88 results about "Maintenance planning" patented technology

The invention discloses a digitized collaborative management and control platform and method for a highway roadbed slope, and belongs to the field of highway engineering. The method comprises the steps that a three-dimensional geological information model of the highway roadbed slope is constructed; sensing and fusing multi-source heterogeneous data; intelligent analysis and early warning are realized; co-processing and closed-loop management; optimization and self-learning of a machine learning algorithm model; and visualizing the slope of the whole highway. According to the invention, through the AI algorithm, the conversion from manual interpretation to intelligent prediction is realized, the early warning time is advanced, and passive response is changed into active prevention and control. According to the invention, information sharing and business collaboration are realized through process driving, and the emergency disposal efficiency is greatly improved. Visual and scientific decision support is provided for slope state evaluation and treatment scheme making based on the digital twinborn model and the multi-source fusion data, and dependence on human experience is reduced. According to the invention, a complete slope electronic health record is established, a data basis is provided for long-term performance evaluation and maintenance planning of the slope, and full-life-cycle management of assets is realized.

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention discloses a remote operation and maintenance diagnosis and predictive maintenance system for heterogeneous equipment, and relates to the technical field of equipment operation and maintenance. The system comprises a data acquisition and access module, a data processing module, a diagnostic analysis module, a prediction maintenance module, a man-machine interaction module and a storage module. The data acquisition and access module supports multiple communication protocols and acquires operation data of heterogeneous equipment; the data processing module preprocesses the collected data and completes multi-source data fusion; the diagnosis analysis module realizes equipment real-time fault detection, fault positioning and fault type association through a two-layer detection mechanism, and generates a fault diagnosis report; and the prediction maintenance module outputs the residual service life and the potential fault risk of the equipment based on the prediction model, and generates a targeted maintenance strategy and plan. According to the system, the problems of difficult access of heterogeneous equipment, poor fault detection real-time performance and insufficient maintenance planning are solved, remote intelligent operation and maintenance are realized, and the equipment shutdown risk and the maintenance cost are reduced.

The invention relates to an equipment maintenance plan dynamic adjustment method based on machine learning. The method comprises the following steps: acquiring real-time operation data and historical operation data of specified equipment, maintenance record information and equipment inherent attribute information; performing data preprocessing on the real-time operation data and the historical operation data respectively, and determining key index parameters of specified equipment and a state definition threshold range of each parameter in combination with maintenance record information and inherent attribute information of the equipment; inputting the preprocessed real-time operation data corresponding to the key index parameters into a pre-trained machine learning model to obtain a fault prediction probability; judging whether the preprocessed real-time operation data corresponding to the key index parameters is in a state definition threshold range or not, and obtaining a judgment result; and according to the judgment result and the fault prediction probability, obtaining a maintenance time node of the specified equipment and updating a maintenance plan. The service life of the equipment is prolonged, and the maintenance resource configuration is optimized.

The invention relates to the technical field of maintenance planning, and particularly discloses a landscape greening maintenance planning method and system based on big data, and the method comprises the steps: constructing a maintenance region model, and updating the maintenance region model based on a shot image; rasterizing the maintenance area model, and determining greening parameters in each grid area; and clustering the grid areas based on the greening parameters, synchronously determining the demand precision of each type of grid areas, obtaining the real-time information of each grid area, feeding back the real-time information to the management end, and receiving a maintenance scheme which is fed back by the management end and contains a maintenance target. According to the method, the landscape model is constructed based on the BIM model, the landscape model is updated based on a large amount of image data, the real-time performance of the landscape model is extremely high, software analysis is performed on the landscape model, a maintenance target can be determined, a more targeted maintenance task can be generated, and the labor workload is greatly reduced under the condition that the maintenance effect is almost unchanged.

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 invention discloses an unmanned aerial vehicle inspection maintenance planning method and device based on natural language man-machine interaction, and relates to the technical field of unmanned aerial vehicle multi-task cooperative scheduling. According to the scheme, natural language tasks are understood through semantic analysis, and maintenance procedures and resource requirements are automatically reasoned by using a knowledge graph; then, a multi-objective optimization model fusing inspection time efficiency, cost, preventive benefits and maintenance technician task load balance is constructed; and adopting an adaptive algorithm to solve and generate a candidate maintenance plan set, determining a target maintenance plan based on multi-dimensional evaluation, and finally synchronously outputting a maintenance scheduling scheme and an updated inspection task allocation scheme. According to the scheme, dependence on data entry of artificial experience and scheme planning is reduced through natural language analysis and knowledge graph reasoning, automation of maintenance decision is achieved, meanwhile, maintenance and inspection are uniformly optimized under a multi-target framework, a preventive maintenance mechanism is introduced, conversion from passive response to active prevention is achieved, and the maintenance efficiency is improved. And the overall utilization efficiency of resources is improved.

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.

The application relates to the technical field of maintenance planning, and particularly discloses a landscape greening maintenance planning method and system based on big data, which comprises the following steps: constructing a maintenance area model, updating the maintenance area model based on a photographed image; performing rasterization processing on the maintenance area model to determine greening parameters in each grid area; clustering the grid areas based on the greening parameters, synchronously determining the demand accuracy of each type of grid area, obtaining real-time information of each grid area, feeding back the real-time information to a management end, and receiving a maintenance scheme containing a maintenance target fed back by the management end. The application constructs a landscape model based on a BIM model, updates the landscape model based on a large amount of image data, the real-time performance of the landscape model is extremely high, software analysis is performed on the landscape model, the maintenance target can be determined, more targeted maintenance tasks can be generated, and the manpower workload is greatly reduced while the maintenance effect is almost unchanged.

The present invention relates to the technical field of intelligent cutting tool management, and relates to a machine learning-based intelligent cutting tool life cycle management method and system. A specific method comprises: establishing a database of quartz machining cutting tools; after operation in a factory is finished every day, recovering and recording cutting tool use and circulation information of the quartz machining cutting tools; establishing a machine learning model, to evaluate and calculate daily wear amounts of the cutting tools, and a daily cutting tool use influence coefficient; evaluating remaining service lives of quartz machining cutting tools having different abnormality grades; arranging remaining service life data of quartz machining cutting tools of a same machining category in order, to form a cutting tool life sequence, and updating the cutting tool life sequence to the database of quartz machining cutting tools in real time; dynamically formulating a factory cutting tool purchase and maintenance plan. The present invention resolves the problems in the prior art of lack of a unified data management platform for quartz machining cutting tools, chaotic tool management at a production site, and inaccurate calculation of lifespans of the cutting tools.

The present application relates to the field of data processing of landscape greening maintenance management, and particularly relates to a landscape greening maintenance planning method and system. The method comprises: obtaining multi-source maintenance data and generating a set of maintenance object portraits by association; extracting requirements and matching task windows to generate a set of maintenance requirements; generating a maintenance task priority queue by fusing multi-level sorting of complaint events, work windows and task pre-post relationships; performing resource matching, executing same-area task merging, cross-area route merging and task splitting for resource conflicts on the matching results to generate the best feasible maintenance planning scheme; mapping dynamic events into planned influence types to update the task queue and resource allocation, and feeding back the execution results to the portrait set to update the planning parameters, forming a self-evolving execution closed loop. The present application significantly improves the resilience of greening maintenance planning under dynamic interference and resource resilience, and realizes the intelligent upgrading from static scheduling to adaptive learning.

To provide a maintenance plan support method capable of properly supporting maintenance activities for a nuclear power plant.SOLUTION: The present invention relates to a maintenance plan support method that supports a maintenance plan for a nuclear power plant by utilizing design and / or information from the field, and when a maintenance plan for preventing a facility management index is made based thereupon in maintenance activities and maintenance actions of facilities which are executed according to the maintenance plan, an update plan for a next maintenance plan which is determined from facility situations in the field and an update plan for a long-period maintenance plan after the next and subsequent maintenance plan are set for a long-period maintenance plan for a plurality of times of periodic inspections planned in advance.SELECTED DRAWING: Figure 1

The invention discloses a multi-technology fusion monitoring and early warning system for elevator safety, which belongs to the technical field of elevator safety monitoring and comprises a historical data analysis module, a knowledge graph construction module, a maintenance plan and early warning module and a visual data output module. The historical data analysis module is used for collecting and analyzing historical operation data and fault data, obtaining health decline curves of all parts of the elevator under different use frequencies, load modes and unstable electric power environments, and constructing a prediction model; through multi-module cooperation and multi-technology fusion, accurate early warning, associated diagnosis and personalized maintenance of elevator faults are achieved, manual dependence and misjudgment are reduced, workers can conveniently master the safety state of the elevator in real time, the problems that traditional elevator maintenance is insufficient in pertinence, fault response lags behind, data integration is difficult and the like are effectively solved, and the maintenance efficiency is improved. And the elevator safety management efficiency and reliability are improved.

The present application relates to the civil aviation engine maintenance technical field, specifically speaking, a kind of civil aviation engine's long-term maintenance plan optimization method under uncertain condition capable of optimizing maintenance cost, under the condition of same scale fleet, the solution result of uncertain fleet maintenance plan model and the solution result of deterministic model are compared, good results are obtained in the number of missing days and the number of wasted days, there is little difference in equilibrium index, the absolute error of optimal value of solution is within the unit of digit, the model under uncertain condition considers engineering practice, triangular fuzzy number is proposed to process fleet engine original data, can be applied to practical engineering, which reflects the advancement and practicality of long-term maintenance plan model of fleet under uncertain condition.

The invention relates to the technical field of factory operation and maintenance, in particular to an intelligent factory operation and maintenance scheduling method and system based on multi-source data. The method comprises the following steps: acquiring optimized data streams of all operation equipment based on a factory sensor network; performing equipment state operation analysis on the optimized data stream to generate an equipment real-time operation curve; performing fault prediction and shutdown maintenance planning according to the real-time operation curve of the equipment, and outputting a shutdown time point; calculating a shutdown period productivity gap index based on the shutdown time point to generate a productivity gap index; and carrying out capacity compensation analysis and production line scheduling planning according to the capacity gap index, and generating a multi-device scheduling instruction. According to the method, predictive maintenance and dynamic productivity optimization from equipment level to system level of the intelligent factory are carried out, and the operation and maintenance efficiency and the overall production stability of the factory are improved.

The present application relates to a kind of asphalt road safety performance test method, device, electronic equipment and storage medium, method includes: obtaining the road surface disease data corresponding to target experimental road, according to road surface disease data, calculate the road surface performance index, road surface damage condition index, road surface driving quality index and road surface rut depth index, then determine the flow number, then calculate the corresponding road surface structure strength index, then determine the safety level of target experimental road, finally determine the road maintenance scheme corresponding to safety level. Compared with the technical index in the existing asphalt pavement maintenance evaluation, all are the functional index of road surface, the present application uses 6 indexes such as road surface performance, material performance, structure performance to divide asphalt road safety level, and carries out maintenance planning to preventive maintenance section, carries out maintenance decision reasonably, can effectively carry out the safety performance test of asphalt road.

The invention provides a virtual power plant multi-adjustable resource optimization scheduling method and system based on a novel power load management system, and the method comprises the steps: building a virtual power plant multi-time scale resource optimization scheduling model for solving the problems of surplus consumption and fluctuation stabilization of uncertain resources under different time scales in the operation process of a virtual power plant; in the day-ahead stage, a photovoltaic cell panel maintenance plan, a gas turbine output plan and the reported electric quantity of electric energy transaction in the next day are determined according to the possible fault condition of the photovoltaic unit; and in the intra-day link, the fluctuation conditions of illumination intensity and user load are considered, and a battery charging and discharging plan, a photovoltaic reactive power compensation plan and an actual power transaction scheme of the battery swap station are obtained. The model comprehensively considers the delicacy management of charging and discharging behaviors of an electrical changing station in a virtual power plant, the user satisfaction problem caused by photovoltaic unit faults and reactive compensation, and the transaction profit condition of participating in power market transaction.

To provide a repair plan support system capable of improving the accuracy of calculating repair cost.SOLUTION: A repair plan support system 100 includes: a three-dimensional point cloud display unit 26 which displays a three-dimensional point cloud of an object to be repaired; a range selection unit 22 which receives a selection of an arbitrary range in the three-dimensional point cloud; a selection operation unit 23 which receives a selection of a construction method for repair construction to be implemented in a selected range which is the range for which the selection has been received by the range selection unit; a polygon input unit 24 which receives an input of polygon indicating an installation range of scaffolding which is used for the repair construction of the object to be repaired; and a cost calculation unit 15 which calculates the cost of the repair construction to be implemented in the selected range on the basis of the magnitude of the selected range, a selected construction method which is the construction method for which the selection has been selected by the selection operation unit 23, and the magnitude of the polygon for which the input has been received by the polygon input unit 24.SELECTED DRAWING: Figure 1

The application discloses a kind of unmanned aerial vehicle inspection maintenance planning method and device of natural language human-computer interaction, it is related to unmanned aerial vehicle multitask collaborative scheduling technical field, the present scheme understands natural language task by semantic analysis, and the maintenance procedure and resource demand are inferred automatically using knowledge graph;Then build the multi-objective optimization model that fuses the time limit for inspection, cost, preventive benefit and maintenance technician task load balancing;Adaptive algorithm is used to solve and generate candidate maintenance plan set, and target maintenance plan is determined based on multidimensional evaluation, and finally the maintenance scheduling scheme and the updated inspection task allocation scheme are output simultaneously.The present scheme reduces the dependence on manual experience data entry and scheme planning through natural language analysis and knowledge graph inference, realizes the automation of maintenance decision-making, simultaneously unifies optimization under multi-objective framework for maintenance and inspection, and introduces preventive maintenance mechanism, realizes the change from passive response to active prevention, and improves the overall utilization efficiency of resources.

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.

The invention discloses a hydraulic turbine set diagram neural monitoring and maintenance planning optimization system, belongs to the technical field of intelligent operation and maintenance of hydraulic power generation equipment, and aims to solve the technical problem that the intelligent degree of maintenance planning of a hydraulic turbine set is insufficient. The system comprises a data acquisition module, a graph structure modeling module, a graph neural network processor, a maintenance optimization calculation module and a man-machine interaction output module. Compared with a traditional method, the maintenance decision accuracy can be effectively improved, the unplanned shutdown time is shortened, and the service life of key components is prolonged.

The invention relates to the field of intelligent monitoring devices in urban rail transit shield construction, in particular to a shield construction operation electromechanical parameter monitoring method and a shield construction operation electromechanical parameter monitoring system. In the test monitoring process, limit data of a tunneling state and existing limit working state data of electromechanical equipment are extracted and can be combined or independently recorded to form data in a conventional state to be compared, an expected residual loss value is obtained through insulation loss calculation, and the expected residual loss value is compared with the expected residual loss value, so that a test result is obtained. Reference expected parameters can be obtained in real time, and the influence on the shield equipment caused by limit parameters generated by the equipment due to special geology in the repair and maintenance process of the shield equipment and the tunneling process can be judged in a preset manner, so that subsequent construction plans and maintenance plans can be judged.

It is not currently feasible to place a sensor within an electrical transformer to directly measure the water content in the solid insulation of the windings. Thus, the degree of degradation of the insulation can generally not be known without a maintenance intervention. Accordingly, disclosed embodiments model thermochemical degradation in a digital twin of the transformer to continuously estimate the water content in sections of the insulation, based on water content in the insulating liquid and the loading on the transformer. This estimated water content may account for diffusion between the insulating liquid and the insulation, as well as the water generated by cellulose degradation in the insulation material. The estimated water content may be used to estimate other parameters, such as the total water content in the transformer, a measure of aging of the transformer, and / or the like, as well as inform maintenance planning for the transformer.

The invention discloses a hollow slab girder bridge performance degradation prediction method and system based on road network level bridge group long-period monitoring data, and belongs to the technical field of bridge state monitoring, and the method comprises the following steps: constructing a hollow slab bridge group multi-source bridge state database based on bridge structure parameters, long-period traffic volume parameters and environmental load parameters; training to obtain a bridge state degradation prediction model based on a neural network by using the road network level region bridge long-period condition data; and utilizing the bridge state degradation prediction model to predict the technical condition of the bridge in the road network level area to obtain an upper structure score, a lower structure score, a bridge deck system score and a bridge technical condition score. According to the bridge state degradation prediction method based on multi-source data of the road network level hollow slab bridge group disclosed by the invention, prediction and evaluation of part-single bridge-road network service state degradation based on machine learning provide reference for medium and long-term maintenance planning of regional bridges.

The invention provides a system grouping maintenance decision-making method based on state autonomous iteration under availability constraint, and a maintenance grouping control optimization model considering a state-strategy information collaborative updating mechanism is constructed for a multi-component system with economic dependence characteristics. According to the method, firstly, the optimal maintenance cycle of each component is determined by calculating component-level cost / availability efficiency indexes; and establishing a system-level maintenance offset cost and availability penalty model, and quantifying economic and availability influences brought by maintenance cycle offset caused by group maintenance. On this basis, a dynamic collaborative iteration mechanism fusing a maintenance strategy and component state information further deduces the overall grouping maintenance cost and availability efficiency of the system, and a reverse sequential optimization dynamic programming algorithm is introduced to realize rapid optimization and updating of a system grouping structure. Therefore, global dynamic adjustment under long-term period-oriented grouping maintenance planning and state awareness is realized.

This invention relates to the field of electrical equipment maintenance technology and discloses an intelligent building electrical equipment maintenance planning system. The system collects outdoor and indoor environmental information data through a data acquisition module; analyzes the outdoor and indoor environmental information data over a preset time period through a detection module to obtain a basic regional heat dissipation index; and then dynamically adjusts the temperature threshold based on the regional basic heat dissipation index, current outdoor and indoor environmental information data through a temperature threshold setting module to avoid frequent start-ups and shutdowns of the air conditioning system, reducing unnecessary energy waste. Simultaneously, it rationally allocates cooling resources according to the regional heat dissipation level, further optimizing energy utilization efficiency. This rational operation control and maintenance planning reduces frequent start-ups and shutdowns of equipment such as compressors, lowers equipment wear and failure rates, and thus extends the service life of the air conditioning system.

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 invention discloses an oil depot storage data management integration and AI decision support integrated system, and relates to the technical field of AI decision support integrated systems, the oil depot storage data management integration and AI decision support integrated system comprises a client, the client comprises a data module, an infrastructure module, an application module and an AI engine module, the data module is composed of a data resource module, a data governance module and a data service module, the output end of the data resource module is connected with the data governance module, and fragmented data dispersed in the warehouse management system, the control system and the environmental protection monitoring equipment are integrated. Standardized processing and consistency verification of data are achieved through the data management module, the problems that real-time verification of inventory data and metering data lags behind, equipment fault early warning and maintenance plans are disjointed and the like are solved, the data can be efficiently circulated and utilized, a unified and accurate data basis is provided for oil depot storage operation, and then the overall operation efficiency is improved.

The invention relates to an automated system for long-term planning and execution of repair works and preventive maintenance, incorporating cost estimation and utilizing artificial intelligence (AI) technologies. The system integrates modules for generating repair schedules and work plans based on technical documentation and regulatory requirements, forecasting repair costs considering market fluctuations and inflation, monitoring actual repair execution, and dynamically adjusting plans in real time. The system implements the integration of technical and financial functionalities within a single platform, providing controlled and segregated access for specialists of various profiles. Additional features include automatic analysis of regulatory documents, market price monitoring, risk management, deviation analysis, occupational safety control, support for a digital technical council, and comprehensive information security compliant with international standards ISO / IEC 27001 and NIST guidelines. The system ensures efficient, accurate, and adaptive repair management and maintenance planning, optimizing resource utilization and reducing operational risks over the long term.

The invention discloses an eVTOL health management and control method and system, and relates to the technical field of data analysis, and the method comprises the steps: dividing equipment operation parameters into a large-scale data set and a small-scale data set; performing feature extraction on the small-scale data set to obtain statistical feature information and engineering feature information, and performing fusion processing to obtain fused feature information; performing rule reasoning based on the fused feature information to obtain an initial fault isolation decision, and adjusting the initial fault isolation decision based on an artificial intelligence lightweight small model; performing equipment fault diagnosis by using the large-scale data set based on an artificial intelligence large model; performing equipment degradation feature extraction on the large-scale data set, and determining a maintenance plan based on equipment degradation feature information and an equipment fault diagnosis result; and performing eVTOL health state management and control based on the maintenance plan and the target fault isolation decision. According to the method, a more accurate maintenance plan can be obtained, and the safety and economy of eVTOL operation are improved.