2204 results about "Digital mockup" patented technology

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention provides a machine room overall electrical scheme configuration method and system for cabinet layout, and relates to the technical field of electrical design, and the method comprises the steps: building a machine room three-dimensional digital model, and carrying out the function partitioning; optimizing the cabinet layout and determining the maximum capacity; constructing an electricity load prediction model; uPS load balancing is carried out by adopting deep reinforcement learning; designing an intelligent bus duct wiring and circuit breaker access scheme; constructing an equipment topological graph and collecting operation parameters; extracting equipment association features based on the graph attention network; and identifying an abnormal power consumption mode and optimizing a regulation and control scheme.

The invention relates to the technical field of construction progress monitoring, and discloses a construction progress monitoring method and system based on big data. The method comprises the following steps: forming a space-time alignment data set through multi-source data acquisition, filtering and quality evaluation; performing feature extraction and registration to generate a digital model; target detection classification is performed to form a completion state table; progress evaluation is achieved through component-task mapping; trend analysis and risk identification are performed to generate a prediction result; decision reference is provided for personalized information screening and augmented reality display. Through multi-source data acquisition, fusion and intelligent analysis, accurate perception, objective evaluation, scientific prediction and visual presentation of the actual state of the construction site are realized, so that a comprehensive, accurate and prospective construction progress monitoring method is provided, the construction period delay risk is effectively reduced, and the construction management efficiency is improved.

A digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (Al) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and / or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

The invention discloses a building measurement method and system based on three-dimensional scanning, a medium and a product, and relates to the field of exploration, and the method comprises the steps: obtaining BIM model data and a measurement parameter threshold value of a target building, and determining a subarea scanning path according to the BIM model data; scanning the target building according to the subarea scanning path to obtain a point cloud data set of the target building; generating a three-dimensional digital model of the target building according to the point cloud data set, and dividing the three-dimensional digital model into a plurality of feature regions; each feature region comprises at least one feature component; extracting a feature point set in the feature region, and establishing a mapping relationship with a corresponding point set of a corresponding region in the BIM model data; calculating actual measurement parameters of the feature region based on the mapping relation; and comparing the actual measurement parameter with the measurement parameter threshold, determining an abnormal area and generating a measurement technical report. By implementing the method, the building data can be more comprehensively acquired, and the precision of building measurement is improved.

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

Methods and systems for a document review process are provided. The method includes receiving an input digital model representation comprising at least one externally-accessible model endpoint for generating a digital artifact. Then, generating a document splice comprising access to multiple document subunits, with at least one document subunit written in a natural language and comprising the digital artifact; the access to each document subunit is provided through an externally-accessible document endpoint for the subunit. Then, generating a document by combining the document subunits, and generating a view associated with the document, based on an user authorization result including selective access rights to the document subunits. The view comprises access to the digital model representation, the digital artifact, each document subunit, and the document. Finally, receiving a user input and updating, via one of the externally-accessible document endpoints, the document splice based on the user input.

The invention relates to the technical field of digital twinning, and discloses a power plant load visualization control method based on digital twinning, and the method comprises the steps: obtaining power plant entity data, constructing and integrating a power plant appearance three-dimensional model and an internal equipment three-dimensional model, and obtaining a static three-dimensional model; constructing a dynamic digital model based on a mechanism modeling method and a data-driven modeling method, and integrating the static three-dimensional model and the dynamic digital model to obtain a digital twinborn model; processing the real-time operation data and the historical operation data of the power plant based on a reinforcement learning algorithm and a deep learning algorithm to obtain a prediction result; performing abnormal state judgment on the prediction result and an early warning threshold value based on the static three-dimensional model to obtain a judgment result; according to a judgment result, evaluating the severity of the abnormal state by using a rule model so as to formulate countermeasures; and based on the digital twinborn model, a dispatching instruction is calculated by using countermeasures and a PID control algorithm, so that the operation state of the power plant is visually monitored and controlled.

The invention relates to the technical field of intelligent early warning of digital twinning, and particularly discloses a slope multi-physics field fusion early warning decision-making system based on digital twinning, which is characterized in that physical monitoring data representing the macroscopic state of a slope and microscopic physical response signals reflecting internal damage evolution are synchronously acquired through a multi-modal data sensing module; space-time alignment, standardization and cross-modal fusion analysis are carried out through a damage eigenstate extraction module, and a unique eigendamage variable for quantitatively representing the real-time degradation degree of the material strength is interpreted; the twinborn self-evolution module takes the variable as a core observed quantity, and drives parameters and states of a slope mechanical model to be cooperatively and dynamically updated by adopting a data assimilation method, so that high-fidelity tracking of a digital model on physical reality is realized; and the prospective early warning decision module deduces a future spatio-temporal evolution path of the material strength parameters based on the calibrated model, and realizes graded early warning and intelligent decision support by combining Monte Carlo simulation and quantification of the instability risk probability.

The invention provides a digital twinning-based stereoscopic warehouse goods allocation and sorting scheduling method and system, and relates to the technical field of digital twinning, and the method comprises the steps: constructing a three-dimensional digital model of a stereoscopic warehouse; goods allocation is carried out based on a deep reinforcement learning algorithm, the goods access frequency, the associated purchase probability and the seasonal demand prediction are used as input parameters, and a goods allocation instruction is generated by taking the shortest sorting path and the associated goods centralized storage as optimization targets; warehousing operation is executed, and a sorting order is received; constructing a virtual potential field in the model, and when the repulsive force between stackers exceeds a threshold value, determining a task priority based on the order emergency degree and triggering obstacle avoidance; and calculating an obstacle avoidance track and generating a cooperative scheduling instruction to execute picking operation. According to the invention, efficient goods allocation and intelligent multi-stacker collaborative scheduling are realized.

The invention relates to the technical field of big data, and discloses an urban rail transit emergency processing method and system based on a visual large model, and the method comprises the steps: collecting visual data, sound data and equipment operation data at a platform and a carriage of urban rail transit, and carrying out the preprocessing and timestamp synchronization; performing dynamic frame difference processing on continuous video frames of the visual data to generate a two-dimensional motion heat map, and performing space-time aggregation; constructing a three-dimensional digital model based on engineering drawings of the platform and the carriage, and establishing a mapping relation between the two-dimensional motion heat map and the three-dimensional digital model; performing abnormal behavior detection on a mapping result, performing multi-modal fusion analysis in combination with sound data and equipment operation data, and determining the severity of an abnormal event; emergency response strategies are formulated according to severity levels of the abnormal events, a control instruction is sent to the rail transit equipment through an industrial protocol interface, and abnormal event information is pushed to a control center, inspection personnel and passenger terminals.

The invention relates to the technical field of bridge construction, solves the technical problem that progress or quality data cannot be updated in real time due to difficulty in direct synchronous data feedback between a construction site and a management system, and particularly relates to a BIM-based intelligent two-dimensional code bridge construction quality and progress management and control system. The geometric information, the construction progress and the quality standard of the bridge structure are recorded in detail, and a unique two-dimensional code is generated for each construction node. A constructor obtains real-time node information by scanning the two-dimensional code, updates the construction progress and uploads quality detection data, and analyzes and feeds back the construction data in real time, so that efficient management and control of the construction progress and quality are ensured. The system overcomes the defects in the prior art, has the characteristics of timely information updating, accurate data, automatic management, high traceability and the like, and can remarkably improve the management efficiency and the quality control level of bridge construction.

The invention discloses a multi-sensor fusion tunnel smooth blasting monitoring system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: preprocessing and integrating monitored data, constructing a tunnel digital model, and applying a static load and a dynamic load; obtaining a safety critical value of each parameter in the tunnel digital model to obtain a tunnel critical value digital model, extracting each parameter exceeding the safety critical value, generating a tunnel abnormal parameter digital model, and identifying an abnormal mode of smooth blasting by using a random forest algorithm; when the abnormal mode of smooth blasting is related to the interior of the tunnel, reconstructing a tunnel digital model; when the optimization suggestion is related to smooth blasting, new blasting parameters are obtained by using a support vector machine model in combination with expert suggestions; a tunnel-smooth blasting correlation model is constructed, correlation rules are identified, and the influence degree of blasting parameters on tunnel monitoring parameters is evaluated; and searching an optimal blasting parameter combination by applying particle swarm optimization to meet a set optimization target.

The invention relates to the field of engineering management, and discloses a wharf steel structure construction progress dynamic management and control and resource allocation system, which comprises a real-time data perception and fusion module for collecting field data and an initial plan and generating a fusion data stream; the dynamic construction digital twinborn construction and evolution module is used for updating the digital twinborn and a dynamic empirical risk layer in real time based on the fusion data flow; the multi-agent self-adaptive decision engine is used for generating a self-adaptive construction scheme based on digital twinborn simulation deduction and feeding back a risk signal; and the man-machine cooperation and closed-loop execution module is used for visualizing the scheme for manual decision making and issuing an instruction to a site. According to the method, the physical field data sensed in real time is fused with the digital model, the dynamic construction digital twin capable of being synchronously updated with the physical construction field state is constructed, and the technical effect that digital information and the physical entity state are kept highly consistent is achieved.

To simulate a 3D image of a subsurface below a surface, the system having a memory device for storing an instruction, a processor in communication with the memory device configured to execute the instruction, and a subsurface image capture module in communication with the processor, the subsurface image capture module having one or more wave generating device and one or more sensor affixed to a vehicle to capture a series of digital image datasets of the subsurface with a coordinate reference data, wherein the processor executes an instruction to generate a digital model of the series of digital image datasets of the subsurface while maintaining the coordinate reference data, wherein the processor executes an instruction to determine a depth map of the digital model, and wherein the processor executes an instruction to identify a key subject point in the digital model, where subsurface includes an internal biology, below ground, underwater.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention provides a manufacturing-oriented test process digital twinning and dynamic optimization management method, which relates to the technical field of testing, and comprises the following steps of: acquiring real-time test data through a multi-source data acquisition device, and establishing a test process digital model; constructing a test process digital twin system; establishing a test process prediction model by using a deep learning algorithm; comparing and analyzing the real-time data and the prediction result, and identifying abnormity and optimization opportunities; generating and implementing a test process optimization scheme; and closed-loop optimization management is realized through feedback control. According to the method, the efficiency and quality of the test process are improved.

The invention provides a high-efficiency ship automatic control system, and the system comprises a comprehensive control platform which carries out the unified integration of subsystems, sets a target function, and carries out the data sharing, collaborative optimization and global control among the subsystems. The intelligent sensing network module is used for collecting historical data and real-time data; the self-learning control module is used for constructing a control strategy generation model; the ship digital model module is used for constructing a digital model for simulating ship navigation so as to test different ship control strategies; the decision execution module is used for generating and executing a control instruction; and the safety monitoring module detects abnormal behaviors of the ship and gives an alarm. According to the invention, a plurality of subsystem modules can be integrated in a unified manner, data sharing, collaborative optimization and global control are realized, an optimal control strategy can be provided, the cost and risk of actual navigation are reduced, and the control precision, response speed and operation efficiency of a ship are improved.

The invention discloses a construction site intelligent safety management system and method based on a BIM technology, and relates to the technical field of constructional engineering, and the method comprises the following steps: S100, building a three-dimensional digital model of a construction site by using BIM software, and collecting construction site data by deploying a video camera, personnel positioning equipment and an environment sensor; s200, preprocessing the acquired data, and performing potential safety hazard identification and environment detection on the construction site; s300, fusing and displaying the generated BIM three-dimensional digital model and the processed data through spatial alignment, data mapping and dynamic updating; s400, calculating the probability of occurrence of potential safety hazards on the construction site and a comprehensive decision score; and S500, carrying out quantitative assessment on the potential safety hazards of the construction site by constructing a risk assessment model, sorting the calculated risk scores of the potential safety hazards, and determining the priority processing sequence of the potential safety hazards of the construction site, so that intelligent safety management of the construction site is realized.

An artificial intelligence (Al) assisted generative digital task fulfillment process within digital model platforms is provided. Disclosed are methods and systems for carrying out digital tasks through generative Al, including tasks related to the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Hie method includes receiving access to a context Al model trained on Internet-scale data, receiving a user prompt indicating the digital task, and generating contextual data based on the user prompt using the context Al model, where the contextual data identifies a syntax Al model. The method includes training the syntax Al model to generate a template script having a placeholder variable for a parameter related to the digital task. The method also includes using a parameter substitution process to generate the orchestration script by substituting the variable with a parameter value.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention discloses a foundation pit digital twinning monitoring method and system and application thereof. The system comprises a foundation pit data acquisition module, a foundation pit digital model simulation module, a foundation pit multi-source data processing and analysis module, a foundation pit abnormal state intelligent prediction module, a foundation pit monitoring and early warning decision module and a foundation pit data storage and backup module. The invention further discloses a foundation pit digital twinning monitoring method, deep learning modeling is carried out in combination with multi-source historical data, and the accuracy and the intelligent level of foundation pit abnormal state monitoring are further improved.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention discloses a dynamic response calculation method of a pure float type photovoltaic array, so as to reduce calculation time and cost. Comprising the following steps: determining a minimum repetitive module of the pure float type photovoltaic array; determining a numerical value equivalent body of the pure float type photovoltaic array, and constructing and simplifying a numerical value equivalent body three-dimensional digital model; according to the working condition and the environment load of the pure float type photovoltaic array and the simplified numerical equivalent three-dimensional digital model, establishing an optimal numerical equivalent simulation model, and calculating a hydrodynamic coefficient and a wind and flow force coefficient of a single optimal numerical equivalent simulation model; connection structures and mechanical parameters between numerical equivalent bodies are determined, and a numerical model under the overall size of the array is established; and performing time domain numerical simulation under a corresponding working condition to obtain a motion response result. According to the method, the concept of the minimum repetition module and the numerical equivalent is introduced, the large pure float type photovoltaic array can be calculated quickly, the calculation efficiency is improved, and the calculation cost is reduced.

The invention discloses a water pump station operation adjusting device and method, and relates to the technical field of pump station operation adjusting, a first prediction result is output through operation data of a water pump station, and whether the first prediction result needs to be corrected or not is judged based on environment characteristics, equipment state data and water quality data to obtain inlet and outlet valve adjusting parameters; constructing a water pump station digital model, receiving a simulation regulation feedback result, and optimizing inlet and outlet valve regulation parameters by utilizing the simulation regulation feedback result; an inlet and outlet valve adjusting stability threshold value is obtained, relation feedback verification is conducted through the inlet and outlet valve adjusting stability threshold value, and whether a protection mechanism is triggered or not is judged through the simulation adjusting feedback result and the relation feedback verification result; and when the protection mechanism is triggered, the dynamic current stabilizer is started, and the current stabilization parameters of the dynamic current stabilizer are adjusted. Intelligent accurate control, active protection and energy efficiency optimization are realized, the system stability is improved, and the operation and maintenance cost is reduced.

The invention relates to a digital twinning-based production line simulation method and related equipment, and the method comprises the following steps: carrying out the historical operation data mining of a target production line, and obtaining the historical time sequence operation data; performing dynamic logic simulation construction on the target production line based on the mapping space digital topological structure and the historical time sequence operation data to obtain a production line operation logic model; analyzing whether an abnormal position exists in the production line operation logic model or not through a preset anomaly detection algorithm, and if yes, labeling the abnormal position to obtain an abnormal labeling position; wherein the abnormal labeling position is a logic conflict point position and a non-optimal configuration area; and performing flow reconstruction on the production line operation logic model based on the abnormal labeling position to obtain a target virtual three-dimensional model, thereby solving the technical problems that the mapping relation between the physical entity and the digital model of the production line is not accurate enough and the actual operation state of the production line is difficult to reflect truly.

Methods and systems for generating a live digital document are provided. The method includes receiving a static document file including human-readable data. Then, parsing the static document file into a plurality of subunits. Then, generating a sharable document splice of the static document file. Hie sharable document splice includes access to a subset of the plurality of subunits, and the access is provided through an Application Programming Interface (API) or Software Development Kit (SDK) endpoint for each subunit in the subset. Then, executing a digital thread script to generate the live digital document from the sharable document splice and an input digital model representation, using API or SDK endpoints in the sharable document splice. The live digital document is configured through the digital thread script to reflect changes in the input digital model representation. Finally, adding execution metadata for the execution of the digital thread script to the live digital document.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The invention provides an engine pipeline measurement method based on laser scanning, which belongs to a non-contact measurement technology, and comprises the following steps: before measurement, selecting a three-dimensional laser scanner, configuring and calibrating the three-dimensional laser scanner, carrying out a scanning test, adjusting a scanning environment, and then obtaining pipeline point cloud data; the outlier error of the pipeline point cloud data is denoised by using a KD-tree (K-dimension tree), and the outlier error of the pipeline point cloud data is denoised by using the KD-tree (K-dimension tree); de-noising system errors and random errors of the pipeline point cloud data by using a Gaussian filter; segmenting the point cloud data of the pipeline, and identifying and isolating all parts (linear segments, elbows and joints) of the pipeline; performing reverse modeling on the pipeline point cloud data to obtain a digital model of the pipeline; and comparing and analyzing the original point cloud of the pipeline and the pipeline model, and evaluating the error of pipeline measurement. According to the invention, the feature extraction and processing analysis of the pipeline point cloud are realized, the time cost of engine pipeline assembly is reduced, and the wear of the pipeline is reduced.