90 results about "Degree function" patented technology

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 invention relates to a hydroelectric generating set mirror plate fault judgment method based on analytic hierarchy process and fuzzy membership, which comprises the following steps: selecting symptom indexes reflecting faults, and systematically analyzing and constructing a hierarchical structure model in a fault type-symptom index association form by using the analytic hierarchy process; a triangular-semi-trapezoidal membership function is used as a basis, a membership function is constructed according to a fault condition level, a symptom index membership matrix is calculated according to a deviation degree between a symptom index real-time value and a health reference value, a fault type fuzzy judgment matrix is further obtained, and a fault type fuzzy judgment matrix is obtained according to a maximum membership principle. And obtaining a diagnosis result whether the hydroelectric generating set has a mirror plate levelness standard exceeding fault. According to the invention, through systematic analysis, objective weight distribution, dynamic reference comparison and a fuzzy determination method, the accuracy of fault determination is significantly improved.

The invention discloses an electronic plant air conditioner control method for setting PID parameters based on a fuzzy algorithm. The method comprises the steps that a design scheme is preliminarily determined according to plant design indexes, and a schematic diagram of a control system is planned; establishing a mathematical model; carrying out PLC control program design and configuration programming; designing a fuzzy controller; fuzzy design of input and output variables; defining an input and output membership function; establishing a fuzzy control table and rules; designing a system fuzzy reasoning algorithm; setting defuzzification output; designing a Simulink system control block diagram, and establishing a real-time control working space; and the communication between the MATLAB and the PLC is realized. According to the method, the control experience of a person is summarized into a group of conditional statements described qualitatively by utilizing a fuzzy logic function of Matlab software, and the conditional statements are quantified by utilizing a fuzzy set theory, so that the controller can accept the experience of the person, simulate an operation strategy of the person and overcome uncertain factors of the person, and the system has higher reliability.

The invention discloses a water resource suitable bearing capacity evaluation method and system considering uncertainty, and belongs to the technical field of water resource management. Constructing a double-layer interval multi-objective planning model, wherein objective functions of the double-layer interval multi-objective planning model comprise an upper-layer objective function and a lower-layer objective function; respectively constructing evaluation functions for the upper-layer objective function and the lower-layer objective function; decomposing the double-layer interval multi-target planning model to obtain two independent interval planning models, respectively constructing a target function lower limit sub-model and a target function upper limit sub-model for each interval planning model, and respectively solving to obtain a lower limit sub-model optimal solution and an upper limit sub-model optimal solution; constructing a membership function, and introducing global satisfaction to establish a satisfaction maximum model; and solving by utilizing the model with the maximum satisfaction degree to obtain a final solution, namely an evaluation result.

The invention provides a method for establishing a precise ammonia injection fuzzy control model based on an LSTM neural network. The method comprises the following steps: S1, analyzing historical data, cleaning, designing a membership function and formulating a fuzzy rule table; s2, constructing a control model by using an LSTM neural network algorithm to obtain an error between a predicted value and an actual value and an error change rate; and S3, comparing a prediction result of the model with an actual output result. According to the invention, an advanced neural network technology and a flexible fuzzy control strategy are fused, so that accurate adjustment of ammonia spraying of the SCR denitration system is realized. A fuzzy expert control system is built, an error, a change rate range of the error and valve opening variation corresponding to different errors and error change rates are obtained by analyzing historical data of ammonia water adjustment, and a fuzzy discourse domain and a fuzzy rule table are designed in combination with a fuzzy control principle and a system working condition. And fuzzy reasoning and fuzzy judgment are carried out on real-time data of the system, and the increment of the ammonia water regulating valve under the current working condition is calculated and regulated.

The invention discloses a method for solving a task-platform optimization configuration problem based on QGA quantum enhancement, and the method comprises the steps: firstly, employing the true randomness and entanglement of quantum, achieving the coding of a corresponding relation between a task and a platform through a quantum state, and determining an optimal individual in a population through a fitness function; secondly, a quantum logic gate is used for achieving selection operation in the algorithm, and the rotation angle and direction of the quantum logic gate are determined through the fitness difference value between each chromosome and the optimal chromosome; then, a quantum logic gate X and a quantum logic gate SWAP are used for achieving mutation operation and crossover operation in the algorithm respectively; the method provided by the invention has high accuracy and timeliness, the number of algorithm iterations and the time complexity are greatly reduced, and the method can be effectively used for solving the task-platform optimization configuration problem in the military C2 organization.

The invention provides a hail probability and size identification algorithm based on fuzzy logic and semi-supervised learning, and aims to improve the accuracy of hail early warning and reduce the false alarm rate. According to the algorithm, the probability comprehensive recognition of fuzzy logic and the Self-training semi-supervised learning technology are combined, and the probability and size of hail occurrence are accurately recognized through deep analysis of dual-polarization radar data and meteorological sounding data. The method comprises the specific steps that firstly, the heights of a 0 DEG C layer and a-20 DEG C layer are calculated through polynomial fitting, then a fuzzy logic algorithm is combined with expert experience to determine a membership function and weight of characteristic parameters, and the probability of hail occurrence is calculated; and then, a semi-supervised learning algorithm is adopted to further refine identification of the hail size. A weak classifier is adopted, and a Self-training iteration process is carried out, so that labeled samples are expanded, and the classification accuracy is improved. A final evaluation result shows that the algorithm is superior to a traditional method in the aspects of the hail recognition rate, the false alarm rate, the accuracy rate and the like, has high application value and can provide effective support for forecasting and early warning of hail weather.

The invention provides a pump station water level dynamic boundary adjusting method based on a fuzzy firefly algorithm, which comprises the following steps of: presetting a target water level in an intelligent water level monitoring system, calculating water level deviation and a water level change rate, and determining a sensitive parameter; presetting a linguistic variable of a fuzzy variable to obtain a fuzzy subset about a high-sensitivity parameter, a fuzzy subset about a low-sensitivity parameter, a fuzzy subset about a dynamic boundary adjustment value and a fuzzy subset about a flow adjustment value; determining a corresponding triangular membership function according to the input variables and the output variables of the different sensitive parameters; setting a fuzzy rule, and calculating the membership degree of each input variable; determining the fuzzy quantity output of the dynamic boundary adjustment value according to the fuzzy rule activation condition and the membership function value; and performing precision on fuzzy quantity output of the dynamic boundary adjustment value by adopting a gravity center method to obtain an accurate dynamic boundary adjustment value. According to the invention, the pump station can be controlled more accurately under various dynamic hydrological conditions.

The present invention relates to the technical field of quality management, and specifically provides a quality management method based on standard data. Specifically, it includes: constructing a dynamic standard database; collecting production data in real time; adopting a hybrid anomaly detection and dynamic interpolation strategy to handle data missing and noise, generating a standardized matching code based on hash and polynomial ring operations to ensure data consistency; determining quality deviations based on a dynamic threshold model and fuzzy logic, quantifying the deviation amount by combining the weighted Euclidean distance, realizing hierarchical early warning through a membership function, and triggering response measures according to the early warning level; using a scoring model and non-linear prediction to evaluate the effectiveness of measures and screening optimization plans; a closed-loop feedback mechanism continuously updates model parameters and the knowledge base. Through dynamic data integration, hybrid detection algorithms and matching code design, the present invention effectively solves the problems of inflexible static thresholds, single anomaly detection, and poor data consistency in traditional quality management, and significantly improves the detection accuracy and system robustness.

The invention discloses an automatic instrument fault prediction method based on fuzzy logic, and belongs to the technical field of industrial automation and equipment fault diagnosis. Comprising the following steps: generating a preprocessed sensor data set; constructing a feature space fusing multi-dimensional information; constructing a three-layer fuzzy reasoning architecture comprising an input layer, a reasoning layer and an output layer; mapping membership function parameters and rule weights in the three-layer fuzzy reasoning architecture into a parameter optimization space comprising a parameter adjustment action, a rule weight optimization action and a reasoning structure adjustment action; self-adaptive adjustment of the membership function parameters is achieved; and calculating the fault probability according to the current optimized membership function parameter, and evaluating the optimization effect of the current parameter configuration in combination with the multi-target reward function. According to the method, through combination of multi-time window dynamic monitoring, multi-type membership function design and a strict hard-soft constraint mechanism, accurate evaluation and stable prediction of the equipment fault probability are realized.

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 discloses a bidirectional research and judgment method for power distribution network faults in combination with an incidence matrix and dynamic Bayesian, and the method comprises the following steps: constructing the incidence matrix through an actual topological structure of a power distribution network, and constructing a fault research and judgment matrix in combination with a fault information matrix; fault criteria under the conditions of node fault information missing report and fault information false report are established respectively, so that fault feeder identification from top to bottom is realized; constructing a dynamic Bayesian network according to an actual topological structure of the power distribution network and a'station-line-transformer-household 'relationship, and reasoning the fault probability of each region; constructing a membership function and a fuzzy inference rule to obtain a fuzzy inference system for adjusting the fault probability; and finally inferring the most probable fault area, and realizing fault area research and judgment from bottom to top. According to the method, on the basis of a traditional matrix algorithm, the fault criterion is improved by constructing the causal incidence matrix, the feeder line identification problem under the conditions of fault information missing report and fault information false report is effectively solved, the calculation amount is reduced, and the fault positioning accuracy is improved.

The invention belongs to the technical field of machine learning and mode recognition, and particularly relates to an intuitionistic fuzzy double support vector machine classification method based on relative density and non-membership, which comprises the following steps: constructing and training an RD-IFTSVM model, and performing data classification through the trained RD-IFTSVM model. According to the method, a relative density membership function is provided, the proportion of local neighborhood heterogeneous samples to global similar density consistent samples is combined, misjudgment caused by the fact that a traditional method only depends on local neighborhoods is avoided, noise is restrained, and effective samples are reserved; optimization problems are respectively constructed for positive and negative classes, independent regularization parameters and slack variables are introduced, key protection of core samples is realized through membership weighting, and robustness of noise samples is improved; meanwhile, a double-hyperplane discrimination criterion based on a geometric distance is provided, and the sample attribution is determined by the minimum distance to the positive and negative kernel hyperplanes.

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

The present invention relates to the technical field of network security management. Specifically, it relates to a network security management system based on big data, which includes a data collection and integration unit, a dynamic threshold setting unit, a fuzzy comprehensive evaluation unit, a machine learning optimization unit, and a control unit. In the present invention, the data collection and integration unit collects multi-source data and cleans it. The collection process has intelligent features and uses blockchain to cache data. The dynamic threshold setting unit constructs a model to adjust the threshold for different types of network crimes by combining the hidden Markov model, kernel density estimation, and reinforcement learning algorithm. The fuzzy comprehensive evaluation unit uses the fuzzy mathematics algorithm to determine the membership function, allocate weights, and calculate the comprehensive risk score of the account. The machine learning optimization unit uses historical data to train and update the model to assist in the determination of the involved accounts, effectively solving the problems of missed and misjudged cases caused by fixed thresholds in traditional account risk assessment, and improving the accuracy of network crime account risk assessment.

The invention provides a pipeline transaction bilateral contract matching method and system, belongs to the field of crossing of metering economics and computer algorithms, and is used for solving the problems of multi-parameter coupling, high research and development uncertainty and low matching efficiency of pipeline transaction contracts in related technologies. A core term is converted into a tensor model, a Markov chain is combined to quantify expected utility, multi-round intelligent matching is realized based on a fuzzy satisfaction function and a sub-scene convex optimization algorithm, a key parameter dynamic calibration mechanism is matched, a complete technical closed loop is formed, the matching accuracy and negotiation efficiency are objectively improved, and the negotiation efficiency is improved. And reliable technical support is provided for complex pipeline transactions.

The present application relates to a kind of video enhancement inference method, device, equipment and medium based on fuzzy control, method includes: initialization multiple different scale pre-training video inference model;Define the fuzzy semantic set of input variable and output variable and membership function;Based on prior knowledge, construct fuzzy rule table;Real-time acquisition input data and complete fuzzy inference, and accurate score is obtained by solving fuzzy output by barycenter method;According to the score value range and anti-shake strategy, the optimal video inference model of next frame is dynamically selected.This method makes full use of the continuity characteristics of target between video frames, realizes the dynamic balance of hardware resources and inference accuracy, the utilization rate of GPU is obviously improved, the target detection rate is also obviously improved, and the temperature is stable within the safety threshold, suitable for real-time video scene such as traffic monitoring, unmanned aerial vehicle inspection.

The invention discloses a multi-AGV formation keeping control method based on IPSO-fuzzy PID. The multi-AGV formation keeping control method is characterized by comprising the following steps: (1) establishing a dynamic self-adaptive multi-AGV pilot following formation control model; (2) constructing a fuzzy PID controller, setting a fuzzy domain of input and output variables, a membership function and a fuzzy control rule, and obtaining the correction of PID parameters through fuzzy reasoning and defuzzification; (3) constructing an improved particle swarm optimization algorithm with dynamic balance of global search and local optimization, improving the inertia weight and learning factor of a particle swarm, and setting a fitness function; and (4) optimizing the input quantization factor and the output scaling factor of the fuzzy PID controller by using an IPSO algorithm, assigning optimized parameters to the controller, and adjusting the linear velocity and the angular velocity of the following AGV. The formation keeping precision, the response speed and the anti-interference capability can be improved.

The application discloses a mechanical arm fuzzy PID control method based on quantum pigeon group optimization, first motion trajectory planning is carried out, and a mechanical arm is controlled to move according to the planned motion trajectory through a fuzzy PID controller; a QAPIO algorithm is used to optimize fuzzy PID control parameters, and each pigeon can be used as a PID control parameter of each joint of the mechanical arm. The QAPIO algorithm of the application modifies a compass operator update equation, a probability density function in quantum representation of real number coding and a quantum rotation gate in the original algorithm, so that the algorithm has a continuous updating capability, can continuously update an optimal value, improves global optimization capability, avoids premature local optimization, and solves the problem that the original algorithm is prone to falling into a [0, 0,..., 0, 0]T point, causing the optimization algorithm to fail. The application effectively realizes optimization of fuzzy PID controller parameters of the mechanical arm, and has good practicability.

The invention belongs to the technical field, and discloses a control method and device for a high-speed motor and electromagnetic bearing system, and the method comprises the following steps: selecting an input variable and an output variable of a fuzzy controller; fuzzifying the input variable and the output variable, and determining a quantization factor, a basic discourse domain and a fuzzy discourse domain; defining a fuzzy subset and a membership function in the fuzzy domain of the input variable and the output variable; the membership degree is calculated through a membership degree function; substituting the membership degree into a fuzzy value formula to obtain a fuzzy value of the output variable, and performing defuzzification to obtain an accurate control quantity of the output variable; and outputting the precise control quantity of the output variable to an electromagnetic bearing model for control. The method provided by the invention can effectively reduce the oscillation and oscillation phenomenon of the system, improves the stability of the whole system, and can adapt to the situation of large disturbance or complex environment.

This invention relates to a vehicle differential lock control method, terminal device, and storage medium. The method includes: S1: constructing a first fuzzy membership function, a second fuzzy membership function, and a fuzzy rule inference table; S2: real-time acquisition of environmental values, road grade, and road curvature values, obtaining the maximum value of road curvature; S3: obtaining all corresponding first fuzzy intervals and fuzzy values ​​from the first fuzzy membership function based on the road curvature ratio; S4: obtaining all corresponding second fuzzy intervals and fuzzy values ​​from the second fuzzy membership function based on the environmental values; S5: matching the first fuzzy intervals with the second fuzzy intervals to obtain a combined fuzzy value of the fuzzy interval combination; S6: taking the fuzzy interval combination corresponding to the maximum value as the fuzzy decision combination, and obtaining the decision result from the fuzzy rule inference table; S7: controlling the differential lock based on the decision result. This invention achieves timely differential locking of the vehicle in weather conditions such as rain and snow when slight slippage occurs during normal driving.

The invention discloses a fixed star spectrum simulation method based on genetic algorithm and fuzzy PID compound control. Firstly, a fuzzy PID controller structure with double inputs: spectral intensity deviation # imgabs0 # and deviation rate # imgabs1 # and three outputs: proportionality coefficient increment # imgabs2 #, integral coefficient increment # imgabs3 # and differential coefficient increment # imgabs4 # is established. Then, basic work such as membership function selection, fuzzy rule making and defuzzification is completed; and finally, by means of a genetic algorithm, through coding mode formulation, initial population generation, fitness function selection and genetic operator determination, a fuzzy PID algorithm process is optimized. Practice verifies that in 3000K-9000K typical color temperature spectrum simulation, compared with a traditional fuzzy PID control algorithm, the error is remarkably reduced, and the simulation precision is greatly improved. And the performance is excellent when the full spectrum band simulation of the AM1.5 solar spectrum is carried out. The algorithm can accurately simulate a spectrum curve containing complex details, greatly enhances the accuracy and reliability of spectrum simulation, and provides key technical support for the fields of solar research, optical detection and the like.

The invention belongs to the technical field of aircraft trajectory planning, and relates to an aircraft trajectory sequence convex optimization method based on a high-dimensional value function, and the method comprises the steps: distributing a target function and a constraint violation degree function in an aircraft trajectory planning problem to a multi-dimensional space, and constructing the high-dimensional value function; a high-dimensional value function is used as an evaluation function of an iterative solution, the iterative solution is solved by adopting a convergence strategy of feasibility first and then optimization, and the convergence strategy is to enable the iterative solution to meet a feasibility sufficient reduction condition first and then enable the iterative solution to meet an optimality sufficient reduction condition; according to the method, the target function and different types of constraint violation degrees can be accurately and separately processed; by constructing a high-dimensional value function and a feasible-first and optimal-second convergence strategy, the convergence performance of the sequence convex optimization method for solving aircraft trajectory planning problems with different types of constraints such as equality constraints, inequality constraints and strong nonlinear constraints is effectively improved.

The invention relates to a video enhanced reasoning method and device based on fuzzy control, equipment and a medium. The method comprises the following steps: initializing a plurality of pre-training video reasoning models of different calculation scales; defining a fuzzy semantic set and a membership function of the input variable and the output variable; constructing a fuzzy rule table based on prior knowledge; collecting input data in real time, completing fuzzy reasoning, and defuzzifying through a gravity center method to output an accurate score; and dynamically selecting the optimal video reasoning model of the next frame according to the score value range and the anti-shake strategy. According to the method, the target continuity characteristic between video frames is fully utilized, dynamic balance between hardware resources and reasoning precision is achieved, the GPU utilization rate is obviously increased, the target detection rate is also obviously increased, the temperature is stabilized within a safety threshold value, and the method is suitable for real-time video scenes such as traffic monitoring and unmanned aerial vehicle inspection.

This invention relates to the field of control, and in particular to a method and system for controlling the operation of a reactor used in resin production. The method involves acquiring real-time operating parameters of the reactor, iteratively training a fuzzy neural network model using a hierarchical collaborative hybrid optimization strategy. The antecedent membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the consequent is a nonlinear polynomial function of the input variables. The training aims to minimize the comprehensive objective function. The optimization strategy involves using an improved quantum particle swarm optimization algorithm for global search to determine the Gaussian mixture model parameters, and using a recursive least squares algorithm for local search after each iteration to determine the consequent coefficients. During training, importance indices are calculated based on the average activation degree of the fuzzy rules and their contribution to the prediction error. Rules with importance consistently below a preset threshold are removed. After training, control commands for controlling the heating system power and material feed rate of the reactor at the next moment are generated and executed based on the real-time parameters.

The embodiment of the application discloses a kind of based on FG-DBN's multi-electric aircraft airborne system reliability analysis method, it is related to reliability design field, can be in fuzzy uncertain condition, to the complex polymorphic system in multi-electric aircraft airborne equipment is carried out dynamic reliability analysis.This application includes: the DBN model of complex polymorphic system in airborne equipment is established, the node in DBN model corresponds the failure state of component in complex polymorphic system;Fuzzy gradient function model containing fuzzy support radius is established, fuzzy gradient function model includes: fuzzy membership model of node in corresponding time slice and state transition matrix corresponding to failure mode;The conditional probability table is established for the failure relationship between nodes in DBN model;The output result of fuzzy gradient function model containing fuzzy support radius and the conditional probability table established are used, the node of weak link in complex polymorphic system is identified and reliability is analyzed.

The present invention provides a fuzzy correction filtering method based on an innovation sequence, comprising: obtaining a filtered innovation sequence, calculating a Mahalanobis distance of the filtered innovation sequence; setting a starting threshold, a robust correction boundary and an adaptive correction boundary; setting a robust correction membership function, an adaptive correction membership function and a fuzzy correction criterion; performing corresponding fuzzy correction on the filter; setting a robust factor to perform robust correction on the filter; setting an adaptive correction factor to perform adaptive correction on the filter; setting an adaptive correction factor and a robust factor to perform robust adaptive correction on the filter; the fuzzy correction filtering method based on an innovation sequence provided by the present invention can take into account both the robustness and adaptability of the filter and improve the performance of the filtering algorithm.

The application belongs to the technical field and discloses a control method and equipment of a high-speed motor and an electromagnetic bearing system, the method comprising the following steps: selecting input variables and output variables of a fuzzy controller; fuzzifying the input variables and the output variables, determining a quantization factor, a basic domain and a fuzzy domain; defining fuzzy subsets and membership functions in the fuzzy domain of the input variables and the output variables; calculating the membership degrees through the membership functions; substituting the membership degrees into a fuzzy value formula to obtain fuzzy values of the output variables, obtaining accurate control amounts of the output variables through defuzzification; and outputting the accurate control amounts of the output variables to an electromagnetic bearing model for control. The method can effectively reduce the oscillation and vibration of the system, improve the stability of the overall system, and adapt to large disturbances or complex environments.

The invention relates to the technical field of intelligent control of rail transit vehicle-mounted equipment, in particular to a quantum fuzzy control method and system for a rail transit vehicle-mounted air conditioner, and the method comprises the steps: collecting the operation data of the vehicle-mounted air conditioner in real time, and carrying out the Kalman filtering noise reduction and EMC anti-interference compensation preprocessing; performing four-dimensional variable quantum bit mapping coding on the preprocessed operation data, and constructing a quantum superposition state including disturbance compensation; a fuzzy membership function is optimized based on quantum probability distribution, a quantum fuzzy rule base is called, and an air conditioner control instruction is generated through quantum annealing algorithm iterative reasoning; an execution mechanism is driven to execute the air conditioner control instruction, the adjusted operation data, the comfort index and the state of the solid state contactor are collected, and deviation is calculated; and if the deviation meets a preset threshold value, maintaining the current parameter, otherwise, returning to re-execute quantum mapping coding and quantum fuzzy reasoning. According to the invention, accurate control of the vehicle-mounted air conditioner of the rail transit vehicle can be realized.

The present invention belongs to the technical field of structural engineering, and specifically discloses a reliability assessment method and system for highway bridge components, including the following steps: S1. Obtain bridge data for assessing the technical state of components; S2. Use the membership function in fuzzy mathematics to represent the uncertainty of the flexural bearing capacity calculation parameters in the technical state of components; S3. Use the residence time of different component technical states to solve the semi-Markov probability transition matrix through the probability density function and survival function of the Weibull distribution, and obtain the probability distribution of the component technical state varying with time. By adopting the above-mentioned reliability assessment method and system for highway bridge components, the present invention combines the membership function with real bridge data to obtain the flexural bearing capacity calculation parameter values, and then obtains the mean value and standard deviation of the flexural bearing capacity, solving the problems of predicting the time-varying reliability of structures and obtaining the mean value and standard deviation of the time-varying flexural bearing capacity in the highway network.