1737 results about "System failure" patented technology

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention discloses a server cooperative control method, a storage medium and electronic equipment, and relates to the technical field of firmware, and the method comprises the steps: employing a programmable logic device to actively take over the control authority of target equipment based on the operation parameters of a sensing device in a server starting stage, stable operation of equipment before initialization of the substrate management control device is not completed is ensured, a mechanism that a preset communication link and the substrate management control device are subjected to interactive verification is adopted, and the functional integrity and communication reliability of the substrate management control device during permission transfer are ensured through bidirectional communication verification. A dynamic permission recovery strategy based on real-time anomaly detection is adopted, control right reverse switching is triggered through a sensing device and communication link state dual monitoring mechanism, and the purpose of actively taking over equipment management responsibilities when a substrate management control device runs abnormally is achieved. Therefore, double improvement of system fault self-healing capability and equipment control continuity is realized, and the problem of difficulty in cooperative control among heterogeneous units is solved.

The invention relates to the technical field of power system fault monitoring, and discloses a 10kV voltage transformer ground fault real-time monitoring method and system, and the system comprises a ground fault data collection module, a waveform distortion analysis module, a transient signal detection module and the like. Three-phase current, zero-sequence current and voltage waveform data are collected, a current harmonic component proportion, a zero-sequence current high-frequency transient energy value and the like are analyzed, a fault type is determined by combining a phase difference, and a fault position is positioned according to topological parameters and line impedance. And the temperature of the grounding down lead can be monitored, and the fault probability is evaluated and the threshold value is dynamically adjusted by machine learning. According to the invention, real-time accurate monitoring can be realized, fault types and positions can be accurately diagnosed, potential hidden dangers can be comprehensively monitored, the intelligence of a monitoring system is improved, the operation and maintenance cost and risks are effectively reduced, and safe and stable operation of the 10kV power distribution network is ensured.

The present invention relates to the field of fault diagnosis technology, specifically to a fault diagnosis method and system for a photovoltaic inverter, comprising the following steps: based on an operating status data set, analyzing a variety of inverter operating data, identifying the volatility of a variety of operating data, extracting statistical features and frequency domain features of the data, adjusting the detection threshold of abnormal data, and generating abnormal fluctuation detection parameters. In the present invention, the abnormal detection threshold is dynamically adjusted by analyzing the operating data, a more sensitive response is made to the data, the accuracy and response speed of the fault warning are improved, the time window segmentation and signal feature extraction are used to timely discover abnormal fluctuations, detect equipment damage or system failures, and the accuracy of fault type identification is improved by combining the comparison and analysis of data features. By analyzing the component types associated with various fault types, the efficiency and accuracy of locating faulty components are improved, and the reliability and economic benefits of the photovoltaic inverter are enhanced.

The invention provides a distributed system fault positioning diagnosis method and system based on log analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting call data between micro-service nodes, constructing a service dependency graph, and calculating the influence weight of each node as a fault source; and modeling the service dependency relationship graph by using a graph convolutional neural network, and constructing a fault propagation prediction model. And when an abnormal node is detected, inputting the node into the prediction model, calculating a fault propagation path and an influence range, outputting an influenced service node list and an influence degree thereof, carrying out risk grade division and alarm, and meanwhile, visually displaying the fault propagation path. According to the method, the fault source can be quickly and accurately positioned, the fault propagation influence is predicted, and the reliability and stability of the distributed system are improved.

The invention provides a DCS system autonomous controllable upgrading and system fault prediction method and system, and relates to the technical field of fault prediction, and the method comprises the steps: obtaining historical operation data and real-time operation data of a DCS system, carrying out the preprocessing, and generating an autonomous controllable upgrading scheme through a deep reinforcement learning algorithm; the method comprises the following steps of: setting specified time for upgrading time, carrying out consistent backup on the state of the DCS, executing software updating, hardware configuration adjustment and system parameter optimization according to upgrading steps, analyzing indexes of the DCS in real time in the process of executing the autonomous controllable upgrading scheme, and recovering the DCS to the state before upgrading when an abnormal condition is detected. And verifying and optimizing the real-time fault prediction result based on a knowledge graph, and generating an explanatory report including a fault prediction and upgrade decision process according to the optimized fault prediction result and the autonomous controllable upgrade scheme.

The invention discloses a fault detection and root cause analysis system for a multi-source energy system, and relates to the technical field of energy management and fault diagnosis. The system obtains various operation data and image data of the multi-source energy system in real time; preprocessing the operation data and the image data; inputting the preprocessed data into a hybrid algorithm combining wavelet transform, CNN and LSTM, and detecting an abnormal mode and a potential fault in the multi-source energy system; the digital twinborn model is dynamically updated according to data acquired in real time; and in combination with the feature extraction capability of a digital twin model and a hybrid algorithm, positioning key causes causing the fault, simulating a system operation state, analyzing potential causes of the fault, and outputting an analysis result. According to the invention, the comprehensive analysis capability of the system on various data sources is effectively improved, and a comprehensive and accurate solution is provided for intelligent operation and maintenance of the energy system.

The invention provides an integrated test scene prediction method and system based on multi-dimensional data, and the method comprises the steps: carrying out the multi-source data collection of an integrated test scene based on a dynamic sampling strategy, obtaining integrated test multi-dimensional data, carrying out the multi-dimensional data preprocessing of the integrated test multi-dimensional data, and obtaining a pre-trained multi-dimensional data analysis model; inputting the pre-processed integrated test multi-dimensional data into the multi-dimensional data analysis model to carry out sub-module data analysis, obtaining a pre-trained intelligent regulation and control model, importing a system diagnosis report and a key index deviation degree report into the intelligent regulation and control model as inputs, generating an intelligent regulation and control decision, and carrying out intelligent regulation and control. By means of the method, system faults, performance bottlenecks and potential problems occurring in the integrated test scene can be accurately predicted, intelligent regulation and control decisions are provided for solving the problems, and accurate system performance evaluation and optimization services can be provided for users.

The invention relates to the field of power systems, in particular to a power system fault prediction and self-healing control method and system. The method comprises the steps of monitoring electrical quantity of a power system in real time to obtain a monitoring data set; predicting potential fault points and fault types based on the monitoring data set; carrying out key monitoring on the area where the potential fault point is located; once abnormity or fault signs are found in key monitoring, fault diagnosis is carried out, the fault position is determined, according to a fault diagnosis report, a fault area is isolated, an instruction is generated and controlled, power supply of a non-fault area is automatically recovered, and a recovery operation log is recorded; a fault recovery strategy is automatically generated, and a control instruction is updated; and adjusting the operation of the power system according to the updated control instruction, and monitoring the operation state of the power system. According to the invention, the accuracy of fault prediction and the self-healing response speed are improved.

Systems and methods for unsupervised multi-modal causal structure learning for root cause analysis. System logs of a cloud system can be transformed to time-series data using a log-tailored language model to obtain system log features of the cloud system. A metric causal graph and a log causal graph can be predicted from modality-specific representations and modality-invariant representations of extracted system metric features and system log features, respectively, using the deep neural network. The metric causal graph and log causal graph can be fused to obtain a fused causal graph. Root causes of system failure can be flagged for system maintenance based on ranked entities obtained from the fused causal graph to obtain flagged root causes. System maintenance can be performed autonomously based on the flagged root causes from identified system entities to optimize the cloud system with an updated configuration.

The invention discloses a service operation and maintenance method and system based on a cloud service framework, and relates to the technical field of cloud computing, and the method comprises the steps: collecting and formatting various link flow data, and carrying out the transverse network performance monitoring and topology generation; acquiring performance index data of the micro-service, and performing multi-dimensional anomaly detection and analysis; and through fault correlation analysis and positioning, automatic repair is carried out. The method provided by the invention is helpful for quickly identifying network bottlenecks and potential safety problems, provides accurate basic data for transverse network performance monitoring and topology generation, optimizes early warning and quick positioning performance, effectively reduces the influence of system faults on services, realizes intelligentization and automation of fault processing, and improves the working efficiency. The fault processing time is shortened, the operation and maintenance workload is reduced, and the stability and reliability of the system are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention relates to the technical field of power monitoring, in particular to a station power utilization system safety and energy efficiency monitoring system based on multi-source information fusion, which comprises a signal analysis module, an anomaly identification module, a risk assessment module and an emergency response module. According to the method, the fineness and the response speed of power station monitoring are improved by integrating wavelet transformation and information entropy analysis, the time-frequency characteristics of signals are deeply analyzed through wavelet transformation, the capturing capacity for short-term and long-term changes is improved, the integrity and the accuracy of data analysis are further ensured, and the accuracy of power station monitoring is improved. The information entropy is introduced to assess the stability and uncertainty of the power station, a scientific basis is provided for predicting and recognizing potential risks of the system, and by combining real-time anomaly recognition and dynamic risk assessment, equipment faults and system failures can be effectively predicted, and energy consumption is optimized, so that early warning and prevention and control are performed in advance, the downtime is remarkably shortened, and the system reliability is improved. The maintenance cost is reduced, and the level and effect of energy efficiency management are improved.

The invention provides an AI-based servo driver fault on-line rapid diagnosis method and system, and the method achieves the quantitative evaluation of fault data through obtaining the candidate system fault monitoring data of a servo driver control system, and converting the candidate system fault monitoring data into a diagnosis difficulty index and a fault description knowledge attribute. When the diagnosis difficulty index exceeds the preset index and the fault data lacks dynamic change information, the method can further utilize a plurality of fault penetration derivation data and the associated fault knowledge networks matched with the data, and effectively identifies and integrates similar fault modes by calculating the state feature distance between the associated fault knowledge networks, thereby improving the fault diagnosis accuracy. Compared with the prior art, the method has the advantages that the accuracy and efficiency of fault diagnosis are remarkably improved, particularly in a complex and changeable fault scene, the fault reason can be quickly positioned, and data support is provided for instant maintenance and recovery of the servo driver, so that the downtime of a system is shortened, and the overall operation reliability and stability of equipment are improved.

In an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the computer system interconnected with a telecommunications system, the method comprising: receiving, at the computer system, data relating to operation of the telecommunication system, obtaining, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunication system, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications;, executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system, and automatically correcting at least some of the detected faults.

An information handling system includes first and second storages, a basic input / output system (BIOS), and an embedded controller. The first storage stores telemetry data associated with the information handling system. The second storage includes a boot partition. The BIOS stores the telemetry data in the first storage. The embedded controller receives the telemetry data stored in the first storage from the BIOS. The embedded controller provides the telemetry data to the boot partition of the second storage over an out-of-band communication channel.

The invention discloses a train control method and a train control system based on credible interval entry check. The train control method comprises the following steps: starting a backup command; the temporary speed limit server starts a sending module used for sending authorization to a train self-protection system; performing interval credible entry check; determining the existence condition of the train in the interval; the temporary speed limit server updates a train existence condition list in the interval; the train obtains a front driving permission; the train runs according to the running permission. According to the train control method and the train control system based on the credible interval entry check, the axle counting equipment is arranged at the interval entrance, software of the temporary speed limit server only needs to adjust individual modules, and the software of the temporary speed limit server can be only used for recording the train condition in the interval and is not used for authorized calculation, so that the train control method and the train control system are convenient to use. The deployment cost of the temporary speed limit server is reduced, the transportation efficiency under the condition of a CTCS-2 system fault is improved, and the toughness of the CTCS-2 system is improved.

The invention relates to the technical field of servers, and particularly discloses a monitoring system, method, device and equipment for a basic input and output system, and a medium. The starting state of the first basic input and output system is obtained after the first controller is powered on, and if it is recognized that the actual starting time exceeds the first starting time corresponding to the starting stage in the starting stage of the first basic input and output system, it is determined that starting of the first basic input and output system fails. And the first change-over switch is controlled to switch the first basic input / output system to the second basic input / output system, so that the first basic input / output system can be switched to the second basic input / output system in time when the first basic input / output system fails, the time for judging the starting timeout of the basic input / output system is shortened, and the starting speed of the server is improved.

The invention provides a cloud native system fault root cause positioning method and device, and the method comprises the steps: determining the abnormal performance index data of each instance and a server based on the micro-service instance of each target micro-service and the performance index abnormal score of the server, so as to construct the nodes corresponding to each micro-service instance and the server, according to a fault propagation direction between the abnormal performance index data, constructing an edge between the nodes to obtain an index-level cause and effect graph; normalizing the performance index anomaly score to obtain a target anomaly score of each node, and constructing a transition probability matrix; and obtaining the access frequency of each node in the causal graph by using a random walk algorithm so as to obtain fault root cause positioning result data of the cloud native system. According to the invention, the automation degree, efficiency and accuracy of fault root cause positioning of the cloud native system can be effectively improved, the efficiency and reliability of fault early warning and recovery of the cloud native system can be effectively improved, and the operation stability of the cloud native system can be improved.

The invention discloses a health evaluation method, electronic equipment, a storage medium and a product, and relates to the technical field of computers, and the method comprises the steps: based on time sequence index data and log data in a to-be-evaluated system, respectively determining a monitoring index deviation degree corresponding to the time sequence index data and a log abnormal probability corresponding to the log data; based on the monitoring index deviation degree and the log abnormal probability, determining an index fusion weight corresponding to the monitoring index deviation degree and a log fusion weight corresponding to the log abnormal probability; and according to the monitoring index deviation degree, the log abnormal probability, the index fusion weight and the log fusion weight, in combination with a health degree scoring function, determining a health degree score of the to-be-evaluated system, so as to execute a recovery operation on the to-be-evaluated system according to the health degree score. The system fault can be detected and evaluated more accurately, the recovery strategy can be dynamically adjusted according to different fault severity degrees and actual conditions of the system, and the recovery efficiency and fault-tolerant capability of the system are remarkably improved.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention discloses a maglev train suspension system electromagnet fault detection algorithm, which comprises the following steps: establishing a suspension system model of a maglev train, and designing a state observer; an electromagnet signal is collected and processed, and a residual value is obtained; the residual value is compared with a threshold value, and fault information of the electromagnet of the maglev train suspension system is obtained; and fault information of the electromagnet is reconstructed. The fault of the electromagnet can be effectively detected and reconstructed by constructing the third-order linear extended state observer (LSEO), system uncertainty, noise and the like can be dealt with through the self-adaption and self-calibration functions of the third-order linear extended state observer, robustness is enhanced, and detection reliability is improved. Fault information of the magnetic levitation system can be quantitatively estimated through LSEO for fault reconstruction, the fault type and position can be accurately identified, and false alarm and missing alarm can be avoided. Meanwhile, fault reconstruction provides an important basis for maintenance, and maintenance personnel can quickly position and take targeted measures, so that the maintenance efficiency is improved, and the cost is reduced.

The invention relates to a context-driven fault solution suggestion generation method, which specifically comprises the following steps of: firstly, collecting various log data generated in system operation, and preprocessing the data as a basis for subsequent analysis; then, a BERTomaly model based on a multi-head cross attention mechanism is constructed, feature extraction, feature fusion and template matching can be performed on the log data, anomaly detection of system faults can be realized, and then, based on the system operation log data, the system fault detection efficiency is improved. The method comprises the following steps of: firstly, constructing a fault solution library which is of a context-driven type and has a dynamic updating capability by using a large language model in combination with the output of a BERTomally model and the output of an FP-Growth association analysis algorithm, and carrying out real-time detection on each fault, so that the fault solution library has a dynamic updating capability. And a targeted solution is output.

An monitoring method for a passenger transportation system embodied as an elevator, escalator, or moving walk applied in a monitoring device is provided, and it relates to the technical field of elevators, escalators and moving walks. The monitoring device includes a plurality of information processing modules. The monitoring method includes: collecting a plurality of operating parameters of the system; executing a first preset operation by at least one of the information processing modules to store the operating parameters that are currently collected, wherein there is at least one different operating parameter that is stored between any two ones of the information processing modules; polling the information processing modules and determining at least one exceptional operating parameter and its exception time information based on polling results to obtain system monitoring data; and publishing the system monitoring data to a message forwarding server end, wherein the system monitoring data is configured to enable a system fault predicting device to predict a system fault according to the system monitoring data based on a support vector machine. A monitoring device, a fault predicting method and device, equipment, medium and program product are also provided.

The embodiment of the invention provides an energy storage system fault processing method and device, equipment, a medium and a product, and relates to the field of photovoltaic power generation, the method solves the problem of low fault processing efficiency in the prior art by constructing a cloud-side-end cooperative fault processing architecture, changes the traditional static threshold judgment logic, and improves the fault processing efficiency. A decision-making mode combining multi-source information fusion and dynamic prediction is adopted, firstly, preliminary diagnosis of real-time data is achieved through edge calculation, the instantaneity of response is ensured, and then historical trend analysis, equipment health prediction and multi-dimensional real-time state evaluation are fused through a cloud platform. According to the method, the dynamic fault evaluation capable of accurately reflecting the actual severity and development trend of the fault is generated, so that the system can be adaptive to equipment aging and environment change, and a grading response strategy accurately matched with the fault grade is triggered, thereby realizing the crossing from passive alarm to active and accurate fault management and control, and improving the fault processing efficiency.

The invention relates to the technical field of new energy power generation monitoring, in particular to a new energy wind power system fault detection method, system and device based on multi-parameter fusion and a medium. The method comprises the following steps: acquiring various operating parameters such as wind speed, wind direction, generator speed, temperature, voltage and current of a wind power system in real time, and performing feature extraction on the operating parameters to obtain key features related to a fault; analyzing the key features based on a deep learning algorithm, and judging the fault type of the wind power system; and outputting a fault diagnosis result in a visual mode, and sending the fault diagnosis result to a control center for remote monitoring and fault early warning. The technical problems that fault detection of a traditional wind power system is poor in real-time performance, not high in accuracy, low in intelligent degree and the like are solved, and reliable guarantee is provided for safe and stable operation of the wind power system.

The invention relates to the technical field of computer system structures and storage, in particular to a non-volatile memory quick recovery method based on metadata priority and on-demand loading. The method comprises the following steps: in response to a system fault signal detected by a voltage monitoring unit, freezing a processor context and traversing a page table structure to extract system configuration information; writing the system configuration information and business data codes in the volatile memory into a nonvolatile medium to generate a persistent state mirror image; analyzing the persistent state mirror image, extracting address conversion metadata, and reconstructing a mapping relation from a virtual address to a physical page frame in a volatile memory; and generating an address mapping table, wherein the physical page frame pointed by the address mapping table is set to be in an existing state but is not associated with the effective service data. According to the method, decoupling of the control flow and the data flow is realized by constructing a virtual ready state, and quick starting of the system and immediate response of key services are realized on the premise of not depending on the total capacity of a memory.

The invention discloses an IT system fault diagnosis method and system based on a large language model, and the method comprises the steps: carrying out the vectorization of a troubleshooting guide TSG through the LLM technology, generating a corresponding TSG vector, and defining an automatic troubleshooting process according to the TSG; according to the received alarm event information, determining an associated IT component and an occurring alarm event by using an IT topological graph; performing vectorization on the alarm event by using an LLM technology to generate an alarm event vector, performing similarity comparison with a TSG vector, and determining a TSG of a similar fault; and executing an automatic troubleshooting process of similar fault TSG, determining a fault root cause according to the obtained diagnosis information, and generating a fault diagnosis report by using an LLM technology. Through the technical scheme of the invention, the fault root cause can be accurately and quickly positioned, automatic fault diagnosis is realized, and the fault handling efficiency is improved.

The invention relates to an electric power informatization system fault cause analysis method, which comprises the following steps: step 1, in an offline stage, constructing a fault analysis decision tree according to an electric power business system document and knowledge and experience of operation and maintenance personnel, and registering various API tools required to be used in a diagnosis process; 2, in an online stage, generating a fault description according to a fault occurring in the system, then matching the fault analysis decision tree in the step 1 according to a keyword in the fault description, traversing the fault analysis decision tree by a control program, and performing fault analysis on the fault analysis decision tree; and combining the fault description, the execution decision of the nodes in the tree, the callable API tool and the historical fault information into a cue word template, and sending the cue word template to the large model, so that the large model analyzes the root cause of the system fault. According to the invention, the troubleshooting and positioning efficiency of the system can be improved.

The invention discloses a power system optimization scheduling method based on artificial intelligence, and relates to the technical field of power system fault testing. The method comprises the steps of obtaining production monitoring data, operation data and management data under a target power system as a power data set, and performing preprocessing; constructing a load prediction model, inputting historical load data, user power consumption behaviors, meteorological data and calendar characteristics, and outputting a predicted power grid load curve; the technical key points are as follows: through combination with an equipment health state evaluation model, dynamic adjustment of health scores and subsequent real-time scheduling optimization, an optimal management scheme with extremely high linkage is formed, the effect of accurately identifying potential fault risks is realized, operation and maintenance personnel can take prevention measures in advance, and the risk of potential faults is prevented. The situation of misjudgment or missing report caused by neglecting external factors is avoided, and the pre-judgment and response capability of the whole scheduling scheme to the equipment health risk is enhanced.