35 results about "Rough set" patented technology

The invention relates to the technical field of foundation pit detection, and discloses a deep foundation pit deformation prediction method and system based on neural network and rough set classification, and the method comprises the following steps: data collection and preprocessing; carrying out attribute reduction based on a rough set theory; constructing and training a model based on an improved attention mechanism recurrent neural network (A-RNN); carrying out credibility verification and dynamic feedback adjustment; and outputting a result and an early warning response. By integrating multi-parameter monitoring data of displacement, soil pressure, underground water level, support stress and the like, driving factors of deformation of the deep foundation pit are comprehensively captured, misjudgment caused by data deviation of a single sensor is reduced, the combination of bidirectional LSTM and an attention mechanism effectively captures local features of long-term dependence and key time points in time sequence data, and the accuracy of deep foundation pit deformation detection is improved. And the accuracy of deformation prediction is obviously improved.

The invention provides an image noise mark feature selection method and system, a storage medium and a computer. The method comprises the steps of obtaining a to-be-processed image noise mark data set; embedding a sample set in the image noise mark data set into a multi-granularity fuzzy cluster to construct a dynamic fuzzy membership evaluation matrix; dynamically evolving a multi-level high-precision granular ball cluster; obtaining mark distribution with high identification degree; constructing a rough perception feature evaluation framework based on granular ball topology driving, extracting decision equivalence classes by combining rough set upper and lower approximation and extended positive domain models, and determining and measuring the contribution degree of each feature to a decision system by fusing multi-granular-ball decision boundary information based on a dependency degree quantitative model; a particle and ball structure consistency verification mechanism is introduced, and multi-level evaluation is carried out on the importance of the features through dependency and consistency. According to the method, the optimal feature subset with strong anti-noise performance and high discrimination capability is obtained, and stable and efficient input support is provided for a subsequent image noise mark learning model.

The invention relates to the technical field of concrete durability evaluation, in particular to a compound green concrete salt freezing resistance durability evaluation system under a frozen soil environment, which comprises a data input unit, a decision information table storage unit, a rough set analysis processing unit and an evaluation output unit, the data input unit receives a sample data set containing freeze-thaw cycle times, dry-wet cycle times, freeze-thaw-dry-wet cycle times, long-term soaking time, composite salt concentration, gas content and fly ash mixing amount, and the decision information table storage unit stores a decision information table constructed based on a rough set theory. The influence factor combination and the relative dynamic elastic modulus percentage are associated, the rough set analysis processing unit establishes a mapping relation through attribute reduction and rule extraction and quantifies the weight of each factor, and the evaluation output unit outputs a relative dynamic elastic modulus predicted value, durability grade classification and key factor contribution sorting. And the salt freezing resistance durability of the concrete under the multi-factor coupling effect is accurately evaluated.

The invention relates to the technical field of medical data mining and mode recognition, in particular to a special child early disease prediction method based on double-layer granular ball knowledge representation BGBK and three-way role arbitration TRA. The method comprises the following steps: firstly, converting original special child data into multi-granularity granular ball representation through an unsupervised granular ball generation algorithm; a BGBK structure is constructed, and coarse-grained particle ball information and fine-grained sample information are fused; a three-boundary neighborhood rough set theory is utilized to endow semantic roles to the granular balls, wherein the semantic roles comprise a core region, an abnormal boundary and a transition boundary; designing a TRA strategy, and calculating a sample abnormal score in combination with the role influence factor; and finally, constructing an abnormal factor by fusing the abnormal scores under the multi-attribute subspace, and realizing accurate early prediction of the special child disease. The method can effectively process complex special child data, has the advantages of high detection precision, strong robustness, good interpretability and the like, and significantly improves the accuracy of early prediction of special child diseases.

The invention relates to the technical field of multi-source information processing, in particular to a multi-source information decision fusion method and system for a multi-granularity multi-view rough set, and the method comprises the following steps: obtaining multi-source heterogeneous data of an object to be decided, constructing a multi-source decision information table through cleaning and standardization processing, and dividing the information table into a plurality of view subsets; and constructing a multi-granularity hierarchical structure based on the view subset, calculating rough set approximation precision and attribute dependency under different granularity hierarchies, and generating a local decision rule set. And performing weighted aggregation on the local decision rule set by using a multi-source information fusion algorithm, and constructing decision evaluation parameters based on consistency measurement. According to the method, the problems of one-sided information under a single view angle and detail loss under a single granularity are effectively solved through the calculation of the multi-view and multi-granularity collaborative mechanism reverse driving decision cost, and the accuracy and robustness of decision analysis under a complex and uncertain environment are remarkably improved based on the feedback adjustment of the dynamic weight; and deep mining and efficient fusion of multi-source information are realized.

The invention belongs to the technical field of new energy automatic optimization, and discloses a fault diagnosis and optimization method based on a rough set and a random forest, and related equipment. The fault diagnosis and optimization method based on the rough set and the random forest comprises the following steps: inputting multi-source heterogeneous operation data into a constructed fault diagnosis model, and outputting early warning information, the early warning information comprising a fault category; wherein the fault diagnosis model training method comprises the steps of performing knowledge reduction on a multi-dimensional data set by adopting a rough set algorithm, inputting an equipment operation sample set after knowledge reduction into a random forest model, performing multi-target optimization analysis through a big data platform based on early warning information and real-time operation data, and generating decision support scheme data; according to the method, a safe and executable operation and maintenance strategy and an operation instruction can be generated in real time by utilizing a multi-objective optimization technology, and the closed-loop technical bottleneck from data fusion, intelligent diagnosis to dynamic optimization is effectively broken through.

The invention relates to the technical field of data fusion, and discloses a multi-sensor data fusion method, device and equipment and a storage medium, and the method comprises the steps: carrying out the synchronization of laser radar data, depth image data and ultrasonic distance data, obtaining a first sensor data set, and constructing an obstacle behavior prediction model; performing Bayesian posterior probability calculation on a second sensor data set acquired in real time according to the obstacle behavior prediction model to obtain a fusion trajectory prediction result and an uncertainty value; performing neighborhood rough set rule reduction based on the fusion trajectory prediction result to obtain a target obstacle avoidance decision rule group; according to the method, the prediction uncertainty is quantified into the risk coefficient to dynamically adjust the conservative degree of the obstacle avoidance strategy, the coupling analysis of the prediction confidence and the control strategy is realized, and the dynamic obstacle avoidance execution instruction is generated. And the cleaning efficiency and the obstacle avoidance safety are effectively balanced.

The invention discloses a leg posture analysis and rating system for a football player in a ball kicking process, and the system comprises a data collection unit which collects four-stage kinematics parameters of kicking, kicking and stretching, backward swinging, forward swinging and ball touching through a three-dimensional motion capture system and a high-speed camera; the data processing unit is used for processing parameters and extracting characteristic parameters of each stage; the expert evaluation interface is used for receiving evaluation data of multiple experts on different ball kicking technology types on the multi-score influence factors; the weight calculation unit is used for calculating an influence factor attribute weight by using a Bayesian method based on expert data; the rating decision matrix construction unit is used for constructing a comprehensive matrix in combination with a weight rough projection method; the multi-angle analysis unit is used for performing multi-angle risk rating by fusing a rough set theory and an approximate ideal solution sorting method; and the result output unit is used for outputting the posture quality grade and the training suggestion. According to the method, the actual requirements of football training on precision, standardization and comprehensiveness of posture analysis can be met.

The application discloses a kind of based on adaptive high-dimensional data local differential privacy protection method, using rough set theory and mutual information, the data of different relevance and sensitivity are clustered grouping, different grouping is used different random response probability, permanent random response and temporary random response are carried out.Local differential privacy processing data, using SNE method for dimension reduction and adaptive sampling gradient optimization in server end, with low-dimensional data joint probability distribution approximates the joint distribution of all attributes, according to the sampling strategy and the approximate joint distribution constitutes release dataset.The application solves the problem of high-dimensional data with dimension disaster and local differential privacy combination and maximum degree maintaining data relevance, and more reasonably allocates privacy budget according to the different data sensitivity to some extent, improves data availability and reduces communication cost.

The invention discloses a garment process parameter adjustable design method and system, particularly relates to the technical field of data processing of computer-aided process design, and is used for solving the problem that an existing digital design method does not consider actual production fluctuation, so that the suitability of a process parameter scheme in actual production fails. The method comprises the following steps: acquiring process fluctuation related data of a garment production end, extracting process fluctuation characteristic parameters and fluctuation tolerance thresholds thereof, grouping initial process parameter adjustment schemes, and establishing hierarchical association with the fluctuation characteristic parameters to judge whether the schemes meet all threshold constraints or not; if not, the adjustment amount of each process parameter is analyzed and determined based on the rough set theory, the fluctuation conduction effect of the adjustment amount is evaluated, and finally, collaborative correction and iterative verification are carried out on the initial scheme according to the adjustment amount and the conduction influence degree to generate a final process parameter adjustment scheme meeting all fluctuation tolerance threshold constraints; and the conversion of process parameter design from ideal condition calculation to actual production fluctuation adaptation is realized.

The application provides a rough set data classification training method and system based on a blockchain, relates to the technical field of the blockchain, and can be applied to the financial field and other fields.The method comprises the following steps: constructing a data matrix by means of a coordination coupling algorithm according to user data of a blockchain client; obtaining a sample data set by selecting the data matrix, constructing an interference data set according to the sample data set and a preset Gaussian noise; constructing a fuzzy decision system according to the interference data set, extracting an attribute reduction core of the fuzzy decision system with respect to data attributes by means of data attribute classification training; and performing attribute reduction on the interference data set by means of the attribute reduction core to obtain training data.

Embodiments of the invention provide a robot process data processing method and apparatus. The method comprises the steps of obtaining a process knowledge graph and a process parameter weight of a robot; key process parameters are determined according to the process parameter weights, the process knowledge graph is mapped into low-dimensional vectors according to a graph embedding algorithm, and node similarity calculation is performed on vectorization features of the key process parameters and process parameter vectors in the low-dimensional vectors of the process knowledge graph; extracting data features associated with the key process parameters from a process knowledge graph according to a similarity result to construct a process connected sub-graph; the method comprises the following steps: extracting process sequence data from a historical process database, performing association rule mining on the process sequence data based on conditional confidence and a rough set algorithm, storing the association rule as a graph structure, performing bidirectional linking on the graph structure and a process communication sub-graph, and determining a corresponding dynamic process rule base to perform process processing. The process programming efficiency and accuracy of the process robot can be improved.

The invention discloses an efficient cleaning method for super-large-scale multi-source heterogeneous data, which comprises the following steps: collecting the multi-source heterogeneous data, and carrying out image, audio and text classification on the multi-source heterogeneous data; respectively extracting an image feature vector, an audio feature vector and a text feature vector, and respectively performing a first round of importance reduction based on a rough set theory; splicing the image feature vector, the audio feature vector and the text feature vector after the first round of importance reduction, and performing a second round of importance reduction based on a rough set theory; a feature set based on images, audios and texts is obtained, and cleaning is achieved. According to the method, the scheme that the strategy of'modal reduction-fusion-global reduction 'is adopted, and engineering practicability and calculation efficiency are extremely high is adopted, the data dimension can be effectively reduced in a staged mode, calculation disasters caused by one-time processing of ultra-high-dimensional data are avoided, and meanwhile contribution in each modal can be better understood.

This invention provides a method and system for fire emergency response plan simulation and verification based on case-based reasoning. The method includes establishing a structured historical fire case database, calculating the mixed similarity between the current scenario and historical cases based on rough set theory and cloud models, and selecting similar source cases; adapting the handling strategies of the source cases to the model by constraining the model to generate an initial plan to be verified; establishing a generalized stochastic Petri net model representing the evolution of the disaster-affected state and the interaction of rescue resources, mapping the initial plan to be verified to the transition rate and initial identifier in the database within the model, performing concurrent conflict detection and temporal logic deduction, and evaluating transient performance indicators; if the target rescue success indicator does not reach a preset threshold, iteratively correcting the resource scheduling parameters of the plan based on the sensitivity analysis results of key transitions, and repeating the deduction steps until the termination condition is met, outputting the target fire emergency response plan.

The invention relates to the technical field of state evaluation, and relates to a health state evaluation method for each subsystem of a rapier loom. The method comprises the following steps: 1, selecting feature parameters, extracting features based on wavelet transform, enhancing abnormal data based on GAN, and constructing a health state model; 2, performing attribute reduction and rule extraction on the evaluation indexes by using a rough set theory, and constructing an initial confidence rule base of each subsystem; 3, fusing the activated rules through an evidence reasoning algorithm, and outputting the confidence coefficient of each subsystem under each health state level; 4, data distribution stratified sampling is carried out to process missing data, and secondary fusion is carried out on all reasoning conclusions by combining a plurality of reasoning results and utilizing an ER algorithm to obtain a final reasoning result; 5, reasoning the error as a target function, and optimizing the parameters of the belief rule base by using a WOA algorithm with interpretability constraint; and step 6, updating the optimized rule base by the incremental rough set, and reserving recent data by using a sliding window, so that the model continuously learns new data. And the evaluation precision and reliability are obviously improved.

The invention discloses a scientific and technological achievement innovation degree evaluation method based on fuzzy comprehensive evaluation. The method comprises the steps that heterogeneous data such as technical indexes and market applications of scientific and technological achievements are collected and stored through a heterogeneous data fuzzy calculation platform; a coupling rough set fuzzy evaluation model is used for screening core evaluation indexes, and index fuzzy membership is optimized through a dynamic PSO fuzzy membership optimization algorithm; performing multi-source information fusion on the optimized membership value by using an evidence theory fuzzy fusion algorithm, and constructing a fuzzy comprehensive evaluation model in combination with a core index system to generate a preliminary evaluation result; and finally, calling historical and industrial standard data to verify the preliminary result through the platform, and outputting a final evaluation result. According to the method, advantages of various algorithms and platforms are integrated, the whole evaluation process is covered, the problems that an existing method is inaccurate in index screening, insufficient in data fusion, insufficient in evaluation precision and the like are solved, systematicness, objectivity and precision of evaluation are improved, and reliable support is provided for scientific and technological achievement innovation degree evaluation.

The invention discloses a nuclide identification method based on KNN classification. The nuclide identification method comprises the following steps: 1) performing dimension reduction on original high-dimensional gamma energy spectrum data of a training sample by using a principal component analysis (PCA); 2) performing attribute reduction on gamma energy spectrum features of the training sample after PCA dimension reduction through a neighborhood rough set, and rejecting redundant features and retaining a feature set with the most discriminant ability through heuristic search based on attribute dependency; and 3) utilizing the feature set obtained in the step 2) to realize rapid nuclide identification of the to-be-detected sample through a K-nearest neighbor classification method. According to the method, PCA dimension reduction, attribute reduction and KNN classification methods are fused, high recognition precision can be kept, meanwhile, better calculation efficiency is achieved, and the method is suitable for portable equipment with limited resources to achieve real-time and accurate nuclide recognition.

The application belongs to the field of air quality data processing, and particularly relates to an air quality data processing method based on neighborhood rough set attribute reduction. The method comprises the following steps: acquiring air quality sample data with multiple air attributes, and performing permutation and combination on each air attribute; using the dependency of the neighborhood rough set and the calculation of the introduced Shapley value, the importance of each air attribute is calculated, then a weighted neighborhood rough set is constructed according to the importance of the air attribute, the upper and lower approximation relationships of the weighted neighborhood are redefined, then the new dependency is defined, finally the reduced attribute subset greater than the set lower limit of the importance is calculated according to the new dependency; and the air quality data is optimized. The application can solve the problem of consistent air attribute weight in the existing air quality data processing process, and the air attribute obtained through the weighted calculation of the neighborhood rough set can make up for the deficiency of the neighborhood rough set model.

The invention belongs to the technical field of numerical control machining temperature monitoring, and particularly relates to a multi-measuring-point combined weighing numerical control machine tool temperature detection sensitive point selection method which comprises the following steps: S1, acquiring thermal error data of numerical control machine tool machining and temperature data of each measuring point; s2, constructing a multi-objective optimization model by taking the importance degree of the maximum measurement point combination to the thermal error and the minimum measurement point number as optimization objectives; s3, quantifying the importance Sig (Ti) of each measuring point Ti based on a neighborhood rough set method NRS; based on the importance Sig (Ti) of each measuring point Ti, calculating the probability pi of the measuring point Ti selected by each measuring point combination in the initial population; generating an initial population according to the probability pi that each measuring point Ti is selected by each measuring point combination in the initial population, the initial population comprising a preset number of measuring point combinations; and solving the multi-objective optimization model by using an ASNSGA-II algorithm to obtain a measurement point combination scheme. The method can effectively balance the importance degree and the number of measuring point combinations, and obtains a stable numerical control machine tool temperature detection sensitive point selection result with excellent comprehensive performance.

The invention discloses an electric power material green package evaluation method combining a network analysis method and a rough set theory, and belongs to the field of electric power material green package evaluation. According to the method, through grading the whole life cycle of typical electric power materials, green packaging related flow nodes of five stages of raw material acquisition, production and processing, transportation and distribution, use and waste treatment are determined, and a multi-level and all-around comprehensive evaluation index system is constructed. And establishing an index full-connectivity relation matrix by using a network analysis method, and realizing quantitative evaluation of green packaging by combining fuzzy mathematics and a weighted scoring algorithm. Furthermore, effectiveness verification and redundancy judgment are performed on the evaluation indexes by adopting a rough set theory, so that the scientificity and simplification of the system are improved. According to the method, a scientific decision basis can be provided for enterprise green purchasing and packaging management, and automatic evaluation and dynamic feedback of the electric power material packaging green degree are realized.

Embodiments of the present specification provide a credit determination method, device and equipment, wherein the method comprises: obtaining an initial rough set; wherein the initial rough set contains a plurality of original feature information of institutions; performing information reduction on the initial rough set by using a discernible matrix reduction algorithm to obtain an evidence information set; each feature information after information reduction serves as a separate evidence; obtaining basic probability assignment values of a recognition framework corresponding to each evidence in the evidence information set; and performing uncertain reasoning by using evidence theory according to the basic probability assignment values of the recognition framework corresponding to each evidence to obtain a representation value of each institution; wherein the representation value is used to represent the credit of the institution. In the embodiments of the present specification, the credit of each institution can be efficiently and accurately determined in the case that the original data amount of each institution is small, and the data exists uncertain or inaccurate knowledge expression.

The invention discloses a network intrusion detection method based on multi-granularity kernel fuzzy rough entropy, which belongs to the technical field of network data analysis, and comprises the following steps: calculating a fuzzy relation matrix for normalized network data by adopting a Gaussian kernel function to obtain a fuzzy information particle set; according to the kernel fuzzy information particles, calculating a knowledge rough entropy under global attributes of the global sample; calculating the weight of each attribute according to the knowledge rough entropy; constructing an attribute subset sequence according to the attribute weight; constructing a multi-granularity kernel fuzzy information particle set, and according to the multi-granularity kernel fuzzy information particles, calculating rough entropies of rough sets after each sample is removed under different kernel fuzzy relations one by one; calculating the importance of the samples according to the rough entropy of the rough set, and calculating the rough entropy outlier factors of the samples one by one in combination with the relative proportion of information particles; and finally, judging whether the outlier degrees of the samples are greater than a threshold value one by one, if so, outputting network data abnormal samples, otherwise, regarding the network data abnormal samples as normal data until all the samples are judged. According to the method, the problems of extraction and fusion of multi-granularity features, mining and extraction of a relation between fuzzy uncertain data and simplification of complicated marking work of existing network intrusion detection are solved.

The application provides an advantage relation rough set classification rule acquisition method, system, device and medium, which comprises the following steps: constructing a corresponding distributed preference information system according to the acquired distributed data set and the preset preference attribute; determining a plurality of decision classes of the distributed preference information system through the advantage relation rough set method according to the classification labels of the distributed data set; acquiring the decision class approximation set of each preference sub-information system; obtaining the decision class approximation set of the distributed preference information system according to the decision class approximation set of each preference sub-information system; and obtaining the classification rule, so that the required global knowledge is synthesized based on the existing local knowledge on the premise that the advantage relation consistency is not affected by the data size, unnecessary repeated calculation of global knowledge acquisition is avoided, the data processing efficiency is effectively improved, the inconsistency problem of the preference multi-attribute decision information system is simply and effectively solved, and the rationality and effectiveness of the global classification rule acquisition are ensured.

The invention belongs to the technical field of data anomaly detection, and particularly relates to an entropy and anomaly detection method based on a fuzzy beta-coverage relation, and the method comprises the following steps: 1, proposing a method for migrating a conventional fuzzy beta-neighborhood operator to a variable-scale fuzzy beta-coverage approximate space; 2, two kinds of parameterized fuzzy beta-co-neighborhoods are provided; step 3, proposing a fuzzy beta-coverage relation and fuzzy beta-coverage information particles based on the fuzzy beta-co-neighborhood; 4, providing a fuzzy beta-coverage entropy and a fuzzy beta-coverage relative entropy; 5, providing a sequence-based fuzzy beta-coverage relative entropy and a weighting function; and step 6, on the basis of the contents from the step 1 to the step 5, constructing an anomaly score, providing an anomaly detection method based on fuzzy beta-coverage entropy, and outputting an anomaly sample obtained by detection according to the input information table. According to the method, the application of the fuzzy beta-coverage rough set theory in the field of anomaly detection is expanded, and the defects of a traditional method in processing data existing in a coverage form are overcome.

This application relates to a method and system for multi-source information fusion in an air-to-ground unmanned system for coastline inspection. The method includes: spatiotemporal synchronization and granular computation of raw data from multiple sensors; filtering high-confidence information granules to generate filtered data; multimodal feature extraction and attention-weighted fusion of the filtered data to extract global environmental features; constructing a factor graph model, using the filtered data as factor edge constraints, calculating the residual Mahalanobis distance of each factor edge, performing fault detection and compensation based on this distance, dynamically generating fusion weights using rough set decision-making to obtain optimal pose estimation, and performing motion distortion correction, dynamic target removal, and map construction on the updated filtered data to achieve target recognition and behavior analysis, generating anomaly event reports. This method can significantly improve the reliability of multi-source data, the robustness of pose estimation, and the accuracy of anomaly target recognition in complex coastline scenarios.

The invention discloses a seismic data denoising method and device based on rough set-deep learning, and the method comprises the steps: constructing a data set for training; constructing a CAP module, wherein the CAP module is used for sequentially performing convolution, attribute reduction and filling operation on the feature map; a neural network denoising model based on rough set-deep learning is constructed, the denoising model comprises a plurality of CAP modules, a plurality of convolution layers, a plurality of one-dimensional convolution layers and a plurality of up-sampling layers, and each convolution layer comprises a convolution kernel, a pooling layer and an activation function; defining a loss function of the denoising model; performing forward propagation and reverse propagation iterative training on the denoising model by using the data set until a loss function value is reduced to a preset value or the number of iterations reaches a set number of times, and completing the training; and de-noising actual seismic data by using the trained de-noising model. According to the method, the generalization ability and the training precision of the model and the ability of deep learning in seismic data denoising can be improved.

The application discloses a DC converter transformer implicit fault diagnosis method and system based on digital twinning, and the method comprises the following steps: collecting the operation state data of an extra-high voltage DC converter transformer; using the operation state data to establish a converter transformer digital twinning model in a virtual space and performing real-time updating on the model; constructing an information system S through the operation state data and the digital twinning model data, and obtaining a characteristic parameter set N of the information system S by using a data processing method of a rough set theory; constructing a deep optimization network through local sparse design and training the network; inputting the real-time collected characteristic parameter set N into the trained deep optimization network to obtain a fault diagnosis result; and the application has the advantages that the efficiency and accuracy of fault diagnosis are improved.

The invention discloses a radar signal sorting method and system based on a rough set theory and adaptive weighting, and relates to the technical field of baseband communication. The radar signal sorting method and system based on the rough set theory and adaptive weighting comprises the following steps: S1, performing whole-process dynamic acquisition on aliasing full-pulse signals to obtain a pulse description word sequence; s2, dividing the full-pulse data stream to obtain a granularity combination entropy, and constructing a global weighted distance matrix; s3, performing local density estimation, constructing a decision diagram and screening pulse points; and S4, constructing a basic probability distribution and uncertainty function value, and outputting a signal sorting result. According to the method, the stability, accuracy and interpretability of radar signal sorting under parameter agility and strong noise conditions are effectively improved, and the problem that existing unsupervised sorting cannot adaptively describe pulse parameter stability differences under fixed distance measurement and fixed weight is solved.

The invention relates to a numerical control driving device health evaluation method based on a rough set theory and a genetic algorithm, and the method comprises the following steps: building connection with a numerical control driving device, collecting index data, carrying out the discretization of the index data, and forming a sample; forming an information system according to a sample of the numerical control driving device; health evaluation indexes of the numerical control driving devices serve as condition attributes, and states of the driving devices serve as decision attributes; obtaining the dependence degree of the condition attribute C on the decision attribute through the rough set; judging whether conditions are met or not; if not, the condition attributes cannot be reduced, and are added into a kernel attribute set; all condition attributes in the kernel attribute set are represented as individuals, and optimized condition attributes, namely reduced evaluation indexes, are obtained through a genetic algorithm; and updating the information system according to the optimized condition attributes so as to carry out health evaluation on the numerical control driving device. According to the method, the indexes are reduced, so that the dimension of the data can be reduced, and the complexity of subsequent analysis and calculation is reduced. In this way, the computing speed can be increased, the requirement for computing resources can be reduced, and the overall system efficiency is improved.

The invention discloses an attribute reduction method, device and equipment based on a coverage information system, and a medium, relates to the technical field of data reorganization, and aims to solve the problems of insufficient classification capability and limited application range of an existing rough set theory in training data attribute reduction. Firstly, an attribute set U of training data is used as a discourse domain, and a coverage information system is constructed; further, an attribute reduction problem is converted into a mathematical problem for solving minimum set coverage of a matrix by defining an attribute pair set and constructing a correlation matrix of the attribute pair set and the coverage, and a reduction result R is finally output through the steps of initialization, row and column traversal, elimination, iterative updating and the like; according to the method, redundant attributes can be accurately removed, the data classification capability is remarkably improved, the method is suitable for a complex military training data set with decision information, resources are saved for subsequent data storage and processing, and a more reliable data basis is provided for command decision.