89 results about "Fuzzy number" patented technology

The invention discloses a medicine cold-chain logistics enterprise recommendation method based on a Fermat fuzzy set. The method comprises the steps of obtaining a transportation quality data set of a plurality of alternative logistics enterprises under evaluation indexes; constructing an expert evaluation matrix through the Fermat fuzzy linguistic variables, and evaluating the importance of experts through the Fermat fuzzy linguistic variables so as to determine the weight of each expert; giving a language evaluation matrix through a Fermat fuzzy language variable and converting the language evaluation matrix into a Fermat fuzzy number; obtaining a Fermat fuzzy group evaluation matrix based on the expert weight and a Fermat fuzzy Einstein interactive integration operator, and performing standardization; based on a Fermat fuzzy set score function and an Einstein interactive integration operator, objective and subjective weights of evaluation indexes are obtained through a Fermat fuzzy variable coefficient method and a Fermat fuzzy step-by-step weight evaluation ratio analysis method respectively, and then a comprehensive index weight is obtained; and according to a Fermat fuzzy set-based scoring function and an Einstein integration operator, obtaining a sequence through an improved comprehensive compromise solution method so as to determine an optimal enterprise.

The invention relates to the technical field of intelligent production and manufacturing, in particular to an energy-saving fuzzy cascade scheduling method and system for regional gathering cooperative production, and aims to solve the composite problems of supply chain cascade scheduling heterogeneous factory resource allocation, multi-stage time accumulation effect, uncertainty interference and the like in regional cooperative transformation in the manufacturing industry. According to the method, a double-layer collaborative optimization framework is assisted through integrated learning, the uncertainty of quintuple interval fuzzy quantization processing, transportation and assembly time is adopted, an initial Q value matrix is generated through a pre-training layer, self-adaptive operator selection is achieved in combination with a dynamic decision-making layer, and local search, damage recombination and genetic operation are executed by multiple sub-groups. According to the method, the energy-saving second-class fuzzy distributed flow shop and multi-flexible job shop cascade scheduling problem model is effectively defined, three-segment coding and full-process energy consumption calculation are supported, and the overall scheduling efficiency and the energy efficiency balance capability of the regional aggregation industry are remarkably improved.

The invention discloses an automatic driving acceleration test method and device and a storage medium, and the method comprises the steps: carrying out the deconstruction of a scene according to the construction demands of an automatic driving test scene, and extracting the static environment information needed by the test scene and the dynamic parameter distribution of traffic participants; static environment information and dynamic parameters of traffic participants are sampled, and a scene is extracted into a plurality of scene sections; scene section generalization generation is realized by using a conditional table adversarial generative network; through each generated scene section, according to scene variable parameter distribution, behavior uncertainty is captured by using a triangular fuzzy number, and a complete fuzzy track sequence is expanded; constructing an automatic driving high-risk fuzzy test scene in combination with the static environment information and the fuzzy trajectory sequence; therefore, not only is the coverage rate of the test scene improved, but also the pertinence and authenticity of the test are enhanced, and more effective support is provided for safety performance evaluation of the automatic driving system in a complex environment.

The invention belongs to the technical field of power grid optimization scheduling, and particularly relates to a load demand clustering evaluation method combining a load step and a game combination weighting method, which comprises the following steps: preprocessing power consumption data of industrial users, and removing redundant load fluctuation; constructing an industrial load demand response potential comprehensive evaluation system; weighting the indexes of the comprehensive evaluation system by adopting a triangular fuzzy number analytic hierarchy process and an improved entropy weight method respectively; fusing the index weights calculated by the two methods to obtain a comprehensive index weight; the TOPSIS method is utilized to comprehensively evaluate the adjustable potential of the industrial users participating in the demand response, and the K-means algorithm is utilized to perform clustering according to the user potential so as to realize the division of the feature group. According to the method, demand potential classification of the feature group is realized, response capacity distribution and power grid load adjustment are facilitated, the user load demand evaluation precision is improved, the power grid dispatching decision is optimized, and the power grid operation stability is improved.

The invention discloses a flexible job shop scheduling method considering fuzzy factors. The method comprises the following steps: constructing a flexible production process model with fuzzy features; initializing a differential evolution algorithm; defining a differential evolution algorithm as a Markov decision process; and strategy optimization is carried out by using deep reinforcement learning, and flexible job shop scheduling is realized. According to the method, the fuzzy set theory and the flexible job shop scheduling problem are combined, uncertain factors in the production process are represented by fuzzy numbers, scheduling imbalance caused by changes of production and processing parameters can be prevented, the anti-interference performance of a scheduling scheme is improved, an optimization algorithm can better cope with a complex and changeable flexible production environment, and the production efficiency is improved. And a more reasonable and effective production scheduling scheme is obtained. And deep reinforcement learning is introduced to learn the variation strategy and the variation rate, and the algorithm is guided to be optimized in a more promising direction, so that the optimization process is more efficient, the solution performance is stronger, the solution result distribution is more concentrated, and the solution quality is higher.

The invention discloses a heterogeneous cluster parallel multi-stage fuzzy job scheduling method, which comprises the following steps of: receiving a processing request which is submitted by a user and contains multi-stage jobs, and modeling task execution time, data transmission time and request deadline into triangular fuzzy numbers, constructing a scheduling model taking the minimization of the total lease cost and the minimization of the request tardiness as targets; carrying out global exploration by adopting an improved second-generation non-dominated sorting genetic algorithm to generate parent and offspring populations; based on population similarity threshold judgment, dynamically triggering local search guided by a multi-agent near-end strategy optimization algorithm, and adaptively selecting a local search operator for each individual to generate an adjacent population; combining various populations, performing non-dominated sorting and crowding distance calculation, and screening out a new generation of populations; and iterating the process until convergence, and outputting a Pareto optimal solution set. According to the method, the problem of multi-target scheduling with fuzzy time variables in a heterogeneous environment is effectively solved, and the quality of a scheduling scheme and the algorithm search efficiency are improved.

The invention discloses a vertical take-off and landing field site selection method based on a fuzzy multi-objective optimization model, and relates to the technical field of low-altitude traffic, and the method comprises the steps: constructing a fuzzy number of a travel demand, and converting the fuzzy number into a deterministic interval; constructing a multi-target site selection model according to the deterministic interval; constructing a solving algorithm of the multi-target site selection model; searching a Pareto optimal solution set of the multi-target site selection model by using a solution algorithm; and determining the site selection of the vertical take-off and landing field according to the Pareto optimal solution set. According to the method, fuzzy parameters and operation uncertainty are considered, so that the multi-target site selection model still has relatively high stability in different scenes, and the scientificity, the practical feasibility and the long-term robustness of a vertical take-off and landing field site selection scheme can be improved by solving the multi-target site selection model.

The invention belongs to the technical field of material type selection, and particularly discloses an improved TOPSIS material type selection method and system based on information entropy, and the method comprises the steps: calculating the information entropy of each attribute of a material, and obtaining a weight coefficient of each attribute; adopting a TOPSIS method, determining positive and negative ideal solutions based on the weight coefficient, calculating the distance between each to-be-selected material scheme of the structure and the fuzzy positive and negative ideal solutions, further obtaining a relative close degree, and determining an optimal material scheme according to the relative close degree; wherein the calculation method of the positive and negative ideal solutions comprises the following steps: constructing a weighted standardized matrix according to a weight coefficient, then calculating the standard deviation of each attribute according to the weighted standardized matrix, and introducing a triangular fuzzy number to construct and obtain the fuzzy positive and negative ideal solutions. The method effectively improves scientificity and reliability of material type selection, is suitable for optimal decision-making of multi-attribute materials under complex working conditions, and particularly has remarkable advantages in lightweight design in the fields of automotive engineering and the like.

The invention relates to the technical field of train wireless network communication, in particular to an information security risk assessment method for a train wireless network control system, which comprises the following steps of: establishing an attack tree model according to a risk assessment object, assessing the occurrence possibility of a security event by using a triangular fuzzy number, analyzing attack paths, and calculating the interval probability of each attack path, the method comprises the following steps: obtaining a point probability according to an attack path interval probability, calculating an attack occurrence possibility, evaluating a security event influence by using a fuzzy analytic hierarchy process, calculating a security event risk value by quantifying the occurrence possibility and an influence value of the security event, and determining a risk level and a security level of a system according to a system risk evaluation value. And formulating corresponding protection requirements. According to the method, the information security risk of the train wireless network control system is effectively evaluated and managed, a basis is provided for formulating an effective security protection strategy, and the overall security capability of the train wireless network is improved.

The present invention discloses a fault diagnosis method for complex industrial systems based on multi-criterion fusion. A fault self-diagnosis model is established with multiple highly sensitive characteristic parameters as inputs and the corresponding operating states and membership degrees as outputs. The quotient between each highly sensitive characteristic parameter of the subsystem to be diagnosed and the corresponding highly sensitive characteristic parameters of other reference subsystems is calculated. A fault mutual-diagnosis model is established with the relative characteristic parameters as inputs and the corresponding operating states and membership degrees as outputs. The highly sensitive characteristic parameters of the status signals collected online are input into the fault self-diagnosis model, the relative characteristic parameters of the subsystem to be diagnosed are calculated and input into the fault mutual-diagnosis model to generate operating state evaluation values in the form of Picture fuzzy numbers for each decision candidate, and a fusion evaluation value is obtained by using the weighted average method. Diagnosis is carried out based on the differences in status information between similar subsystems, which is beneficial to eliminating random factors inside complex industrial systems and improving the diagnosis accuracy and stability.

The invention relates to a lithium ion energy storage prefabricated cabin risk toughness assessment method based on a fuzzy Bayesian network model. According to the method, real-time operation data, including risk factors such as battery voltage, current, temperature, available capacity, dew point temperature, dust concentration, air velocity and altitude, of the energy storage prefabricated cabin and equipment state data of a battery management system, a fire prevention system and a gas monitoring system are collected. Through processing and calculation of the data, key risk indexes related to the fire risk are determined, and the fault probability of each system is calculated. A risk level of a risk index is converted into a fuzzy number by using a fuzzy set theory, and then the fuzzy number is converted into a prior probability through a left-right fuzzy sorting method. In combination with historical statistical data of the energy storage prefabricated cabin, a Bayesian network total probability formula is used to calculate the probability of fire occurrence, and a reverse reasoning mechanism is used to identify a risk factor which is most likely to cause the fire. And quantitative and dynamic evaluation of the fire risk of the energy storage prefabricated cabin is realized.

The invention relates to a hesitant fuzzy number-based high-voltage circuit breaker operating mechanism voiceprint state detection method and device, and a medium. Extracting features of an original voiceprint data sequence of the high-voltage circuit breaker operating mechanism, wherein the features comprise a Mel-frequency cepstrum coefficient, short-time energy and a zero-crossing rate; forming a symptom feature vector by the symptom parameters, inputting the symptom feature vector into a fuzzy depth residual shrinkage network, constructing a membership function, and outputting a membership value of each state; constructing hesitant fuzzy numbers according to the membership values to form a collective hesitant fuzzy evaluation matrix; determining the evidence weight of each input evaluation model through an optimal-worst method, deriving a state risk weight through a TOPSIS method in combination with a language Z number, and performing weighted fusion on the collective hesitation fuzzy evaluation matrix to obtain a health index; and dividing the health indexes into normal, attention and dangerous health levels by adopting a K-means clustering algorithm. Compared with the prior art, the method has the advantages of high accuracy, high robustness, high flexibility and the like.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

The invention belongs to the technical field of industrial facility safety assessment, and particularly discloses an overdue service pressure pipeline risk assessment method and terminal equipment, and the method comprises the steps: constructing a fault tree with the failure of a pressure pipeline as a top event; obtaining a fuzzy number corresponding to a plurality of expert ratings of each bottom event in the fault tree, and determining a relative consistency degree of the expert ratings according to the fuzzy number; clustering the fuzzy number by using a similarity clustering method, and determining an optimal relaxation factor according to a clustering result; determining the failure probability of each bottom event according to the relative consistency degree of expert rating and the optimal relaxation factor; and determining the failure probability of the top event according to the failure probability of each bottom event. According to the method, the optimal relaxation factor is determined by combining the fuzzy number of expert rating, and the relaxation factor is improved, so that the influence of subjectivity is remarkably reduced, and the reliability of risk assessment of the overdue service pressure pipeline is greatly improved.

The invention discloses a ship plane segmentation workshop fuzzy scheduling method, which comprises the following steps of: firstly, establishing a ship plane segmentation fuzzy scheduling model, adding uncertainty of processing time to the traditional scheduling model, and representing the processing time by using a triangular fuzzy number; secondly, improving the Harlisia eagle algorithm, introducing nonlinear decreasing escape energy and simulated annealing temperature parameters, solving the problems of premature convergence and falling into a local optimal solution of the algorithm, and improving the quality of an initial population by adopting an improved NEH algorithm and chaotic mapping; and finally verifying the validity of the algorithm through a reference function, and applying the optimization method to an engineering example. The method is helpful for improving the efficiency of a ship plane section processing workshop, and has important practical significance.

The invention relates to the technical field of data processing, and discloses a roadway average air volume prediction method and system. The method comprises the following steps: acquiring roadway parameters to obtain resistance interval data; quantifying the air volume uncertainty to obtain an air volume interval range; converting into a triangular fuzzy number to obtain air volume fuzzy distribution data; obtaining average air volume fusion data through weight operator fusion; and air volume prediction data is obtained based on historical sequence error correction. The prediction accuracy of the average air volume of the roadway is improved.

The invention provides a sewage treatment effect real-time evaluation method based on multi-dimensional indexes, which comprises the following steps: firstly, an index monitoring subsystem adopts a dynamic weight sliding window sampling algorithm to collect three kinds of multi-dimensional indexes of water quality, microorganisms and energy consumption; secondly, constructing a fuzzy comprehensive evaluation system, and introducing an improved triangular fuzzy number comment quantization algorithm to determine a factor set and a comment set of'excellent, good, medium and poor '; thirdly, calculating each index weight vector by adopting a grey correlation-analytic hierarchy process coupling algorithm, and ensuring that the weight fits the relevance between the index importance and the actual performance; constructing a fuzzy evaluation matrix, and calculating the membership degree of the index to the comment set through a sectional self-adaptive membership degree function generation algorithm; and finally, calculating a comprehensive evaluation result by using an evidence theory-fuzzy matrix fusion algorithm, and determining an evaluation grade according to a maximum membership degree principle. The method solves the limitation of traditional single index evaluation, improves the evaluation accuracy and real-time performance, and provides support for stable operation of a sewage treatment system.

The invention discloses a lithium iron phosphate battery fault diagnosis algorithm. The algorithm comprises the following steps: collecting an information source data set of lithium iron phosphate battery faults; a genetic algorithm is adopted to search the information source data set, a search result serves as an initial clustering center to be input into a fuzzy C-means clustering algorithm for means clustering, and the clustering number and distance measurement are determined; the expert weight is calculated by using the distance measurement of the trapezoidal fuzzy number, and the attribute weight is calculated by using a deviation maximization method; calculating positive and negative ideal solutions based on the expert weight; and based on the attribute weights and the positive and negative ideal solutions, calculating group benefits, personal regret values and compromise compromise solutions to obtain a fuzzy fault sequence. According to the method, the genetic algorithm is combined with the FCM, the FCM is prevented from falling into a local minimum value in the iteration process, and finally the search result of the genetic algorithm is used as the initial clustering center of the FCM, so that the fault recognition rate of the algorithm is improved.

This invention discloses a method, storage medium, and device for aquaculture water quality assessment based on intuitionistic fuzzy set distance measures. The method includes: constructing an expert comment set based on water quality datasets collected under aquaculture conditions, and converting the expert comment set into an intuitionistic fuzzy matrix for aquaculture water quality assessment; constructing a Gaussian kernel-based dynamic hesitancy assignment model based on the intuitionistic fuzzy matrix to determine the hesitancy assignment of intuitionistic fuzzy numbers; forming quadruples of the intuitionistic fuzzy numbers and their corresponding hesitancy assignments in the intuitionistic fuzzy matrix; and calculating the intuitionistic fuzzy set distance measures with the optimal and worst water quality based on the quadruples of the intuitionistic fuzzy numbers to assess the aquaculture water quality. This invention can significantly improve the accuracy of aquaculture water quality assessment and enhance the timeliness and effectiveness of aquaculture water quality early warning.

The application discloses a waiting time constraint batch processing pipeline crane scheduling method based on an improved slime algorithm and belongs to the field of intelligent scheduling of industrial production. The method comprises the following steps: a scheduling model is constructed, the waiting time fuzzy interval is defined by a trapezoidal fuzzy number, and the maximum completion time is minimized as the target; the waiting time uncertainty is accurately characterized by the fuzzy interval, and the fluctuation range of the waiting time in actual production is completely covered; based on the model, a hybrid improved slime algorithm is used for solving; a matrix coding and One-Hot mapping are adopted to adapt to the discrete scheduling scene; a good point set initialization method is introduced to construct an initial population, and multiple scheduling schemes are obtained; and a double tabu table strategy is combined to optimize the local search efficiency and convergence stability. The application is superior to traditional genetic algorithms, discrete slime algorithms and other comparative algorithms in terms of solution quality, convergence speed and robustness, can efficiently adapt to the crane scheduling demand of process-type industries, and provides a stable and reliable solution for multi-crane collaborative scheduling in a complex uncertain environment.

The invention discloses an improved FMEA method combining component importance and fuzzy-entropy weight-TOPSIS. The method comprises the following steps: S1, constructing a system component index original decision matrix; s2, carrying out data normalization and forward processing; s3, calculating the importance weight of the system component; and S4, calculating a system fault mode priority sequence by applying an improved FMEA method. According to the method, component importance is introduced as a constraint, a triangular fuzzy number is applied to replace accurate values of risk factors O, S and D, the influence of subjectivity of FMEA team member group decision-making on evaluation values of the risk factors O, S and D is reduced, in addition, interaction and influence among different risk factors are fully considered in the improved method, and the accuracy of the FMEA team member group decision-making method is improved. The weight among the risk factors O, S and D is calculated by applying an entropy weight method, and the accuracy of system fault mode risk priority ranking is improved.

The invention discloses an integrated decision-making method for evaluating the digital transformation capability of a logistics enterprise, and the method comprises the steps: constructing an evaluation index system comprising a plurality of dimensions according to the digital transformation characteristics of the logistics enterprise; evaluation information is given according to the evaluation index system and the candidate logistics enterprises for evaluation to obtain evaluation data; expressing evaluation preferences by using linguistic variables in a spherical fuzzy set, and converting the linguistic variables into spherical fuzzy numbers; determining an evaluation weight through a spherical fuzzy score function; fusing the evaluation information by using a spherical fuzzy Sugeno-Weber priority operator to generate a comprehensive evaluation matrix; in combination with an SPC model and an SWARA method, comprehensive weights of evaluation indexes are determined from objective and subjective perspectives; and sorting the candidate logistics enterprises by using a spherical fuzzy regret AROMAN method, calculating a comprehensive score of each candidate enterprise through an ideal point vector and a regret matrix, and finally determining priorities of the candidate logistics enterprises. According to the method, the uncertainty of the evaluation process can be comprehensively and accurately reflected, and extraction and recommendation can be carried out.

The application claims a data integrity audit matching method based on psychological perception and preference, comprising the following steps: S1: calculating the subjective and objective weights of each evaluation index, and calculating the subjective and objective combination weights of the index based on the weights, and then calculating the weight of each audit scheme by using the improved Topsis comprehensive evaluation method; S2: calculating the user preference weight by using the triangular fuzzy number and Topsis method according to the user preference set, and integrating the weights of the two; S3: quantitatively calculating the comprehensive preference value of both parties according to the demand preference of the audit task and the execution preference of the audit resource, and calculating the preference utility value matrix of both parties; S4: calculating the perception utility value matrix of both parties based on the improved disappointment theory; S5: standardizing the perception utility value matrix, taking the maximization of the standard perception utility value of both parties as the optimization goal, constructing a double-objective optimization model, and solving the model. The application effectively improves the comprehensive satisfaction of both parties in the case of considering the psychological factors and preferences of both parties.

This application relates to the field of data processing technology and discloses a method and system for predicting average air volume in roadways. The method includes: collecting roadway parameters to obtain resistance range data; quantifying air volume uncertainty to obtain the air volume range; converting it into triangular fuzzy numbers to obtain fuzzy air volume distribution data; fusing it using a weighted operator to obtain fused average air volume data; and obtaining predicted air volume data based on historical sequence error correction. This application improves the accuracy of average air volume prediction in roadways.

This invention discloses a method, apparatus, medium, and equipment for constructing quality control charts based on fuzzy numbers, belonging to the technical field of quality supervision information systems. The method includes: obtaining key quality characteristics corresponding to key processes in the manufacturing process, wherein these key quality characteristics are controlled by control charts; defining fuzzy numbers that satisfy conditions based on the key quality characteristics corresponding to the key processes, and determining the possible value range of these fuzzy numbers; and constructing a control chart corresponding to the uncertain quality characteristics by performing quality factor analysis on the key processes in the manufacturing process, based on the fuzzy numbers that satisfy the conditions, the possible value range of these fuzzy numbers, and the uncertain quality characteristics in the production process. The apparatus, medium, and equipment can be used to implement this method. It can accurately determine random errors in the manufacturing process and more accurately reflect the actual product quality situation.

The invention belongs to the technical field of interferometer direction finding, and particularly relates to a rapid defuzzification direction finding method for a quintuple planar array interferometer, which comprises the following steps of: substituting all possible fuzzy number combinations of a reference baseline group one by one to defuzzify a phase difference measured value, and constructing a fast defuzzification direction finding method for a quintuple planar array interferometer under the assumption that a fuzzy number combination for constructing a complex modular square threshold is satisfied; the fuzzy number estimation fitting error of the fitting baseline group is calculated one by one, elimination is performed according to the fitting error threshold, the minimum value of the total fitting error is searched, the corresponding fuzzy number estimation value of the long baseline group is reserved, and finally, the long baseline group is used for defuzzification and direction finding, so that the number of loop operations of fuzzy number traversal is greatly reduced. Therefore, quick ambiguity solution direction finding is realized; the method is high in applicability, can be widely compatible with two-dimensional direction finding of an atypical irregular planar array interferometer, and can be popularized and applied to ambiguity resolution direction finding of irregular planar arrays with more array elements such as a seven-element array and a nine-element array.

The application discloses a kind of nuclear power equipment value chain manufacturing risk assessment methods.The present application determines risk group and risk factor in nuclear power equipment manufacturing from risk database;Adopt hesitating fuzzy language term set method, define a set of risk grade evaluation language set, define semantic function as conversion standard;According to risk group, risk factor and risk grade evaluation language set, obtain the evaluation information of each nuclear power equipment value chain manufacturing risk factor from risk database;The evaluation information is converted into triangular fuzzy number and aggregated, the risk importance and risk grade of each risk factor and risk group are determined, and the overall risk importance and risk grade are determined.The present application effectively and accurately processes fuzzy information in risk assessment, comprehensively analyzes the risk importance of each risk factor and risk group, and provides a systematic method for developing nuclear power equipment value chain manufacturing risk assessment.

The application relates to a kind of vehicle engine system electromagnetic vulnerability evaluation methods, belong to electromagnetic environment effect evaluation field.Establish the engine system bayesian network fault model, get the triangular fuzzy number corresponding to the fuzzy expression of the failure correlation between each node in the bayesian network fault model through semantic contrast table, calculate fuzzy conditional probability, convert it into interval grey number conditional probability;Determine electromagnetic stress probability density function and component sensitivity threshold probability density function, calculate component layer interval grey number failure probability, and calculate engine system interval grey number failure probability in combination with interval grey number conditional probability.Affirmative effect is: determine the weight of each sensor through fuzzy analytic hierarchy process, solve the shortcoming that traditional analytic hierarchy process is more subjective, combine grey system theory and fuzzy theory with bayesian network, can more accurately express the uncertain information in the whole evaluation process and carry out failure probability calculation.

The invention provides a two-layer decision optimization method for oil field fire coal coupling biomass emission reduction and heat production, and belongs to the technical field of economic environment decision methods, and the method comprises the steps: selecting influence factors of oil field fire coal coupling biomass emission reduction and heat production according to an environment protection target of a first decision maker and an economic profit target of a second decision maker, data are collected; describing the influence factor by adopting a fuzzy number, and calculating an expected value of the influence factor; constructing a two-layer multi-objective decision optimization global model; converting the two-layer multi-objective decision optimization global model into a two-layer single-objective decision optimization global model; and solving the two-layer single-objective decision optimization global model. According to the method, an expected value of a fuzzy number is introduced to describe an influence factor, a two-layer single-target decision optimization global model is established, the model is solved, an optimal decision solution in the coal-fired coupled biomass power generation process is obtained, and objective conflicts among decision makers are solved.

The present application relates to a kind of ship shore end control system risk assessment method based on interval two fuzzy numbers, comprising the following steps: by the function analysis of system, obtain the relevant data including multiple sub-modules / components, assess the failure probability and loss severity of each sub-module / component, wherein the failure probability and loss severity are interval two fuzzy numbers;According to the failure probability and loss severity of each sub-module / component, the fuzzy risk of system or component is estimated using the improved arithmetic operation rule of interval two fuzzy numbers, wherein interval two fuzzy numbers are composed of elements and high, and the arithmetic operation rule of improved interval two fuzzy numbers is obtained by the arithmetic operation rule of the high of unified interval two fuzzy numbers;The deviation degree and similarity of the results under different operation rules are compared and analyzed to verify the results.