155 results about "Fuzzy number" patented technology

The invention provides a coal rock interface identification method based on an improved DS evidence theory, and belongs to the technical field of data analysis and multi-source information fusion. A plurality of information source signals in the cutting process are collected; constructing a recognition framework; obtaining a basic probability distribution function of each information source for elements in the recognition framework through the triangular fuzzy number and the trapezoidal fuzzy number; fusing the basic probability distribution function of each information source by using a Dempster combination rule, including introducing a conflict degree, a difference degree and a dispersion degree to define a new evidence conflict coefficient; correcting the evidence body according to the new evidence conflict coefficient; and based on the fused basic probability distribution function, selecting an element with the maximum probability in the recognition framework as a recognition result. According to the method, the application of a multi-source information fusion algorithm in engineering practice is realized, and compared with the traditional DS evidence theory, the method can more comprehensively reflect the relationship between evidence bodies, reduce the conflict between the evidence bodies and improve the accuracy of an identification result.

The invention discloses an automatic driving high-risk fuzzy test scene generation method, 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 discloses a least square improvement-based nearest projection point ambiguity resolution method. The method comprises the following steps of: constructing a relation model between ambiguity phase differences corresponding to various baselines of a double-long-baseline phase interferometer; grid division is carried out on the field angle of the double-long-baseline phase interferometer, and a phase difference fuzzy graph is constructed; expanding the relation model of the double-long-baseline phase interferometer to a multi-long-baseline interferometer, constructing a baseline vector, a theoretical fuzzy phase difference vector and a fuzzy number vector, and determining a hyperplane perpendicular to a phase line in the phase difference fuzzy graph; starting point fuzzy phase difference vectors of all phase lines and phase difference projection points of the phase lines on a hyperplane are determined based on the phase difference mode fuzzy graph; and performing least square fitting on an actually measured fuzzy phase difference vector corresponding to the actually measured multi-snapshot target signal, searching a fitted fuzzy phase difference vector with the minimum variance, determining the nearest phase difference projection point, and obtaining a corresponding fuzzy number vector to realize ambiguity resolution.

The invention provides a neural collaborative filtering recommendation model considering multi-dimensional user preferences, which comprises the following steps: acquiring and cleaning data information from a database to obtain an original user item score matrix; based on a Picture fuzzy set theory and a designed filling strategy, a user item scoring matrix is divided into four matrixes including a membership degree P, a non-membership degree N, a hesitation degree H and a rejection degree R so as to correspond to four interest preference states including user liking, disliking, hesitation and rejection; fitting the four preference matrixes by using a de-noising auto-encoder model, learning interests and preferences of the user in four dimensions, and constructing a user interest and preference model; performing normalization processing on the Picture fuzzy number (interests and preferences in four dimensions) of the user on the item obtained in the previous step, and obtaining an interests and preferences value of the user on the item through a scoring function; learning user behavior preferences by using a residual neural network fused with an attention mechanism, and generating a user behavior preference value by taking historical score information of a user on an article as input; and fusing the user interest preference and the user behavior preference in a weighting manner to form multi-dimensional user preference predicted values, and sorting the multi-dimensional user preference predicted values to generate a recommended item list.

The invention provides a shield soil full-period risk assessment method based on interval spherical fuzzy analytic hierarchy process, and relates to the technical field of shield soil risk assessment. The method comprises the following steps: establishing a shield soil full life cycle risk assessment model which comprises a target layer, a standard layer and an index layer; performing model quantification, including constructing a paired comparison matrix, converting an interval value spherical fuzzy number into a matrix form, determining the consistency of expert judgment through consistency check, calculating a fuzzy weight based on a fuzzy theory, performing defuzzification processing on the weight by using a score function, and performing normalization processing; and weight sorting: carrying out priority sorting on the standard layer according to the local weight, then carrying out further priority sorting on the index layer based on the global weight, and completing weight sorting of the model through comparative analysis with actual data or existing theories. According to the method, the accuracy and consistency of risk assessment are improved by introducing the interval value spherical fuzzy number and consistency check.

The invention provides an adaptive sliding mode trajectory tracking control method based on a fuzzy multi-objective function model, and the method comprises the steps: firstly, building a kinematic model of an omnidirectional mobile robot, and generating a virtual path between the mass center of the mobile robot and a preset point in real time; a final sliding mode control law is obtained by defining a sliding mode surface and designing a sliding mode switching function and constructing a switching control law and an equivalent control law; for a plurality of sub-optimization targets, fuzzy logic is adopted to perform fuzzification processing, and a membership function is constructed by using a hexagonal fuzzy number, so that optimization of the controller is realized; besides, an adaptive controller is designed, and by combining an adaptive sliding mode control strategy of equivalent control and switching control and fusing a fuzzy multi-objective function model, the controller can adaptively adjust behaviors of the controller according to different external interferences and system states, and high-precision trajectory tracking is realized. According to the invention, the linear tracking trajectory and the circular tracking trajectory are simulated and controlled, and the trajectory tracking control performance of the mobile robot is improved.

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 relates to an ultra-short baseline positioning method for solving phase ambiguity based on a vector baseline fusion algorithm. The invention relates to the ultra-short baseline positioning method for solving the phase ambiguity based on the vector baseline fusion algorithm. The objective of the invention is to solve the problems of low ambiguity resolution success rate and high calculation overhead of the existing algorithm. The method comprises the following steps: step 1, in an N-element circular array, calculating a pseudo-fuzzy number combination set in each baseline combination through the phase of a received signal; step 2, obtaining a pseudo-fuzzy number combination of each baseline of the N-element array; 3, establishing a condition judgment loss function; calculating the error of the pseudo-fuzzy number combination; step 4, obtaining a combinatorial optimization model, and defining the combinatorial optimization model as a comprehensive loss function; obtaining a phase difference after ambiguity resolution of the corresponding baseline based on the comprehensive loss function; and step 5, calculating an azimuth function based on the obtained phase difference after ambiguity resolution of the corresponding base line, and solving the azimuth of the target based on the azimuth function. The method is applied to the field of ultra-short baseline positioning.

The present invention provides a risk warning method for hot work in a tank area of ​​a chemical enterprise, comprising: obtaining accident precursors; based on the accident precursors, obtaining corresponding accident hazards and accident consequences in the OSHA case library, and establishing a hot work accident chain based on the accident hazard source, the event precursors, and the accident consequences; based on the hot work accident chain, determining the nodes of a Bayesian network; quantifying the Bayesian network nodes based on a triangular fuzzy number evaluation method; according to the Bayesian network, calculating the posterior probability by quantizing the nodes, and establishing a hot work risk warning model based on the Bayesian network; using the accident precursors as variables, inputting them into the hot work risk warning model to achieve risk prediction. The present invention improves prediction accuracy and reliability.

The invention relates to a method for acquiring the speed of a target object, a method for acquiring a Doppler fuzzy number, a sensor, computer equipment and a storage medium, and relates to the technical field of sensors. According to the method, two chirp signals are transmitted at a time through a first transmitting channel, namely, one chirp signal is added in front of or behind a signal transmitted by one transmitting channel in a traditional FMCW sensor, and the phase difference of the two chirp signals at the target peak position in an echo signal is obtained, so that a Doppler fuzzy number (namely q) is obtained; and the Doppler fuzzy number can be used for solving velocity ambiguity so as to determine the velocity of the target object.

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 provides a distributed fuzzy flexible job shop scheduling method and system based on an MSDBO algorithm, a storage medium and electronic equipment, and relates to the field of flexible job shop scheduling. According to the method, firstly, production resources and workpiece machining tasks are obtained; secondly, representing the machining time of each process of any workpiece on the machine and the transfer time of any workpiece on different machines by adopting a triangular fuzzy number so as to obtain the fuzzy completion time of any workpiece; thirdly, on the basis of the production resources and the workpiece machining tasks, a mathematical model is constructed with the minimum maximum fuzzy completion time as an optimization target; and finally, solving the mathematical model by adopting a multi-strategy dung beetle optimization algorithm to obtain an optimal machine distribution sequence and an optimal workpiece processing sequence. The fuzzy machining time is introduced, the actual production condition is met, meanwhile, production resources of multiple factories are utilized, reasonable distribution of workpieces is achieved, and beneficial help is provided for improving the production efficiency of enterprises.

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 application discloses a fuzzy remanufacturing system scheduling method based on an improved migratory bird migration optimization algorithm, belongs to the technical field of remanufacturing system scheduling, and solves the problems that the prior art ignores the fuzzy uncertainty of processing time, uses a deterministic model, leads to disconnection with actual production, and does not establish a suitable mathematical model and a solving framework for the special structure of a 3T-RSSP. The method comprises the following steps: establishing a fuzzy mixed integer linear programming model, and solving the fuzzy mixed integer linear programming model based on the improved migratory bird migration optimization algorithm. The application constructs a fuzzy mixed integer linear programming model suitable for the special structure of a 3T-RSSP, accurately quantifies fuzzy completion time based on the conventional operation rules of triangular fuzzy numbers, and accurately depicts the strong coupling logic of "job sequence-workstation allocation" by using the improved migratory bird migration optimization algorithm, so that the global optimization ability and convergence stability of the algorithm in a fuzzy environment are significantly improved, and the efficient scheduling demand of a remanufacturing system in an uncertain environment is met.

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 discloses an equipment rotation decision-making method, equipment, a medium and a product, and relates to the field of data processing. The method comprises the following steps: replacing a fuzzy number of a rule antecedent in a belief rule base with a language Z number to construct a rotation decision model; obtaining a historical operation task and an optimal rotation strategy corresponding to the historical operation task; taking the historical operation tasks and the optimal rotation strategy corresponding to the historical operation tasks as training data to optimize the rotation decision model; acquiring a current operation task of the equipment; and for the current operation task, reasoning is carried out by adopting the optimized rotation decision model, and an optimal rotation strategy is obtained. According to the method, the reasonability and accuracy of decision making can be improved, and decision making technical support is provided for long-time and large-scale operation of equipment.

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.