46 results about "Fuzzy inference engine" patented technology

The invention relates to the field of power equipment data processing, in particular to a biomass power generation combustion parameter deep learning method and system, and aims to solve the problem of phase mismatch caused by sampling frequency difference and clock reference offset of multi-source heterogeneous time sequence data. A parallel multi-scale convolution and bidirectional long-short-term memory network hybrid model is constructed, transient fluctuation and long-period trend features are extracted, and combustion stage feature weights are dynamically distributed through a gating attention mechanism. The optimization control module generates a multi-target constraint condition, an operation instruction is output in combination with a fuzzy inference engine, and a digital twin platform simulates an extreme working condition to enhance model robustness. A closed-loop feedback mechanism dynamically adjusts model parameters through combustion efficiency monitoring data and simulation results, and a two-stage fault-tolerant strategy realizes sensor abnormity compensation and historical control strategy backtracking. The problem of asynchronous data stream feature misalignment is effectively solved, and the combustion efficiency prediction precision and the control decision reliability are improved.

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 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 intelligent door and window control method and system, and the method comprises the steps: S1, obtaining multi-source environment data; s2, inputting the multi-source environment data into a fuzzification module, and converting the multi-source environment data into a fuzzy variable through a preset ambiguity function; s3, dynamically generating an environment parameter weight vector according to the real-time scene parameters; s4, inputting the fuzzy variable and the environmental parameter weight vector into a fuzzy inference engine, executing logical operation of a fuzzy rule base, and outputting comprehensive evaluation values of window opening suitability, window closing urgency and shading demand; and S5, generating a final control instruction through a defuzzification strategy, and executing the final control instruction. According to the method, the problem of frequent invalid operation caused by fluctuation of the environmental parameters near the boundary value is solved, the environmental parameter weight vector is dynamically generated based on the time parameters, the user preset mode and the meteorological data confidence factor, adaptation to seasons, time periods, user states and other scenes is achieved, and decision logic can be flexibly adjusted according to actual requirements; and a contradictory decision under a traditional fixed rule is avoided.

An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks. The dynamic modeling module determines connectivity values between clusters of formations based on nodal connectivity of neighboring clusters, assigns pressure values across the volume of interest, and generates a three-dimensional dynamic model for the volume of interest based on the pressure values.

The invention relates to an intelligent control method, system, equipment and medium for automatic discharging mining tunneling cutting picks, and the method comprises the following steps: monitoring the flowing state of coal rock slag in a spiral discharging channel in real time, collecting a channel pressure difference signal and a cutting pick vibration signal, and generating discharging state characteristic parameters; blocking risk quantification is carried out on the characteristic parameters, and a real-time blocking index is generated through weighted fusion calculation; based on the index, a cutting head rotating speed optimization adjustment amount is generated through a fuzzy reasoning mechanism; and finally, generating a rotating speed control instruction through dynamic amplitude limiting processing and sending the rotating speed control instruction to a variable-frequency driving system. By means of the method, real-time sensing and dynamic self-adaptive adjustment of the blockage risk of the discharging channel are achieved, the problem that the discharging channel is blocked due to coal rock property changes is effectively solved, the discharging efficiency and the stability of tunneling operation are improved, and the service life of cutting teeth is prolonged.

The invention relates to a multi-motor cooperative control method, system and device based on position and speed double feedback and a self-adaptive sliding mode and a medium, and relates to the technical field of multi-motor cooperative control. The multi-motor cooperative control method comprises the following steps: acquiring an actual position signal and an actual speed signal of each motor, and converting and calculating a position synchronization error and a speed synchronization error between the motors; calculating a position tracking error and a speed tracking error of each motor according to a preset reference position signal and a reference speed signal; fusing the weighted operation position synchronization error, the speed synchronization error, the position tracking error and the speed tracking error to construct a sliding mode surface function; iteratively optimizing the sliding mode surface function according to a preset fuzzy reasoning mechanism, outputting a self-adaptive adjustment amount, updating the original switching gain, and calculating a new switching gain; and calculating the final control quantity of each motor according to the new switching gain, the sliding mode surface function and the saturation constraint function, and outputting the final control quantity to a corresponding motor driver to drive the motor to move.

The invention relates to the technical field of automatic control, and discloses a sliding mode control buffeting suppression method based on an interval type-2 fuzzy rule. Dynamic balance of buffeting suppression and robustness is realized through deep cooperation of an interval type-2 fuzzy logic system and sliding mode control: an interval membership function is utilized to accurately capture an uncertainty boundary of the system, a double-layer fuzzy reasoning mechanism is combined to cooperatively optimize switching gain and boundary layer thickness, and high-precision tracking is maintained under strong disturbance; the control signal high-frequency oscillation energy is monitored in real time to drive parameter reconstruction, and buffeting is eliminated from the source; the modular design can be directly embedded into an industrial control system, abrasion of an executing mechanism is remarkably reduced, the service life of equipment is prolonged, and the inherent contradiction between dynamic response and stability of a traditional method is solved.

The invention discloses a flight control method and system for a vertical take-off and landing tilting quad-rotor unmanned aerial vehicle, and aims to solve the technical problem of poor time-varying adaptability of a control system in a nacelle tilting transition stage. According to the method, in a rotor wing flight mode and a nacelle tilting transition mode, attitude angle deviation, deviation change rate and flight speed of the unmanned aerial vehicle are obtained in real time, a fuzzy inference engine is adopted to dynamically output basic control quantity based on a predefined rule base, and a final control instruction is generated in combination with pneumatic feed-forward compensation quantity based on speed to drive a pneumatic control surface. According to the method, fuzzy intelligent decision making and feed-forward dynamic compensation are fused, the limitation that traditional control depends on an accurate model is broken through, the control precision and adaptability of a transition stage are remarkably improved, overshoot is effectively restrained, the adjusting time is shortened, the robustness of a system under model parameter changes and external interference is enhanced, and the system reliability is improved. And a reliable technical guarantee is provided for full-mode flight of the tilt-rotor unmanned aerial vehicle.

This invention relates to a cascaded fuzzy feedforward direct-drive speed control method and system based on LC filtering. The method includes: obtaining a speed error and error rate of change from a reference speed and a real-time measured speed; inputting the speed error and error rate of change into a Mamdani fuzzy inference engine to output a power setpoint coefficient; inputting the power setpoint coefficient and a normalized measured speed signal into a two-dimensional torque-current reference lookup table, performing bilinear interpolation calculation, and outputting an electromagnetic torque command; converting the electromagnetic torque command into a quadrature-axis command current, generating a PWM signal through current closed-loop regulation and SVPWM modulation, and driving a two-level voltage-type inverter; the output of the two-level voltage-type inverter drives a low-voltage permanent magnet synchronous motor through an LC sine wave filter with an RC damping branch. Compared with the prior art, this invention has advantages such as low computational load, improved dynamic response, and enhanced disturbance rejection.

The invention relates to the technical field of avionics, and discloses a friend behavior prediction and multi-platform autonomous decision-making method and system during communication interruption, and the method comprises the steps: constructing a multi-level fuzzy inference tree, and carrying out the fuzzy processing of multi-source heterogeneous input information, and forming a plurality of fuzzy sets; decomposing the complex input relation into a plurality of subsystems based on a hierarchical fuzzy reasoning mechanism, and obtaining a subtask response benefit value through layer-by-layer reasoning; input variables are fuzzified based on a triangular membership function, and a potential task allocation scheme of the friend formation is predicted in combination with a hierarchical rule reasoning mode. According to the method, centerless, low-dependence and high-robustness multi-platform autonomous task allocation can be realized, and the collaborative decision-making capability and task execution efficiency of formation members in a complex environment under an incomplete information condition are improved.

The present invention proposes a terahertz inversion method and system based on fuzzy reasoning and teaching optimization algorithm, which belongs to the field of terahertz inversion. The method includes: using a terahertz time-domain spectrometer to collect time-domain signals and obtain frequency-domain signals through Fourier transform, and performing preprocessing; constructing a transmission propagation model of terahertz waves in the sample to be measured, and obtaining a model terahertz frequency-domain signal based on the model; establishing a composite objective function based on the 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 teaching optimization algorithm to obtain the optimal individual; the present invention realizes accurate matching of key features of terahertz signals by constructing a composite objective function that integrates the overall frequency domain error, peak frequency error and peak amplitude error. By introducing the fuzzy reasoning mechanism, the problem of premature convergence of the algorithm or falling into local optimum is effectively avoided, the convergence speed is improved, and the inversion process is made more efficient and stable.

The invention provides a boundary flow control method and system based on a busy water area macroscopic basic diagram, electronic equipment and a storage medium, and the method comprises the steps: building a water area macroscopic basic diagram which can accurately reflect the macroscopic law of mixed traffic flows of different ship types through collecting and processing the data of a ship automatic recognition system; determining an optimal accumulated ship number target when the overall navigation efficiency of the system is highest; delimiting an easily-congested key area in the target water area as a core water area, and establishing a ship traffic flow dynamic balance model taking the optimal target as a balance point for the core water area as a control object; a fuzzy self-adaptive PID controller is designed and applied, the PID controller can sense the deviation between the actual number of ships in a core water area and a target value and the variation trend of the deviation in real time, control parameters are intelligently adjusted on line through a built-in fuzzy inference engine, the optimal boundary flow control rate is dynamically calculated and output, and therefore the flow of the ships entering the core water area is accurately adjusted. The number of the ships in the water area is actively and stably maintained in the optimal interval.

This invention discloses a fuzzy logic energy-saving control method for a mechanical jigging screen, relating to the field of mineral sorting equipment control technology. The method includes real-time acquisition of raw multi-source state data from the equipment, preprocessing to generate a standardized state feature set containing normalized feed load characteristics, particle size distribution feature vectors, bed thickness variation trend characteristics, and motor power spectrum characteristics. This set is then input into a preset fuzzy inference engine, where fuzzy logic operations are performed using a predefined and tuned fuzzy rule base. The output is a fuzzy set of optimal driving parameters covering fuzzy suggestions for screen stroke, stroke frequency, and acceleration. This set is then defuzzified and converted into deterministic driving command parameters. This method enables precise adaptation of the equipment's multi-source operating states and driving parameters, enhances the collaborative adjustment capability of multi-dimensional driving parameters, optimizes equipment operating adaptability, and achieves the technical effects of dynamic matching of operating conditions and energy-saving regulation.

The invention discloses a power transmission and transformation project electronic file verification method based on a fuzzy reasoning mechanism. The method comprises the following steps: carrying out consistency verification on metadata of an electronic file through a Transform model, an LSTM network and Hash verification; on the basis of a conditional weight dynamic adjustment mechanism, screening out features most relevant to the target task in the electronic archive management system, and removing redundant data; according to an input condition, adaptively adjusting a scoring standard, and according to a fuzzy reasoning mechanism and the scoring standard, scoring the screened features to obtain a target feature score; and generating a final verification result of the electronic archive based on the target feature score. The design is suitable for integrated processing and accurate verification of large-scale and multi-modal data, and the accuracy and flexibility of electronic file verification can be effectively improved.

Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a reservoir model is received. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of input features is generated from the static model and the dynamic model. The set of input features is related to a drilling attractiveness of a target region of the reservoir using a set of rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.

The invention belongs to the technical field of oil and gas field development, and discloses a crude oil treatment inlet liquid steady flow control method based on fuzzy cascade PID, which comprises the following steps: determining fuzzy cascade PID regulation loop control logic of an inner loop control loop and an outer loop control loop, the input and output of the outer loop control loop are respectively a pressure buffer tank target liquid level and an inlet pressure buffer tank flow control valve opening; the input and the output of the inner ring control loop are respectively the opening degree of a flow control valve of an inlet pressure buffer tank and the opening degree of a gulp valve; determining initial domains of discourse of input and output parameters of a fuzzy inference engine, and performing fuzzification processing; determining fuzzy rule tables of the inner and outer ring fuzzy inference engines; the target liquid level of the pressure buffer tank serves as loop input, initial parameters of an inner ring PID controller and an outer ring PID controller are determined, the parameters are continuously optimized under the action of a fuzzy inference engine, and steady flow control is achieved. The stability of crude oil liquid inlet flow can be greatly improved, the processing capacity of a process rear-end device is met, and light-load operation of the pressure buffer tank is guaranteed.

The invention discloses an efficient full feeding system and method for a high-pressure roller mill, and belongs to the technical field of high-pressure roller mill feeding control. According to the system, multiple parameters such as the ore feeding amount, the material level and the pressure are collected in real time through the sensing module, the optimal pressure set value is dynamically calculated through a fuzzy inference engine of the control module, then an adjusting instruction is generated through the cascade control module, and finally the roller is driven by the execution module to change the speed. According to the invention, the problems that the high-pressure roller mill is easy to cause material blockage and production halt due to unstable material level of the feeding hopper and the working pressure and frequency are difficult to accurately adjust manually are solved. According to the invention, the full ore feeding state can be automatically maintained, and the working pressure and frequency are stabilized in a high-efficiency interval, so that material blockage accidents are effectively prevented, continuous and stable production is guaranteed, the rolling efficiency is improved, the product quality is stabilized, and the energy consumption is reduced.

The application discloses a kind of intelligent high-voltage switch's circuit breaker body fault diagnosis method, device, equipment and storage medium, method includes: obtaining the historical fault data of circuit breaker in high-voltage switch, according to historical fault data determine suspected fault component set;Based on suspected fault component set, historical alarm information and fault reason are classified, determine the alarm information and fault reason corresponding to each fault component;Alarm timing information in historical fault data is checked with timing information constraint, and removes error fault information;The mapping relationship between the fault reason of each fault component and alarm information is analyzed using fault tree module;Based on the mapping relationship, the alarm information of target fault event is obtained by matrix form iteration operation by fuzzy inference mechanism, and the fault probability representation of high-voltage switch circuit breaker is obtained, and the fault diagnosis result is determined according to the fault probability representation of high-voltage switch circuit breaker.Effectively improve fault diagnosis efficiency.

The invention relates to the technical field of aquaculture, in particular to an aquatic product gradient temperature control system and method for deep feature extraction of heat pump data, and the system comprises a data collection module, a feature extraction module, a deep learning prediction module, an energy efficiency optimization module, a fuzzy control module and an execution control module. By collecting heat source side inlet and outlet temperature, user side inlet and outlet temperature, system flow and system power data, the data are preprocessed, and multi-dimensional feature vectors including temperature gradient features, temperature difference features and frequency domain features are extracted. And predicting the energy efficiency ratio of the system by using a hierarchical neural network structure, constructing a temperature difference-energy efficiency mapping relation, and determining an optimal temperature difference set value. Control parameters are generated through the three-dimensional fuzzy inference engine, the rotating speed of the water pump and the running state of the heat pump are adjusted, gradient temperature control of the aquaculture environment is achieved, and the aquaculture requirements of high-value aquatic product species sensitive to temperature can be met.

This invention discloses an energy management method for hybrid energy storage systems based on fuzzy model predictive control. It constructs a linear variable parameter model predictive control architecture incorporating fuzzy decision-making for closed-loop optimization control. First, an LPV prediction model for the system is established, and the load power sequence is introduced as a measurable feedforward disturbance into the state equation. During the rolling optimization preparation stage, a fuzzy inference mechanism is used to analyze multi-dimensional operating conditions such as system power fluctuations, SOC, and SOV in real time, dynamically adjusting the weighting factors of energy tracking, battery loss, and current smoothing in the cost function to achieve an adaptive trade-off in the control strategy. Simultaneously, a hierarchical setpoint tracking strategy is adopted, intelligently switching the voltage state reference target based on the real-time energy level of the supercapacitor. Finally, based on the time-varying model, dynamic weights, and the reference target, a quadratic programming problem is solved, outputting the optimal control quantity applied to the power converter, thereby significantly improving the system's adaptive coordination capability and overall operating efficiency under complex and variable operating conditions.

The application provides a TBM jamming risk intelligent early warning method and system based on knowledge-data double driving, which comprises the following steps: acquiring multi-source parameter information collected in the TBM tunneling process of TBM tunneling cases under different stratum tunneling conditions, and constructing a multi-source parameter database; processing the data in the multi-source parameter database, and calculating data-driven jamming probability based on a robust time sequence anomaly detection model for abnormal changes; extracting expert experience knowledge based on TBM jamming literature research and experience knowledge in actual construction cases, and constructing a TBM tunneling jamming event knowledge base; determining the membership degree and the reliability of the fuzzy condition, constructing a TBM tunneling jamming event rule reasoning machine, and calculating knowledge-driven jamming probability based on a fuzzy reasoning machine model; multiplying the data-driven probability and the knowledge-driven probability by using a factor multiplication method, calculating the TBM tunneling jamming probability, and obtaining the TBM tunneling jamming risk prediction result based on the knowledge-data double driving.

Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a reservoir model is received. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of input features is generated from the static model and the dynamic model. The set of input features is related to a drilling attractiveness of a target region of the reservoir using a set of rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.

The invention provides a multi-robot smell source positioning method based on fuzzy reasoning parameter adaptive control, and aims to realize parameter adaptive adjustment in a smell source positioning task of multiple robots based on an improved particle swarm optimization algorithm of fuzzy reasoning and improve the search efficiency, the success rate and the environmental adaptability. Comprising the steps of establishing a smell source positioning simulation environment model, designing an improved particle swarm optimization algorithm, designing a fuzzy controller and positioning a smell source. According to the method, parameter adaptive control is realized through a fuzzy reasoning mechanism, and the intelligence, the stability and the robustness of the source positioning system in a complex plume environment are effectively improved in combination with the improved particle swarm optimization algorithm with enhanced guide items. The method is simple in algorithm structure and excellent in performance, and has wide simulation application potential and engineering popularization value.

An adaptive, fuzzy logic-based system (100) for controlling rainwater quality for the intelligent detection of first runoff, comprising: a roof water inlet connection (102) designed to receive rainwater runoff from the roof; a removable dirt filter assembly (104) arranged downstream of the roof water inlet connection (102) to remove coarse contaminants from the incoming runoff; a sealed sensor chamber (106) hydraulically connected to the dirt filter assembly (104); a plurality of sensor units (108) arranged within or connected to the sensor chamber (106), the sensor units comprising at least a turbidity sensor, a conductivity or total dissolved solids sensor, a flow sensor, a storage tank level sensor, and optionally a pH sensor;a programmable controller (110) comprising a processor, memory, and a fuzzy inference engine (112) configured to receive sensor readings, normalize the received readings, generate fuzzy membership values, execute a stored fuzzy rule base, and continuously calculate a rainwater suitability value corresponding to the quality of the collected runoff water; an electronically actuated three-way valve (114) functionally coupled to the programmable controller (110) and designed to selectively direct the collected rainwater, according to the continuously updated rainwater suitability value, to a discharge outlet (116), a secondary use outlet (118), and a primary storage outlet (120);wherein the programmable controller (110) continuously adjusts the diversion decision during a rainfall event in response to changing runoff quality and the availability of storage tanks, thereby maximizing the amount of recoverable rainwater while preventing contaminated runoff from entering a primary storage tank.

The present invention discloses an intelligent detection and evaluation method and system for dielectric loss, withstand voltage and partial discharge of distribution network cables, which relates to the technical field of power equipment condition monitoring, and includes: synchronously collecting the dielectric loss spectrum, withstand voltage leakage current curve and partial discharge pattern of the distribution network cable through a phase-resolved measurement device, and applying a frequency-domain adaptive filter to process the partial discharge pattern to generate a denoised partial discharge characteristic pattern; constructing a hypersphere model of the normal domain of cable multi-dimensional parameters and calculating the deviation distance value; inputting the deviation distance value into a fuzzy inference engine, performing non-linear conversion according to a preset fuzzy rule base, and generating an insulation state evaluation index and a fault risk probability distribution map of the distribution network cable, so as to realize the accurate evaluation of the cable insulation state. The present invention realizes the accurate evaluation of the cable insulation state and the accurate positioning of the fault location, and improves the operation reliability and maintenance efficiency of the distribution network.

The invention provides an intersection signal period level risk analysis method based on fuzzy logic, and belongs to the technical field of intelligent traffic, and the method comprises the steps: obtaining an intersection overlook video through employing an unmanned plane shooting mode; a YOLOv8 target detection algorithm and a BoTSORT tracking algorithm are adopted to detect and track a vehicle, and a vehicle track is obtained through Kalman filtering smoothing processing; based on the collision time difference, defining a collision time difference intrusion time proportion; according to the method, conflict indexes are adopted as quantitative factors of collision risks, fuzzy processing is carried out on the conflict indexes, an IF-THEN scheme is used for establishing a fuzzy rule base, different fuzzy logics used for collision risk assessment are developed, defuzzification is carried out through a gravity center method, and an assessment result of the collision risks is obtained; and quantizing the risk of each phase at the signal period level of the intersection through a traffic conflict index and a fuzzy inference device. According to the method, the signal period level collision risk of each phase of the intersection can be accurately obtained, and a decision basis is provided for signal control strategy optimization and active safety intervention.

The invention relates to the technical field of photoelectric pods, and discloses a fuzzy PID control method and servo system for a photoelectric pod, and the method comprises the steps: 1, obtaining the speed error e, the speed error change rate ec and the speed gain Kv of a brushless motor in real time; 2, e, ec and Kv serve as three-dimensional input variables to be sent into a fuzzy inference engine, and proportional gain correction delta Kp, integral gain correction delta Ki and differential gain correction delta Kd are output; 3, performing gravity center method ambiguity resolution on the delta Kp, the delta Ki and the delta Kd to obtain real-time PID (Proportion Integration Differentiation) parameters Kp, Ki and Kd; and 4, calculating a PWM duty ratio through real-time PID parameters, and driving the brushless motor to drive the photoelectric pod frame to rotate. According to the method, the speed gain serves as fuzzy input, so that the control algorithm can output more reliable control quantity, and the requirements for high precision and rapidity when the photoelectric pod is controlled by using a brushless motor can be met.

The invention discloses an industrial equipment fault diagnosis method and equipment based on machine learning and a medium, and the method comprises the steps: collecting operation data of target equipment, and carrying out the preprocessing of the operation data, so as to obtain monitoring data; a pre-trained fault diagnosis model is determined, the fault diagnosis model is an improved graph neural network model, and an input layer of the improved graph neural network model adopts a fuzzy reasoning mechanism; and inputting the monitoring data into the fault diagnosis model to determine the equipment state and the fault type of the target equipment. By using the graph neural network, the topological structure relation between samples can be fully mined and utilized, the method is particularly suitable for processing complex data from a multi-source sensor, interdependence between nodes can be captured, and therefore the diagnosis accuracy is improved. A fuzzy reasoning mechanism is adopted in an input layer of the graph neural network and used for processing uncertainty in data, adjustment can be carried out according to fuzziness of the data, and the capacity of processing uncertain data is improved.

The present disclosure provides an aluminum electrolytic alumina concentration online identification method and system based on fuzzy reasoning, which comprises: obtaining apparent alumina concentration and comprehensive discharging period through a time sequence knowledge graph, calculating the PDHC value of the current comprehensive discharging period and the previous comprehensive discharging period; inputting the PDHC value of the current comprehensive discharging period and the previous comprehensive discharging period into a preset fuzzy reasoning machine to obtain the fuzzy reasoning output variable of the alumina concentration state and the alumina concentration change trend of the current comprehensive discharging period; and performing anti-fuzzification processing on the fuzzy reasoning output variable of the alumina concentration state and the alumina concentration change trend of the current comprehensive discharging period to obtain the identification evaluation value of the alumina concentration state and the alumina concentration change trend of the current comprehensive discharging period.