93 results about "Membership function" patented technology

The invention relates to the technical field of computing power resource allocation, and particularly discloses a computing power resource allocation method and system based on multi-dimensional dynamic evaluation and a product. The method comprises the steps of obtaining evaluation index data of a to-be-scheduled task in multiple dimensions; dynamically configuring a weight value of each evaluation index according to a task attribute and a system state; the evaluation index data is fuzzified by using a preset membership function, a fuzzy evaluation matrix is constructed in combination with the weight value, and comprehensive calculation is performed through a fuzzy inference rule to obtain fuzzy comprehensive evaluation data of the task, so that an accurate evaluation result is generated; modeling a task execution process by adopting a multi-layer perceptron model, and predicting the execution performance of the task; and based on the evaluation result and the prediction result, dynamically selecting a proper strategy from a plurality of predefined task scheduling strategies, and scheduling the tasks to optimize computing power resource allocation. According to the method, the resource utilization efficiency is remarkably improved through multi-dimensional evaluation and a dynamic scheduling mechanism.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention provides a crop moisture stress diagnosis method and system based on a fuzzy discrimination theory and multi-source information fusion, and the method comprises the steps: collecting multi-source monitoring data which comprises but is not limited to soil moisture, crop physiology, remote sensing images and meteorological parameters; preprocessing the data, and calculating at least one moisture related index; constructing a membership function of the indexes based on a fuzzy set theory; fusing the indexes by adopting a fuzzy comprehensive discrimination method to obtain a comprehensive moisture stress index; and outputting a mild, moderate or severe crop water stress level according to the index. The system comprises a data acquisition, data processing, fuzzy discrimination and result display modular structure according to the method. By fusing multi-source monitoring data and adopting a fuzzy set theory and a fuzzy comprehensive discrimination method, accurate diagnosis and graded output of crop water stress are realized, the utilization efficiency of water resources is improved, and the method has remarkable water-saving and efficiency-increasing values.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention provides an intelligent proportion control method and system for an antistatic agent synthesis process, and the method comprises the steps: fuzzifying real-time parameters through real-time and historical process parameters by using an asymmetric membership function constructed based on data distribution skewness and kurtosis; historical data samples are mapped into graph theory nodes, communities are divided through a community discovery algorithm to generate fuzzy rules, initial weights are set, and an initial rule base is constructed; using a recursive least square method to identify rule consequent parameters, combining redundancy rules according to cosine similarity, and combining a particle swarm optimization algorithm to optimize antecedent parameters; and inputting the fuzzification real-time parameters into the optimized fuzzy neural network, calculating the activation intensity of the rule, adjusting the weighted average weight based on the information entropy of the current activation intensity, and obtaining the proportion control quantity of each component after defuzzification.

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

The invention discloses a large-model intelligent question setting system and method based on fuzzy mathematics fine tuning, and belongs to the technical field of intelligent question setting. Comprising a fuzzification processing module, a multi-dimensional reward feedback module, a rule-driven attribute reasoning module, an attribute accurate quantification module, a self-adaptive membership degree adjustment module, a strategy optimization and intra-cluster standardization module and a question generation and structured output module. According to objective fields such as post specifications, post levels, technical stack depth and team scales, hard indexes are converted into soft boundary semantics through five membership functions, and then quantitative attributes of surface test questions on difficulty, openness, prejudice and distinction are reasoned by using N rules without subjective wording. And the business result data is used for driving rewards to be updated online, and finally, daily automatic fine tuning is realized on the 32B large model, so that each subjective surface test question generated by the AI not only meets the objective requirements of posts, but also has explainable, auditory and iterable closed-loop capabilities.

The invention provides a geotechnical engineering geological disaster prediction method and system for mining exploitation, and relates to the technical field of geotechnical engineering, and the method comprises the steps: S1, obtaining multi-dimensional data from different regions of a mining area in real time through a plurality of data collection devices, S2, carrying out the denoising, abnormal value correction, space-time alignment and missing value supplementation of the multi-dimensional data obtained in S1, and obtaining a prediction result; and the dynamic time window technology is adopted to process the time sequence data, so that the data under different time scales can be effectively synchronized, and accurate alignment and dynamic updating of the data are ensured. According to the geotechnical engineering geological disaster prediction method and system for mining exploitation, the fusion problem of multi-source heterogeneous data is effectively solved by combining the advantages of fuzzy logic and a neural network through an adaptive neural network-fuzzy hybrid algorithm. The fuzzy logic model carries out modeling on spatial and temporal changes of different data sources, and the influence degree of each data source on disaster prediction is accurately evaluated by dynamically adjusting parameters of a membership function.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment process parameter adjusting method and system based on fuzzy reasoning, and the method comprises the steps: obtaining to-be-treated sewage and a historical treatment log set, carrying out data cleaning on the historical treatment log set to obtain a cleaned log set, and obtaining a membership function set, performing fuzzy classification based on the membership function set and the cleaned log set to obtain a fuzzy historical data set, constructing a fuzzy rule base, performing water quality detection on to-be-treated sewage to obtain real-time water quality data, summarizing the real-time water quality data and a process parameter set to obtain a real-time treatment log, and adding the real-time treatment log to a historical treatment log set. And on the basis of the updated processing log set and the standard real-time water quality data, sewage treatment process parameter adjustment based on fuzzy reasoning is completed. The intelligent level and stability of parameter adjustment of the sewage treatment process can be improved.

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

Methods for determining the reliability of a technical system using fault tree analysis, comprising the following steps: S1 - Defining basic events of fault tree analysis; S2 - for each basic event, defining a fuzzy linguistic variable and a set of membership functions; S3 - at least partially based on real-time embedded software, defines a minimal cutting set for linguistic fault tree analysis; S4 - at least partially based on the minimum intersection theorem, determining the reliability of the technical system. Furthermore, the invention relates to a control unit, vehicle system, a computer program product and a computer-readable medium for executing the method.

The application relates to a T-S fuzzy semantic intelligent liver cancer staging method and system. The method comprises the following steps: S1. liver cancer feature extraction, key liver cancer features are extracted from obtained digital radiology diagnosis reports of patients to form a data set; S2. liver cancer feature fuzzification, a plurality of fuzzy semantic sets are used for fuzzy representation, and relevant membership functions are designed; S3. T-S fuzzy semantic model construction, comprising the following steps: designing a T-S fuzzy semantic rule, solving the membership degree of each rule, identifying the antecedent parameter membership function, identifying the consequent parameter, and outputting the T-S fuzzy semantic model; S4. liver cancer intelligent staging and performance evaluation verification, the liver cancer categories in the Chinese CNLC standard are subjected to digital label processing, a training set and a test set are divided, and the staging performance of the intelligent staging model is verified. The T-S fuzzy semantic intelligent liver cancer staging method can divide the liver cancer into Ia, Ib, IIa and IIb-IV, and is more accurate in grading and staging the liver cancer. The application greatly reduces the estimation error and classification error of the liver cancer state.

The application provides a CLAHE image blur enhancement method based on a mixed membership function. The method improves the single membership function in the existing CLAHE method based on the fuzzy theory into a mixed membership function, dynamically fuses the exponential and linear membership functions in the threshold clipping process, realizes the differential processing of the flat area and the area rich in details, and thus adaptively adjusts the clipping threshold, so as to solve the problems of the insufficient histogram equalization capacity, the easy amplification of noise and the poor edge retention effect of the prior art. Compared with the traditional method, the image enhancement quality of the method is significantly improved under multiple scenes.

This invention relates to a knowledge and data fusion-driven, interpretable primary headache auxiliary identification method. The method includes: collecting patient information and preprocessing it to obtain a multi-dimensional feature vector; inputting the multi-dimensional feature vector into an interpretable primary headache auxiliary identification model to obtain a probability distribution of headache identification results; outputting the headache type with the highest probability and its identification confidence, and generating a risk warning; wherein, the hierarchical fuzzy logic layer of the interpretable primary headache auxiliary identification model, based on the multi-dimensional feature vector, calculates the fuzzy membership degree of each feature when it belongs to different headache types according to the differentiated membership function of different headache types, obtaining fuzzy membership degree features; the KAN feature fusion network fuses the fuzzy membership degree features with the original vector to obtain classification features; the classifier outputs the probability distribution of headache types based on the classification features. Compared with existing technologies, this invention provides a solution for intelligent headache diagnosis that combines accuracy, robustness, and interpretability.

The application discloses a voltage sag region online identification method and system based on a dynamic fuzzy rule base, and belongs to the field of power system power quality analysis and protection. The method comprises the following steps: calculating initial voltage sag critical points on each line in a power system to generate an initial voltage sag region AOV; acquiring voltage amplitude deviation, phase jump rate and fault point electric distance data of a load point, and performing fuzzy processing on the data by using a membership function to obtain a membership set, inputting the membership set into a TSK fuzzy reasoning model based on a dynamic updating rule base to output a critical point correction amount; and adaptively updating a region boundary according to the initial voltage sag critical points and the critical point correction amount to output an accurate voltage sag region. By introducing a dynamic fuzzy reasoning and online updating mechanism, the application effectively improves the accuracy and adaptability of voltage sag region identification, and solves the problems that existing methods cannot comprehensively consider multiple influence factors and lack online updating capability.

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

The invention provides a boiler soot blowing control method and a related device, and relates to the technical field of boiler operation control. Performing image processing on the initial image of the boiler heating surface to obtain a processed image, performing mapping operation on pixel coordinates of the processed image and actual reference coordinates of the boiler heating surface to obtain a mapping result, and obtaining a region image corresponding to the soot blower based on the mapping result, carrying out dust deposition identification operation on the regional image to obtain a dust deposition identification result, calculating regional attribute information of the regional image according to the dust deposition identification result, determining a fuzzy membership function boundary value corresponding to the regional attribute information, constructing a self-adaptive fuzzy membership function by using the fuzzy membership function boundary value, and obtaining a self-adaptive fuzzy membership function; and determining a fuzzy quantity corresponding to the regional attribute information by using an adaptive fuzzy membership function, determining a soot blowing parameter of a soot blower corresponding to the regional image according to the fuzzy quantity corresponding to the regional attribute information and the fuzzy rule base, and controlling the corresponding soot blower to perform soot blowing operation according to the soot blowing parameter.

The application discloses a weather radar echo type identification method and device, a medium and equipment. The application obtains preset polarization radar observation data, accurately calculates the distribution characteristics of ocean clutter by using a statistical method, and further constructs a membership function, thereby providing a quantitative basis for a fuzzy logic algorithm. Through fuzzy logic algorithm processing, the membership degree of each pixel belonging to a preset echo category is obtained, and finally the echo category is determined according to the membership degree. The application solves the problem that the echo type of the weather radar cannot be accurately identified in the prior art.

The invention discloses a low-voltage transformer state evaluation method based on a fuzzy grey fusion algorithm. The method comprises the steps of multi-dimensional data index collection and self-adaptive preprocessing; and based on the preprocessed data indexes, state evaluation of fuzzy grey fusion is carried out. During work, a self-adaptive preprocessing system is constructed, a sliding window is used for dynamically normalizing and adjusting a boundary to solve dimension and working condition drifting, and a time-varying weight model is constructed in combination with a load fluctuation rate and an environment deviation degree to improve evaluation sensitivity; an intelligent completion strategy based on a gray model is designed, and the data integrity is effectively improved; a fuzzy grey fusion evaluation framework is innovated, a semi-trapezoidal membership function is used for eliminating state boundary jump, a fuzzy membership degree and a dynamic weight are coupled through double-layer grey correlation analysis, evaluation continuity and accuracy are improved, and technical support is provided for fine operation and maintenance and risk early warning of the low-voltage transformer.

The invention discloses a pipe-jacking tunneling machine type selection evaluation system and method based on fuzzy mathematics. The method comprises the steps that firstly, a multi-dimensional key evaluation index system covering planning and design requirements, hydrogeological conditions, surrounding environment factors and cutterhead configuration characteristics is constructed; secondly, establishing each index membership function aiming at different jacking pipe types and cutterhead configuration combinations by applying a fuzzy mathematics theory, and generating a membership matrix of jacking pipe adaptive type selection; and thirdly, determining a comprehensive index weight by adopting a subjective and objective weighting method fusion strategy, and finally constructing a fuzzy comprehensive evaluation model based on the membership matrix and the comprehensive weight, thereby realizing the adaptability quantitative evaluation of the pipe-jacking tunneling machines with different types and cutterheads. According to the method, the defect that traditional model selection depends on subjective experience is overcome, quantitative evaluation of pipe-jacking model selection under the complex stratum condition is achieved, the model selection accuracy and efficiency are improved, a reliable solution is provided for scientific model selection of the pipe-jacking tunneling machine, and the method has outstanding practicability and wide application prospects.

The application provides an aircraft intention recognition probability estimation method and system, and relates to the technical field of aircraft intention recognition. Basic information of a target aircraft is acquired, attribute information and state information are determined in the basic information; semantic information fuzzy sets are generated according to the attribute information and the state information to serve as antecedent structure parameters; target task information is acquired, and membership functions in consequent structures are determined according to the target task information; the intention probability prediction model of the target aircraft is generated by combining the basic information and the target task information and through aggregation of preset semantic rules; the basic information of the current target aircraft is acquired, and the flight intention is determined in combination with the intention probability prediction model. Through multiple rules, complex nonlinear relationships are approximated, various complex data are fitted, each rule has semantic characteristics, and strong interpretability can be achieved, so that the technical effect of efficiently and accurately identifying the aircraft intention is realized.

This invention discloses a data security governance method based on data classification and grading, belonging to the field of data security governance technology. The method includes: collecting resource data and classifying it according to structural type; generating standardized resource datasets and preprocessed record resource packages through differentiated preprocessing; performing sensitivity analysis on the standardized data to obtain comprehensive sensitivity values; constructing a four-level sensitive fuzzy set and corresponding normal membership function; calculating and correcting the membership vector; grading according to the maximum membership principle and completing boundary verification; performing correlation analysis on data of the same grade to determine a segmentation template; segmenting to obtain resource sub-data; determining access permission ranges through association analysis; desensitizing the resource sub-data according to the permission range and sharing it; and simultaneously using a full-process dynamic optimization mechanism to adaptively iterate the parameters of each stage. The advantages are: abandoning the fixed threshold grading mode, achieving refined adaptive sensitivity grading, balancing data security and business availability, and forming a closed-loop governance system throughout the entire process.

The invention discloses a smart tourism recommendation method based on granular computing and a type-2 fuzzy set. The method comprises the following steps: acquiring multi-source heterogeneous tourism data; performing uncertainty modeling on the multi-source heterogeneous tourism data by using a Bayesian neural network, outputting posterior distribution, and mapping the posterior distribution into membership function parameters of a generalized type-2 fuzzy set to obtain multi-modal type-2 fuzzy information particles; based on a particle calculation framework, with coverage rate-specificity collaborative maximization as a target, performing optimal particle size distribution on the multi-mode type-2 fuzzy information particles to generate optimal particle size information particles; inputting the optimal granularity information particles into a two-channel deductive learning framework, realizing knowledge-data dynamic fusion by the two channels through a shared attention layer, and outputting a fused tourist preference feature vector; and based on the preference feature vectors, constructing a multi-granularity graph neural collaborative filtering model, respectively generating a personalized recommendation list, a group recommendation list and a socialized recommendation list, and outputting interpretable rules.

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

The invention provides a measurement and control signal fine capturing method and device based on fuzzy control, and belongs to the field of satellite-borne measurement and control responder signal processing, and the method comprises the steps: respectively obtaining a pseudo code delay coarse estimation value and a Doppler frequency coarse estimation value; determining a value range corresponding to the input variable of the pseudo code delay dimension and a value range corresponding to the input variable of the Doppler frequency dimension; determining an input fuzzy set of the dimension and a corresponding input membership function according to the input variable of the pseudo code delay dimension, and determining an input fuzzy set of the dimension and a corresponding input membership function according to the input variable of the Doppler frequency dimension; for each dimension, executing deviation value estimation operation in the fuzzy set corresponding to the input variable of the dimension to obtain a deviation estimation value corresponding to the dimension; and determining a fine capture value of the dimension according to the rough estimation value and the deviation estimation value corresponding to the input variable of the dimension. The estimation precision of the measurement and control signal can be improved.

The invention relates to a river ecological environment forecasting method, in particular to a river ecological drought probability forecasting system based on fuzzy mathematics. The objective of the invention is to provide a fuzzy mathematics-based river channel ecological drought probability forecasting method so as to realize high-precision accurate forecasting of river channel ecological drought. According to the technical scheme, the river ecological drought probability forecasting method based on fuzzy mathematics comprises the following steps of 1, collecting and investigating basic data of a research area; 2, determining a fuzzy ecological drought membership function; 3, a natural runoff probability forecasting method; 4, calculating a fuzzy ecological drought occurrence possibility; and 5, evaluating the fuzzy ecological drought forecast precision.

This application provides a reservoir permeability prediction model generation and prediction method, apparatus, equipment, and medium, relating to the field of petroleum exploration technology. The model generation method includes: generating multiple target data; determining multiple reservoir permeability intervals and their corresponding target datasets based on the target data; constructing membership functions corresponding to the reservoir permeability intervals based on fuzzy logic and determining the parameter values ​​in the membership functions; determining the target sample set corresponding to each reservoir permeability interval according to the membership degree corresponding to each target data under different membership functions; dividing the target sample set corresponding to the reservoir permeability interval into a first sample set and a second sample set, and training the initial reservoir permeability prediction model separately to obtain two target reservoir permeability prediction models corresponding to the reservoir permeability interval. This application can effectively solve the problem of low prediction accuracy in predicting reservoir permeability based on the correlation between porosity and reservoir permeability.

The invention discloses a method for evaluating fish habitat and ecological flow regulation and control based on a data-driven fuzzy model, belongs to the field of water conservancy and hydropower engineering environmental protection and ecological hydraulics, and particularly relates to a method for evaluating fish habitat and ecological flow regulation and control. According to the method, a hydraulic parameter membership function is automatically deduced from on-site fish behavior observation data through a fuzzy C-means clustering (FCM) algorithm, a fuzzy rule base is constructed, a habitat suitability index (HSI) model under the synergistic effect of multiple factors such as water depth and flow velocity is established, and an environment flow threshold value based on weighted available area (WUA) maximization is output. According to the method, the defects that a traditional habitat model excessively depends on expert experience and multi-factor interaction processing is insufficient are overcome, objective quantification of model parameters and accurate capture of a complex nonlinear relation are achieved, quantifiable and species-specific decision support is provided for cascade reservoir ecological scheduling, and the method has the advantages of being high in practicability and easy to popularize. And hydroelectric benefits and ecological protection requirements are effectively balanced.

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

The application provides a medical data-oriented deep convolution fuzzy neural network and a training method thereof, and comprises a medical data explainability prediction model (IP-DCFNN) based on a deep convolution fuzzy neural network. The IP-DCFNN is composed of three parts: a fuzzy logic antecedent part, a deep convolution calculation part and a fuzzy result representation part. The fuzzy logic antecedent part extracts input data, and the input data is converted from a numerical value into a set of membership degree values for fuzzy language scalars through the operation of a membership function in the fuzzy logic antecedent part. The deep convolution calculation part extracts hidden features in input rule weights, and converts hidden layer weights into high latitude information representation. The fuzzy result representation part is used to process the defuzzification process in fuzzy reasoning. The application relates to the technical field of computer technology, and the IP-DCFNN adds the concept of a deep convolution neural network on the basis of a fuzzy reasoning system to achieve the explainability prediction capability for medical data.