177 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 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 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 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 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 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 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.

The invention discloses a calender control system for thin photovoltaic glass production, and belongs to the technical field of photovoltaic glass production control. The problems that an existing calender control system is insufficient in control precision, response speed and stability, and the high requirement for ultrathin photovoltaic glass production is difficult to meet are solved. The pressure sensor is used for monitoring the pressure of the compression roller in real time, anomaly detection and smooth processing are conducted in combination with the box plot method and the moving average method, the PID control model is adopted for dynamically adjusting the pressure, and equipment damage or the product quality problem is avoided; the temperature sensor monitors the temperature of the water-cooling and air-cooling systems in real time, accurate regulation and control are performed by using a discrete time PID algorithm, and an alarm mechanism is combined, so that the temperature stability is ensured, and the efficiency and quality are improved; based on a fuzzy control model, temperature and pressure data are integrated, the rolling speed is dynamically optimized through fuzzification, rule base, reasoning and defuzzification, the method adapts to complex nonlinear scenes, and the production stability and the product consistency are remarkably improved.

The invention provides an air energy heat pump direct current motor intelligent speed regulation method and system based on self-adaptive fuzzy control. The method comprises the steps of obtaining original operation data; extracting dynamic characteristics of a short-time window from the original operation data set, and calculating to obtain a thermal load trend index; constructing a fuzzy controller, wherein the fuzzy controller comprises fuzzification, rule reasoning and defuzzification output; in the fuzzification, a thermal load trend index is mapped into a five-level fuzzy level by adopting a triangular membership function; combining the thermal load trend index with an operation mode to calculate a target wind speed; and the target wind speed is converted into a control signal, feedback data is collected after each regulation and control period is finished, a performance deviation index is constructed based on the feedback data, and correction parameters are output. According to the invention, a resonance frequency point avoidance mechanism and a voltage margin correction strategy are embedded, so that the system is kept stable, low in noise and high in efficiency under high-speed, low-speed or medium-speed operation conditions.

The invention discloses a variable transmission ratio parameter optimization method for a steer-by-wire control system, which comprises the following steps of: designing a variable transmission ratio controller based on fuzzy control, taking a vehicle speed and a steering wheel angle as input variables, taking a transmission ratio as an output variable, performing fuzzification processing on the input and output variables by adopting a Gaussian membership function, and designing a fuzzy rule; carrying out defuzzification by adopting a gravity center method, and converting fuzzy output into a specific transmission ratio numerical value; parameters of the fuzzy controller serve as optimization objects, the parameters of the fuzzy controller are optimized through a simulated annealing algorithm, an initial solution is an initial value of the parameters of the fuzzy controller, steering response, stability and driving comfort of a vehicle are comprehensively considered, an energy function is designed, the performance of the fuzzy controller is evaluated, and the optimal parameters are searched through iteration. And minimizing the energy function to obtain the optimal parameter of the fuzzy controller. The method does not depend on vehicle model precision and expert experience, does not need manual parameter adjustment, has interpretability, and accords with the steering expectation of a driver.

The invention discloses a software quality evaluation method based on a fuzzy theory. The method comprises the following steps: obtaining a quality factor set according to a Meiger software quality model; determining an evaluation grade set, and dividing the evaluation grade set into five evaluation grades; constructing a membership function by adopting a kernel density estimation method; constructing a judgment matrix through the triangular fuzzy number and calculating a defuzzification weight; and converting the score into a fuzzy synthesis value through fuzzy synthesis operation, and generating a fuzzy comprehensive evaluation score through weighted average and normalization processing. According to the method, the fuzzy theory and the Meicole model are combined innovatively, and uncertainty in subjective and objective evaluation is processed by applying fuzzy mathematics. The objectivity, scientificity and repeatability of an evaluation result are effectively improved through a membership function and a weight set constructed based on historical data of similar software. The method is suitable for quality evaluation and optimization decision making of various complex software systems.

The invention relates to the technical field of deep and far sea culture, in particular to an underwater intelligent light supplementing method and system for a deep and far sea culture net cage, and the method comprises the steps: obtaining video image data of fish school movement, and carrying out the preprocessing of the obtained video image data; taking the preprocessed video image data as input to construct a fish identification model, performing fish school behavior analysis by using an output result of the identification model, generating a dynamic light supplementing strategy through a fuzzy PID control algorithm based on a fish school behavior analysis result, converting the dynamic light supplementing strategy into electric signal driving, and performing fish school behavior analysis; the light supplement strategy is adaptively optimized according to the real-time illumination parameters, the weight of the fuzzy rule base is updated online through the recursive least square method, the light intensity error and the change rate of the light intensity error serve as input, the fuzzy rule base is constructed through the fuzzy PID control algorithm, the PID parameter adjustment amount is calculated in a defuzzification mode in combination with the gravity center method, and the light supplement strategy is optimized. The light supplementing strategy can be accurately adjusted according to the fish shoal behavior and the actual illumination condition.

The invention relates to the technical field of data communication, in particular to a data communication system for multi-level linkage agent deduction, which is characterized in that in a data acquisition end, basic data of an agent is acquired and verified, and the data is subjected to multi-node processing to obtain deduction data; in the data transmission end, the deduction data is encrypted to obtain encrypted data, the block chain technology is adopted to transmit the encrypted data through multiple industrial control communication protocols, and the encrypted data is verified and monitored in the transmission process; in the data receiving end, decrypting the encrypted data, and performing error correction on the decoded data according to verification and monitoring results in the data acquisition end and the data transmission end; and synchronizing the verified data to a corresponding agent for data updating by adopting a distributed cache technology, performing deduction analysis on the synchronized updated data by adopting a deep learning algorithm, and performing defuzzification on a deduction analysis result by utilizing a fuzzy mathematical algorithm to obtain a deduction decision of the agent.

The application discloses a kind of information system access risk assessment methods based on access context logical inference, according to the security element of access subject context, the accessible asset set of access subject is obtained by logical inference of trust calculation and access policy set, then the risk degree of single access subject's comprehensive trust degree, access environment and its actual access asset value degree are carried out fuzzy processing, fuzzy inference and defuzzification, obtain the session access risk of single subject, after processing, the session access risk of entire information system is obtained.The application obtains the access permission of access subject by using trust calculation and logical inference, and introduces access context and reasoning mechanism into risk assessment, carries out fuzzy processing to these contexts related to access, such as environmental risk degree, asset value degree, by quantifying each element of access context process in information system, combined with logical inference and fuzzy inference, the comprehensive evaluation of information system session access risk is realized.

The invention discloses a method and system for controlling the height adjustment rate of a closed air suspension system, and the method comprises the following steps: firstly, the system receives the target height of a vehicle and the actual height obtained by a sensor, and calculates the height difference and the change rate thereof; then, the height difference and the change rate are fuzzified, and parameters Kp, Ki and Kd of a PID controller are generated based on a preset fuzzy rule; then, accurate PID control parameters are obtained through defuzzification, and a PID controller is designed and used for height adjustment of the air suspension system; the rotating speed of the compressor is adjusted according to the Kp value output by the PID controller, and the gas flow rate is controlled, so that the height adjusting rate is adjusted, and stable change of the suspension height is ensured. The control strategy can be dynamically optimized according to different vehicle working conditions and driving states, higher adjusting precision and response speed are provided, and driving comfort and stability are improved.

This application relates to the field of stationary time series analysis and prediction technology, and discloses a fuzzy prediction method for non-stationary time series, including: determining the fuzzy posterior probability corresponding to the current sequence; updating the fuzzy parameters of the fuzzy set based on the fuzzy posterior probability; generating a rule base associated with the updated fuzzy parameters; performing probabilistic defuzzification prediction based on the rule base to obtain the prediction result; updating the model parameters of the Gaussian mixture model based on the prediction error set corresponding to the prediction result; determining the KL divergence corresponding to the prediction error set based on the parameters in the model parameters, the first half error set corresponding to the first half window, and the second half error set corresponding to the second half window; wherein the current window consists of a first half window and a second half window; the prediction error set consists of the first half error set and the second half error set; and performing prediction based on the KL divergence to obtain the output sequence. This solution can meet the practical prediction needs of non-stationary time series.