499 results about "Fault rate" patented technology

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 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 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.

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 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 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 a coal machine equipment operation and maintenance method and system based on a data integration platform, and relates to the technical field of coal machine equipment operation and maintenance, and the method comprises the steps: collecting and fusing sensor data and external data of coal machine equipment in real time through the data integration platform; performing anomaly detection and fault prediction on the coal machine equipment based on the fault prediction model; and according to a fault prediction result, generating a personalized operation and maintenance strategy and automatically updating a strategy library. According to the coal machine equipment operation and maintenance method based on the data integration platform, by designing an intelligent filtering mechanism and a time window screening mechanism, the false alarm rate and the missing report rate are reduced, the adaptability is enhanced, the equipment fault rate and the operation and maintenance cost are reduced through an intelligent anomaly detection and fault prediction model, the perspectiveness and the effectiveness of operation and maintenance are improved, and the operation and maintenance efficiency is improved. Through personalized operation and maintenance strategy generation and feedback closed loop, the equipment fault risk is reduced, the service life of the equipment is prolonged, and better effects are achieved in the aspects of false alarm rate, adaptability and operation and maintenance cost.

The invention provides a fault diagnosis method and system based on multi-band multi-scale fuzzy entropy fusion for a traction rectifier with the highest fault rate in an electric locomotive traction transmission system. The method comprises the following steps: firstly, on the basis of an optimal wavelet basis function, decomposing fault signals under different working conditions and different operation modes by a wavelet packet to obtain a series of optimal frequency band information; secondly, performing coarse graining processing on the sequence of each frequency band, and calculating a multi-scale fuzzy entropy; and finally, solving the energy value of the multi-scale fuzzy entropy of each frequency band, and taking the energy value as a fault feature vector. The multiband fuzzy entropy feature obtained based on the optimal wavelet basis function has certain robustness to noise, and the fault diagnosis rate can be further improved through the proposed multi-information fusion algorithm. Compared with other methods, the method has the advantages of higher diagnostic rate and stronger robustness.

The invention discloses an underground parking space new energy automobile lithium battery cooling and fire extinguishing device which comprises a double-control module, and the double-control module comprises a main control module A and a main control module B which are used for regularly sending detection signals between the main control module A and the main control module B, detecting faults, exchanging sensor data and synchronously storing the data in a local database; the multi-mode sensing layer is arranged at a parking space and used for detecting whether a vehicle is parked or not and whether the temperature is too high or not, and the multi-mode sensing layer is connected with the main control module A and the main control module B; and the spraying assembly is located at the parking space and connected with the fire fighting pipeline, an electromagnetic valve is arranged at the joint, and the electromagnetic valve is connected with the main control module A and the main control module B. The system has the effects of rapid intervention, accurate cooling, remote early warning closed loop and the like, meanwhile, the fault rate of the system is reduced to 0.0002 times per year by adopting the double main control modules, and the false alarm rate is only 0.1 times per thousand of vehicle days and is reduced by 87% compared with a single-point threshold value detection mode.

The invention provides an anomaly analysis method combining a fault propagation path and a topological structure, and the method comprises the steps: calculating an overall recovery time estimation value under different topological structures according to the business importance and a recovery time target, and obtaining a candidate topology scheme set meeting the recovery time requirement; for the candidate topology scheme set, key nodes and key links are extracted, a fault scene is simulated based on component fault rate and link fault rate parameters, a fault propagation path is analyzed, and the influence of faults on service quality recovery time is evaluated; obtaining an optimal topology scheme considering the recovery time and the dependency strength constraint through the service recovery time output by the service recovery time model; in the solving process, load balancing capacity and fault-tolerant backup capacity are introduced as constraint conditions.

The utility model provides a gear mechanism, a driving device, a cleaning mechanism, cleaning equipment, a cleaning base station and a cleaning system. The gear mechanism comprises a first gear, a second gear and a rotation driving part. The second gear has a meshing state of being meshed with the first gear and a separation state of being separated from the first gear, and the first gear at least drives the second gear to rotate from the first angle position to the second angle position in the first direction and / or to rotate from the second angle position to the first angle position in the second direction. The rotation driving piece drives the second gear to rotate from the first angle position to the third angle position in the second direction. Thus, the movement gap between the first gear and the second gear can be guaranteed, the service life of the gears and the cleaning equipment is effectively prolonged, the reliability of the cleaning equipment is effectively improved, and the fault rate of the cleaning equipment is reduced.

The invention discloses a traffic equipment remote monitoring and maintenance management system and method, and the method comprises the steps: collecting the voltage, current, temperature, humidity and state data of traffic equipment through a sensor, and uploading the data to a remote platform in real time; setting a threshold value, triggering an alarm when abnormity occurs, and notifying operation and maintenance personnel; in combination with real-time and historical fault data, fault types are automatically identified, graded classification is performed, and priority processing is performed; a regular maintenance plan is generated according to the operation duration of the equipment, and maintenance personnel are intelligently scheduled; through historical data analysis, a prediction model is established, an alarm is given in advance, and preventive maintenance suggestions are provided; real-time data visualization is carried out, an operation report is generated, and the state, the fault rate and the maintenance record are analyzed; setting multi-level permissions to guarantee data security, supporting operation and maintenance feedback, and performing continuous optimization; according to the traffic equipment remote monitoring and maintenance management system and method, sensor data, historical records and real-time monitoring are combined, an intelligent algorithm is applied to develop a precise fault diagnosis system, and the recognition accuracy is improved.

The invention provides a method for predicting the working failure rate of a railway vehicle relay, and the method comprises the steps: classifying data according to a preset dimension to generate a structured feature set, constructing a time sequence feature matrix, and defining a multi-dimensional analysis coordinate system based on the classification dimension of the feature set; determining an initial prediction constraint condition according to the relay life distribution model and the fault mode library, executing dynamic prediction simulation on the feature set based on the time sequence feature matrix, simulating degeneration behaviors of the relay under various working conditions through a state transition simulation engine, generating an initial fault rate curve, and calculating the fault rate of the relay according to the initial fault rate curve. And finally, carrying out iterative correction on the initial fault rate curve by adopting an integrated optimization algorithm to generate a multi-dimensional fault rate prediction model. The invention further provides a system for predicting the working failure rate of the railway vehicle relay. According to the method, the adaptability and accuracy of prediction are greatly improved, and the degeneration behavior of the relay under the actual complex working condition can be simulated more truly.

An autonomous driving control apparatus requests information associated with a plurality of parts of an autonomous vehicle from an external electronic device and calculates a fault rate for each of the plurality of parts using the information associated with the plurality of parts. The apparatus identifies that a first fault rate corresponding to a first part among the calculated fault rates is greater than or equal to a specified first value and calculates a first failure rate at which the first part causes a failure of the autonomous vehicle using the first fault rate. The apparatus stores the first fault rate and information associated with the first part in storage and stops performing a function associated with the first part or stops performing the entire autonomous driving function of the autonomous vehicle after a fault or a failure of the autonomous vehicle occurs.

The invention relates to the technical field of model training, in particular to a power transmission line fault rate prediction model training method and system based on machine learning. According to the method, an improved inverse distance weighted interpolation algorithm is adopted, timeliness weight, reliability weight and terrain constraint weight are introduced, regional meteorological grid point data are mapped to a tower coordinate position, and a time sequence is aligned; carrying out statistics on occurrence frequencies of meteorological factors in the fault samples and the non-fault samples, and calculating a single-factor association weight and a multi-factor collaborative association weight; combining the meteorological factor feature vector, the association weight and the fault tag to form a training data set; and constructing a neural network model containing an attention layer, calculating an attention weight based on a single-factor association weight, and training the model. According to the method, the attention mechanism is guided through the association weight, so that the model preferentially pays attention to the high-association-degree meteorological factors, and the prediction precision is effectively improved.

The invention relates to the technical field of port scheduling, and discloses a port multi-carrier collaborative scheduling optimization system based on deep reinforcement learning, and the system comprises a state sensing module which obtains the position, state and task progress data of multiple carriers; the multi-carrier scheduling optimization model module constructs a collaborative scheduling optimization problem model, wherein the collaborative scheduling optimization problem model comprises a target function and a constraint condition; the scheduling problem solving module is used for solving the collaborative scheduling optimization problem model and generating a collaborative scheduling strategy with the minimum energy consumption and the shortest time consumption; the action execution module converts the collaborative scheduling strategy into an actual port operation instruction; the fault statistics module calculates a carrier fault rate and formulates a carrier maintenance strategy; and the maintenance management module carries out maintenance recording on the fault carrier so as to carry out preventive maintenance management. According to the method, multi-source data are integrated, the collaborative scheduling optimization model is constructed, dual-target solution is realized, and the technical problems of low scheduling efficiency, high energy consumption and maintenance strategy lagging in the traditional technology are solved by combining fault statistics and preventive maintenance.