1127 results about "Full life cycle" patented technology

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention relates to the technical field of grinding wheel monitoring, in particular to a centerless grinding machine full-life-cycle grinding wheel state monitoring system and method.According to the centerless grinding machine full-life-cycle grinding wheel state monitoring system and method, through combined difference extraction of a main shaft current mean value fluctuation ratio and a power gradient continuous variation value and assisted by transverse comparison of periodic response intervals, effective recognition of time period boundaries of working condition states is achieved; according to the method, operation stage division does not depend on static threshold judgment any more, a track trend is established through linkage judgment of an amplitude kick point and a slope extreme value, a dynamic evolution path of signal response is clearer, the identification resolution of abnormal changes and local deviation trends is improved, response synchronous identification of parameter state changes is achieved, and the identification accuracy is improved. In multi-signal collaborative analysis, directivity comparison between a temperature rise mutation fragment and a current residual error response is carried out, so that the consistency analysis capability of response logic between different monitoring paths is enhanced, and the real-time performance, stability and anti-interference capability of state identification are improved.

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a multi-sensor fusion heat pump full life cycle AI maintenance early warning system, and relates to the technical field of new energy utilization, and the early warning system comprises a data collection module which obtains operation parameters in a heat pump full life cycle based on a sensor array, and constructs a data set after preprocessing the parameters; the operation parameters comprise temperature, pressure, flow and micro vibration; the data fusion module is used for extracting trend correlation characteristics and parameter coupling characteristics from temperature, pressure and flow parameters by adopting a dynamic sliding window adaptive to a working condition, and preserving core nonlinear information through KPCA dimension reduction; the micro-vibration signal extraction comprises frequency domain and time domain features. According to the method, features are extracted through a working condition adaptive dynamic sliding window, then through cross-space mapping and a life cycle-working condition double-attention mechanism, the analysis and early warning module depends on a core feature mapping library and a two-dimensional dynamic baseline, through instantaneous and accumulated deviation double judgment, abnormal accurate recognition and stage division are achieved, and early warning perspectiveness is high.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

The invention discloses a bus duct full life cycle health management system based on digital twinning, and relates to the technical field of health management. The multi-source sensor collects operation parameters and static information, the digital twin modeling and mapping module constructs a physical model and associates real-time data, and a thermal-electric coupling equation is used for simulating temperature; the state monitoring and fault diagnosis module compares data to judge states and diagnoses faults by means of methods such as a fault tree, the health assessment and decision making module constructs an index system to assess health and makes a maintenance decision, the data management and interaction module stores data and realizes visual interaction and system integration, and the intelligent optimization module performs intelligent optimization based on operation and maintenance data. And optimizing model parameters and a decision strategy. According to the invention, intelligent health management of the bus duct is realized, multi-source data acquisition is accurate and comprehensive, fault diagnosis is more accurate and prediction is more timely through combination of digital twinning and an algorithm, and intelligent assessment assists scientific maintenance decision; and the operation and maintenance efficiency and the power transmission stability are improved through system integration and edge calculation.

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention discloses a lithium battery full life cycle quality tracing system based on digital twinning, and relates to the technical field of lithium battery quality tracing, and the system comprises a physical entity layer which integrates multiple types of sensors and detection equipment to collect full-link data; the digital twinning modeling layer is used for constructing a multi-scale model to map a battery state; the data acquisition and transmission layer is used for realizing data real-time transmission preprocessing through an edge gateway and an encryption protocol; the data storage and management layer is used for establishing a data warehouse by using a distributed database and performing backup; the intelligent analysis and decision-making layer integrates an algorithm to realize anomaly detection, performance prediction and tracing; the visualization and interaction layer is used for providing a multi-terminal visualization and interaction function; and the block chain evidence storage layer constructs an alliance chain to ensure that the data cannot be tampered. According to the invention, full-life-cycle data connection and credible tracing of the lithium battery are realized, the quality problem positioning efficiency is improved, intelligent early warning and optimization capabilities are provided, the operation threshold is reduced, and industrial quality control and green development are facilitated.

The invention relates to the technical field of informatization management systems, and particularly discloses an AI-aid decision-making product full-life-cycle informatization management system which comprises a full-link data sensing module, a digital main line construction module, an AI decision-making center module, a service execution interface module and a man-machine collaborative interaction module. The method comprises the following steps: collecting multi-source heterogeneous data of each stage of a full life cycle of a product through a full-link data sensing module to form a full-amount data sequence with a space-time stamp; and in combination with a data fusion engine, an association graph construction unit and a twinborn model driving unit of the digital main line construction module, data cleaning, alignment, semantic fusion and cross-stage association are realized, a dynamically updated product digital twinborn model is constructed, real-time mapping of a physical entity and a virtual model is achieved, and the real-time mapping of the physical entity and the virtual model is realized. Unified and complete data support is provided for full-link decision making, and the problems of data fragmentation and prominent collaborative barriers of a traditional system are thoroughly solved.

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention discloses a digital twinning-based building full life periodicity performance evaluation method and device, and belongs to the field of building construction.The evaluation method comprises the following specific steps that I, in the building planning and design stage, a building foundation digital model is constructed, meanwhile, various construction data are collected in real time through a multi-modal sensor, and block chain storage is established; iI, performing real-time sensor data preprocessing and updating based on the building basic digital model so as to perform multi-physics field coupling simulation, and dynamically updating and predicting a building state; the method overcomes the limitation that traditional simulation is large in calculation amount and difficult to update in real time, improves evaluation reliability, achieves dynamic calibration and precision enhancement, enables material aging and repair prediction to be more accurate, provides a solid basis for durability evaluation, improves the economy and reliability of the whole life cycle of a building, and has a wide application prospect. And a complete closed loop of coverage planning-construction-use-maintenance is formed, so that the performance evaluation can be continuous, dynamic and traceable.

The invention provides an mbse-based system full life cycle management method and system, and relates to the technical field of data synchronization, and the method comprises the steps: obtaining to-be-synchronized data of a source system, constructing a semantic graph structure, generating a mapping strategy based on implicit association between semantic matching learning systems, executing transactional data transmission, and constructing a time sequence traceability tree to track change propagation. And adaptive reconstruction is carried out when a constraint conflict is detected. According to the method, the problem of data synchronization among heterogeneous systems is solved, and the model consistency maintenance capability and the adaptive conflict processing efficiency are improved.

The invention relates to the technical field of smart home, discloses a full-life-cycle fault prediction and management method and system for smart home equipment, and aims to solve the problems of high fault rate and low operation and maintenance efficiency of the smart home equipment in the prior art. The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; time domain and frequency domain features are extracted and fused to generate a comprehensive feature vector; predicting the fault probability and type by using a deep transfer learning model; a dynamic risk score is calculated through a weighted risk assessment model, and risk grades are divided; and triggering a differential response strategy according to the risk level. The system comprises a multi-source heterogeneous data sensing module, a dynamic feature extraction and fusion module, an adaptive fault prediction module, a quantitative risk assessment module, an intelligent response and decision module and a full life cycle data management module. According to the technical scheme, comprehensive and accurate monitoring, efficient fault prediction and intelligent management of the smart home equipment can be achieved, the fault rate is remarkably reduced, and the overall performance of the system and the user satisfaction degree are improved.

The invention relates to a full-life-cycle digital passport management system and method for electromechanical equipment, which take dynamic data in an operation and maintenance stage as a core and depend on linkage of an intelligent algorithm and full-cycle data. The problems of equipment parameter deviation, performance degradation and the like can be accurately identified based on five-dimensional full-period data integrated by a data passport construction module; and a fault influence range and associated equipment are positioned according to physical, logic and power dependency relationships presented by the equipment dependency graph construction module, so that a basis is provided for decision correction. Besides, through unique identification of a gene coding module, historical data and responsibility subjects of equipment can be traced, BIM model dynamic updating, feedback design and construction stage data verification optimization are driven, and a closed loop of operation and maintenance feedback-model correction-full-cycle data iteration is formed. The module solves the problems of traditional operation and maintenance data isolation and model static state, enhances the timeliness and accuracy of block chain evidence storage data, and supports applications such as intelligent service layer predictive maintenance and fault self-repairing.

The invention relates to a house building construction stage carbon emission dynamic estimation method and system based on full life cycle data, and the method comprises the steps: dividing a construction region based on a BIM model, laying a multi-source sensor network, fusing static BIM data and a dynamic Internet of Things monitoring flow, building a four-dimensional space-time label data set, and carrying out the construction stage carbon emission dynamic estimation based on a double-layer dynamic correction model. Kalman filtering is utilized to eliminate instantaneous interference, LSTM transfer learning is combined to predict mechanical efficiency attenuation, a dynamic correction coefficient matrix of a mechanical aging rate, operator skills and environmental factors is embedded, and a carbon emission value is calibrated in real time; when abnormal emission is detected, a key pollution source is traced and positioned through a graph neural network, a three-dimensional thermodynamic diagram is generated in the digital twinborn model, and component-level carbon flow analysis and early warning are performed, so that the carbon emission estimation precision is remarkably improved, the abnormal traceability time consumption is shortened, and the timeliness is remarkably improved.

The invention discloses a city update district full life cycle monitoring and optimizing system based on digital twinning, and the system comprises a data collection module which collects physical world data in real time through a plurality of sensors; the digital twinborn model building module is used for receiving the data and building a model containing three-dimensional geographic information, a building information model and crowd behavior simulation; the data analysis module processes original data and feeds back the original data for model updating; the dynamic monitoring module is used for displaying a state and early warning based on a model and analysis data; the optimization decision-making module is used for generating a decision-making scheme through analog simulation and an optimization algorithm; and the system interaction module provides a multi-terminal interface and collects user feedback for scheme adjustment. According to the method, the problems of data islands, monitoring lag and decision-making experience in traditional city updating can be solved, and complete-cycle data integration, dynamic monitoring and intelligent optimization are realized.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention particularly relates to an intelligent factory integrated operation management method based on multi-system integration, and relates to the technical field of intelligent factory operation management. Performing intelligent planning and robustness decision making; performing production execution and monitoring; and carrying out equipment full-life-cycle management. According to the method, the limitation of a traditional static plan is thoroughly broken through through a collaborative mechanism of multi-dimensional risk index linkage, dynamic time buffering and a plan chain; on one hand, the DTB performs real-time calculation based on a supply chain risk index, an equipment health index and a process stability index, approaches reference buffer to avoid resource waste when the risk is low, automatically expands to absorb disturbance when the risk is high, and ensures dynamic balance of plan efficiency and disturbance resistance; and on the other hand, the pre-generated plan chain is rapidly triggered when the disturbance exceeds the limit, global rearrangement is not needed, and continuous production can be guaranteed only through local adjustment.

The invention discloses a battery pack full life cycle internal resistance evolution tracking method, which belongs to the technical field of battery state monitoring, and comprises the following steps: identifying an operation gap window in the operation process of a battery pack; applying a micro excitation signal to the battery pack in the operation gap window to generate a voltage response signal; orthogonal component extraction and amplitude calculation are carried out on the voltage response signal, and a current internal resistance measurement value is obtained through calculation; jointly storing the current internal resistance measurement value and the corresponding battery state parameter to obtain an internal resistance data sequence; performing trend extraction and deviation modeling on the internal resistance data sequence to obtain an updated internal resistance evolution prediction model; and performing health state mapping analysis on the updated internal resistance evolution prediction model to generate a battery health state evaluation parameter. According to the method, a dynamic excitation adjustment technology is adopted, and a multi-source data time alignment strategy is combined, so that accurate tracking of full life cycle evolution of the internal resistance of the battery pack and accurate evaluation of the battery health state can be realized.

The invention relates to the technical field of industrial big data storage and management, and discloses an industrial data full life cycle management method and system.The method comprises the steps that original time sequence data of industrial equipment is obtained, Shannon entropy calculation is carried out, and a time sequence importance score sequence is obtained; performing threshold comparison according to the time sequence importance score sequence, and determining a key transient window and a stable normal window; performing lossless differential coding on the key transient window to obtain a high-fidelity data segment; performing feature extraction and sparse processing on the stationary normal window, and performing time domain splicing and index construction to obtain a primary hybrid storage structure; and continuously monitoring the query access path to generate a popularity distribution graph to obtain a hierarchical archiving data set. According to the method, value-aware hierarchical storage and dynamic full-life-cycle management of massive industrial data can be realized.

The invention discloses a dynamic semantic SLAM (Simultaneous Localization and Mapping)-driven robot full life cycle navigation method, which comprises the following steps: synchronously acquiring multi-modal data through a multi-modal sensor array, and carrying out joint coding based on a cross-modal contrast learning framework to obtain embedded features. A dynamic three-dimensional Gaussian radiation field is constructed, a scene is represented as a parameterized Gaussian kernel set, and mixed Gaussian field representation is obtained through micro rendering and joint training optimization. A Gaussian mixture field is divided into a global static layer and a local dynamic layer, retention constraints are applied to the static layer, and an incremental online updating strategy is adopted for the dynamic layer. Based on the optimized Gaussian field parameters and the multi-modal data, a factor graph containing Gaussian rendering residual factors and inertia pre-integration factors is constructed and solved, the corrected robot pose is obtained, and the space occupancy probability is generated. Operation control parameters are adjusted on line according to the pose and the occupancy probability through a strategy network, and autonomous navigation is achieved in combination with path planning and trajectory tracking.

The invention provides an API full-life-cycle management method and system based on database management and control. The method comprises the steps that basic information of an API is received through a standardized interface, and the basic information is written into a database after verification is passed; an accessible user role is configured for the registered target API, and the permission association relationship of the target API is written into a database; configuring a read-write attribute, an instance library access rule and a classification permission level of the target API, and writing configuration data into a database; accessing the verified API to a service gateway to open a calling entry; aPI calling data is collected and written into a database, and multi-dimensional analysis is carried out to comprehensively monitor the running state of the API; the system comprises an API registration module, an authority configuration module, an attribute configuration module, an operation monitoring module and a background management module. According to the technical scheme, authority control, attribute configuration and link monitoring are integrated through the centralized database, and the method has important application value.

The invention provides a drug temperature and humidity digital monitoring and quality tracing system based on a full life cycle, and relates to the technical field of digital monitoring, and the system comprises the steps: firstly collecting the temperature and humidity data of the whole link of drug production, storage, transportation to terminal sales, and obtaining a standardized data set through the verification and packaging of an industrial standard library; performing secondary screening through primary screening in combination with a dynamic temperature and humidity screening model to obtain a precise abnormal data set; associating drug full-cycle multi-class information, and constructing a temperature and humidity-drug state mapping relation based on a time sequence association algorithm to form a full-dimension association data set; based on the data set, combining with a quality risk assessment model for early warning, realizing full-period traceability through a reverse traceability algorithm, and generating an assessment result and a traceability link; and finally, relevant data are converted into a dynamic temperature and humidity monitoring curve, a quality risk early warning report and a full-life-cycle tracing atlas, and an interactive report is assembled, so that the temperature and humidity state of the medicine can be checked conveniently.

The invention relates to the technical field of safety valve fault monitoring, in particular to a safety valve full-life-cycle fault detection method and system, and aims to solve the problems that sudden safety accidents occur frequently due to the fact that a traditional method depends on leakage flow monitoring and cannot capture hidden faults such as valve element jamming and spring fatigue, a power supply system mostly adopts a single power generation mode, and the power consumption is low. In a low discharge scene, the electric quantity of the energy storage module is used up, and monitoring interruption is caused. According to the invention, the problems of safety valve hidden fault monitoring deficiency and unstable power supply are effectively solved. By means of multi-dimensional data collection, spring elasticity attenuation and valve element movement abnormity can be found in advance, and faults are prevented from being evolved into serious leakage. The composite power supply system maintains basic monitoring in the non-discharge stage, energy is quickly supplemented in the discharge stage, and continuous operation of the whole life cycle is guaranteed. A model analysis result can guide maintenance personnel to accurately position a fault part, and unnecessary shutdown maintenance is reduced.

The invention relates to the technical field of modularized laboratory project management, and discloses a modularization-based laboratory project management method, which comprises the following steps: S1, system architecture design: adopting a micro-service distributed architecture, and cooperatively working through a plurality of business modules; s2, module management and project creation; the invention also provides a laboratory project management system based on modularization, which comprises a project management engine module, an intelligent key node reminding system, a sample full life cycle tracking management module and a multi-party collaborative feedback management module, the project management engine module comprises three core business modules, namely, a project management module, a sample management module and a contract management module, standardized management and efficient operation of laboratory projects are achieved through a project module engine, an intelligent node reminding mechanism and a full-process tracking algorithm, the project management efficiency can be remarkably improved, and the project management efficiency is improved. And the time cost of a project preparation stage is saved.

The invention provides an intelligent prediction and data traceability management method for a lubricating system, and the method comprises the steps: obtaining the multi-source operation data of the input, operation and disposal stages of a lubricating medium in the whole life cycle, building the cross-stage data association based on a unique batch identifier and space-time metadata, and forming a traceable operation data chain. According to the method, dynamic correlation characteristics among equipment operation efficiency, lubricating medium states and environmental conditions are quantified through a multi-dimensional correlation analysis module, and a state evolution decision model is generated in combination with a knowledge rule base. Based on the model, the system calls a risk assessment knowledge base to generate oil replacement prediction suggestions, a component risk priority list and a maintenance strategy report. A maintenance result is fed back to the operation data chain, decision model updating and strategy optimization are driven through a knowledge base self-adaptive calibration mechanism, closed-loop optimization of intelligent lubrication management is achieved, and the fault tracing efficiency, prediction reliability and the maintenance cost-effectiveness ratio are effectively improved.

The invention relates to the technical field of crack monitoring, in particular to a concrete structure crack full-life-cycle monitoring method and system, and the method comprises the following steps: arranging sound wave points to obtain a stress change to generate an initial signal, extracting an image feature recognition extension path, analyzing a size acceleration to generate an extension identifier, and screening an active cycle at intersection time to generate an evolution fragment. And classifying crack behaviors according to displacement and width trends to generate an annotation set. According to the method, stress evolution is reflected based on interval transit time, a crack expansion trend is extracted in combination with an image feature point track, an active state is identified by superimposing crack size speed increase synchronism, a high-frequency change fragment is positioned through multi-index time intersection, and crack behavior types are divided according to a displacement direction and a width increase trend. Continuous tracking and classified marking of the whole process from crack starting to evolution are achieved, the response capacity to abnormal crack behaviors is enhanced, and the precision and timeliness of structural safety state recognition are improved.

The invention provides a pressure vessel full life cycle stress fatigue assessment and predictive maintenance system based on digital twinning. The system comprises a model construction module used for constructing a three-dimensional digital twinning model corresponding to a solid pressure vessel; the data synchronization module is used for integrating historical test data and actual operation load data of the solid pressure vessel into the three-dimensional digital twinborn model and simulating dynamic stress distribution of the solid pressure vessel in an actual operation load process; the evaluation and maintenance module is used for evaluating the accumulated fatigue damage of the solid pressure vessel and the residual fatigue life in the current state based on the dynamic stress distribution, and determining a potential risk area and a risk time window of the solid pressure vessel based on an evaluation result; and on-demand inspection plans and predictive maintenance strategies of the entity pressure vessels are generated based on the potential risk areas and the risk time windows. Stress fatigue evaluation of the whole life cycle is achieved, and the accuracy and reliability of life prediction are remarkably improved.