167 results about "Fuzzy clusters" patented technology

The invention relates to the technical field of digital image processing and pattern recognition, discloses a fuzzy clustering steel plate surface defect detection method based on pre classification and aims to overcome defects of judgment missing and mistaken judgment by the existing steel plate surface detection method and improve the accuracy of steel plate surface defect online real-time detection effectively during steel plate surface defect detection. The method includes the steps of 1, acquiring steel plate surface defect images; 2 performing pre classification on the images acquired through step 1, and determining the threshold intervals of image classification; 3, classifying images of the threshold intervals of the step 2, and generating white highlighted defect targets; 4, extracting geometry, gray level, projection and texture characteristics of defect images, determining input vectors supporting a vector machine classifier through characteristic dimensionality reduction, calculating the clustering centers of various samples by the fuzzy clustering algorithm, and adopting the distances of two cluster centers as scales supporting the vector machine classifier to classify; 5, determining classification, and acquiring the defect detection results.

The invention provides an intelligent fusion identification network state prediction and congestion control system. The invention discloses a method and a system for realizing network state predictionand data packet queue congestion control on a programmable data plane by combining a machine learning model method based on P4. The method comprises the following steps: collecting network state feature information in real time through an in-band network telemetry (INT) technology; and achieving network state characteristic value prediction by adopting an 'LSTM-fuzzy clustering' model method combining a long short term memory (LSTM) neural network model and a fuzzy clustering algorithm, and achieving the prediction of the network state characteristic value by adopting a fuzzy clustering algorithm. According to the obtained network state characteristic value, fuzzy clustering is carried out to obtain four network states; a normal state, a congestion early warning state, a continuous congestion state and a congestion alleviation state; corresponding strategies are formulated for different network states, the controller issues corresponding flow tables and formulates switch actions in different network states, and a comprehensive and dynamic queue feedback mechanism is provided to ensure that data packet queuing delay is as small as possible and achieve the effect of congestion control.

The invention discloses a load prediction system of a regional power grid and a method thereof, wherein the load prediction system comprises a reverse isolation device, a data processing module, a load classification module, a load prediction module and a database; the data processing module is used for preliminarily processing load data collected by the reverse isolation device into load sample data; the load classification module is used for utilizing FCM fuzzy clustering to carry out classification on the load sample data and selecting mean values of all kinds of load curves as a typical daily load curve; and the load prediction module is used for using the typical daily load curve as an input variable of a BP nerve network, carrying out prediction, and obtaining a load predicted value of the regional power grid. With comprehensively considering influences of season and weather factors on loads, the pre-processing module is improved, the loads are classified based on the FCM fuzzy clustering, the typical daily load curve is accurately found, the load predicted value of the regional power grid is obtained by the BP nerve network, and the inherent law of history load data is fully extracted, so that the prediction precision is greatly improved.

This invention relates to a method for identifying and classifying carcinomas on the skin of a subject by a FTIR or Raman spectrometer coupled with a micro-imaging system.

The invention discloses a multi-source image change detection method based on clustering guided deep neural network classification, which avoids the step of difference figure generation in the early stage in traditional change detection and overcomes the defect that difference figure generation is needed in multi-source image change detection. The method comprises the following steps: inputting a gray matrix of an optical image; carrying out fuzzy clustering on the optical image to get a segmented gray matrix; marking the optical image after clustering segmentation; sampling the optical image and a TM image; selecting a training sample from the TM image; training a stacked sparse automatic encoder SAE; using a tag to fine-adjust network parameters; inputting the TM image to a network, and outputting a classified image; working out the log ratio of the two classified images; and getting a change detection result. The link of difference figure construction is abandoned. The method is applicable to multi-source remote sensing image change detection, and has the advantages of little noise influence, high precision of change detection result classification, and the like.

The invention belongs to the technical field of radar. The invention relates to a flight path optimization method, in particular to a target track optimization method based on dual-fusion maximum entropy fuzzy clustering JPDA. The method comprises the following steps: (1) performing state prediction and updating by adopting a Kalman filter on the basis of a maximum entropy fuzzy clustering method,and performing preliminary screening on a target trace point set obtained at a moment k + 1according to an elliptical wave gate rule during scanning and tracking; (2) multiplying the measurement membership degree taking the wave gate center as the clustering center by the corresponding position of the wave gate membership degree taking the effective measurement data as the clustering center to obtain the bidirectional membership degree between each effective measurement and all the wave gate centers; and (3) analyzing clutter distribution and combining the bidirectional membership degree to obtain a final association probability, obtaining state estimation and estimation error covariance of the target according to a standard JPDA algorithm filtering program, and finally iterating trackingtrack information of the target. The method is high in tracking precision, and avoids complex correlation matrix splitting.

The invention discloses a fuzzy clustering analysis method-based method for determining a critical rainfall threshold of a landslide. The method comprises the steps of building an algorithm empirical model, determining the critical rainfall threshold of the landslide, and performing fuzzy clustering factor selection through a Delphi method; based on this, performing fuzzy clustering analysis of the critical rainfall threshold of the landslide, creating a data matrix and a standard data matrix, and establishing a fuzzy similar matrix; and finally performing fuzzy clustering and determining the critical rainfall threshold. According to the fuzzy clustering analysis method-based method for determining the critical rainfall threshold of the landslide, the rainfall threshold is determined by fully considering influence factors of regions through strict mathematic algorithm derivation, statistics technologies and physical means. The method is high in accuracy; the requirements of current landslide forecasting and warning can be met; the threshold accuracy is greatly improved; and reasonable determination of the critical rainfall threshold plays an important role for guaranteeing the landslide forecasting and warning accuracy.

Aiming at the problems of low cluster stability, more isolated nodes existing in the existing vehicular ad hoc network clustering algorithm under the situation of frequent network topology changes, the invention discloses a fuzzy clustering algorithm of a vehicle-mounted ad hoc network. In a cluster generation algorithm, the driving direction of the vehicle is divided at first, and then an ability parameter capable of balancing a vehicle node and becoming a cluster head is defined in combination with the idea of fuzzy clustering; and in a cluster maintenance algorithm, the position of a cluster member is firstly predicted based on a kalman filter, and then a cluster maintenance mechanism with a critical cluster member as the core is provided based on position prediction and driving direction judgment. The fuzzy clustering algorithm uses the fuzzy clustering idea, meanwhile predicts the position of the cluster member, thereby improving the stability of the cluster in the network, reducing the number of isolated nodes in the network and reducing the network communication cost, and the fuzzy clustering algorithm is suitable for straight roads, T-intersections, crossroads and other scenes, and thus having a good application prospect.

The invention provides a level set medical image segmentation method based on heredity kernel fuzzy clustering and relates to the application of medical image segmentation. According to the level set medical image segmentation method disclosed by the invention, a heredity kernel fuzzy clustering algorithm is utilized to obtain an optimal clustering result of a medical image to be treated and then the clustering result is applied to an initial outline of an LBF (Local Binary Fitting) model to carry out the segmentation on the image, so that a blood vessel image has high segmentation efficiency and accuracy.

The invention discloses a short-term photovoltaic power generation prediction method considering the correlation degree of weather and meteorological factors. The method comprises the following steps:1, rejecting bad data through an iForest algorithm; 2, respectively calculating Pearson correlation coefficients R of the photovoltaic power generation power and the five meteorological factors underfour weather types, and normalizing the Pearson correlation coefficients R; 3, performing fuzzy clustering on the five meteorological factors of the to-be-measured day, and obtaining a correlation coefficient of the historical day and the to-be-measured day; 4, a correlation coefficient normalization value is introduced, and the correlation degree between a historical day and a to-be-measured dayis solved; and 5, taking the historical day with high correlation degree as historical data, inputting the historical data and the meteorological factors of the day to be measured into the improved ACO-BP neural network, and finally obtaining a predicted value of the solar photovoltaic power generation to be measured; and 6, determining a neural network correlation coefficient, and performing simulation. The method aims to improve the photovoltaic power generation prediction precision, improves the practicality of a prediction model, and plays a great role in the combination of scheduling andprediction.

The invention discloses a blast furnace bosh gas volume prediction method and program for a multi-clustering prototype-based T-S model. The method comprises the steps of performing data preprocessingon blast furnace data, and deleting abnormal values; performing variable screening by utilizing a Spearman rank correlation coefficient; selecting a hyperspherical clustering prototype-based FCM fuzzyclustering algorithm and a hyperplane clustering prototype-based NFCRMA fuzzy clustering algorithm to perform fuzzy clustering on the blast furnace data, performing calculation by using membership functions corresponding to the two fuzzy clustering algorithms to obtain two types of different T-S model antecedent rule fitness, sorting the two types of the rule fitness from high to low, and finallycalculating a weighted sum of the two types of the rule fitness to obtain weighted rule fitness; and calculating consequent parameters of the T-S model by using a least square method, and finally adjusting a weighted coefficient to improve the prediction precision of the model. The method can accurately predict a value of a blast furnace bosh gas volume index at a next moment.

The invention provides a fuzzy cluster SAR image segmentation method based on Gamma distribution. The method comprises the following steps: taking ray values of pixels of an SAR image to be segmented as sampling points, and constructing an FCM object function with a Gamma distribution function; determining solution formulas of parameters in the FCM object function; carrying out fuzzy clustering on the SAR image to be segmented by use of the FCM object function with the Gamma distribution function to obtain fuzzy membership degree matrixes when the gray value of each pixel of the SAR image to be segmented belongs to each ground object type; and performing defuzzification on the fuzzy membership degree matrixes according to a maximum membership degree principle so as to realize SAR image segmentation. According to the invention, a dissimilarity measure from pixel points to clusters is described by use of negative logarithms of a Gamma distribution probability density function, through accurate fitting of SAR image distribution features, the influence exerted by noise in the SAR image on a segmentation result is further overcome, the segmentation precision is improved, and the fitting and segmentation precision of the SAR image is effectively improved.

The invention belongs to the technical field of personalized recommendation and discloses a cluster group discovery-based recommendation system and method and a personalized recommendation system. According to collection of user operation behavior data by an online system, a user-project matrix is extracted; an entire user-project data set is divided into multiple subgroups that are highly internally correlated to each other; via combination of Euclidean and other similarity calculation methods and clustering algorithms, recommendation results are obtained after a collaborative filtering algorithm is used directly in the subgroups. Via use of the cluster group discovery-based recommendation system and method and the personalized recommendation system, data preprocessing operation is performed before a recommendation algorithm is used directly, fuzzy clustering is adopted for grouping the data, and a final recommendation result is obtained after predicted scores of all groups are integrated. While normal operation of the entire recommendation system is ensured, a fuzzy cluster group discovery-based data preprocessing method allows the recommendation algorithm to be applied directlyto individual groups of data that are strong in correlation instead of all data, and therefore the recommendation system can be improved in expansibility and accuracy in the face of mass data.

Provided is a method of extracting an infrared pedestrian object by utilizing an improved fuzzy clustering algorithm, comprising the steps of: step 1, detecting the position of a pedestrian object symmetric axis; first, employing a significance algorithm to obtain an area where a target is located, then utilizing a fuzzy C mean value algorithm to perform primary classification on the area where a target is located, summing each column of a primary classification result image to obtain a column summarization curve, and then solving the extreme value of the curve, the position of the extreme value being the position of the pedestrian object symmetric axis; step 2, in order to reduce the influence of intensity inhomogeneity in an infrared image to a clustering process, employing morphological close operation to preprocess an original image; step 3, employing an improved fuzzy clustering algorithm to perform clustering segmentation on the image after preprocessing; and steps 4, in order to obtain a segmentation result with a complete outline and smooth rim, performing subsequent processing on the clustering result: rejecting a non pedestrian target region and a small area region, and smoothening the rim.

The invention discloses a single-cell transcriptome sequencing data clustering recommendation method based on two-dimensional distribution structure judgment, which comprises the following steps of: acquiring a gene expression matrix obtained by single-cell transcriptome sequencing data of a plurality of cells, and after filtering and standardization processing, constructing a two-dimensional feature matrix and carrying out linear normalization; calculating an Euclidean distance between cells according to the normalized two-dimensional feature matrix, thereby establishing a cell minimum spanning tree; cutting the cell minimum spanning tree through a self-adaptive threshold, and determining a two-dimensional distribution structure of data according to the balance of clusters formed after cutting; and recommending and applying a hierarchical clustering algorithm for data with fuzzy inter-cluster boundaries and a continuous two-dimensional distribution structure, and recommending and applying a spectral clustering algorithm for data with obvious inter-cluster boundaries and a block two-dimensional distribution structure. According to the method, a method which is more suitable for a two-dimensional distribution structure of single cell transcriptome sequencing data in hierarchical clustering and spectral clustering can be recommended to serve as a downstream clustering analysis method, and the clustering accuracy is improved.

The invention discloses an image segmentation method. The method comprises: step one, a historically stored image is divided with K view angles and historical clustering centers are obtained by a classical FCM algorithm; step two, when a new noisy image is processed, a to-be-processed image is divided into K view angles, a clustering center, obtained at the step one, of a related historical similar image is fused based on the classical FCM algorithm;, and a new objective function of a multi-view-angle FCM algorithm with introduction of a transfer learning mechanism is constructed; and step three, a final membership degree matrix of a currently processed image is obtained according to membership degree matrixes of all view-angle images of the current images and view-angle weight vectors W, defuzzification is carried out to obtain a space division result of the current image. According to the image segmentation method, a multi-view-angle fuzzy clustering image segmentation method having the transfer learning capability is employed; and on the basis of the transfer capability, multi-view-angle coordinated image segmentation is realized, so that the segmentation precision is improved.

The invention discloses an equivalent range-of-motion (ROM) distance calculation method based on a dynamic-time-warping (DTW) cluster fuzzy clustering self-organizing-maps (SOM) neuron. A feature vector is established, the feature vector is processed by an initial-state Kalman filter algorithm to remove secondary Gaussian covariance white noises to obtain data, and then a sample sequence for a dynamic time warping algorithm is obtained; the sample sequence is transformed into X-axis and Y-axis amplitude values of an acceleration signal acceleration amplitude SVM and the SVM amplitude values are classified into different groups by using a fuzzy clustering neuron algorithm; the group values of the two axial directions of the SVM(x) and the SVM(y) are combined with group clustering centers and gravity centers corresponding to the group values relatively; and then an ROM equivalent value is obtained by combining the group clustering gravies and the centers and a distance is calculated. Therefore, spatial offset distances between different group sensors can be found out and the path time of the end-to-end posture sensor is reduced.

The invention discloses a fuzzy clustering color image segmentation method based on cuckoo optimization. An image to be segmented is input, and color feature is extracted from the image; a cuckoo algorithm is used to optimize a clustering center of a fuzzy clustering algorithm; an improved fuzzy clustering algorithm is used to cluster pixel points in a color space of the image; the clustering center is output, and a membership degree matrix is calculated; and pixels of the image are divided according to the output clustering center and the membership degree matrix, and the image is segmented. An HSV color space which is suitable for sensing of human eyes, the segmentation effect can be improved, an iteration process that the cuckoo algorithm is used to optimize the fuzzy clustering center is provided for overcoming the defect that a traditional fuzzy clustering algorithm tends to fall into the optimum, the operation speed and the convergence speed of the clustering algorithm are improved, the problem that the initial value of the clustering center has much influence on the clustering algorithm, and the clustering effect is good.

The invention discloses a household energy double-layer optimization method for realizing interaction between a power grid side and a user side. The method comprises the steps of: reading a daily loadcurve of a user, carrying out dimensionality reduction on high-dimensional load data based on a feature index, and obtaining a dimensionality reduction curve; calculating the membership degree of thedimension reduction curve based on an FCM fuzzy clustering algorithm, and completing the classification of the daily load curve; classifying resident users, and designing a power package for each type of resident users; simulating a day-ahead load curve, and performing real-time monitoring and load decomposition on the household appliances of the users by utilizing a non-intrusive optimization model; dynamically adjusting a peak-valley electric quantity coefficient, and switching on and switching off equipment in real time according to the dynamic priority of the household appliances. According to the household energy double-layer optimization method for realizing interaction between the power grid side and the user side of the invention, the power package considering the time-of-use electricity price and an incentive subsidy mechanism is implemented to the scheduling of the specific load equipment of the household users, and is used for improving the peak-valley difference of the resident load curve, improving the interaction degree between a power utilization end and a power supply end, and enabling the resident users to participate in the optimal scheduling of the power grid more actively.

Equipment operation state judgment method based on the fuzzy clustering optimal k value selection algorithm comprises the steps of collecting test data according to the operation condition of equipment to be tested, and preprocessing the data; establishing a double-target model according to the processed test data; using a CDG optimization algorithm to carry out optimal solution on the double-target model; converting the result after optimization solution by using a DB index, and calculating to obtain an optimal clustering number k; according to the obtained optimal clustering number k, analyzing the preprocessed test data by using a fuzzy clustering algorithm FCM, and dividing the test data into k clusters; and counting the data center of each cluster, the characteristics of the data in each cluster and the range included by each cluster, and judging the running state of the current equipment according to the characteristic conditions of the clusters. According to the method, the result error caused by determining the optimal clustering number k in the clustering algorithm is reduced, and the method can be more accurately used for judging the running state of the equipment.

The invention provides a thermal ablation area monitoring method and system based on radio frequency processing and fuzzy clustering. The thermal ablation area monitoring method based on radio frequency processing and fuzzy clustering comprises the following steps: S1, acquiring radio frequency data in a thermal ablation process, and processing the acquired radio frequency data; s2, related to fuzzy clustering; s3, performing iterative processing on fuzzy clustering according to the processed radio frequency data and set fuzzy clustering parameters to obtain a clustering result; and S4, displaying a thermal ablation area according to the obtained clustering result. On one hand, the difference between thermal ablation and normal tissue is maximized and highlighted from the perspective of signal processing; on the other hand, an unsupervised fuzzy clustering algorithm is used for automatically identifying the thermal ablation area, the scheme can be well compatible with a current ultrasonic system, and compared with other disclosed thermal ablation monitoring methods, the method has the advantages of being reliable in result, easy to operate and easy to implement.

The invention discloses a confidence rule automatic generation method based on fuzzy clustering, and belongs to the technical field of data mining. In order to solve a problem that time and labor areconsumed when a confidence rule base is established completely depending on artificial experience, on the basis of an extended confidence rule base method, firstly, fuzzy clustering is carried out onsample data, and a fuzzy clustering center of a sample and the sample with the clustering center as the circle center and the distance between the sample and the clustering center being a certain setvalue serve as basic data for generating an extended confidence rule base; and calculating a premise attribute weight and a rule weight according to the variable association relationship and the sample membership matrix. The objective of the invention is to solve the problems of difficult selection of sample data and unsuitability for large sample data modeling in the extended confidence rule basemethod. According to the method, the dependency degree of experience setting premise attribute weights and initial rule weights in the existing extended confidence rule base method on expert experience is reduced. By adopting the method, the confidence rule is extracted from the actual production data, and compared with manually set initial confidence rules, the generation efficiency and the reasoning precision of the confidence rule base are improved.

A fast image segmentation algorithm based on artificial bee colony optimization fuzzy clustering is proposed and optimizes the sensitivity of a traditional FCM algorithm to the initialization of clustering centers by using the intelligent behavior of bee colony in nature. The algorithm starts with bees looking for food sources. An improved fitness function value which is described in the description is used to express the nectar content of the food source, according to the greedy algorithm, the old and new food sources are selected. After the bee finishes searching, the information is transmitted to the follower bee. The bee chooses a food source according to the probability P related to the nectar amount, and at the same time, the bee searches the neighborhood near the food source. When the nectar amount is not improved after limited searches in the vicinity of a food source, the nectar source is abandoned, and the bees associated with the food source are replaced by scouting bees toindependently and randomly search for the nectar source, and the location of each food source represents a possible solution of the optimal clustering center of the image to be segmented.

The invention provides a predictive control method and a predictive control device for a piezoelectric ceramic actuator based on a fuzzy TS (Takagi-Sugeno) model. The method comprises the steps of acquiring a voltage displacement modeling data set of the piezoelectric ceramic actuator; performing fuzzy clustering on a voltage displacement sample data set according to the fuzzy TS model, acquiring a plurality of linear sub rules, and acquiring parameters of each linear sub rule according to a least square identification method; determining a sub predictive control law corresponding to each linear sub rule under a performance index optimum condition; acquiring a voltage displacement predictive data set of the piezoelectric ceramic actuator in a sampling period; and performing weighted average on the sub predictive control laws, acquiring a final predictive control law and using the final predictive control law as a control output function. The predictive control method provided by the invention can realize real-time and accurate control for the piezoelectric ceramic actuator.

The invention discloses an online fuzzy least square support vector machine sintering process kinetics modeling algorithm. The algorithm fuzzily divides online input k+L groups of data vector space by using a fuzzy c mean value objective function, provides an input space fuzzy membership computing method, constructs a Langrange function according to a structural risk minimization principle, designs a model optimization object of the k+L groups of data according to a KKT optimal solution condition, and solves a model parameter and a model solution, determines the rolling characteristic of the data and whether to roll the online data to k+L+1 to update the model. The modeling algorithm introduces an input data fuzzy clustering idea so as to enhance the generalization capability of the model, and introduces the concept of a rolling time window so as to enhance similar solid sintering process online modeling practical performance.

The invention provides a fuzzy clustering algorithm-based information fusion method applicable to a vehicle ad hoc network and relates to the technical field of computer networks. The method comprises the following steps: selecting speed value, brake frequency and acceleration value as information attributes and classifying local message objects by the fuzzy clustering algorithm to form different message sets; and fusing the information objects with the same attributes into a message. The method has the advantages of improving the fusion efficiency, obviously reducing transmission flow and guaranteeing the accuracy of road state information.

The invention discloses a fuzzy clustering system based on user behavior data, and relates to the technical field of wireless Internet behavior analysis and prediction. In order to construct a profitmode of precision marketing, the fuzzy clustering system specifically comprises a data acquisition module, a data analysis module and an output unit, wherein the data acquisition module is used for collecting user behavior data of user operation time and sending the user behavior data to a server, and the user behavior data comprises static data and dynamic data. According to the fuzzy clusteringsystem, after the user classification is obtained, the long-term behavior prediction aiming at the user classification and the short-term behavior association aiming at the individual behavior are obtained through data mining; the real-time behavior prediction accuracy is continuously updated and perfected in the running time; the equipment capacity can be flexibly expanded; the equipment performance is improved; the flexibility of technical upgrading and equipment updating is achieved; the flexibility of supporting expansion, adjustment and reconstruction of service functions is achieved; therequirements and hobbies of customers are known; and browsing and interaction behavior data of the customers are emphasized.

The invention discloses an Internet financial platform fraud behavior detection method based on a fuzzy C-mean algorithm, and the method comprises the steps: carrying out the Z-score normalization anddimensionality reduction standard processing of collected information obtaining real-time measurement point data during the registration of a client account of an Internet platform, dividing the datainto a training set and a verification set, initializing the parameters of a fuzzy C-mean, and adopting a fuzzy clustering effectiveness function to automatically optimize the initial clustering number, obtaining a fuzzy C-mean clustering model through a target function, determining a classification decision rule according to the training set, classifying the verification set, and optimizing themodel with the application behavior and post-loan performance of the user; and deploying the optimized fuzzy C-mean value model to the rear end of an internet financial platform to perform online anomaly detection and monitoring on application behaviors of customers, sending out system early warning for applications in suspected abnormal states, and performing manual approval links or rejecting the applications. The method is advantaged in that high early warning result accuracy and strong fraud identification capability are achieved, and financial fraud risks are reduced.

The invention relates to a Gaussian fuzzy clustering computing method for differentiating and diagnosing chronic bronchitis. The method comprises the steps of obtaining inspection data of diagnosed chronic bronchitis patients from an electronic medical record system; calculating the initial clustering number through utilization of a hierarchical clustering algorithm; randomly selecting a clustering center according to the initial clustering number; mapping the clustering center and samples to a Hilbert space through mapping; calculating a membership matrix of the samples according to the clustering center in the Hilbert space; calculating a new clustering center through utilization of the calculated membership matrix; continuously and iteratively calculating the membership matrix and the clustering center until the change of the clustering center is smaller than a threshold value; calculating clustering granularity values according to the obtained clustering centers; circulating all initial clustering numbers and carrying out the steps; and taking the clustering center with the minimum granularity value as a final clustering result. The method can be used for finely classifying chronic bronchitis symptoms, and certain facilitation for diagnosing the chronic bronchitis is provided.

The invention relates to data mining in bioinformatics, and particularly to a method for identifying the cancer-related miRNA through a miRNA-gene regulation module. The method comprises the steps ofperforming difference comparison of gene expression data; processing the gene expression data and miRNA expression data; constructing the miRNA-gene interaction matrix; calculating a miRNA-gene correlation coefficient, obtaining a miRNA-gene correlation matrix, performing fuzzy clustering on the miRNA; constructing a combined miRNA-gene interaction matrix and a miRNA-gene correlation matrix, calculating absolute average correlation degree of the gene with each miRNA, adding the gene into the miRNA according to absolute average correlation degree for constructing the miRNA-gene regulating module; calculating the correlation degree of the miRNA in each module, and ordering according to the correlation degree. The main process is presented in a graph 1. The method can be used for acquiring the cancer-related miRNA for searching the function and the mechanism in a cancer development and generating process, screening the miRNA biological mark in cancer early-period diagnosis, and acquiringtargets in targeted treatment of the cancer.