127 results about "Maintenance planning" patented technology

The invention discloses an intelligent property management system based on artificial intelligence, and relates to the technical field of intelligent property, and the system comprises a data management center which is in communication connection with a data collection and transmission module, a fault recognition and diagnosis module, a knowledge graph auxiliary decision module, an intelligent repair customer service module and an equipment maintenance management module. And the data acquisition and transmission module is used for acquiring and preprocessing operation data of the facility equipment based on various sensors deployed on the property facility equipment. According to the invention, real-time analysis is carried out on facility equipment operation data through an artificial intelligence algorithm, potential faults can be identified in advance and early warning can be given out, fault expansion is avoided, fault causes are rapidly positioned, clear guidance is provided for maintenance personnel, maintenance time is shortened, a knowledge graph is combined with a fault identification result, a solution strategy and decision support are provided, and maintenance efficiency is improved. Maintenance personnel are helped to quickly make a reasonable maintenance plan, resource allocation is optimized, and the fault processing efficiency is remarkably improved.

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 composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

A system and method for automated post-mission logging of unmanned vehicle operations using generative AI. Flight telemetry, operator notes, and maintenance data are aggregated and normalized into a standardized dataset. A large language model interprets the dataset to generate user-readable debriefs, compliance records, and maintenance logs. The system ensures each post-mission report conforms to regulatory standards and automatically flags anomalies or compliance deviations. The generative AI module may be fine-tuned on domain-specific datasets to produce logs that are structurally consistent with predefined templates and compliance forms. The resulting reports can be stored, retrieved, and shared via secure cloud services, streamlining flight record-keeping and maintenance planning.

The invention relates to a dynamic early warning and maintenance strategy adjusting method based on residual life, and belongs to the technical field of equipment management in the oil refining and chemical engineering field. The method comprises the following steps: acquiring multi-source operation data of equipment through an online acquisition module, and constructing a structured equipment state parameter set; based on the parameter set, establishing a residual life admission judgment model and generating a residual life accounting result of the current equipment; further adopting a corrosion rate-residual life two-factor coupling model to generate an equipment risk influence rate curve, and constructing a dynamic risk prevention and control boundary; and according to the risk level of boundary division, a dynamic maintenance strategy adjustment module is linked to perform early warning response, strategy execution, response recording and self-learning optimization are realized through a closed-loop management module, real-time evolution of a fortification value and a maintenance plan along with an equipment state is realized, and the accuracy of risk response and the intelligent level of equipment maintenance are improved.

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 discloses a dynamic intelligent maintenance path planning method and system for a generator set, relates to the technical field of intelligent maintenance of power generation equipment, and solves the problems of insufficient dynamic quantification of potential safety hazards and low response efficiency of sudden working conditions in conventional maintenance planning of the generator set. The method comprises the steps that a constrained directed graph model is constructed based on a three-dimensional topological relation of equipment, nodes represent a to-be-overhauled part, and edges represent a feasible moving path; a temperature risk sensing mechanism is introduced, and the safety coefficient in the path edge weight is dynamically adjusted; and designing a hybrid optimization algorithm to solve the maintenance sequence with the lowest total cost. Project verification shows that the high-temperature operation risk can be effectively reduced, the construction period is shortened, and the operation and maintenance intelligence level of the generator set is remarkably improved.

The invention provides a civil aviation fleet-oriented maintenance plan and work package integrated optimization method, which comprises the following steps of: firstly, taking minimization of total maintenance cost as a target function; constructing a mathematical planning model for integrated optimization of the fleet maintenance plan and the work package by taking a maintenance item number constraint, a maintenance item and maintenance work package corresponding relation constraint, a maintenance resource constraint, a maintenance interval constraint, a variable relation constraint, a variable value constraint and the like as constraint conditions; then, auxiliary variables are introduced to carry out linearization processing on the established mathematical programming model; and finally, solving the linearized mathematical programming model by adopting a differential evolution-adaptive large-scale neighborhood search two-stage heuristic algorithm, namely, continuously iteratively solving through a designed two-stage interaction strategy, so as to achieve the purpose of integrated optimization. According to the method, the dual goals of reducing the total maintenance cost and improving the maintenance scheme making efficiency are achieved, and a scientific solution is provided for the complex optimization problem in civil aviation maintenance management.

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 relates to the technical field of big data analysis and application operation and maintenance, and discloses a multi-data fusion-based application operation and maintenance planning method, which comprises the following steps of: combining multi-source sensor data into a multi-dimensional state vector representing a local space-time state, and comparing the multi-dimensional state vector with a baseline vector representing a normal operation mode to generate a drift vector; according to the method, the stability of the correlation mode in the multi-dimensional data is analyzed, and then the stability of the modulus length and the direction of the drift vector in the time sequence is analyzed, so that potential risks which cannot be perceived by a traditional threshold value mode can be recognized. The focus point of risk recognition is transferred from isolated value out-of-limit to analysis of the stability of the correlation mode in the multi-dimensional data; the early gradual change risk formed by the synergistic effect of a plurality of physical quantities can be effectively identified, and the risk source can be traced based on the analysis process, so that the predictability and decision reliability of operation and maintenance planning are remarkably improved.

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 offshore wind turbine generator reliability distribution modeling and maintenance decision-making method under long-life management, and the method employs a multi-source heterogeneous data fusion and meta-transfer learning technology to improve the prediction accuracy and adaptability of the health condition and remaining service life of key parts of an offshore wind turbine generator. Based on the RCM theory and the system analysis component structure and influence factors, uncertainty in fuzzy theory processing evaluation is introduced, predicted health and degradation information is combined, an objective and quantitative reliability index distribution model of the key components is established, and the reasonable reliability index distribution weight of each key component is determined; according to the method, the maximum average reliability of an offshore wind turbine generator system and the minimum full-life-cycle operation and maintenance cost serve as targets, a long-life-period multi-target optimization model considering part degradation, the maintenance effect and multiple constraints is constructed, an intelligent algorithm is adopted for solving, and an optimal preventive maintenance plan is obtained.

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.

An automated test equipment (ATE) for testing semiconductor devices, the test equipment comprises a test handler, a spare part, or a contactor socket, and a semiconductor devices tester, The spare part comprises an electronic component for storing and / or processing data regarding the spare part or a portion thereof, The test equipment comprises an operator terminal comprising a display or GUI and a data exchange interface which is connected or connectable to the electronic component within the spare part, for at least displaying data stored therein. The ATE further comprises a data buffer unit for buffering the data, a maintenance planning and control unit for planning and controlling maintenance actions of the test equipment, and a dedicated database residing in a control computer.

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.

This invention discloses a method for determining subway maintenance plans and related equipment, relating to the field of intelligent planning and scheduling. It primarily addresses the problem of treating similar parts as a whole in subway maintenance plans, which can lead to waste of maintenance resources and the lack of intelligent manual planning. The method includes: obtaining subway operating mileage and part status, where the part status includes the remaining operational mileage of the part corresponding to the part status; determining a target monthly maintenance plan based on the subway operating mileage, the part status, and a preset monthly maintenance plan; and determining a target daily maintenance plan based on the target monthly subway maintenance plan and a preset daily maintenance plan, combining constraints and optimization objectives. This invention is used in the process of determining subway maintenance plans.

The invention belongs to the technical field of data intelligent processing, and provides an XML format maintenance plan data module rapid compilation method, which comprises the following steps: S1, selecting a maintenance project table recording maintenance plan information for a word file recording maintenance project data; s2, the maintenance item information of each row in the maintenance item table is analyzed in sequence, character data in cells are separated through separators, and the content in the maintenance item table is converted into character string matrixes in sequence; wherein the table line is converted into a line of character string data; s3, according to a conversion rule of the character string matrix, converting each row of character strings into task definition data of an XML format maintenance plan type data module according to the conversion rule; and S4, generating maintenance plan type data module contents meeting standard mode requirements. According to the method, the maintenance plan content is quickly converted into the XML format data module from the source document, the compiling efficiency is improved, and human errors are reduced.

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.

The invention discloses an efficient operation and maintenance method and device for a large model system, and belongs to the technical field of artificial intelligence, and the implementation of the method comprises the following steps: constructing an operation and maintenance agent cluster of the large model system, which comprises a model deployment agent, a model detection agent, a model operation agent, a model upgrading agent and an operation and maintenance planning agent; after an operation and maintenance task is input, the operation and maintenance planning agent receives an input instruction, performs task understanding and analysis, and divides the task into a conventional operation and maintenance mode and a customized operation and maintenance mode; setting a conventional operation and maintenance mode by using the operation and maintenance planning agent, and calling the model detection agent and the model operation agent to guarantee and maintain function indexes and operation states of models in the system; and setting a customized operation and maintenance mode by using the operation and maintenance planning agent, and calling the model deployment agent and the model upgrading agent to complete customized deployment and upgrading of the model. According to the method, efficient deployment, continuous verification, dynamic expansion and intelligent upgrading of the model are realized.

The application discloses a self-adaptive control method and device for cloud native cluster intelligent operation and maintenance, and relates to the technical field of cloud native application.The operation and maintenance demand of a K8s cluster is described through a YAML format file, and the operation and maintenance demand is acquired by analyzing the YAML format file; the planning control logic and the index analyzer are generated according to a rule trigger condition and an operation and maintenance operation supported by the cluster; the index collector type is determined according to an index type and an index parameter, and the corresponding index collector is generated according to a template; the index monitor to be created is determined according to the analyzed operation and maintenance demand, the index collector and the index analyzer are combined to obtain the index monitor, the index is automatically collected through the index collector, and the operation and maintenance planning logic corresponding to the operation and maintenance demand is determined through the index analyzer; the index monitor and the planning control logic are integrated, the code of the K8s controller is generated and deployed into the K8s cluster, and the self-adaptive control for the cloud native cluster intelligent operation and maintenance is realized.

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 provides a battery pack energy efficiency optimization method and system, and the method comprises the steps: recognizing the structure information of riding equipment, carrying out the structure matching with an equipment battery pack of the riding equipment, and outputting a battery adaptation parameter; acquiring riding data, and performing equipment power distribution on the riding data and the battery adaptive parameters to obtain an energy efficiency control threshold value; identifying battery state data of the equipment battery pack based on the battery adaptation parameter, performing thermal safety boundary calculation on the battery state data and the energy efficiency control threshold, and outputting attenuation trend data; and performing battery maintenance analysis on the equipment battery pack according to the attenuation trend data to generate subsequent maintenance planning information. The battery performance degradation or thermal runaway risk can be pre-warned, and the safety of battery use is improved.

The present application relates to a railway track quality status assessment method, device, computer equipment, railway track quality status assessment system and storage medium. The method comprises: obtaining geometric dynamic detection data of a section of the railway track to be assessed at a first moment; based on a track irregularity quality index evaluation algorithm, performing calculations based on the geometric dynamic detection data of the section to be assessed at the first moment to obtain the track irregularity quality index of the section to be assessed at the first moment; performing calculations based on the track irregularity quality index and amplification factor of the section to be assessed at the first moment to obtain a track quality status index; the amplification factor is determined based on the track quality degradation rate of the section to be assessed during the tamping cycle and the standard value of the track quality degradation rate; and evaluating the track quality status of the section to be assessed based on the track quality status index. The use of this method can improve the accuracy of railway track quality status assessment and improve the scientificity and economy of maintenance plans.

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.