141 results about "Fault tree analysis" patented technology

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

The invention relates to a rail transit energy efficiency balance dynamic monitoring method and system, relates to the field of rail transit energy conservation and monitoring, and solves the problems that in an energy consumption monitoring link, simple statistical analysis and a rule-based system are difficult to adapt to a complex and changeable operating environment due to fixed threshold values and rules, and misjudgment and missed judgment are easy to occur. The method comprises the following steps: according to a comparison result of actual energy consumption of equipment and a personalized energy efficiency standard threshold value, determining whether the corresponding equipment has energy consumption abnormity or not; if yes, the personalized threshold value and historical energy consumption data of the equipment are combined, the built comprehensive energy loss analysis model based on the fault tree and the Bayesian network is utilized, the fault mode of the equipment is started, the operation history, the maintenance record and the environmental factors of the equipment are combined, and Bayesian network reasoning and dynamic fault tree analysis are carried out; and the source reason of the energy loss is positioned and sent to the terminal held by the person in charge. The method has the following effect that the energy efficiency management level of rail transit is effectively improved.

The invention discloses a wind turbine generator running state analysis method and system based on rotating speed monitoring, and relates to the technical field of wind power generation, and the method comprises the steps: collecting wind turbine generator data and historical data of the wind turbine generator data, and carrying out the preprocessing of the collected wind turbine generator data; performing rotation speed monitoring verification based on fault tree analysis, training a machine learning model in combination with historical data, and performing fault diagnosis by using a rotation speed monitoring verification result; and automatically generating a repair scheme based on the fault diagnosis result, executing the repair scheme, and recovering the normal working state of the wind turbine generator. According to the wind turbine generator running state analysis method based on rotating speed monitoring, data modeling is carried out by using the optimal characteristics of the wind turbine generator, the correlation among the characteristics is captured through the multi-dimensional Gaussian model, the fault probability of each component is calculated, the fault diagnosis precision is improved, and the fault diagnosis efficiency is improved. And the limitation of only depending on a single data source in the prior art is overcome.

The invention discloses a vehicle communication fault source determination method and related equipment, and relates to the technical field of vehicle communication, the method comprises the following steps: obtaining communication operation information of a plurality of processing levels in a vehicle communication system, the plurality of processing levels comprising a hardware layer, a middle layer, a security layer and a network layer; based on a preset log format, recording the communication operation information, and generating an encrypted log; and performing fault root cause analysis on the encrypted log through a preset fault tree model to obtain a target fault source. Through multi-level log acquisition and fault tree analysis, in combination with a structured format and encrypted storage, vehicle communication fault diagnosis with accurate and efficient fault positioning, data traceability, safety and credibility can be realized.

The invention discloses a power transmission and transformation equipment state evaluation method and system based on digital twinning. The method comprises the following steps: acquiring data of power transmission and transformation equipment and preprocessing the data; the LSTM algorithm model predicts the running state of the equipment; the fault tree analysis module evaluates possible fault types and fault probabilities; an interaction mechanism of the LSTM algorithm model and the fault tree analysis module is established, and when the LSTM algorithm model predicts that the operation state of the equipment is abnormal, related data and a prediction result are input into the fault tree analysis module, and a fault tree is guided to locate a fault; when the fault tree analysis module finds a potential fault risk, related information is fed back to the LSTM algorithm model; and performing weighted fusion on the LSTM algorithm prediction result and the fault tree analysis result according to the confidence of the LSTM algorithm prediction result and the fault tree analysis result to obtain an evaluation result. A model interaction mechanism is established, information sharing and cooperative work of the two are realized, and the capability of capturing the abnormal state and the potential fault risk of the equipment is enhanced.

The invention discloses an adaptive direct-current partition protection method based on a reconfigurable topology perception fault tree and an improved LSTM (Long Short Term Memory), which is suitable for fault diagnosis and partition protection of a direct-current system of a transformer substation. According to the algorithm, improved dynamic fault tree analysis and a long short-term memory (LSTM) network model are combined, and rapid positioning and selective isolation protection of direct-current system faults are achieved. The method comprises the following steps: firstly, constructing an updatable dynamic fault tree model based on fault historical data, and correcting a fault event weight in real time by adopting a Bayesian method to realize dynamic reconstruction of a fault propagation path; secondly, a multi-target particle swarm optimization algorithm is introduced, optimization is carried out between the minimum protection response time and the minimum isolation range, and an optimal partition scheme is generated; multi-dimensional time sequence characteristics such as fault current and bus voltage are extracted through a multi-channel LSTM model, and the fault development trend is predicted; and finally, dynamically adjusting a trigger threshold and a delay parameter of a protection action in combination with an online reinforcement learning strategy network of an Actor-Critic architecture. The method has the advantages of quick fault response, flexible partition, adaptive protection strategy and the like, and the safety and the stability of the direct current system can be remarkably improved.

The invention discloses a water and electricity intelligent management method and platform based on electric power prediction, and belongs to the technical field of water and electricity management, and the method specifically comprises the steps: collecting hydrological forecast data, electric power load data, equipment operation state data and historical scheduling records, building a unified data storage library, and guaranteeing the real-time updating and format standardization of various types of data; the method comprises the following steps: generating uncertainty quantitative indexes of each source by adopting a probability distribution model, error sequence analysis and fault tree analysis methods according to three sources of hydrological forecast errors, load prediction deviations and equipment fault probabilities; constructing a mapping model; according to the mapping result, establishing a risk grade evaluation system, and outputting the specific grade of each scheduling risk in real time; and according to the risk level, formulating a targeted scheduling strategy, executing the generated scheduling strategy, synchronously monitoring and predicting data change and risk level fluctuation, and immediately triggering dynamic adjustment of the scheduling strategy when an uncertainty index exceeds a threshold value.

The invention provides a port dangerous cargo risk assessment method and system based on a V-HAZOP model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining the feature information and additional information of port dangerous cargos, and building a risk assessment basic data set; preprocessing the risk assessment basic data set based on an HAZOP method, and establishing a risk data index library; analyzing the risk data index library based on a correlation analysis algorithm, and updating the risk data index library; obtaining final risk index data on the basis of a Bayesian network conversion method based on the fault tree analysis model; performing visual data modeling according to the final risk index data, and constructing a V-HAZOP model; displaying the V-HAZOP model by using a VIEW-Glass multi-dimensional visualization technology so as to obtain data; according to the invention, the management difficulties of various types of dangerous goods, different physicochemical properties, high operation requirements and the like in the existing port can be solved, the safety production operation of the dangerous goods storage yard is optimized comprehensively, and the professional storage yard intelligent operation management of the dangerous goods in the port is realized.

The invention discloses a construction whole-process carbon emission metering method based on multi-source data fusion and dynamic feedback correction. The method comprises the steps that multi-source data such as construction equipment operation, building material logistics and environmental parameters are collected through Internet of Things equipment and preprocessed; using the improved neural network model to classify the associated data, and outputting the comprehensive energy consumption and carbon emission potential of the construction link; a metering model covering transportation, equipment and personnel activities is constructed, and link calculation is achieved in combination with dynamic parameters; and through real-time CO2 monitoring and comparison with a predicted value, analyzing a positioning deviation and correcting the model based on a fault tree. Through multi-source data fusion and dynamic feedback correction architecture, total element coverage and real-time self-adaptive correction of carbon emission in each construction stage are realized, the metering precision is improved, a construction unit is supported to quickly identify a high-carbon link and optimize the process, and the method is high in compatibility, can adapt to different construction projects and has remarkable engineering application value.

The invention discloses an operation and maintenance fault positioning method and device based on a large model, equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: if a system fault is monitored, collecting current system operation and maintenance data in real time, and carrying out the preprocessing of the current system operation and maintenance data, so as to obtain target preprocessed system operation and maintenance data; extracting data features corresponding to the system operation and maintenance data after target preprocessing, inputting the data features into a preset large model, and analyzing the data features through the preset large model to obtain a fault analysis result; and constructing a fault tree corresponding to the fault analysis result based on a fault tree analysis method, determining a fault reason of the system fault through the fault tree, and analyzing the fault reason to complete fault positioning. Therefore, when a fault occurs, various information related to the fault can be rapidly collected, the root cause of the fault can be rapidly and accurately positioned, the troubleshooting time is shortened, and the fault processing efficiency is improved.

The invention discloses a power equipment operation intelligent monitoring analysis method based on artificial intelligence, and relates to the technical field of power system operation and maintenance. Equipment operation parameters, equipment maintenance and operation records, equipment appearance state data, vibration waveform data and sound characteristic data are covered. According to the power equipment operation intelligent monitoring analysis method based on artificial intelligence, by implementing a multi-stage fault prediction scheme, the fault development stages are divided, the feature threshold value of each stage is determined, the change rate of the real-time fusion feature and the equipment load are combined, and the power equipment operation intelligent monitoring analysis method based on artificial intelligence is realized. Predicting the remaining time of the fault developing to the critical stage through time sequence analysis; meanwhile, relying on a part association relationship in the digital simulation model, an improved fault tree analysis method is adopted to trace direct and indirect inducements, and a visual traceability map is generated; and fault prediction is changed from post-event identification to pre-event early warning, so that non-planned shutdown is effectively avoided.

The invention discloses a hydropower station GIL equipment insulation state detection and evaluation method based on an industrial internet platform, and the method comprises the steps: collecting the characteristic parameters of voltage, current, temperature and the like through an online monitoring system, a live detection device or a power failure high-voltage test, and generating an original characteristic set; through photoelectric conversion, filtering and other processing, key features are extracted to generate a structured feature set, and the structured feature set is serialized and uploaded to a data center. A phase resolution graph and a dielectric spectrogram are calculated by utilizing technologies such as partial discharge analysis and a knowledge graph, fault diagnosis is carried out, and information such as fault positions and types is determined. And based on a diagnosis result, evaluating an insulation state risk through methods such as fault mode and influence analysis and fault tree analysis, quantifying defects, safety and economic risks, and outputting an alarm level and a processing suggestion. And if the risk is triggered, performing convolutional neural network analysis on the specific signal to update the fault position, and finally generating a diagnosis report according to the fault influence range, the diagnosis result and the alarm level, thereby realizing comprehensive monitoring and accurate evaluation of the insulation state of the GIL equipment.

The invention provides a new energy automobile high-voltage accident grading response and emergency rescue operation guiding method and system, and is applied to the technical field of data processing. Multi-source data of voltage, current and battery states of a high-voltage system are collected, a standardized association sequence is generated after denoising, then the standardized association sequence is converted into a three-dimensional risk map, electric leakage, overvoltage and thermal runaway scenes are analyzed and subdivided through a fault tree, and a classified accident grading model is constructed. Extracting hazard weight factors to establish a dynamic evaluation matrix, and cooperatively calculating multi-source data to obtain key response indexes; parameters are extracted according to accident stages, response levels are divided, and a feature matrix is generated; real-time data and historical data are compared, and correction factors are generated by means of the accident curve slope. And in combination with vehicle types, architecture and environment grouping, key factors are screened by using a support vector machine, a personalized rescue model is established by fusing multiple constraints, and finally, a real-time graded response instruction and scene-divided rescue guidance are output.

The invention relates to a bank network system stability evaluation method and system, and the method comprises the steps: analyzing a top event of a bank network system based on a fault tree analysis method, and determining a plurality of basic factors affecting the stability of the bank network system to construct a fault tree; wherein the top event is an event related to service interruption of the bank network system, and the plurality of basic factors include risk factors affecting all aspects of stability of the bank network system; analyzing the fault tree based on a similarity aggregation method to determine the failure probability of the plurality of basic factors; analyzing a plurality of basic factors of the fault tree and mutual influence and dependency among the plurality of basic factors based on a network analysis method (ANP); and based on an ANP analysis result and the failure probabilities of the plurality of basic factors, determining a key factor sequence of the top event.

According to the improved FMEA method based on the interval type-2 fuzzy set and the DEMATEL-VIKOR mixed multi-criterion decision, on the basis of traditional FMEA, a more perfect IMS key hardware risk assessment model is constructed by improving related methods. A related failure mode is obtained through a fault tree analysis method. According to the invention, the interval type-2 fuzzy set and the FMEA are combined to solve the problem of inaccurate RPN value in the traditional FMEA; calculating comprehensive weights of experts by utilizing a similarity aggregation method and combining personal backgrounds of the experts; in the aspect of risk factors, a combined weight method is adopted, so that the analysis precision can be improved, and the contribution of each factor to the overall risk can be evaluated more comprehensively by comprehensively considering the weights of a plurality of indexes; the DEMATEL-VIKOR hybrid multi-criterion decision-making method for preference relationship analysis fully considers the relevance between factors and the comprehensive influence of multiple criteria, and provides relatively comprehensive and visual decision-making support.

The invention discloses a distributed photovoltaic power station intelligent operation and maintenance method based on fault tree analysis. The method comprises the following steps: S1, constructing a distributed photovoltaic power station fault tree model; s2, collecting operation data of the distributed photovoltaic power station in real time; s3, calculating the occurrence probability of each bottom event of the fault tree according to the collected operation data by using a fault tree analysis method; s4, identifying key fault factors according to the bottom event occurrence probability; and S5, making an intelligent operation and maintenance strategy according to the key fault factors. According to the distributed photovoltaic power station intelligent operation and maintenance method based on fault tree analysis, key fault factors are accurately identified, the operation and maintenance strategy is dynamically adjusted, the fault positioning and repairing efficiency is improved, the operation and maintenance cost is reduced, and the distributed photovoltaic power station is promoted to be upgraded to active intelligent operation and maintenance.

The invention discloses a medical equipment remote fault diagnosis, maintenance and guidance system based on a cloud platform. According to the invention, the deep reconstruction of the medical equipment maintenance ecology is realized by constructing the digital service network covering the whole process. The AR cooperation platform of the maintenance engineer end and the real-time data intercommunication of the hospital end break through the space-time limitation of technical support in a traditional maintenance mode, so that remote experts can directly participate in field fault diagnosis and cooperate with historical maintenance archives and fault tree analysis provided by the intelligent work order system, and the first repair rate is greatly improved. An intelligent order sending algorithm of the management background is combined with an order grabbing mechanism of the mobile terminal, so that the geographical distribution efficiency of engineer resources is optimized, precise docking is realized through skill label matching, the industrial pain point of scarcity of special equipment maintenance resources is solved, and the service life of the mobile terminal is prolonged. Modular design and data interconnection characteristics bring systematic improvement to medical equipment operation and maintenance management, and fine control of equipment management cost and continuous improvement of service quality are realized.

The invention relates to the technical field of chemical production safety, in particular to a chemical production safety risk intelligent reasoning method and system and a storage medium, and the method comprises the steps: building a multi-level flow model according to a chemical production flow chart, and constructing a fault tree analysis qualitative model for the multi-level flow model through a danger and operability analysis technology; fusing and correcting the multiple groups of evidence sources according to the D-S evidence theory to obtain the initial abnormal probability of each basic event; and establishing a dynamic Bayesian network quantitative model in combination with a logic gate rule, and performing forward reasoning and backward reasoning. According to the invention, forward reasoning and backward reasoning can be carried out on abnormal event nodes in the chemical production process, causal traceability is completed, an abnormal propagation path is explored, and an effective management means is provided for safe production in the chemical process.

The embodiment of the invention provides an intelligent coal bunker safety protection system and method. The device comprises the steps that sensing parameters are detected; wherein the sensing parameters comprise contact type temperature measurement data, infrared thermal imaging data and coal seam three-dimensional point cloud data; the contact type temperature measurement data and the infrared thermal imaging data are fused, and a three-dimensional temperature gradient model of the coal bunker is established; inverting a smoke traceability path based on a computational fluid mechanics algorithm; calculating a coal pile slippage risk coefficient according to the coal seam three-dimensional point cloud data; a multistage threshold triggering mechanism associated with a temperature gradient extreme value, an inversion smoke traceability path and a coal pile slippage risk coefficient is configured; if the multi-level threshold triggering mechanism is triggered, an emergency disposal scheme based on fault tree analysis is called, and a corresponding emergency device is controlled to operate; real-time and comprehensive monitoring of parameters of the coal bunker is realized, the prediction precision is high, potential safety hazards are found in time, and safe operation of a coal yard is guaranteed.

The invention provides a shelter equipment remote power supply monitoring method and system based on the Internet of Things. The method comprises the following steps: monitoring each electric device in real time, and sending monitoring data to a cloud server to obtain real-time monitoring data; generating an accurate power demand prediction report according to the real-time monitoring data; based on the power demand prediction report, when power supply and demand imbalance is predicted, starting an optimization scheduling strategy based on a particle swarm optimization algorithm, dynamically adjusting the output power of a generator set or the working schedule of electric equipment, and evaluating potential risk points by using a fault tree analysis technology; generating an emergency response plan and an optimal scheduling result; and carrying out automatic management on a power resource shortage condition based on the optimal scheduling result, ensuring normal operation of high-priority equipment, and generating a power management decision suggestion. According to the technical scheme provided by the invention, the flexibility of power supply monitoring can be improved.

The invention relates to the technical field of automotive electronics and intelligent control, and discloses a domain controller failure mode judgment and redundancy control method and system, and the method comprises the steps: collecting the operation state information of a chassis domain controller; a failure tree analysis method is adopted, a failure mode library of the chassis domain controller is constructed according to the operation state information, failure modes are classified, and the failure mode library comprises controller failures, communication link anomalies, sensor failures and power supply anomalies; in combination with real-time monitoring data, the health state is judged by utilizing a machine learning algorithm, the fault mode is predicted, and the severity of the fault mode is determined; according to the method, the corresponding redundancy control strategy is determined according to the severity of the fault mode, the redundancy control strategy is used for redundancy control, the redundancy control is carried out according to the severity of different fault modes, continuous and stable operation of key chassis functions is ensured, and the safety and reliability of the chassis domain controller are improved.

The invention belongs to the technical field of multi-level RCM, and discloses a multi-level RCM analysis and reliability structure construction method and a related device. The multi-level RCM analysis and reliability structure construction method comprises the following steps: establishing a unit system function model after constructing a unit system database, performing multi-level classification on equipment of a unit system in combination with a fuzzy clustering algorithm, and then determining levels and weights of various types of equipment in the unit system function model, analyzing a failure mode of each type of equipment after multi-level classification through a harmfulness analysis method in combination with a fault tree analysis technology, establishing a correlation model between the equipment failure mode and a unit system function, determining an influence weight of the equipment failure mode on the reliability of the unit system, and establishing a unit system reliability model; inputting the unit system database into the unit system reliability model to obtain a unit system maintenance strategy library; the analysis result accuracy and the system reliability can be greatly improved.

The application discloses an embedded control system fault self-recovery method and system, and belongs to the technical field of embedded systems. The method comprises the following steps: setting and dynamically adjusting a fault threshold value based on historical data and current working conditions; triggering a pre-warning when a real-time parameter exceeds the dynamic threshold value, and confirming by comparing main and redundant sensor data to distinguish between sensor abnormalities and real equipment faults; intelligently diagnosing based on a knowledge base using methods such as fault tree analysis to determine the fault type, location and severity; the system matches candidate self-recovery strategies from a strategy library, selects and executes the optimal strategy by calculating the value and execution priority of each strategy, and completes self-recovery control through software and hardware execution mechanisms. The application significantly improves the accuracy and reliability of fault detection through dynamic fault threshold values and redundant data confirmation, effectively avoiding false positives; precise positioning of faults and optimal self-recovery strategy selection are achieved.

The invention relates to the technical field of equipment management, and discloses an intelligent equipment state monitoring and operation and maintenance management system based on the Internet of Things, which comprises a system module assembly, and the system module assembly comprises an edge data acquisition unit, a fault tree analysis engine, an edge intelligent reasoning node, a distributed collaborative analysis platform and a cloud edge collaborative operation and maintenance center. The edge data is deployed in a sensor network of an equipment end, collects operation data in real time, supports multi-protocol access and adapts to heterogeneous equipment, and the fault tree analysis engine constructs a dynamic fault tree model based on historical fault data, optimizes an event association rule through machine learning, calculates the probability of a top event in real time, and analyzes the fault data in real time. And risk levels are divided. According to the method, a dynamic fault tree evolution mechanism is adopted, a cooperative fault is combined to predict the equipment degradation trend, the fault tree structure is adaptively adjusted, and the multi-modal data is fused with the vibration signal, the infrared thermal imaging and the current harmonic data to improve the diagnosis precision.

The invention belongs to the technical field of fault detection and recovery, and particularly relates to a fault self-diagnosis and self-adaptive recovery method for an embedded controller, which comprises the following steps of: starting a fault self-diagnosis process by adopting a periodic triggering mode and an event triggering mode, and acquiring, processing and generating a diagnosis data source with time sequence characteristics in real time; step-by-step anomaly recognition is carried out through a three-level anomaly recognition mechanism, an anomaly feature set is extracted, cooperative fault positioning is carried out in combination with a weighted cosine similarity algorithm and fault tree analysis, fault types and severity levels are judged, and a fault positioning report is generated; the optimal self-adaptive recovery strategy is matched to carry out preliminary recovery, the strategy is divided into light, medium and deep levels according to serious levels and is provided with priorities, and the scheme with the minimum influence on the core service is preferentially selected; the recovery effect is verified through multi-dimensional comparison, and processing is carried out according to the recovery effect. According to the method, the real-time accurate diagnosis, intelligent positioning and self-adaptive recovery of the fault of the embedded controller are realized, and the reliability and self-healing capability of the system are improved.

The invention discloses an equipment failure risk analysis method based on a probability hesitant fuzzy evidence theory, and relates to the field of failure risk analysis, and the method comprises the steps: S1, building a fault tree structure, and constructing a reference failure rate set of basic events in a fault tree; s2, acquiring evaluation data of an expert on a reference failure rate set of the basic event, and obtaining an adjustment score and a tendency degree of the basic event; s3, converting the adjustment score into an evaluation probability by using a score conversion rule; s4, establishing a qualification weight according to the qualification of the expert; quantizing and adjusting the uncertainty of the score and the tendency degree to obtain an evaluation uncertainty weight, and carrying out weighted summation on the two weights to synthesize a total weight; s5, based on the evaluation probability and the total weight, synthesizing a final evaluation probability through a Dempster-Shafer evidence theory; and S6, performing fault tree analysis based on the final evaluation probability. According to the method, the probability hesitant fuzzy set and the Dempster-Shafer evidence theory are introduced, the diversity and uncertainty of expert evaluation are reserved, and conflicts and consistency between evidences are effectively processed.

The invention provides a soft start cooperative control method and system for a large industrial air conditioning unit, and the method comprises the steps: a central cooperative controller collects the operation parameters and start dependence conditions of all parts through a high-speed real-time communication network, builds a fault feature database, and carries out the real-time recognition of an abnormal signal; a fault tree analysis module is adopted, faults are divided into three levels according to the types and severity of the faults, if communication interruption occurs, a hardware watchdog circuit of a safety protection unit executes emergency shutdown within 500 ms, out-of-control is prevented, and the method relates to the technical field of large-scale industrial air conditioning unit control. The fault grade and the influence range are dynamically evaluated, rapid positioning and collaborative response of faults are achieved, fault spreading is avoided, the safety and reliability of the large industrial air conditioning unit starting process are improved, the problems of power grid impact and system reliability during multi-device starting are solved, and the method is suitable for large air conditioning units of data centers, factories and the like.

The invention discloses an intelligent detection method, system and device for a core control board of a distribution network hot-line work robot and a storage medium, and relates to the technical field of detection of the core control board of the distribution network hot-line work robot, and the method comprises the steps: building a key grade evaluation model based on the functional characteristics of each assembly of the core control board, and executing a dynamic detection process according to a grading result; an exception handling engine is built by combining a state machine model, and the equipment state is monitored in real time; the root cause of equipment state data before abnormality is analyzed and positioned through a fault tree, fault information is recorded by adopting an enhanced watchdog mechanism, and the waiting time length of function detection is dynamically adjusted; and dynamically adjusting the waiting time of function detection based on the real-time monitoring of the response states of the control panel and the equipment. According to the method, the detection efficiency and reliability are improved, and the large-scale deployment of the distribution network hot-line work robot is met.

This invention relates to a method and system for decision-making on retaining wall deformation control measures based on fault tree analysis. The method includes the following steps: using foundation pit monitoring data as input parameters for a retaining wall maximum deformation prediction model, and obtaining the predicted maximum deformation value of the retaining wall through the model; acquiring the retaining wall deformation in real time using the foundation pit monitoring data; when the retaining wall deformation exceeds the predicted maximum deformation value, making a deformation control scheme decision based on the foundation pit monitoring data and the retaining wall deformation control measure decision-making model, thereby obtaining the retaining wall deformation control measures. Compared with existing technologies, this invention achieves both the prediction of retaining wall deformation and the decision-making on retaining wall deformation control measures, improving construction efficiency.

The present invention belongs to the technical field of aviation airborne system safety analysis, and specifically relates to a method and system for generating FMES data for airborne system safety analysis. The method comprises: S1: acquiring FMEA data of the airborne system, performing data processing, and extracting data features using the BERT model; S2: using sinusoidal similarity to measure the FEMA structured data feature vectors and calculate the similarity of the FEMA structured data features; S3: obtaining the FEMA structured data feature similarity matrix and performing a first agglomerative hierarchical clustering analysis; S4: performing a second clustering analysis on the tree-like clusters of the FEMA structured data features based on fault tree analysis (FTA); and S5: generating structured Failure Mode and Effect Summary (FMES) data based on the second clustering results. The present invention proposes a complete framework for automatically generating FMES data, and improves the quality of the automatic generation through secondary clustering.