218 results about "Defuzzification" patented technology

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention provides a river crab feed feeding mixed monitoring method, system and device and a medium, and belongs to the field of aquaculture intelligent monitoring. Data is collected and normalized, and a chain type data collection system is constructed; the edge computing and block chain technology is adopted to realize data distributed storage and transmission; a fuzzy neural network hybrid model is constructed, fuzzification processing, feature mining and defuzzification output are integrated, and a feeding amount prediction value is generated; associating the feed proportioning library with the knowledge graph, and iteratively training the model and generating a multi-target feeding decision parameter; feeding equipment is controlled to execute actions according to the decision parameters, and the real-time monitoring model outputs and triggers an early warning signal; and updating the knowledge graph and the feed matching library based on the feedback data to form closed-loop self-adaptive regulation and control. According to the method, various factors can be comprehensively considered, scientific and intelligent feed feeding decision is realized, feed waste and insufficient feeding are effectively avoided, and the growth quality of the river crabs is improved.

The invention discloses an intelligent circuit breaker system based on a fuzzy control theory. In the intelligent circuit breaker, current changes are monitored in real time through a fuzzy controller, an input variable is converted into an input fuzzy quantity for expression according to a membership function, fuzzy reasoning is carried out on the converted input fuzzy quantity according to a preset fuzzy rule base to obtain an output fuzzy quantity, and finally the output fuzzy quantity is defuzzified. And a final circuit breaker action instruction is obtained. The method has the beneficial effects that the safety stability and the response flexibility of the system are improved, and the protection strategy is dynamically adjusted, so that various complex power failure scenes can be more flexibly and efficiently handled.

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 fabricated beam bridge finished product quality evaluation method fusing fuzzy reasoning and a neural network, and belongs to the field of comprehensive evaluation, and the method comprises the steps: constructing a fabricated beam bridge finished product quality intelligent evaluation system which comprises a first-level index and a second-level index; adopting a fuzzy number to describe quantitative and qualitative indexes, and constructing a fuzzy judgment matrix; calculating an initial weight vector by using an analytic hierarchy process and a defuzzification technology; calculating an improved regularization item balancing data driving and expert priori; and performing weight optimization and weighted summation on the parameters of each component by using a weighted neural network, and outputting a finished product quality score of the fabricated beam bridge. The invention aims to construct an intelligent evaluation system for the quality of the finished product of the fabricated beam bridge, realizes collaborative evaluation of expert knowledge and a data driving method by fusing a deep learning technology, improves the accuracy and robustness of the quality evaluation of the finished product of the fabricated beam bridge, and provides an efficient and reliable technical means for the intelligent evaluation of the quality of the fabricated beam bridge.

The invention relates to an amphibious vehicle path planning method and system based on fuzzy logic rules and reinforcement learning. The planning method comprises the following steps: collecting scene parameters by using a sensor according to an application scene of the amphibious vehicle, and taking the scene parameters as input parameters; performing fuzzification processing on the input parameters, performing reasoning by using a fuzzy rule base to obtain fuzzy output, and performing output defuzzification on the fuzzy output by using a gravity center method; defining a state space and an action space, resetting an environment, executing an action and calculating a reward, and finishing the step when a turn ending condition is met; an Actor-Critic neural network is initialized, and stable learning of a strategy is realized through experience playback and an updating mechanism of a target network; and training and optimizing the Actor-Critic neural network to obtain an optimal path planning strategy. According to the amphibious vehicle path planning method based on the fuzzy logic rule in combination with reinforcement learning, efficient solution of a path planning problem in a complex environment is realized through combination of a fuzzy logic system and a reinforcement learning algorithm, and the amphibious vehicle path planning method has a wide application prospect.

The invention relates to the field of temperature control, in particular to a solution conductivity meter temperature control method based on fuzzy PID, and the method comprises the steps: collecting the temperature data of a measurement solution through a high-precision temperature sensor; acquiring temperature data of the measurement solution, and calculating a temperature deviation value and a temperature change rate of the measurement solution; inputting the temperature deviation value and the temperature change rate into a fuzzy PID (Proportion Integration Differentiation) control module; according to the input temperature deviation value and the temperature change rate, the input of the PID controller is corrected through a fuzzification and fuzzy rule table in the fuzzy PID control module and a corresponding variable quantity output by defuzzification; according to an algorithm, updated PID parameters are obtained, and then a pulse width control signal is obtained by an incremental PID control algorithm to control a driving circuit to dynamically adjust a TEC semiconductor cooler, so that the temperature of the measured liquid is controlled, the control speed is effectively improved, the robustness of the system is enhanced, and the temperature is stabilized near the target temperature.

The invention provides an electro-hydraulic servo pump control method and system for controlling hydrogen compression based on fuzzy nerve, and relates to the technical field of hydrogen compression, and the method comprises the steps that the current state of a hydraulic cylinder controlled by an electro-hydraulic servo pump is determined, and an input control variable is obtained; determining an output control variable, establishing a fuzzy control rule, and dividing a basic discourse domain; performing composite defuzzification, and obtaining a corrected weight coefficient through a feedforward neural network; the discrete rule amplitude correction factor and the weight coefficient correction are dynamically optimized and corrected; neural network training is established, a loss function evaluation model is utilized, and hydrogen compression control is achieved through a double-buffering mechanism. Fuzzy PID control is adopted, composite defuzzification is carried out according to the real-time state change of a system in combination with fuzzy logic, a membership function output by the fuzzy PID is divided according to regions, a control signal of a driver is synthesized, and hydrogen compression control is achieved; system output is adjusted according to different working conditions, power loss is reduced, and system control precision is 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 an intelligent infusion evaluation reminding method and system, and solves the problems that it is difficult to accurately grasp the illness state change and infusion requirements of each patient, and omission or delayed processing is likely to occur. The method comprises the steps that the environment noise value and the working state of the environment where medical staff currently works are obtained, performing fuzzification processing on the acquired environmental noise value and the working state of the medical staff, converting the environmental noise value and the working state of the medical staff into elements in a fuzzy set, and establishing a logical relationship among the environmental noise, the working state of the medical staff and the reminding level according to a pre-established fuzzy rule base; and generating corresponding fuzzy output results for different reminding levels through fuzzy reasoning, determining a specific reminding mode corresponding to the reminding levels through defuzzification calculation, and executing the corresponding specific reminding mode. The system has the advantages that efficient management and optimization of the infusion process are achieved, the working efficiency of medical staff is improved, and safety and effectiveness of infusion treatment are guaranteed.

The invention discloses a production execution MES system, and particularly relates to the technical field of MES systems, which comprises the steps of collecting energy consumption information, historical information and operation information generated by production equipment in the production process of a product, obtaining an energy consumption change condition of the production equipment according to the energy consumption information generated by the production equipment, and calculating the energy consumption change condition of the production equipment; according to historical information of energy consumption information generated by production equipment, a clustering algorithm is used for identifying potential risks of the production equipment, key features of replaceable equipment are extracted, a fuzzy rule base is constructed, defuzzification processing is carried out, operation scores of the production equipment are obtained, and production process information and production quality information are collected. The matching degree of products in the production process is quantified, the genetic algorithm is used, the optimal manufacturing path of the products in the MES system is obtained, clear and intelligent decision support is provided for selectively and preferentially shutting down the equipment poor when multiple pieces of production equipment work at the same time, and the fault risk of production is reduced.

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 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 provides a self-adaptive interval type-2 fuzzy logic enhanced intelligent manufacturing control method, which comprises the steps that a fuzzy set influencing a target system is predicted through a particle swarm optimization algorithm, the fuzzy set comprises one or more dimensions, and each dimension determines a numerical range capable of generating corresponding influence through an upper membership function and a lower membership function; activating a corresponding fuzzy reasoning rule in response to the obtained fuzzy set output by the particle swarm optimization algorithm, performing fuzzy reasoning based on upper and lower membership functions in the fuzzy set, and obtaining a reasoning set; and performing defuzzification on the reasoning set to obtain a control instruction, and controlling the target system to perform intelligent manufacturing based on the control instruction. According to the method, the capability of representing and processing uncertainty in the intelligent manufacturing control process can be improved, so that a more accurate decision instruction is provided to improve the product quality and the production performance.

The invention provides a flow control method and device for optimizing fuzzy PID based on particle swarm optimization, and relates to the technical field of intelligent control, and the method comprises the steps: taking a sample flow error and a sample error change rate as sample input variables, taking a first control parameter Kp, a second control parameter Ki and a third control parameter Kd as sample output parameters, and creating a PID controller model; performing fuzzification processing on a current input variable to generate a fuzzy set, matching a weighting rule based on the fuzzy set, inputting the fuzzy set into the PID controller model to complete defuzzification, and obtaining a corrected output parameter; optimizing the corrected output parameter by adopting a particle swarm algorithm to obtain a target output parameter; and determining a flow control parameter in the current gas flow control environment based on the target output parameter. According to the invention, the fuzzy PID controller is designed and optimization is carried out by using the particle swarm optimization, so that a more accurate and stable control effect is realized in gas flow control.

The invention discloses an adaptive modulation coding method and system based on fuzzy logic, and belongs to the field of wireless communication, and the system comprises a dynamic window module which dynamically adjusts the window size of a fuzzy set according to the received signal strength indication and the change trend of the packet reception rate; the input fuzzification module takes the actual received signal strength indication and the packet reception rate as input and converts the actual received signal strength indication and the packet reception rate into fuzzy variables through fuzzification processing; the fuzzy reasoning module is used for defining different membership functions, mapping continuous received signal strength indications and packet reception rate measurement values into a fuzzy set, considering membership value combinations of all RSSI and PRR fuzzy categories, calculating a weighted total score and outputting a fuzzy decision value; and the defuzzification module is used for converting into an actual modulation coding selection scheme through a defuzzification process. By adopting the method and the system, the adaptability and the response speed of the system to complex and changeable channel conditions are improved, and the accuracy and the reliability of adaptive modulation coding selection are improved.

The embodiment of the invention provides an intelligent bone drilling self-adaptive control method for a mechanical arm, which comprises the following steps: acquiring displacement deviation of the tail end of a drill bit of an orthopedic surgical robot, obtaining an error change rate through differential calculation, and combining a bone layer type as an input variable of a fuzzy controller and a feeding speed as an output variable; input variables are fuzzified through a membership function, and a fuzzy language variable set is obtained; according to the set and a preset rule base, constructing a multi-rule group structure comprising three types of scenes of free space, cancellous bone and cortical bone, and setting corresponding fuzzy rule subsets for different bone layers; designing a ''minimum-maximum'' rule activation and combination strategy for reasoning by adopting a Mamdani type fuzzy reasoning method according to fuzzification input and a multi-rule group structure, and generating a fuzzification output variable set; and defuzzification is performed through a gravity center method to obtain a continuous speed instruction, and dynamic regulation and control of the drill bit feeding speed are achieved. When approaching a penetration point, the device automatically decelerates and safely stops, so that the safety and accuracy of an operation are effectively guaranteed, and over-drilling injury is prevented.

The embodiment of the invention discloses a GIS fault diagnosis and identification method based on multi-source information fusion. The method comprises the steps that GIS signals are collected through a plurality of sensors installed on GIS equipment; extracting a plurality of pieces of feature information based on the GIS signal; obtaining a fuzzy rule base, wherein the fuzzy rule base comprises a matching relationship between the feature information corresponding to the historical GIS signal and the fault type; fuzzifying the plurality of pieces of feature information to obtain fuzzified feature information; determining total fuzzy output of the fuzzification feature information based on the fuzzy rule base, wherein the total fuzzy output comprises multi-dimensional feature information; and performing defuzzification on the total fuzzy output to determine the GIS equipment fault type. According to the invention, a plurality of sensors installed on GIS equipment are used for acquiring signals, and the sensors acquire equipment operation data from different angles and layers, so that multi-source data fusion is realized. The multi-source data can reflect the operation state of the equipment more comprehensively, and the risk of fault misjudgment is reduced.

The invention discloses a non-meteorological target identification method based on meteorological feature image clustering and fuzzy fusion. Firstly, millimeter wave cloud radar meteorological data are preprocessed; and then generating a meteorological data parameter feature image, and converting the two-dimensional meteorological data array into a two-dimensional pseudo-color image. According to the method, distribution characteristics of meteorological targets and non-meteorological targets on a data image are analyzed, a meteorological data parameter characteristic image is divided into different potential target areas by using an image segmentation large model, and parameter characteristics in the potential target areas are extracted; and calculating the average radial texture and inter-library change degree of the reflectivity factor, the regional average value and variance of the radial speed, and the regional average value parameter characteristic statistical information of the speed spectrum width. A fuzzy logic fusion algorithm is adopted to determine a fuzzy membership function of statistical information, a fuzzy rule is set to calculate a fuzzy membership degree of each data point, and a weighted summation method is adopted to perform defuzzification to obtain a judgment value compared with a threshold value. According to the invention, the accuracy of identifying the non-meteorological target is improved.

The invention provides a fuel cell energy management method and device based on fuzzy control, and the method comprises the steps: carrying out the fuzzy processing of a current data value corresponding to an impact factor, and determining a target fuzzy set discourse domain corresponding to the current data value of the impact factor, and the membership degree of the target fuzzy set discourse domain in the target fuzzy set discourse domain; performing fuzzy reasoning in combination with a target fuzzy set discourse domain corresponding to the influence factor and a preset fuzzy rule table, and determining a fuzzy set discourse domain to which the output power of the fuel cell belongs; and according to the fuzzy set discourse domain to which the output power of the fuel cell belongs and the membership degree of the influence factor on the target fuzzy set discourse domain to which the influence factor belongs, performing sharpening processing on the output power of the fuel cell by using a preset defuzzification method, and determining a predicted value of the output power of the fuel cell. Fuel cell power output management is achieved through the fuzzy control theory, the energy utilization efficiency of the fuel cell hybrid power system is improved, and the driving experience of a driver is improved.

The invention discloses a network congestion control method and device based on a fuzzy rule, and the method comprises the following steps: setting a corresponding fuzzy rule according to network types which comprise a data center network and a wide area network; network parameters are obtained, fuzzification processing, fuzzy reasoning and defuzzification are carried out on the network parameters, an adjustment factor delta F is obtained, and the network parameters comprise the bandwidth utilization rate, the ECN marking rate and the queue length; obtaining the size of the current congestion window, and adjusting the size of the congestion window in real time according to the delta F; a current network type is detected between the fuzzification processing step and the fuzzy reasoning step, a corresponding fuzzy rule is selected according to the detected network type in the fuzzy reasoning process, and a weight coefficient corresponding to each network parameter is dynamically adjusted. According to the network congestion control method based on the fuzzy rule, the network congestion window can be adjusted in real time, intelligence and automation of network congestion control are achieved, and the network management efficiency is improved.

The invention discloses an automatic regulation and control method and device for a valve of a heat supply pipeline and a medium, and relates to the field of automatic control over a heat supply system.The method comprises the steps that the opening value of the valve of a target heat supply pipeline is obtained in real time, and according to the opening value, the heat difference and the heat difference change rate of the target heat supply pipeline are predicted through a fitted polynomial regression model; converting the predicted heat difference and the predicted heat difference change rate into corresponding membership values through a membership function, and obtaining a total membership value of a preset fuzzy set; performing matching in a fuzzy rule base based on the membership value, and determining a fuzzy value; performing defuzzification on the fuzzy value according to the total contribution and the total membership value of a preset fuzzy set to obtain a specific value of the valve adjustment amount; and adjusting the control parameters of the automatic controller based on the specific value. And by continuously optimizing automatic control parameters, the steady-state error of the system is reduced, the control precision of the system is improved, and it is ensured that the heat supply effect is more stable.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

The invention discloses a data center air conditioning equipment control method and system. Comprising the steps of obtaining a first environment temperature and a second environment temperature of a target space where the data center air conditioning equipment is located at a first sampling moment and a second sampling moment, and determining a temperature error and an error change rate of the target space at the first sampling moment by combining an expected environment temperature, the temperature error and the error change rate serve as target input variables to be matched with a preset fuzzy rule base, a target output fuzzy set is obtained, defuzzification processing is conducted on the target output fuzzy set, and a corresponding target output variable is obtained; and a control signal is determined according to the temperature error, the error change rate and the target output variable, and equipment operation parameters of the data center air conditioning equipment are adjusted through the control signal. The technical problem that the effect of controlling the equipment operation parameters of the data center air conditioning equipment through the control signals generated through the fixed PID parameters is poor is solved.

The invention relates to the technical field of load segmented transfer, in particular to a 10kV distribution line load segmented transfer method and device based on an electric energy meter and a storage medium. Comprising the steps that S1, electrical parameters of key nodes of a 10kV distribution line are monitored in real time based on an intelligent electric energy meter, and the electrical parameters at least comprise voltage, current and a power factor; s2, mapping the monitored electrical parameters to a predefined fuzzy set by using a fuzzy logic algorithm, and reasoning and quantitatively evaluating the overload degree of the equipment based on a fuzzy rule; selecting a to-be-transferred load path based on a Dijkstra algorithm; and after it is confirmed that the load verification of the to-be-transferred path is passed, carrying out load transfer. According to the method, the electrical parameters on the key nodes are monitored in real time, overload evaluation is carried out in combination with the fuzzy logic algorithm and the optimized defuzzification algorithm, and the overload condition can be recognized more accurately even under the condition that current and power factors fluctuate sharply due to frequent starting and stopping of large industrial equipment.

The invention relates to the technical field of virtual reality interaction, and particularly discloses a multi-modal fused virtual exhibition hall immersive interaction system, which comprises the following steps of: acquiring real-time multi-modal interaction data of a user and performing synchronous preprocessing; inputting the orientation semantic words into an interval type-2 fuzzy logic system to generate a three-dimensional space semantic membership field; according to the gesture pointing vector and the hand shaking amplitude, a pointing conical area is constructed, two-dimensional Gaussian distribution is established in a layered mode, and a gesture pointing probability field is generated; taking a semantic membership field, a gesture pointing probability field and a gaze point Gaussian kernel density field as independent evidences, introducing a wall boundary and a floor channel as spatial topology constraints, and fusing by adopting an evidence theory combination rule to generate a three-dimensional intention probability distribution field; carrying out gravity center defuzzification processing on the distribution field to extract a navigation target area, and planning a navigation path by taking the current position of the user as a starting point and taking a nearest floor channel entrance as a passing point; according to the method, the problems of multi-mode fuzzy intention understanding and space adaptation are solved.

The invention relates to the technical field of wireless power transmission, in particular to a fuzzy PID wireless power transmission control method, system and device based on load estimation, and the method comprises the steps: measuring an output voltage and an output current, and estimating the load information of a wireless power transmission line; an equivalent circuit model is constructed based on the load information, and the relation between the output active power of the full-bridge inverter and the load is established; monitoring load information in real time, identifying system interference and load change according to a preset change rate threshold value, and judging whether to execute fuzzy PID self-tuning control or not; when fuzzy PID self-tuning control is executed, fuzzy control rules are established by fuzzifying the input quantity error and the error change rate; and performing fuzzy reasoning according to a fuzzy control rule to obtain a control quantity, and generating a corrected PID control parameter through a defuzzification method. According to the invention, the problem of limitation of a traditional PID control method in a wireless power transmission system when facing nonlinearity, dynamic change and interference is effectively solved.

The invention relates to the technical field of gas sensors, in particular to a temperature control method, device and equipment of a gas sensor and a medium, and the method comprises the following steps: acquiring the current temperature of the gas sensor; generating a fuzzy set according to the current temperature and the target temperature range of the gas sensor; generating a fuzzy PID control action according to the fuzzy set, and performing defuzzification processing on the fuzzy PID control action to obtain a target PID control action; and controlling the current temperature of the gas sensor based on the target PID control action. Therefore, the problems of inaccurate detection result and the like caused by the fact that the detection of the gas sensor is easily influenced by the environment temperature in the prior art are solved.

The invention discloses a power distribution network dynamic safety space solving method and system for virtual power plant aggregation, and relates to the technical field of power distribution network safety operation, and the method comprises the steps: obtaining the network topology information of a power distribution network, and carrying out the calculation based on the network topology information of the power distribution network to obtain a hyperplane representation power distribution network safety domain; obtaining a resource fuzzy boundary uploaded by the virtual power plant, performing optimization decision calculation based on the resource fuzzy boundary uploaded by the virtual power plant, and generating a power distribution network resource fuzzy model by using an optimization decision value; inputting the fuzzy model of the power distribution network resource into a hyperplane representation power distribution network security domain, outputting to obtain a fuzzy power distribution network dynamic security space, and performing reduction and defuzzification on the fuzzy power distribution network dynamic security space by using an improved enhanced opposite search algorithm to obtain a power distribution network dynamic security space; inputting the dynamic safety space of the power distribution network into a pre-established aggregation model of a virtual power plant, and outputting to obtain aggregation capacity of the virtual power plant; and calculating the virtual power plant blocking capacity caused by the dynamic safety space of the power distribution network in combination with the original virtual power plant aggregation capacity.

The invention discloses a water turbine primary frequency modulation method adopting feedforward double fuzzy control and adapting to working condition changes, which comprises the following steps of: firstly, inputting a frequency deviation into a PID (Proportion Integration Differentiation) controller when the frequency deviation exceeds a primary frequency modulation dead zone, inputting a water head signal and a power signal into a fuzzy controller I, and outputting PID controller parameter Kp, Ki and Kd values after fuzzification, fuzzy reasoning and defuzzification; the PID output and the feed-forward signal are added and then converted into a hydraulic signal through an electro-hydraulic converter; and then the opening degree deviation signal and the derivative of the signal are input into a fuzzy controller II, an input electric signal of an electro-hydraulic converter is output, a movable guide vane is operated through a hydraulic execution mechanism to control the output of the water turbine, and a primary frequency modulation action is completed. According to the method, adaptability improvement is carried out on different working conditions of the water turbine governor based on fuzzy logic, meanwhile, in order to limit the water hammer effect, the second fuzzy controller is added in front of the electro-hydraulic converter, and the adaptability, quickness and stability of the primary frequency modulation process are effectively improved.