823 results about "Fault rate" patented technology

The invention discloses a power distribution cabinet fault detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: laying a multi-modal sensor array, constructing a multi-dimensional data collection network, collecting various types of sensing data in real time, and carrying out the data preprocessing; establishing a multi-modal data fusion model by using a deep learning algorithm, and forming a fault feature mapping relation through feature extraction, data correlation analysis and cross validation to realize collaborative perception of a data level; and constructing a multi-dimensional fault diagnosis decision tree based on a collaborative perception result, accurately positioning a specific device position where a fault occurs in combination with weight distribution and confidence evaluation of data of each sensor, and giving out evaluation results of a fault type and severity. The system effectively reduces the equipment failure rate, prolongs the service life, reduces the maintenance cost, improves the power supply reliability, promotes the development of an electric power system towards the intelligent operation and maintenance direction, and is of great significance to the improvement of the operation reliability and maintenance efficiency of a power distribution system.

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

The invention provides an AI-based assembly state intelligent monitoring method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a multi-angle real-time assembly process image of a target product, constructing an assembly digital twin model, inputting the image into a hybrid neural network in combination with an assembly knowledge graph, analyzing the assembly node coordinate and torque data deviation, and obtaining an assembly state of the target product. And obtaining an optimal assembly path and torque control parameters, and finally generating an assembly quality evaluation matrix. Through fault feature extraction and mode recognition, potential faults are predicted, and a dynamic risk coefficient is calculated. And when the fault occurrence probability exceeds a preset threshold value, the position and torque compensation parameters are calculated, an execution instruction sequence is generated to control the assembly execution mechanism to perform adjustment, closed-loop optimization control in the assembly process is achieved, the assembly precision and efficiency are improved, and the fault rate is reduced.

The invention provides a power equipment operation and maintenance state intelligent monitoring system, and relates to the technical field of power equipment operation and maintenance. According to the system, fault prediction and remote control are realized by monitoring the operation state of the power equipment in real time. The system comprises a data acquisition module, a data processing module, a fault prediction module and a remote control module. The data acquisition module is responsible for acquiring operation data of equipment, and the data processing module processes and analyzes the acquired data and extracts characteristic parameters related to the state of the equipment. And the fault prediction module performs fault prediction by using a machine learning algorithm based on the characteristic parameters, and provides early warning information for operation and maintenance personnel. And the remote control module allows operation and maintenance personnel to remotely monitor and control the equipment through a network, so that quick response and processing are realized. According to the system, through the functions of real-time monitoring, intelligent operation and maintenance, personalized configuration and the like, the operation and maintenance efficiency is effectively improved, the equipment failure rate is reduced, and remarkable improvement and promotion are brought to operation and maintenance management of power equipment.

The invention relates to the technical field of motor temperature intelligent control, and particularly discloses a motor temperature monitoring and regulation method and system based on intelligent control and a storage medium, and the method comprises the steps: carrying out data collection circulation: judging whether a sampling period is reached, if yes, reading multi-mode sensing data, and outputting a calibrated temperature value T, and if not, outputting a calibrated temperature value T; if yes, vibration noise is eliminated, the environment temperature is compensated, multimode sensing data is added, and then the calibrated temperature value T is output; intelligent analysis is conducted on the calibrated temperature value T obtained through data collection circulation, if the temperature is normal, data analysis and prediction are conducted, and if the temperature is abnormal, the calibrated temperature value T is analyzed, fault self-diagnosis is conducted, abnormal reasons are judged, the system is controlled to conduct self-adjustment, and temperature early warning is conducted. The problems that in the prior art, monitoring is delayed, regulation and control are rigid, and maintenance is passive, so that energy efficiency is wasted, the failure rate is high, and operation and maintenance cost rises are solved; and a real-time temperature monitoring means and an intelligent control system are lacked.

The invention relates to the technical field of power transmission lines, in particular to a power transmission line toughness evaluation method and system considering important loads under an ice disaster. The method comprises the following steps: firstly, constructing an icing thickness real-time growth model considering multiple parameters such as water-ice density, an ice precipitation rate, a wind speed and air water content, carrying out regionalization analysis on a power transmission line according to weather parameters such as the wind speed and the precipitation rate, respectively calculating a unit length fault rate of each section, and comprehensively obtaining an overall fault probability of the whole line; then establishing a toughness evaluation system comprising a disturbance performance index, a recovery speed index and an important load maximum loss index, and dividing the affected process into five stages of system normal operation, performance reduction, performance maintenance, preliminary recovery and complete recovery; and finally, carrying out normalization processing on the evaluation indexes, determining the weight by adopting an optimization algorithm, and calculating the toughness value of the system. The fault prediction accuracy and the system recovery efficiency are remarkably improved through regional analysis and multi-dimensional evaluation, and the power supply reliability of important loads is effectively guaranteed.

The invention relates to the technical field of electric digital data processing, in particular to a fault online monitoring method and system for a cable braiding machine, and the method comprises the steps: obtaining a vibration monitoring signal and a tension monitoring signal when the cable braiding machine works; at different gear rotating speeds, fluctuation of the vibration monitoring signals caused by gear abrasion is analyzed, and the gear vibration abnormal degree of the cable braiding machine is obtained; analyzing the time synchronism of the vibration monitoring signal and the tension monitoring signal to obtain a sudden change position overlap ratio, and analyzing the amplitude change consistency of the vibration monitoring signal and the tension monitoring signal to obtain amplitude change relevance; and determining the gear fault rate of the cable braiding machine based on the gear vibration abnormal degree, the sudden change position overlap ratio and the amplitude change relevance, and determining a fault maintenance strategy of the cable braiding machine based on the gear fault rate. According to the invention, the electric digital data of the cable braiding machine during working are processed, and real-time and accurate online monitoring is carried out on faults.

The invention discloses a multidimensional monitoring system of an on-load tap-changer for a converter transformer and a fault prediction method, the system comprises a motor, a change-over switch core and other parts, and a data acquisition module is provided with a current, vibration and oil chromatography acquisition unit and a vacuum tube life monitoring unit. The current sensor collects motor current, the vibration sensor collects vibration signals of the change-over switch, the oil chromatography collection unit analyzes an oil sample, and the vacuum tube life monitoring unit evaluates the residual life of the vacuum tube. And a data transmission and processing analysis module. The fault prediction method comprises the steps of data acquisition, preprocessing, multi-dimensional data fusion, fault prediction model construction, feature extraction and selection, fault prediction and evaluation and the like. And screening characteristic parameters and fusion data by adopting a plurality of technical means, and constructing a machine learning model to predict faults. According to the invention, multi-source data acquisition and multiple analysis methods are integrated, the equipment state can be comprehensively reflected, the monitoring reliability is improved, faults can be predicted in advance, and the equipment fault rate is reduced.

The invention provides an air conditioning system fault diagnosis method and device based on a parameter correlation Bayesian network, and the method comprises the steps: rapidly determining the real-time numerical values of key performance parameters of parts through the correlation between the material parameters of the parts, comparing the calibration numerical values of the parts when the parts leave a factory, and determining the real-time fault rates of various faults of the parts; and in combination with a Bayesian network model of the air conditioning system, calculating the real-time fault rate and the real-time posterior probability of each stage of sub-node in sequence, and judging whether the sub-node is a fault node or not by comparing the difference between the statistical posterior probability and the real-time posterior probability. According to the method, in real-time data measurement and processing, measurement is convenient, and operability is high; according to the method for judging the faults through the Bayesian network and the posterior probability, the diagnosis process is more flexible, fault positioning is more accurate, the method can be suitable for fault diagnosis of various types of air conditioning systems through slight adjustment, and high practical generalization is achieved.

The invention discloses an elevator fault prediction method and system based on sound and vibration fusion, and the method comprises the steps: carrying out the weighted fusion of a vibration signal and a sound signal according to a signal-to-noise ratio, and then combining with a feature matrix composed of a wavelet packet transformation normalized energy value, a time domain and a frequency domain, applying a Sodat swarm algorithm to an optimization support vector machine model, and carrying out the prediction of an elevator fault. And carrying out feature classification on the feature matrix by using the model. According to the invention, the accuracy of fault prediction can be improved, and the risk of sudden faults is reduced, so that the maintenance efficiency is optimized and the fault rate is reduced.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention discloses an error correction method of a neural network processor and a related device, and belongs to the technical field of neural networks. The method comprises the steps that according to a fault PE table obtained through error detection, the fault PE table comprises a two-dimensional list of fault occurrence and position information, a newly detected fault PE is positioned according to the fault PE table, and a multi-stage fault-tolerant decision is executed: i, a fine-grained error correction stage; ii, a coarse granularity error correction stage; according to the current error correction mode, a pipeline submission mechanism or a submission mechanism is selected, and a calculation result is output. According to the method, a mixed granularity fault recovery scheme is adopted, and the error correction function can be achieved under the condition that any standby PE is not added. The fine-grained scheme provides more flexibility and does not waste PE, and the coarse-grained scheme ensures that the reasoning precision is not reduced along with the increase of the failure rate.

The invention discloses an intelligent production line fault prediction and adaptive adjustment system and method based on artificial intelligence. In the modern manufacturing industry, a traditional production line maintenance mode is difficult to find potential faults in advance, so that shutdown loss is huge, and the problems of insufficient data utilization, poor prediction and adjustment effects and the like exist in the prior art. The system comprises a data acquisition module, a data processing module, a fault prediction module and a self-adaptive adjustment module. The data acquisition module acquires multi-modal data through various sensors and carries out edge calculation preprocessing on the multi-modal data. The data processing module cleans data by using deep learning, extracts features, and stores the features by using a block chain technology; the fault prediction module fuses multiple technologies to improve the prediction precision; and the adaptive adjustment module generates and optimizes an adjustment strategy by using reinforcement learning. The method comprises the steps of data acquisition, processing, fault prediction and adaptive adjustment. According to the method, accurate fault prediction and efficient adaptive adjustment are realized, the intelligent level of a production line is improved, the fault rate and the production cost are reduced, the enterprise competitiveness is enhanced, and the method has a wide application prospect in the field of intelligent manufacturing.

The invention discloses an offshore wind turbine generator fault prediction method and device considering the influence of environmental factors, and the method comprises the steps: dividing an offshore wind turbine generator into a sensor system, a mechanical system and a tower structure system according to the correlation between the fault rate and the environmental factors, and building fault detection models respectively; time convolutional network prediction models are respectively established in combination with unit operation data and environment data to predict the operation state of each system, and fault occurrence is predicted in combination with a fault prediction model; coupling each system fault prediction model, constructing a coupling fault prediction model based on unit operation and an offshore wind turbine generator fault tree, carrying out label classification on predicted faults of each system fault model, carrying out verification through the fault tree, carrying out analysis and classification on fault causes, and supplementing the fault tree when judging that a new fault type appears; according to the method, the correlation between the offshore environment and the fault generation increments of different parts in the wind turbine generator is considered, so that the fault generation prediction accuracy of the offshore wind turbine generator is improved.

The invention discloses an information energy equipment operation reliability evaluation method and system and electronic equipment, and relates to the field of power system reliability determination. According to the method, the Foster thermal resistance model is adopted to determine the junction temperature of the selected device in the information energy equipment, so that the average junction temperature and the junction temperature fluctuation are obtained. Adopting a Coffin-Manson-Arrhenius model to determine the number of failure cycles of the selected device based on the average junction temperature and junction temperature fluctuation so as to determine the degree of damage caused by the set number of junction temperature stress cycles; determining the fault rate of the selected device based on the damage degree, and obtaining the fault rate of the H bridge + DAB combination unit; the fault rate of the half-bridge arm can be obtained on the basis of the fault rate of the H-bridge + DAB combination unit, and the fault rate of the rectification subsystem can be accurately obtained under the condition of considering the bridge arm inductance fault rate of the half-bridge arm, so that the reliability of the digital battery energy storage system can be simply and effectively analyzed.

The present invention discloses an intelligent data processing system for accessing new energy to a power grid, and relates to the technical field of power data processing. The intelligent data processing system for accessing new energy to a power grid includes: a data acquisition and preprocessing unit, a regularity prediction module, an anomaly detection module, a geographic location analysis and processing module, an equipment health management and predictive maintenance unit, and an equipment input power compensation unit; by detecting the data of the access of new energy power generation equipment to the power grid, and analyzing the regularity and abnormal data of its fluctuations, on the one hand, it is convenient to better control the safety of the power grid, and through abnormality analysis, it can be analyzed whether there is abnormal operation of new energy power generation, and its health can be evaluated, so as to facilitate maintenance in advance and avoid its failure rate; the application of the equipment input power compensation unit can complement the power of different power generation equipment connected to the power grid, and avoid the problem of instability of the local power grid caused by fluctuations.

The invention belongs to the field of equipment evaluation, particularly relates to an intelligent evaluation method and system for the operation quality of a flight simulator, and aims to solve the problems of single evaluation dimension, poor real-time performance, low intelligent degree and lack of quantitative standards in the prior art. The method comprises the steps that operation data such as fault time, duration and available time are collected in real time through a sensor network; calculating core indexes such as fault frequency per unit time, accumulated duration and malignant fault rate; dynamically weighting and fusing the indexes to generate a single-machine health index; a correlation model is constructed based on fault space-time correlation between devices, and cluster health scores are output in combination with single machine indexes; when the malignant fault exceeds a threshold value, driving causal inference by utilizing a knowledge graph containing entity causal relationships of fault types, equipment parts and the like, and outputting a root cause diagnosis and maintenance scheme; and acquiring intervened data to update atlas parameters and models to form closed-loop optimization. Multi-dimensional real-time monitoring and diagnosis closed loop of the operation state are realized, and the maintenance efficiency is improved.

The invention discloses a box-type substation remote monitoring method and system, and the method comprises the steps: obtaining and analyzing the historical operation monitoring data of a box-type substation, and determining the key operation parameters of the operation of the box-type substation; features of the key operation parameters are extracted, a prediction model is constructed based on the features and a preset model for prediction, and key operation parameter prediction data are obtained; analyzing the key operation parameter prediction data, and determining a fault mode of each key operation parameter; determining data features from the data change curve, and performing abnormal degree evaluation based on the data features to obtain an abnormal degree evaluation value; and determining the abnormal level of the box-type substation based on the fault mode and the abnormal degree evaluation value of each key operation parameter, and generating an early warning scheme based on the abnormal level and the fault mode. Remote monitoring data are analyzed, the operation state of the box-type substation is monitored in real time, abnormal conditions are found in time, early warning is carried out, and therefore the operation efficiency is improved, the fault rate is reduced, and safe and reliable operation of a power system is guaranteed.

The invention discloses an ultrahigh-pressure jet cleaning system with an intelligent adjusting function. According to the system, the control algorithm sub-module is integrated with the PID controller, the fuzzy logic controller and the machine learning model, so that the control algorithm sub-module can realize accurate monitoring and intelligent adjustment of the system operation state, and the efficiency and the stability of the cleaning process are ensured. The PID controller quickly responds to errors, eliminates steady-state errors, reduces system overshoot and oscillation, and improves real-time performance and accuracy of control; the fuzzy logic controller is used for processing nonlinearity and uncertainty of the system, so that the adaptability of the system to complex working conditions is enhanced; and the fault rate and the maintenance cost of the equipment are reduced through predictive maintenance, so that the operation convenience, the operation reliability and the economical efficiency of the system are integrally improved, and efficient and intelligent cleaning experience is brought to users.

The invention discloses a maintenance and operation fault dynamic early warning system based on a high-speed railway communication network, and relates to the technical field of high-speed railway communication networks. The invention aims to solve the problems of inaccurate fault early warning, slow response speed and the like in the prior art. The system realizes real-time monitoring and intelligent analysis of a railway line through three-level elastic section classification, four layers of intelligent processing modules and a cross-domain cooperation module. The monitoring intensity is dynamically defined according to the annual average failure rate in the third-level elastic section classification; the four-layer intelligent processing module has the functions of multi-module sensing, edge calculation, digital twinning, decision control and the like; and the cross-domain collaboration module ensures the overall efficiency of the system. According to the invention, the accuracy and timeliness of fault early warning are improved, the maintenance cost is reduced, and a powerful guarantee is provided for safe operation of the high-speed railway. In the future, the system is continuously optimized, more application scenes are explored, and the system performance and the user experience are improved.

A safety apparatus for a lifting machine platform and a three-dimensional goods shelf, said apparatus comprising a blocking assembly (100) that is located at the connection point between a lifting machine platform (10) and a three-dimensional goods shelf (30), a guide assembly (200), and a rotary bracket (300). The blocking assembly is rotatably arranged and is provided with a small blocking end (100a) and a large counterweight end (100b) which switch between high and low positions by means of rotation. When no external force is applied to the blocking assembly, under the action of gravity provided by the large counterweight end, the small blocking end is located at the high position in an upward orientation, so as to create blockage to a shuttle vehicle (40) in a direction of movement of the shuttle vehicle. The guide assembly and the blocking assembly are arranged to fit by means of offset collision in a direction of lifting, and in an offset collision state, the guide assembly collides with the large counterweight end, such that the blocking assembly rotates to switch the high and low positions of the small blocking end and the large counterweight end, and the small blocking end rotates to the low position to clear blockage to the shuttle vehicle. The described safety apparatus can effectively prevent losses such as falling of the shuttle vehicle caused by various uncertain factors in smart warehousing, thus reducing the fault rate and improving the working efficiency of three-dimensional warehousing.

The invention discloses a complex production line reliability analysis method combining a fault operation and maintenance knowledge graph with a fault tree, and the method comprises the steps: constructing a multi-level production line fault operation and maintenance knowledge graph based on a production line-equipment-fault-method, inputting fault information, carrying out the fault matching query of a production line layer, constructing an available equipment sub-graph, and carrying out the fault matching query of the equipment sub-graph; a production line fault tree is constructed by analyzing the logic relation between the production line faults and the factors causing the faults, and finally qualitative analysis is conducted on the production line fault tree, the production line fault rate is quantitatively calculated, and reliability analysis of the production line is achieved. The method is simple and easy to implement, reliability analysis of a current production system can be improved from an equipment level to a whole production line level considering coupling between equipment, a mode layer is reversely standardized and verified by utilizing characteristics of the production line and fault knowledge, production management personnel are helped to understand the rule of equipment or system fault generation, and the reliability of the production system is improved. And a reasonable maintenance strategy is formulated.

The invention discloses a fault prediction method and system for deviation rectification of a laser die cutting and winding all-in-one machine, and belongs to the technical field of intelligent manufacturing and industrial equipment fault prediction.The method comprises the steps that multi-dimensional sensor data of a deviation rectification mechanism is collected and preprocessed; a sequence variability coefficient is calculated through a sliding window, and a high-risk abnormal time period is screened in combination with a dynamic threshold value; an improved Transform prediction model is constructed to carry out offset trajectory prediction; fault judgment is carried out, and the model is deployed in edge computing equipment or a server to realize efficient prediction; according to the fault prediction method and system for deviation correction of the laser die cutting and winding all-in-one machine, prediction errors can be reduced, the early warning advance and accuracy can be improved, redundant calculation can be reduced, and the model efficiency can be improved to adapt to industrial deployment; potential fault recognition and early warning are achieved, the equipment fault rate and the shutdown risk are reduced, and remarkable economic and social benefits are achieved.

The invention relates to the technical field of power system reliability analysis, and particularly discloses a source-load interaction fault coupling charging station and power distribution network cooperation reliability evaluation method comprising the following steps: collecting data parameters of a power distribution network line and a charging station; based on data parameters of a power distribution network line and a charging station, an association probability matrix of line faults and charging station outage is constructed, Poisson distribution fault rate parameters of independent faults of the charging station are integrated, and a two-way joint probability model is formed; constructing a hierarchical index system, and forming a multi-dimensional evaluation system; a bidirectional trigger mechanism for simulating a power distribution network line fault and charging station shutdown is adopted, a line overload secondary fault caused by load transfer is analyzed, and a reliability evaluation report is generated. According to the method, the limitation of traditional one-way evaluation is broken through, the'fault-load transfer-overload 'chain reaction is accurately restored, the dynamic simulation precision is improved, a multi-dimensional index scientific decision is adopted, and a quantitative basis is provided for reliability evaluation.

The invention provides a method and a device for determining the failure rate of nuclear power station mechanical equipment, which can be applied to the technical field of safety maintenance of nuclear power station equipment. In specific implementation, the equipment type of target mechanical equipment of the nuclear power station and current environment characteristic parameters related to the target mechanical equipment can be firstly obtained; according to the equipment type of the target mechanical equipment, determining a matched preset equipment fault rate model from a preset equipment fault rate model library as a target equipment fault rate model; meanwhile, according to the current environment characteristic parameters, current environment influence factor data values matched with the target mechanical equipment are obtained; and processing the current environmental influence factor data value by using the target equipment fault rate model, and determining the current fault rate of the target mechanical equipment. Therefore, the specific failure rate of the mechanical equipment in the nuclear power station in different environment scenes can be efficiently and accurately determined, errors are effectively reduced, and the operation safety of the nuclear power station is ensured.

The invention provides a rolling mill equipment design parameter optimization method and device, equipment and a storage medium, and relates to the technical field of equipment parameter optimizing.The rolling mill equipment design parameter optimization method comprises the steps that original design data of to-be-optimized rolling mill equipment is obtained, and an equal-proportion roll changing force model is established based on the original design data; analyzing the roll changing force model to obtain key design parameters of the rolling mill equipment to be optimized; and classifying the key design parameters to obtain a plurality of classification data, and performing parameter optimization on each classification data according to a preset condition to obtain actual design parameters of the rolling mill equipment to be optimized. Through systematized data acquisition, model establishment and parameter analysis and optimization, the production requirements can be met, so that the system can operate more efficiently in the production process, the product quality and the production efficiency are improved, and meanwhile, the fault rate and the maintenance cost are reduced.

The invention relates to the technical field of data processing, and particularly provides an automatic fault tracing method, system and device based on topological coding and a medium, and the method comprises the steps: distributing a unique topological code reflecting the hierarchical position of physical equipment for the physical equipment in a factory power distribution system; the method comprises the following steps: collecting real-time operation data through an edge collection terminal, and binding topological codes to form a real-time data stream with a label; constructing a topology dependency degree model, and defining the inherent failure rate of each node and the dependency degree of each node on a superior node; when the fault of the target equipment is detected, reversely traversing upstream nodes along a topological path based on the model, and calculating the fault suspicion probability of each upstream node by adopting a probability propagation algorithm; and carrying out normalization processing on the probabilities, fusing real-time state data, carrying out weighted correction, and finally generating a fault diagnosis report sorted according to the probabilities. According to the invention, by introducing a multi-level topological coding system and a probability propagation algorithm, rapid and accurate positioning and intelligent diagnosis of faults are realized.

The invention provides a switch redundant resource scheduling method and device based on fault prediction, electronic equipment and a medium, and the method comprises the following steps: firstly, collecting multi-dimensional operation state data (such as port state, flow, environment parameters and the like) of a switch, and carrying out the standardization processing to generate a normalized feature matrix; calculating a fault rate estimation value and a health degree score based on the standardized data, and taking the values as prediction inputs; predicting a fault probability by using a deep learning model and combining health degree calibration to obtain an adjusted fault probability; based on the calibration probability and the health degree score, redundant resource priorities are calculated and sorted; finally, path selection is optimized according to the priority, pre-switching redundant resources are dynamically scheduled, rapid switching during faults is ensured, and fault extension is avoided. According to the invention, through reasonable scheduling, path optimization and load balancing, the fault response capability, resource utilization efficiency and stability of the switch system are effectively improved.

The embodiment of the invention relates to the field of smart home, and discloses a smart socket and a sewing machine fault early warning method. The method comprises the following steps: if an intelligent socket registration request is received, performing Bluetooth connection with an intelligent socket; acquiring first equipment information of the intelligent socket, and registering the intelligent socket to an Internet of Things platform according to the first equipment information and the Wi-Fi distribution network information; if the sewing machine registration request is received, second equipment information of the sewing machine connected to the intelligent socket is determined, and the sewing machine is registered to the Internet of Things platform; and monitoring whether the intelligent socket and the sewing machine have fault monitoring data in real time, if so, generating alarm information according to the fault monitoring data, and sending the alarm information to an intelligent terminal of a user. According to the scheme, through the functions of automatic equipment registration, real-time fault monitoring, intelligent alarm, remote management and the like, the intelligent management level of the equipment can be improved, the user experience is improved, the fault rate is reduced, the maintenance cost is saved, and the safety and reliability of the equipment are enhanced.

The invention discloses an electrical coupling system operation reliability evaluation method, which comprises the steps of analyzing a network topology, an energy flow direction and an element logic relationship of an electrical coupling system, and constructing a Bayesian network model of the system from a power supply source to a load point; establishing an element reliability model, and obtaining different failure rate information of the non-key element by changing different age limits and load rates; taking different failure rate information of the non-key elements as input data of nodes of the Bayesian network model, outputting an analytical model coefficient, and obtaining a reliability index in combination with a key element combination state probability; training an LLSVM model by taking different failure rate information of the non-critical elements as input and taking an analytical model coefficient as output; and during online application prediction, obtaining different failure rates of corresponding non-key elements by combining element long-term and short-term prediction results with the element reliability model, inputting the different failure rates into the trained LLSVM model, and outputting a reliability index. According to the method, effective, rapid and efficient reliability evaluation of the electrical coupling system is realized.