540 results about "System failure" patented technology

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 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 a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

The invention relates to the technical field of industrial intelligent control, in particular to a combustion system fault correlation analysis and diagnosis method and system based on a knowledge graph. The method comprises the following steps: constructing a mechanism topological graph of a combustion system, obtaining real-time load data of the combustion system, calculating dynamic conduction lag time of a parent node influencing a child node in the mechanism topological graph based on a fluid mechanics load correction mechanism in combination with a flow resistance correction factor and reference transmission time, performing time sequence alignment based on dynamic conduction delay time, calculating causal association strength of a father node pointing to a child node and performing confidence attenuation processing, performing reverse search on a mechanism topological graph when an abnormal trigger point is monitored, calculating accumulated abnormal energy of each father node based on the causal association strength, and determining the abnormal trigger point according to the accumulated abnormal energy. And determining the father node with the highest accumulated abnormal energy as a fault root cause. According to the scheme of the invention, accurate time sequence alignment under working condition fluctuation can be realized, diagnosis failure can be prevented, and fault root causes can be accurately positioned.

The invention belongs to the crossing field of artificial intelligence and mechanical engineering, particularly relates to a micro-motor fault prediction and health management system, and aims to solve the problems that early faults of a micro-motor are difficult to recognize, degradation modeling is inaccurate and maintenance lags. The system collects multi-source data through high-density sensing, combines denoising reconstruction, composite feature extraction and time-varying weighted fusion to generate health indexes, identifies health stages by using a segmented hidden Markov model, iteratively updates residual life prediction based on a Wiener process, outputs an estimation result with a confidence interval, and links a hierarchical maintenance strategy. And continuous optimization of the model is realized through federal learning. The system improves the fault early warning accuracy and prediction reliability, and reduces the operation and maintenance cost.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

The invention discloses an energy storage system fault database indexing method, and particularly relates to the technical field of fault prediction. The method comprises the following steps: firstly, constructing an energy disturbance vector sequence matrix in a unified time window, extracting features based on a continuous variation rate and energy residual distribution, and generating an energy disturbance feature spectrogram; establishing an energy propagation path atlas in combination with a system module topological relation, and introducing a time sequence consistency identifier; a fault evolution fingerprint is generated through graph embedding coding, similarity index matching is carried out in combination with a standard fault trajectory, and a fault type and a positioning weight are output; real-time energy indexes are further fused, a micro-fault position prediction map is generated, and dynamic early warning is achieved; according to the method, accurate identification and visual positioning of the micro-fault of the energy storage system in a complex scene can be realized, and the operation safety and the intelligent operation and maintenance capability of the system are improved.

The invention provides an MCU function security implementation system based on a domain controller, and relates to the technical field of domain controllers, the system realizes dual backup storage and on-site execution of program codes by combining a security MCU without a built-in program memory with at least two redundant plug-in nonvolatile storage units, and the security of the MCU is improved. Consistency verification is carried out on key codes or data in the running process, and system failure caused by single-point storage faults or data tampering is effectively prevented; meanwhile, read-write operation without losing data during power failure is isolated through the independent data storage unit, and resource conflict and interference between program execution and data storage are avoided; the ASIL D-level power management unit supporting two-way fault monitoring is matched, resetting can be triggered in time or the MCU enters a safe state when a power supply is abnormal or an MCU internal fault occurs, and therefore a high-reliability and high-safety domain controller MCU system is constructed on the hardware architecture level, and the capacity of the MCU for meeting the ASIL D function safety level in a plug-in storage scene is improved.

The invention relates to an aircraft fuel system fault isolation and maintenance decision-making method based on a knowledge graph, and the method comprises the steps: employing an equipment tree analysis method to disassemble an aircraft fuel system into a plurality of maintenance units of different levels, and obtaining system knowledge data; obtaining fault knowledge data and maintenance knowledge data of each maintenance unit; constructing a fault isolation and maintenance knowledge graph according to the obtained system knowledge, fault knowledge and maintenance knowledge; and obtaining a to-be-matched fault early warning result output by the fault early warning system, searching a matched entity in the fault isolation and maintenance knowledge graph, and outputting a corresponding fault isolation and maintenance decision. According to the invention, accurate fault isolation suggestions and maintenance decisions can be quickly generated according to fault information, the maintenance work efficiency can be greatly improved, and accurate maintenance of equipment is realized.

The invention provides a power failure prediction and recovery method and system based on multi-cloud collaboration, and the method comprises the steps: collecting the real-time operation data and meteorological parameter data of power transformation equipment in each region based on a constructed multi-cloud collaboration architecture, based on the real-time operation data and the meteorological parameter data in combination with a pre-constructed power transformation fault knowledge graph, performing fault risk identification on the power transformation equipment in each region to obtain a fault risk identification result of the power transformation equipment in each region; determining a fault recovery plan for the power transformation equipment with the fault risk based on the fault risk identification result in combination with resource configuration information of each cloud platform in the multi-cloud collaborative architecture; and optimizing the fault recovery plan based on the power transformation fault knowledge graph to obtain a target recovery scheme, and executing fault recovery operation on the power transformation equipment with the fault risk based on the target recovery scheme. According to the invention, the overall efficiency and quality of power system fault processing are improved.

The invention relates to an artificial intelligence and multi-agent system technology, in particular to a multi-agent collaborative decision-making method and system based on a large language model, and the system comprises a perception analysis agent, a collaborative decision-making agent, a resource scheduling agent, an execution monitoring agent and a learning optimization agent. The system carries out linkage evaluation through three quantitative indexes of global reward value variance, accuracy improvement rate and cooperation strategy consistency, sets a differentiation threshold value and yellow, orange and red three-level early warning, accurately recognizes training states such as strategy oscillation, local optimum and strategy instability, triggers a progressive response from enhanced monitoring to immediate intervention, and improves the early warning efficiency. The system failure risk caused by abnormal training is obviously reduced; after the early warning is triggered, the system starts a structured four-step analysis process including anomaly positioning, causal verification, root cause determination and report generation, and outputs an executable report including an evidence chain and optimization suggestions by calling a large language model and automatically executing verification codes, so that the operation and maintenance transparency and the decision-making efficiency are improved.

The application provides a CPU platform fault log recording method and device, comprising: receiving the running log data stream output by the CPU debugging serial port in real time; collecting the sampling value of the key power supply voltage and monitoring the hardware signal line to obtain the restart event signal and the shutdown event signal; packaging into a structured log recording unit; and writing the compressed log data block back to the flash memory; in response to the external reading instruction, reading the log data block from the specified log area of the flash memory and transmitting to the host end diagnostic tool; reorganizing and displaying in time sequence for fault analysis. The application can construct a log recording channel completely independent of the main CPU and operating system without increasing any additional hardware cost, ensuring that even in the most extreme system failure scenario, the complete context information at the fault moment can be reliably saved, thereby greatly improving the diagnosis efficiency and accuracy of the occasional fault.

The invention discloses a primary and secondary fusion pole-mounted switch fault studying and judging method based on edge computing, and relates to the technical field of power system fault detection and diagnosis. Primary side electric parameters and secondary side state data are synchronously collected under the unified time scale of edge computing nodes, a primary and secondary fusion time sequence data set is constructed, and the primary and secondary fusion pole-mounted switch fault studying and judging method based on edge computing is achieved; the problem that fine time sequence correlation analysis cannot be carried out due to asynchronous data time marks in a traditional method is solved fundamentally, and a reliable data foundation is laid for follow-up accurate analysis. The change trend of the primary response time difference sequence and the secondary response time difference sequence in multiple cycles is extracted and analyzed, progressive defects such as slow drifting of action time caused by aging and jamming of a switching mechanism or a control loop can be effectively recognized, early warning of latent faults is achieved, and the limitation that judgment is prone to missing in single-cycle threshold judgment is broken through.

The invention discloses an electric power system fault diagnosis method and system based on multi-sensor fusion, and relates to the technical field of electric power fault diagnosis, electric power data of an electric power system are collected based on multi-array sensors, and a health data set of the electric power system is generated by adjusting element parameters of the electric power system; constructing an electric power digital twinborn model of the electric power system by using the element parameters and the health data, and inserting a distributed fault analysis unit into the electric power digital twinborn model to generate a parallel fault analysis model; real-time electric power data of the electric power system are collected in real time, when a fault trigger signal is detected, a driving data flow is generated, the driving data flow is introduced into the parallel fault analysis model to position a fault source, and a fault report is output; according to the method, the high-fidelity digital twinborn model and a parallel fault analysis mechanism are constructed, so that rapid, accurate and automatic positioning and diagnosis of the faults of the power system are realized.

The invention provides a log data-oriented system fault mining method, which belongs to the technical field of information, and comprises the following steps: collecting original log data of a system, and preprocessing original system logs, including analysis, key information extraction, vectorization and generation of a structured log event sequence; processing the log event sequence by adopting a two-stage model fusing anomaly detection and mode clustering so as to distinguish accidental anomaly and a real fault mode; and for each candidate fault mode cluster, analyzing a time sequence causal relationship between internal events, and positioning a root cause event according to the causal flow score of the node. The method has the advantages that dependence on artificial experience is reduced through full-process automatic modeling and analysis; according to the method, log analysis, feature extraction, anomaly detection and root cause positioning are all automatically completed by adopting a preset algorithm process, and the detection effect is maintained through self-adjustment of the model, so that the flexibility and sustainability of fault mining are improved.

The invention relates to the technical field of data processing, in particular to an incinerator system fault early warning method based on digital twinning, and the method comprises the steps: collecting the operation data of an incinerator; based on the distribution characteristics of the operation data in the data set coordinate system, obtaining the initial dependency degree of the target point on the neighbor point; fusing the deviation degree between the target point and the recent historical operation data and the fluctuation degree difference between the recent historical operation data and the long-term historical operation data to obtain the abnormal degree of the target point; obtaining the target dependency degree of the target point on the neighbor point according to the abnormal degree; and a self-adaptive K value is obtained through the target dependency degree of the target point on the neighbor point, the self-adaptive K value is used for monitoring the abnormity of the incinerator through FastABOD, and fault early warning is achieved in combination with digital twinning. The self-adaptive K value is obtained through the distribution characteristics of the data, and the limitation of using a fixed K value is broken through.

The invention provides a failure mode identification method and device of a new energy ship power system, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring real-time multi-modal data of a new energy ship power system, and extracting multi-modal fusion features based on the real-time multi-modal data; based on the multi-modal fusion feature, performing retrieval in a pre-constructed failure mode knowledge graph, and obtaining each knowledge graph association result associated with the real-time state of the new energy ship power system; splicing the multi-modal fusion feature with the map feature of each knowledge map association result to obtain a feature splicing result; and inputting the feature splicing result into a large language model to obtain a failure mode recognition result for the new energy ship power system. By adopting the method, the failure mode of the new energy ship power system can be accurately and reliably identified.

The invention discloses a reliability evaluation method for a power distribution network containing network construction type energy storage and flexible resources, and the method comprises the steps: firstly constructing a wind power and photovoltaic combined output probability distribution model considering the output uncertainty and spatial-temporal correlation of new energy, and a typical load scene; then establishing a'operation-fault 'two-state shutdown model, and generating a system operation state through sequential Monte Carlo simulation; simulating the power distribution network according to the fault information, and analyzing the power output characteristics of the constructed network type energy storage and the voltage support effect of the constructed network type energy storage on the near-area distributed new energy; and finally, constructing an optimal load shedding model considering the network-forming type energy storage power support effect, calculating the fault consequence of the system, and calculating the reliability index of the system according to the fault consequence. According to the method, the improvement effect of network construction type energy storage on the reliability of the power distribution network is analyzed; and meanwhile, the accurate reliability evaluation capability is still kept in a high-permeability scene of the distributed power supply, and a theoretical support is provided for network construction type energy storage planning investment for improving the reliability of the power distribution network.

The invention provides a fault prediction system, method and device, a storage medium and a program product, and relates to the technical field of computer application. The fault prediction system comprises a data acquisition module for acquiring a multi-source log of a target application system in real time, extracting a key field after standardization processing, and storing the key field in an original log table; the data processing module is used for acquiring business semantic information and a fault influence coefficient according to the data of the original log table, constructing a business topological graph according to the business semantic information and the fault influence coefficient and storing graph information to a business graph relation table; and the fault prediction module is used for predicting the risk level of the fault of the target application system according to the service atlas characteristics in the service atlas relation table. By adopting the fault prediction system provided by the invention, coupling analysis of business logs and system logs and visual association of fault propagation chains can be realized, and the fault risk level of the application system is predicted based on the association map characteristics, so that the hidden fault risk is identified in advance.

The invention discloses an intelligent system failure analysis method based on fault recognition, and relates to the technical field of intelligent system reliability analysis, and the analysis method comprises the specific steps: employing Modelica to construct a multi-physical domain digital twinborn model, combing historical data, and building a structured fault mode library; developing a fault injection algorithm to acquire data to form a standardized data set; preprocessing data to construct a causal link and a probability flow network; carding task scenes to construct a reliability evaluation model and training the reliability evaluation model; inputting actual parameters to trace faults, generating an optimization plan, and outputting an analysis report and a plan after verification; according to the method, the problem of insufficient fault correlation analysis caused by modeling scale splitting in a traditional method is solved by constructing a multi-physical domain digital twinborn model covering microcosmic, mesoscopic and macroscopic and establishing a structured fault mode library of a standardized feature description system containing three types of faults of hardware, software and environment interaction.

The invention discloses a fan fault analysis and operation and maintenance decision support method based on a new energy monitoring system, belongs to the field of new energy power generation monitoring, and aims at solving the problems that an existing system is low in fault recognition precision, depends on artificial experience and is not fully mined in data value. According to the method, a wind turbine generator SCADA system, meteorological data and operation log multi-source data are integrated, and a five-dimensional fault feature tag set of electrical and mechanical faults is constructed; after module pre-classification, a DBSCAN clustering algorithm is adopted for analysis, and samples with the similarity matching degree smaller than 60% of a new clustering center and an original clustering center serve as newly-added typical cases to update a fault processing knowledge manual; when the matching between the unit parameters and typical faults is greater than or equal to 80%, automatic early warning is carried out and a processing scheme is pushed; carrying out preventive maintenance in combination with a time sequence model, optimizing a spare part inventory according to a weighted model, and pushing a case when the matching between a fault and a historical case is greater than 90%; the method improves the fault diagnosis precision, shortens the processing time, reduces the operation and maintenance cost, and is suitable for various manufacturer models.

The invention discloses a server hardware link diagnosis method and a server hardware link diagnosis system, relates to the technical field of computer system fault diagnosis, and discloses the server hardware link diagnosis method and the server hardware link diagnosis system. Through the steps of obtaining a diagnosis rule configuration file, collecting software and hardware state data, carrying out matching analysis to generate a diagnosis path, executing diagnosis processing, carrying out correlation analysis, generating a fault report and the like, the problems of software and hardware diagnosis splitting and path stiffness in the prior art are solved, and collaborative diagnosis and dynamic path generation of software and hardware faults are realized. And the fault positioning accuracy and the system recovery timeliness are improved.

The invention discloses a method and device for determining an initial fault line combination of a power system considering cascading failure propagation, electronic equipment and a storage medium, and belongs to the technical field of power system fault recognition. The method comprises the following steps: constructing an attack and defense double-layer optimization model comprising an upper-layer attack model and a lower-layer defense model, wherein the upper-layer attack model aims at maximizing the load loss value, and the lower-layer defense model aims at minimizing the load loss value; the method comprises the following steps of: randomly generating a plurality of candidate fault line combinations containing a preset number of fault lines, respectively simulating a cascading fault propagation process caused by the candidate fault line combinations, obtaining a power grid working condition after a fault is ended, and calculating a load loss value of each candidate combination by utilizing a lower-layer defense model; and selecting the candidate fault line combination with the maximum load loss value as an initial fault line combination of the power system. By implementing the method, the problem that the initial fault line combination of the power system is not accurately determined in the prior art can be solved.

The invention relates to the technical field of heating and ventilation fault recognition, in particular to a heating and ventilation system fault recognition method based on operation state offset modeling and a control system. The method comprises the following steps: acquiring steady-state operation data of the heating and ventilation system and extracting actual parameters of equipment; inputting the environment and load parameters into the lightweight integrated tree base line model, and outputting an equipment operation parameter reference value; performing baseline drift correction on the reference value in combination with the accumulated operation duration of the equipment, calculating a dynamic difference value between an actual value and a calibration reference value, and constructing an operation state offset characteristic matrix; and inputting the matrix into a preset fault diagnosis rule base, matching a hidden fault in combination with the dynamic tolerance interval and the multi-dimensional Boolean logic, and giving an alarm. Performing reference compensation by using the accumulated operation time length in combination with a linear or exponential progressive attenuation model; on the premise of not consuming high computing power to re-train an underlying algorithm model, the interference of natural physical aging of the equipment on residual calculation is eliminated.

The invention provides a carrier rocket time sequence control full redundancy implementation method, and aims to improve the fault adaptability and the working reliability of a time sequence control system. The full-redundancy design method of three-path information source redundancy, three-path transmission line redundancy, two-out-of-three voting control redundancy and five-tube relay instruction driving redundancy is adopted in the sequential control system, the capacity of adapting to all first-degree faults and most second-degree faults is achieved, a single-point failure link does not exist, and the reliability is high. Normal work of the sequential control system can be ensured in the flight process of the carrier rocket. Based on a three-bus communication electrical architecture of a new generation carrier rocket electrical system, a full-redundancy design implementation method of information source redundancy, transmission line redundancy, voting control redundancy and instruction driving redundancy is adopted, high-reliability carrier rocket time sequence control is completed, the fault adaptive capacity of the time sequence control system is improved, and the time sequence control system has a wide application prospect. And the working reliability of the system in the rocket flight process is ensured.

The invention belongs to the technical field of distributed control and intelligent operation and maintenance, particularly relates to a synchronous timing system fault diagnosis method based on unsupervised learning, and aims to solve the technical problems that fault early warning is lagged, hidden coupling faults are difficult to recognize and fault samples are scarce in the prior art. Constructing a time sequence of the multi-dimensional state space; s2, calculating a dynamic control entropy feature representing the disorder degree of the control loop; s3, obtaining a reconstruction sequence of the input sequence, and calculating a reconstruction error vector; and S4, calculating a real-time abnormal score of the synchronous timing system, and judging whether the synchronous timing system enters a metastable fault state or not according to the real-time abnormal score. According to the invention, the early warning of the fault is realized, and the early warning can be given out when the SoftPLL enters the metastable state and does not lose the lock yet.

Method and process of fault detection in power systems using feature extraction from the measurements of the voltages and currents in the power system, as well as machine learning methods. A computer processor, such as an industrial computer, is connected to various voltage and current measurement systems in various locations. This, in turn, is connected to a server computer and control system. Electrical power fault signals (features) propagating through the power system network are analyzed using various measurements and feature extraction methods. These can be used to detect, classify, and localize the fault. The present methods are particularly useful for hard-to-analyze faults such as high impedance faults and faults occurring due to inrush currents, bidirectional power flows, and islanding issues.

The invention belongs to the technical field of computers, and particularly relates to a system fault response method, a system fault response device and electronic equipment. The method comprises the following steps: acquiring a fault injection event; mapping the fault injection event according to the fault injection model to obtain a fault score corresponding to the fault injection event; mapping the fault injection event according to a strategy matching model to obtain a target response strategy and a strategy matching score corresponding to the target response strategy; updating model parameters of the fault injection model or the strategy matching model according to a numerical relationship between the fault score and the strategy matching score; and generating a response strategy for a real system fault detected by the transaction management system according to the strategy matching model after the model parameters are updated. According to the invention, system fault response intelligence and automation can be realized.

The invention discloses a semiconductor equipment temperature control system fault prediction method based on digital twinning, which relates to the field of temperature control fault prediction, and comprises the following steps: configuring a structural topological graph of a temperature control system, defining a health parameter vector of the temperature control system, configuring an online grey box digital twinning model, and obtaining temperature setting parameters and process stage state parameters. Temperature control response segments are divided, response feature vectors are configured based on the temperature control response segments, and event labeling is carried out. A temperature control system grey box digital twinborn model with on-line updating capability is constructed on the basis of a temperature control system structure topological graph, a corresponding state space relation is established, and a health parameter vector formed by a circulating pump efficiency parameter, a heat exchanger heat transfer capability parameter, a fluid regulating valve opening offset parameter and a temperature sensor zero offset parameter is matched. And the operation state of the temperature control system has good physical interpretability.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to dynamic system state capture for troubleshooting of medical devices. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components that can collect, in response to a service request in connection with a system failure of a medical device and via an artificial intelligence model, troubleshooting data based on a time range or an error type of the system failure. In various aspects, at least one of the computer executable components can further generate a system state digest of the troubleshooting data. In various instances, the system can further dynamically generate an encryption of the system state digest.