15 results about "Rough set" patented technology

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 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.

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.

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 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 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 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.

The application belongs to the technical field of fault diagnosis of marine diesel engines. A fault diagnosis method based on a deep space-time convolutional network ST-NN comprises: acquiring original oil spectrum and / or ferrography data and original vibration signals; performing dimension reduction on the original oil spectrum and / or ferrography data by using a rough set attribute reduction method to remove redundant features and obtaining a reduced oil and / or ferrography feature set; performing time-frequency conversion on the original vibration signals by using a continuous wavelet transform to obtain a time-frequency graph sequence; performing three-dimensional convolutional neural network fusion on the reduced oil and / or ferrography feature set as an additional feature channel and the time-frequency graph sequence, and introducing a self-attention mechanism to dynamically weight time steps to obtain a health index sequence; and performing fault trend prediction on the health index sequence by using a Gaussian process regression model to obtain a fault prediction result. The method effectively identifies the operating state of a diesel engine and improves the accuracy and reliability of fault diagnosis.