143 results about "Fuzzy matrix" patented technology

The invention discloses a risk assessment method based on a fuzzy matrix and an analytic hierarchy process. The risk assessment method based on the fuzzy matrix and the analytic hierarchy process comprises the steps that a plurality of evaluation indexes are set to generate an evaluation index domain; a plurality of evaluation scales are set to generate an evaluation scale domain, and each evaluation scale corresponds to a scale fuzzy subset; the membership of a dangerous source for each scale fuzzy subset is calculated, and a membership matrix is constructed; weight vectors of the evaluation indexes are calculated; a fuzzy comprehensive evaluation result vector of the dangerous source is synthesized; a risk assessment value is calculated; a risk value is calculated, and the risk condition of the dangerous source is judged according to a risk matrix and the risk value. Through the application of the fuzzy matrix and the analytic hierarchy process in risk assessment, the risk assessment method is used for analyzing various undetermined factors and indexes in the risk assessment process; first-scale indexes and second-scale indexes are set for evaluation from multiple aspects, so that analysis is more comprehensive and a danger value of possibility and a danger value of severity are formed; curving of a risky area chart guarantees stable risk conditions of the dangerous source.

The invention relates to an image recognition method based on multi-column convolutional neural network fuzzy evaluation. By combining an image preprocessing technology, a convolutional neural network and a fuzzy mathematical method, interference information in an image sample acquired in the real environment is overcome by adopting different preprocessing technologies from different perspectives respectively, feature information beneficial to recognition is strengthened, the preprocessed image is input to the preprocessed and optimized multi-column convolutional neural networks having different structures respectively, then fuzzy evaluation based on comprehensive affiliation degree and discrete affiliation degree is performed on the output results of the multi-column convolutional neural networks by adopting a fuzzy matrix to determine a fuzzy evaluation manner suitable for the image, and a recognition result is finally accurately obtained.

The invention discloses a highway tunnel traffic accident risk evaluation method based on fuzzy analytic hierarchy process. The method includes the following steps: formulating a plurality of evaluation indices to obtain an evaluation index domain, dividing a highway tunnel into three research sections, establishing respective index system for each research section; formulating a plurality of evaluation levels to obtain an evaluation level domain; separately computing the fuzzy degree of membership of respective evaluation index to respective evaluation level, constructing a degree of membership matrix; computing a weight vector of the evaluation indices; on the basis of the weight vector and the degree of membership matrix, synthesizing a comprehensive evaluation vector; computing a risk value, on the basis of the risk value, determining risk conditions. According to the invention, by using the fuzzy matrix and the application of the analytic hierarchy process to risk evaluation, the method can analyze respective uncertain factor and index that appear in the course of risk evaluation; by setting a first level index and a second level index, the method can consider from a multiple aspects and can deliver more comprehensive analysis by dividing the tunnel into three regions and make related talks, the method herein takes the minimum evaluation value as a final evaluation result. The evaluation is more much reasonable.

The invention relates to the field of pattern recognition, and discloses an unbalanced data classification method based on intuitive fuzzy integration and a system based on the method. The method comprises the following steps of: a) cleaning original data, and classifying original point-of-sale (POS) class samples according to intra-class positions to generate POS class artificial samples; b) training a base classifier by using different sample sets of inter-class approximate balance; c) converting the classification output equal utility of the base classifier into an intuitive fuzzy matrix; and d) integrating samples to be classified into the membership and the non-membership of the POS class and the negative (NEG) class by combining the weight of the base classifier, and making a classification decision. The invention has the advantages that: over learning is avoided by integrating over sampling and under sampling; the training samples of the base classifier are different, so that the difference of the base classifier is ensured; the base classifier is not specifically limited, so the method has good expandability; the intuitive fuzzy reasoning method quantitatively describes the uncertainty in classification so as to improve the performance of integrated learning; therefore, the system based on the method can better support the medical diagnosis decision and the like.

Disclosed is a spatial gravity model based fuzzy c-means remote sensing image automatic classification method which is suitable for automatic segmentation and classification of remote sensing images, medical images and other image. The spatial gravity model based fuzzy c-means remote sensing image automatic classification method comprises the steps of determining the number of pixels of a remote sensing digital image and performing clustering on the image through a standard FCM (Fuzzy C-Means) model to obtain an initial fuzzy membership matrix and clustering center; solving the spatial gravity and spatial constraint penalty factors between every pixel and other pixels in neighborhood windows of the pixel in turn to obtain a fuzzy factor as the formula finally; adding the fuzzy factor as the formula to the standard FCM model to obtain a new clustering objective function; solving the fuzzy matrix as the formula and the clustering center as the formula in a circulating mode until the clustering center does not continue to be changed any longer or the operation reaches the maximum number of iterations; performing class marking on every pixel point of the remote sensing image through the maximum membership matrix criterion according to the finally solved fuzzy membership matrix as the formula to determine the class of every pixel to form a remote sensing image classification thematic map so as to implement the automatic classification of remote sensing digital images. The spatial gravity model based fuzzy c-means remote sensing image automatic classification method is simple, high in automation degree, small affected by image noise and high in image segmentation classification accuracy.

The present invention relates to image fusion and gait recognition of human body, and especially to a super-resolution image restoration method in gait recognition. The present invention provides a super-resolution image restoration method in gait recognition. The image quality of video sequence is increased through the restoration of super-resolution image. The changing interference of background image is greatly reduced thereby reducing the calculation amount in human body detection process. The invention adopts a technical scheme that the prior gait image is degraded to low-resolution gait image in each iteration. The degraded gait image and the error and fuzzy matrix of actual measured low-resolution gait image are used for forming a reverse projection operator for computing the next high-resolution gait image. Namely the high-resolution image (HR image) is obtained by simulating the low-resolution image (LR image) and observing the low-resolution image (LR image) for executing reverse projection. The process is iterated continuously until the time when the error amount reaches least. The super-resolution image restoration method in gait recognition according to the invention is mainly used for personal identification through the gait of human body.

The invention discloses a method for clustering low-voltage distribution network transformer districts based on fuzzy clustering. The method comprises the steps that characteristic indexes of the low-voltage distribution network transformer districts are established; characteristic index data to be analyzed are input, and then an original data matrix is established; standard processing is conducted on the original data matrix, so that a fuzzy matrix is obtained, and a fuzzy similar matrix of the fuzzy matrix is established according to the Euclidean distance algorithm; a fuzzy equivalent matrix is established, the fuzzy equivalent matrix is converted into a Lambda-cut matrix equivalent to the fuzzy equivalent matrix, a dynamic clustering diagram is formed, clustering analysis of the low-voltage distribution network transformer districts to be analyzed is achieved, and after the number of categories is determined, a clustering result of the low-voltage distribution network transformer districts is output according to analysis demand; according to the clustering result of the low-voltage distribution network transformer districts, data characteristics of the transformer districts of each category are analyzed, whether the transformer districts of each category are in urgent need for treatment is judged, the transformer districts in urgent need for treatment are screened out, and a follow-up treatment scheme is provided preliminarily. The method for clustering the low-voltage distribution network transformer districts based on fuzzy clustering has the advantages that the recognition speed is high, the classification accuracy is high, and classification effectiveness is high.

The present invention discloses a method for rapidly screening a raw material used to make a rice product. The method comprises the following steps: (1) collecting a representative rice sample; (2) determining physical and chemical indicators of different varieties of rice raw materials; (3) processing the different varieties of rice raw materials into rice products; (4) for a feature of each variety of rice products, establishing a multi-level evaluation indicator factor set of quality of rice products, and obtaining a weight of each evaluation indicator by analytic hierarchy process; (5) determining a degree of membership of each evaluation factor and constructing a fuzzy evaluation matrix; (6) by adopting a fuzzy matrix compound operation, obtaining a fuzzy comprehensive evaluation value; (7) by regression analysis, obtaining a mathematical model between a comprehensive evaluation value of rice products and a characteristic of raw materials; and (8) by using the mathematical model, predicting suitability of the different varieties of rice raw materials for being processed into rice products. By adoption of the method disclosed by the present invention, the suitability of the different varieties of raw materials for being processed into rice products can be calculated quantitatively, so as to support scientific utilization of raw materials.

The invention discloses a risk assessment system based on a fuzzy matrix analytic hierarchy process. The system comprises an assessment index system generation module, an assessment level system generation module, an index quantification module, a index weight calculating module, a membership degree matrix construction module, a fuzzy comprehensive result calculating module and a risk assessment module, wherein the risk assessment module comprises a risk situation assessment sub-module, a risk analysis sub-module and a risk control sub-module. The risk assessment system based on the fuzzy matrix analytic hierarchy process of the invention can analyze various uncertain factors and indexes appearing in the risk assessment process by using the fuzzy matrix and the analytic hierarchy process (AHP) in the risk assessment.

The invention discloses an AHP (Analytic Hierarchy Process) based assessment method of the teaching quality of fuzzy comprehensive remote education, comprising the following steps of: determining index weight by applying AHP; and determining a total assessment result by applying a fuzzy comprehensive assessment method. A method for determining the index weight by applying the AHP comprises the following steps of: establishing a hierarchical structure of a researched problem; judging a matrix by determining paired comparison; carrying out hierarchical single sequencing; carrying out hierarchical total sequencing, and calculating the combinational weight of all elements of a same hierarchy; and carrying out consistency check. The assessment method of the teaching quality of quantitative remote education carries out the data processing by establishing a fuzzy comprehensive assessment model based on the AHP, introducing a theory into the assessment of the teaching quality of the remote education, determining the weight of assessment factors by using the AHP and then establishing a mathematical model by using a fuzzy comprehensive assessment method, thereby transforming the assessment problem into the multiplication problem of the fuzzy matrix for processing.

A single-image super-resolution method based on the multi-scale structural self-similarity and the compressive sensing comprises the following steps of: firstly setting an initial estimated value of a high resolution reconstructed image, setting a stopping error and the maximum time of iteration, determining a downsampling matrix and a fuzzy matrix according to the process of image degradation to construct an image pyramid, and building a dictionary by using the image pyramid as a training sample of the K-SVD (K-singular value decomposition) method; secondly, according to a Nonlocal method, searching for similar image blocks with the same scale in the current high resolution reconstructed image and determining a weight matrix; thirdly, updating the estimated value of the high resolution reconstructed matrix, updating the sparse representation coefficient, and updating the estimated value of the high resolution reconstructed matrix again; and fourthly carrying out the next iteration until two sequential high resolution reconstructed matrixes meet the corresponding requirement or reach the maximum time of iteration. The single-image super-resolution method of the invention adds the additional information contained in a multi-scale self-similar structure of an image into the high resolution reconstructed image through a compressive sensing frame, thereby having a high computational efficiency.

The invention relates to a method for evaluating the reliability of the performance state of a reciprocating compressor. The method comprises the following steps of: determining a factor set U, wherein the factor set is a set formed by taking various factors influencing an evaluated object as elements, and the comprehensiveness, sensitivity, chanciness and stability of indexes are required to be taken into consideration when the indexes are selected; establishing a weight set A, and giving a corresponding weight to each factor, wherein the weight set is required to meet conditions of normalization and non-negativity; determining a remark set V, wherein the remark set is a classic set consisting of possible evaluation results of the evaluated object, the number m of remark grades is more than 3, but less than or equal to 9 and is an odd number; evaluating a single factor, and establishing a fuzzy relation matrix R; after the factor weight set A and the evaluation matrix R are determined, obtaining a fuzzy comprehensive evaluation set B according to the multiplying operation of the fuzzy matrix; and processing the evaluation indexes by a maximum membership grade method or a weighted averaging method. The method is simple and practical and is easy to popularize and apply.

The invention discloses an improved self-adaptive multi-dictionary learning image super-resolution reconstruction method, which comprises the steps of: (1) determining a downsampling matrix D and a fuzzy matrix B according to a quality degradation process of an image; (2) establishing a pyramid by utilizing the self-similarity of the image, regarding an upper-layer image and a natural image of the pyramid as samples of dictionary learning, constructing various types of dictionaries Phi k by adopting a PCA method, and regarding a top-layer image of the pyramid as an initial reconstructed image X<^>; (3) calculating a weight matrix A of nonlocal structural self-similarity of sparse coding; (4) setting an iteration termination error e, a maximum number iteration times Max_Iter, a constant eta controlling nonlocal regularization term contribution amount and a condition P for updating parameters; (5) updating current estimation of the image; (6) updating a sparse representation coefficient; (7) updating current estimation of the image; (8) updating a self-adaptive sparse domain of X if mod(k, P)=0, and using X<^><k+1> for updating the matrix A; (9) and repeating the steps from (5) to (8), and terminating iteration until the iteration meets a condition shown in the description or k>=Max_Iter.

The invention discloses a motion information-based method for a monocular camera to acquire the depth information. The method comprises the following steps of taking at least two pictures different in sharpness by means of a camera during the movement process of the camera, wherein the pictures are the same in illumination condition; secondly, conducting the blocking treatment on the obtained pictures and selecting small blocks of images that are required for computation; conducting the filtering treatment on each block of clear pictures according to different fuzzy matrixes, and finding out a fuzzy matrix highest in level of similarity through matching fuzzy images; obtaining the depth information of the block based on the camera imaging principle by utilizing the above fuzzy matrix highest in level of similarity; repeating the above steps to obtain the depth information of the entire scene. By adopting the above method, the front and rear distances of an object in a picture can be calculated based on a simple algorithm. The method is applicable to both the short-distance measurement and the long-distance measurement.

The invention discloses an airspace operation situation evaluation and classification method based on fuzzy reasoning and belongs to the technical field of airspace situation evaluation and classification. The method comprises the following steps: 1, processing airspace operation situation samples in a to-be-processed sector; 2, establishing a primary fuzzy reasoning system based on the airspace operation situation samples in the to-be-processed sector; 3, optimizing interpretability and accuracy of the fuzzy reasoning system on the basis of a multi-target population adaptive immune algorithm.The method provided by the invention can aim at large-scale and high-dimensionality sector operation data and center on airspace operation situation evaluation accuracy and interpretability, the multi-target immune optimization algorithm is used, and the airspace situation evaluation accuracy is optimized. In addition, when the immune algorithm is realized, the condition that the fuzzy matrix scale is in exponential growth in the process of processing high-dimensionality data is avoided, time complexity and space complexity needed by the algorithm are greatly reduced, and the convergence precision is improved.

The invention provides a frequency diverse array MIMO radar target positioning method based on a fuzzy function. The positioning method comprises the steps of building a frequency diverse array MIMO according to a frequency control transmission array of an antenna, a phased array receiving array, the equal interval of a frequency control array transmission array element, the equal interval of a phased array receiving array element and a moving target at a far field; building a signal matrix received by a frequency diverse array MIMO receiving array according to the frequency diverse array MIMO; and then solving angle dimension and distance dimension information of a radar target by using the fuzzy function and a multi-signal classification algorithm, and thus completing the positioning ofthe radar target. According to the positioning method provided by the invention, the fuzzy matrix is used for replacing the related matrix to solve a noise sub-space and a signal sub-space via featurevalue decomposition, and then the two-dimensional search is performed at the time-frequency spectrum to acquire the angle and distance dimension information of the target; by using the target positioning method, the far field target positioning performance can be greatly improved, and the problem that the frequency diverse array MIMO cannot accurately estimate the target information under the condition of relatively low signal to noise ratio is solved.

The embodiment of the invention provides a tunnel surrounding rock safety grading method. The method mainly includes the following steps: according to a factor set and an evaluation set of tunnel surrounding rock grading, a factor grade evaluation matrix R is established; according to the influence level of each factor in the factor set of tunnel surrounding rock grading, a factor weight set A is established; compositional operation for fuzzy matrixes is carried out on the factor grade evaluation matrix R and the factor weight set A, so that primary fuzzy matrixes are obtained, the primary fuzzy matrixes are arranged sequentially, so that a secondary fuzzy matrix is obtained, and according to the secondary fuzzy matrix, an evaluation result of tunnel surrounding rock grading is calculated by the maximum membership degree method or the weighted average method. The embodiment of the invention mainly aims at special surrounding rock conditions without stability, for grading indexes, advanced deformation and failure conditions of surrounding rock under the influence of tunnel construction are mainly taken into consideration, surrounding rock deformation, failure area scope and distribution pattern can be adopted to represent safety, the essential characteristics of complex tunnel surrounding rock is caught by the grading indexes, and thereby the safety of tunnel surrounding rock can be graded accurately and effectively.

The invention relates to an examination data analysis method on the basis of a fuzzy synthetic evaluation model. The method comprises following steps: 1) an evaluation indicator system of examination data is established, wherein the evaluation indicator system directed at the examination data comprises following objects: test question analysis, examination analysis and student analysis; 2) evaluation factor sets and remark sets are determined; 3) the weight of each evaluation factor is determined through an analytic hierarchy method; 4) membership grade of each evaluation factor is determined and a fuzzy evaluation matrix is constructed; 5) a fuzzy synthetic evaluation result is obtained through the fuzzy matrix compound operation. By means of the method, there is no need to gather experts to analyze examination data so that subjective influence is reduced, the whole evaluation time of the examination data is greatly shortened, the work efficiency is increased, the project management cycle is shortened, and the score results are made to be more objective. Therefore, the method can be widely used in the field of examination data analysis. Therefore, the method can be widely used in the field of examination data analysis.

A process defect risk analysis method based on a procedure association relationship is disclosed, which comprises the steps of S1, analyzing a RPN (risk priority number) of each defect mode accordingto a traditional PFMEA (process failure mode and effects analysis); S2, establishing a process defect fuzzy matrix; S3, performing a fuzzy defect risk analysis; S4, establishing a mutual association matrix between defects; and S5, establishing an associated risk analysis model. The advantages of the method of the invention are that firstly, the association relationship between the defects is considered, the ranking result of obtained defect risk values is compared with that of a traditional RPN analysis method, the risk evaluation result of the fuzzy semantics is quantitatively analyzed, and the degrees of risk of defects with the same or similar RPN values are differentiated; and secondly, the proposed risk analysis method considers the mutual influence relationship of different defects between procedures and thus can reflect the actual processing situation better with respect to the risk evaluation result and have higher reference and application values.

The invention provides a maintenance method and device based on wind generation set part importance evaluation. The method comprises the following steps: dividing importance grades of parts and a factor set of evaluation, and calculating an interval number grey fuzzy matrix according to importance degree of each factor in the obtained factor set with respect to each grade in the importance grades, and sufficient degree of information included in the factor set; according to an interval analytic hierarchy process, calculating weight of each factor for wind generation set part significance evaluation, and determining a weight matrix of the factor set; and on the basis of an OWA operator, according to the interval number grey fuzzy matrix and the weight matrix of the factor set, determining importance degree of a part of a wind generation set, and determining a corresponding maintenance strategy according to the importance degree of the part of the wind generation set. The method and device fully consider the degree of membership of evaluation factors to the evaluation grades as well as influence of sufficient degree of the information on importance evaluation, thereby improving accuracy of wind generation set part importance evaluation.

The invention discloses a wheel set dimension measurement result credibility interval fuzzy evaluation method based on a laser displacement sensor. First of all, a fuzzy integrated evaluation index system is established according to factors affecting wheel set dimension measurement results, and according to requirements for accuracy of the measurement results, wheel set dimension measurement evaluation grades are set; then, through calculating interval properties of membership grades and weight vectors of the measurement result influential factors and analyzing nonlinear features of fuzzy matrix weighting operation, relative dominance of a fuzzy mathematic theory and an interval number vector matrix is introduced, and a wheel set dimension measurement result credibility secondary fuzzy integrated evaluation calculation model is established; and finally, according to the dominance, wheel set dimension measurement result credibility grades are analyzed and determined. According to the method, credibility of a wheel set measurement result can be reasonably evaluated, and the operationality is quite high.

The invention belongs to the technical field of image processing, in particular to an image division method based on biogeography optimization capable of being used for image enhancement, mode identification, target tracking and the like. The method comprises the following steps that parameters are initialized; pictures to be divided are input, and clustering centers are initialized; a fuzzy matrix is calculated; the clustering centers are calculated again; the fitness value of each clustering center is extracted; the immigration rate and the emigration rate of each emigration center are extracted; the emigration centers are updated according to mutation operators; and division results are output. Because the biogeography migration strategy is adopted for optimizing the clustering centers, the calculation quantity is reduced, the selected clustering centers after the optimization have the overall situation characteristics, the problem of initialization sensitivity of the traditional clustering algorithm is solved, and the stability and the clustering performance of the clustering algorithm are improved. Because the immigration and emigration strategies are introduced for optimizing the clustering centers, the data center distribution characteristics can be more accurately reflected, in addition, the corresponding clustering center updating rules are designed, and the calculation quantity is reduced.

The invention discloses a built-in PID fuzzy control method based on a PLC board card. Fuzzy control rules are determined through sampling data or experience values and are utilized to conduct fuzzification processing on a measurement error e and an error change rate ec to obtain the membership degrees of fuzzy subsets, membership assignment tables of the fuzzy subsets and parameter fuzzy controlmodels are established according to the obtained membership degrees of fuzzy subsets, fuzzy synthesis reasoning is applied to design a fuzzy matrix table for fractional order PID controller parameters, correction parameters are calculated to serve as inputs of the PID controller, values of three control parameters output by the PID controller are automatically adjusted according to the state of acontrolled object, the error e and the error change rate ec serve as inputs, the fuzzy rules are utilized to conduct self-adaptive setting on the output parameters of the PID controller, so that the controlled object is kept in a good dynamic and static state, a user only needs to input the corresponding control parameter values and does not need to compile PLC control programs, the operation difficulty is low, and the stability and reliability are high.

The invention discloses a data analysis method aiming at evaluation of a server hardware statement. Based on a fuzzy comprehensive evaluation method, the method is characterized in that a fuzzy comprehensive evaluation index system for hardware state evaluation is established from perspectives such as representativeness, completeness and applicability according to actual situations of hardware running; a CRITIC assignment method is used to determine weights of different indexes, so that a weight set of the evaluation indexes is constituted; a 4-grade scale is used to establish an evaluation grade set; single-factor fuzzy evaluation is carried out, and an evaluation fuzzy matrix is determined; an operator is used to conduct fuzzy comprehensive evaluation to compositional operation, so that fuzzy subsets on the evaluation grade set are obtained; and finally, a final fuzzy comprehensive evaluation result is determined. In comparison with the prior art, the invention provides a fuzzy evaluation model which is improved based on the CRITIC assignment method; and the model is applied in the evaluation of the server hardware state. In this way, the method can help a server management worker better master the hardware state, adjust resources in time and increase working efficiency.

The invention discloses an elevator risk assessment system. The elevator risk assessment system comprises an elevator risk assessment index hierarchical structure generation module, an information acquisition module, a weight calculation module, a remark definition module, a membership matrix construction module, a risk assessment module and a risk analysis module. According to the invention, the fuzzy matrix, the artificial neural network and the analytic hierarchy process are employed in the elevator risk assessment, various uncertain factors and indexes which occur in the risk assessment process can be analyzed, the elevator running state can be assessed timely, sensitively and reliably, and a relatively-accurate risk grade of an elevator target floor can be given out quickly.

According to the channel estimation method for the large-scale MIMO system provided by the invention, with the increase of the number of antennas, the calculation complexity of the channel estimationmethod is not more complicated, and the channel estimation of the large-scale MIMO system can be completed by utilizing the existing equipment. The method comprises the following steps: S1, accordingto the MIMO system model, a received signal vector r is utilized to construct a sample covariance matrix; S2, on the basis of the sample covariance matrix (shown in the specification), the signal subspace RS is solved by adopting an FSCPI subspace tracking algorithm; S3, the pilot frequency sequence PHi is used to obtain the initial estimation of the channel gain matrix based on the pilot frequency; S4, solving a fuzzy matrix Ej by using the signal subspace RS and the initial estimation of the channel gain matrix (please see the formula in the specification); And S5: based on the signal subspace RS, Initial estimation of the fuzzy matrix Ej Initial estimation of the channel gain matrix is estimated again by means of an ILSP algorithm, and the final estimation of the channel gain matrix isobtained.

The invention discloses an electric power communication network risk assessment system, which includes a risk assessor and a plurality of electric power communication devices connected to the risk assessor through a network interface. The risk assessor comprises an electric power communication network evaluation index system generation module, an evaluation level system generation module, an index quantification module, an index weight calculation module, a membership degree matrix construction module, a fuzzy comprehensive evaluation result calculation module and a risk evaluation module. The electric power communication network risk assessment system of the invention can analyze various uncertain factors and indexes appearing in the risk evaluation process through the application of a fuzzy matrix and an analytic hierarchy process in the risk evaluation, and is high in evaluation speed and accuracy.

The invention relates to an image editing method for editing an original image, and the original image at least comprises a first object and a second object. The image editing method comprises the following steps: obtaining a first distance between the first object and a lens; obtaining a second distance between the second object and the lens; obtaining a fuzzy matrix system according to the first distance and an optics parameter; obtaining a first fuzzy matrix from the fuzzy matrix system according to the second distance; and forming a fuzzy second object after performing a fuzzy processing action on the second object according to the first fuzzy matrix, wherein the first object and the fuzzy second object form a simulation image.

The invention relates to the field of geological environment, and discloses a pyrite mountain geological environment evaluation method, which comprises the following steps: S1, carrying out field investigation and data collection on a mine, and carrying out data arrangement and analysis on investigation results and data; S2, screening out evaluation indexes from the sorted and analyzed data, and grading the evaluation indexes; S3, performing weight calculation on the screened evaluation indexes; and S4, establishing a fuzzy matrix for evaluation through the weight vectors of the evaluation indexes and the evaluation elements so as to construct an evaluation system. The evaluation indexes are established on the basis of existing data and actual investigation, more emphasize on the mine geological environment of the pyrite, and have better applicability and specificity; the fuzzy matrix is established by adopting the weights of the evaluation indexes and the evaluation elements to establish the evaluation system, and the evaluation result is more accurate, quicker and more efficient.