7 results about "Grey clustering" patented technology

The invention relates to the technical field of tunnel engineering, and discloses a tunnel soft rock large deformation assessment method based on multi-source parameter analysis, and the method comprises the steps: firstly constructing an assessment model based on a grey clustering theory, and carrying out the preliminary risk classification of tunnel surrounding rock; after a tunnel face is revealed through tunnel excavation, apparent characteristic data are quantitatively collected, a comprehensive score is calculated, and an apparent large deformation level is determined; meanwhile, a BP neural network model optimized through a particle swarm optimization algorithm is utilized, and the cumulative deformation of the surrounding rock is predicted based on multi-source input parameters; calculating a strength-stress ratio by combining ground stress and rock mass strength data actually measured on site, verifying and correcting the preliminary grade, and determining a final comprehensive large deformation grade; and finally, mapping the grades into support parameters, and executing dynamic adjustment based on monitoring data. According to the method, through the fusion of theoretical calculation, field quantitative scoring and intelligent prediction, accurate judgment and closed-loop control of large deformation of the soft rock are realized, and the construction risk is effectively reduced.

The application discloses an anesthesia hypotension prediction method and system based on FMD data mining of a grey system, and belongs to the field of electric digital data processing. The application carries out preprocessing, interval grey number conversion and dimension standardization processing on brachial artery blood flow mediated vasodilation function data and clinical baseline data, generates a standardized interval grey number feature set, completes feature screening and coupling relationship mining through grey nonlinear correlation analysis, completes risk simulation calculation through a non-equidistant metabolism GM(1, N) power model, and divides and outputs a hypotension risk level after anesthesia induction relying on grey clustering evaluation. The system is electrically connected by multiple functional modules in sequence, and can realize missing data completion, model precision verification and feature contribution output. The application is suitable for a poor information small sample scene, reduces the interference of data uncertainty and dimension difference, improves the reliability and interpretability of risk prediction, and provides evaluation support for perioperative hemodynamic management.

This invention relates to the field of dynamic prediction technology, specifically to a dynamic prediction method for slope stability based on heterogeneous Transformer integration and grey clustering association. This invention solves the technical problems of single-model feature expression, gradient vanishing in traditional recurrent networks, and insufficient capture of long-range dependence and causal relationship of slope parameters by integrating the global coupling feature capture capability of BERT, the causal relationship mining capability of GPT2, and the basic nonlinear fitting capability of MLP.

The invention relates to a molten salt system safety evaluation method based on HAZOP-grey clustering analysis. The method comprises the following steps: collecting real-time operation data of each component of a molten salt system; the real-time operation data are input into a back-propagation neural network BP, the security level of each component of the molten salt system is output, the back-propagation neural network BP is optimized through a GA genetic algorithm and is obtained through training based on a training set, and the training set comprises historical operation data and the corresponding historical security level; and performing grey clustering analysis on the safety level of each component of the molten salt system to obtain the comprehensive safety level of the molten salt system. The method can effectively identify the potential risk, improves the safety of the molten salt system, and has an important practical value.

The application discloses a kind of network equipment early warning evaluation method and system based on SNMP, belong to network equipment state monitoring early warning evaluation technical field, method includes: using SNMP protocol and equipment OID information acquisition network equipment's key state information and store to database;Real-time early warning is carried out to key state information based on early warning filter pool, and real-time early warning result is obtained;Current data, historical data are recalled and combined with real-time early warning result, and state evaluation is carried out using improved grey clustering algorithm, with early warning result as the determination reference of index abnormal degree and the basis for grey class correction, the state evaluation result associated with early warning result is output by calculating clustering coefficient and determining grey class.The application realizes the deep fusion of early warning and evaluation, improves the accuracy and timeliness of network equipment state evaluation, provides strong support for preventive maintenance, and can be widely applied to the operation and maintenance of various network equipment.

The application discloses a bonding encapsulation equipment health state evaluation method based on twin data, classifies and combs three major functional components of an ultrasonic welding head system, a motion control system and a machine vision system of the bonding encapsulation equipment, establishes a hierarchical structure model and a fault tree, constructs a three-level health evaluation model including a component layer, a functional component layer and a whole machine layer, divides equipment health states into four qualitative grades of excellent, good, general and observation, introduces a health degree into quantitative characterization in combination with a fuzzy mathematics theory, adopts a multi-channel neural network model based on an attention mechanism to recognize the health state of a vibration signal, adopts a grey clustering model for health indexes which are not vibration type and are difficult to directly quantitatively evaluate, calculates clustering coefficients of the health indexes belonging to various health states based on a whitening weight function, and determines the health state; and adopts an entropy weight method based on twin data driving to determine the weight of each health index, carries out grey class weighted fusion, and outputs the real-time health degree and the health state grade of the equipment. The application improves the reliability, accuracy and equipment operation efficiency of the equipment health state evaluation, shortens the maintenance time, and reduces the production cost.

The invention discloses an artificial universal intelligent security assessment method and system based on an AHP and a genetic algorithm. According to the scheme, firstly, a data acquisition module extracts data features related to security from an AGI model; then the index hierarchy construction module establishes a safety index system based on an analytic hierarchy process; the weight calculation module calculates an initial weight and performs consistency verification; if the consistency ratio (CR) does not meet the threshold value, the genetic algorithm module executes optimization and correction; the optimized weight is input into a grey clustering module to calculate a clustering coefficient and a comprehensive safety score; and finally, the result output module generates a safety assessment report and a risk level. According to the scheme of the invention, a hierarchical structure system of AGI security risks is established through AHP, and consistency correction and global optimization are carried out by using a genetic algorithm, so that evaluation weight calculation is more objective and stable; and meanwhile, a grey clustering model and a whitening weight function are combined to realize quantitative classification of different risk types, so that a comprehensive safety score with dynamic adjustability and interpretability can be output.