107 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 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 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 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.

The present invention provides a method for reconstructing the direction pattern of an opportunistic array radar for a dynamic multi-node platform, including: initializing task objectives and constraint condition parameters; selecting fuzzy numbers according to requirements and establishing a fuzzy chance-constrained programming model for reconstructing the direction pattern of the dynamic multi-node opportunistic array radar; and solving the model by using the matrix pencil method combined with fuzzy simulation for multi-plane array approximation. The general model and solution method constructed by the present invention can establish corresponding programming models under different task requirements, effectively solve the problem of system robustness caused by the influence of the dynamic environment involved in the direction pattern reconstruction problem in practical applications, and the node resources and position information of the opportunistic array radar can be dynamically adjusted according to the environment tasks, which is more in line with practical applications, realizes the adaptability of the opportunistic array radar, that is, the characteristic of adjusting system parameters according to different task requirements, and provides a reference for modeling and solving other problems related to the opportunistic array radar.

The invention discloses a multi-feature target recognition method, belonging to the technical field of target recognition. The method comprises: acquiring feature data of a target to be recognized collected by multiple sensors, wherein the feature data of the target to be recognized comprises sample data to be tested; generating triangular fuzzy numbers according to a target sample data set and the sample data to be tested; generating a neutral intelligence set representation of each target feature under each target type according to the triangular fuzzy number matching of the target sample data set and the sample data to be tested; converting the neutral intelligence set representation of each target feature under each target type into a basic probability assignment; using weighted average to fuse the basic probability assignment of each target feature under each target type to obtain the basic probability assignment of each target type; fusing the basic probability assignment of each target type, and determining the target type to which the target to be recognized belongs according to the fusion result; and outputting the target type to which the target to be recognized belongs. The method can improve the accuracy of target recognition and is used for multi-feature target recognition.

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.