245 results about "Full life cycle" patented technology

This invention relates to the field of engineering structure performance prediction and optimization technology, specifically disclosing an engineering full life cycle performance prediction and optimization system. The system collects time-series data of load, acceleration, and strain of engineering structures under extreme loads; constructs a physical consistency mapping operator based on real-time input data and finite element prior information, outputting a predicted response field; performs intrinsic orthogonal decomposition on the finite element prior information to extract the basis vector and integral point subset, trains the time-series evolution rule of the basis coefficients, and outputs low-dimensional basis coefficient prediction values; compares the real-time input data with the predicted values, and triggers online basis assimilation update when the deviation exceeds a threshold, assimilating the low-dimensional basis coefficients to output a corrected stiffness field; substitutes the corrected stiffness field into the time-series evolution rule to extrapolate the future response field, generating a smooth control force command for the active mass damper; this invention achieves real-time closed-loop optimization control of engineering structures under extreme loads.

The application provides a kind of epoxy resin glue machine full life cycle management method, system and device for ship, method includes the following steps: install multiple data acquisition devices on epoxy resin glue machine;Synchronous acquisition of the original data of multiple data acquisition devices;Signal conditioning is carried out to original data to obtain conditioning data;Time-frequency conversion is carried out to conditioning data, time-domain signal is converted into frequency-domain signal, and characteristic data representing the state of epoxy resin glue machine is extracted;Based on the comparison of characteristic data and preset threshold, the running state of epoxy resin glue machine is judged, and early warning information is generated when abnormal. It can effectively carry out accurate early warning on failure, and avoid relying on point-by-point detection by artificial.

The application provides a kind of blow molding product full life cycle quality tracing and process sharing system based on blockchain, belongs to intelligent manufacturing and industrial data security technical field.The system includes data acquisition and storage module, for obtaining production information and storing key characteristic value or hash value in blockchain;Multi-party collaborative management module, for configuring dynamic data access rights for participants through smart contract;And process knowledge asset management module, for extracting process parameter set from blockchain data that is strongly associated with high-quality output results, encapsulating as verifiable process knowledge package, and realizing its pricing, transaction and feedback loop through smart contract.The method realizes cross-enterprise trusted data tracing, safe sharing and value realization of process knowledge through the collaborative work of the above modules, improves product quality consistency and industry knowledge reuse efficiency.

This invention discloses a closed-loop compensation method and system for the entire lifecycle accuracy of high-end industrial machine tools, belonging to the field of high-end CNC machine tools and industrial machine tools technology. This system constructs a complete system encompassing machine tool accuracy forward design, full-dimensional error modeling, online real-time detection, closed-loop dynamic compensation, and lifecycle accuracy maintenance. It can achieve a positioning accuracy of ≤0.0005mm and a repeatability of ≤0.0003mm under rated load for domestically produced machine tools, stably achieving IT4 level machining accuracy, with accuracy retention improved by 3 times compared to conventional domestically produced machine tools. This invention breaks through the core bottleneck of low accuracy upper limit, poor accuracy retention, and long-term reliance on imported CNC systems in domestically produced high-end machine tools. It is fully compatible with domestically produced machine tools and CNC systems, 100% independently controllable, and can be widely applied in scenarios such as army-specific five-axis CNC machine tools and artillery / ammunition precision machining centers.

The application discloses a kind of highway full life cycle ecological influence dynamic grading evaluation method and system, belong to ecological environment evaluation technical field.The multi-source time series monitoring data along the highway is collected, engineering interference index and ecological system resilience index are calculated, and the ecological system resilience index is used to correct the engineering interference index, the dynamic critical interference threshold value is calculated in combination with the ecological system resilience index, and then the correction factor is generated by migration learning combined with the similarity of current engineering characteristics and historical cases, the dynamic critical interference threshold value is optimized, and finally the ecological impact index is calculated based on the corrected engineering interference index and the corrected dynamic critical interference threshold value, and the dynamic grading is completed.The application realizes the intelligent, dynamic and accurate evaluation of the ecological impact of the whole life cycle of highway, can adapt to different engineering scenarios and ecological states, and improves the accuracy and adaptability of evaluation.

The application relates to the technical field of digital twinning, in particular to a highway carbon emission full life cycle accounting system based on digital twinning, which comprises a front-end interaction framework and a back-end accounting framework, the front-end interaction framework and the back-end accounting framework realize data bidirectional interaction through network communication, the front-end interaction framework is used for realizing the human-computer interaction functions of digital twinning visual display, carbon emission accounting data operation and platform management instruction issuing. The application realizes accurate carbon emission full life cycle accounting, solves the pain point of the incomprehensive traditional accounting range, realizes the deep fusion of digital twinning and carbon emission data, improves the visualized control level, realizes multi-module collaborative linkage, improves the accounting efficiency and the control standardization, provides standardized data support, guarantees the consistency of the accounting accuracy, simultaneously, helps the green development of highways, and the system can realize the input of carbon reduction measure data and the accurate accounting of carbon reduction amount.

The application discloses a kind of underwater vehicle degradation characterization method, belong to underwater vehicle technical field, steps are as follows: the full life cycle data collected by sensor carried on underwater vehicle is collected, data is preprocessed;Feature extraction is carried out to the data after pre-processing;Design a kind of layered cooperation particle swarm optimization algorithm based on fuzzy analytic hierarchy process, the selected feature is obtained by screening the extracted feature, and the final optimal feature combination is obtained;Dynamic correlation mahalanobis distance is used to weight and fuse the selected feature, and the fusion feature is obtained;Using the UMAP dimension reduction method with degradation perception, the degradation process is divided into three stages of early, middle and late according to the full life cycle data, an independent UMAP submodel is trained in each stage, and then the degradation index is fused by transfer learning.This application breaks through the limitation of single signal, realizes multi-component coupling degradation recognition, strengthens early degradation capture, reduces the rate of missed judgment and fault risk, has environmental dynamic adaptability, and adapts to complex deep-sea working conditions.

The application discloses a kind of full life cycle of research and development project multi-source data fusion management system, it is related to research and development project management technical field, including project management module, planning management module, data collection module, project tracking module, resource management module, report output module, item management module, management maintenance module.The full life cycle of research and development project multi-source data fusion management system, from system architecture level, constructs project control, resource collection, report output integrated architecture, effectively integrates each stage data of research and development project, realizes the full-process digital management from project establishment to item by introducing workflow engine, ETL tool, BI tool, ensures the real-time circulation and sharing of project data, which not only greatly improves the efficiency and quality of project management, but also significantly reduces project risk and cost.

The application provides a kind of oil and gas maintenance site's explosion-proof monitor whole life cycle tracing method and system, including RFID reader passes through the analysis mixed baseband signal constellation chart distortion degree in collision time slot, according to the preset mapping relationship estimation collision label quantity;Combining the success and idle time slot number of MAC layer statistics, the total number of to be identified label is calculated, to set the frame length of next frame, and get collision density index by the ratio of collision time slot number and frame length;Based on the index, adjust the transmission power of reader;For each successfully identified explosion-proof monitor label, the label ID, maintenance record, time stamp, reader position and collision density index are encoded into data block, the current abstract is generated by hash operation combined with the hash value of the previous data block and stored, to build a complete whole life cycle tracing chain.

The application relates to a rotating machine residual life prediction method and system, and belongs to the field of mechanical life prediction. The method comprises the following steps: collecting signals capable of reflecting mechanical life of a rotating machine to be measured at least at two positions, extracting one-dimensional time characteristic data from the signals capable of reflecting mechanical life by using principal component analysis pooling, training a related vector machine by using the one-dimensional time characteristic data, so that the full life cycle of the rotating machine is divided into a period without obvious failure trend and a failure tendency period; training a deep separable convolution gate recurrent unit network by using data of the failure tendency period, and predicting the residual life of the rotating machine in the failure tendency period. The application can improve the prediction accuracy when the residual service life of the rotating machine is predicted.

The application relates to an online multi-element cloud exhibition hall system based on 3D visualization, and relates to the technical field of information processing technology. The full life cycle management of works and materials is realized through a workbench module, the flexible definition of a multi-modal interaction component is driven by a hot spot configuration module, the scene switching and media playing rhythm are accurately controlled based on a time axis global synchronization mechanism by a guide management module, a multi-theme module supports the dynamic loading and mixed superposition of multiple sets of theme resources, and finally high-precision 3D rendering and cross-terminal adaptation are realized by a rendering engine module integrating the above instructions. The application realizes the stereoscopic interactive display of text / PDF / audio and video through multi-modal information fusion, intelligently adjusts the guide path and node time length by adopting a dynamic Bezier algorithm to eliminate visual angle jumping, supports seamless switching and on-screen comparison of multiple themes by combining layered rendering and resource on-demand loading technology, and realizes performance optimization while guaranteeing visual effects.

The present application relates to the technical field of equipment life cycle management, and specifically includes an aviation equipment full life cycle management method and system based on digital threads, the method comprising: acquiring process parameters of each stage, determining first digital thread master data and a first layer of associated test sequences, second digital thread master data and a second layer of associated test sequences, mapping the deviation of the physical equipment state and the virtual simulation state, and identifying the performance difference point under the cross-stage data correlation link; setting a management strategy for collaborative optimization. The technical problem of being unable to identify the cross-stage performance difference point equipment performance fluctuation root cause positioning in real time, causing the reliability of aviation equipment to be limited is solved, the first and second digital thread master data and the associated test sequences are constructed, the deviation of the physical equipment and the virtual simulation state is accurately mapped, the management strategy is set in combination with the double thread data and the performance difference point, and full life cycle collaborative optimization is performed, and the technical effect of effectively improving the equipment reliability is achieved.

The application discloses a kind of based on building engineering full life cycle's collaborative management system, it is related to building engineering management technical field, including: data acquisition module: obtaining the engineering task information and engineering entity information of each stage of building engineering full life cycle;Data analysis module: based on the engineering task information, determine task node and the task dependency between task node;State agent generation module: based on the task node, for each engineering entity generates corresponding state agent object, and the state agent object is used to represent the real-time state of the engineering entity under specific task node.By generating the state agent object of each engineering entity throughout full life cycle, and state agent object is associated with specific task node, the deep fusion of engineering task execution state and engineering entity real-time state is realized.

The application discloses an industrial full-link through implementation method based on unique identification and double contract anchoring, and belongs to the technical field of intelligent manufacturing informatization. The method takes a plan number as a unique and unchangeable identification throughout the full life cycle from raw material warehousing to finished product warehousing, takes a contract hash value as a cross-table association anchor point, adopts a step-by-step anchoring mechanism of sales contract priority binding and procurement contract subsequent binding, and combines batch processing, transaction safety control and full-chain backtracking synchronization technology to realize bidirectional synchronous updating of procurement and sales contract information in full life cycle data tables such as raw material inventory tables and production process quality inspection record tables. The application breaks the strong coupling of the procurement and production processes, supports cost information post-recording, solves the pain points of data disconnection, untraceability, low efficiency and maintenance difficulty of traditional industrial software, has a simple architecture, high performance and stable operation, and is suitable for various intelligent manufacturing systems such as MES and ERP. The application has been applied in actual industrial scenes and has industrialization popularization capability.

The application provides an AI-driven software project full life cycle intelligent management method and system, relates to the technical field of software project management, and comprises the following steps: realizing demand structural analysis by fusing NLP and a knowledge graph, automatically supplementing implicit demand and compliance requirements; completing task automatic disassembly, work hour estimation and resource dynamic scheduling based on reinforcement learning; identifying risks in real time by fusing multiple source data and generating a treatment scheme; completing online verification according to an optimal path and realizing data closed loop self-optimization; the system comprises five modules, namely, demand analysis, task management, risk control, online verification and data storage, and breaks data islands by means of a unified data platform, a distributed message queue and a globally unique identification system. The application effectively solves the problems of demand deviation, progress lag, risk lag and data island, significantly improves demand accuracy, management efficiency, risk response speed and delivery quality, and is suitable for intelligent management of software project full life cycle in multiple fields.

This application relates to the fields of artificial intelligence and computer technology, specifically providing a risk decision-making method and system for the entire lifecycle of power data. The method includes: acquiring multi-source heterogeneous log data from the entire lifecycle of a power information system; using an unsupervised feature extraction module based on a variational autoencoder to extract latent feature vectors and determine the VAE reconstruction error; performing spatiotemporal feature extraction on the latent feature vectors to obtain the temporal behavior probability from the time-series analysis stream and the spatial graph embedding distance from the spatial analysis stream; and constructing a dynamic decision-making module based on a Bayesian network, using the VAE reconstruction error, temporal behavior probability, and spatial graph embedding distance as multi-source evidence nodes for the dynamic decision-making module to calculate the posterior probability of risk events. This application, through the collaborative fusion of spatiotemporal features and causal probabilistic inference, can significantly improve the safety risk perception and dynamic control capabilities of power data throughout the entire process of acquisition, transmission, and processing.

The application discloses a bridge full life cycle management and maintenance system based on digital twinning, which is used for the full life cycle management and maintenance of a cable-stayed bridge and comprises a data acquisition and fusion module, a digital twinning model construction and dynamic updating module and a structure health assessment and prediction module.The data acquisition and fusion module is used for acquiring monitoring data of the cable-stayed bridge in real time and fusing and processing multi-source data by using an adaptive weighting method.The digital twinning model construction and dynamic updating module is used for constructing and continuously calibrating a parameterized finite element simulation model based on bridge design parameters and fused data output by the data acquisition and fusion module, so as to serve as a digital twinning model corresponding to the physical bridge, and the digital twinning model can simulate material performance degradation.The structure health assessment and prediction module is used for assessing the current health state of bridge components by using residual characteristics of digital twinning model output and measured data based on the updated digital twinning model output by the digital twinning model construction and dynamic updating module.The application realizes intelligent management and maintenance of the full life cycle of the cable-stayed bridge.

The application discloses a rolling bearing degradation trend prediction method based on linear regression and TCN, characterized in that: firstly, the full life cycle vibration data of the rolling bearing is collected through an acceleration sensor, the sample entropy of the data is calculated and obtained as the performance degradation index of the bearing, the sample entropy is preprocessed smoothly, then the SPT point of the bearing is determined according to the linear regression model and mu+3delta, the influence of different window lengths on the determination of the SPT is analyzed, finally the preprocessed sample entropy value is input into the trained TCN network for degradation trend prediction. The application can effectively and timely determine the SPT point of the bearing, the TCN can well fit the performance degradation trend of the bearing, and the problem that the equipment cannot be safely operated due to the too late discovery of the bearing fault is avoided; compared with the method of adopting the recurrent neural network prediction, the accuracy of the method for the equipment health management is obviously improved, and a new idea is provided for the degradation trend prediction method of the bearing.

A comprehensive energy system hydrogen storage optimization configuration method based on health status perception and full life cycle optimization includes: establishing a fuel cell health status perception model using an exponential decay model; formulating multi-module collaborative operation strategies to address the dynamic differences in module health; constructing an energy conversion model for hydrogen storage units using a unified energy index; establishing a full life cycle optimization configuration model based on the adaptability of long-term cost and short-term operation; constructing a full life cycle health perception multi-energy optimization model by integrating the coupling requirements of electricity, heat, and gas; and solving the full life cycle health perception multi-energy optimization model by inputting the time-series data of the park's source and load and equipment parameters to obtain the number of modules, capacity, and load allocation strategy. This method achieves coordinated optimization of the economy, reliability, and low-carbon characteristics of hydrogen storage configuration in multi-energy coupling systems by constructing a dynamic health status model of equipment, designing multi-module collaborative operation strategies, and integrating full life cycle cost accounting.

The application discloses a contract full life cycle processing and examination system based on artificial intelligence, and belongs to the technical field of artificial intelligence and contract management. The system adopts a three-layer architecture of a core function layer, an auxiliary support layer and a data layer, the core function layer integrates four modules, the auxiliary support layer contains three components such as a knowledge graph, and the data layer realizes data intercommunication through distributed storage. The intelligent examination module integrates multi-model fusion analysis capability, and the remaining modules realize contract drafting and performance closed loop, automatic extraction of key information and multi-version semantic comparison respectively. Through incremental training, dynamic rules and knowledge graph updating, the application realizes contract full life cycle closed loop processing, solves the defects of the prior art, improves processing efficiency and examination accuracy, reduces enterprise cost and risk, and is suitable for personalized needs of various enterprises.

The application relates to the technical field of pipeline data management, and discloses an oil and gas station pipeline whole life cycle data management method and system, which comprises the following steps: extracting pipeline geographic information, extracting spatial correlation features through a graph neural network, combining pressure time sequence data to determine a pipeline conduction mode through a preset long short-term memory network; quantitatively evaluating the conduction mode according to historical maintenance and abnormal data, extracting topological nodes if the value exceeds a threshold, combining a preset attention mechanism to obtain a space-time coupling vector; extracting node correlation features from the space-time coupling, quantitatively fusing to obtain a heterogeneous node set, generating an abnormal atlas through graph convolution; extracting risk indexes, if upstream conduction effects are shown, obtaining a damage prediction sequence through a preset recurrent neural network, fusing topological and time sequence data to determine a health score vector, analyzing the matching degree of the health score vector with historical records, and generating a risk traceability warning if the matching degree is lower than a threshold. The method can realize accurate positioning and evaluate the overall health state of a pipeline system.

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 discloses a kind of management systems and implementation methods of full life cycle operation and maintenance, it is related to full life cycle technical field.Constructs full cycle initial dynamic operation and maintenance hypergraph;Generation represents the dynamic hypergraph time sequence evolution sequence of system topology evolution along with life cycle;Generation full cycle space-time fusion operation and maintenance representation;Output the multidimensional abnormal risk prediction distribution atlas of the future operation trend of representation system;Based on the full life cycle management library, the corresponding resource degradation, isolation or expansion strategy is matched, and the adaptive operation and maintenance scheduling execution instruction set for directly intervening target system is generated.The application makes that when processing the full cycle long sequence data of mass node scale, the time axis resolution of operation and maintenance characteristics is significantly improved, especially in the correlation tracing of long-range latent fault.

This invention discloses a method for visually tracking the entire lifecycle of chip sales orders, relating to the field of order management technology based on knowledge graphs. The method includes: acquiring order events and packaging identifiers of sales orders, identifying multiple sub-batches, and extracting event interval sequences for each sub-batch; querying the humidity clock semantics of each interval using a knowledge graph based on the order event information corresponding to the event interval sequences, determining the allowable exposure time and duration of each interval in conjunction with the packaging identifier, and generating a clock calculation sequence; calculating the remaining humidity clock quantity for each sub-batch, and constructing a set of remaining humidity clocks for each order sub-batch; extracting the conservative deliverable status, clock fork quantity, and dominant sub-batch for each lifecycle node, constructing an order node status group, and generating visual tracking results for the order. This invention can accurately reflect the state differentiation during the unpacking and batching of humidity-sensitive chips, improving the authenticity and targeted management of order fulfillment tracking.

The application discloses a kind of construction methods of automobile parts full life cycle digital twinborn model, it is related to digital twinborn technical field, including, life cycle dataset is input initial life digital twinborn model, time domain local feature fusion is carried out in feature extraction layer, degradation trend is captured in time series analysis layer, form the life prediction value of automobile parts, extract actual detection value in life cycle dataset, calculate the deviation of life prediction value and actual detection value of automobile parts, deviation is mapped in time series relationship, generate dynamic causal graph, graph traversal is carried out to dynamic causal graph, obtain key degradation path node, correlation analysis is carried out to key degradation path node using dynamic causal reasoning, generate degradation characteristic parameter, the application is realized by using depth-first search and analytic hierarchy process, dynamic causal correlation accurate modeling of life digital twinborn model, significantly enhance the robustness of life digital twinborn model under actual complex working conditions.

This invention relates to the field of geological archive digital resource management technology, specifically a cloud-edge-device collaborative method and system for the full lifecycle management of geological archives. The method includes: acquiring geological archive data, constructing a geoscientific semantic basis and generating acquisition basis records, and generating a forward lifecycle trajectory starting node based on the acquisition basis records; constructing a forward lifecycle trajectory, encapsulating the geoscientific semantic basis, the generated prescription chain, and the regeneration verification anchor into an archive seed package object; performing regeneration adjudication on the verified archive deliverables and performing lifecycle closure verification on the archive seed package object; selecting a target based on the archive seed package object to generate a prescription chain and construct regeneration instructions, which are then regenerated by edge nodes to obtain archive deliverables; generating correction task records and distributing them to edge nodes. This invention achieves regenerable, verifiable, traceable, and corrective management of geological archives during edge acquisition, edge processing, and cloud archiving.

The application discloses a kind of track traffic full life cycle economy estimation system based on multidimensional data, it is related to track traffic engineering economic estimation technical field, the system includes following component parts: data acquisition module, time sequence linkage mapping module, substage estimation model module, feedback correction module and dynamic output module;The application is through covering track traffic full life cycle, establishes effective association, to substage estimation model accurately calculates each stage economic cost, provides solid data support for economic analysis;Dynamic feedback correction mechanism is built, real-time response deviation changes, improve estimation accuracy;Whole-process closed-loop optimization design, solve prior art defects, adapt to project dynamic change, provide timely reliable reference for project full cycle economic management.