207 results about "Maintenance system" patented technology

A data registration apparatus (200) processes original data saved in a data source and registers the processed data. A data verification apparatus (300) computes a digest value of reference combined data which is generated based on a digest value of the original data and a digest value of the processed data as a reference digest value. The data verification apparatus computes a digest value of the registered processed data as a provisional digest value, retrieves digest value saved in the data source as a provisional digest value, computes a digest value of verification combined data which is generated based on the provisional digest value of the original data and the provisional digest value of the processed data as a verification digest value, and compares the verification digest value with the reference digest value.

This invention provides an intelligent operation and maintenance system for prefabricated substations based on big data, belonging to the field of prefabricated substation operation and maintenance technology. The data access unit unifies the time alignment of electrical operation, environmental monitoring, and historical operation and maintenance data, constructing a constrained time-series data structure based on equipment topology; the state evolution modeling unit extracts multi-scale features, generating three sets of evolution parameters: impedance, capacity, and load; the control variable reconstruction unit generates enhanced control input vectors through component mapping and iterative feedback; the real-time control execution unit outputs reactive power regulation and voltage control commands accordingly; and the human-machine feedback unit embeds manually corrected information and updates control parameters. This invention solves the problems of lack of topological constraints in multi-source data and the difficulty of embedding human experience into automated control, improving the intelligence level, control accuracy, and adaptability of prefabricated substation operation and maintenance.

The application provides an AI-driven adaptive rendering energy storage system intelligent operation and maintenance system, which comprises a terminal interaction layer, the terminal interaction layer is configured to obtain energy storage modeling image data of a to-be-rendered area screen and interaction behavior data of interaction behavior between a user and the to-be-rendered area screen; an AI decision layer, the AI decision layer is configured to perform multi-factor fusion calculation according to the energy storage modeling image data and the interaction behavior, obtain a dynamic LOD switching strategy and hierarchical rendering optimization parameters, and adjust the dynamic LOD switching strategy and the hierarchical rendering optimization parameters; and a rendering execution layer, the rendering execution layer is configured to perform image rendering on a modeling model in the to-be-rendered area screen according to the dynamic LOD switching strategy and the hierarchical rendering optimization parameters, and monitor real-time frame numbers of the to-be-rendered area screen in real time. The application solves the problem that a large-scale energy storage power station digital twin scene cannot be rendered smoothly on a Web end in the prior art through the above system.

This application discloses a natural language-driven edge-cloud collaborative intelligent operation and maintenance system and its control method, belonging to the field of intelligent operation and maintenance technology. The system includes a management plane, a control plane, an edge agent, an intelligent operation and maintenance center, a task center, and a skill center. The intelligent operation and maintenance center uses a directed graph state machine engine to convert natural language operation and maintenance goals into executable steps. The task center atomically distributes tasks to the edge agent and streams execution fragments. The skill center implements version management and AI-assisted generation of operation and maintenance skills. This invention achieves a fully automated closed loop from understanding operation and maintenance intentions to edge execution and knowledge accumulation, significantly shortening fault repair time, improving operation and maintenance security and knowledge reuse capabilities, and has outstanding substantive features and significant progress.

Embodiments of the present application disclose a routing device operation and maintenance method, an operation and maintenance system, an electronic device and a storage medium. The routing device operation and maintenance method comprises: obtaining state data of a routing device; performing combined processing on the state data to obtain a plurality of state data groups; each state data belongs to one or more state data groups; each state data group corresponds to a data category; based on a pre-configured correspondence between prompt information and data categories, target prompt information corresponding to each data state group is obtained; each prompt information describes a data analysis strategy; based on the target prompt information and the state data in the state data group, a first large model is used for data analysis processing to obtain a data analysis result; and operation and maintenance processing is performed on the routing device according to the data analysis result. The present application can improve the operation and maintenance efficiency of the routing device.

The application discloses a multimodal intelligent interaction method based on a large language model, and the interaction method comprises the following steps: receiving multimodal input of a user; analyzing the user's intention, decomposing a complex task into multiple subtasks, and coordinating the execution of various professional tools; processing multiple types of input data; integrating various financial professional analysis tools and services; managing and maintaining the knowledge base of the system, dynamically updating and real-time searching knowledge; and providing the infrastructure required for system operation. Through automatic task decomposition and tool calling, the manual maintenance workload is reduced.

This application belongs to the field of chemical equipment maintenance management technology, specifically providing a predictive maintenance management system for the entire lifecycle of chemical equipment. It mainly includes: a data standardization module: acquiring at least three operating parameters of the target chemical equipment within a first time period, standardizing these parameters to obtain standardized operating parameter data; and a coupling analysis module: based on the standardized operating parameter data, analyzing the dynamic coupling relationship between multiple operating parameters, and calculating the real-time correlation coefficient between parameter pairs to form a real-time correlation matrix. This application effectively solves the problem in existing technologies where maintenance methods struggle to identify early potential problems from subtle changes in parameter correlations. It achieves dynamic and accurate assessment of equipment health status, provides a comprehensive basis for maintenance decisions, reduces resource waste and failure risks, and improves the adaptability of the maintenance system.

This invention relates to the field of glass production technology, specifically disclosing a glass edging system. The invention includes a distributed sensing unit, a closed-loop control unit, an active execution drive unit, and a low-power wireless communication unit. The distributed sensing unit is used to collect real-time data on glass edge stress and clamping gap for glass edging adaptation. This invention achieves real-time, comprehensive, and accurate collection of core operating conditions of the glass edging system across all dimensions through the distributed sensing unit, effectively capturing early hidden dangers in the glass and edging system. Through the coordinated linkage of the closed-loop control unit and the active execution drive unit, a complete closed loop of "monitoring-judgment-execution-feedback" is constructed, enabling proactive intervention and precise elimination of abnormal operating conditions. The low-power wireless communication unit enables bidirectional data interaction between the system and external operation and maintenance platforms, seamlessly connecting to intelligent operation and maintenance systems in scenarios such as smart buildings, intelligent connected vehicles, and rail transit.

The application relates to an on-orbit maintenance method for a self-repairable satellite-borne DSP, which is realized by an on-orbit maintenance system and is used for maintaining and managing a DSP chip; the on-orbit maintenance method comprises the following steps: S1, powering on the on-orbit maintenance system; S2, performing three-mode redundancy comparison error correction and CRC check word checking to judge whether the checking is passed; S3, mapping the DSP chip to a target chip area address and performing DSP program data loading; S4, judging whether the monitoring function of the DSP chip is normal; and S5, downloading the chip area address of the DSP program data which is not passed in the telemetry and executing satellite platform instructions to repair the DSP program data. The application has simple architecture, low implementation difficulty, can effectively prolong the on-orbit life of a satellite and other space vehicles while keeping low cost.

A predictive maintenance system includes an irrigation system including a plurality of components and configured to irrigate an area of tillage. The predictive maintenance system includes a sensor configured to generate a signal indicative of a condition of at least one of the plurality of components of the irrigation system based on network power quality. The sensor is disposed at a central pivot of the irrigation system or a main break of a utility. A memory includes instructions that, when executed by a processor, cause the predictive maintenance system to receive the sensed signal, determine a change in the condition of the at least one component, and predict a maintenance need of the at least one component based on predetermined data.

The application provides a device maintenance system and method based on big data analysis, the system comprising a device basic data unit, a maintenance specification confirmation module, an operation early warning module and a device point inspection module, wherein the device basic data unit comprises a device static data module and a device dynamic data module, the device static data module is configured to add, delete, query and modify device static data, and the device dynamic data module is configured to add, delete, query and modify device dynamic data; the maintenance specification confirmation module is configured to confirm a maintenance threshold; the operation early warning module is configured to perform big data statistics and analysis on the device static data and the device dynamic data, and automatically generate a warning notification when the maintenance threshold is reached; and the device point inspection module is configured to generate an inspection plan through the device static data, the device dynamic data and the warning notification, and update the device dynamic data. The application can reduce the device failure rate and intelligently assist device managers and operators in device maintenance.

This invention relates to the field of railway bridge pier maintenance technology, specifically a smart maintenance system for high-speed railway bridge piers. It includes two symmetrically locked, arc-shaped support frames on the upper arc-shaped structure of the pier. Each support arc-shaped support frame is connected to a spraying component. Locking components are provided on the sides of the support arc-shaped support frames corresponding to the pier for securing them to the pier. This system can perform comprehensive spraying maintenance on bridge piers of different models. Simultaneously, the locking components in this system can stably lock the support arc-shaped support frames to the arc-shaped structure at the upper end of the pier and its top connecting protrusion, thus preventing the spraying pipes from loosening. The system can automatically control the system to perform timed spraying maintenance on the bridge piers through a central control module. A humidity sensor can determine whether it is raining; spraying maintenance will not be performed during rainy weather, and spraying maintenance will stop if rainfall occurs during the spraying process.

The application discloses a link detection method and device, an electronic device and an operation and maintenance system. The method comprises the following steps: determining at least two first sub-domains in a network based on the device state of each device in the network, wherein each sub-domain comprises at least one device; issuing a first message to a near-field integrated server, wherein the first message carries the path identification related to each first sub-domain in the at least two first sub-domains; sending a detection packet to a first loop, wherein the first loop is connected based on the first message; and obtaining the monitoring result of the first loop based on the measurement result corresponding to the detection packet reported by the at least two first sub-domains.

This invention proposes a multi-robot operation and maintenance system and method based on digital twins and cloud-edge-device fusion. It belongs to the interdisciplinary fields of intelligent manufacturing, industrial IoT, and human-machine collaborative operation and maintenance. The method includes: constructing a human-machine collaborative data acquisition network by integrating industrial-grade biometric sensors and distributed data acquisition units; deploying standardized interfaces based on a microservice architecture to perform layered preprocessing of multi-source heterogeneous data to generate a fused dataset; and achieving a breakthrough improvement in operation and maintenance efficiency and security through deep integration of biometric perception and digital twin technologies.

The application discloses a bridge full life cycle management and maintenance system based on digital twinning, which is used for the full life cycle management and maintenance of a cable-stayed bridge and comprises a data acquisition and fusion module, a digital twinning model construction and dynamic updating module and a structure health assessment and prediction module.The data acquisition and fusion module is used for acquiring monitoring data of the cable-stayed bridge in real time and fusing and processing multi-source data by using an adaptive weighting method.The digital twinning model construction and dynamic updating module is used for constructing and continuously calibrating a parameterized finite element simulation model based on bridge design parameters and fused data output by the data acquisition and fusion module, so as to serve as a digital twinning model corresponding to the physical bridge, and the digital twinning model can simulate material performance degradation.The structure health assessment and prediction module is used for assessing the current health state of bridge components by using residual characteristics of digital twinning model output and measured data based on the updated digital twinning model output by the digital twinning model construction and dynamic updating module.The application realizes intelligent management and maintenance of the full life cycle of the cable-stayed bridge.

This application discloses a smart operation and maintenance system and management method for broadcast television transmitting stations, relating to the field of operation and maintenance management technology. It includes: a provincial center platform, branch center platforms, a smart station platform, and an operation and maintenance platform. The provincial center platform can interact with both the branch center platform and the smart station platform; the branch center platform can interact with the smart station platform; and the smart station platform can interact with the operation and maintenance platform, thereby achieving integrated management among the provincial center, branch centers, smart stations, and operation and maintenance. The smart station system includes a comprehensive display subsystem, a mobile application subsystem, an equipment operation management subsystem, a broadcast signal monitoring subsystem, a maintenance and repair management subsystem, a technical business management subsystem, a station comprehensive management subsystem, a smart operation and maintenance auxiliary subsystem, and a display system. This application aims to solve the problems of decentralized station operation and maintenance and difficulties in coordination.

The present application relates to a kind of based on data processing operation and maintenance factory visualization system, belong to the data processing field specially applicable to administrative, commercial, financial, management, supervision or prediction purpose, more specifically, it relates to big data processing field.The present application is based on data processing and artificial intelligence technology, and a set of operation and maintenance factory visualization system is developed, by real-time acquisition and analysis the massive operation and maintenance data of the IT equipment and application of the set IDC machine room, using the AI algorithm realized based on intelligent analysis prediction model carries out intelligent analysis and prediction, to obtain the equipment failure information and application failure information of the set IDC machine room in the current time section next time section, so that IDC machine room operation and maintenance system in the visual interface will the various operation and maintenance work of the set IDC machine room be carried out transparent factory type's assembly line visual display, equipment failure information and application failure information are synchronously shown, to help operation and maintenance personnel promptly master the running state of the set IDC machine room.

An automated maintenance system for solar components is disclosed. The system includes a condensate reservoir, a wiping element, a motor, and a fluid delivery element. The fluid delivery element is configured to transport a fluid from the condensate reservoir to a solar component, and the motor is configured to operate the wiping element via a controller. The system is designed to clean solar components automatically, without the need for manual intervention. The system is efficient, cost- effective, and environmentally friendly.

This invention relates to the field of equipment data processing, and discloses an interactive data processing method, system, device, storage medium, and program product. The interactive data processing method provided in this invention includes: collecting multi-source data information from steel equipment, including a three-dimensional spatial visual image and sensor information of the steel equipment; uniformly semantically encoding the data information of different formats from the multi-source data information and inputting it into a pre-acquired semantic reasoning model, so that the semantic reasoning model generates structured instructions based on the multi-source data information; responding to the structured instructions, generating three-dimensional spatial rendering information and interactive feedback information of the steel equipment, and superimposing the three-dimensional spatial rendering information and interactive feedback information on the three-dimensional spatial visual image of the steel equipment. The method provided in this invention improves data processing efficiency and the accuracy of fault identification in steel equipment, realizing precise and immersive guidance for the AR operation and maintenance system of steel equipment.

The application provides a heat-aware storage engine performance optimization method, which comprises the following steps: determining a target database, initializing a maximum heap in the memory of the target database, and maintaining the file heat of the system; reading file heat information from the log of the target database, and recovering the file heat identification mechanism based on the maximum heap; writing the file heat information into a Memtable of an LSM-tree memory component by a storage engine, and making the data of the Memtable fall to the disk and generate a CuckooTable file; establishing a Bloom filter for the CuckooTable file on an NVM; recording the corresponding relationship between the Bloom filter and the file in an index queue; and updating the index queue when the number of files in the LSM-tree reaches a preset threshold. The method provided by the application can be used to optimize the performance of a storage engine designed with a LSM-tree structure.

The application discloses a special vehicle engine digital maintenance system based on big data analysis, comprising: a data set generation module for forming an engine time series data set; a feature generation module for forming a window feature sequence; a prototype construction module for forming a working condition prototype set and an ordered working condition label sequence; a similarity generation module for forming a directed transition similarity matrix; a cost generation module for forming an adaptive segmentation cost sequence; an initial segmentation module for forming an initial working condition state segment set and an initial state segment boundary by using an improved SegmenTier algorithm; an evidence interval generation module for forming a precursor evidence interval; and a re-segmentation output module for outputting a working condition recognition result, a precursor segment positioning result and a maintenance triggering result. The application improves the working condition segmentation accuracy and the maintenance triggering reliability.

The application relates to the technical field of industrial control and process automation, and discloses a remote diagnosis predictive maintenance system for an industrial instrument cluster, which comprises a topology evolution and diagnosis module, a residual error decoupling and false alarm stripping module and a health assessment and strategy generation module.The topology evolution and diagnosis module combines a static space topology with real-time actuator state feedback to calculate a dynamic connection weight matrix, and outputs a suspected fault node set through a graph neural network.The residual error decoupling and false alarm stripping module calculates an estimated controlled deviation based on a first-order inertia transfer function, strips the estimated controlled deviation from an original residual error to obtain a decoupled residual error sequence, and performs false alarm identification and determination.The health assessment and strategy generation module extracts a degradation characteristic parameter based on the decoupled residual error sequence, calculates an instrument health index, divides a health grade, and outputs a corresponding control protection strategy.The application can effectively distinguish normal process disturbances from real instrument faults, quantitatively evaluate the gradual degradation process of the instrument, reduce the false alarm rate of monitoring, and avoid unexpected chain shutdown intervention caused by instrument degradation.

The application discloses an intelligent predictive maintenance system for an external pressure type ultrafiltration membrane based on a pollution state sensing, relates to the technical field of ultrafiltration membrane maintenance, and is characterized in that the system is a closed-loop intelligent maintenance system, which is constructed through a collaborative architecture of a sensing layer and an execution layer, data sharing and dynamic optimization decision-making, forms a closed-loop maintenance process of'sensing-analysis-early warning-decision execution', breaks the problem of fragmentation of each link of a traditional system, improves the intelligent level and operation efficiency of the maintenance system, promotes the transformation of ultrafiltration membrane maintenance from 'after-the-fact remediation' to 'prior prediction', and provides a core technical guarantee for the large-scale and efficient application of the ultrafiltration membrane technology.

An operation and maintenance method, a model training method, and related apparatus are disclosed. The operation and maintenance method includes: acquiring a user's input question; inputting the input question into a first model to output an identifier of a target operation and maintenance module; and calling the target operation and maintenance module according to parameter information indicated by the input question to obtain the answer to the input question. The first model is trained on a training dataset related to a second operation and maintenance system. In this application, the first model is trained on a training dataset related to the second operation and maintenance system, so the target operation and maintenance module identified by the first model can be an operation and maintenance module within either the first or second operation and maintenance system. Furthermore, users can use the first operation and maintenance system to operate and maintain network devices managed by other operation and maintenance systems, improving the convenience of user operation and maintenance work.

This application discloses a substation operation and maintenance system and method based on digital twins, belonging to the field of operation and maintenance management technology. It includes: constructing a substation digital twin based on substation operating status data to generate a substation digital twin model; performing coupled simulation analysis on the substation digital twin model to confirm the substation coupled data; performing anomaly detection and comprehensive analysis of substation equipment operating status based on the substation coupled data to obtain comprehensive indicator data of equipment operating anomalies and equipment fault trend prediction data; evaluating the operation and maintenance priorities of the substation digital twin model to generate a substation equipment operation and maintenance priority sequence; generating substation operation and maintenance strategies based on the substation equipment operation and maintenance priority sequence to obtain a substation operation and maintenance strategy set; and distributing the substation operation and maintenance strategy set to the control terminal to execute intelligent substation operation and maintenance operations. This application has the effect of improving substation operation and maintenance efficiency.

This application provides a remote operation and maintenance system and method for petrochemical equipment based on the Industrial Internet of Things (IIoT). It collects operational monitoring data from petrochemical equipment and determines the explicit behavioral feature vectors corresponding to the equipment. Through feature learning using these explicit behavioral feature vectors, it determines the equipment health indicators. It collects coolant information corresponding to each reactor node in the petrochemical equipment and performs delayed correlation analysis on this information to obtain implicit behavioral feature vectors. Based on these implicit behavioral feature vectors, it performs implicit risk analysis. The remote operation and maintenance center then executes corresponding remote operation and maintenance strategies based on the implicit risk information. This system enables risk identification through the implicit behavioral analysis of petrochemical equipment, achieving real-time control of implicit risks and improving the operational safety of petrochemical equipment.

The present application relates to the field of building intelligence, in particular to a digital twin hybrid cloud edge collaborative intelligent real estate building group operation and maintenance system, the system collects building basic information, equipment operation data and environmental parameters, establishes a digital twin scene database, and synchronizes to edge devices, uses BIM, GIS, Internet of Things, 5G and AI technology to build a virtual and real digital intelligent building scene, realizes real-time synchronization of virtual scene and physical environment through AR / VR devices, the operation and maintenance module includes multi-source heterogeneous data fusion, edge intelligent analysis and decision-making, self-adaptive model training and iterative optimization, predictive maintenance algorithm of virtual-real mapping and multi-level collaborative decision-making and autonomous scheduling mechanism, realizes full digital management of building group, and the monitoring module monitors the state and operation of the edge device, provides data services, supports management decision visualization, and the system effectively improves the intelligent and digital level of building group operation and maintenance.

The application discloses a water diversion channel drag chain type maintenance system and a maintenance method thereof, and the system comprises a drag chain vehicle system, a support fixing assembly and a maintenance assembly; the drag chain vehicle system provides driving force when traveling along the wall surface of the water diversion channel; the support fixing assembly comprises a diagonal support assembly and a horizontal support assembly which are hinged to the drag chain vehicle system, the end of the diagonal support assembly abuts against the inner wall surface on the opposite side when the diagonal support assembly is unfolded, and the horizontal support assembly is hung below the diagonal support assembly; the maintenance assembly comprises a slewing assembly and a working basket which are connected to the horizontal support assembly, and the slewing assembly drives the working basket to horizontally rotate to closely abut against the inner wall surface for maintenance. The drag chain vehicle system drives the maintenance assembly to travel and work in the water diversion channel, the maintenance complexity is reduced, the delicacy, safety and reliability of the maintenance work are ensured, and full coverage maintenance of the water diversion channel is realized.

During disasters, widespread power outages occur, and after power is restored, power shortages arise, making it impossible to maintain power supply to evacuation centers, medical facilities, and communication equipment. [Solution] The present invention provides a power supply maintenance system that enables the maintenance of power supply both before and during supply by identifying the required amount of power corresponding to the supply period or the remaining period of the supply period based on the load to be supplied, identifying the amount of power that can be supplied by multiple power sources, excluding or limiting power sources that do not meet the required amount of power from candidates, determining the configuration of a power source or group of power sources that will satisfy the required amount of power over the supply period, and changing the configuration if there is a possibility that the supply maintenance conditions will not be met during supply. Furthermore, this invention is applicable to various uses, including power supply to evacuation centers, medical facilities, and communication equipment during disasters.