559 results about "Fuzzy rule" patented technology

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 present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention relates to a city planning checking method and system based on natural language processing, and the method comprises the steps: receiving a user request instruction, carrying out the analysis, and obtaining the checking intention information and target region information; retrieving and matching a corresponding region rule set based on the target region information, and generating a checking task list and a data demand list; obtaining multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain standardized check data and historical fusion features; executing a preset differentiation checking process according to the task type; generating a checking report based on the differentiated checking process result; in conclusion, according to the method, the user request is processed and analyzed through the natural language, the planning knowledge base is constructed, deterministic and fuzzy rule tasks are processed in combination with the differentiated checking process, cooperation of rule terms and case features is achieved, the problems that a traditional checking method is low in automation degree and insufficient in fuzzy term processing capacity are solved, and the efficiency of checking is improved. The method has the effects of improving the checking efficiency and enhancing the interpretability of decision suggestions.

The invention relates to the technical field of automatic control, in particular to a multi-parameter cooperative control method and device for a clamping system of a precision boring and milling machine. According to the method, an industrial camera is used for collecting and processing a workpiece image and extracting visual features, and the visual features are fused with process parameters of a workpiece and geometric distribution parameters of a workpiece key area obtained from a process database; generating a comprehensive state vector; based on the vector, outputting a target clamping force adjustment coefficient by a pre-trained MLP decision model, and calculating a final target clamping force in combination with a safe clamping force range; in the main clamping stage, vibration, temperature and pressure signals are synchronously collected, multi-modal disturbance characteristics are extracted, observation vectors are constructed, the observation vectors are input into a preset fuzzy rule base for reasoning, and compensation activation factors are obtained; and finally, a current correction instruction is dynamically generated according to the clamping force deviation and the compensation factor, and an electro-hydraulic proportional valve is driven to achieve accurate compensation. According to the invention, the stability and the machining reliability of the clamping system are obviously improved.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably improved.

The invention discloses a road risk grading early warning method and system based on a Beidou satellite system, and the method comprises the steps: firstly carrying out the real-time positioning of a vehicle through Beidou dual-frequency signals, inertial navigation data, road side unit differential data and vehicle-mounted sensor data, and obtaining the precise position information; fusing the real-time position of the vehicle with meteorological data, vehicle-mounted OBD parameters and social media public opinion data to generate a dynamic risk factor matrix; a fuzzy rule base is constructed based on expert experience, the fuzzy weight of each risk factor in a matrix is obtained, meanwhile, the time sequence weight of each factor is predicted by means of an LSTM model, and a final road risk score is obtained through dynamic weighting. And performing graded early warning on the road risk in combination with the driver portrait, and feeding back and updating the fusion parameter, the fuzzy rule base or the LSTM model parameter according to the early warning effect to form a closed-loop optimization mechanism. Dynamic coupling analysis of multi-dimensional risk factors is realized, and the real-time performance and accuracy of road risk early warning are improved.

The invention discloses a power equipment fault diagnosis method based on adaptive fuzzy reasoning. The method comprises the following steps: S1, constructing a multi-source normalized feature vector set; s2, constructing a power system diagram; s3, executing an attention graph convolution operation, and outputting a node embedding state vector set; s4, obtaining an edge synapse updating weight matrix; s5, obtaining a fuzzy rule base; s6, executing a multi-stage fuzzy reasoning process, and outputting a fault diagnosis judgment result; s7, writing a fault diagnosis judgment result into a state memory unit of a corresponding node to form a node state memory record and an edge state evolution record; and S8, when the confidence coefficient of a fault diagnosis judgment result is lower than a set threshold value, calling a node state memory record and an edge state evolution record, executing time sequence sliding window backtracking processing, updating a node embedding state vector set, and repeating the step S6 to execute re-judgment. According to the invention, the graph neural network and adaptive fuzzy reasoning are fused, and intelligent diagnosis of power faults is realized.

The invention relates to the field of artificial intelligence and computer science, in particular to a causal structure and latent variable-based adaptive reinforcement learning reasoning migration method, which comprises the following steps of: constructing a causal world model fused with multi-modal observation and a decoupling latent variable space; establishing a hierarchical inference engine comprising an intuition layer, a conventional layer and a planning layer; pre-training a quick response and judicial planning dual-mode strategy and generating an interpretable fuzzy rule base; performing calculation level coarse tuning based on task identification and causal complexity; evaluating the real-time state criticality through an adaptive neural fuzzy system and dynamically switching a decision mode; after the action is executed, the threshold and the rule are subjected to closed-loop optimization, and cross-environment efficient migration is realized by utilizing a causal modularization characteristic. According to the technical scheme, consumption of computing resources is remarkably reduced on the premise that decision precision and safety are guaranteed, and the response speed and cross-scene adaptive capacity of a system on edge equipment are improved.

The invention provides a JKW control method based on adaptive fuzzy control, which belongs to the technical field of reactive power compensation of a power system, and comprises the following steps of: interacting reactive power compensation states of JKW devices in adjacent regions based on an edge physical agent, constructing a regional reactive power topological graph in combination with local power grid topological data, generating a regional coordination instruction according to a supply-demand relationship, and establishing a regional coordination graph according to the regional coordination instruction; the method comprises the following steps: acquiring proportion deviation, power factor deviation and reactive power change gradient of a mutual inductor, constructing a standard fuzzy input vector in combination with a regional cooperation instruction, mapping the standard fuzzy input vector into a fuzzy linguistic variable through a dynamically updated fuzzy rule base, and reasoning and outputting an initial switching strategy; and comparing and analyzing the proportion deviation and the power factor deviation through a hardware comparator, determining a trigger condition and existence of a fault, and dynamically optimizing an initial switching strategy by combining a regional reactive topological graph to realize the control of the JKW. And the operation stability of the power grid is obviously improved.

The method comprises the following steps: judging whether each wheel clamping force sensor works normally, if so, directly obtaining a brake clamping force as a final brake clamping force, if not, obtaining a first brake clamping force through a motor model, and if not, obtaining a second brake clamping force through a motor model; second brake clamping force is obtained through the brake friction plate model, and fusion is conducted through the fuzzy logic weight algorithm to obtain final brake clamping force; judging whether the wheel has a locking risk or not, if not, calculating the current ground longitudinal force of the wheel according to a kinetic model of the wheel, if so, triggering longitudinal force locking logic, and acquiring the current locking ground longitudinal force as the current ground longitudinal force of the wheel; calculating the current utilization adhesion coefficient of each wheel in combination with the vertical force; and determining the road surface type of the vehicle by using a fuzzy rule based on the adhesion coefficient of each wheel. According to the invention, the problem of low identification precision is solved.

The invention provides a high-performance shield intelligent synchronous grouting control system based on solid waste residue soil resource utilization, a grouting material and a preparation method, and relates to the technical field of shield engineering, the high-performance shield intelligent synchronous grouting control system comprises a parameter obtaining module used for obtaining key parameters, the actual grouting amount and the actual grouting pressure; the error calculation module is used for calculating a grouting amount error and a grouting pressure error; the error fuzzy classification module is used for fuzzy grade classification; the membership calculation module is used for calculating the membership of the grouting amount error and the grouting pressure error to each fuzzy grade category; the fuzzy rule base construction module is used for establishing an initial fuzzy rule base and adjusting and optimizing the initial fuzzy rule base to obtain a final fuzzy rule base; and a grouting process adjusting module. The solid waste muck is used for replacing bentonite and part of sand aggregate, so that the material cost is remarkably reduced, outward transportation and landfill of waste are reduced, and the burden on the environment is reduced.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention relates to the technical field of voltage control, and discloses an ESC voltage regulator voltage control method and system based on fuzzy adaptive PID, and the method comprises the steps: collecting the output voltage of an ESC voltage regulator and a preset target voltage in real time, calculating a voltage error and an error change rate after filtering, and carrying out the calculation of the error change rate; dividing fast tracking, precise adjustment and anti-interference stability maintenance stages according to errors and change rates and generating control signals, calling corresponding fuzzy rule subsets to deduce proportional, integral and differential gain adjustment amounts, generating real-time gains in combination with initial PID parameters, calculating control amounts by using position type PID based on the real-time gains, converting the control amounts into signals to drive power switch tubes, and controlling the power switch tubes. And closed-loop adjustment is formed. According to the method, the problem that a traditional control method is poor in performance under multiple working conditions is solved, cooperative control over rapid tracking, precise adjustment and anti-interference stability maintenance of the output voltage of the ESC voltage regulator is achieved, it is ensured that the ESC voltage regulator can output stable voltage under the working conditions of starting, steady-state operation and load sudden change, and the requirement for high voltage control precision and dynamic response is met.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention discloses an integrated circuit source measurement unit control method for improving a raccoon algorithm and optimizing fuzzy PID. The method comprises the steps that an output voltage control system of an SMU is set up; acquiring output end voltage data of the SMU system in real time; calculating a voltage error and a voltage error change rate; a fuzzy inference rule base is constructed, fuzzy controller parameters are dynamically adjusted based on the fuzzy rule base according to the voltage deviation value and the voltage error change rate, and then PID parameters are dynamically adjusted; a control signal is generated according to the set PID parameter, and the output voltage of the SMU is adjusted; and S4, optimizing the fuzzy PID voltage controller obtained in the step S4 by adopting an improved raccoon algorithm, selecting a decision variable as a parameter of a fuzzy controller, and assigning the optimized parameter to the fuzzy PID voltage controller to realize control on the output voltage of the SMU. According to the invention, the improved raccoon optimization algorithm and the fuzzy PID controller are combined and applied to the integrated circuit source measurement unit, so that the control performance of the SMU system is improved.

The invention provides a battery cell fault real-time monitoring method and system based on multi-parameter analysis and calculation, and relates to the field of battery safety monitoring. The method comprises the following steps: synchronously acquiring voltage, temperature and internal resistance signals of a battery cell, and preprocessing to obtain original data; and performing voltage correlation analysis, internal resistance-temperature collaborative judgment and isolated forest anomaly detection on the original data in sequence to form a progressive anomaly judgment chain, and extracting multi-dimensional battery cell state features. And dynamically correcting the voltage protection threshold value based on the battery health state attenuation rate and the real-time temperature. And fusing multi-dimensional battery cell state characteristics through a fuzzy rule base, and calculating a comprehensive fault probability to divide a fault probability grade. And triggering a matched response strategy based on the fault probability level. The technical problems that single-parameter monitoring is low in sensitivity and high in false alarm rate, and the real-time monitoring requirement is difficult to meet by depending on historical data are solved.

The invention discloses a wind turbine generator self-adaptive control method, medium and system based on environmental turbulence, and the method comprises the steps: S1, collecting the power of a generator and the current pitch angle in the normal operation process of a wind turbine generator; s2, estimating the real-time wind speed based on the power of the generator and the inherent parameters of the unit; s3, calculating real-time environmental turbulence intensity according to the real-time estimated wind speed; s4, inputting the real-time environmental turbulence intensity and the current pitch angle into a fuzzy controller, and calculating a variable pitch control parameter coefficient according to a preset fuzzy rule; and S5, based on the variable-pitch control parameter coefficient, the variable-pitch control parameter is adjusted to carry out variable-pitch control, and self-adaptive stable operation of the unit is achieved. According to the method, no hardware needs to be additionally installed, unit load impact and rotating speed fluctuation caused by large turbulence can be effectively restrained, and on the premise that it is ensured that the limit load of key components does not exceed the limit, the operation stability of the unit is improved, the power generation loss is reduced, and cost reduction, efficiency improvement and safe and stable operation are achieved.

The invention discloses a storage tank pressurization control method and system based on LNG gas station fuzzy self-adaptive PID control, and aims to solve the technical problems that a traditional pressurization control method of an LNG storage tank is low in control precision, poor in stability and rapid in equipment abrasion. According to the control method, dynamic compensation for the target pressure is introduced, so that the system can aim at a more accurate control target in the initial stage, the steady-state control deviation is reduced, and the control precision is improved. And static pressure head change disturbance caused by liquid level change is overcome through dynamic compensation, the adjusting burden of the controller is reduced, the controller can focus on processing other dynamic disturbance, and therefore the control process is more stable, and overshoot is smaller. Through the design of a dynamic compensation mechanism and a special fuzzy rule base, the system can automatically adapt to various complex working conditions, and fast, stable and overshoot-free pressure control is always kept. Therefore, frequent and large-amplitude action and surge phenomena of the valve are avoided, and the energy consumption of an executing mechanism is reduced.

The invention relates to the technical field of servo drivers, in particular to a servo driver control method, and the technical scheme comprises the steps: carrying out the online identification of load inertia and external disturbance through a self-adaptive observer, generating a dynamic compensation parameter, constructing a multi-model parallel self-adaptive observer group, and carrying out the online identification of the load inertia and external disturbance through the self-adaptive observer group, a dynamic confidence evaluation mechanism is designed, disturbance dynamic compensation is realized, parameter sudden change and slow change scenes can be taken into consideration, the real-time performance of dynamic response can be ensured, compensation parameters are input into a fuzzy neural network controller, an optimal control quantity is generated in combination with a preset control target, a multi-source compensation parameter input channel is constructed, and the optimal control quantity is generated. A dynamic input weighting module is designed, fuzzy rule confidence is updated through an online strategy gradient algorithm to generate an optimized control quantity, the working condition adaptability can be enhanced, a dynamic target can be dealt with, finally, a sliding mode variable structure algorithm is adopted to carry out high-frequency buffeting suppression on the control quantity, and a final driving signal is output to a power module. And the control precision of the servo driver is 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 application discloses a network congestion control method and device based on fuzzy rules, and the method comprises the following steps: setting corresponding fuzzy rules according to network types, wherein the network types include a data center network and a wide area network; obtaining network parameters and performing fuzzy processing, fuzzy reasoning and defuzzification on the network parameters to obtain an adjustment factor ΔF, wherein the network parameters include a bandwidth utilization rate, an ECN marking rate and a queue length; obtaining the size of a current congestion window and adjusting the size of the congestion window in real time according to the adjustment factor ΔF; the steps of fuzzy processing and fuzzy reasoning further comprise detecting the current network type, and selecting corresponding fuzzy rules and dynamically adjusting the weight coefficients corresponding to each network parameter in the process of fuzzy reasoning according to the detected network type. The network congestion control method based on fuzzy rules can adjust the network congestion window in real time, realizes the intelligentization and automation of network congestion control, and improves the network management efficiency.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

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.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention provides a fuzzy knowledge graph construction method related to a wafer manufacturing process, and relates to the technical field of fuzzy knowledge graphs, and the method comprises the steps: employing a three-dimensional convolutional network to extract a spatial defect feature X of a scanning electron microscope image, carrying out the coding of sensor time series data through a bidirectional LSTM, and obtaining a feature set Y, obtaining electrical performance parameters of the wafer based on impedance spectroscopy analysis from the text to obtain a feature set Z; building fuzzy knowledge based on the text to reinforce the feature sets X, Y and Z, building a fuzzy feature set and a time sequence feature set, building a weighted scoring model, and screening a candidate set from the text according to the weighted scoring model; information is extracted from the candidate set, and a tetrad structure G = (entities, relationships, time attributes and fuzzy rules) is constructed; and taking G as input, generating a time sequence fuzzy rule through a recursive reconstruction contribution method, and incrementally updating the knowledge graph. According to the method, the characterization bottleneck of space-time coupling and uncertainty correlation in a traditional method is broken through.

The invention relates to the technical field of semiconductor manufacturing, and discloses a semiconductor heater temperature control method based on self-adaptive PID (Proportion Integration Differentiation). The method comprises the steps of collecting temperature data of multiple monitoring points of the semiconductor heater in real time, and constructing a temperature field distribution matrix to completely present temperature distribution of the heater; generating a set temperature track based on the matrix, and implementing closed-loop regulation on the heating power through a self-adaptive PID (Proportion Integration Differentiation) controller, so that the temperature stably fits the set track; in closed-loop regulation, a PID parameter fuzzy rule base is dynamically reconstructed according to the temperature fluctuation trend, and the adaptive capacity of the controller is enhanced; when a temperature abnormal area is detected, a multi-level compensation mechanism is triggered to carry out phase correction on the controller, and abnormity is rapidly handled. The method can optimize the accuracy and stability of temperature control, cope with temperature fluctuation and abnormity, and meet the strict requirements of semiconductor manufacturing for temperature control.

The invention discloses an anesthesia post-operation awakening delay risk assessment method based on adaptive fuzzy reasoning. The method comprises the following steps: S1, obtaining a normalized multi-modal physiological information data stream with consistent time sequence; s2, dividing the standardized multi-modal physiological information data stream into a high-frequency physiological information data substream and a low-frequency physiological information data substream according to a sampling frequency; s3, generating a high-frequency fuzzy reasoning result based on the initial high-frequency fuzzy rule base and an adaptive membership function; s4, generating a low-frequency fuzzy reasoning result based on the initial low-frequency fuzzy rule base and an adaptive membership function; s5, outputting a multi-scale fusion risk scoring curve; and S6, generating a wake-up delay risk alarm signal and recording a risk driving factor list. According to the method, the risk identification sensitivity and robustness of high-risk patients, complex operations and multi-complication cases are remarkably improved, and missing report and false report caused by a single-dimension or static threshold value are avoided.

The invention discloses a power coordination control method of a high-temperature gas cooled reactor nuclear steam supply system, which relates to the technical field of nuclear power, and comprises the following steps of: coupling multiple modules in the high-temperature gas cooled reactor nuclear steam supply system through a common conventional island; the key state parameters of the to-be-adjusted module are used as main control variables, and the other modules are used as auxiliary control variables, so that the mutual influence relationship among the modules can be comprehensively considered; reasoning the control quantities based on a preset fuzzy rule to determine a fuzzy control strategy, so that adverse effects among modules can be counteracted, overall parameters are kept in a design value range, and safe and stable operation of the system is guaranteed; flexible adjustment can be performed according to key state parameters of different modules by using a fuzzy control strategy, the power of the reactor is quickly and accurately controlled, the dynamic response capability of the system is improved, and the requirement that the power of the reactor is quickly adjusted along with the load requirement of the steam turbine generator unit is met.