimal control data set. According to the invention, the digestion furnace can still maintain a reliable temperature monitoring effect under a complex working condition.

The application discloses a kind of power transformer health state determination methods, including obtaining the attribute data of power transformer;Attribute data is normalized and corresponding grey correlation coefficient is calculated;The weight value of attribute data information is calculated using analytic hierarchy process, and grey correlation degree value is calculated;The attribute of power transformer is divided into health grade and the priority of each health grade is determined by comparison;Health grade is associated with grey correlation degree value to construct fuzzy logic control rule;The health state determination of actual power transformer is carried out using fuzzy logic control rule.The application also discloses a kind of system for realizing the power transformer health state determination method.The application obtains and processes the data of power transformer, and predicts the state of power transformer based on analytic hierarchy process and grey correlation analysis scheme, so that the application can not only realize the health state determination of power transformer, but also has high reliability and good accuracy.

The invention provides an intelligent energy-saving building ventilation system self-adaptive to environmental changes, and relates to the technical field of intelligent buildings. The system comprises a hardware layer and a functional layer, the hardware layer comprises a sensor group, an actuator and a controller, and the functional layer is composed of a plurality of program modules operated by the controller. Wherein the data sensing and processing module is responsible for collecting indoor and outdoor environment parameters and preprocessing the data; the deep learning prediction module predicts a future environmental parameter trend and system energy consumption by using historical environmental data, an equipment operation state and external environmental data on the basis of an LSTM (Long Short Term Memory) network; the fuzzy logic control module combines the real-time environment data and the prediction data, and generates a preliminary control instruction according to a fuzzy rule. According to the system, through cooperative work of all the modules, intelligent adjustment and energy-saving optimization of the building ventilation system are achieved, energy consumption is reduced, and meanwhile the indoor comfort degree and the air quality are remarkably improved.

This invention discloses a digital identification system and method for high-pressure testing instrument detection data. The system includes: a data acquisition and conversion module for acquiring and converting analog signals; a data preprocessing module for noise removal and anomaly detection; a key parameter extraction module for extracting and optimizing key parameters; a data analysis and identification module for analyzing data and issuing early warnings of anomalies; a feedback adjustment module for optimizing test parameters; and a display and control module for displaying data, analysis results, and alarm prompts. This invention combines sliding window time series analysis, Kalman filtering, temporal convolutional neural networks, and adaptive fuzzy logic control algorithms to intelligently analyze and process the detection data of high-pressure testing instruments, enabling fault early warning, precise optimization, real-time adjustment, and maintenance decision-making. The system features high precision, strong real-time response capabilities, and significant optimization and maintenance effects, significantly improving data processing capabilities and decision-making efficiency during high-pressure testing.

The present application relates to the technical field of building automation, in particular to an outdoor louver canopy automatic control method and system, comprising the following steps: based on historical and real-time environmental data, using an autoregressive integrated moving average model, analyzing the trend and seasonal variation of illumination, temperature and wind speed data, using a random forest algorithm to process the nonlinear relationship of the data, constructing a model to predict environmental changes, analyzing and predicting the future trend of illumination and temperature parameters, and generating environmental prediction data.In the present application, the autoregressive integrated moving average and random forest algorithm accurately predict environmental changes, the gradient boosting machine algorithm reduces prediction error, the long short-term memory network timely identifies structural damage and functional failure, enhances reliability and safety, the fuzzy logic control handles input uncertainty, improves control accuracy, the PID algorithm adjusts the control amount according to real-time data, realizes accurate adjustment, real-time data stream analysis monitors the state, and ensures effective execution of the strategy.

This invention discloses a maximum power point tracking (MPPT) control system and method for a pump-driven turbine power generation system. It calculates output power by acquiring DC-side voltage and current, and also acquires rotational speed. Within each MPPT cycle, the operating state is identified by the power-speed slope feature G: when operating conditions change rapidly, the weight of the disturbance observation branch is increased; when approaching the maximum power point, the weight of the adaptive scale fuzzy logic control branch is enhanced. The two branches achieve branch current fusion according to the weights constructed using (|G|), outputting the q-axis stator current increment. A dq current closed loop is used to output a PWM drive signal to control the machine-side converter, adjusting the electromagnetic torque of the permanent magnet synchronous generator, thereby achieving efficient MPPT under time-varying head / flow conditions. This invention relies only on measurable power and speed signals, requiring no precise hydraulic model or data-intensive training, resulting in low computational load, ease of embedded implementation, improved energy recovery efficiency, and suppression of power fluctuations and torque pulsations.

An intelligent centrifugal separation system and method for protease production, a process parameter monitoring module is used for real-time acquisition of fermentation broth temperature, separation cavity pressure, drum running vibration amplitude and feed liquid level as an initial process data set; the data processing module is connected with the process parameter monitoring module, the data processing module is used for preprocessing the collected initial process data set, and an input feature vector is obtained; the parameter optimization module is connected with the data processing module, the parameter optimization module uses an expert rule base to adjust the operation parameters in real time based on a fuzzy logic control model; the execution control module is used for receiving the instruction of the parameter optimization module, and is used for dynamically adjusting the centrifugal separation operation according to the optimized parameters. The overall structure is simple, the process parameter monitoring, data processing, fuzzy logic control and execution adjustment are organically combined, and the intelligent control of the centrifugal separation operation in the protease production process is realized.

This invention provides a primary frequency regulation method for a wind-storage integrated system based on multivariable fuzzy logic control, comprising: establishing a wind-storage integrated system model, dividing wind speed regions, and establishing a multivariable fuzzy logic control method. The primary frequency regulation method for a wind-storage integrated system based on multivariable fuzzy logic control provided by this invention can realize power allocation for wind power and energy storage participating in primary frequency regulation, fully leverage the frequency regulation advantages of DFIG and energy storage, and improve the frequency stability of power systems with high wind power penetration.

The present invention discloses a method, system and device for monitoring a vehicle fuel cell. The method collects battery operation data through a data acquisition subsystem, stores and processes the data, and uses it to construct 1D-CNN and LSTM models, thereby deeply analyzing the real-time data and providing early warning and control based on the results. The improved model enhances feature extraction and analysis capabilities, and fuzzy logic control and other means optimize control and early warning. The monitoring system includes multiple units such as data acquisition and aggregation, storage and preprocessing, which work together to achieve efficient data processing and transmission. The monitoring device integrates multiple functional modules to ensure stable operation from the hardware level. The invention can accurately monitor the operating status of vehicle fuel cells, effectively improve the safety and reliability of the battery system, extend the service life of the fuel cell, reduce the risk of vehicle downtime and maintenance costs, and provide solid technical support for the commercial operation of fuel cell vehicles.