977 results about "Real time data acquisition" patented technology

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention relates to the technical field of data processing, in particular to a distributed multi-source heterogeneous sensor data processing method and system. The method comprises the following steps: collecting environmental parameters of a leakage area in real time through a distributed sensor array; performing coordinate system unification and timestamp alignment on the environmental parameters of the leakage area to generate a standardized leakage situation data set; extracting gas diffusion characteristics and thermal imaging characteristics of the standardized leakage situation data set to calculate a gas cloud cluster diffusion path probability; performing risk decision instruction generation on the gas cloud cluster diffusion path probability based on a preset leakage level classification neural network to obtain a risk decision instruction; calling a matched emergency plan based on the risk decision instruction; and analyzing the implementation steps of the emergency plan and performing instruction conversion to generate an emergency plan instruction. According to the invention, through real-time data acquisition, intelligent risk assessment and automatic emergency response, the timeliness responsiveness of data processing is improved.

The invention discloses a building ground real-time settlement monitoring device and a use method thereof, and belongs to the field of building structure safety monitoring. The monitoring device comprises a hierarchical sensor network which is used for carrying out multi-time-scale real-time data acquisition and comprehensively obtaining deformation data and related environmental parameters of a building structure; the data processing and analyzing module is used for performing real-time processing and intelligent analysis on the acquired data, and identifying and classifying abnormal deformation characteristics of the building structure in time; the deep learning prediction module is used for quantitatively predicting the probability state and the evolution trend of building settlement by constructing a multi-scale time sequence prediction model; the multi-factor analysis module is used for carrying out coupling modeling and comprehensive analysis on the environmental factors, the structural characteristics and the abnormal evolution process so as to identify key influence factors and action mechanisms thereof; and the risk assessment and early warning module is used for performing grading assessment on the building settlement risk based on the prediction and analysis result and generating corresponding early warning information and decision support schemes.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

The invention discloses a water conservancy reservoir group joint scheduling optimization system based on digital twinning, and the system comprises the following steps: a real-time data collection and preprocessing module which is used for collecting and processing meteorological data; the reservoir group digital twinning modeling module is used for constructing a dynamic digital twinning reservoir group simulation model; the future state prediction module is used for predicting the future state change trend of the reservoir group; the joint scheduling optimization module is used for optimizing a multi-target joint scheduling scheme; the self-adaptive memory mechanism management module is used for maintaining an excellent scheduling strategy memory bank and carrying out intelligent disturbance adjustment; the execution and dynamic adjustment module is used for executing the scheduling scheme and dynamically correcting the scheme; the emergency response module is used for generating an emergency scheduling plan; and the feedback and adaptive optimization module is used for feeding back the real-time execution result to the twin model for dynamic correction. According to the invention, real-time optimization and intelligent emergency response of the reservoir group scheduling system are realized, and the scheduling efficiency and the system stability are obviously improved.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of industrial data processing, in particular to a suspension spring multi-station production line collaborative scheduling system based on digital twinning, which comprises a station control programming module, a multi-station autonomous collaborative module, a quality pre-control module, a manufacturing process tracing module and a multi-machine execution safety module, according to the system, through real-time data acquisition and monitoring of an integrated digital twin platform, self-adaptive generation of control programs of all stations and programmed issuing of production instructions are carried out; when the production disturbance is sensed, dynamic optimization of a task sequence and real-time self-adaptive adjustment of processing parameters are executed; feedforward control and active compensation are realized in combination with a quality prediction model; constructing a digital thread to support tracing and analysis of the whole manufacturing process; and dynamic program verification of security interlocking and resource conflicts is cooperatively executed on multiple machines. Intelligentized, high-efficiency and high-quality integrated optimization control over the multi-station production process of the suspension spring is achieved.

The invention provides a gynecological disease diagnosis and treatment method and system fused with an electronic medical record, and relates to the technical field of medical informationization, and the method comprises the steps: collecting the multi-modal diagnosis and treatment data of a patient, and constructing a personalized medical database through a self-encoding network; deploying a plurality of agents based on a dynamic causal network, and generating an initial treatment scheme in combination with deep reinforcement learning; through real-time data acquisition and a multi-objective optimization algorithm, the scheme is dynamically optimized by fusing expert decision features. According to the invention, personalized precise treatment of gynecological disease diagnosis and treatment can be realized, the treatment effect is improved, and the complication risk is reduced.

The invention relates to the technical field of new energy automobile electric control, and discloses a new energy automobile electric control fault prediction system. The system comprises a real-time data acquisition module, a dynamic fault prediction model construction module, a fault difference calculation module, a multi-dimensional anomaly analysis module, a fault probability positioning module and a self-adaptive maintenance strategy module. The real-time data acquisition module acquires sensor data of the electric control system in real time; the dynamic fault prediction model construction module constructs a dynamic fault prediction model based on historical fault data; the fault difference calculation module inputs real-time data into the model and outputs theoretical fault indexes; the multi-dimensional anomaly analysis module compares the theory with the actually measured fault indexes to generate an anomaly difference matrix; the fault probability positioning module inputs the matrix into a space correlation network to generate a fault probability distribution diagram; and the adaptive maintenance strategy module configures maintenance parameters according to the distribution diagram. According to the system, the fault prediction accuracy and real-time performance can be improved, and stable operation of the new energy automobile electric control system is guaranteed.

The invention discloses a power supply fault rapid detection system and method based on an intelligent diagnosis engine, and belongs to the technical field of power electronic equipment fault diagnosis, and the system comprises a function region division module which is mainly used for dividing a plurality of function regions according to a power supply topological structure; the real-time data acquisition module is mainly used for acquiring parameter data of each functional area in a power supply operation stage; the current model building module is mainly used for building a current time sequence prediction model; the trend prediction module is mainly used for generating a future current change trend; the abnormity judgment module is mainly used for identifying an abnormal condition by performing similarity matching with real-time data; the fault positioning module is mainly used for matching with a fault mode based on an abnormal current waveform and constructing a directed graph to position a fault position; the method has the advantages that early warning and accurate positioning of the power supply fault are realized through multi-modal data fusion and a deep learning model, and the operation and maintenance efficiency and reliability of the power electronic equipment are remarkably improved.

The invention relates to the technical field of data acquisition and processing, and provides a real-time data acquisition and processing method and system for a distributed control system, and the method comprises the steps: collecting a data flow in real time through a terminal sensor, analyzing the probability distribution of a data change rate through a sliding window, and obtaining a data change rate; and a sensitivity threshold is intelligently generated by combining dynamic factors such as equipment electric quantity and network load. When data mutation exceeds a threshold value, the system automatically extracts a key change section and generates time, period and variable quantity three-dimensional features, and meanwhile, the frequency domain analysis result is fused to enhance the anti-interference performance. And the event emergency degree is calculated based on the time-space correlation of the spectrum energy entropy and the characteristic parameters, and finally a processing queue sorted according to priorities is formed and the bandwidth is dynamically allocated. According to the method, through multi-dimensional data fusion and resource adaptive adjustment, the response speed of the system to emergencies, the data discrimination precision and the stability in a complex environment are improved.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention relates to the technical field of mine safety production analysis, and discloses a mine safety production data fusion analysis method based on multi-source perception, which comprises the following steps: step 1, carrying out real-time data acquisition on physical parameters related to safety production in a mine environment through various types of sensors arranged in the mine environment, the collected data are stored as independent data streams with timestamps, and the data of different sensors have time inconsistency according to the difference set by the sampling frequency and the clock; and step 2, carrying out time delay compensation processing on the multi-source data with the timestamps acquired in the step 1. An optimization model is established in combination with a variational method to perform time delay compensation and time synchronization processing on sensor data, the problem of time inconsistency caused by different sampling frequencies and clocks of sensors is eliminated, fusion processing of multi-source sensor data on a unified time reference is realized, and an accurate data basis with consistent time sequence is obtained.

The invention relates to the technical field of quality detection in material manufacturing and coating processes, in particular to an intelligent color difference analysis and detection method for paint, which comprises the following steps: S1, acquiring a multispectral dynamic compensation image; s2, texture-color coupling separation is carried out; s3, carrying out adaptive chromatic aberration modeling; s4, process correlation visualization is carried out, color difference data is mapped into a scalable thermodynamic diagram, and grid density is dynamically adjusted according to detection precision; and analyzing geometric feature parameters of the abnormal region, matching a process defect mode library to generate an optimization suggestion, and superposing the optimization suggestion on the thermodynamic diagram by a visual identifier. Through real-time data acquisition, texture interference elimination, model adaptive optimization and process defect analysis, the overall detection precision and efficiency are improved, the intelligent adaptive characteristic is achieved, and different production environments and base material types can be adapted, so that the coating production process is optimized, and the consistency of the coating quality is ensured.

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention relates to the technical field of fire extinguishing control, in particular to an unmanned aerial vehicle fire extinguishing control method and system. The method comprises the following steps: carrying out hot-flow wind field real-time data acquisition on a high-rise building fire scene through a sensor carried on an unmanned aerial vehicle, carrying out approaching hot-flow wind field intensity difference analysis, and then carrying out unmanned aerial vehicle flight attitude instability numerical value quantification to obtain flight attitude instability numerical value quantification data; time-varying attitude spraying strategy adjustment is carried out according to the flight attitude instability numerical value quantization data, and a time-varying attitude spraying adjustment strategy is obtained; building a fire extinguishing spraying control model for the time-varying attitude spraying adjustment strategy based on a strategy gradient algorithm to obtain a fire extinguishing spraying control model; and sending the fire extinguishing spraying control model to the terminal. The fire extinguishing control technology is optimized, so that the fire extinguishing control technology is more perfect.

The invention discloses a wind power generation system operation optimization method based on intelligent control, and the method comprises the following steps: S1, collecting the real-time data during the operation of a wind power generation system, including the wind speed, the wind direction, the rotating speed of a generator, the output power, and the environment temperature; s2, constructing a performance model of the wind power generation system according to the real-time data; s3, based on the performance model, adjusting the operation parameters of the generator in real time to optimize the power generation efficiency of the system; and S4, when detecting that the system is abnormal, automatically triggering a fault diagnosis module, generating a fault processing suggestion and executing an optimization strategy. Through real-time data acquisition, performance model construction, operation parameter dynamic adjustment and intelligent fault diagnosis, the problems that a traditional wind power generation system is low in efficiency and not timely in fault response are solved, and the system has the advantages of dynamically optimizing power generation efficiency, timely processing system abnormity and improving operation stability.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention discloses a load control method based on a charging pile ad hoc network, and relates to the technical field of charging pile ad hoc network load control, and the method comprises the following steps: firstly, carrying out the real-time collection of various parameters in the operation process of a charging pile, guaranteeing the obtaining of high-quality original data, and providing a solid data basis for the subsequent analysis and model evaluation; according to the invention, through real-time data acquisition, intelligent feature extraction, deep learning evaluation and adaptive dynamic regulation and control, the problems of harmonic resonance, power distribution imbalance and power grid instability in the operation of the charging pile are solved. Charging pile parameters are collected in a high-precision mode, harmonic key features are extracted through a detection window mechanism, intelligent evaluation is conducted through a deep learning model, and the harmonic abnormal trend is predicted. When the resonance risk is detected, the power of the charging pile and the reactive compensation strategy are intelligently adjusted, the load distribution is optimized, the harmonic resonance risk is reduced, the power supply quality of the power grid is improved, and the intelligent charging network has higher stability, reliability and scheduling capability.

The invention discloses intelligent part damage identification and quantitative analysis based on multi-modal fusion, and particularly relates to the technical field of intelligent part damage identification. According to the method, synchronous or asynchronous real-time data acquisition is carried out on a target part, multi-modal features are extracted in combination with a heterogeneous feature extraction network, confidence scores of all modals are calculated, mutual information between the modals is fused, and an attention weighted fusion process is guided; when it is detected that the modality is abnormally suppressed, feature enhancement and dynamic weight adjustment are implemented, key weak signals are prevented from being ignored, fusion features are input into a damage identification model, and a damage identification result, modal weight visualization and early damage risk scoring are output; the technology effectively improves the recognition capability of the model for early and hidden damage, is especially suitable for sensitive capture and fusion judgment of weak modal signals in high-safety scenes such as wind power and aviation, and significantly enhances the early warning accuracy and maintenance foresight of the system.

The invention provides an electric energy load real-time data acquisition and analysis method based on a low-cost scheme, and the method comprises the following steps: constructing a distributed data acquisition network, employing a combined hardware architecture of a current / voltage sensor and a low-power-consumption MCU, and achieving the data acquisition optimization through a dynamic sampling rate adjustment mechanism, when the load fluctuation exceeds a set threshold value, the sampling rate is automatically increased, when the load is stable, the sampling rate is reduced to a calibration sampling rate, and a Kalman filtering algorithm is adopted to carry out real-time data preprocessing; a three-level data transmission system is established, the three-level data transmission system comprises an edge acquisition node layer, a convergence gateway layer and a cloud processing layer, an optimized AODV routing protocol is adopted among nodes to construct a star-shaped and net-shaped hybrid network topology, and an optimal transmission path is dynamically selected according to the signal strength and the network congestion condition; a differentiated QoS transmission strategy is designed, and data is divided into three priorities of fault alarm data, real-time monitoring data and historical data.

The invention discloses a CPLD-driven IGBT short-circuit current dynamic monitoring method and system, and relates to the technical field of power electronics, and the method comprises the specific steps of S100 real-time data acquisition, S200 dynamic threshold calculation, S300 short-circuit state judgment, S400 rapid protection execution, and S500 fault data recording and uploading. Accurate and prospective judgment of the short circuit state of the IGBT is achieved, two stress parameters including the direct-current bus voltage and the junction temperature serve as independent variables, when the bus voltage rises, the voltage stress borne by the IGBT in the turn-off period is increased, meanwhile, the current peak at the short circuit moment is more remarkable, early warning is conducted in advance by dynamically lowering the threshold value, and therefore the short circuit state of the IGBT is accurately judged. According to the mechanism, the monitoring system does not simply respond to the occurred overcurrent, but can pre-judge the safety boundary of the device under the current working condition, so that intervention is implemented before the current really reaches a dangerous peak value, and a protection blind area under a severe working condition is effectively avoided.

The invention relates to a network topology dynamic optimization method and equipment for large-scale power supply and distribution equipment networking. The method comprises the following steps: step S101, real-time data acquisition and state sensing; step S102, carrying out network topology modeling and performance evaluation; step S103, dynamic risk assessment and optimization target generation; s104, carrying out topological optimization decision making based on a feasibility maintenance type genetic algorithm; step S105, carrying out optimal strategy verification and seamless switching; selecting an optimal network topology reconstruction scheme from the Pareto optimal solution set according to a preset decision strategy; and after the reconstruction scheme is verified on a control level, generating an equipment cascade and open circuit control instruction sequence, and guiding related nodes to complete undisturbed switching of the network topology in a preset time window through a distributed cooperative control mechanism. According to the method, generation of invalid solutions can be avoided, and the convergence efficiency of a large-scale network topology optimization algorithm is remarkably improved.

The invention relates to the technical field of safety management, and discloses a hydraulic engineering construction potential safety hazard monitoring method based on Internet of Things, which comprises the following steps: S1, real-time data acquisition: arranging a plurality of sensors at a construction site to acquire data and uploading the data in a wireless communication mode, the method comprises the steps of S1, data acquisition and analysis, S2, data pre-processing and analysis: pre-processing acquired data at a cloud or an edge computing platform, S3, adaptive adjustment: automatically adjusting sensor layout and monitoring frequency according to construction stages and environment changes, S4, multi-source data fusion, S5, alarm and emergency: triggering alarm when monitoring data is abnormal, providing emergency measures by means of an intelligent decision support system, and carrying out emergency management. S6, visualization and decision support; and S7, intelligent feedback and optimization. By adopting a dynamic threshold adjustment mechanism and a multi-modal data fusion technology, a more accurate hidden danger identification effect is achieved, the phenomena of missing report and false report are remarkably reduced, and the effectiveness of early warning is improved.

The invention discloses a twinborn cooperative intelligent operation and maintenance method for a wind power tower group, and relates to the technical field of wind power generation towers, and the method comprises the following steps: S1, sensor deployment and real-time data collection; the method comprises the following steps: acquiring operation state data and environmental parameters through a sensor network deployed in each wind power generation tower in a wind power plant; s2, digital twinborn body construction and early warning are carried out; constructing a corresponding digital twinborn body for each wind power generation tower, wherein the digital twinborn body integrates a physical mechanism model and a prediction module driven by real-time data; generating a fault early warning signal and a health degree evaluation result based on the deviation between the simulation output of the digital twin and the sensor data; s3, multi-agent collaborative decision making is carried out; step S4, performing closed-loop optimization; according to the twinborn cooperative intelligent operation and maintenance method for the wind power tower group, the fault early warning precision is improved, the service life prediction error is compressed, the maintenance efficiency is improved, and the wake flow loss is remarkably reduced.

The embodiment of the invention discloses a collaborative optimization method and device for a distributed micro-grid and a medium, and relates to the technical field of energy management, and the method comprises the steps: carrying out the real-time data collection through a multi-element sensor network disposed at each distributed energy node in advance, so as to obtain the real-time energy data corresponding to each distributed energy node; according to the edge node corresponding to each distributed energy node, carbon emission of the distributed energy nodes is predicted by using the real-time energy data, and node-level predicted carbon emission parameters are determined, so that single-node optimization reference information corresponding to each distributed energy node is determined; through a preset global center node, collecting single node optimization information corresponding to each distributed energy node for aggregation, and determining global model gradient information; and based on the global model gradient information and the single-node model gradient information, carrying out collaborative optimization on the single-node optimization strategy to determine a collaborative optimization strategy corresponding to each edge node.

The invention discloses a stamping equipment construction method based on digital twinning, and the method comprises the steps: firstly constructing a digital twinning system of stamping equipment, which comprises a physical layer, a digital layer, a virtual layer and an application layer; then, three-dimensional modeling and multi-physical-quantity simulation of the stamping equipment are carried out on a physical layer, and the real form of the equipment is reflected; real-time data acquisition and processing of the stamping equipment are carried out on the digital layer, a digital twin engine is constructed, virtual-real combination and real-time linkage processing are carried out on the virtual layer, and data analysis and optimization of the stamping equipment are completed; finally, man-machine interaction and visualization processing are carried out on an application layer, user interactivity and the intelligent level of equipment are improved, and intelligent decision support is carried out through data integration and dynamic adjustment. According to the stamping equipment construction method, high efficiency and intelligence of stamping equipment construction are achieved, the design period is remarkably shortened, the production efficiency and the equipment performance are improved, and meanwhile the cost and the operation and maintenance risk are reduced.

The invention discloses a ship-shore cooperation-based cabin intelligent diagnosis management system, which comprises a ship-end intelligent sensing layer, a ship-shore cooperation communication layer and a shore-based intelligent decision-making layer, and is characterized in that the ship-end intelligent sensing layer comprises a ship-end data acquisition module, and the ship-shore cooperation communication layer comprises a data transmission module, a shore-end data center and a cloud server; the shore-based intelligent decision-making layer comprises an intelligent diagnosis module, a predictive maintenance module and a ship-shore cooperation module. The invention belongs to the technical field of cabin intelligent management, and particularly provides an intelligent cabin diagnosis management system based on ship-shore cooperation and a predictive maintenance method, which can reduce the equipment failure rate, optimize the maintenance cost and prolong the equipment life cycle through real-time data acquisition, cloud intelligent analysis and dynamic maintenance decision.