90 results about "Maintenance plan" patented technology

The utility model provides a kind of vacuum pump test system, it is related to vacuum pump technical field.Specifically, the vacuum pump test system includes test cover, detection module, gas supply module, period test module and reading module.Wherein, test cover is used to connect with the import of the vacuum pump to be measured, to simulate the process chamber connected with the vacuum pump to be measured.And gas supply module, period test module and reading module are connected with test cover.Wherein, gas supply module is used to supply gas to test cover and monitor gas supply flow, to simulate the load condition of the vacuum pump to be measured in actual working scene. Reading module is used to read the vacuum degree of test cover, to facilitate the evaluation of the key performance indicators such as pumping efficiency and ultimate vacuum degree of the vacuum pump to be measured.And period test module is used to supply gas to test cover and simulate test environment, to carry out period test to the vacuum pump to be measured, to predict the service life of the vacuum pump to be measured, to formulate maintenance plan accordingly.

Provided are an information processing method, a program, and an information processing system that facilitate efficient maintenance of equipment. In the information processing method, prescribed component information related to a prescribed component from among a plurality of components included in equipment to be maintained is acquired (S41). In the information processing method, a schedule (second maintenance plan) including second maintenance that is associated with a sign of an unexpected abnormality of a prescribed component and can be performed aperiodically unlike first maintenance performed periodically is generated on the basis of the acquired prescribed component information (S44). The information processing method includes outputting the generated schedule.

The present application relates to the technical field of micro-grid, and provides a maintenance plan generation method and system suitable for high-load local isolated network, comprising: for each month in each maintenance plan date combination, calculating the power shortage when each thermal power unit is shut down; based on the power shortage, combining the new energy output in previous years, calculating the new energy supplement power when each thermal power unit is shut down in each month; based on the new energy supplement power, calculating the daily power purchase amount when each thermal power unit is shut down in each month; for each maintenance plan date combination, accumulating the daily power purchase amount of all days to obtain the grid power purchase amount corresponding to the maintenance plan date combination, and taking the maintenance plan date combination corresponding to the minimum grid power purchase amount as the annual optimal maintenance plan. By using the certain periodicity of new energy in time, the power gap in the power grid power shortage state is made up, the multi-energy complementary effect is realized in the maintenance plan aspect, and the external power purchase amount is reduced.

Quantum entanglement neuro morphology bridge disease holographic monitoring repair method and system: deploy quantum entanglement sensor array to obtain micro-disease information and fuse infrared sensor, laser radar, industrial camera data, encrypt transmission preprocessing through quantum communication network to generate fusion feature vector, disease category probability, severity score and comprehensive risk index; build bridge three-dimensional coordinate system to reconstruct three-dimensional geometric model, map the index to three-dimensional coordinate system, output three-dimensional disease holographic model through spatial clustering; weighted sum of comprehensive risk index, average severity, structure and traffic importance weight to get repair priority score and sort, under the constraint of cost and construction period, take repair cost, time, risk reduction and life increment as the comprehensive optimization objective, solve the construction control parameter and dynamically evaluate the repair quality to generate structured repair record; continuously collect monitoring data to calculate health indicators, establish degradation model to determine failure probability and remaining life, form maintenance plan and update parameters.

This invention presents an intelligent global dynamic maintenance method for multi-component systems considering imperfect maintenance, based on deep reinforcement learning. First, it acquires the degradation observation sequence of each component through periodic inspections, establishes a stochastic process model of component degradation, and achieves iterative RUL prediction through offline training and online Bayesian updates. Then, it determines the baseline predictive maintenance timing and, considering imperfect maintenance improvement factors, determines the baseline delayed maintenance time. Simultaneously, it constructs a maintenance timing deviation penalty cost model for group maintenance. Next, it uses the PPO algorithm to perform rolling optimization on the dynamic global group maintenance (DGGM) to output maintenance combinations and maintenance execution times. When a sudden failure occurs during operation, dynamic opportunistic imperfect maintenance (DOIM) is triggered, performing corrective maintenance on the failed component and determining the opportunistic maintenance component set, synchronously updating subsequent maintenance plans, thus achieving intelligent global dynamic maintenance decision-making in an infinite time domain. This invention realizes joint optimization of predictive and opportunistic maintenance for multi-component systems.

The application provides a method and system for checking the cross-modal information consistency of a maintenance plan based on a large model, and relates to the technical field of big data analysis. The method comprises the following steps: identifying a target maintenance plan through a power large model, determining key maintenance content, an unstructured proportion, and an attachment type complexity; obtaining a text data complexity and a business risk index based on key maintenance content analysis, as a first checking difficulty coefficient and a second checking difficulty coefficient; determining a third checking difficulty coefficient according to the unstructured proportion and the attachment type complexity evaluation; taking the expected review time limit of the target maintenance plan as a constraint, formulating an adaptive checking depth and an adaptive checking breadth based on the first checking difficulty coefficient, the second checking difficulty coefficient, and the third checking difficulty coefficient; and performing cross-modal information consistency checking on the target maintenance plan according to the adaptive checking depth and the adaptive checking breadth. The dynamic allocation of resources for checking the cross-modal information consistency of a maintenance plan is achieved.

The application discloses a kind of plan optimization systems of distribution network maintenance, it is related to power grid management technical field, including intelligent maintenance dispatching optimization module, equipment monitoring and prediction module, panoramic maintenance plan simulation module, dynamic risk assessment module, inspection robot management module.Intelligent maintenance dispatching optimization module is used to analyze distribution network data, equipment monitoring and prediction module is used to monitor distribution network equipment operating state, and future fault occurrence time and position are predicted.Panoramic maintenance plan simulation module carries out dynamic simulation to maintenance scheme.The application introduces multiple models, effectively overcomes the deficiency of traditional technology in flexibility, real-time and accuracy, so that the system has adaptive learning and dynamic adjustment capability, can quickly respond to failure in complex and changeable working environment, optimize resource scheduling, and significantly reduce maintenance cost and time, the system greatly improves the stability and reliability of distribution network equipment, provides solid technical support for intelligent power operation and maintenance.

This invention belongs to the field of energy simulation technology and relates to an energy flow network simulation system and method for steel enterprises. It includes: a system configuration module for configuring parameters based on user-inputted information and forming a structured configuration data package; a front-end operation module for configuring a visual interactive interface based on the configuration data package; generating a Gantt chart based on pre-set production and maintenance plans; and drawing a dynamic energy flow diagram based on user-defined equipment and energy nodes; and a back-end service module for calculating energy prediction results based on the Gantt chart, dynamic energy flow diagram, and configuration data package using a pre-set production and consumption model, and performing scheduling optimization when energy prediction results are unbalanced to obtain an energy scheduling balance result. This invention achieves collaborative simulation of production and energy, more intuitively displays the energy balance process, provides a more comprehensive simulation platform for the steel industry, and facilitates systematic management by enterprises.

The application relates to the technical field of information management, and provides a smart factory production line management and control method, device, equipment and medium, the method comprises the following steps: acquiring working data streams and category information corresponding to a plurality of production devices on a production line in a smart factory; obtaining a target state judgment rule according to the working data streams and the category information corresponding to the plurality of production devices; performing state judgment on the working data streams corresponding to each production device according to the target state judgment rule to obtain a state judgment result corresponding to each production device; performing fault trend prediction on the production line according to the state judgment result corresponding to each production device to obtain a target fault trend; and generating a production line maintenance plan according to the target fault trend, so that the plurality of production devices on the production line can be maintained according to the production line maintenance plan. The scheme in the application improves the stability and efficiency of the production of the production line in the smart factory.

This invention discloses an air-storage cavern structure based on UHPC lining and distributed optical fiber, and its monitoring method, belonging to the field of underground cavity engineering technology for air-storage. The structure includes a concrete lining structure set on the inner wall of the natural cavern; a concrete reinforcement structure set on the inner side of the concrete lining structure, forming an energy storage chamber inside; and an optical fiber sensor embedded in the airtight protective layer. By integrating ultra-high performance concrete layered lining with distributed optical fiber sensors, a cavern gas storage system integrating structural load-bearing, multiple sealing and intelligent sensing is constructed. Based on a coupled deformation model and a leakage threshold model, a multi-dimensional early warning index and a graded early warning mechanism are established to accurately capture early micro-damage and gas leakage precursors, and automatically generate maintenance plans. This effectively solves the technical problems of the disconnect between high-performance sealing materials and intelligent monitoring systems, and the inability to identify early hidden damage in a timely manner.

A method of ranking recommendations includes receiving, by one or more processing circuits, a recommendation including an indication of an equipment fault, retrieving, by the one or more processing circuits, context data associated with the equipment fault, the context data including at least three of a first priority associated with the equipment fault, a work order status for a work order associated with the equipment fault, a monetary value associated with the equipment fault, a health parameter associated with the equipment fault, or a second priority associated with at least one of (i) a piece of equipment, (ii) a space, or (iii) an individual associated with the equipment fault, calculating, by the one or more processing circuits, a score for the recommendation based on the context data, and modifying, a work order queue or maintenance schedule based on the score.

The present application relates to the technical field of railway maintenance plan management, and particularly relates to a railway ladder window maintenance plan management system, which comprises a central processor, a basic data management module, a disease driving plan generation module, a process approval engine, a registration and cancellation verification unit and a whole-cycle traceability database. The central processor constructs a multi-dimensional association graph containing equipment, area, power supply and disease nodes based on heterogeneous data; an initial maintenance plan draft is generated in response to a disease trigger signal; a multi-level signing is performed through the approval engine and a flow log is generated; in the execution stage, the registration and cancellation data are collected by the registration and cancellation verification unit and compared with the plan item point list, and the operation difference result is output; finally, the whole-process data is stored in the traceability database to form a digital archive. The present application realizes the whole-cycle closed-loop management of diseases, supports the automatic generation of maintenance plans, conflict verification and whole-process traceability, and improves the safety and management efficiency of maintenance operations.

This application relates to the field of power system maintenance optimization technology, and in particular to a method for rapidly generating power system maintenance plans. The method includes: first, collecting data on power grid topology, operation, maintenance requirements, and resource constraints to construct a topology model; then, calculating electrical distances based on node impedance matrices and performing clustering to obtain initial partitions. Next, optimizing the initial partitions using tie-line transmission power merging to determine sub-regions and cross-regional equipment lists. Maintenance tasks are allocated to each sub-region according to equipment location, and computing nodes are assigned to load data in parallel to generate a preliminary plan. A model is constructed based on operation and resource constraints, and cross-regional conflicts are identified and resolved according to the cross-regional list to form an integrated plan. Finally, the target plan is output after verification by the topology and constraint model. This application achieves rapid generation of large-scale power grid maintenance plans through adaptive partitioning, parallel solving, and conflict resolution.

This invention discloses a dynamic assessment method for sedimentation load in sewage pipe networks based on graph convolutional networks (GCNs). It relates to the field of GCN technology and aims to solve the technical problem of disconnect between dredging and anti-corrosion operation and maintenance decisions caused by a vicious cycle of mutually reinforcing corrosion and sedimentation in high-salt, high-corrosion industrial parks. The method includes: S100, constructing a training dataset; S200, training a sedimentation load assessment model; S300, generating real-time multi-dimensional node feature vectors; S400, model inference and outputting assessment results; S500, calculating a composite risk index and generating an operation and maintenance plan; and S600, incremental learning and updating the model. This invention designs a salt corrosion coupling feature weight adjustment algorithm, combined with a spatiotemporal feature fusion architecture of GCN graph convolutional layers and GRU temporal convolutional layers, to construct a salt corrosion coupling constraint module, achieving accurate quantification of the interaction between salinity concentration and corrosion rate in high-salt, high-corrosion industrial parks.

This application provides a vehicle maintenance method, apparatus, and computer equipment. The vehicle maintenance method includes: in response to a maintenance request for the vehicle, collecting real-time status data of the vehicle and obtaining damage data and abnormal driving behavior data from an abnormal event database; wherein the damage data is determined based on video data collected in the vehicle's sentry mode; the real-time status data characterizes the current usage level of each component in the vehicle; and based on the real-time status data, damage data, abnormal driving behavior data, and the vehicle's historical maintenance plans, generating a target maintenance plan for the vehicle. This method can reduce over-maintenance or missed maintenance of vehicles, improving the accuracy of vehicle maintenance and user experience.

The application discloses an industrial equipment predictive maintenance method and system based on digital twinning and AI, the method constructs and adaptively updates a digital twinning model containing geometry, physics and behavior, collects and fuses multiple source data in real time, inputs an AI model trained by transfer learning to perform health state evaluation and residual life prediction, compares AI prediction results with digital twinning simulation output, generates early warning information based on a dynamic threshold, and finally outputs a refined maintenance plan containing a maintenance time window; the application realizes data in-situ processing by integrating edge computing nodes, improves prediction accuracy by using multi-modal fusion and ensemble learning, realizes visual interaction by combining augmented reality technology, realizes deep fusion of data driving and mechanism model, and greatly improves the accuracy and advance of fault early warning, effectively reduces unplanned downtime and maintenance cost.

The application discloses a multi-type property maintenance work order automatic generation and management method and system, and the method comprises the following steps: triggering a timing task according to the execution time of a maintenance plan, and judging whether the maintenance plan is overdue; when the maintenance plan is not overdue, creating a work order record and writing the work order record into a business data table, and starting a work flow instance with a business primary key in the same database transaction; the work flow instance triggers an execution listener in a node flow process, updates a work order processing state, and executes corresponding business logic according to a node identifier; a timing job record is written in a user task node, a work order timeout mark is updated and a timeout alarm is sent when a timeout deadline arrives, so that the work order automatic generation, process linkage and timeout control are realized.

This invention relates to an intelligent drawing review and maintenance support decision-making method and system based on image recognition, specifically as follows: Drawing input and preprocessing: Receiving multi-format drawings and performing adaptive optimization to provide high-quality data for subsequent recognition; Multimodal element extraction: Extracting graphic components, text information, and attribute parameters from the drawings to establish a multimodal dataset; Multi-dimensional review and defect identification: Reviewing drawings from three dimensions: compliance, logical consistency, and maintenance relevance, locating and classifying defects; Defect-maintenance correlation analysis: Establishing a mapping relationship between design defects and maintenance plans, quantifying the impact of defects; Maintenance plan optimization generation: Selecting the optimal maintenance plan through a multi-objective optimization algorithm; Result output and model iteration: Outputting a standardized review report and maintenance plan, and optimizing the model based on feedback; This invention has the advantages of a multimodal fusion architecture, a closed-loop process, adaptive and self-learning capabilities, and the ability to achieve quantitative decision support.

This invention relates to the field of equipment operation and maintenance, and particularly to a predictive maintenance method, system, equipment, and medium for rail transit platform screen doors. The method includes: acquiring multimodal sensing data and time-varying operating condition characteristics of the station area; inputting the sensing data into a multimodal fusion model, and extracting physical loss, time-series load, and operation and maintenance semantic feature vectors through heterogeneous parallel branches; performing self-learning alignment and fusion of features based on a cross-attention mechanism, and outputting an operating status score and associated text; inputting the associated text into a large language model, using a fault causal logic chain to back-verify and correct candidate root causes; analyzing the fault root causes and multimodal sensing data to obtain an initial maintenance strategy, and performing dynamic optimization to generate a maintenance plan. Therefore, this invention, through deep fusion of multimodal data and causal logic reasoning, realizes fault early warning, root cause localization, and operation and maintenance resource scheduling for rail transit platform screen doors, improving the intelligence level of operation and maintenance management and the equipment operation guarantee capability.

The application discloses an electric power maintenance equipment expansion method and system, and relates to the technical field of electric power equipment maintenance.The method comprises the following steps: firstly, based on the power grid equipment account data, the maintenance equipment terminology library and the artificial definition rule, a mapping relationship dictionary describing the connection, subordination and expansion relationship between the equipment is pre-constructed; secondly, a simplified maintenance equipment input list is received, and based on the dictionary, multiple rounds of iterative reasoning and condition judgment are carried out until the equipment set no longer changes, and a complete maintenance equipment set is generated; finally, the set is subjected to integrity and consistency verification and then output to a maintenance task system.The system comprises corresponding mapping construction, iterative expansion and verification output modules.The application combines the pre-constructed mapping with dynamic iterative reasoning, realizes automatic, accurate and efficient expansion of the maintenance equipment, solves the problems of long time consumption, easy omission in the traditional manual mode and the incapability of the existing static rule method to deal with complex chain type association, and significantly improves the efficiency and reliability of the maintenance plan making.

The present application belongs to the field of generator unit maintenance plan arrangement, and relates to a kind of construction method of high-efficiency generator unit maintenance model based on unit aggregation and linear relaxation, based on unit combination model, establishes the basic UC maintenance model, and models the generator unit maintenance problem as a mixed integer programming problem;Further, for the problem that the production and operation constraints of thermal power generating units in the medium and long term maintenance problem of generator unit are complex and difficult to solve, the RCUC model is embedded into the maintenance model by introducing unit aggregation and linear relaxation technology, and the RCUC maintenance model is established;For the maintenance constraints of unit cluster, an improved RCUC maintenance model is proposed, which uses integer variables instead of 0-1 variables for modeling, reduces the size of integer variables and constraints, and improves the solving efficiency of the generator unit maintenance model.

This invention provides a green building life cycle assessment method and system based on pile foundation data. It acquires material and energy consumption data during the construction phase, including pile foundation data, and calculates the building's inherent environmental load by combining this data with an environmental impact factor database. During the service life, it collects pile foundation structural response monitoring data and uses a structure-foundation correlation model to invert and obtain the equivalent load time series of the superstructure reflecting the building's actual usage state. The equivalent load time series is input into a pile foundation performance degradation model to generate a maintenance plan and calculate the maintenance environmental load. Simultaneously, it identifies the building's actual usage mode and calibrates the building energy consumption model to calculate the operational environmental load. Based on the predicted state of the pile foundation performance degradation model at the end of the planned lifespan, it assesses the remaining bearing capacity of the pile foundation, formulates demolition, renovation, or reuse disposal strategies, and calculates the disposal environmental load. Finally, it summarizes the inherent, maintenance, operational, and disposal environmental loads to obtain the building's full life cycle environmental load assessment results.

The application discloses a power system dispatching automation technical field, and relates to a power supply balance risk prevention and control decision method, system, medium, equipment and product for a power outage maintenance plan. The method comprises the following steps: calculating the total output of all support newly started units and the total transmission capacity of all support standby tie-line channels in the power balance risk area during the maintenance period; if the first criterion is met, selecting the first support newly started unit in the order of the regulation cost index from small to large to carry out risk prevention and control; if the first criterion is not met and the second criterion is met, selecting the first support standby tie-line channel in the order of the regulation cost index from small to large to carry out risk prevention and control; if the first criterion is not met and the second criterion is not met and the third criterion is met, the remaining power gap is used to reduce the load power in advance. The application can effectively reduce the power outage risk and improve the safety and reliability of power maintenance.

The application discloses a kind of remote authorization locking systems of energy storage power station based on Internet of Things, and the application relates to the technical field of high-end equipment manufacturing and intelligent manufacturing equipment industry, cloud comprehensive management platform is used to globally dispatch management for the intelligent equipment and operation and maintenance personnel in energy storage power station, cloud comprehensive management platform includes work order dispatching module, permission configuration module and global situation awareness module, wherein work order dispatching module is used to generate operation and maintenance work order according to power grid dispatching demand and operation and maintenance plan, the information cooperation between the cloud comprehensive management platform, mobile operation and maintenance terminal and RFID intelligent lock node is established by the application, combined with the edge control gateway based on Internet of Things technology, multiple accurate control from "cloud authorization" to "bluetooth near field communication" to "RFID physical verification" is realized, and the technical problems that traditional lock management is chaotic and difficult to trace are effectively solved.

This invention provides a system and method for equipment fault diagnosis based on collaborative reasoning using a large model and knowledge graph. It relates to the field of equipment fault diagnosis technology and includes: a data layer for acquiring real-time operating data of the equipment under test and preprocessing the data; a knowledge layer connected to the data layer for analyzing the preprocessed real-time operating data to determine the presence of fault data; and a model layer connected to the knowledge layer for analyzing the fault data when the analysis results output by the knowledge layer determine the presence of fault data, thereby obtaining the cause of the fault and the corresponding maintenance plan. This invention deeply integrates data processing, fault identification, intelligent analysis, predictive warning, and lifespan assessment, achieving an upgrade in equipment fault diagnosis from passive response to proactive prevention, and from single-data judgment to multi-dimensional collaborative reasoning.

This invention relates to the field of intelligent logistics technology and discloses an express delivery order process operation and maintenance system based on AI intelligent agents. The system includes: leveraging the intent recognition and automatic operation and maintenance plan generation functions of the intelligent agent orchestration layer, combined with the hierarchical retrieval and self-evolving knowledge base of the knowledge enhancement layer, to quickly match cross-platform business rules and historical cases, improving the response speed and scientific nature of anomaly handling. It uses the MCP standardized protocol to uniformly call multi-platform interfaces, resolving issues of inconsistent interface specifications and poor integration, ensuring compliant and unified operation and maintenance. The data and observability layer provides full-process auditing, playback, and monitoring functions, enabling traceability of operation and maintenance trajectories and improving process transparency. A priority-based mechanism prioritizes the handling of core order anomalies, reducing operational risks. Simultaneously, the fully intelligent process significantly reduces reliance on manual intervention, effectively addressing pain points such as state confusion and low efficiency in existing technologies, and significantly improving the stability of logistics services and user experience.

The application provides a mold maintenance management system, comprising: a storage unit for storing information of a setting unit, a mold information input unit, a maintenance information input unit and a notification unit; a central processing unit for calling and receiving the information stored in the storage unit; the setting unit is used for displaying a mold maintenance plan, a maintenance plan reminding content and a reminding receiver setting interface; the mold information input unit is used for displaying a mold information input interface and obtaining mold information; the maintenance information input unit is used for displaying a mold maintenance plan information input interface and generating a maintenance record; the notification unit is used for issuing a maintenance reminder; the query unit is used for querying the information stored in the storage unit; and the display unit is used for displaying the information queried by the query unit. The application improves the convenience of querying and statistics of the mold maintenance related information, avoids missing or repeated maintenance, and improves the maintenance efficiency.

A system is provided that automatically notifies a user to perform maintenance for one or more parts of an appliance. The system automatically detects that an appliance has been purchased by a user and, in response to automatically detecting that the appliance has been purchased by the user, stores, in a database of an online electronic transaction platform, an appliance profile for a user comprising a list of appliances including the appliance associated with the user, the list of appliances being associated with a plurality of manufacturers. The system selects, in the database of the online electronic transaction platform, an individual appliance from the list of appliances and determines a maintenance schedule for one or more parts associated with the individual appliance. The system automatically notifies the user to perform maintenance for the one or more parts according to the maintenance schedule.

This invention belongs to the field of glass curtain wall maintenance technology. It discloses a method and system for full lifecycle management of glass curtain walls, including the following modules: a data acquisition module for collecting the status parameters of each glass panel in the glass curtain wall. This invention sets up a remaining lifespan prediction model to assign predicted lifecycle values ​​to all glass panels, and visualizes them in a three-dimensional digital twin model as a lifespan topographic map. Spatial statistical methods are used to identify clusters with significantly low remaining lifespans, i.e., lifespan depressions. By performing synergistic analysis and causal inference on the lifespan curves of the glass panels within these depressions, common causes leading to cluster performance degradation can be intelligently diagnosed. Based on this diagnosis, a unified investigation and collaborative maintenance plan for the entire depression can be generated, thereby achieving a systematic improvement from treating individual defects to enhancing regional performance. This avoids repetitive inspections and fragmented repairs, saving significant manpower and resources.

The present application relates to the technical field of management platform, especially to a maintenance plan and power generation scheduling collaborative optimization system and method based on deep reinforcement learning. The method is: collecting power grid multi-source heterogeneous data, standardizing and extracting features of the multi-source heterogeneous data, and constructing a multi-dimensional state feature vector; constructing a deep reinforcement learning model; inputting the real-time generated multi-dimensional state feature vector into the trained deep reinforcement learning model to output a comprehensive decision vector; generating a maintenance work order and a power generation scheduling instruction based on the comprehensive decision vector, and issuing and executing; collecting power grid actual operation data after the instruction is issued and executed, calculating the evaluation results based on the preset evaluation index system, and adjusting the parameters of the deep reinforcement learning model according to the evaluation results to drive the continuous optimization of the model. The present application improves the collaboration of maintenance plan and power generation scheduling, improves the efficiency of power grid operation, guarantees the safety and stability of power supply, and reduces the operation and maintenance cost.