18 results about "Reliability management" patented technology

The invention belongs to the technical field of nuclear power DCS health management, and aims to provide a DCS health management platform for nuclear power plant DCS operation and maintenance business application. The invention discloses a DCS (Distributed Control System) health management platform based on agile development and a general-branch architecture, which comprises a display layer, a business application layer, a model and service layer, an operation and maintenance deployment layer and an infrastructure layer, and is characterized in that intelligent operation and maintenance management, software and hardware state remote monitoring and service life prediction of a nuclear power plant DCS are realized through the business application layer; and a dynamic logic deduction and fault tracing mechanism is constructed, full-process digital operation management and control, overall planning of a maintenance plan, authority auditing and intelligent guiding are carried out, and a full-life-cycle data system is established. According to the invention, DCS operation and maintenance service application is provided for the nuclear power plant, the reliability management of the DCS of the nuclear power plant and the risk analysis level of an instrument control system can be improved, and the probability of unexpected unit operation events caused by the DCS is reduced.

The embodiment of the invention discloses an insulation monitoring and early warning method and device for high-voltage equipment, equipment and a medium. A specific embodiment of the method comprises the steps of obtaining a multi-source signal set corresponding to high-voltage equipment in a smart power grid; performing interference separation processing on the partial discharge pulse signal to obtain a processed partial discharge pulse signal; performing first regulation and control processing on the high-voltage equipment based on the processed partial discharge pulse signal and the temperature distribution signal; performing optimization processing on the dielectric loss factor signal based on the temperature distribution signal to obtain an optimized dielectric loss factor signal; performing second regulation and control processing on the high-voltage equipment based on the processed partial discharge pulse signal and the optimized dielectric loss factor signal; and executing early warning processing according to the acquired first identification information and second identification information. According to the embodiment, insulation on-line monitoring and early warning are achieved, and a reliable decision basis is provided for safe operation and maintenance and reliable management of high-voltage equipment in an intelligent power grid.

The invention provides an industrial equipment preventive maintenance decision-making method based on a discrete time Markov chain, and relates to the technical field of industrial equipment reliability management and maintenance strategy optimization. The method comprises the following steps: based on historical maintenance record statistics, establishing a discrete time Markov chain maintenance effect state transition model and determining a state transition probability matrix thereof, carrying out eigenvalue decomposition on the state transition probability matrix to obtain state probability distribution after n times of maintenance, and determining each state probability; carrying out weighted fusion on the random intensity functions of the three maintenance processes through corresponding state probabilities, and constructing a mixed fault rate function; calculating an expected failure frequency based on the mixed failure rate function; and based on the expected failure times, establishing a long-term cost rate function, and solving to determine an optimal replacement period. According to the method, three basic maintenance effect types are integrated through the dynamic probability, and accurate prediction of the number of failure times of equipment and calculation of the optimal maintenance period are achieved.

The invention relates to the technical field of power system reliability management, and discloses a power reliability management platform based on defect integration and grading classification, which comprises a defect acquisition module, a grading classification module, a reliability evaluation module, a dynamic monitoring module, a mode switching module, an emergency response module and a risk early warning module. The platform provides reliability evaluation in a conventional mode and an emergency response mode by integrating defect information of power equipment and performing grading and classification processing, dynamically adjusts an evaluation process in combination with real-time operation state data, and predicts a potential risk level through a risk early warning module. The method can improve the reliability management level of the power system, is suitable for a complex and changeable operation environment, and achieves the precise evaluation and dynamic optimization.

The application discloses a power outage data credible management method based on a block chain, relates to the technical field of power supply reliability management, and comprises the following steps: data chain management: obtaining power outage events and reliability data, performing hash calculation, generating a unique data fingerprint, and writing the hash value and related metadata into a block chain; chain configuration management: performing failure compensation operation after a chain failure event is triggered; on-chain verification management: comparing the hash calculation with the original block chain storage, automatically and manually verifying the consistency of business data; verification task management: comparing the hash calculation of power supply reliability power outage data with the on-chain hash value, marking the inconsistent items and recording; on-chain traceability management: displaying a traceability list in reverse chronological order, locating corresponding items according to query conditions, and checking the on-chain storage track and operation history. The application can improve the defects of the prior art and improve the efficiency and accuracy of data verification.

A compute apparatus and method provide redundancy-aware dynamic power and performance balancing. When redundant columns are activated to replace defective columns, a control processor or local controller automatically adjusts clock frequency, voltage, and workload mapping to maintain uniform timing, throughput, and thermal conditions across compute rows. Compensation is determined using telemetry feedback and predictive models and applied through per-row voltage regulators and clock circuits. The system preserves consistent performance after redundancy reconfiguration without manual recalibration, linking reliability management with real-time adaptive power control.

The invention relates to the technical field of degradation prediction of power electronic devices, in particular to a short-time overload degradation prediction method and system for a power electronic device. The method comprises the following steps: acquiring short-time overload data; performing feature extraction according to the short-time overload data to obtain short-time overload feature data; performing time-frequency diagram construction according to the short-time overload characteristic data to obtain time-frequency diagram data; performing acoustic pseudo-spectrum feature generation according to the time-frequency graph data to obtain acoustic pseudo-spectrum feature data; and performing degradation prediction on the acoustic pseudo-spectrum characteristic data to obtain short-time overload degradation prediction data. According to the method, the pseudo-spectrum characteristics can be constructed through the short-time overload electric parameters under the condition that no extra sensor is needed, early perception and degradation trend prediction of device micro-damage are achieved, and the reliability management capacity of a power electronic system under the high-frequency dynamic working condition is improved.

The application discloses an artificial intelligence silent evolution control system based on a fusible, contractible, auditable and interpretable edge protocol and belongs to the technical field of artificial intelligence system safety and reliability management. The system comprises an edge protocol bus, an interpretable module, a fusible module, a contractible module, an auditable module and a silent evolution management layer. The interpretable module calculates the confidence and the feature attribution path of the model output. The fusible module triggers the fuse according to the preset confidence condition and publishes an event. The contractible module executes dynamic resource contraction in response to the fuse event. The auditable module generates a three-tuple association log of the confidence, the fuse action and the contraction action. The edge protocol bus provides standardized event communication, module registration and version negotiation. The application solves the technical problems that the existing AI management module is isolated and cannot actively defend and heat evolve based on the output quality, and is suitable for high-risk AI application scenarios such as finance, medical treatment and automatic driving.

A method for managing reliability of a cloud application includes deploying one or more detection engines into a customer environment. The method also includes monitoring data related to the reliability of the cloud application from within the customer environment. The method further includes detecting, by the one or more detection engines, a parameter affecting the reliability based on a set of rules. In addition, the method includes generating a response to interrogate the customer environment based on the parameter.

The present application relates to the technical field of power electronic device degradation prediction, and particularly relates to a power electronic device short-time overload degradation prediction method and system. The method comprises the following steps: obtaining short-time overload data; performing feature extraction according to the short-time overload data to obtain short-time overload feature data; performing time-frequency graph construction according to the short-time overload feature data to obtain time-frequency graph data; performing acoustic pseudo-spectrum feature generation according to the time-frequency graph data to obtain acoustic pseudo-spectrum feature data; and performing degradation prediction on the acoustic pseudo-spectrum feature data to obtain short-time overload degradation prediction data. The present application can construct pseudo-spectrum features through short-time overload electrical parameter data without additional sensors, realize early perception and degradation trend prediction of device micro-damage, and improve the reliability management capability of the power electronic system under high-frequency dynamic working conditions.

The invention discloses a mining equipment fault causal analysis method based on a knowledge graph, equipment and a medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: collecting real-time sensor data flow, historical operation data, maintenance cost records, production plan data and unstructured historical maintenance reports of gold mine key equipment; constructing an equipment fault causal knowledge graph; according to a causal analysis path packaged in the operation and maintenance decision script, directionally collecting refined data of gold mine key equipment, performing logic consistency verification, and determining a fault root cause and a causal chain influencing a production plan; and generating a decision scheme based on the fault root cause and the causal chain influencing the production plan. According to the method, the equipment fault causal knowledge graph is constructed, so that early-stage accurate early warning and predictive maintenance decision support of the gold mine key equipment hidden fault are realized, and the equipment reliability management level is improved.

The invention belongs to the technical field of nuclear power plant equipment reliability management, and particularly relates to a nuclear power plant conventional island pipeline health management and state evaluation system and method. Comprising an external data module, a standardized input tool, a solver and a prediction module, after information is input into external data in the external data module through the standardized input tool, intermediate process data is generated through calculation of the solver, an output result is evaluated through the prediction module, and corresponding operation and maintenance decision suggestions are generated according to evaluation results of different degrees. The beneficial effects are that health management and state evaluation are carried out for fault modes of nuclear power plant steam-water pipeline corrosion, small branch pipe vibration fatigue failure and the like, and the nuclear power plant pipeline operation and maintenance efficiency is improved.

The invention discloses a power plant equipment two-dimensional code and data synchronization information management and control system. The system comprises a multi-source data access module, a state abstract generation module, a two-dimensional code graphic rendering module, a code scanning interaction module, a log auditing module, a clock synchronization module, a centralized configuration operation and maintenance module and an emergency bypass hardware module. The system collects DCS, EAM and point inspection historical data in real time to generate a compressed and encrypted equipment state abstract, the abstract is rendered into a high-resolution two-dimensional code with a state color band, and the equipment operation state, the maintenance plan and the point inspection record can be obtained by scanning the code on site. And the mobile terminal dynamically displays information according to role permission after scanning the code, and supports offline caching and code scanning record write-back. And the log auditing module records the abstract message, the two-dimensional code version number and the code scanning event. According to the system, real-time visualization, safety, controllability, traceability and high-reliability management of equipment information are realized, and the operation and maintenance efficiency and safety of a power plant are remarkably improved.

The invention relates to a hard disk state determination method and device, computer equipment and a readable medium. The method comprises the steps that according to the current state of a target hard disk in a storage pool, reliability management is conducted on the target hard disk, a management result is determined, the actual state of the target hard disk is determined from a hard disk state configuration table according to the management result, and the target hard disk is any hard disk existing in the storage pool at the current moment or in the historical time period; the hard disk state configuration table comprises a plurality of states configured for the hard disk in advance in different operation stages. According to the method, after the current state of the target hard disk in the storage pool is obtained, the real state of the target hard disk is determined by conducting reliability management on the target hard disk, and therefore the precision of the determined actual state of the hard disk is improved.

The application discloses a video storage method, system, device and computer readable storage medium, relates to the technical field of video storage and reliability management, obtains existing consumption data of a target video on a storage space; a distribution model of the existing consumption data is generated; the distribution model is subjected to time correlation modeling to generate a prediction model; future consumption data of the target video on the storage space is generated based on the prediction model; based on the future consumption data, the target retention duration of the target video in the storage space is simulated; and in response to the target retention duration being less than a set retention duration, the storage mode of the target video is adjusted. The application generates future consumption data that conforms to overall distribution characteristics and time coherent changes with the aid of the prediction model, and the target retention duration obtained based on the future consumption data is more in line with the storage consumption of the target video and is more accurate. Therefore, when the storage mode of the target video is adjusted based on the target retention duration, it can be ensured that the retention duration of the target video meets the demand.

The invention provides a reliability prediction method and device based on vehicle after-sales data and electronic equipment, and relates to the field of vehicle reliability prediction.The method comprises the steps that the vehicle after-sales data of multiple data sources are preprocessed, and standard vehicle after-sales data are obtained; extracting prediction features related to vehicle reliability from the standard vehicle after-sales data; training the prediction features by adopting a machine learning algorithm to obtain a reliability prediction model; and processing the current vehicle after-sales data of the target vehicle by adopting the trained reliability prediction model, and outputting a reliability prediction result of the target vehicle. Through multi-source vehicle after-sales data preprocessing, prediction features are accurately extracted, and a reliability prediction model is trained in combination with a machine learning algorithm, so that the potential fault type, occurrence probability and prevention measures of the target vehicle can be accurately output, and the problem of low prediction precision caused by experience or single data traditionally is solved; and the full-life-cycle reliability management level of the automobile is obviously improved.

The application discloses a multi-battery cabin EMU cooperative safety upgrading method and system, and belongs to the technical field of electrochemical energy storage system operation and maintenance. The method comprises the following steps: constructing a dedicated high-reliability management network isolated from a business network and performing hierarchical health diagnosis; performing cluster state sensing and parallel file distribution based on adaptive sliding window and hash check; before executing an upgrading instruction, forcibly performing business safety interlocking inspection including bus voltage, cell temperature, state of charge and system silent window, triggering upgrading only under safe working conditions, and synchronously starting cluster state cooperative monitoring; after upgrading is completed, automatically performing full-cluster software version consistency check, and generating a structured audit report and an automatic fault disposal work order based on the check result. The application deeply embeds safety logic into the upgrading process, realizes cluster cooperative sensing and automatic operation and maintenance closed loop in the upgrading process, and significantly improves the safety, reliability and operation and maintenance efficiency of large-scale energy storage system software upgrading.