89 results about "Dynamic maintenance" patented technology

The invention discloses a building waterproof construction quality monitoring and early warning system based on the Internet of Things, relates to the technical field of building engineering, and realizes omnibearing and real-time monitoring and intelligent early warning of a building waterproof construction area through a multi-layer architecture formed by a data acquisition layer, a data transmission layer, a data processing layer and an application layer. The data processing layer analyzes data features from a time sequence and a spatial distribution dimension through normalization, denoising processing and a multi-head attention mechanism in a Transform model, and accurately recognizes the abnormality and risk trend of a weak point in combination with dynamic threshold and trend analysis, and the application layer determines the risk trend of the weak point through multi-dimensional data visualization, historical playback and real-time interaction. And the early warning module is combined with intelligent reasoning to generate dynamic maintenance suggestions, a maintenance scheme is optimized, the construction quality and the maintenance efficiency are improved, the intelligent level of building waterproof monitoring is improved, risk prediction and accurate maintenance are realized, the management cost is reduced, and the system has wide application value.

The invention discloses an industrial equipment intelligent operation and maintenance management system and method based on 5G-MOM, and belongs to the technical field of industrial internet and intelligent manufacturing. The system comprises a multi-source heterogeneous data acquisition layer deployed in industrial equipment, an edge computing node cluster based on 5G, a cloud intelligent analysis platform and a man-machine collaborative operation and maintenance terminal. The method comprises the following steps of collecting equipment vibration, temperature and current multi-dimensional working condition data in real time through a 5G network; performing data cleaning and feature extraction by using edge computing nodes, and constructing an equipment operation digital twin model; a cloud deep neural network is adopted to carry out fusion analysis on the multi-dimensional time series data, and self-adaptive diagnosis and residual life prediction of a fault mode are realized; a dynamic maintenance strategy is generated based on an MOM system, and field personnel are guided to execute precise maintenance through an AR terminal. According to the invention, 5G ultra-low time delay communication and an industrial mechanism model are creatively combined, and real-time visual management and predictive maintenance decision optimization of the equipment health state are realized.

The invention discloses a mold life prediction and maintenance strategy making method based on big data analysis, and particularly relates to the field of mold life prediction and maintenance, and the method comprises the following steps: collecting and associating multi-source heterogeneous data in a mold production process; comprising process parameter time sequence data, on-line monitoring time sequence data and structured and image data of off-line inspection; performing time domain alignment and collaborative feature extraction on the data, and constructing a structured training data set taking a production cycle as a unit; inputting the data set into a dynamic evolution prediction model, outputting a mold health state score, and generating a dynamic maintenance strategy through a multi-objective optimization model in combination with production context information; and finally, visually displaying the strategy, converting the strategy into a control instruction, issuing the control instruction to a production management system, and automatically triggering maintenance execution. The method can realize high-precision life prediction, dynamically optimize the maintenance decision, significantly improve the service life and production efficiency of the mold, and reduce the maintenance cost and production risk.

The invention discloses an equipment prediction maintenance framework based on probability residual life, and belongs to the technical field of fault prediction and health management (PHM). According to the framework, through combining a Bayesian neural network and a reinforcement learning technology, probability prediction and dynamic maintenance decision optimization of the residual life of equipment are realized. The method specifically comprises the following steps: acquiring sensor data in equipment operation, and preprocessing to generate a training data set; constructing a Bayesian neural network (BNN), utilizing variation reasoning to approximate posteriori distribution, and outputting probability distribution of residual life through Monte Carlo sampling; based on a probability prediction result, constructing a reinforcement learning environment model, and defining a state space containing residual life distribution, a spare part state and a maintenance action and a reward function; a Double DQN algorithm is adopted to optimize a maintenance strategy, and intelligent decision-making of the optimal maintenance time and the optimal ordering time of equipment is dynamically realized through an epsilon-greedy algorithm, so that the maintenance cost and the fault risk are minimized. According to the method, probability residual life distribution and reinforcement learning decision are innovatively combined, the problem that a traditional point estimation model ignores uncertainty is solved, an intelligent maintenance framework is constructed through a reinforcement learning method, and dynamic optimization of maintenance decision is achieved. The NASA aero-engine data set verification shows that compared with a traditional method, the optimization effects of prediction errors, uncertainty quantification, the maintenance cost rate and the like are remarkable, and the reliability and economical efficiency of equipment are effectively balanced.

The invention discloses an intelligent concrete automatic curing system and method based on real-time monitoring, and the system comprises a multi-parameter sensing module which collects the environment parameters and internal structure parameters of a concrete structure through a plurality of sensors and label identification codes, and an edge calculation module which is used for carrying out the preprocessing, feature extraction and emergency decision-making of collected data. The cloud intelligent module is used for storing data, establishing a prediction model and generating a dynamic maintenance strategy; the maintenance execution module is used for adjusting and executing spraying, temperature control or crack repair operation according to the maintenance strategy; by integrating structural strain, crack detection and environment air pressure monitoring, the limitation of traditional single temperature and humidity monitoring is broken through, multi-dimensional sensing and accurate diagnosis of concrete are achieved, meanwhile, the system combines a physical model and a data driving model, the self-adaptive optimization capability is achieved, and the system is suitable for large-scale popularization and application. And the edge gateway is adopted to process emergencies, and the cloud platform is responsible for global optimization, so that the system gives consideration to the real-time performance and the calculation depth, and the maintenance efficiency and the concrete quality are effectively improved.

The invention relates to a database table field description intelligent generation and dynamic maintenance method and device based on RAG and a large model. The method comprises the following steps: constructing a domain-based dynamic knowledge base system through multi-source corpus collection, domain adaptation vectorization model processing and a dynamic knowledge base construction and updating process; receiving a description generation request of a table or a field, recalling related knowledge from the knowledge base through an RAG technology, assembling cue words, and then calling a large model for reasoning to generate a description result; the generated description result is confirmed through manual auditing, the knowledge base is updated according to confirmation feedback, and continuous optimization of the knowledge base is achieved. According to the method provided by the invention, the problems of low accuracy, high maintenance cost, poor interpretability and the like in a traditional method are effectively solved by constructing a field-enhanced RAG architecture and combining a three-layer knowledge system and a self-adaptive prompt framework which can be dynamically adjusted according to a business scene, and a new technical implementation path is provided for table and field description in intelligent data governance.

The invention discloses a dynamic correction method and system for equipment fault mode occurrence degree, and belongs to the technical field of power plant electrical overhaul and maintenance, and the method comprises the steps: collecting operation parameters, maintenance records and historical fault data of target electrical equipment, and recognizing a potential fault mode set of the target electrical equipment; failure mode and influence analysis is carried out on each failure mode, and the initial occurrence degree, severity and detectability of each failure mode are determined; calculating an initial risk priority number of each fault mode, and determining an initial risk according to a preset risk grade division rule; dynamically adjusting the initial occurrence degree, recalculating the updated risk priority number, and generating a corrected risk level; and constructing a dynamic maintenance decision model, and outputting a maintenance priority and a maintenance time window for each fault mode. According to the method, the defects of real-time performance, comprehensiveness and flexibility of electrical operation and maintenance of the power plant are overcome, and efficient and accurate data support is provided for operation and maintenance of electrical equipment.

The invention discloses a geological archive structure integrity guarantee method and system based on map dynamic maintenance, and the method comprises the steps: obtaining an archive circulation path which is directly and indirectly connected with an archive for peripheral associated data marked as an undeletable archive, analyzing a potential breaking point in the path, and obtaining a risk region which may cause a data isolation problem; performing deep scanning on archive data in the risk area to obtain content features and historical circulation records of archives, judging whether the content features and the historical circulation records meet data accuracy standards or not, and determining a non-key archive list needing to be deleted; according to the non-key file list, analyzing the operation influence range of each file to be deleted, obtaining potential impact on the stability of the whole system after the file is deleted, and if it is judged that the information network integrity is damaged due to deletion, moving the file out of the deletion list; and for the finally determined deletion list, acquiring backup data and a flow path log of the archive, and updating the archive connection atlas through a dynamic maintenance mechanism to obtain a new information network structure.

The invention relates to an intelligent equipment fault self-diagnosis and optimal maintenance system. The intelligent equipment fault self-diagnosis and optimal maintenance system comprises a data acquisition and transmission module, a data storage and preprocessing module, a fault diagnosis and prediction module, an intelligent maintenance optimization module, an environment perception and adaptive adjustment module and a closed-loop feedback and self-repairing module. The system collects equipment operation data in real time through a sensor, fault diagnosis and trend prediction are achieved through a deep learning algorithm after preprocessing, a dynamic maintenance strategy is generated in combination with reinforcement learning and optimization technologies, and the equipment operation state is adjusted according to environment changes. And a closed-loop feedback mechanism further optimizes the maintenance effect, and rapid repair is realized in combination with an automatic repair technology. According to the invention, the problems of untimely diagnosis and unreasonable maintenance in traditional equipment maintenance are solved, the operation reliability and management efficiency of the equipment are improved, and the method is suitable for intelligent operation and maintenance scenes of various types of equipment.

The invention discloses a wire feeding processing intelligent operation and maintenance system based on cloud computing, and relates to the technical field of industrial automation and cloud computing. The problems that in an existing system, due to data transmission delay, abnormal response of equipment is not timely, system stability is affected by unreasonable computing resource scheduling, and the operation and maintenance effect is poor due to poor adaptability of a dynamic maintenance strategy are solved. Comprising an edge layer, a transmission layer, a scheduling layer, a cloud layer and an application layer. The edge layer collects data and makes an emergency response; the transmission layer optimizes data interaction based on network parameters; the scheduling layer balances task allocation through a deep reinforcement learning algorithm; the cloud layer constructs a prediction model and generates a dynamic maintenance strategy according to a result; the application layer visually displays and distributes operation and maintenance tasks; according to the method, the fault response speed, the resource scheduling balance and the maintenance strategy adaptivity are greatly improved, and the production continuity and the product quality are remarkably improved.

The invention discloses a repair and conservation construction method for damaged travertine natural landscapes and geological landscapes. The method comprises the steps that the travertine landscape damage type and grade are evaluated based on the travertine damage area, depth and range; the method comprises the following steps: selecting homogeneous and in-phase high-calcium mineral materials and microbial agents to prepare primitive materials, and carrying out grading modulation, so that the primitive materials have a multi-stage pore structure; preparing a calcareous ecological restoration material by adopting an element material; the natural travertine building is repaired, constructed, subjected to infiltration reduction, subjected to leaking stoppage, maintained, conserved and expanded by using the calcium ecological restoration material through a series of construction methods; and after dynamic maintenance of the travertine construction body, restoration and conservation of natural landscape and geological landscape of travertine are completed. The method has the advantages of good environmental compatibility, high repairing efficiency and strong authenticity. The permeability coefficient of the repaired landscape reaches 10 <-4 >-10 <-2 > cm / s, the self-bonding strength is 1-25 MPa, biological co-deposition can be effectively adhered, continuous deposition of newborn travertine on the surface of travertine is promoted, and the method has wide application prospects.

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

The invention is suitable for the technical field of industrial dust remover equipment operation monitoring and maintenance, and provides an industrial dust remover operation state monitoring method and system, and the method comprises the steps: obtaining an initial maintenance period of a target dust remover, extracting a plurality of historical samples which have the same type as the target dust remover, but have different high-chlorine treatment frequencies and the same preset work cycle from the database of the target dust removal plant; by constructing the comprehensive corrosion index and analyzing the change trend of the comprehensive corrosion index along with the increase of the high-chlorine treatment frequency, the key inflection point of rapid development of corrosion caused by damage of the protective film can be captured, the future high-chlorine treatment frequency is predicted in combination with the database, and the relative deviation value of the comprehensive corrosion index and the critical point is calculated and predicted. A maintenance period correction factor is generated, the initial maintenance period is revised in real time, and prospective early warning and dynamic maintenance of the potential accelerated corrosion risk of the equipment are achieved.

The invention discloses an RCM-based power plant maintenance strategy making method and a related device, and belongs to the technical field of power plant maintenance. The method comprises the following steps: constructing a maintenance database of power plant equipment according to historical maintenance records; acquiring operation parameters of the power plant equipment, inputting the operation parameters into a pre-established state analysis model, and outputting a fault type analysis result; matching the fault type analysis result with a maintenance database to generate a candidate maintenance strategy set; and based on the candidate maintenance strategy set, calculating a risk priority number RPN corresponding to each strategy, and generating a dynamic maintenance plan based on the RCM according to the risk priority number RPN. The maintenance plan can be dynamically generated according to the real-time operation state of the equipment and the fault type analysis result, and the flexibility and adaptability of the maintenance plan are improved.

The invention relates to the technical field of industrial equipment control, and discloses a dynamic maintenance management method and system based on the full life cycle of test equipment, and the method comprises the steps: obtaining a time domain vibration signal and torque current data during the operation of the equipment through a high-frequency vibration sensor and a current loop feedback interface; calculating an accumulated damage gradient parameter representing the physical recession degree of the mechanical transmission chain; the parameters are input into a preset damage stress coupling model to solve the maximum radial impact force allowed to be borne by the damaged part, and the impact force is mapped into a dynamic capability boundary for carrying out logic constraint on the motion planning instruction; and the motion controller reconstructs an ideal speed curve by using the boundary in an interpolation period, and reshapes a torque load spectrum output to a servo motor by reducing the jump of acceleration and deceleration sections. The problem that an existing static control strategy is mismatched with the dynamic aging characteristic of equipment is solved.

The invention relates to the technical field of renewable energy sources, in particular to an intelligent photovoltaic power station operation and maintenance management method and system, and the method comprises the steps: obtaining multi-modal data, such as a hyperspectral image and an electroluminescent image, and carrying out the time-space registration; cross-modal feature fusion is realized through an attention mechanism; environmental stress and material degradation influence are separated by using a variational auto-encoder, and an aging feature map is generated in combination with a three-dimensional convolutional neural network; and generating a dynamic maintenance path based on the topology constraint graph and a reinforcement learning algorithm, and adjusting the priority of the path through real-time data. The system can comprehensively capture the aging characteristics of the photovoltaic module, improves the aging prediction precision and maintenance efficiency, reduces the operation and maintenance cost, and promotes the intelligent management of a photovoltaic power station.

The invention discloses a dynamic optimization management system and method for monitoring an inventory state in real time, and relates to the technical field of inventory management of industrial mechanical and electrical equipment manufacturing enterprises, and the system comprises a real-time inventory state data collection module which is used for collecting real-time inventory state data through deploying RFID tags, bar code scanning equipment and an IoT sensor network in an industrial mechanical and electrical equipment warehouse; collecting inventory state data of equipment physical positions, equipment quantity, equipment warehousing states and warehouse environment parameters in real time; according to the invention, by deploying the RFID tag, the bar code scanning device and the IoT sensor network and docking the ERP / MES system, the inventory state data and the repair warehousing work order data are acquired in real time, the maintenance emergency degree is calculated by using the improved TOPSIS algorithm, and the maintenance resources and dynamic allocation are optimized by using the multi-agent strong chemistry MARL, so that the problems of inventory overstock and inventory resource occupation are avoided, and the maintenance efficiency is improved. And dynamic maintenance resource allocation and maintenance quality are also efficiently scheduled.

The invention relates to the field of civil engineering and structural seismic resistance, in particular to an in-service steel pier seismic toughness improving structure and a construction method thereof. The method comprises the following steps: obtaining structural health data of an in-service steel pier based on an intelligent sensor network, and carrying out pier aging and stress distribution analysis to obtain a degradation parameter sequence of an old structure; performing comparative analysis on the degradation parameter sequence and new material performance data, generating an optimized new material and old structure connection scheme, and obtaining a cooperative work model of new and old materials; inputting the model into an intelligent feedback adaptive system, and performing construction parameter adjustment based on real-time construction data to generate an optimal construction scheme; based on the connection scheme and the real-time monitoring data, long-term health monitoring is carried out, and a dynamic maintenance strategy sequence is generated. Dynamic adjustment of construction parameters is achieved through the intelligent feedback self-adaption system, seamless connection and cooperative work of new and old materials are ensured, the shock resistance toughness of the in-service steel pier is effectively improved, and the service life of the in-service steel pier is effectively prolonged.

The invention belongs to the technical field of boiler maintenance, and particularly relates to a boiler four-pipe foreign matter blockage early warning method and system. The method comprises the steps of obtaining boiler four-tube foreign matter blockage historical data; collecting boiler four-tube real-time state data of a reheater, an induced draft fan and a flame detection cooling fan; a fault mode-influence-risk mapping relation is established by adopting FMEA based on historical data, and a risk assessment framework is formed; generating an initial risk level; training a machine learning algorithm model by adopting historical data and generating a blockage probability; and dynamically adjusting the initial risk level according to the blockage probability, generating a final risk level and outputting a corresponding maintenance strategy. The system comprises a data acquisition module, an equipment monitoring module, a risk division module, a risk assessment module, a jam prediction module and a decision module. According to the scheme, the FMEA and the machine learning algorithm are fused, early warning, accurate risk assessment and dynamic maintenance decision making of boiler foreign matter blockage are achieved, and boiler operation safety and maintenance efficiency are improved.

The invention relates to the technical field of predictive maintenance and health management of industrial equipment, in particular to an intelligent electromechanical integrated monitoring system based on the industrial internet, which comprises a data acquisition module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module acquires an equipment dynamic operation parameter set, inherent physical attribute parameters and historical maintenance data of target electromechanical equipment; calling preset process topology data, economic loss data and maintenance cost data; the state evaluation module iteratively calculates an accumulated degradation index representing the health condition of the equipment; the importance quantification module calculates a process importance coefficient representing the criticality of the equipment in the production line based on the process topology data; the risk calculation module generates a production risk degree which quantifies the shutdown influence; the decision generation module generates a dynamic maintenance decision instruction; according to the invention, systematic risk cognition from the individual equipment to the overall production line is realized, extensive management of all equipment is avoided, and allocation of maintenance resources is more strategic and targeted.

The invention discloses a dynamic maintenance method for a distribution network transient recording event sample library and computer equipment. The method comprises the steps of obtaining a first preset database; receiving a maintenance operation executed on the first preset database based on the target account; based on the maintenance operation, a log is generated and stored in a second preset database, and the log comprises operation time corresponding to the maintenance operation, an identifier corresponding to the power distribution network transient recording event sample and a target account identifier; based on the preset interval duration, whether an abnormal identifier exists in a first preset database is detected, and the abnormal identifier represents that the identifier of the corresponding power distribution network transient recording event sample does not exist; and under the condition that the abnormal identifier does not exist in the first preset database, determining that the maintenance operation is executed successfully. According to the method, the technical problem that certain risks exist in data errors due to implicit association of waveform data and label information when a power distribution network transient recording event sample library is maintained at present is solved.

The invention relates to the field of elevator maintenance, and discloses an elevator dynamic maintenance period optimization method and system combined with Internet of Things analysis, and the method comprises the following steps: collecting multi-modal data of an elevator in a manner of deploying a heterogeneous sensor array, and determining elevator part damage root cause associated nodes; and the aim of constructing an elevator fault prediction model is fulfilled by combining a federal learning mode. And through the elevator fault prediction model, risk simulation analysis is carried out in combination with a parallel Monte Carlo simulation method, and a periodic optimization optimal solution of elevator dynamic maintenance is output according to an analysis result. According to the method, the continuity and intelligence of data inspection can be realized in combination with an advanced data analysis algorithm, the operation and maintenance cost is remarkably reduced, and the resource utilization efficiency is improved.

The invention belongs to the field of hydroelectric generating set RCM maintenance, and discloses a hydroelectric generating set RCM maintenance strategy determination method and system based on risk priority, and the method comprises the steps: building an FMEA analysis database based on the historical operation data, fault event ledger and equipment state monitoring data of a hydroelectric generating set; performing quantitative evaluation on each fault mode of the FMEA analysis database from occurrence degree O, severity S and undetectability D, and calculating a risk priority number RPN of each fault mode; risk grades are divided according to the risk priority number RPN, a dynamic maintenance strategy is generated based on the risk grades, the dynamic maintenance strategy is output to the operation and maintenance management system, the actual operation condition and fault risks of equipment are fully considered, the reliability and safety of the hydroelectric generating set are effectively improved, the maintenance cost is reduced, and the non-planned downtime is shortened.

The invention provides a method and a system for dynamically maintaining a Token, computing equipment and a computer readable storage medium, in the method, after a server generates a first Token after login verification of a client is passed and before the first Token is returned to the client, the first Token is stored in a first database, and an index mapping table of a unique identifier of the Token is established; 'pseudo-stateful 'management of the Token is realized by intercepting a client request; when the risk of Token leakage occurs, the corresponding record in the first database can be directly deleted or the interface is called to forcibly expire the Token. Furthermore, all active Tokens of the user are stored by taking a user name as a key to form a global view of the login user, an automatic cleaning function, manual revocation and a self-defined capacity control strategy are integrated through a dynamic maintenance strategy, the Token number upper limit and validity period rule of a single user are supported to be dynamically adjusted, and flexible management of concurrent login of multiple devices is realized.

The invention discloses a die full life cycle management system based on the Internet of Things, and relates to the technical field of die management, and the die full life cycle management system comprises the steps: obtaining a pre-correlation parameter and a post-correlation parameter of a process in each stage in a die full life cycle, and obtaining a pre-correlation feature and a post-correlation feature; dynamically maintaining equipment in the process based on the real-time parameters; the method is used for solving the problems that in an existing mold full-life-cycle management method, the state of a mold is predicted in a one-sided mode only through collection of single data, and when the process where the mold is located changes and other variables influence the state of the mold, the relation between the single data and the variables cannot be effectively judged, and the mold state cannot be effectively predicted. Therefore, the problem that effective management cannot be performed based on the subsequent state of the mold is solved.

This invention provides a dynamic management method and system for multi-level assessment indicators of the resilience of nearshore bridges throughout their entire life cycle, relating to the field of structural resilience assessment and management technology. The method includes: acquiring multi-source heterogeneous maintenance data streams throughout the entire life cycle of the nearshore bridge; processing these data streams through time-series alignment and feature dimensionality reduction mapping to obtain a basic feature matrix of the bridge's service status; and performing conditional routing and data stream processing based on this basic feature matrix to obtain corrosion evolution calculation potential field distribution data, structural dynamic stiffness attenuation tensor, and structural life-cycle degradation confidence distribution vector. This invention accurately obtains resilience indices that truly reflect the bridge's resistance to degradation through multi-step quantification calculations, clarifies the sensitivity differences of assessment nodes, formulates differentiated dynamic maintenance strategies, and constructs a closed-loop management system throughout the entire process, thereby achieving intelligent bridge maintenance, improving maintenance efficiency, and reducing costs.

The application provides an image processing-based intelligent analysis method and platform for a power system, a control collection device acquires icing data of a power line in a monitoring destination, determines a heavy icing point according to the icing data, determines a dynamic maintenance range of a target line according to a working range of a cooperative maintenance device, acquires a dynamic working duration of the cooperative maintenance device based on the heavy icing point in the dynamic maintenance range, acquires deicing detection data of the cooperative maintenance device based on the dynamic maintenance range and the dynamic working duration, updates the dynamic maintenance range and the dynamic working duration according to the deicing detection data, and obtains linkage deicing data of the cooperative maintenance device.

The embodiment of the invention discloses an equipment maintenance method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring historical event data of equipment; inputting the historical event data into a maintenance time prediction model to obtain predicted dynamic maintenance time; generating a dynamic maintenance plan according to the dynamic maintenance time; and maintaining the equipment according to the dynamic maintenance plan. Therefore, the scheme can improve the probability of equipment maintenance.

The invention discloses a dynamic management method and system for optimizing the full life cycle cost of special equipment, and relates to the technical field of equipment management, and the method comprises the steps: collecting the operation data and environment data of the special equipment, preprocessing the data, and inputting the data into a fault prediction sub-model to predict the fault occurrence probability and fault parts of the special equipment; constructing a fuzzy comprehensive evaluation model to divide risk levels of the special equipment; combining the risk level of the special equipment and the prediction benefit of the benefit prediction sub-model, and making a dynamic maintenance strategy of the special equipment through the maintenance cost; and overhauling the special equipment according to the dynamic overhauling strategy, and optimizing the data model and the dynamic overhauling strategy according to the actual overhauling cost. By predicting the equipment fault occurrence probability, the optimal maintenance time of the equipment is dynamically determined, the maintenance cost and the shutdown loss cost are reduced, the maintenance efficiency is improved, the service life of the equipment is prolonged, and the whole life cycle cost of the equipment is controlled from the source.

The invention relates to a bolted connection local contact stiffness sudden change threshold prediction method, which comprises the following steps of: 1, constructing a three-dimensional model of a bolted connection composite board, and performing finite element simulation analysis on the three-dimensional model; 2, performing preliminary screening and normalization on data obtained by modal analysis and harmonic response analysis; 3, constructing a machine learning prediction model by using the training set; 4, the precision of the machine learning prediction model is tested and verified through the test set, and evaluation is carried out through the evaluation indexes. According to the method, the time cost and complexity of manual testing are reduced, the dynamic overhaul period can be formulated according to the predicted threshold value cycle index, and the monitoring efficiency and the production efficiency are improved.