1354 results about "Simulation based" patented technology

The invention discloses a power equipment defect detection method based on image recognition, and belongs to the technical field of power equipment defect detection, and the method comprises the steps: carrying out the defect simulation based on physical mechanism driving according to an equipment three-dimensional model and physical field simulation parameters, and obtaining a defect simulation data set; according to the defect simulation data set and the real inspection data, training a cross-modal deep learning network based on physical law constraint to obtain a defect identification model; performing time-space diagram neural network modeling according to the historical time sequence inspection data and the defect identification model to obtain a state evolution model; and inputting inspection data acquired in real time into the equipment health state evolution model, and performing online reasoning to obtain a defect detection result. The problems that an existing electrical equipment defect detection method excessively depends on scarce real defect samples, the generalization ability for complex working conditions is weak, and the defect evolution trend prediction ability is lacked are solved.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention discloses a wind wave-seabed-pile foundation-photovoltaic array multi-physics field coupling calculation system for an ocean photovoltaic power station. The system comprises a wave model construction module, a photovoltaic array wind load numerical simulation module and a multi-physics field coupling numerical model integration module. The wave model construction module is used for extracting wave elements and constructing a wave model; the photovoltaic array wind load numerical simulation module is used for performing photovoltaic array wind load numerical simulation based on the extracted wave elements and the constructed wave model; and the multi-physics field coupling numerical model integration module is used for carrying out multi-physics field coupling numerical model integration, solving and verification based on a numerical simulation structure. According to the method, the wind wave load, the seabed seepage field, the pile foundation stress field and the photovoltaic array wind field are cooperatively calculated by building the multi-field dynamic coupling model, and therefore the accuracy and reliability of offshore photovoltaic power station pile foundation bearing performance evaluation are improved.

The invention relates to the technical field of dynamic regulation, in particular to a CFD (computational fluid dynamics) simulation-based flue gas flow field dynamic regulation system, which comprises a multi-dimensional data collaborative acquisition module, an anti-ash deposition sensor array is deployed at a key position of a flue, and a time-space fusion input data set is generated; the dual-mechanism CFD dynamic coupling module operates the main CFD prediction model and the simplified fluid network model in parallel, compares output differences through an evidence theory fusion algorithm, corrects boundary conditions and reconstructs network parameter characteristics when the confidence coefficient is lower than a threshold value, and updates a dynamic parameter knowledge base; the multi-target self-adaptive regulation and control module is used for solving a Pareto optimal strategy meeting the safety constraint by taking the baffle opening degree and the pump frequency as variables based on the updating model, driving an actuator and feeding back flow field response data; and the system forms a closed-loop adaptive mechanism, so that the CFD model continuously tracks the degradation state of the equipment, and the effectiveness of the regulation and control strategy is maintained.

The invention discloses a multi-scattering SAR (Synthetic Aperture Radar) imaging simulation method, system and device based on physical consistency modeling and a medium. The method comprises the following steps: constructing a three-dimensional electromagnetic model of a target; constructing a geometric model under the working mode of the ray tracing auxiliary transmitting-receiving co-located SAR; a high-frequency electromagnetic scattering algorithm based on ray tracing is adopted, multiple scattering paths of a first order, a second order and a higher order are considered, superposition and amplitude modulation are carried out on scattering contributions of all orders, scattering field distribution of a target under the far field condition is calculated, and a far-field scattering field is obtained; performing spatial propagation mapping on the total far-field scattering field, and mapping a far-field scattering result into a radar platform near-field echo signal; correcting the amplitude of the echo signal to obtain a two-dimensional SAR near-field echo matrix; sAR imaging processing is carried out, wherein the SAR imaging processing comprises distance compression, range migration correction (RCMC) and azimuth compression. According to the invention, the authenticity and precision of SAR imaging simulation in a complex target scene are improved.

Techniques for generating simulations to evaluate a performance of an object controller. The controller may be configured to control one or more functionalities of a simulated object (e.g., a “smart object”) in a simulation, such as make the smart object operate like a human in a given scenario. A simulation system may generate one or more simulations based on log data captured by a vehicle operating in a physical environment. The simulation system may cause the object controller to control at least one smart object in the simulation, the smart object being representative of an actual object detected in the physical environment. The simulation system may compare a smart object trajectory to an actual object trajectory of the object to determine a performance metric associated with the object controller. Based on a determination that the performance metric satisfies a threshold, the simulation system may validate the object controller.

The invention discloses a bipolar plate structure optimization method and system based on flow field data analysis, and relates to the technical field of fuel cells. The method comprises the following steps: establishing a parameterized bipolar plate runner geometric solid model; performing compression deformation simulation of a gas diffusion layer on the bipolar plate flow channel geometric solid model to obtain physical field data of the gas diffusion layer, and calculating a porosity distribution field and a local mass transfer impedance coefficient field based on the physical field data; based on the porosity distribution field and the local mass transfer impedance coefficient field, multi-physics field coupling simulation is carried out, and a local performance contribution degree field is obtained; constructing a comprehensive loss function according to the local mass transfer impedance coefficient field and the local performance contribution degree field; and with the purpose of minimizing the comprehensive loss function, iterative optimization is carried out on the design variables through an optimization algorithm until convergence conditions are met, and an optimal design variable combination is output. Through parametric modeling, multi-physics field coupling simulation and comprehensive loss function evaluation, efficient optimization of the bipolar plate design is realized.

The invention provides a method and system for improving the flutter derivative recognition precision of a large-span bridge, and relates to the technical field of road and bridge safety, and the method comprises the steps: obtaining basic data and section geometric parameters of a target bridge; according to the geometric parameters of the section, constructing a model by combining the bluff body streaming characteristics of the section of the bridge to obtain a virtual model; performing forced vibration simulation on the virtual model based on preset working condition parameters to obtain a simulated self-excitation force amplitude; obtaining a virtual model based on the simulated self-excitation force amplitude and the actually measured self-excitation force amplitude, and outputting a simulated phase difference; based on the simulation phase difference and the actually measured phase difference, phase compensation is carried out on the virtual model after force amplitude correction, and individual digital twin bodies are constructed and obtained; and performing global digital twinborn construction according to the individual digital twinborn to obtain a flutter derivative global prediction model. According to the method, the global prediction model is established, the flutter derivative recognition precision is remarkably improved, and an analysis tool considering reliability and high efficiency is provided for bridge wind resistance design.

The invention discloses a power module multi-objective optimization method and device, and the method comprises the steps: determining a key structure parameter and a constraint range of a to-be-optimized power module, and constructing a multi-dimensional parameter space based on the key structure parameter and the constraint range; performing global uniform sampling on the multi-dimensional parameter space based on a Sobo sequence to generate a sample point set; performing electromagnetic-thermal coupling simulation based on the sample point set to obtain a target performance index, and constructing a training data set based on the target performance index and the sample point set; constructing an agent model of the MLP, and training the agent model based on the training data set to obtain a target optimization model; and constructing a multi-target optimization model based on the target performance index, solving the multi-target optimization model based on a tabu search algorithm, a fast non-dominated sorting genetic fusion algorithm and the target optimization model, and obtaining optimal key parameters of the to-be-optimized power module.

In various examples, a technique for generating simulation data includes generating, via one or more simulations, simulation data associated with operation of a first machine in an environment. The technique also includes determining a command to the first machine based at least on the simulation data and a goal associated with the first machine and updating the simulation data based at least on the command. The technique further includes storing the simulation data, the command, and the updated simulation data in one or more data records, and causing a second machine to perform one or more actions based at least on the one or more data records.

The invention provides an adhesive particle dynamic repose angle prediction method based on a discrete element-machine learning coupling model. The method comprises the steps that a repose angle experimental device is built, the repose angle of the adhesive particles is measured, and a repose angle data set is obtained; constructing a geometric model of the experimental device, performing discrete element numerical simulation on the adhesive particles based on the basic parameters under the characteristic working conditions, obtaining repose angle data corresponding to the characteristic parameters of the adhesive particles, comparing the repose angle data obtained by the experiment and the repose angle data obtained by the simulation one by one, obtaining an optimal parameter combination, and verifying the reasonability of the simulation; the method comprises the following steps: firstly, determining the influence of characteristic parameters on a dynamic repose angle based on the characteristic parameters of a simulation process, then constructing a machine learning prediction model according to repose angle data corresponding to the characteristic parameters under an obtained optimal parameter combination, and carrying out index evaluation and optimization. According to the method, by fusing discrete element numerical simulation and machine learning, the environmental influence is greatly reduced, and the method has excellent accuracy for predicting the dynamic angle of repose under variable working conditions.

The invention provides an FPGA parallel Isin model optimization method and system based on a GHMC simulated annealing algorithm, relates to the field of power system combination optimization, and solves the technical problem that an existing FPGA-based Isin model solver adopts a traditional Monte Carlo method and is low in convergence precision and solution quality. The method comprises the following steps: receiving a question input by a user through an upper computer, and mapping the received question into a Hamiltonian parameter of an Isin model; the Hamiltonian parameter is transmitted to an FPGA through a communication interface, and a mixed GHMC sampler and a temperature scheduling module are arranged in the FPGA; the hybrid GHMC sampler carries out parallel solution on Hamiltonian parameters; and the temperature scheduling module gradually reduces the system temperature according to a preset annealing curve, and drives the Isin model to converge to a global optimal solution. The method is used in the power system combination optimization process.

The invention provides an MES-based solar cell production and manufacturing execution method and system, and the method comprises the steps: collecting equipment states and technological parameters in real time, combining a bar code system to achieve the single-chip-level metadata association, constructing a standardized data architecture, building a multi-dimensional index, converting the expert experience into reasonable rules, and carrying out the calculation of the reasonable rules. The method comprises the following steps: simulating the influence of parameter combination on product performance, triggering an edge computing node to call a knowledge graph to perform parameter optimization and production simulation based on a dynamic threshold early warning mechanism, identifying process improvement opportunities through a batch tracing report and clustering analysis, forming knowledge base iteration update, predicting inventory risks in combination with a production plan and equipment load, and performing production management. According to the method, intelligent purchasing is automatically triggered, raw material distribution is optimized, technological parameter self-optimization, abnormity self-healing, quality traceability and supply chain collaboration are achieved through data driving, the production efficiency, the product consistency and the resource utilization rate are improved, and a whole-process digital solution is provided for photovoltaic manufacturing.

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

The invention provides a narrow gap swing laser welding pool wettability prediction method based on CFD simulation. The method comprises the following steps that an interaction platform is constructed through Python + PyQt5, and geometric modeling, UDF compiling and a material library module are integrated; the groove parameters and the swing track are dynamically loaded to a Fluent solver, and a full-coupling transient model of a steam recoil pressure-buoyancy-molten drop mass source item is established; in CFD-POST, the widest section of the molten pool is positioned on the basis of the contour surface with the volume fraction being 0.5, and Z-direction displacement-flow velocity data are extracted along the two side wall surfaces; fusing defects are predicted through double criteria of a bilateral average flow velocity difference threshold value and F test variance homogeneity; according to the method, dynamic quantitative evaluation of wetting behaviors can be achieved, the process development period is shortened, the defect detection rate is increased, and the method is suitable for aerospace thick plate welding quality control.

The invention discloses a mechanical arm simulation system supporting industrial quality inspection, which relates to the technical field of industrial automation and comprises a multi-physics field simulation engine module, a digital twin interface module, a defect sample library module and a cooperative control module. According to the method, traditional entity mechanical arm debugging is replaced by rigid body dynamics simulation based on a physical engine, loss and production line shutdown caused by repeated trial and error of equipment are avoided, rapid construction of the digital twin environment of the production line is achieved by means of an efficient CAD model conversion process and a lightweight loading technology, and high-fidelity optical simulation is achieved through a parameterized defect model library and high-fidelity optical simulation. Various common industrial defects and complex optical effects are covered, the modeling bottleneck of a quality inspection scene is broken through, real-time closed-loop interaction of visual inspection and motion control is realized by using thread separation and an efficient communication technology, and the recognition robustness of a visual algorithm to tiny defects is guaranteed; the reliability of algorithm verification is improved through the quantitative output of algorithm performance indexes and the comprehensive test of extreme working conditions by the simulation system.

The invention relates to the field of impact failure full-process prediction of complex-structure die castings, and discloses a data-driven impact failure full-process prediction method for complex-structure die castings, which comprises the following steps: step 1, performing mold flow simulation on a target part to obtain the solidification time and porosity of any position of the target part; 2, the solidification time and porosity of any position of the target part are input into a multi-layer perceptron model, and the multi-layer perceptron model outputs mechanical parameters of any position of the target part; the multi-layer perceptron model is trained based on data of actually measured mechanical properties; 3, acquiring a collision simulation grid model of the part, mapping the mechanical parameters to each grid unit of the collision simulation grid model, and generating an input file for collision simulation; and 4, performing collision simulation based on the input file in the step 3.

The invention relates to the technical field of data sampling, in particular to a neural network training data set generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining the shape data, material data and target load position space of a part virtual model; determining boundary constraint conditions of the part virtual model according to the shape data and the material data; carrying out load point stratified sampling in the target load position space to obtain a plurality of sampling points and sampling parameters; according to the boundary constraint condition and the sampling parameters of the plurality of sampling points, constructing a simulation working condition, performing topological structure simulation under the simulation working condition, and determining a part topological structure corresponding to the simulation working condition according to a simulation result; and generating a training data set based on the simulated working condition and the corresponding model topological structure, training a neural network model by using the training data set, and generating a part topological structure under the target real working condition by using the trained neural network model. Therefore, the problem that the quality of the training data set is poor in the related technology is solved.

The invention relates to the technical field of wind power plants, in particular to a wake flow evaluation method and device for a floating wind power plant, equipment and a storage medium. According to the method, high precision of numerical simulation and high efficiency of data driving are combined, namely, CFD is firstly adopted for simulation, and then a symbol regression model based on a particle swarm optimization algorithm is trained based on data obtained through simulation, so that a wake flow velocity loss expression is constructed, and the influence of wake flow interference existing in the floating wind power plant is simulated. And meanwhile, compared with the traditional method that fan integrated numerical calculation is carried out by using CFD, the calculation time consumption and the cost are very high, the method carries out multi-equation comparison through a particle swarm optimization algorithm, an optimal symbol regression equation is optimized, and rapid prediction of a key region of interest in engineering is realized. Besides, for the wake flow area, a smooth step function is adopted to replace an indicator function, so that the problem that the wake flow expansion rate contains jump discontinuity, and sharp change of the wake flow width can be caused can be avoided.

The invention relates to an intelligent management method and device for science and technology projects, equipment and a medium. The method comprises the following steps: acquiring full-life-cycle heterogeneous data of science and technology projects, and preprocessing the full-life-cycle heterogeneous data of the science and technology projects to obtain project ontologies; embedding entity dynamic state time sequence data into the project ontology, and constructing to obtain a semantic-time sequence coupling knowledge graph; utilizing a semantic-time sequence coupling knowledge graph to constrain a causal exploration space, and performing ontology causal structure extraction to obtain causal priori; performing multi-strategy time sequence simulation based on a project digital twin model to obtain expected index distribution of each strategy; performing causal enhancement risk prediction according to the semantic-time sequence coupling knowledge graph, causal priori and expected index distribution of each strategy to obtain a risk prediction report and a causal interpretation report; and based on compliance requirements, performing multi-objective optimization to obtain a project association scheduling scheme. By adopting the method, causal traceability and simulation pre-judgment can be realized, and compliance and high-efficiency promotion of science and technology projects can be guaranteed.

The invention relates to the technical field of semiconductor device preparation, in particular to a super-junction MOSFET preparation process parameter optimization method based on simulation analysis, which comprises the steps of generating a simulation model according to a preparation process, executing simulation based on a plurality of groups of initial process parameter sets and the simulation model, performing simulation electrical performance test on a plurality of simulated super-junction MOSFETs, and performing simulation analysis on the simulated super-junction MOSFETs. Performing mapping relation learning on the plurality of groups of initial process parameter sets and the plurality of electrical performance data sets by utilizing a pre-constructed back propagation agent model, performing optimization solution on a mapping relation model, performing decision screening on an optimal solution set, and importing an optimal process parameter combination into a pre-constructed TCAD simulation model for verification to obtain a TCAD simulation model; and completing super junction MOSFET preparation process parameter optimization based on simulation analysis. According to the method, accurate estimation of the optimal process parameter combination is realized through simulation analysis, and dynamic correction is carried out when the optimal process parameter combination does not reach the expectation, so that the super-junction MOSFET process parameter combination which is better in performance and feasible to prepare is obtained.

The invention relates to the technical field of underwater propeller modeling, and discloses an underwater propeller annular propeller modeling method based on analog simulation. The method comprises the following steps: constructing an initial blade fluid domain grid model by using a discrete element simulation algorithm based on three-dimensional geometric parameters and material attribute data of an annular propeller; after multiphase flow coupling boundary conditions are applied to the initial model, fluid dynamic load distribution data are obtained through a transient flow field solver; generating a blade deformation characteristic value according to the load data, and if the blade deformation characteristic value exceeds a preset tolerance interval, activating a dynamic grid reconstruction instruction; fluid-structure interaction interface pressure pulsation data is extracted based on the instruction, a stress concentration coefficient is calculated in combination with material attributes, and an abnormal signal is generated when a safety threshold value is exceeded; and when the signal is triggered, the vortex field tracking module is called to update the fluid domain grid model.

The invention discloses an aircraft simulation method, device and equipment based on parallel block sparse matrix incomplete LU decomposition and a medium, and relates to the technical field of simulation, and the method comprises the steps: building a block sparse matrix comprising a plurality of zero sub-blocks and non-zero sub-blocks based on the topological relation of each grid point in an aircraft grid and simulation physical parameters, determining dependency pairs respectively corresponding to a plurality of non-zero sub-blocks from the non-zero sub-blocks, and storing the dependency pairs by adopting a preset data structure; wherein the dependent pair comprises a pair of matched non-zero sub-blocks; respectively distributing the plurality of non-zero sub-blocks to a plurality of GPU thread bundles for parallel processing so as to update the corresponding non-zero sub-blocks based on the updated dependency pairs after the GPU thread bundles complete updating on the dependency pairs corresponding to the corresponding non-zero sub-blocks; and after the plurality of non-zero sub-blocks are updated, updating the block sparse matrix based on the plurality of updated non-zero sub-blocks, and determining an incomplete LU decomposition result according to the updated block sparse matrix so as to perform aircraft simulation.

The embodiment of the invention provides a pipeline inner wall spraying simulation control method and device based on digital twinning, and the method comprises the steps: importing a guide rail model and a motion mechanism model into offline programming software, configuring a kinematic pair, and constructing a virtual scene corresponding to an actual pipeline inner wall spraying production line based on a digital twinning technology; importing a pipeline three-dimensional model, configuring workpiece parameters, and calibrating coordinate data; identifying the boundary of a spraying area on the inner wall of the pipeline, and determining the spraying range; an initial spraying track is generated, parameters are adjusted according to boundary conditions, and the track is optimized; configuring spray gun parameters, importing a flange plate and a spray gun model, and completing basic tool configuration; a tool model is imported, tool parameters are configured, spraying parameters are configured based on the sampling data, and a spraying tool is obtained; according to the embodiment of the invention, simulation is carried out based on the virtual scene, the optimized track and the spraying tool; and presenting the simulation effect in a three-dimensional visualization manner in real time. Spraying track optimization, parameter dynamic matching and real-time visualization of the spraying effect are achieved.

The invention discloses an airport area underground water and surface water system coupling seepage regulation and control method and system, and the method comprises the steps: employing a multi-source data fusion investigation mode to find out regional stratum and structure characteristics, and determining an underground water occurrence migration rule and a surface water and underground water supplement and drainage relation; constructing a coupling seepage model of the underground water and the surface water system under the current condition by applying a numerical simulation method, and defining a dynamic association mechanism of the underground water and the surface water system in time and space; based on a simulation result, determining a transformation scheme that a main stream adopts a double-hole box culvert and a branch stream adopts a single-hole box culvert, and arranging an electric gate at the box culvert; arranging monitoring points of groundwater level, pore water pressure and surface water system flow in combination with field spring water and well distribution, and constructing a cooperative monitoring network; the coupling seepage law of underground water and surface water systems is analyzed based on monitoring data, and the opening degree of a box culvert electric gate is dynamically adjusted to control rising of the underground water level in the flood period. The stability of a water system in an airport area is ensured.

The invention relates to the technical field of geotechnical material local modeling, in particular to a geotechnical material data driving local modeling method and device based on a knowledge migration strategy, and the method comprises the steps: building a first mapping data pair based on the stress-strain physical quantity of a test curve sampling point, and building a high-fidelity data set of a geotechnical material; constructing a second mapping data pair based on the stress-strain state of the simulation curve sampling point and the intermediate physical quantity label, and establishing a low-fidelity data set of the geotechnical material; pre-training the target data-driven constitutive model by using the low-fidelity data set to generate an initial geotechnical material data-driven constitutive model; and driving the model to be fine-tuned by using the high-fidelity data set for the initial geotechnical material data, and generating final geotechnical material data to drive the model. Therefore, the problem of insufficient generalization ability and prediction precision of a data-driven model due to limited training data and unpredictability of a stress path in the prior art is solved.

The invention provides a tree wind-induced response evaluation and analysis method, and relates to the technical field of disaster prevention and reduction engineering, and the method comprises the steps: carrying out the construction of a geometric model for a target tree; on the basis of the geometric model, CFD analog simulation is carried out, and wind pressure time history data and crown load data of CFD grid points of the branches under different wind field conditions are obtained; finite element mesh generation is carried out on the geometric model, a finite element model is constructed, and mesh unit types in the model comprise hexahedron units, tetrahedron units and wedge-shaped units; the branch wind pressure time history data and the crown load data are mapped to the surface of a finite element model, structural finite element analysis is carried out, and cloud chart data of stress and displacement of the target tree changing along with time under different wind field conditions are obtained; and carrying out wind-induced response analysis and wind resistance evaluation on the target tree based on the cloud picture data and the wind-induced damage criterion of the tree. According to the technical scheme, a scientific basis can be provided for understanding the response behavior of the target tree under the strong wind condition.

The invention discloses a forming method of a composite material special-shaped yacht cab in the technical field of composite material manufacturing, which comprises the following steps of: performing resin flow simulation based on a three-dimensional model, and identifying a high-resistance area; a flow guide system and a layering scheme are optimally designed; arranging a diversion net by adopting a layered laying strategy; the flow effect is optimized through experimental verification; a vacuum system is constructed, and multi-inlet synchronous glue injection is carried out; and gradient curing molding and post-treatment are carried out, so that the production of products is realized. According to the method, through CFD flow simulation and dynamic diversion system optimization, flow lag of high-resistance areas such as curved surfaces and sharp corners is accurately predicted and compensated, the infiltration effect of resin in a complex structure is remarkably improved by combining layered diversion net design and a stress-oriented layering strategy, common defects such as dry spots and pores in a traditional process are fundamentally eliminated, and the method is suitable for large-scale production. And the forming quality of complex components such as the special-shaped yacht cab is greatly improved.

The invention provides a double-line shield tunnel induced ground surface settlement curve intelligent prediction method and product, and relates to the technical field of shield tunnel engineering and ground surface settlement prediction. In the embodiment of the invention, the VL-GP (volume loss and grouting pressure combination) mixed double-line shield tunnel two-dimensional simplified numerical model considering the construction process is established, and the calculation cost is greatly reduced and the simulation precision and efficiency are improved by adopting the two-dimensional simulation method while the influence of the shield tunnel construction process is considered; ground surface settlement curve data are obtained through simulation based on the two-dimensional simplified numerical model, control parameters of a ground surface settlement curve are obtained through empirical formula fitting, and an efficient database is established. An AutoML method is adopted to establish an intelligent prediction model of ground surface settlement curve control parameters, the workload of manual modeling and feature extraction is greatly reduced, and the modeling efficiency and prediction precision are improved.

The invention provides a network simulation method, device and system based on digital twinning, and belongs to the technical field of network simulation, and the method comprises the steps: receiving real-time operation data of a physical layer device group in an actual network environment; performing preliminary analysis on the real-time operation data; uploading the real-time operation data and the preliminary analysis result to a network cloud decision platform under the condition of determining that the physical layer equipment group has a fault risk according to the preliminary analysis result; receiving a fault solution issued by the network cloud decision platform; performing simulation test on the fault solution by using a digital twin topology module; and a test result is uploaded to the network cloud decision-making platform, so that the network cloud decision-making platform determines the feasibility of the fault solution. According to the invention, all edge computing nodes are utilized to carry out simulation testing on the fault solution, full-architecture simulation based on the actual network environment is realized, and the deviation between the simulation result and the actual operation condition is reduced.