242 results about "Fault management" patented technology

The invention discloses a computer memory bank fault prediction method and system based on deep learning, and relates to the technical field of computer hardware fault diagnosis, and the system comprises a multi-source time sequence data collection module which is used for obtaining memory bank operation state data in real time; the dynamic feature enhancement module is based on a composite architecture of a generative adversarial network and transfer learning, comprises a fault mode generator, and generates synthetic data consistent with real fault distribution by using an LSTM network; aligning feature spaces of different hardware platforms through a maximum mean difference loss function; the multi-modal fusion deep learning model comprises a space-time convolutional network, a graph attention network and an adaptive weight adjustment mechanism; and the fault early warning analysis module is used for analyzing a fault probability predicted value, an interpretable thermodynamic diagram and a maintenance suggestion. According to the invention, passive maintenance is changed into active prevention and control, and preposition and precision of fault management are realized through dual mechanisms of long-term trend prediction and short-term risk early warning.

The invention relates to the technical field of lithium battery diagnosis, in particular to a lithium battery fault diagnosis system based on real-time impedance monitoring, which comprises a disturbance signal acquisition module, an impedance track extraction module, a polarization response comparison module, an abnormal period judgment module and a structural risk output module. According to the method, a dynamic comparison mechanism of polarization response time difference and structure state parameters is introduced, periodic risk grading and characteristic quantity marking are linked, tiny response variation in the lithium battery operation process is subdivided, quantitative output of key nodes is achieved by fusing pole piece dynamic characteristics, periodic risk state traceability and abnormal node tracking are supported, and the reliability of the system is improved. According to the invention, the method can achieve the fine structural abnormality marking and multi-stage alarm, improves the timeliness and foresight of structural risk management, can achieve the parallel collection and processing of multi-element response signals, promotes the high-reliability operation and distributed fault management and control of a lithium battery system, expands a safety monitoring boundary, and improves the precision and granularity of active early warning and management.

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

The invention relates to the technical field of automatic control, in particular to an automatic control method of a BOG low-temperature recovery system for a storage tank for an LNG (liquefied natural gas) ship, which comprises the following steps: acquiring BOG pressure fluctuation data and temperature difference trend, analyzing pressure rise rate and cooling efficiency change, identifying a pressure peak value and a cooling lag section, screening a low-efficiency section and marking an optimization unit. And identifying an energy matching degree, analyzing energy loss distribution, identifying an over-limit node and a fault signal, and obtaining an energy and fault synchronous control table. According to the system, through real-time data monitoring and intelligent analysis, the BOG recovery process can be accurately controlled, energy circulation and cooling treatment can be optimized, the efficiency of a low-temperature recovery system can be improved, BOG pressure and temperature difference changes can be monitored, pressure fluctuation and cooling lag can be analyzed, continuous improvement of condensation efficiency can be ensured, control parameters can be accurately adjusted, energy loss is avoided, and the energy efficiency of equipment can be improved; energy consumption and equipment fault management are optimized, and efficient and safe operation of the low-temperature recovery system is ensured.

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 field of power distribution control, and particularly discloses a self-healing control system and method based on multi-agent collaboration, and the system comprises a regional agent and a local agent which are disposed in a power distribution network. Wherein the local intelligent agent is configured to obtain real-time operation data of a monitoring point in the power distribution network, and perform rapid fault detection on the real-time operation data; the regional intelligent agent is configured to fuse the fault analysis information of the plurality of local intelligent agents to determine fault position information when the power distribution network has a fault, generate a self-healing control strategy according to the fault position information, and send the self-healing control strategy to the local intelligent agents for execution; through cooperative work of the distributed intelligent agents, the problems of low response speed, low positioning precision, weak autonomous capability and the like in traditional power distribution network fault management are solved. In addition, the system does not need to depend on centralized control of a master station, can still operate autonomously when communication is interrupted, and improves the efficiency and reliability of fault processing through cooperation of multiple agents.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

The invention provides an intelligent management method and system for global environmental control heat dissipation of a wind power plant, and is applied to the technical field of data processing. According to the application, wind power plant equipment operation and environment parameters are obtained, a global environment control sensing module is used, a core equipment state is monitored by means of multi-sensor fusion, and an equipment-environment association model is constructed to obtain a preliminary heat dissipation demand; an intelligent heat dissipation regulation and control module optimizes a strategy to generate a precise control instruction; the frequency converter fault-free management module dynamically adjusts parameters to form fault-free management information; coordinating equipment linkage through a joint optimization strategy to generate a global environment control scheme; and finally, the operation efficiency evaluation module outputs evaluation information containing efficiency and a safety coefficient, so that intelligent management of heat dissipation of the wind power plant is realized.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

A method for reporting data to a Northbound Management System (NBMS) via a Network Function (NF) level Northbound FCAPS Reporting Microservice (MS) in an NF of a Radio Access Network (RAN), the method including receiving, by a first microservice (MS) from at least one other MS of the NF, data, the first MS being one of a Fault Management (FM) MS and a Performance Management (PM) MS and the data comprising performance data for the PM MS or event data for the FM MS; processing, by the first MS, the received data; obtaining, by the FCAPS Reporting MS, the processed data; and reporting, from the FCAPS Reporting MS to the NBMS, the processed data.

The invention provides a power distribution communication network fault management process optimization method, which is based on a fault evolution reverse deduction technology of historical repair knowledge, and realizes efficient fault root cause positioning and repair recommendation by combining a dynamic space-time atlas and a graph database technology. The method comprises the following steps: constructing a dynamic space-time atlas, and storing power distribution communication network topology and fault data; constructing a fault causal relationship model, and describing a fault state transition probability and an evolution causal chain; multi-path hypothesis testing is executed, possible fault sources and evolution paths are generated, and confidence scores are distributed; similar fault modes and repair measures are retrieved from a historical repair knowledge base, and a directional repair strategy is recommended; and space-time backtracking analysis is realized, and the whole fault evolution process is visually displayed. According to the method, the root cause positioning efficiency and accuracy are remarkably improved, the fault processing flow is improved, the fault prediction capability is improved, the preventive maintenance level is enhanced, and continuous accumulation and optimized application of maintenance knowledge are realized.

The invention discloses a memory fault management system and method, a server and electronic equipment, and relates to the technical field of computers, the memory fault management system comprises a processing circuit of a hardware memory, a hardware layer and a processor are arranged on the processing circuit, and the processor is further divided into a kernel layer, a user layer and an input and output layer. Through cooperative work of the hardware layer and each software layer, hierarchical detection, classified processing and automatic isolation of memory error data are realized, and server downtime caused by memory fault error data is effectively prevented. And meanwhile, through a linkage mechanism of a kernel mode and a user mode and in combination with visual display, the monitorability and maintainability of memory errors are enhanced, operation and maintenance personnel can quickly position and repair problems conveniently, and the operation and maintenance cost of the system is reduced.

An enhanced lighting control system is provided that utilizes updated communications technology to provide a cost and resource efficient lighting control system that allows for a retrofit installation into existing lighting systems. The lighting control system utilizes single pair ethernet connection protocols, as well as a pulsed power source, to enable the efficiencies.

The invention relates to a low-voltage distribution network topology identification method based on voltage-current correlation feature mining, and the method comprises the steps: carrying out the time synchronization processing of user-side multi-source electric parameter data after obtaining the user-side multi-source electric parameter data, and forming a unified electric parameter data set; after further preprocessing the integrated electric parameter data, extracting a voltage feature sequence based on the preprocessed point parameter data; the method comprises the following steps: establishing a user group, calculating voltage curve similarity among users, establishing a feature matrix and performing clustering analysis to preliminarily identify the user group, then establishing a mixed integer programming model by combining a Kirchhoff's current law, optimizing a phase-line-user relationship, and finally outputting a topological structure result. The problems that the low-voltage power distribution network topological structure is insufficient in recognition precision, poor in data consistency and low in dynamic adaptive capacity are solved, so that efficient and reliable network topology recognition is achieved, and monitoring and fault management of the power distribution network are effectively supported.

The invention provides a vehicle cloud collaborative vehicle fault monitoring method and system and an electric vehicle, and relates to the technical field of electric vehicles. The method comprises the steps that a vehicle-mounted system collects actual operation data of a vehicle in real time, preliminary fault diagnosis is conducted on the electric vehicle based on the actual operation data of the vehicle, and a local fault diagnosis result is obtained; vehicle actual operation data and a local fault diagnosis result are uploaded to a cloud server; the cloud server inputs the actual operation data of the vehicle into a pre-constructed vehicle dynamic model to obtain theoretical parameter information of the electric vehicle under the current working condition; cloud fault diagnosis is carried out on the electric vehicle according to the theoretical parameter information of the electric vehicle under the current working condition and the actual operation data of the vehicle to obtain a cloud fault diagnosis result, and a corresponding processing instruction is sent to the vehicle-mounted system according to the cloud fault diagnosis result and a local fault diagnosis result. Local end diagnosis-cloud end diagnosis-double-end decision making is realized, and the timeliness and reliability of fault management are improved.

The invention relates to a network fault root cause positioning system based on multi-modal learning and causal inference, and belongs to the technical field of network fault diagnosis and positioning. The system comprises a data processing and association mining module, an intelligent fault diagnosis and evaluation module, a root cause positioning module and a system optimization module which are connected in sequence. The data processing module collects data through edge nodes, constructs a hierarchical knowledge graph and outputs a feature matrix; the diagnosis module performs fault detection by adopting a multi-modal model of a fusion graph neural network, integrates a small sample learning mechanism and outputs a fault event with confidence; the positioning module constructs a causal graph based on a knowledge graph, fuses multi-source evidences and realizes root cause tracing through a random walk algorithm; and the optimization module adjusts diagnosis parameters by utilizing reinforcement learning, expands a sample set based on the generative adversarial network, and realizes continuous optimization of the model through an automatic assembly line. Closed-loop self-optimization from fault sensing to root cause positioning is realized, and the network fault management capability is improved.

Provided are a method and system for handling FCAPS reporting for the FM MS and PM MS. The method may be performed by at least one processor, for reporting data to a northbound management system (NBMS), the method including: receiving, by a FCAPS reporting microservice (FCAPSR MS) from an Fault Management microservice (FM MS) of a first Radio Access Network (RAN) Network Function (NF), first data comprising fault event data, the FCAPSR MS being a host level MS; receiving, by the FCAPSR MS from an Performance Management microservice (PM MS) of the first RAN NF, second data comprising performance data; and reporting, from the FCAPSR MS to the NBMS, at least one of the first data and the second data.

The invention relates to the technical field of fault detection, in particular to an electronic component fault management method and system. The method comprises the following steps: collecting real-time data and constructing a real-time fault monitoring data set; reading historical data and constructing a historical reference data set; comparing the real-time fault monitoring data set with the historical reference data set item by item in a corresponding time window to obtain a drift amplitude, and mapping according to the drift amplitude to generate a reverse excitation amplitude instruction; a reverse current signal according to the reverse excitation amplitude instruction is injected into the loop node to be measured, and a Hall null drift amount is determined according to Hall output; outputting an electronic disturbance quantity based on the Hall null drift quantity; therefore, through dynamic data cooperation and an active excitation response mechanism, the problems of missing detection recessive degradation, misjudgment transient interference and single diagnosis dimension of traditional fault management are solved, and the hidden danger recognition precision and the protection reliability are remarkably improved.

The invention relates to the technical field of network communication, in particular to an intelligent visual edge gateway control method based on a 5G network. According to the method, real-time transmission and timely control of edge gateway information are achieved through the 5G network, meanwhile, the operation state of the edge gateway is visually and comprehensively mastered through the information, timely finding and processing of problems are facilitated, the visual quality and the data transmission quality are ensured along with channel communication quality evaluation and analysis of the 5G network, and the service life of the edge gateway is prolonged. The resource allocation is dynamically adjusted in an information progressive mode, the transmission timeliness of priority data is guaranteed during channel load fluctuation, the problem that a fixed scheduling strategy is poor in adaptability is solved, meanwhile, the operation state information of the edge gateway is further analyzed, and the service life of the edge gateway is prolonged. According to the method, reasonable fault management can be carried out according to hardware or software faults of the edge gateway, namely, the hardware faults and the software faults are analyzed, so that the faults of the edge gateway can be found and processed in time, manual intervention is reduced, and the reliability of the system is improved.

The invention discloses a cluster fault management method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining performance index data of a multi-field unit in a cluster; constructing a dynamic fault propagation model of the cluster through the performance index data based on the graph model; according to the dynamic fault propagation model, constructing four-dimensional fault visualization based on a time sequence; and cluster fault analysis is carried out through four-dimensional fault visualization. Therefore, on the basis of the time sequence, the performance index data of the different field units are constructed into the dynamic fault propagation model, cross-domain fault positioning, analysis, monitoring and deduction based on the time axis are realized, cross-domain fault space-time correlation splitting caused by independent fault monitoring of each field unit is avoided, and the fault positioning efficiency and accuracy are improved. In addition, based on the four-dimensional fault visualization expression of the time sequence, the multi-screen switching monitoring complexity of operation and maintenance personnel is reduced, and the cluster fault management efficiency is improved.

The invention discloses a CDN fault node isolation method and system based on telecom operator network management data, and relates to the technical field of CDN fault management and operation and maintenance, and the method comprises the steps that an operator network management center collects multi-stage network equipment and link data, and a CDN health center collects indexes such as node packet loss rate; comparing an index and a threshold value quality index degree which exceeds a threshold value, and marking a suspected node; querying network management data to locate a fault category; calculating a score by combining a fault position weight, and isolating if the score exceeds a threshold value and the fault position is a node or a direct connection link; eliminating nodes and scheduling traffic; and quickly triggering the alarm, subscribing the network management alarm, directly marking the suspected node and triggering the process. According to the method, the isolation accuracy of the CDN fault node is improved, and misjudgment caused by an upstream network fault is reduced; the isolation response time delay is shortened; congestion aggravation is inhibited; a quantifiable and configurable isolation decision system is formed, the operation and maintenance intelligence is improved, and the service quality and the user experience are optimized.

In some examples, a system monitors for a memory fault associated with execution of software. Based at least on receiving an indication of a possible memory fault, the system scans a first area of the memory based on a data structure that indicates that the first area of the memory stores a portion of parameters of the software that affect an accuracy of the software if there is a memory fault in the first area of the memory. Based at least on the scanning indicating that there is a memory fault in the first area of the memory, the system remaps the portion of parameters of the software from the first area to a second area of the memory that is determined to be free.

The application discloses a kind of air pump unit fault management systems, it is related to air pump fault management technical field, real-time acquisition and air pump safety operation related multi-source data are obtained by sensing equipment, processing module is preprocessed to the multi-source data collected, including data cleaning, denoising, etc. Operation is to ensure data quality and accuracy. Feature data is extracted from the data after preprocessing, analysis module will be obtained into air pump prediction model and comprehensively analyzed after feature data, air pump prediction model is air pump and outputs an operation assignment, regional division module will air pump be divided into corresponding area according to the comparison result of operation assignment and gradient threshold value, and according to the regional division result of air pump, corresponding management strategy is generated, and management strategy is sent to administrator. The management system can effectively predict the fault of air pump in the operation process, so as to carry out early warning and management before air pump failure, guarantee the safety and stability of air pump operation.

A device and method for fault management for an electric power distribution network incorporating intermittent generation sources. The method involves configuring multiple hyperparameters for a series of Convolutional Neural Networks (CNNs). A first CNN is trained using current signal imagery from phasor measuring units (PMUs) during fault conditions to classify faults. A second CNN is trained with signal images from PMUs captured during pre-fault and fault cycles for identifying fault sections. Similarly, a third CNN is trained using these images to determine the exact fault location. Once trained, the CNNs are employed sequentially. The first CNN classifies the fault, the second detects the fault section, and the third ascertains the fault location. Subsequently, a comprehensive fault management strategy is deployed.

The invention provides a communication fault determination method and equipment, which can be applied to the technical field of communication technology and fault management. The communication fault determination method comprises the following steps: responding to received register error information from a central processing unit, analyzing the register error information to obtain route identification information which is used for positioning error triggering equipment; wherein the register error information is obtained by the central processing unit from a register related to the baseboard management controller based on the target error event; under the condition that the route identification information indicates the out-of-band management service module, determining that the register error information is target communication type information; wherein the substrate management controller, the central processing unit and the out-of-band management service module are connected through a communication link of a target communication type; and determining a communication fault type according to the configuration information of the communication link, and sending the communication fault type to the central processing unit.

A high voltage AC power fault managed power system (100) is provided that includes accurate fault detection. The high voltage AC power fault managed power system (100) includes a transmitter device (1) and a receiver device (2) that are coupled via a transmission line (3). The transmitter device (1) and the receiver device (2) obtain status information by monitoring a voltage (8) or current (9) on the transmission line (3), and share the detected status information with each other. High voltage power may continue to be transmitted through the transmission line (3) when safety conditions are met based on the communication of status information between the transmitter device (1) and the receiver device (2).

The invention relates to the technical field of subway fault management, in particular to a subway train fault management system based on semantic recognition, which comprises a train information base; the information acquisition module is used for acquiring fault information and operation information of the subway train every first set time; the fault analysis module is used for constructing a fault tree model based on the historical fault information, determining a plurality of fault correlation parameters corresponding to the current fault information, and determining a current fault element based on a comparison result of the current fault information obtained at the current time point and the fault information obtained at the adjacent time point and each fault correlation parameter; the fault judgment module is used for judging whether the current fault information triggers the updating of the fault tree model or not, and determining a fault updating node and an abnormal fault node; and the fault early warning module is used for determining whether to send out early warning information and generating a fault report based on the fault updating node or the abnormal fault node. According to the invention, the accuracy and efficiency of fault analysis can be improved.

Provided are a method, computer readable medium, and system for reporting data to a Northbound Management System (NBMS) via a Network Function (NF) level Northbound Fault reporting Microservice (MS) in an NF of a Radio Access Network (RAN). The method includes: receiving, by a Fault Management (FM) MS from at least one other MS of the NF, events data; processing, by the FM MS, the events data; obtaining, by the Fault reporting MS, the processed data; and reporting, from the Fault reporting MS to the NBMS, the processed data.

The invention provides a distributed mirror image library management method, which belongs to the technical field of distributed storage, and comprises the following steps of: counting the number of times that a mirror image file is requested, identifying a hotspot mirror image and a non-hotspot mirror image, respectively determining a corresponding cache space and a cache strategy from a mirror image-configuration table, obtaining a cache result, and creating a mirror image library framework. The method comprises the following steps of: setting a fragmentation strategy about initial resources of a mirror image library, constructing the mirror image library, setting an access strategy according to a user access log, performing mode division on the access strategy to obtain an access mode set, further determining an access rule, optimizing an access path of the mirror image library based on the access rule, and determining a fault-tolerant strategy of the mirror image library based on an optimization result. A fault processing mechanism is set, fault management is carried out on the mirror image library, caching, storage and access strategies and a fault-tolerant mechanism are systematically integrated, the performance, availability and expandability of the distributed mirror image library are effectively improved, and the requirements of large-scale data storage and access are better met.

The invention discloses a network fault management system and method based on multi-source data, and relates to the technical field of server hard disk fault monitoring, and the method specifically comprises the following steps: collecting hardware health data, service performance data, network flow data and historical fault records, and determining the collection frequency; key features of server hard disk faults are obtained, and data quantification processing is carried out on the key features; taking the preprocessed data as a reference, selecting key feature data values of a plurality of server hard disks, and constructing a server hard disk fault probability prediction model through logistic regression; obtaining real-time key feature data, calculating the fault occurrence probability of the server hard disk through the server hard disk fault probability prediction model, and comparing a preset grading range to obtain a fault grade; and according to the fault level, triggering an automatic response action. According to the invention, the troubleshooting time and the business interruption risk are effectively reduced, and more intelligent and efficient network fault management is realized.