29 results about "Fuzzy matrix" patented technology

The invention belongs to the technical field of oil and gas chemical engineering risk evaluation, and discloses an oil and gas pipeline dynamic risk evaluation method, system, equipment and medium based on human error operation, and the method comprises the steps: analyzing an accident case of an oil and gas pipeline, and constructing a human error operation risk evaluation index system based on an analysis result; calculating the dynamic weight of each risk evaluation index in the human error operation risk evaluation index system in combination with an analytic hierarchy process; constructing a three-dimensional fuzzy matrix of risk levels of different risk evaluation indexes in each time period; and calculating the risk priority of each risk evaluation index based on the three-dimensional fuzzy matrix and the dynamic weight. The method not only can obtain an accurate risk evaluation result, but also has higher time stability and risk distinction degree.

The invention provides a traffic special bottleneck monitoring method based on multi-source road data, and the method comprises the following steps: S1, collecting road data of a plurality of intelligent network connection data clients, and building a road information database of different road segments; s2, performing precision complementation and error ablation on the road information of the same road section of a plurality of intelligent network connection data clients, outputting multi-source road information, and calculating overall evaluation parameters of multi-dimensional deep road section ablation; s3, calculating a road data fuzzy matrix; s4, setting and calculating the critical entropy of the traffic event; calculating a fuzzy entropy in combination with the multi-source road information and the road data fuzzy matrix; and when the fuzzy entropy is greater than the critical entropy, constructing a special event list, and listing the special event list for continuous tracking. Through the multi-dimensional multi-source deep road data ablation algorithm, the deep road data fuzzy method and the road data fuzzy entropy fluctuation recognition algorithm, the accuracy of monitoring the special traffic events is improved from the way of processing the multi-source road data.

The invention provides a vehicle-mounted voice interaction system test method and system, and a storage medium, and relates to the technical field of new energy vehicle evaluation, and the method comprises the steps: constructing a test system comprising subjective indexes, basic indexes and corrected objective indexes, and combining subjective scores, objective data (wake-up rate, recognition rate and the like) and corrected data (sight line deviation and steering wheel hand separation); and generating a standardized score through historical database order score conversion, and finally calculating a comprehensive test result by using a fuzzy matrix for weighted fusion. The test deviation is comprehensively corrected, and an accurate optimization basis is provided for automobile manufacturers.

The invention discloses a Bayesian reasoning method for high-voltage power supply reliability under common cause failure influence, and belongs to the technical field of high-voltage power supply reliability analysis. Constructing a dynamic fault tree of the high-voltage power supply; acquiring the proportion of common cause failure in each pair of bottom event failures, and collecting evaluation suggestions for the proportion of the bottom event to construct a fuzzy matrix; determining common cause failure factors of common cause failure of each pair of bottom events; the dynamic fault tree of common cause failure is converted into a discrete time Bayesian network, and a Bayesian network graph, a conditional probability table and a marginal probability table are obtained respectively; and performing bidirectional reasoning on the dynamic reliability of the high-voltage power supply, and calculating reliability and maintainability indexes of the high-voltage power supply in each time period. According to the method, the common cause failure factor is determined through the fuzzy theory, and the dynamic fault tree considering common cause failure is mapped into the Bayesian network for reliability analysis, so that the problems that the common cause failure factor is difficult to determine and the reliability analysis calculation amount is too large are solved, and the high-voltage power supply reliability analysis precision and calculation efficiency are effectively improved.

The invention provides an aerospace titanium alloy ecological design performance evaluation method, device, equipment and medium, and relates to the field of new materials.The aerospace titanium alloy ecological design performance evaluation method comprises the steps that life cycle evaluation software is utilized, and based on life cycle list data of a target titanium alloy, environment influence data and environment contribution data of the whole production and manufacturing process of the target titanium alloy are calculated; performing weighted scoring on the full performance and the working condition service life performance of the target titanium alloy to obtain a service life evaluation result of the target titanium alloy; and obtaining a comprehensive ecological design evaluation result by using a fuzzy matrix and a scoring card model based on the environmental influence data and the environmental contribution data of the target titanium alloy whole production and manufacturing process and the life evaluation result. Thus, the influence of the titanium alloy whole production and manufacturing process on the environment is considered, a life evaluation result is obtained through weighted scoring of the performance, finally, a fuzzy matrix and a scoring card model are utilized, environment factors and performance life factors are combined, and a comprehensive ecological design evaluation result is obtained. And basis and data support are provided for type selection and use of titanium alloy in the aerospace field and industry green carbon reduction.

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 belongs to the technical field of man-machine cooperation and multi-modal perception, and particularly relates to a multi-modal human body action recognition method and system based on credible belief redistribution. The method specifically comprises the following steps: acquiring a multi-modal signal containing three-dimensional skeleton data, an electroencephalogram signal and an electrocardiosignal; preprocessing the signal to obtain a multi-source input sequence with consistent time sequence; respectively constructing an action recognition basic classifier based on each mode, and outputting basic trust distribution; calculating a discount factor by using a K-nearest neighbor algorithm; constructing a fuzzy matrix according to the confusion probability between action categories in the training set; carrying out credible redistribution on the belief value of each classifier according to the discount factor and the fuzzy matrix; and adopting a Dempster-Shafer evidence fusion rule to carry out fusion on the redistributed belief, and outputting a final action recognition result. The method can effectively model the uncertainty between modes, improves the accuracy and robustness of motion recognition in a complex environment, and is suitable for intelligent manufacturing, rehabilitation training and man-machine cooperation scenes.

The present invention discloses a density-based fuzzy clustering method, comprising the following steps: obtaining current frame data, obtaining the number of clusters k, calculating the maximum difference in lateral distances between each cluster, updating the number of clusters, constructing an objective function, solving the objective function, obtaining a primary fuzzy clustering result, repeating until a termination condition is met and updating the membership matrix k and cluster centers, using the updated primary clustering result as input initial values to construct a new objective function, solving the objective function again to obtain a secondary fuzzy clustering result, iteratively updating the membership matrix and location center points, continuing if the secondary fuzzy clustering meets the iteration condition; otherwise, repeating, outputting the updated membership matrix and location center points to obtain a secondary fuzzy clustering result. The updated membership matrix is the final membership fuzzy matrix. The present invention utilizes this density-based fuzzy clustering method to optimize clustering results and effectively address the problem of adjacent vehicles of the same speed being clustered together.

The invention discloses an automatic driving risk avoiding decision-making weight determination method fusing driving styles, and belongs to the technical field of automatic driving, and the method comprises the following steps: collecting subjective score data of a driver, defining a conflict scene and quantifying a risk level, normalizing the data, clustering the data to obtain three types of driving styles, and constructing a scene-style unit; through fuzzy matrix construction and deblurring, AHP subjective weight calculation and consistency check, factor correlation coefficient and conflict index calculation, CRITIC objective weight calculation and subjective and objective weight fusion, a weight mapping table of a matching scene and a driving style is output, and personalized decision is supported. According to the automatic driving risk avoiding decision-making weight determination method fusing the driving style, automatic driving risk avoiding decision individuation is achieved, the risk resisting ability of different traffic participants is considered, the weight can be flexibly adjusted according to the conflict scene and the driving style, and the decision reasonability and adaptability are improved.

The present application belongs to the technical field of equipment maintenance support decision, discloses a kind of equipment maintenance task priority evaluation method and system, the present application obtains maintenance task dataset and attribute evaluation set, based on scene discrimination parameter selection evaluation path;In hesitant fuzzy evaluation path, hesitant fuzzy matrix is constructed, the expected value and variance value are calculated, the weight optimization objective function is constructed based on attribute correlation degree to solve weight vector, normalization and comprehensive evaluation sorting are carried out;In analytic hierarchy process-approximate ideal solution evaluation path, hierarchical structure model and judgment matrix are constructed to calculate weight, standardized using Logistic function, determine positive and negative ideal solution, calculate closeness degree and sort, the present application realizes the unity of wartime rapid response and daily fine evaluation by the scene adaptive double-path evaluation mechanism, effectively solves the problem of insufficient description of decision ambiguity and lack of dynamic adaptation ability.

The invention provides a denoising and non-uniform blurring-removing image enhancement method based on a multi-scale pyramid, and the method comprises the steps: firstly constructing a multi-scale Gaussian pyramid image, determining a blurring layer and a mask through fuzzy segmentation in order to improve the accuracy, carrying out the layered estimation of a motion blurring layer, and determining a blurring matrix of each image through a non-blind method, the method comprises the following steps: firstly, removing low-frequency noise by using a Gaussian low-pass filter, removing high-frequency noise by using wavelet transform, then performing deconvolution deblurring in a Gaussian pyramid, fusing two processing results through weighted multi-scale, and finally, performing inverse operation through a multi-scale pyramid to synthesize a clear picture. According to the method, the fuzzy kernel is solved in a layering mode, the Gaussian pyramid is used for denoising and deblurring, meanwhile, the high-frequency noise problem which is prone to being ignored in a traditional algorithm is considered, adaptability to pictures of different sizes is enhanced through the pyramid, uneven blurring caused by camera movement is removed, and the PSNR is increased by about 2.74 d compared with the PSNR in a traditional method.

The invention relates to the technical field of electrolytic aluminum production pole change prediction, and discloses a method for predicting pole change quality based on image information and cell control data, and the method comprises the steps: carrying out the CV large model training through the cell control data; fuzzy classification is carried out on video information and cell control information 2 hours after pole changing, a fuzzy matrix is constructed, then self-adaptive iterative adjustment is carried out on weights by utilizing historical data, finally, the weights are converged, 18-24-hour conduction condition prediction of an anode guide rod is carried out, and the situation that the state of an electrolytic cell is influenced for a long time due to the pole changing operation problem can be reduced.

The invention discloses an input measurement method and system based on enterprise data elements, and relates to the technical field of input measurement, and the method comprises the following steps: S1, obtaining data elements, cost data and a time span, and carrying out the cleaning and classification of the data elements, the cost data and the time span; s2, presetting a feature extraction strategy, and extracting core feature parameters and external supplementary parameters; s3, performing time sequence division to obtain a time sequence set; s4, performing fuzzification processing, and setting a time sequence fuzzy rule; s5, generating a time sequence fuzzy matrix, and generating a time sequence fuzzy result; s6, constructing a multi-dimensional input measurement model, and calculating input measurement parameters; and S7, verification optimization is carried out, and an evaluation report is generated. According to the method, internal core data and external supplementary data of an enterprise are acquired, cleaned and classified, and clear definition of time span parameters is combined, so that the problems of single data dimension and time dimension deficiency in traditional input measurement are solved.

The invention discloses an image information anti-counterfeiting encryption method and application thereof in transparent plastic package anti-counterfeiting encryption, and the image information anti-counterfeiting encryption method comprises the steps: extracting a gray scale coding matrix of original image information, and carrying out the fuzzy processing of the gray scale coding matrix of each channel, and obtaining a fuzzy image information matrix; generating a random fuzzy matrix for each channel by using a random function; generating an encryption share matrix based on each channel fuzzy image information matrix and the random fuzzy matrix; performing error diffusion and channel combination on the random fuzzy matrix and the encryption share matrix of each channel to generate a first encryption share and a second encryption share; superposing the first encryption share and the second encryption share to obtain a decrypted image approximate to the target image; the method realizes lossless encryption of gray and color images, ensures that the encrypted image is consistent with the original image in size, is simple and convenient in encryption process and visual in decryption, and is adaptive to a transparent plastic bag printing process.

The invention discloses a road surface green low-carbon evaluation method. The method comprises the following steps: S1, selecting road surface carbon emission evaluation indexes based on related specifications of road construction, operation and maintenance processes; s2, carrying out index classification on the selected road surface carbon emission evaluation indexes according to multiple dimensions; s3, determining the weight of each index; s4, constructing an index hierarchy analysis and evaluation model; s5, respectively establishing an evaluation factor set and a weight set according to the index structure, hierarchical division and index weights; s6, according to the difference between the characteristics of the evaluation object and the actual situation, setting the evaluation standard as a comment set with different grades; s7, evaluating the index layer according to the comment set, performing quantitative processing on the indexes to obtain the membership degree of the ith factor to the jth evaluation, and establishing a fuzzy relation matrix; s8, performing fuzzy operation on the weight and the fuzzy matrix to obtain a row vector index layer fuzzy evaluation result; and S9, determining a final evaluation result of each level index.

The invention discloses an analysis method, device and equipment for pipeline failure, a medium and a program product, and relates to the field of risk analysis of pipeline failure, and the key points of the technical scheme are as follows: obtaining element sets corresponding to a plurality of elements contained in influence factors for evaluating the pipeline failure; determining a hesitant fuzzy element of each element according to the maximum value and the minimum value of the plurality of first failure evaluation values; subtracting the minimum value from the maximum value of the hesitant fuzzy element to obtain a hesitant degree matrix of each element, substituting the hesitant fuzzy element into the hesitant degree matrix, and calculating the hesitant fuzzy entropy of each element; enabling the hesitant fuzzy entropy of each element to be in one-to-one correspondence with a preset graduatory fuzzy matrix, determining the membership degree of each element, and calculating the non-membership degree of each element according to the membership degree and a hesitant degree matrix; according to the membership degree and the non-membership degree, calculating a graduatory fuzzy number of each first failure evaluation value; and the failure value of each element is calculated according to the graduatory fuzzy number.

The application discloses a kind of based on fuzzy comprehensive evaluation's advantage seepage channel identification method, it is related to oil reservoir development technical field.The method includes: obtaining oil reservoir related data, screening evaluation index and constructing evaluation index system;Using analytic hierarchy process to determine the weight of each index, and obtaining weight vector;According to the distribution characteristics of each index, membership function is constructed;Membership degree is calculated to each index observation value, and fuzzy relation matrix is formed;The weight vector and fuzzy relation matrix are carried out fuzzy matrix multiplication, and the fuzzy comprehensive evaluation result vector is obtained;Whether there is advantage seepage channel in oil reservoir area is determined according to the comparison result of evaluation value and preset threshold value, and dynamic data and / or numerical simulation result is verified.The application can effectively handle the fuzziness and uncertainty of oil reservoir data, improve the accuracy and reliability of advantage seepage channel identification, and optimize oil reservoir development decision.

The present application provides a hierarchical relationship analysis method and device for influencing factors, including: determining the first influencing factors for target keyword information contained in the first document data corresponding to the target keyword information, determining the relevance of the first document data to each first influencing factor, and determining the influence of the first document data on each first influencing factor; based on the relevance and influence, determining the second influencing factors corresponding to the target keyword information from each first influencing factor; based on the correlation score between each second influencing factor, determining the first fuzzy matrix corresponding to the second influencing factor; wherein the rows and columns of the first fuzzy matrix correspond one-to-one to each second influencing factor, respectively; for the target keyword information, based on the first fuzzy matrix, determining the hierarchical relationship between each second influencing factor, can reduce the time cost of data analysis to a certain extent and improve the accuracy of data analysis.

This invention belongs to the field of grouting evaluation technology, specifically relating to a method for evaluating the effect of grouting sealing and reinforcement. The method includes: S1. Determining the influencing factors of the grouting sealing and reinforcement effect; establishing a hierarchical evaluation level for grouting sealing based on these influencing factors; S2. Establishing a judgment matrix for the influencing factors based on the hierarchical evaluation level, assigning importance values ​​to each influencing factor, and calculating the weight vector of the judgment matrix; S3. Verifying the consistency of the judgment matrix; S4. Determining the influence weights of secondary evaluation indicators on the grouting sealing evaluation level, establishing an evaluation matrix through primary and secondary evaluation indicators, assigning specific numerical values ​​to the evaluation levels, and using the fuzzy matrix single-valued method to determine the sealing and reinforcement effect of fractured and fissured sudden water inrush. This improves the systematicness and accuracy of the grouting sealing and reinforcement effect evaluation method.

The invention discloses a target signal enhancement method based on a generative adversarial network, solves the problems of low signal-to-noise ratio and blind signals in a complex electromagnetic environment in the prior art, and realizes detection accuracy and generalization ability in a complex background. The method comprises the following steps: setting related parameters of a receiving station, and determining a reference signal and a noisy echo signal; carrying out time-frequency transformation on the two-dimensional mutual fuzzy matrix to generate a two-dimensional mutual fuzzy matrix; extracting a distance dimension slice set of the target feature region in the two-dimensional mutual fuzzy matrix, and performing spatial sampling on each distance dimension slice to generate a noisy signal set corresponding to each distance dimension slice; inputting the noisy signal set into a pre-trained signal enhancement model to obtain an enhanced signal set corresponding to each distance dimension slice; counting the continuous correct matching times of the enhanced signal in the target position neighborhood, and judging that the target exists when the times reach a set threshold value; and if wrong matching occurs, resetting continuous counting and continuing detection until a threshold value is reached or scanning is ended.

The invention discloses a rainstorm magnitude calculation method in combination with objective weather typing and a relative deviation fuzzy matrix. The method comprises the following steps: carrying out objective typing on historical atmosphere reanalysis data; carrying out clustering analysis on the typing result and the mode forecast by adopting a clustering algorithm, and judging similar types; historical ground precipitation data are divided into different rainstorm magnitudes, and a relative deviation fuzzy matrix comprehensive evaluation technology is utilized to obtain rainstorm forecasting schemes of all the magnitudes; constructing a rainstorm potential calculation operator of each magnitude, calculating the rainstorm probability of each magnitude, and taking the rainstorm magnitude with the maximum probability as output; and adopting an exponential method to carry out empty elimination correction on the output rainstorm magnitude according to a mode forecast result. According to the method, scheme and weight distribution is carried out on main environment variable parameters influencing a weather system through the relative deviation fuzzy matrix, graded rainstorm potential forecasting is achieved in combination with a rainstorm potential calculation operator, and technical support is provided for quantitative and refined rainstorm magnitude early warning.

A bayesian inference method for reliability of high-voltage power supply under common cause failure influence belongs to the technical field of high-voltage power supply reliability analysis. A dynamic fault tree of the high-voltage power supply is constructed; the proportion of common cause failure occurrence in each pair of bottom event failure is obtained, a fuzzy matrix is constructed by collecting evaluation opinions for the proportion of bottom events; the common cause failure factor of each pair of bottom events is determined; the dynamic fault tree of the common cause failure is converted into a discrete-time bayesian network, and the bayesian network graph, the conditional probability table and the marginal probability table are obtained respectively; the bidirectional inference is carried out on the dynamic reliability of the high-voltage power supply, and the reliability and maintainability indexes of the high-voltage power supply in each time period are calculated. The common cause failure factor is determined through the fuzzy theory, and the dynamic fault tree considering the common cause failure is mapped into the bayesian network for reliability analysis, so that the problems of difficult determination of the common cause failure factor and large calculation amount of the reliability analysis are solved, and the precision and calculation efficiency of the high-voltage power supply reliability analysis are effectively improved.

The application discloses an electric water heater protection control system based on clustering processing, which comprises a data acquisition module, a basic clustering generation module, an optimal fuzzy matrix learning module, a sample screening module, an optimization strategy design module and a protection control module. The application belongs to the field of data processing and specifically refers to an electric water heater protection control system based on clustering processing. The scheme introduces a joint target of physical space Euclidean distance and entropy regularization to reduce misjudgment of a real fault boundary. A physical distance penalty term is introduced to improve the sensitivity to real fault state changes. The fault tolerance to sensor noise and boundary state is enhanced through fuzzy neighbors. Noise and fault points are removed in combination with fuzzy similarity and outlying degree to perform all-around abnormal filtering. The risk of equipment failure is reduced. Random coefficients and difference information are combined to adjust hyperparameters, so that good protection control effects can be achieved under different working conditions.

The invention relates to the field of risk assessment, and discloses an enterprise audit risk assessment method and device based on big data analysis, and a medium, and the method comprises the steps: obtaining log data of enterprise business behaviors, and carrying out the preprocessing of the log data, and obtaining an assessment matrix for the audit risk assessment; constructing a risk assessment model, and training the risk assessment model by using the preprocessed data and the assessment matrix to obtain a trained risk assessment model; utilizing the trained risk assessment model to complete enterprise audit risk assessment; the method has the advantages that the uncertainty and dispersity of enterprise behavior data are solved, the factor set and the fuzzy matrix obtained through risk regularization clustering are used for training the evaluation model, finally the purpose of directly inputting the data into the model to obtain the risk level is achieved, the evaluation result is more accurate compared with a traditional method, and the method is suitable for large-scale popularization and application. Compared with a traditional method, the evaluation result is more accurate.

The invention provides a building risk assessment method and device, a medium and a product, and relates to the technical field of building risk assessment, and the method comprises the steps: employing a PS-InSAR technology to obtain PS points on all buildings in a monitoring area; obtaining structural damage factors of each building according to the PS point, and constructing a building stability data set; inputting the building stability data set into a pre-trained LightGBM model, and outputting a building risk level of each building; selecting a risk building higher than a preset building risk grade threshold value, and obtaining a social economic environment index of the risk building; and adopting a G-1 method to endow the social economic environment indexes with weights and calculate membership degrees, and multiplying weight vectors of the social economic environment indexes by a fuzzy matrix of the membership degrees to obtain a building comprehensive evaluation result. According to the invention, the problem of inaccurate building risk assessment caused by difficulty in comprehensive assessment of building structure damage and influence of social economic environment in the prior art is solved.

The invention discloses a communication network quality evaluation method based on data driving, and relates to the field of communication, and the method comprises the steps: setting a first-level evaluation index and a state index, and determining an association fuzzy triangular interval of the first-level evaluation index and the state index, and a demand fuzzy triangular interval of the first-level evaluation index; acquiring a state index parameter of a communication terminal in the communication network; constructing an association fuzzy matrix based on the association fuzzy triangular interval of the first-level evaluation index and the state index; obtaining the evaluation weight of the state index based on the demand fuzzy triangular interval of the first-level evaluation index and the association fuzzy matrix; obtaining a network quality quantized value of the communication terminal based on the evaluation weight of the state index and the state index parameter; determining a quality evaluation weight of the communication terminal based on the state index parameter; and obtaining a network quality evaluation quantized value of the communication network based on the network quality quantized value of the communication terminal and the quality evaluation weight of the communication terminal. According to the invention, the complexity of communication network quality evaluation is reduced.

The invention discloses an electric water heater protection control system based on clustering processing. The system comprises a data acquisition module, a basic clustering generation module, an optimal fuzzy matrix learning module, a sample screening module, an optimization strategy design module and a protection control module. The invention belongs to the field of data processing, and particularly relates to an electric water heater protection control system based on clustering processing, according to the scheme, by introducing a joint target of a physical space Euclidean distance and entropy regularization, misjudgment on a real fault boundary is reduced; a physical distance penalty term is introduced, so that the sensitivity to real fault state change is improved; the fault-tolerant capability of the sensor noise and the boundary state is enhanced through fuzzy neighbors; noise and fault points are eliminated in combination with fuzzy similarity and outlier degree, and all-directional anomaly filtering is carried out; the risk of equipment failure is reduced; and the hyper-parameter is adjusted by combining the random coefficient and the difference information, so that a good protection control effect can be realized under different working conditions.

The present invention discloses an image de - streaking method based on a fuzzy matrix, which includes the following steps: Collect the image I to be processed ref (M, N), and perform downsampling; Convolve the downsampled original image with an anisotropic Gaussian filter kernel to obtain a blurred image; The original downsampled image I ref (M1, N) is subtracted from the blurred image I mod (M1, N) to construct a streak feature image I gas (M1, N); Judge whether each pixel needs to be processed to obtain a truth value matrix; Perform statistical processing on the streak feature image I gas (M1, N) to obtain a streak noise feature vector; Use the feature vector to perform a mapping subtraction operation on I ref (M, N) to obtain the final reconstructed image. The image de - streaking method based on a fuzzy matrix proposed by the present invention not only considers the structural characteristics of the target noise, but also considers the discontinuous streaks caused by camera sampling in a way with less resource consumption.

The invention discloses a food quality prediction system based on big data and fuzzy mathematics, and relates to the technical field of big data and fuzzy teaching. Specific environment conditions corresponding to food are set through a food data monitoring center, food data of the food are monitored in real time according to the specific environment conditions, and the food data are classified and connected to generate a food data matrix of the food; setting a food data fuzzy rule, obtaining a food data input fuzzy matrix of the food data matrix, and constructing a fuzzy evaluation model corresponding to the food data input fuzzy matrix based on the food data fuzzy rule; evaluating whether the food data is good or not based on the fuzzy evaluation model to obtain a food data fuzzy output result set; processing the food quality according to the food data fuzzy output result set; the comprehensiveness and rapidity of the food quality are effectively improved.