153 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 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 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 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 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 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 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 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.

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 present invention relates to the technical field of data processing, and is a sewage treatment intelligent dosing adjustment system based on fuzzy logic, comprising: a data acquisition module, which collects water quality data of sewage and optimizes the data acquisition process, including chemical oxygen demand, ammonia nitrogen content, total phosphorus content and sewage flow; a data analysis module, which is used to receive real-time data and perform small batch gradient descent analysis on the data to obtain analysis results; an optimization module, which is used to optimize the parameters and rules of the fuzzy logic controller through differential evolution according to the analysis results to obtain optimized parameters and rules; a fuzzy logic control module, which performs fuzzy logic calculation of dosing amount through simulated annealing according to the received water quality parameters and optimized rules, and converts it into a dosing control signal. The present invention realizes comprehensive intelligent management from data acquisition to dosing adjustment by integrating advanced data acquisition, analysis, optimization and control technologies.

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 discloses a method and a device for automatically positioning the underwater depth of a test rod, and particularly relates to the technical field of underwater detection. A pressure sensor, a sonar ranging module and an inertial navigation unit are integrated on a measuring rod, temperature-salinity-depth sensor data obtained in real time are combined, a pressure-depth conversion result is dynamically corrected, a high-precision target water depth value is generated through data fusion, and meanwhile, the system utilizes inertial navigation information and a target depth change trend to determine the water depth of the target. A fuzzy logic control method is adopted to realize real-time adaptive adjustment of the attitude and depth position of the measuring rod, and the method effectively improves the positioning precision and environmental adaptability of the underwater measuring rod in a complex layered water body, and has good practicability and innovativeness.

The invention discloses a pump valve parallel electro-hydraulic servo system and a control method thereof, and the pump valve parallel electro-hydraulic servo system comprises a pump control loop and a valve control loop; the control method is based on a pump valve parallel electro-hydraulic servo system, self-adaptive control parameters are defined, and a hydraulic system model is constructed; a self-adaptive robust controller of a self-adaptive compensation item and a robust compensation item is introduced, an intelligent agent SACI is constructed, a reward function of the SACI is constructed according to displacement accuracy, flow accuracy and motion stability, guided reinforcement learning training optimization is carried out in combination with an outer-layer self-adaptive robust algorithm ARCI and a fuzzy logic control algorithm FLCI, and a space a1, a space a2, a space a3 and a space a4 are determined. Calculating the total flow required by the hydraulic system according to an inner-layer adaptive robust algorithm ARC II; and an intelligent agent SAC II is constructed, a fuzzy logic control algorithm FLC II is combined to reinforce learning training and optimize an output space a2, a pump valve flow smooth switching coefficient and a switching reversing valve input signal are obtained, a pump valve loop smooth switching strategy is determined, and flow distribution of the hydraulic system is achieved.

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 discloses a vehicle fuel cell monitoring method, system and device, and the method comprises the steps: collecting battery operation data through a data collection subsystem, storing and processing the battery operation data, building 1D-CNN and LSTM models, carrying out the deep analysis of real-time data, and carrying out the early warning and control according to a result. The improved model improves feature extraction and analysis capability, and control and early warning are optimized by means of fuzzy logic control and the like. The monitoring system comprises a plurality of units such as a data acquisition and convergence unit and a storage and preprocessing unit, and efficient data processing and transmission are cooperatively realized. The monitoring device integrates multiple functional modules, and stable operation is guaranteed from the hardware level. The system can accurately monitor the running state of the vehicle fuel cell, effectively improves the safety and reliability of a cell system, prolongs the service life of the fuel cell, reduces the shutdown risk and maintenance cost of the vehicle, and provides a solid technical support for commercial operation of the fuel cell vehicle.

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 discloses a multilayer fusion dynamic optimization expert model architecture for a photovoltaic production line and an implementation method. The implementation method comprises the following steps: 1, generating a high-dimensional feature vector by using a convolutional neural network algorithm; 2, defining intelligent optimization modules designed for different key processes of the photovoltaic production line as expert models, wherein each model focuses on a dynamic optimization task of a specific process; 3, the welding optimization expert model uses deep reinforcement learning to optimize the combination of welding temperature, pressure and current; the lamination optimization expert model outputs lamination temperature and time based on an evolutionary optimization predictive control algorithm; the glue filling and curing optimization expert model is combined with Bayesian optimization and fuzzy logic control to adjust the glue amount, the glue filling speed and the environment temperature and humidity; and 4, taking the parameter result obtained by the optimization as the input of the next optimization to form a continuous iterative optimization process, so that the process parameters are continuously and effectively close to the optimal solution, and the assembly quality and the resource utilization efficiency of the production line are improved.

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.