16 results about "Grey clustering" patented technology

The invention provides a highway bridge intelligent maintenance decision-making system and decision-making method. The system comprises a data acquisition module, a processing and storage module, a bridge condition evaluation module, a maintenance decision making module, an implementation and management module, and a monitoring and feedback module. The data acquisition module collects various types of data of bridges, environments, traffic and the like through multiple means such as fiber bragg gratings and vibration sensors. And the processing and storage module cleans the data by using a DBSCAN algorithm, processes the classified data by one-hot coding, and constructs distributed database storage based on a block chain. And the evaluation module adopts an Isomap algorithm to evaluate technical conditions and predict diseases. And the decision module generates a scheme by using a particle swarm optimization-simulated annealing algorithm, and analyzes and optimizes by using an analytic hierarchy process-grey clustering. And maintenance implementation is managed by means of a BIM model. The monitoring module collects data in real time and feeds back an adjustment strategy. The system can acquire data in real time through multiple sensors, accurately process data, accurately evaluate diseases, optimize maintenance decisions, dynamically adjust strategies, guarantee bridge safety and prolong the service life.

The invention discloses a state monitoring system for a railway electric switch machine. The state monitoring system comprises a multi-mode sensor module, a data preprocessing module, an intelligent analysis and decision module and a visualization and interaction module. The multi-mode sensor module is distributed at a key part of the switch machine and is used for acquiring multi-dimensional data such as the rotating speed of a power transmission shaft, displacement and vibration of an actuating rod, an expression notch image, temperature and humidity, air pressure and the like; the data preprocessing module performs noise reduction through a Kalman adaptive filtering algorithm and fuses asynchronous data by using a space-time alignment technology; the intelligent analysis and decision-making module constructs a hierarchical index system, determines a weight in combination with an analytic hierarchy process and an entropy weight method, dynamically evaluates a health level through a fuzzy grey clustering algorithm, predicts and represents a gap size trend by adopting an ARIMA model, performs monthly early warning on jamming faults by applying a random forest model, and optimizes and maintains resource configuration according to historical data; the visualization and interaction module develops a Web mobile terminal interface and supports multi-level health state display, real-time alarm and maintenance work order generation.

The invention belongs to the technical field of circuit breakers, and particularly relates to a circuit breaker health state evaluation method and computer equipment. The method comprises the following steps: obtaining a measured value of each secondary index under each primary index reflecting the health state of the circuit breaker, and calculating a deterioration degree used for judging the aging degree of the measured value of the secondary index according to the measured value of each secondary index; constructing whitening weight functions of different grey classes of the second-level indexes, and substituting the degradation degrees of the second-level indexes into the whitening weight functions of different grey classes to obtain grey clustering coefficients of the second-level indexes; and obtaining an evaluation result of the health state of the circuit breaker according to the gray clustering coefficient of each secondary index and the weight corresponding to each primary index. The accuracy of the health state evaluation result of the circuit breaker is improved, the reliability of the circuit breaker is improved, faults are avoided, and normal operation of the circuit breaker is ensured.

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 invention discloses a comprehensive benefit evaluation method and system for a power distribution system, and the method comprises the steps: firstly, collecting the multi-dimensional benefit index data of a power distribution system scheme, and constructing a contribution cloud picture through parameter inversion, so as to describe the fuzziness and uncertainty of the data; secondly, fusing subjective and objective weights by adopting a combined weighting method so as to balance expert experience and data laws; then, performing weighted fusion on the cloud model parameters of the plurality of indexes to quantify the overall benefit performance of the scheme; and finally, in combination with an improved grey clustering analysis method, introducing a Logistic curve to fit a benefit level boundary, in combination with a graph neural network, extracting a complex relation structure between the schemes, fusing a chaos theory to improve a fuzzy boundary identification capability, replacing a traditional whitening function, calculating a membership degree of each scheme to a benefit level, and completing sorting. Compared with a traditional method, the method is better in processing nonlinearity, uncertainty and dynamic information fusion, and the scientificity and decision credibility of comprehensive benefit evaluation of the power distribution system are effectively improved.

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 invention discloses a safety evaluation method for an intelligent network connection mixed driving mountain city expressway confluence area. The method comprises the following steps: constructing a simulation scene of the mountain city expressway confluence area; confluence behaviors in a mixed driving scene are simulated, and rear-end collision and lane change collision data are extracted; constructing a traffic conflict prediction model under different intelligent network connection permeability, and outputting a traffic conflict number prediction value through the traffic conflict prediction model; the traffic conflict rate is used as an evaluation index to construct a safety evaluation index system, the comprehensive weight of the evaluation index is calculated, and the safety level is divided. By constructing the mountain city expressway confluence area traffic conflict prediction model and the conflict rate evaluation index system and comprehensively integrating the empowerment and improving the gray clustering model, the method accurately reflects the traffic safety condition, adapts to the intelligent network connection mixed driving environment, provides a scientific basis for traffic optimization, effectively improves the safety level, reduces accidents and improves the traffic safety. And resident travel safety is guaranteed.

The present invention discloses a WSN topology control method based on grey clustering and dynamic slicing, which is used to improve the security of data during transmission. The method includes: evaluating the trust values of adjacent cluster head nodes in a way of inter-cluster security awareness, and the cluster head nodes adopt dynamic slicing technology to slice data packets and send them to candidate cluster head nodes. The present invention makes certain improvements in data slicing. It uses a clustering algorithm to divide the security states of each candidate cluster head, and defines the number and proportion of data slices of the cluster head nodes according to the security states and location information of the candidate cluster heads, reducing the threat of malicious nodes to data during network communication and also controlling the energy consumption at a relatively low level.

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 invention discloses a safety risk assessment and early warning system for a complex interleaving area in an intelligent network connection mixed driving environment, and the system comprises the following steps: N1, the current situation analysis of the complex interleaving area, including data collection and traffic operation characteristic analysis, N2, the construction of a traffic conflict calculation model, including the design of a conflict simulation experiment in a mixed driving environment, and the design of the traffic conflict calculation model through an orthogonal experiment design method. Constructing a simulation experiment orthogonal table; carrying out a simulation experiment on the complex interleaving area by utilizing SUMO software, exporting corresponding data, and establishing a conflict calculation model; n3, constructing a traffic safety evaluation method: establishing traffic safety evaluation indexes, constructing a traffic safety evaluation method based on a grey clustering model, introducing a sine curve type possibility degree function, and determining the weight of each evaluation index through combined weighting of a fuzzy consistency matrix and an entropy evaluation method; and N4, performing security level division and verification. According to the method, gray fixed weight clustering evaluation is improved, and the traffic safety pre-judgment capability of the complex interlaced area is improved.

The invention discloses a city open block evaluation analysis method and system, and the method comprises the steps: setting an initial open block influence region of a city, obtaining the traffic volume of each road in the influence region before and after the opening of the initial open block influence region, calculating the saturation degree of each road before and after the opening, determining an affected road according to the road saturation degree, and carrying out the evaluation analysis of the urban open block. Determining the influence range of the influence area of the open block; constructing an evaluation analysis index system; establishing an evaluation analysis method model: determining an index grey class level quantitative standard according to an index layer, calculating a whitening weight function characteristic value, and constructing a whitening weight function; weighting each index layer, and calculating a gray clustering coefficient and a clustering coefficient vector; and according to the obtained clustering coefficient vector, determining a grey class to which the block is opened and affiliated, and further outputting a result for evaluating whether the block is suitable for being opened or not. From qualitative and quantitative angles, the invention provides an effective and reliable evaluation analysis method for block opening.

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 present invention provides an intelligent maintenance decision-making system and decision-making method for highway bridges. The system includes data acquisition, processing and storage, bridge condition assessment, maintenance decision-making, implementation and management, monitoring and feedback modules. The data acquisition module collects various types of data such as bridges, environment, and traffic through multiple means such as fiber Bragg gratings and vibration sensors. The processing and storage module uses the DBSCAN algorithm to clean the data, one-hot encoding to process the categorical data, and constructs a distributed database based on the blockchain for storage. The assessment module uses the Isomap algorithm to evaluate the technical condition and predict diseases. The decision-making module uses the particle swarm optimization-simulated annealing algorithm to generate solutions and optimizes them using the analytic hierarchy process-grey clustering analysis. The maintenance implementation is managed with the help of a BIM model. The monitoring module collects data in real time to feedback and adjust strategies. This system can collect data in real time with multiple sensors, process the data accurately, evaluate diseases accurately, optimize maintenance decisions, dynamically adjust strategies, and ensure the safety of bridges and extend their service life.

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.