52 results about "Planned maintenance" patented technology

The invention discloses a quick-freezing equipment fault prediction and health management system based on digital twinning, and particularly relates to the field of quick-freezing equipment operation and maintenance. The theoretical fault index generation module is used for generating a multi-dimensional theoretical fault index matrix; the real-time fault index calculation module generates a real-time fault index; the deviation threshold value library establishing unit forms a dynamically updated deviation threshold value library; the residual life prediction module calculates the residual running time of the equipment from the current state to the fault; the health management module calculates equipment health scores and divides equipment health states into four grades; the visual display module provides a visual interface for a user; compared with the traditional operation and maintenance that the residual life cannot be estimated, the method can enable the operation and maintenance personnel to plan maintenance and replace parts in advance and reasonably arrange a production plan, reduces the risk of sudden failure, and enables the equipment operation and maintenance to be changed from passive response to active prevention.

The invention discloses a safety production standardized comprehensive management system and method, and relates to the technical field of safety production management, the system comprises the following components: a data acquisition module, a data preprocessing module, a model construction and training module, a prediction analysis module and a maintenance management module; according to the method, the time sequence data in the full life cycle of the equipment are continuously collected, the time sequence data comprise key parameters such as operation duration, start-stop times and maintenance records, the improved LSTM neural network is utilized to construct the equipment safety life prediction model, and the model can not only predict the overall life of the equipment, but also can predict the service life of the equipment. The method can accurately predict the residual safe use cycle of the easily-worn part, the accurate prediction capability enables an enterprise to plan a maintenance plan in advance, production interruption and safety accidents caused by sudden equipment faults are avoided, and the accuracy and foresight of equipment safety management are remarkably improved.

The invention discloses a water conservancy pipeline data management system based on cloud computing, and relates to the technical field of intelligent management, the system comprises an analysis module, a prediction module and a decision module, through first analysis and second analysis, time features and spatial features are obtained, fault probability, fault degree and fault interval duration are obtained, and early warning is carried out. According to the invention, the analysis module carries out deep time and space dimension analysis on pipeline historical data, can find potential fault risks in advance, provides powerful guarantee for stable operation of a water conservancy pipeline system, can help managers plan maintenance tasks in advance and reasonably arrange maintenance resources by accurately predicting the fault probability and degree, and improves the maintenance efficiency of the water conservancy pipeline system. And unnecessary maintenance work is avoided, so that the maintenance efficiency is remarkably improved, and the maintenance cost is reduced. Meanwhile, according to the fault interval duration, the potential risk of the associated pipeline is predicted, the maintenance sequence and resource allocation are optimized, priority maintenance of the key pipeline is ensured, and the resource utilization efficiency is further improved.

The invention discloses a health monitoring system of a speed reducer, and belongs to the field of mechanical equipment state monitoring. Comprising a sensor module, a data acquisition module, a data processing module, an early warning module and a display module, the sensor module comprises a displacement sensor, a pressure sensor, a rotating speed sensor, a vibration sensor and a temperature sensor, and the displacement sensor and the pressure sensor are arranged at the position of a brake piston of the speed reducer and used for monitoring piston displacement and brake pressure in real time; the rotating speed sensor is arranged at the output end of the reducer and used for monitoring the output rotating speed of the reducer. Real-time monitoring of key parameters of the speed reducer is achieved through multi-sensor fusion, potential faults can be found in time, and fundamental transformation from passive maintenance to active early warning and from planned maintenance to predicted maintenance is achieved. The brake performance and the abrasion degree of a friction plate are accurately evaluated through combined monitoring of displacement and pressure of a brake piston; and early fault diagnosis is realized through spectrum analysis of the vibration signals.

The invention discloses motor dismounting equipment in the technical field of motor dismounting, and the motor dismounting equipment comprises a telescopic supporting frame, a bracket and a buffer assembly, the telescopic supporting frame comprises a fixed supporting frame fixedly mounted on a supporting base and a movable supporting frame connected with the fixed supporting frame in an up-down sliding manner; a pressing handle connecting rod mechanism used for controlling the movable supporting frame to ascend and descend is arranged outside the fixed supporting frame, the bracket comprises a lifting plate fixedly installed at the top end of the movable supporting frame and protective baffles rotationally installed on the two sides of the lifting plate, and an adjusting piece used for controlling the two ends of the lifting plate to stretch out and draw back is arranged at the bottom of the lifting plate. The problems that in the prior art, when 50-kilogram-level light motor device components are subjected to left-right displacement and lifting maintenance in space, large hoisting is difficult to implement, the space is limited when professional electric hoists and hand-drive hoists are used for hoisting, other tools need to be operated by multiple persons and are difficult to flexibly arrange in narrow space, and the maintenance cost is low are solved. The maintenance and inspection progress is limited; the planned maintenance and inspection time is prolonged; and the potential safety hazard of manual brute force carrying exists.

The invention discloses a multi-parameter fusion driven cable full life cycle monitoring method, which relates to the technical field of cable monitoring, and comprises the following steps: acquiring risk data of the surrounding environment of a cable and historical operation data of the cable, and performing calculation and analysis to obtain a fault risk prediction index; the fault risk prediction index is used for reflecting the possibility of cable fault risk; dividing the priority of cable maintenance according to a comparative analysis result of the fault risk prediction index of the cable and a preset fault risk upper limit threshold and a fault risk lower limit threshold; according to the comparison result of the fault risk prediction index and the preset fault risk threshold value, the maintenance priority is divided into the emergency maintenance level, the planned maintenance level and the maintenance-free level, the emergency maintenance situation is immediately alarmed and dispatched for maintenance, the planned maintenance is processed after a scheme is formulated within the specified time, normal operation is kept if maintenance is not needed, and the maintenance efficiency is improved. Operation and maintenance resource distribution is optimized, unnecessary shutdown is reduced, and the operation reliability and the service life of the cable are improved.

The utility model discloses a rotary drum stirring suspension shaft assembly which comprises a suspension shaft, a bearing box, a bearing gland and a self-aligning roller bearing, the self-aligning roller bearing is connected to one end part of the suspension shaft in a sleeving manner; the bearing box covers the outer parts of the self-aligning roller bearing and the suspension shaft; and a bearing gland is arranged at an opening, close to the self-aligning roller bearing, of the bearing box. The shaft sleeve is transformed into a rolling bearing supporting structure, so that the abrasion of the suspension shaft is obviously limited. Therefore, the risk of abrasion and leakage of the box body is reduced. The equipment operation period is prolonged, the maintenance frequency is reduced, the bearing is convenient to disassemble and replace during planned maintenance, the drum core does not need to be hung, and the maintenance risk and the maintenance time are reduced.

The utility model discloses a hydro-generator thrust bearing elastic oil tank which comprises an oil tank seat and supporting iron, the supporting iron is installed in the oil tank seat, the upper end of the supporting iron is fixedly connected with the oil tank seat, a cavity is formed between the interior of the oil tank seat and the supporting iron, the side wall of the oil tank seat is of a corrugated pipe structure, and the bottom supporting face in the oil tank seat is a downwards-concave arc face. The lower end part of the supporting iron is a downwards convex cambered surface; and the curvature radius of the bottom supporting surface is greater than that of the lower end part. According to the utility model, the lower end part of the supporting iron is improved to be the downwards convex cambered surface, the bottom supporting surface in the oil tank seat is improved to be the downwards concave cambered surface, and the curvature radius of the bottom supporting surface is greater than that of the lower end part, so that the supporting iron can be adaptively swung and finely adjusted after the elastic oil tank loses efficacy, and the thrust bearing bush is stressed more uniformly; the elastic fuel tank can still insist to run for a period of time after failure, and preparation time is provided for planned maintenance.

The embodiment of the invention discloses a solar cell surplus capacity determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining the yield information and shutdown information of single-machine equipment of a plurality of processes in a first preset time period, and determining the single-machine restoration daily capacity of each single-machine equipment; aiming at each working procedure in the plurality of working procedures, acquiring planned maintenance information of all single-machine equipment in the working procedure and determining working procedure restoration daily productivity of the working procedure; based on the first pass yield of the current working procedure and the first pass yield of all working procedures after the current working procedure, the working procedure reduction daily productivity of the current working procedure is converted into the working procedure reduction daily productivity of the last working procedure; according to the method, the required capacity of each process is determined based on the incidence relation between the minimum value in the conversion capacity and the first pass yield, then the surplus capacity of each process is determined, information of multiple processes in the solar cell production process can be collected and analyzed in real time, therefore, the surplus capacity can be analyzed in real time, and the analysis efficiency is higher.

The invention discloses a 5G power production management system based on AI driving, and relates to the technical field of intelligent power grids and industrial Internet of Things. The system is mainly characterized in that a data acquisition module is connected with an Internet of Things sensor through a 5G network to acquire power, current, voltage, insulation, load and safety state data of equipment in each link of power generation, power transmission, power transformation and power distribution in real time; the AI processing module carries out fusion analysis on multi-source data through a mixed deep learning model, thereby realizing fault early warning, accurate state evaluation and operation trend prediction. The control management module issues instructions of power regulation, circuit breaker operation, load switching and the like to the power equipment based on an AI result and a network slicing technology, a maintenance management sub-module is arranged in the control management module, planned maintenance is carried out on fault equipment according to a maintenance complexity score, and optimization of maintenance resources is ensured; and multi-terminal visual interaction is supported, and the intelligent level and the operation and maintenance efficiency of power production are improved.

The invention discloses a fuselage maintenance task pool generation method and device, equipment, a medium and a program product, and belongs to the technical field of aviation maintenance engineering. The method comprises the following steps: acquiring planned maintenance requirement data of a target fleet, and analyzing the planned maintenance requirement data to obtain an analysis result; determining a fuselage maintenance task based on the analysis result, and obtaining an MRS number, an applicability definition and an interval definition of the fuselage maintenance task; obtaining a target work card number according to the mapping relation table and the MRS number; performing matching according to the applicability definition to obtain a target aircraft, and obtaining an aircraft sampling type and a tail number of the target aircraft; generating a task interval type based on the historical task processing state, the aircraft sampling type and the interval definition; calculating a task expiration date of the body maintenance task according to the task interval type; and constructing a fuselage maintenance task pool. According to the embodiment of the invention, the accuracy and efficiency of calculating the expiration date can be improved, so that the efficiency of generating the fuselage maintenance task pool is improved.

The invention provides a rail vehicle direct maintenance cost target allocation method, which comprises the following steps of: 1, determining influence factors, and identifying seven types of core influence factors, namely complexity, technical maturity, environmental factors, utilization rate, severity, accessibility and repair material cost, based on a plan maintenance cost model and a non-plan maintenance cost model of a DMC; 2, determining an assignment range and a standard of each influence factor; step 3, according to the range and the standard of the step 2, assigning each influence factor of each system; 4, calculating a DMC comprehensive score of each system; 5, calculating a DMC proportion and a DMC distribution value of each system, judging whether the distribution value needs to be adjusted, if so, returning to the step 3, and if not, executing the step 6; and step 6, outputting a final DMC distribution result. According to the method, seven types of core influence factors are identified through the planned maintenance cost model and the non-planned maintenance cost model based on the DMC, and the accuracy and rationality of DMC distribution are remarkably improved.

This invention discloses a time-axis-based method and device for visualizing the risks of optical transmission systems, relating to the field of risk visualization technology. This invention constructs a mapping between the time axis and the maintenance link by acquiring planned maintenance information, combines link disconnection simulation to locate target services affected at different times, and then assesses the risk level of the service links and maps them into a topology visualization graphic. This achieves a closed-loop process for handling affected services in scenarios with simultaneous maintenance of multiple transmission paths or nodes, moving from inefficient manual analysis to automated system identification, quantitative assessment, and intuitive presentation. This significantly improves the efficiency and accuracy of service impact analysis and overcomes the decision-making lag problem caused by cumbersome manual derivation in complex maintenance scenarios using traditional methods.

Method for planning maintenance measures for a railway technical system The invention comprises a method for planning maintenance measures for a railway technical system (BTS), comprising a functional component (FK) that performs a function belonging to railway operation, a monitoring device (UV) that monitors the function of the functional component (FK) and generates monitoring data (UD) describing this function, a diagnostic device (DGN) that receives messages containing monitoring data (UD) generated by the monitoring device (UV) and generates computer-aided diagnostic data (DD) that describe a need for maintenance measures.A computer-aided planning module (PLM) is used, which employs a Large Language Model (LLM) to evaluate messages containing diagnostic data (DD) and to generate analysis data (AD) that describe the diagnostic data (DD) in human language. The evaluation is performed with regard to maintenance requirements and / or maintenance measures. A description of the maintenance requirements and / or maintenance measures is generated in human language. Output data (OD) representing the description of the maintenance requirements and / or maintenance measures is output via a first interface (S1). Furthermore, the invention comprises a railway engineering system, a computer program product, and a computer-readable storage medium. The advantage lies in the fact that various types of documentation describing the railway engineering system can be evaluated automatically.

The invention discloses a garbage incinerator adaptive control method fused with a natural language, and the specific implementation scheme comprises the steps: S1, obtaining manual inspection information and planned maintenance information, inputting the manual inspection information, the planned maintenance information and a control instruction into a working condition description instruction generation model in combination with cues, and generating working condition description information; s2, inputting the working condition description information into a pre-action generation model to generate pre-action generation description; s3, converting the pre-action generation description into a 1024-dimensional integral fusion feature through a vectorization model; and S4, inputting the overall fusion features and field sensor data into a data-driven action model to output a control command of an execution mechanism. The method solves the problems that the model is difficult to train and the working condition adaptability is poor due to the fact that the quantization instruction feature space of the controller is very complex.

The invention discloses a conical connecting structure of a hot mill frame roller and a spline shaft and an operation method of the conical connecting structure, and belongs to the technical field of hot mill frame rollers. The structure mainly comprises a rack roller, a spline shaft and a conical sleeve. One end of the spline shaft is of an external cone structure; the inner conical surface of the conical sleeve is matched with the outer conical surface of the spline shaft, and the outer cylindrical surface of the conical sleeve is in interference fit with a connecting hole in the end part of the rack roller. The spline shaft is provided with a high-pressure oil way with an oil outlet and provided with a fastening screw used for axial fixing. The operation method comprises the assembly process and the disassembly process, disassembly damage is concentrated on low-cost parts through the conical sleeve which is a replaceable protection piece, and the precious rack roller and spline shaft are effectively protected. By means of the synergistic effect of conical surface matching and high-pressure oil, convenient and low-loss disassembly of the spline shaft is achieved, high-cost destructive maintenance is converted into repeatable planned maintenance, and the maintenance cost of equipment in the whole life cycle is reduced.

The utility model relates to the technical field of mechanical transmission lubrication, in particular to a monitoring device for thin oil lubrication, which comprises a shell, a plurality of filter plates which work in turn and are used for filtering metal scraps are arranged in the shell, and after the filter plates filter for a set time, the collected metal scraps are moved to the top of a weighing unit to be weighed. The top of the shell is provided with a brush plate in a penetrating and sliding manner, the brush plate is used for cleaning the filter plates, the top of the shell is communicated and fixedly provided with a liquid inlet pipe, and the bottom of the shell is communicated and fixedly provided with a liquid outlet pipe. Collected metal scraps are moved to the weighing unit to be weighed, then the weight of the metal scraps in set time is obtained, whether metal part abrasion is normal or not is judged, data are sent to a control center, and if the data are abnormal, planned maintenance or emergency shutdown is conducted according to program setting, and loss expansion is avoided.

The invention provides an intelligent load transfer auxiliary decision-making method and system, and belongs to the technical field of power grid dispatching automation. In order to solve the problems that an existing intelligent decision-making system cannot be used when data is missing, wrong or delayed due to excessive pursuit of data completeness and algorithm accuracy, and even misleads decision making, a light-weight decision-making design concept oriented to data authenticity is provided. The system actively abandons dependence on non-essential data, strictly limits a data input source as a limited data set which is highly trusted and inevitably used in daily work of a dispatcher, and the limited data set comprises switch displacement, recent steady-state load stored before a fault, an equipment rated parameter table and planned maintenance information.

The invention relates to an automatic control method and system for a concrete mixing plant, and belongs to the technical field of concrete production control, and the method comprises the steps: collecting the operation state data of key equipment in a mixing plant production line in real time; inputting the operation state data into a preset equipment health prediction model to generate predictive maintenance information for the key equipment; when the early warning key equipment meets the condition that the corresponding predictive maintenance information meets a preset early warning condition, a production scheduling plan is dynamically adjusted based on the predicted fault time point corresponding to the early warning key equipment, and a planned maintenance window before the corresponding predicted fault time point is planned for the early warning key equipment; the maintenance personnel are reminded to maintain the early warning key equipment during the planned maintenance window; wherein in the planned maintenance window, other parts except the early warning key equipment normally operate on the production line of the mixing plant. The device has the effect of improving the production efficiency under the abnormal condition of the stirring station equipment.

The present application relates to the technical field of structural health monitoring and artificial intelligence, and discloses a steel structure corrosion prevention evaluation system based on environmental data and AI, which comprises a data sensing module, an environmental index generating module, an impact function generating module, a stress-corrosion coupling effect model constructing module, a steel structure life prediction module, a grading early warning module and a system self-learning module; the present application can quantify the corrosion impact of environmental emergencies and realize advanced early warning; for the first time, real-time stress monitoring data is directly incorporated into a corrosion life evaluation model, and the coupling effect of stress corrosion is accurately depicted; modeling and prediction are carried out for the unique environment and stress state of each specific structure, and the results are more instructive; the present application realizes the transformation from planned maintenance to condition-based maintenance, avoids waste caused by excessive maintenance, prevents risks caused by insufficient maintenance, and significantly reduces the life cycle cost.

The invention relates to a road condition index prediction method and system based on index attenuation. The method comprises the following steps: firstly, obtaining historical and planned maintenance information, road condition data and road surface types of a target road, calculating annual index attenuation values of various road surface types, extracting sample paragraphs of specified road surface types, calculating historical annual interval attenuation values of the sample paragraphs, and further obtaining a latest annual attenuation value through a weighted average algorithm; dividing a target road into basic road units, determining the latest road surface type of each unit by using a time sequence covering method, and matching a corresponding attenuation value; and finally, subtracting the attenuation value from the latest annual road condition index value to obtain the predicted annual road condition index of each unit. According to the method, data-driven attenuation law analysis is adopted, weighted average and time sequence coverage technologies are combined, the accuracy of road surface technical condition index prediction is improved, and maintenance road section selection, scheme making and resource allocation decision making can be effectively guided.

The utility model discloses a cylinder bolt sealing device, and belongs to the technical field of coal water slurry process gasification equipment. The lining plate is arranged in a rod mill barrel, the barrel bolt freely penetrates through the lining plate and penetrates through a bolt hole in the rod mill barrel, the self-locking nut is in threaded connection with the barrel bolt and is located outside the rod mill barrel, and the axis of the barrel bolt is perpendicular to the central axis of the rod mill barrel. According to the cylinder bolt sealing device, the thickened rubber pad, the bowl-shaped structure gland and the base are arranged, so that the position of the cylinder bolt is limited, sealing of the cylinder bolt can be effectively controlled, coal water slurry leakage is prevented, on-site civilized production is guaranteed, raw materials are saved, more importantly, the emergency shutdown overhaul frequency is greatly reduced, and the service life of the cylinder bolt is prolonged. Periodic planned maintenance is basically realized, and no leakage in one operation period is realized.

The invention provides a power distribution network planned maintenance optimization method based on network reconstruction interval optimization. The method comprises the steps that an upper layer maintenance time optimization model with the minimum total maintenance cost as a target function is constructed; constructing a lower-layer power distribution network reconstruction interval optimization model taking the minimum energy loss in the maintenance period as a target function; and loop solution iterative calculation is carried out on the upper-layer maintenance time optimization model and the lower-layer power distribution network reconstruction interval optimization model, and the optimal maintenance time period of the power distribution network equipment and the power distribution network reconstruction strategy of the power distribution network equipment in the corresponding time period are determined. The problems that the uncertainty of operation of the power distribution network is not fully considered in traditional maintenance optimization, and a maintenance scheme pays attention to economy and looks light on power supply reliability to a certain extent are solved, and a more economical and reliable power distribution network maintenance plan optimization scheme is obtained.

The invention provides a method for predicting fatigue cracks of a welding structure. The method comprises the following steps: S1, accurately acquiring the crack length and the maximum crack depth of a sample under different cycle times; and S2, obtaining a fatigue crack propagation model formula, namely, the unit is as follows. S3, if the crack depth is L1 when the fatigue test is circulated for N1 times, and the cycle index corresponding to the crack depth L2 is N2, N2 is calculated according to the following formula. According to the method, the maintenance interval can be scientifically prolonged or maintenance can be arranged in advance by establishing the crack propagation model and predicting the residual life of the structure, and if the residual life predicted by the model is far longer than the next planned maintenance period, the maintenance period is properly prolonged to save docking cost, labor loss and the like; if the residual life predicted by the model is lower than a safety threshold value, overhauling is arranged in advance, and hidden dangers are eliminated to avoid sudden disasters and accidents.

The application provides a health state-based complex product maintenance support auxiliary decision method and system, the method takes health state core data of complex product subsystems / parts as core input, cooperates with multi-source data support of maintenance task original data, spare part original list data, cost-life basic data and health management real-time data, realizes maintenance support auxiliary decision through five core steps of combined maintenance decision model construction, plan maintenance task group optimization, maintenance plan dynamic equalization, condition-based maintenance and spare part support joint optimization and maintenance detection interval period quantitative solution, and the output results of each step jointly constitute maintenance support auxiliary decision results; the maintenance support auxiliary decision system adapting to the method realizes automatic and integrated execution of the above method through data interaction of the data acquisition module and the multifunctional processing module. The application can realize real-time and comprehensive grasp of the health condition of the complex product, and timely find and warn potential failure trend.

The invention provides a nuclear power equipment maintenance isolation ring planning method and system, and the method comprises the steps: collecting equipment information and region division information of a nuclear power station, rating each to-be-maintained equipment according to the equipment information, and obtaining a rating report of each equipment, the equipment information represents the current condition of the equipment, and the region division information represents the current condition of the equipment; the device level in the rating report is used for determining an isolation index; acquiring historical maintenance data of each device, and preprocessing the historical maintenance data; performing comparison processing analysis on the rating report and historical maintenance data, and determining an overhaul risk grade of each device; and dividing a corresponding maintenance isolation ring for each device in combination with the region division information and the maintenance risk level and maintenance efficiency of each device. According to the method, the difference between the set area size of the maintenance isolation ring and the actual demand can be reduced, and the reasonability and accuracy of the divided maintenance isolation ring are improved.

The invention discloses a process curve deviation alarm system and method for industrial temperature control. The process curve deviation alarm system comprises a parameter setting module, a reference curve recording module, an EEPROM (Electrically Erasable Programmable Read-Only Memory) storage unit, an operation monitoring module and an alarm output module, the invention relates to the technical field of industrial temperature control. According to the process curve deviation alarm system and method for industrial temperature control, temperature values of a plurality of time points are recorded to form a reference curve, and the current temperature value and the reference curve are compared in the whole process according to the same time axis in the subsequent operation process, so that traditional point monitoring is upgraded to line monitoring; according to the method, the recessive temperature curve form abnormity caused by equipment element aging and sudden working condition fluctuation in industrial production can be effectively identified, the omission ratio is reduced, and meanwhile, the equipment maintenance is changed into planned pre-maintenance from traditional passive first-aid repair through whole-process deviation analysis and alarm signals of a reference curve, so that the equipment maintenance efficiency is improved. And the risk of accidental interruption of the production line caused by sudden failure of components is effectively reduced.

The invention provides an operation and maintenance strategy optimization method and system applied to a wind turbine generator and electronic equipment. The method comprises the steps of collecting multi-dimensional operation data of a wind turbine generator; calculating a DHDI index of a key component of the wind turbine generator based on the multi-dimensional operation data; calculating an OMWS score of the wind turbine generator based on the multi-dimensional operation data; the DHDI index is compared with various threshold values of DHDI threshold values, whether the wind turbine generator enters an emergency maintenance mode or a planned maintenance mode is determined based on the comparison result of the DHDI index and the DHDI threshold values, and the DHDI threshold values comprise an attention threshold value, a warning threshold value and a critical threshold value; and in an emergency maintenance mode or a planned maintenance mode, outputting a corresponding operation and maintenance decision of the wind turbine generator according to a comparison result of the OMWS score and an OMWS minimum threshold. According to the method, the accuracy and foresight of fault early warning can be improved, the opportunity cost for maintaining the execution opportunity is minimized, and the transparency and credibility of the decision making process are enhanced.

The invention relates to the technical field of intelligent maintenance of the Internet of Things, and discloses a UV printing remote monitoring and maintenance system based on the Internet of Things, and the system comprises a data collection and preprocessing module which collects the operation parameters and environment data of UV printing equipment, and carries out the data preprocessing and state evaluation; the maintenance scheduling and resource coordination module is used for dividing the UV printing equipment network into a plurality of regional maintenance centers, constructing a deep Q network maintenance scheduling model, learning an optimal maintenance strategy and realizing resource coordination among regions; the intelligent contract and path optimization module automatically distributes and executes maintenance tasks and plans maintenance paths of technicians; the maintenance quality evaluation module is used for evaluating the maintenance task completion degree and updating the reputation value of a technician; the knowledge sharing and model optimization module is used for realizing maintenance knowledge sharing and model optimization among equipment; according to the invention, a decentralized architecture is adopted to eliminate the single-point fault risk, and the stability and reliability of the whole system are ensured.

The application discloses a capsule cabin equipment health management and predictive maintenance system based on digital twinning, relates to the technical field of health management, and comprises an LPWS index calculation module, a maintenance frequency calculation module and a deviation index calculation module.The LPWS index calculation module is used for constructing a geometric twin of a target capsule cabin team and calculating the LPWS index of each capsule cabin in the target capsule cabin team according to the geometric twin.The maintenance frequency calculation module is used for calculating the planned maintenance frequency of each capsule cabin according to the equivalent convergence width of each capsule cabin, the LPWS index and the management and maintenance cycle of the target capsule cabin team.The deviation index calculation module is used for calculating the static deviation index of the capsule cabin according to the measured standby current of the capsule cabin and the reference standby current of the capsule cabin.The application improves the ability to identify potential convergence angles and drainage failure areas.