742 results about "Virtual model" patented technology

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention discloses a live-action three-dimensional dynamic change modeling method based on digital twinning. The method comprises the following steps: collecting multi-source data; carrying out space registration and time synchronization processing, and outputting a fusion data set; carrying out three-dimensional modeling, optimizing model parameters, and generating a live-action three-dimensional model; virtual-real mapping is carried out, and generated virtual space data and state parameters thereof are stored; performing differential analysis to generate a change detection result data set; performing dynamic change modeling to generate an updated virtual model; executing geometric accuracy optimization and semantic consistency check, and outputting an optimized virtual model; bidirectional data flow association and dynamic updating are executed, and the live-action three-dimensional dynamic change modeling process is completed. The real scene dynamic updating is realized by adopting the improved Gaussian sputtering modeling, and the method has the advantages of high precision, strong real-time performance and virtual-real synchronization.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.

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 discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention provides a self-adaptive spraying mechanical arm based on multi-mode perception and a control method of the self-adaptive spraying mechanical arm. The method comprises the steps that multi-source perception data of a to-be-repaired area is obtained through a dual-mode vision system and a laser scanning device, a two-dimensional repairing area segmentation map is generated, and space registration and local geometric modeling are achieved in combination with three-dimensional point cloud; on the basis of local geometric features extracted by the model, retrieving matched process parameters from the knowledge graph, generating a digital spraying instruction set containing tracks, postures and dynamic process parameters, and performing simulation verification in a digital twin environment; the mechanical arm state and the virtual model are synchronized in real time in the execution process, the paint film thickness is dynamically monitored, and compensation adjustment is triggered; and after spraying is completed, the coating quality is detected, and a result is fed back to the knowledge graph to update the parameter mapping relation. According to the method, closed-loop control from sensing, decision making, execution, evaluation to learning is achieved, and the spraying uniformity, the self-adaptability and the intelligent level under the complex working condition are improved.

The invention relates to the technical field of chemical enterprise production management, in particular to a chemical enterprise safety production informatization management system which comprises a sensing network layer architecture, a digital twin management platform, a risk management and control and decision center, a process closed-loop management center and an emergency cooperative processing platform. According to the chemical enterprise safety production informatization management system and method, through construction of a chemical knowledge graph, on the basis of a dynamic risk matrix and an AI prediction model library, equipment remaining service life prediction, process tiny anomaly capture and accident consequence simulation processing are realized; meanwhile, a root cause analysis module is used for associating low-level alarm and traversing a knowledge graph to automatically infer a fault root, physical equipment and a virtual model are bound through an entity-model mapping engine, the model state is dynamically updated in combination with a real-time rendering technology, and technological parameter optimization, accident deduction and scheme verification can be completed in an analogue simulation engine; and the management intuition and the decision-making scientificity are effectively improved.

The invention discloses a municipal road intelligent construction and collaborative management method based on digital twinning, and belongs to the field of digital twinning, and the method comprises the steps: S1, building a unified data twinning body composed of BIM, construction monitoring, environment and equipment data; s2, real-time synchronization of the virtual model and the entity is realized through time sequence mapping; s3, performing full-period control by adopting a multi-agent collaborative optimization algorithm; s4, embedding a risk evolution prediction model to carry out structure and material risk assessment; s5, multi-work-type tasks are distributed based on scene hierarchy; s6, progress self-checking and closed-loop control are achieved through two-way communication; s7, performing multi-stage data fusion to realize model consistency updating; s8, establishing a full life cycle carbon emission and energy consumption evaluation system; and S9, outputting a comprehensive decision report, and realizing integrated management of planning, construction and operation and maintenance. The method has the beneficial effects of improving construction efficiency and decision-making precision.

The invention belongs to the field of bridge engineering construction monitoring and intelligent control, and provides a Beidou digital twinning system for bridge steel member precision hoisting and a hoisting method aiming at the problems of low efficiency and poor real-time performance of on-site hoisting of a segment steel member in a large-span bridge structure. The system comprises a multi-source data acquisition module, a coordinate conversion module, an attitude calculation module, a three-dimensional virtual model construction module, a hoisting motion state monitoring module and a digital twinning module. The attitude resolving module calculates a standard attitude vector by using a comprehensive deep learning model according to the degree-of-freedom data, the attitude information and the movement speed, and performs attitude resolving based on the standard attitude vector to obtain an attitude angle of the bridge steel member; the hoisting motion state monitoring module can perform safety early warning and posture adjustment when the bridge steel member is abnormal; and the digital twin module can synchronously update the attitude information and the three-dimensional virtual model of the bridge steel member to obtain the current space trajectory and historical state of the bridge steel member.

The invention discloses a heat supply pipe network operation fault diagnosis method based on digital twinning, and the method comprises the following steps: S1, constructing a digital twinning architecture of a heat supply system, and constructing a complete digital twinning system based on a physical entity, a virtual model, a data synchronization layer and a decision control layer; s2, multi-source data fusion and state mapping; s3, a fault prediction and diagnosis mechanism is adopted, an SVM-LSTM joint model is adopted to carry out time sequence anomaly detection and fault prediction, an XGBoost classifier is optimized in combination with an improved sparrow optimization algorithm, and a classification result is output; and S4, performing self-healing control and cooperative regulation and control. According to the invention, a complete digital twin architecture is constructed, through a five-layer system of a physical entity, a sensing and execution network, a data synchronization layer, a virtual model layer and a decision control layer, a full-link closed loop from data acquisition, model driving to intelligent decision is realized, and systematic support is provided for fault diagnosis and self-healing control.

The invention discloses a booster station barrier-free channel digital twinborn evacuation early warning method. The problem of path planning misalignment caused by insufficient multi-source sensing data fusion is effectively solved. Through spatial data integration of the digital twin model, the available state of the barrier-free facility and the disaster influence range can be accurately identified. A dynamic path planning algorithm continuously optimizes an evacuation route in the disaster evolution process, and people are prevented from entering an affected dangerous area. The multi-modal instruction generation mechanism ensures that people with different physiological characteristics can obtain adaptive evacuation guidance, the retention risk caused by a single information transmission mode is reduced, the response speed and reliability of the emergency evacuation system are further improved through the real-time state monitoring and path updating functions, the geometric dimension and space coordinates of facilities are accurately collected, and the emergency evacuation system is more accurate in real-time state monitoring and path updating. The consistency of a virtual model and an actual environment is ensured, and data support is provided for facility availability evaluation in a disaster environment by configuring material characteristics and accessibility parameters.

The invention discloses a scraper conveyor middle trough production line operation state monitoring method based on digital twinning, and belongs to the technical field of industrial intelligent manufacturing. The method comprises the following steps: constructing digital twin bodies in one-to-one correspondence with physical production line elements; establishing a real-time data driving channel between the digital twin and the physical elements; based on the channel, driving the digital twin to synchronously map the real-time operation parameters, and generating a virtual operation state of the production line; based on the virtual operation state, performing time sequence deduction on the processing flow in the digital twin according to the processing technology logic and the equipment performance parameters to obtain a production line pre-estimation state of a future time point; and comparing the parameters in the pre-estimated state with a preset threshold range to generate prediction information. According to the invention, real-time data driving and bidirectional interaction of the physical production line and the virtual model are realized, the problems of data isolation and feedback lag of a traditional monitoring mode are overcome, the prediction capability is provided, and the operation reliability and intelligent management of the production line are effectively improved.

The invention provides a power equipment three-dimensional dynamic updating method and system for virtual reality training, and the method comprises the steps: collecting the working state data of power equipment in real time through a sensor; processing the working state data based on a preset function model, and calculating the physical state change of the power equipment; according to the physical state change, adjusting physical attributes of the three-dimensional model of the power equipment in real time so as to simulate physical effects of thermal expansion, stress concentration or structural deformation; loading the adjusted three-dimensional model into a virtual reality environment, and performing real-time display through a virtual reality rendering platform; and after the user executes the interactive operation through the virtual reality equipment, the working state of the power equipment is updated, and the influence of the operation on the three-dimensional model of the equipment is dynamically fed back, so that a virtual-real linkage closed-loop training process is formed. The virtual model can change in real time along with the collected data, and the technical verisimilitude and operability of virtual training are remarkably improved.

The invention discloses a field crop full-period planting management system and method based on digital twinning. Comprising a multi-source sensing and data acquisition module, a crop-environment digital twinborn modeling module, a full-cycle growth state evaluation module, an intelligent planting decision generation module, a precise execution and process control module, a dynamic growth prediction module and a twinborn model iteration and optimization module. According to the method, a space-time alignment algorithm and a mapping precision function are introduced, dynamic association of physical and virtual environments is constructed, it is ensured that a virtual model can accurately reflect the real state of crops and the environment, and a reliable basis is provided for management decision making; a management scheme adaptive to a growth period is deduced and generated based on twinborn simulation, advanced intervention is realized in combination with dynamic prediction, decision logic is continuously optimized through a model iteration mechanism, and scientificity and adaptability of decision are improved.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention provides an energy efficiency optimization and predictive regulation and control method for a building electromechanical system based on digital twinning. The method comprises five layers of architectures including a physical entity layer, a data acquisition and interaction layer, a digital twinning model layer, an energy efficiency analysis and optimization layer and a predictive regulation and control layer. The dynamic digital twinborn body interacting and synchronizing with the physical electromechanical system in real time is constructed, fundamental transformation from passive lag control to prospective predictive control is achieved, a high-fidelity virtual model is used as a nondestructive testing platform, countless operation strategies can be safely and rapidly simulated and evaluated in a virtual space, and therefore accurate optimization is achieved, and the real-time dynamic digital twinborn body is obtained. The overall energy efficiency of the system is remarkably improved, the dilemma that the traditional method makes consideration and loses others is eliminated, and the leap-type improvement of the comprehensive operation quality is achieved.

The invention relates to the technical field of biological process intelligent control, and discloses a pomegranate leaf fermentation process optimization and intelligent control system based on digital twinning. According to the system, the Internet of Things and a visual sensor are integrated through a multi-source data sensing module, so that synchronous acquisition and fusion of temperature, pH, dissolved oxygen, flora density and image data are realized; the intelligent data preprocessing module extracts high-quality features by adopting a self-adaptive filtering technology; the digital twin simulation engine module drives the virtual model to dynamically update based on real-time data; the deep prediction module accurately predicts the fermentation trend through a deep learning algorithm; the intelligent diagnosis module realizes abnormal root cause analysis by using a knowledge graph; the reinforcement learning optimization module autonomously generates an optimal control strategy based on the diagnosis result; and the self-adaptive execution module accurately regulates and controls fermentation parameters. According to the system, multi-dimensional perception, high-precision prediction and adaptive optimization control of the fermentation process are realized, and the accuracy and stability of process control are effectively improved.

The invention relates to the technical field of gastrointestinal tracts, in particular to a gastrointestinal tract ultrasonic virtual simulation nursing teaching platform which comprises a function module and a hardware configuration module. The function module comprises an anatomy learning module, an ultrasonic simulation module, a case training module and an examination and evaluation module; the hardware configuration comprises a VR head-mounted display, an ultrasonic simulator and an operation panel. The system has the advantages of safe and risk-free training, standardized process learning, immersive scene experience and instant feedback evaluation, a high-fidelity gastrointestinal tract ultrasonic virtual simulation teaching platform for critically ill patients is constructed, a 3D virtual model is constructed by using a virtual simulation technology, knowledge is presented dynamically and visually, real situations such as ICU wards and the like are simulated through VR, and real-time feedback evaluation of the gastrointestinal tracts of the critically ill patients is realized. The learning initiative and the sense of participation are stimulated; an ICU environment is virtualized, an unlimited'trial-and-error 'practice space is provided, the equipment cost is reduced, potential safety hazards are avoided, and space-time and safety barriers are broken.

The invention discloses a deviation correction method, system and equipment based on a tool bit, and relates to the technical field of tool bit correction.The method comprises the steps that after a tool bit replacement correction request sent by a user side is received, the system executes first-time destination cutting, generates a destination cutting groove virtual model, extracts cutting parameters and compares the cutting parameters with preset parameters, and destination cutting deviation data is obtained; the system compensates the tool bit track and then executes second-time return cutting, a return cutting groove virtual model is generated, cutting parameters are extracted, and return cutting deviation data are obtained; the residual deviation is compared with a preset residual deviation threshold value, and whether the tool bit is corrected or not is judged; and if the correction is not completed, calculating a correctable value, and judging whether the return cutting deviation data can be continuously used for subsequent compensation or not. According to the method, whether the tool bit deviation can be compensated or not can be dynamically judged according to actual conditions, system oscillation caused by blind correction is avoided, rapid detection, effective compensation and intelligent decision making of the tool bit deviation are achieved, and the cutting precision and the system stability are improved.

The invention discloses a digital twinning multi-agent reinforcement learning intelligent decision-making system and method with a secure memory playback mechanism, and the system comprises a digital twinning module which is used for constructing a virtual model and synchronizing the virtual model with a physical entity in real time; the multi-agent reinforcement learning module is used for carrying out strategy learning based on a constrained Markov decision process and balancing performance and safety through a Lagrange multiplier; the safe memory playback module is used for weighting and playing back the experience samples according to the risk and the timeliness so as to improve the learning safety; the reversible grey influence network module is used for causal modeling and reasoning and enhancing decision interpretability; the double-loop self-constraint control module ensures that a control action is always in a physical safety boundary through a barrier function and safety projection; and the convergence and stability criterion module is used for verifying strategy security convergence and system asymptotic stability. According to the method, the problems of strategy border crossing, virtual-real mismatching and the like in the high-risk manufacturing process are solved, and multi-target optimal control under the safety constraint is realized.

The invention discloses a five-dimensional motion vector real-time construction and calibration method based on multi-sensor fusion, and relates to the technical field of multi-sensor information fusion and dynamic state estimation, and the method comprises the steps: collecting and preprocessing motion carrier data, obtaining a preprocessing data set and a feature data set, inputting the feature data set into a long short-term memory network model, and obtaining a multi-sensor fusion model; and outputting a sensor error offset prediction vector to the extended Kalman filtering model, outputting a preliminary five-dimensional motion vector, and performing consistency verification and correction on the preliminary five-dimensional motion vector through a kinematics constraint equation to obtain a corrected five-dimensional motion vector so as to drive a virtual model corresponding to a motion carrier to perform synchronous position and attitude updating. According to the method, the sensor error is predicted through the long-short-term memory network model, and the dynamic motion model constraint module is additionally arranged to perform physical constraint correction, so that the problems of inaccurate error compensation and lack of physical authenticity of the calculation result under the dynamic working condition are solved, and the construction precision and reliability of the five-dimensional motion vector are improved.

The invention discloses a special gas cylinder state real-time monitoring and abnormal behavior recognition system, which belongs to the technical field of gas cylinder safety monitoring, and comprises a gas cylinder state data acquisition module for continuously acquiring the pressure, the temperature, the position and the gas concentration of a gas cylinder in real time within one second; the gas cylinder digital twinning module constructs a gas cylinder virtual three-dimensional digital model based on gas thermodynamics physical constraints, realizes double-source fusion of physical constraints and actually measured data by establishing an equivalent mapping relation between the virtual model and a gas cylinder real-time state, and performs adaptive correction on the equivalent mapping relation based on a deviation decomposition attribution mechanism; the deviation decomposition attribution mechanism divides deviations into random errors, systematic accumulated deviations and abnormal deviations, and differential correction strategies are adopted for different types; the equivalent mapping dynamic updating unit continuously optimizes a mapping function through an online learning algorithm, so that the model has a self-evolution capability; and conversion from passive monitoring to active prediction is realized.

The invention relates to the technical field of numerical control machine tool monitoring, and discloses a numerical control machine tool intelligent monitoring system based on digital twinning. A digital twin modeling module of the system obtains geometric parameters, physical attributes and historical operation data of a numerical control machine tool in real time, and constructs a dynamically updated virtual machine tool model; the working condition sensing module collects vibration, temperature and current data through a multi-source sensor, and generates a running state feature set through time sequence alignment processing. The exception response module is used for matching an exception mode library according to the feature set and outputting an exception type identifier and an influence range parameter; the decision optimization module generates an optimization instruction set containing maintenance priority ranking based on the parameters; and the strategy iteration module monitors an instruction execution result, calculates a deviation value between an actual effect and a virtual model prediction effect, and dynamically updates the abnormal mode library. According to the system, accurate sensing of the operation state of the numerical control machine tool, quick response to abnormity and dynamic optimization of a maintenance strategy are realized, and the intelligent level of monitoring is improved.

The invention relates to the technical field of environment adaptability testing, and discloses an identification key performance comprehensive environment adaptability testing system. The system comprises a multi-environment factor acquisition and processing module for acquiring data and performing normalization preprocessing; the environment scene dynamic modeling module is used for constructing a multi-dimensional association graph and updating a topological structure; the performance index evaluation module evaluates a performance degradation index in a composite environment by means of a spatial-temporal feature fusion algorithm; the failure mode prediction module is used for generating a potential failure mode under multi-environment coupling according to the performance degradation trend and the stress accumulation effect; the environment tolerance analysis module is used for integrating data to establish a virtual model and outputting a tolerance residual error of a specific environment section; the test scheme optimization module is used for generating a self-adaptive test sequence according to the residual error and the map; and the environment scene simulation control module is used for regulating and controlling parameters of the test cabin in combination with multiple information. The system can comprehensively and accurately evaluate the performance and endurance capability of equipment in a composite environment, and has a dynamic adjustment characteristic.

The invention relates to the technical field of metal composite materials, in particular to a metal composite material production process dynamic simulation method based on digital twinning. Comprising the following steps: constructing a digital twin model comprising a physical entity layer, a virtual model layer and a connection layer, and establishing a Fourier neural operator model embedded with a Navier Stokes equation and a heat conduction equation constraint; real-time molten pool monitoring data are mapped into model input through a connecting layer, and advanced time step deduction is executed by using a deep learning model to predict the state of a molten pool; based on the differentiable characteristic of a physical information neural operator, calculating the gradient of a prediction state and an ideal target by using an automatic differential algorithm, generating a process parameter field through reverse iteration updating, and discretizing the process parameter field into a galvanometer control instruction; and when the risk of aggregation of the reinforced phase particles is predicted to exist, automatically generating a swing scanning strategy to realize active suppression. The method gives consideration to simulation precision and real-time performance.

The invention belongs to the related technical field of waterlogging simulation, and particularly relates to an urban waterlogging simulation device and method based on a digital twin system, and the device comprises the digital twin system, a physical sand table, a sensor, and a microcontroller. The twin system is connected with the physical sand table through a microcontroller and sensors arranged on the physical sand table, and is used for simulating the underground pipe network and surface ponding conditions of the urban target area under the rainfall condition, and regulating and controlling the physical sand table through the microcontroller to realize dynamic verification of the urban inland inundation process; the sensor is used for collecting underground pipe network and surface water accumulation condition data in the physical sand table in real time and transmitting the data back to the twin system, bidirectional interaction and synchronous display of the virtual environment and the physical sand table are achieved, and dynamic simulation and verification of the urban inland inundation process are achieved on the whole. Wherein the twinborn model is composed of a pipe network model, a road model and a building model. According to the invention, real-time mapping and interaction of the physical experiment and the virtual model of the urban drainage system can be realized.

The invention discloses a digital twinning monitoring and predicting method and system for a ball mill, and relates to the technical field of ball mills. According to the digital twinning monitoring and predicting method for the ball mill, a ball mill monitoring and predicting control framework based on the digital twinning technology is constructed. According to the principle, a high-fidelity virtual model which is synchronous with a physical ball mill in real time is established, key parameters are directly measured and predicted through the model, and advanced simulation and intelligent decision making are carried out on the basis. According to the digital twinborn monitoring and predicting method for the ball mill, digital twinborn bodies are driven to carry out high-fidelity real-time simulation by receiving multi-source sensor data and generating fusion feature vectors, early-stage and quantitative evaluation of the swelling risk is achieved by fusing digital twinborn body output, data prediction and expert rules, and when the risk exceeds the limit, the risk of the swelling risk can be accurately evaluated. And performing simulation optimization in the digital twinborn model by utilizing reinforcement learning, outputting an optimal collaborative regulation instruction and automatically executing the optimal collaborative regulation instruction, thereby ensuring safety.

The invention relates to a jet flow electrochemical deposition rate self-adaptive control method and system, and belongs to the technical field, and the method comprises the steps: carrying out the physical field model construction of collected data through a digital twinning technology, and constructing a jet flow electrochemical deposition rate self-adaptive control model based on deep learning and a physical model; therefore, optimized working parameters of the chemical additive manufacturing equipment are generated through the jet flow electrochemical deposition rate self-adaptive control model, a visualization system is constructed based on the collected data, and finally interaction control is conducted on the chemical additive manufacturing equipment through the visualization system. According to the method, a high-fidelity virtual model which synchronously runs with physical equipment is created, and before a complex spiral structure is printed, full-process simulation can be performed in a digital twinning body, which corners possibly have defects due to rate mismatch can be found in advance, and optimized manufacturing production parameters are generated; therefore, dynamic fine adjustment is carried out according to real-time current and image data, and closed-loop control is realized.

The invention belongs to the technical field of electric ships and fault detection. The invention discloses a digital twin electric ship power battery fault detection method based on artificial intelligence. The method is characterized by comprising the following steps: step 1, a data acquisition layer captures a battery operation state and environmental parameters in real time by deploying multiple types of sensors; 2, the digital twinborn layer realizes dynamic mapping of a physical battery system by constructing a high-fidelity virtual model; establishing a multi-physical field coupling model based on electrochemical characteristics, thermodynamic behaviors and structural parameters of the power battery; 3, the fault prediction layer extracts the harmonic component of the voltage curve, the spatial distribution mode of the temperature gradient and other multi-dimensional features through time-frequency analysis, and captures the gradual change trend of the internal resistance of the battery in combination with a sliding window technology; and 4, the application layer converts a prediction result into an operable engineering decision, and constructs a closed-loop operation and maintenance system. According to the method, efficient battery fault detection can be realized.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method and system for an automatic tensioning control valve of a scraper conveyor. The method comprises the following steps: acquiring pressure and flow data of a main flow channel and an auxiliary flow channel through a sensor array, and extracting frequency domain characteristics to obtain a standardized frequency spectrum; detecting parameter coupling abnormity based on the spectrum cross correlation coefficient and generating an abnormal index; environment interference and medium viscosity data are fused, and non-linear feature distribution and correction deviation representing the mechanical state are obtained through classification and filtering analysis; matching the correction deviation with a preset fault mode, determining a fault type, and generating a fault positioning coordinate in a three-dimensional space by using an optimization algorithm; and finally, performing fluid dynamic simulation through the high-fidelity virtual model to verify the coordinate stability, and outputting accurate early warning when conditions are met. Through cooperation of multi-source information fusion and an intelligent algorithm, the accuracy of early-stage internal fault diagnosis of the complex double-flow-channel valve is improved.