468 results about "Fuzzy reasoning" patented technology

The invention discloses an energy-saving intelligent street lamp automatic emergency response system and a control method thereof, relates to the technical field of industrial Internet of Things control, and solves the problems that an existing intelligent street lamp system is poor in dynamic scene adaptability, single in emergency response strategy and insufficient in communication stability. According to the method, a dynamic priority scheduling matrix is generated through multi-source data fusion and an adaptive weighted decision tree, and an intelligent dimming strategy is trained in combination with improved fuzzy reinforcement learning; a multi-level fuzzy control and fuzzy reasoning system is used for generating emergency parameters driven by accident levels; dynamically selecting an optimal communication link transmission instruction based on a multiple access protocol and multi-scale channel sensing; an IEEE 1588PTP protocol and Bayesian clock drift correction are adopted to guarantee time sequence consistency, and energy consumption and safety balance are optimized through multi-target reinforcement learning; the dynamic adaptive capacity, the emergency response accuracy and the communication reliability of a complex scene are remarkably improved, and collaborative optimization of energy-saving efficiency and road safety is realized.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention discloses an aircraft cabin environment personalized adjustment method based on sentiment analysis, and the method comprises the steps: collecting the facial expression, voice waveform and environment parameters of a passenger in real time through a cabin multi-source sensor, and generating a standardized physiological signal matrix and anonymized voice features through noise reduction and feature extraction; inputting the physiological signal matrix and the voice features into a pre-trained deep learning model, outputting an emotion index and a classification label, and updating model parameters through a federal learning framework; dynamically generating temperature, humidity and oxygen concentration adjusting instructions and dynamic weights by adopting a fuzzy reasoning system in combination with the emotion indexes and passenger preset preferences; environment adjustment is executed through a closed-loop control system, and environment parameter errors are fed back; synchronously updating a fuzzy inference system rule base and deep learning model parameters by utilizing reinforcement learning in combination with environmental parameter errors and emotion index changes, and completing optimization of a closed loop; according to the invention, real-time dynamic regulation and control and continuous adaptation optimization of the personalized cabin environment can be realized.

The invention relates to the technical field of grease production control, in particular to a grease product production control method and system based on intelligent digitization. The method comprises the following steps: monitoring temperature, viscosity and turbidity data in a grease crystallization process in real time, and generating grease crystallization state snapshot data; performing crystallization state prediction according to the grease crystallization state snapshot data, and performing reaction kettle cooling rate adjustment to obtain a target control cooling rate; performing real-time stirring rotating speed adjustment fuzzy reasoning on the grease crystallization state snapshot data to generate a corrected grease stirring speed; and performing control instruction conversion on the target control cooling rate and the corrected grease stirring speed, executing a production control instruction, and generating a grease crystallization process control report. By accurately controlling the cooling rate and the stirring speed, intelligent control over the grease crystallization process is achieved, and the quality and stability of grease products are remarkably improved.

A genetic algorithm optimization-based fuzzy PID control method and apparatus, and a device. The method comprises: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, on the basis of a preset system parameter of the surface density measurement system, setting a data sampling interval; S3, on the basis of the data sampling interval, continuously sampling a number N of pieces of data using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean value of the N-2 pieces of data, and eliminating sampling value deviation of the arithmetic mean value, to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; S5, on the basis of the applicable PID parameters, using a genetic algorithm to control a servo motor of the surface density measurement system. Thus, the technical problems of parameter tuning being difficult and the robustness of closed-loop control being low are solved.

The invention relates to the technical field of water pump fault diagnosis, discloses a condensate pump shafting vibration fault diagnosis method and device based on multi-parameter analysis and a medium, and solves the problems of sensor signal distortion and noise interference of a condensate pump under high-temperature and high-humidity working conditions through multi-source data fusion and environment adaptive compensation. Dynamic time-frequency domain feature extraction and an improved lightweight residual network are adopted, and efficient decoupling and accurate recognition of multi-fault concurrent features are achieved; the fuzzy inference rule base and the shafting fluctuation propagation model are combined, the fault positioning precision and the early fault detection rate are remarkably improved, scientific and reliable technical support is provided for diagnosis and maintenance of the shafting vibration fault of the condensate pump, and safe and stable operation of factory equipment is effectively guaranteed.

The present application relates to an output power adjustment method and apparatus, and a computer device and a storage medium. The method comprises: acquiring sampling data corresponding to sampling parameters, and determining a fuzzy set to which the sampling data belongs, wherein the sampling parameters are related to the maximum power point of a photovoltaic power generation system; on the basis of a fuzzy rule corresponding to the fuzzy set, performing fuzzy reasoning on the sampling data, and determining fuzzy control information matching the sampling data, wherein the fuzzy control information comprises a parameter value adjustment direction of working parameters and a step-size adjustment direction of parameter value change amounts; performing defuzzification processing on the step-size adjustment direction to obtain a step-size adjustment value; and outputting a control signal matching the parameter value adjustment direction and the step-size adjustment value, wherein the control signal is used for changing an output power of the photovoltaic power generation system by adjusting parameter values of the working parameters. By using the method, an output power adjustment effect can be improved.

The invention discloses a visual inspection method for the surface quality of a precise injection mold blank, relates to the field of visual inspection of quality inspection, and is used for solving the problems that the efficiency and quality of weld surface detection of the injection mold blank can be reduced due to image light spots or apertures, and the precision of a final detection result is reduced due to multi-angle image processing. The method comprises the following steps: randomly collecting an initialized image sample, obtaining an image surface light spot area ratio mean value and an image light spot appearance probability, determining an analysis angle of a further detection image, and collecting the similarity of a current angle image welding position and the current angle mold blank reflected light intensity; constructing a support vector machine model to perform angle positioning judgment on the injection mold base, screening out corresponding angle images according to an output result, making a group of fuzzy rules for the reserved angle images according to a surface defect detection coefficient and a surface smoothness coefficient to perform fuzzy reasoning, determining a surface quality result of the injection mold base, ensuring production consistency, and improving the quality of the injection mold base. And the mold blank surface quality detection difference is reduced.

The invention provides a terahertz inversion method and system based on fuzzy reasoning and a teaching optimization algorithm, and belongs to the field of terahertz inversion. The method comprises the following steps: acquiring a time domain signal by using a terahertz time domain spectrometer, obtaining a frequency domain signal through Fourier transform, and preprocessing the frequency domain signal; constructing a transmission propagation model of terahertz waves in the to-be-detected sample, and obtaining a model terahertz frequency domain signal based on the model; establishing a composite objective function based on the actually measured terahertz frequency domain signal and the model terahertz frequency domain signal; performing parameter inversion on the composite objective function based on fuzzy reasoning and a teaching optimization algorithm to obtain an optimal individual; according to the invention, the precise matching of the key features of the terahertz signal is realized by constructing the composite objective function fusing the frequency domain overall error, the peak frequency error and the peak amplitude error. By introducing a fuzzy reasoning mechanism, the problem of premature convergence or falling into local optimum of the algorithm is effectively avoided, the convergence speed is improved, and the inversion process is more efficient and stable.

The invention discloses an automatic temperature control heating method and system. The method comprises the steps that heating state data are obtained in real time and preprocessed; a deep neural network is adopted to predict the heating state data to obtain heating control parameters; predicting the historical heating state sequence data by adopting a long short-term memory network to obtain a future parameter change value; a reinforcement learning algorithm is adopted for reinforcement feedback, and an optimization control signal for the heating control parameters is obtained; performing fuzzy reasoning by adopting an adaptive neural fuzzy reasoning system, and adaptively adjusting rules and weights to obtain a fuzzy control signal; and integrating the parameters and the signals, and generating a heating control signal for the heating equipment through weighted fusion. According to the technical scheme, intelligent automatic control over the heating equipment is achieved, the monitoring capacity and the process management and control capacity of the heating equipment are greatly improved, the heating quality of products is improved, the rejection rate of the products is reduced, and the automation and intelligence level is improved.

The invention discloses a power grid voltage fluctuation suppression method and system based on dynamic voltage regulation, and the method comprises the steps: synchronously collecting a three-phase voltage signal through a sensor array, generating a multi-dimensional feature vector through Park transformation, and extracting a time-frequency joint feature through a mixed multi-scale decomposition algorithm, and inputting a model fusing fuzzy reasoning and a space-time diagram neural network, generating a cooperative adjustment strategy in combination with power grid topology and equipment constraints, and optimizing model parameters through a closed-loop correction mechanism driven by a dynamic forgetting factor. The system comprises a signal acquisition module, a characteristic decomposition module, an intelligent decision module and a closed loop correction module. According to the method, high-precision suppression of power grid voltage fluctuation is realized, the multi-node cooperative adjustment capability is improved, the method can be adaptive to power grid operation state changes, technical feasibility and economic rationality are considered, and an effective scheme is provided for modern power grid voltage stable control.

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

The invention discloses an industrial control network data monitoring system based on a cloud platform, and particularly relates to the technical field of industrial control network communication, comprising a data acquisition module for acquiring communication protocol data between an industrial control network and the cloud platform in real time to form a data set; the data analysis module analyzes the data set, calculates indexes such as network timeliness, throughput capacity and transmission efficiency, and generates a performance evaluation data set; the fuzzy reasoning module is used for deducing communication protocol performance based on the data set and dividing the communication protocol performance into high-class, medium-class and low-class; the feedback module sends corresponding signals according to protocol performance classification, and the adjusting module adjusts the size of a data monitoring window according to feedback signals; the method can be suitable for various industrial communication protocols, provides sufficient basis for whether the communication protocols need to be improved or not through a unified analysis mechanism, adapts to dynamic changes of an industrial network, achieves self-adaptive adjustment, and improves the stability and efficiency of the industrial control network.

The invention discloses an automatic control system based on full-automatic intelligent surface treatment equipment, which relates to the field of industrial production surface treatment and comprises a data acquisition module, a data analysis module, a process decision module, a control execution module, a fault monitoring module, a man-machine interaction module, a data storage and management module, a self-adaptive learning module, a remote monitoring and maintenance module and a green energy-saving module. An advanced sensor is combined with a micro-nano sensing technology, a deep reinforcement learning and transfer learning combined algorithm is applied, genetic optimization fuzzy reasoning is adopted, through magnetic suspension and compound control, faults are monitored based on deep learning, a VR technology is introduced to realize human-computer interaction, and a block chain technology is adopted to store data. And a meta-reinforcement learning algorithm is used to automatically adjust decision rules and parameters, and the remote collaborative robot carries out maintenance operation on the equipment. The system is accurate in data acquisition, scientific in analysis and decision, accurate in control, timely in fault diagnosis, convenient in man-machine interaction, efficient in data management, energy-saving and capable of comprehensively improving the production efficiency.

The invention discloses a new energy battery load optimization system and method based on an adaptive algorithm, and relates to the technical field of battery control, and the system comprises a multi-source collection module, an analysis decision module and an optimization adjustment module. The multi-source acquisition module acquires driving state data, battery state data and road surface state data; the analysis decision-making module obtains the remaining capacity of the battery through a Kalman filtering algorithm in combination with the battery state data, reconstructs the road surface state data into a road surface three-dimensional model through a fusion three-dimensional reconstruction algorithm, and obtains the slope of the road surface through a sampling analysis method; the optimization adjustment module obtains the driving state data, the battery remaining capacity and the road slope, performs fuzzy reasoning and defuzzification according to a membership function and a fuzzy rule to determine the optimal battery output power, comprehensively evaluates the fuzzy reasoning benefit based on a reward function, optimizes the membership function parameter through reinforcement learning, and obtains the optimal battery output power; and intelligent optimization matching of the battery output power, the driving state and the driving road condition is realized.

The invention discloses an unmanned aerial vehicle adaptive path planning system and method based on information fusion and collaboration, and the system comprises an environment sensing module which is used for collecting the landform, obstacle and threat information of a mining area in real time, and constructing a three-dimensional digital elevation model; the global path planning module is used for generating an initial global path by adopting an improved Theta * algorithm, and optimizing a path turning point through a father node relink; the dynamic obstacle avoidance module is used for generating a local obstacle avoidance path by combining real-time obstacle information with a speed sampling range constrained by an unmanned aerial vehicle kinetic model based on an improved DWA algorithm; the autonomous decision module is used for dynamically adjusting the man-machine authority level based on fuzzy reasoning and switching a planning mode; the execution module is used for realizing trajectory tracking through motor rotating speed control and outputting a rotating speed change curve of each motor and unmanned aerial vehicle state parameters; and the data fusion module is used for synchronizing the remote sensing data and the path planning result to the cloud and supporting multi-machine collaboration and historical data backtracking.

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data processing, and the method comprises the steps: extracting low-altitude multi-target decision features of a low-altitude semantic decision map, carrying out the fuzzy reasoning of the low-altitude multi-target decision features, and generating an anti-interference control instruction set; based on the anti-interference control instruction set, fault logs and task execution data during operation of the unmanned aerial vehicle are collected, and an evolution strategy library is generated through association rule mining; and performing causal analysis according to the evolution strategy library, generating a cluster coordination rule upgrade package, performing dynamic verification on the cluster coordination rule upgrade package, and outputting a self-healing strategy. According to the method, the reliability and the safety of task execution of the unmanned aerial vehicle are improved by constructing the semantic association large model and generating the anti-interference control instruction set.

The invention relates to the technical field of robot health physical examination, and discloses a robot noninvasive health physical examination and monitoring platform. A health data acquisition module of the system integrates physiological parameters acquired by a multi-modal sensor to generate a standardized health data set; the physical sign load analysis module calculates parameter fluctuation difference to generate a physical sign load distribution state value; the physical examination parameter optimization module screens optimal physical examination parameters to generate an operation parameter set; the environment adaptability adjusting module generates an environment response physical examination scheme in combination with the environment and body position data; a health threshold value dynamic setting module adjusts an abnormal judgment threshold value to generate a grading early warning threshold value; the health trend prediction module generates a health risk prediction value through fuzzy reasoning; and the physical examination feedback control module adjusts operation parameters and scanning frequency to generate a self-adaptive regulation and control strategy. According to the platform, the accuracy and the intelligent level of health physical examination are improved, and the non-invasive health monitoring requirement of the user can be better met.

The invention belongs to the technical field of mechanical lifting platforms, and particularly relates to a pushing chain type synchronous lifting platform and a control method.The synchronous lifting platform is formed by combining two lifting platform bodies, and each lifting platform body comprises a pushing chain driving system and a guiding mechanism; the multi-axis virtual axis electronic gear is combined with a fuzzy reasoning library to realize synchronous accurate control of the double servo motors, and the synchronous control system has the advantages of high speed, high bearing capacity, multi-point control, high precision, low initial height, large stroke, small occupied space, no maintenance, high efficiency, energy conservation and the like.

The invention discloses a mechanical arm control method and system based on fuzzy self-adaptive PID control, relates to the technical field of mechanical arm control, and solves the problem that mechanical arm control is insufficient in flexibility when facing different working conditions. According to the embodiment of the invention, the universe expansion and contraction factor is adjusted through the error, then the close linkage of the quantization factor and universe expansion and contraction is realized through the self-adaptive quantization factor calculation, the fuzzy reasoning technology is combined, the intelligent mapping from the error information to the PID parameter increment is realized, the real-time updating of the PID parameter and the control output calculation are realized, and the real-time updating of the PID parameter is realized. The flexibility and adaptability of the control system are improved; and then output is controlled through incremental PID, so that a complete control link from parameter increment to actuator driving is realized, a complete closed loop is formed by a cascade control architecture, functional modules are tightly coupled, parameters are coordinated, and the whole-process intelligence from error sensing to execution control is realized.

The invention relates to the technical field of electric energy quality management of an electric power system, and discloses an industrial power grid voltage fluctuation suppression method and system, and the method comprises the steps: firstly building a system-level voltage and current forward transmission model composed of a power unit, a filter circuit and power grid impedance which are cascaded; reversely constructing a feedforward controller based on a voltage and current forward transfer model, and generating a main compensation voltage instruction according to the power grid disturbance quantity detected in real time; state parameters of each power unit are obtained, a differential equation is constructed, and a comprehensive real-time health degree index is obtained through fuzzy reasoning; dynamically correcting an equivalent impedance parameter in the forward transfer model by using the index, and calculating a voltage distribution weight of each unit; and decomposing the total compensation voltage instruction into each unit modulation signal according to the weight. Through systematic modeling, intelligent state evaluation and dynamic fault-tolerant distribution, high-speed, accurate and high-reliability suppression of the voltage fluctuation of the industrial power grid is realized.

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention relates to the technical field of power equipment, in particular to an intelligent circuit breaker electric operation mechanism integrating multi-parameter detection and an intelligent monitoring system.The intelligent circuit breaker electric operation mechanism comprises a basic mechanism unit, a detection unit, an intelligent control unit and a communication unit, and the basic mechanism unit comprises a double-layer sliding block compensation system and a transmission mechanism; the double-layer sliding block compensation system is composed of a first sliding block, a second sliding block and a spring set, and a thin film pressure sensor is integrated on the bottom face of the second sliding block. According to the intelligent circuit breaker electric operation mechanism integrated with multi-parameter detection and the intelligent monitoring system, the health state and fault assessment system achieves fault assessment through data receiving, feature fusion, health modeling and fuzzy reasoning, and the intelligent monitoring system comprises a remote monitoring platform. And data display, fault early warning, remote control and reference model optimization are supported, intelligent control and multi-dimensional state monitoring of the circuit breaker are realized, and the fault diagnosis accuracy and the reliability of a power system are improved.

The invention provides an automobile wire harness short circuit fault detection method, system and device and a medium, and the method comprises the steps: extracting the transient characteristic quantity of a current signal of each monitoring node, and obtaining the amplitude attenuation characteristics of the current signal of each monitoring node in a wire harness propagation process based on the mutual information intensity inversion of the transient characteristic quantity between adjacent monitoring nodes; further identifying an abnormal wire harness path which is obviously influenced by common-mode interference based on the fluctuation difference degree of the amplitude of the common-mode current between adjacent monitoring nodes in spatial distribution, and constructing a common-mode interference topological graph which represents the abnormal state of the wire harness in combination with the response intensity of the common-mode current; and performing fuzzy reasoning on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topological map to obtain the fault membership degree of each monitoring node, and performing short-circuit fault correlation positioning on the target automobile wire harness through all the fault membership degrees. By adopting the scheme of the invention, the associated positioning of the short-circuit fault of the automobile wire harness under the common-mode interference condition can be realized.

The invention discloses a supply chain financial risk assessment system based on a block chain technology and an assessment method thereof, and relates to the technical field of block chains, and the method comprises the steps: deploying system nodes on a server of each participant of supply chain finance, and constructing a block chain network; selecting nodes with high priorities according to the node weights to participate in block verification and confirmation to obtain transaction confirmation time; obtaining a compatibility index of a new version logic contract and an old version after the smart contract upgrade proposal, calling a parameter contract interface to adjust a financing interest rate, and determining a smart contract upgrade effect; and evaluating a supply chain financial risk condition based on fuzzy reasoning according to the transaction confirmation time in combination with an intelligent contract upgrading effect, and adjusting a consensus process of the block chain network and an intelligent contract construction process according to an evaluation result. The problems that the transaction confirmation time of the block chain is relatively long and the smart contract is difficult to flexibly adjust according to the change of the actual business condition are solved; and the processing efficiency and the system adaptability of the supply chain financial business are improved.

The invention relates to the technical field of solid waste incineration automatic control, and discloses a solid waste incineration equipment automatic control method and system.The method comprises the steps that real-time data of solid waste components in a hearth are collected, and an initial multi-dimensional parameter set is obtained through preprocessing; calculating a balance point of the heat value and the water content through fuzzy reasoning; when the balance point exceeds a threshold value, parameter interaction influence is predicted, and a multi-dimensional control vector is generated; performing data fusion and historical data matching in combination with real-time incineration process indexes to obtain an enhanced load distribution adjustment factor; carrying out weight comparison based on the adjustment factor and the balance point, judging a load balance influence index, and carrying out matching correction if the index exceeds a compatible threshold value; updating a load distribution scheme according to a correction result, and generating a final operation strategy; and a control instruction is sent through a feedback cycle mechanism and is continuously optimized, so that closed-loop control is realized. The device can adaptively treat dynamic changes of solid waste components, and the incineration efficiency and stability are improved.

The invention discloses a diamond cutting tooth self-adaptive vibration suppression method based on a fuzzy control algorithm. The diamond cutting tooth self-adaptive vibration suppression method comprises the following steps that S1, a multi-channel signal data set in the diamond cutting tooth machining process is collected in real time; s2, acquiring a preprocessed multi-channel signal data set; s3, carrying out integration to form a feature vector containing multi-dimensional dynamic features; s4, inputting the feature vectors into a fuzzy control module, and performing fuzzy processing on the feature vectors of the channel signals to realize fuzzy quantization of the processing state; s5, generating a control output quantity by using a fuzzy reasoning algorithm according to the fuzzy quantization result, and converting the control output quantity into an executable process control instruction through a defuzzification algorithm; and S6, the process control instruction is fed back to a machining execution mechanism. The method has higher sensitivity and adaptability in unstable machining state recognition, the judgment accuracy of the initial vibration stage can be remarkably improved, and the judgment basis of a control system under complex disturbance is enhanced.

The invention relates to the technical field of 5G communication, in particular to an optimization method of a high-performance 5G communication module. Comprising the following steps: acquiring key operation parameters in a multi-carrier concurrent state, and constructing a characteristic graph model reflecting a parameter evolution relationship; mining a state clustering mode through a graph polarization spectral clustering algorithm, and identifying key paths and core parameter nodes which influence communication performance in combination with a multi-hop path optimization algorithm of a graph attention mechanism; taking the key path strategy graph as input, combining target communication scene features, and adopting a graph structure alignment meta-learning algorithm to generate an optimal parameter configuration strategy; the strategy is applied to an actual module, a self-adaptive control model fusing fuzzy reasoning and reinforcement learning is constructed, and dynamic evaluation of the strategy execution effect and fine parameter tuning are achieved. According to the invention, the performance scheduling efficiency of the 5G communication module under the complex network condition is improved, and the intelligence, generalization ability and scene adaptability of the system are obviously enhanced.

The invention discloses an automobile armrest dynamic damping adjustment system and method based on multi-sensor fusion, particularly relates to the technical field of automobile armrest adjustment, and is used for solving the problem of poor armrest damping self-adaptive adjustment precision. Multi-source data such as vehicle postures, power behaviors, contact behaviors and hand torque are collected, resampling, denoising and phase compensation are carried out, a fusion state vector is constructed, the future state of the vehicle is predicted based on a short-time historical state sequence, state increment is extracted, and a dynamic risk index is formed; a current state and a risk trend are combined to construct an optimization model integrating supporting, smoothing and restraining, controller parameters are dynamically adjusted through a fuzzy reasoning mechanism, self-adaptive adjustment of armrest damping is achieved, and in the damping execution process, cooperative features and dynamic correction strategies of a control instruction and feedback output are analyzed, so that the damping performance is improved. Therefore, the response precision and stability of the dynamic damping of the automobile armrest under the complex working condition are improved.

The invention provides an argon flow and micro-positive pressure linkage control method for an electroslag furnace. The method comprises the steps that the oxygen content and micro-positive pressure signals in the furnace are collected; the future furnace condition is predicted through the deep belief network model; optimizing initial parameters of the fuzzy PID controller in an off-line manner by using a particle swarm algorithm; on the basis of the real-time signal and the prediction result, adaptively adjusting controller parameters on line; and finally, an argon flow and dust removal valve opening control instruction is output in a linkage mode. According to the method, future furnace conditions are predicted through the deep belief network model, the control advancement is realized, and the problem of large lag of the system is solved; offline parameter optimization is carried out on the fuzzy PID controller through a particle swarm algorithm, and excellent initial performance adapting to different working conditions is provided for the fuzzy PID controller; the parameters of the controller are adaptively adjusted through online fuzzy reasoning, so that the system has robustness for coping with a dynamic process; finally, accurate and stable linkage control of the oxygen content in the furnace and the micro-positive pressure is achieved through linkage calculation of the argon flow and a dust removal valve opening instruction.