37 results about "Fuzzy matrix" patented technology

The invention provides a deep tunnel time-delay rockburst comprehensive early warning method based on an acoustoelectric technology. The method is applied to the technical field of tunnel and underground engineering disaster early warning and comprises the steps that a micro-seismic sensor array is arranged in a deep tunnel rock engineering target area, and a micro-seismic monitoring system is built; positioning a micro-seismic event and seismic source parameter information for the collected micro-seismic signals, and determining a key monitoring area of a rock mass fracture damage area; carrying out point location monitoring on the key monitoring area of the rock mass fracture damage area by using an electromagnetic radiation monitoring instrument; establishing a time-delay rockburst early warning index system; a comprehensive early warning model is established, and fuzzy mathematics comprehensive evaluation is conducted on the time-delay rockburst; obtaining a rockburst main control factor membership degree function according to a micro-seismic and electromagnetic radiation monitoring data statistical result; obtaining a fuzzy matrix R of the time-delay rockburst according to the membership function of the main control factors of the rockburst; and outputting an early warning result. In this way, the accuracy of rockburst early warning can be improved.

The invention discloses a method for adjusting an optical lens, and relates to the technical field of optical lens adjustment. The specific implementation process comprises the following steps: firstly, in combination with Hough linear transformation, a neighborhood fuzzy evaluation method and edge color change, comprehensively analyzing geometric distortion, fuzziness and chromatic aberration generated by a lens imaging result so as to obtain a distortion offset matrix, a fuzzy matrix and a color offset matrix; secondly, proposing a lens adjustment model based on a generative adversarial network, introducing an adversarial loss and cyclic loss optimization model, and further obtaining a lens adjustment pose matrix through a generator so as to adjust the position and angle of the lens; and finally, the adjustment accuracy is designed, so that the cyclic execution of the adjustment process is realized until the adjustment accuracy reaches a preset threshold value, the high definition of the image, the accurate color restoration and the reduction of optical distortion are ensured, and the adjustment precision and the imaging quality of the lens are improved.

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

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

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

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

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

The invention provides a sewage treatment effect real-time evaluation method based on multi-dimensional indexes, which comprises the following steps: firstly, an index monitoring subsystem adopts a dynamic weight sliding window sampling algorithm to collect three kinds of multi-dimensional indexes of water quality, microorganisms and energy consumption; secondly, constructing a fuzzy comprehensive evaluation system, and introducing an improved triangular fuzzy number comment quantization algorithm to determine a factor set and a comment set of'excellent, good, medium and poor '; thirdly, calculating each index weight vector by adopting a grey correlation-analytic hierarchy process coupling algorithm, and ensuring that the weight fits the relevance between the index importance and the actual performance; constructing a fuzzy evaluation matrix, and calculating the membership degree of the index to the comment set through a sectional self-adaptive membership degree function generation algorithm; and finally, calculating a comprehensive evaluation result by using an evidence theory-fuzzy matrix fusion algorithm, and determining an evaluation grade according to a maximum membership degree principle. The method solves the limitation of traditional single index evaluation, improves the evaluation accuracy and real-time performance, and provides support for stable operation of a sewage treatment system.

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

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

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

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

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

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

The present invention provides a detection method for identifying electricity theft based on cumulative probability distribution, including the following steps: Step 1: Obtain power consumption data; Step 2: Establish the probability distribution range of daily power scatter points of users; Step 3: Obtain the corresponding cumulative probability; Step 4: Establish the corresponding fuzzy matrix; Step 5: Calculate the density of all power data in the power dataset; Step 6: Calculate the average distance density value of the power consumption data; Step 7: Classify different cluster distances C X ; Step 8: End clustering when the clustering dataset is an empty set; In Step 9: Update the data center; Step 10: Obtain the cluster center sequence #imgabs0#; Step 11: Calculate the similarity of each cluster center; Step 12: Measure the similarity of the curves; Step 13: When the cumulative result is lower than η , it is considered that the user's power consumption is abnormal. Applying this technical solution can realize inferring whether there is an abnormality in the metering device or user electricity theft.

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

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

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

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

The present invention firstly performs segmentation processing on the source image and the target image by adopting the simple linear iterative clustering method of gradient descent, then searches for the most similar superpixel in the target image for each superpixel in the source image, and then performs color migration between the matched superpixel pairs, and finally eliminates the erroneous color migration and unnatural brightness transition problems caused by the mismatching generated by the over-segmentation processing. Firstly, a fuzzy matrix is ​​introduced in the first matching migration process, and then the global statistical information of the migration result obtained for the first time is used to perform secondary color migration on the source image to obtain a more natural color migration effect; on the one hand, the unnatural migration result problem caused by the erroneous color migration of certain areas generated by over-segmentation is corrected; on the other hand, the application scope of the algorithm is improved, the robustness, robustness and generalization ability of the method are improved, and the color migration effect is improved while improving the efficiency.

The invention provides a method for processing an error virtual loop after verification of a secondary virtual loop of an intelligent substation, and belongs to the technical field of intelligent substation model verification. Comprising the following steps: acquiring all possible error reasons of the secondary virtual loop; acquiring knowledge of the secondary loop which is called and still not matched correctly; defining a fuzzy matrix between the secondary loop knowledge and the error reason; according to the fuzzy vector of the to-be-detected error virtual loop, utilizing the fuzzy matrix to obtain a fuzzy vector of an error reason; and diagnosing the error cause of the to-be-detected error virtual loop according to the sorting result of the error cause fuzzy vectors. According to the method, the error result of the virtual loop verification is analyzed and processed, the error reason of the wrong virtual loop can be positioned, a user is helped to quickly position the problem, and the integrity of the virtual loop verification is improved.

The invention provides a safe operation assessment method for a new energy power system, and the method comprises the steps: constructing an SWOT matrix based on an SWOT analysis method, and determining the advantages, disadvantages, opportunities and threat factors of the new energy power system; based on a fuzzy AHP method, constructing a hierarchical structure model, obtaining expert judgment, establishing a fuzzy pairwise comparison matrix, and calculating a weight value of each SWOT factor; based on a fuzzy MARCOS method, determining an evaluation standard, obtaining fuzzy judgment of experts on the improved strategies, calculating a weighted fuzzy matrix, and sorting the improved strategies through a utility function; and comprehensively considering the utility value of each improvement strategy, and evaluating the safe operation of the new energy power system. By combining SWOT analysis, fuzzy AHP and fuzzy MARCOS methods, the new energy power system can be comprehensively evaluated from multiple dimensions, not only are internal and external factors of the system considered, but also advantages and disadvantages of different improvement strategies are compared, and comprehensiveness and objectivity of an evaluation result are ensured.

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

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

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

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

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

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

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

The embodiment of the present application provides a method and system for comprehensive identification of aerial targets with multi-attribute decision-making, which relates to the field of target identification technology. The method includes: obtaining multiple identification results of aerial targets; generating intuitive fuzzy matrix data according to multiple identification results; obtaining identification weight set data corresponding to multiple identification means according to the intuitive fuzzy matrix data; obtaining basic probability assignment data according to evidence theory and intuitive fuzzy matrix data; correcting the basic probability assignment data according to the identification weight set data to obtain comprehensive identification result matrix data corresponding to multiple detection cycles; obtaining period identification weight set data corresponding to multiple detection cycles according to the conflict degree data of multiple detection cycles; performing multi-period information fusion on the comprehensive identification result matrix data to obtain multi-period comprehensive identification results. The method can achieve the technical effect of improving the reliability and effectiveness of aerial target identification.