1115 results about "Model simulation" patented technology

The invention relates to the technical field of bridge bearing capacity evaluation, and discloses an existing continuous beam bridge bearing capacity comprehensive evaluation method which comprises the steps that multi-source data are collected to construct a three-dimensional damage voxel field, and an initial finite element model is generated; simulating a heavy load identification control section, and designing an equivalent static load test scheme according to an equivalent internal force principle; executing a field static load test, collecting elastic response data and constructing an actual measurement response vector; calculating errors and iteratively adjusting parameters in a voxel field constraint range to finish model correction; and generating an operation space-time security envelope based on the correction model, and comparing the state in real time to output a control instruction. A three-dimensional damage voxel field is constructed, voxels are used as a normalized data carrier, discrete apparent and internal damage data such as three-dimensional laser scanning point cloud, ultrasonic velocity and rebound strength are mapped into a continuously distributed space field, and the space variability of the rigidity of an existing bridge material can be represented in a refined mode. And the accuracy of describing the physical current situation of the structure by the initial finite element model is improved.

The present application relates to the field of nuclear reactor simulation. Disclosed are a fast simulation method and system for multi-physical field coupling and a storage medium. The method comprises: at intervals of a first preset interval time, collecting state information of each physical field of a nuclear reactor in a transient process, so as to obtain physical field snapshots of a plurality of physical fields, the transient process being simulated by a reactor simulation model corresponding to the nuclear reactor; stitching the physical field snapshots of the plurality of physical fields, so as to obtain a fully-concatenated physical field; constructing a matrix on the basis of the fully-concatenated physical field; performing order reduction processing on the matrix, so as to construct a timing model corresponding to the fully-concatenated physical field; and decomposing the timing model corresponding to the fully-concatenated physical field, so as to obtain timing models corresponding to the plurality of physical fields. The fast simulation method for multi-physical field coupling of the present application can reduce the inconsistency between different physical fields while increasing the computation speed.

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 discloses a stamping die health state assessment method and system based on digital twinning, and relates to the technical field of die health state assessment, and the method comprises the following steps: a physical sensor network deployed on a stamping die collects die stamping process data in real time; constructing a finite element analysis simulation FEA model to simulate the working condition of the stamping die based on the geometric structure, the material attribute and the stamping process parameters of the die; according to the invention, the data of the die stamping process are collected in real time through the physical sensor network, and real-time calculation is carried out in combination with the finite element analysis simulation model, so that transient stress field, strain field and temperature field data of the die can be output in a short time, and real-time monitoring of the health state of the die is realized; by considering the degradation of the mold material performance along with the use time and the dynamic process of quantitative damage accumulation, the damage accumulation value is accurately calculated through the dynamic material performance database and the continuous damage mechanical model, and the accuracy of the evaluation result is improved.

The invention discloses an urban inland inundation simulation prediction method and system based on interpretable machine learning, and the method comprises the steps: obtaining multi-driving-factor data, and obtaining urban inland inundation data through the combination of physical model simulation; constructing a spatial analysis unit of the city, taking multi-driving factor data as an input feature and waterlogging data as an output target variable, training the candidate machine learning models, comparing the prediction precision of each candidate machine learning model which is completely trained, and determining an optimal prediction model; and based on the optimal prediction model, key driving factors and a nonlinear influence mechanism thereof are identified in combination with an interpretability method, the influence of interaction among different factors on the waterlogging risk is analyzed, and finally, a prevention and control and treatment scheme is formulated and output. According to the method, the specific influence of each driving factor on urban waterlogging and the interaction relationship among the factors are disclosed, and a scientific basis is provided for urban climate risk treatment and prevention and control.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention belongs to the technical field of engineering data processing, and particularly relates to a digital twinning-based engineering completion delivery data information extraction method, which comprises the following steps of: acquiring completion site entity data, constructing a digital twinning body of building information model and live-action three-dimensional fusion, and fusing semantic tag information based on an improved ICP (Inductively Coupled Plasma) algorithm. Performing space coordinate alignment and attribute association on the digital twinborn body to generate a completion information dynamic twinborn mapping map; a multi-level rule inference engine is used for extracting information items conforming to delivery standards, a dynamic risk propagation model is used for simulating the influence of data deviation on a delivery result through multi-node consensus verification data of a construction party, a construction party and a supervisor, and information extraction weight parameters are corrected in real time; and dynamically generating a delivery data packet and a visual auditing report and synchronously updating the digital twin archive library in combination with engineering change records and acceptance specifications. Therefore, the problems of incapability of capturing engineering parameter fluctuation, poor transmission efficiency and the like in the prior art are solved.

The invention discloses a water supply pipeline leakage detection method and system, and relates to the field of artificial intelligence, and the method comprises the steps: collecting a multi-source signal through a plurality of sensor nodes disposed on a pipeline; preprocessing and space-time alignment are carried out on the collected multi-source signal at an edge computing node to obtain a standardized signal; analyzing the standardized signal by using a preset lightweight one-dimensional convolutional neural network model at the edge computing node to obtain a preliminary leakage detection result and a corresponding confidence coefficient; executing a hierarchical response strategy according to the confidence coefficient; and in the cloud analysis module, multi-model fusion analysis is performed in combination with the feature vectors, historical time sequence data and a hydraulic model simulation result, so that leakage points are accurately positioned and verified, and a leakage result is determined. According to the invention, a real-time, accurate and reliable complete solution can be provided for pipeline leakage monitoring.

The invention relates to the technical field of water resource management and forecasting, in particular to a deep learning model building and forecasting method based on hydrological mechanism fusion. The method specifically comprises the following steps: constructing a snow melting calculation module and a soil calculation module, splicing to form a runoff production model, constructing a confluence calculation module, executing confluence evolution simulation of a calculation result of the runoff production module by using the confluence calculation module, and constructing a confluence model. In confluence model simulation calculation, according to the number of calculation units, a broadcast calculation strategy and an array-based ordinary differential equation solving method are adopted, parallel calculation of the multiple calculation units is achieved, and runoff production calculation results of the multiple units are obtained; and integrating and calculating runoff production calculation results of each unit through a confluence model to obtain a runoff prediction result of the modeled drainage basin after confluence. The method provided by the invention solves the problems of low model construction efficiency and poor practical application effect faced by the application of the deep learning technology in the hydrological model at present.

The invention discloses a greenhouse gas concentration time sequence prediction method based on a sudden change perception attention mechanism. The method comprises the steps of data preprocessing, sudden change intensity sequence construction with boundary processing, time sequence feature coding, sudden change perception attention weight calculation, context vector generation and concentration prediction, model training and optimization and model prediction. The method aims to solve the problem that a standard deep learning model is slow in sensing and lagged in prediction for a sudden change event in a concentration sequence, and finally realizes high-precision prediction for future concentration change, especially a sudden concentration peak value by endowing the model with the capability of actively identifying and reinforcing the learning of a historical sudden change mode. The urgent demand for early warning of abnormal emission in practical application is met. The method is particularly suitable for processing foundation observation data with small resolution and even higher resolution, has the core value of improving the prediction capability of concentration dramatic change driven by sudden emission events, and can be widely applied to key scenes such as accurate carbon emission monitoring, environmental pollution early warning and climate model simulation.

The invention provides a wind field inversion method and device based on multi-source prior data, equipment and a medium, and the method comprises the steps: building a target function of a three-dimensional fusion wind field based on the multi-source prior data and a fluid mechanics model simulation wind field, carrying out the iterative optimization of the target function, and determining the three-dimensional fusion wind field; constructing an initial deep learning neural network model, taking the three-dimensional fusion wind field as a truth value label, inputting the urban underlying surface features, the terrain elevation and the numerical weather forecast wind field into the deep learning neural network model, and training to obtain a target deep learning neural network model; and inputting the new numerical weather forecast wind field, the urban underlying surface features and the terrain elevation into the target deep learning neural network model, and outputting a refined wind field. According to the technical scheme provided by the embodiment of the invention, through the trained deep learning neural network model, high-precision rapid inversion of the low-altitude wind field is realized without depending on laser radar and ground observation data and depending on prior information such as numerical prediction and terrain.

The invention discloses a shale gas well fracturing parameter optimization design method and system based on a depth Q network, and relates to the technical field of shale gas development, and the method comprises the following steps: S1, collecting geological parameters, fracturing construction parameters and productivity data of a shale gas well in advance; s2, constructing an agent model and performing environment simulation; and S3, constructing a DQN model. According to the method, a LightGBM algorithm is adopted to construct a data-driven proxy model to simulate a real fracturing environment, an optimization model is constructed based on a deep Q network (DQN), state, action, reward and epsilon-greedy strategies are defined, and technologies such as a variable step size search mechanism, experience playback and target network soft update are combined, so that the real fracturing environment is simulated. The problems that a traditional optimization method is weak in local search capability and low in convergence speed are effectively solved, multivariable synchronous optimization of the unit perforation length proppant dosage and the fracturing fluid dosage is achieved, and a global optimal parameter combination can be rapidly explored.

The invention relates to a full-convolution deep-convolution generative adversarial network fusion process constraint-based artificial intelligence pressure-resistant shell design method, which comprises the steps of constructing a simulation model, the method comprises the steps of setting a pressure-resistant shell shape parameter range and step length and a pressure-resistant shell material, setting a rib parameter range and step length, performing simulation software modeling, and calculating the volume, the weight and the maximum pressure-resistant value of the pressure-resistant shell. Constructing a data set, and generating a plurality of basic samples according to the parameters of the simulation model; an AI model is constructed, the AI model is a full-convolution deep-convolution generative adversarial network architecture, the AI model comprises a forward prediction model and a reverse design model, the forward prediction model comprises three convolution layers of a full-convolution structure, and the reverse design model comprises three transposed convolution layers of the full-convolution structure; training and optimizing the AI model; and intelligently designing the pressure-resistant shell by using the AI model. The design efficiency is improved, the feasibility of the design scheme is high, and the withstand voltage value prediction precision and the parameter generation accuracy are improved.

The invention provides an ecological system service flow simulation and collaborative tradeoff relation quantification method based on a graph neural network, and relates to the technical field of ecological system service simulation and quantification. According to the method, the Huaihe River basin serves as a research area, firstly, a landscape is abstracted into a graph structure, ecological patches serve as nodes and are endowed with multi-dimensional features, ecological process flow paths serve as edges, and flow attributes are quantified; then, constructing a graph neural network model, and learning inherent rules of generation, flow and consumption of an ecological system service through training; and finally, realizing ecological system service, particularly dynamic simulation of supply service and adjustment service, and quantifying the collaborative tradeoff relation by adopting a multi-index fusion method. According to the method, the nonlinear correlation of the ecological process can be accurately captured, the flow simulation and relation quantification precision is improved, scientific support is provided for ecological planning and management of the Huaihe River basin, and compared with an InVEST model, the simulation error is reduced by 20%-30%, and the collaborative tradeoff recognition accuracy is improved by 15% or above.

The invention discloses a photovoltaic power station fault diagnosis method based on digital twinning and transfer learning, and belongs to the technical field of photovoltaic power generation fault detection. According to the method, a photovoltaic power station digital twin model based on a physical mechanism is constructed, operation behaviors in normal and multiple fault states are simulated, and a simulation data set with an accurate label is generated; in combination with the acquired real operation data, a fault diagnosis model is trained by adopting a transfer learning framework containing a domain adaptation loss item, so that the distribution difference between simulation data and real data is effectively reduced; a physical consistency loss item is innovatively introduced, and a physical rule is used as a constraint embedded model, so that the reliability and interpretability of a diagnosis result are improved; and finally, performing accurate fault diagnosis on the real-time operation data by using the trained model. According to the method, the model training problem caused by scarcity of real fault data is solved, the diagnosis precision and generalization ability are remarkably improved, and the method is suitable for intelligent operation and maintenance of the photovoltaic power station.

The invention discloses a power distribution network area low voltage prediction method, and relates to the technical field of voltage prediction, and the method comprises the steps: S1, data preprocessing; s2, establishing a GA-BP neural network load prediction model; s3, GA-BP load prediction model simulation is carried out; s4, establishing an LSTM neural network voltage prediction model; s5, performing model simulation and result analysis; and S6, voltage prediction and treatment suggestion. According to the power distribution area low voltage prediction method, firstly, a GA-BP neural network is utilized to predict area loads, influence factors such as temperature, humidity, date types and different moments in one day are fully considered, and predicted load data are utilized as input to construct an LSTM model to predict area voltages; the transformer area low voltage is effectively predicted through the GA-BP-LSTM combined model, the model is high in fitting degree and low in prediction error, the low voltage degree of a user is reflected in real time, and more information support is provided for follow-up transformer area low voltage treatment.

The invention discloses a coal-series gas yield increasing and CO2 geological sequestration integrated simulation method and system, and relates to the technical field of gas exploitation of each layer, and the method comprises the following specific steps: building a geological model representing a multi-lithology superposed coal-series reservoir; the coal series reservoir is a heterogeneous reservoir and comprises shale, a coal seam and sandstone, the shale and the coal seam are double-hole double-permeability media, and the sandstone is a single-hole single-permeability medium; based on the geologic model, establishing a multi-physical field coupling model of a coupling temperature field, a seepage field and a stress field; performing discretization solution on the multi-physics field coupling model to obtain a numerical simulation result; constructing a numerical model, and performing inversion correction on model parameters of the numerical model based on experimental data and the numerical simulation result; and utilizing the corrected numerical model to simulate different injection-production working conditions so as to carry out double-target evaluation and scheme optimization of coal-series gas yield increase and CO2 geological sequestration. The method provides a reliable decision basis for engineering scheme design.

The invention discloses a multi-scale simulation method for a thermal compression deformation behavior of TC4 titanium alloy, and belongs to the field of metal material processing simulation. Comprising the following steps: 1, carrying out thermal compression experiments at different temperatures to obtain a deformed sample; 2, analyzing the grain orientation, orientation difference angle and texture evolution of the sample by using an electron back scattering diffraction technology; 3, constructing a crystal plasticity finite element model, and simulating a polycrystalline deformation process; 4, constructing a molecular dynamics model, and simulating phase change and dislocation evolution in thermal compression and cooling processes; and 5, based on a multi-scale simulation result, hot working parameters are optimized, dynamic recrystallization and a beta-to-alpha phase change path are regulated and controlled, and grain refinement and performance homogenization are achieved. The invention discloses a correlation mechanism of the microstructure and the mechanical property in TC4 titanium alloy thermal compression deformation, and provides a theoretical basis for optimization of a thermal processing technology of a high-performance titanium alloy component in the aerospace field.

The invention provides a watershed flood early warning system and method based on hydrological model and mobile application coupling, and belongs to the field of watershed flood early warning. In the system, a communication and data transmission module collects data for hydrological model simulation in a watershed area; the model simulation module simulates hydrological data of rainfall runoff in a watershed area in real time by using a hydrological model based on the collected data, and transmits a simulation result to the flood risk forecasting module through the communication and data transmission module; the flood risk forecasting module generates a flood risk map based on the received simulation result and transmits the flood risk map to the communication and data transmission module; and the mobile application display module receives real-time early warning information generated by the communication and data transmission module based on the flood risk map, and displays the real-time early warning information to a user. The watershed flood is simulated in real time through the hydrological model, the flood forecasting and early warning information is sent to people in a watershed flood submerging range through the mobile application, and the accuracy and timeliness of watershed flood early warning can be improved.

The invention relates to the technical field of CAM intelligent programming, and discloses a CAM programming algorithm and system based on a generative artificial intelligence large model. The algorithm comprises the steps of analyzing a CAD document, fusing geometric features, tolerances and material information through a generative artificial intelligence large model, and generating a structured semantic skeleton. Then, the semantic skeleton is matched with a process knowledge base, a preliminary processing instruction cluster is generated, and a dynamic processing graph with a node dependency relationship is constructed; secondly, receiving an editing intention of a user for the graph, analyzing the intention by using a generative artificial intelligence model, and automatically reconstructing node logic and connection of the graph to form a corrected processing logic network; and finally, before execution, simulating a network execution process by using a generative artificial intelligence model, and identifying potential logic and resource conflict nodes. According to the invention, intelligent conversion from design to processing instructions, and intent-driven process chain dynamic optimization and conflict pre-verification are realized.

The invention relates to an automatic driving training method and system based on a differentiable world model simulation system, computer equipment and a medium. The method comprises the following steps: constructing the differentiable world model simulation system; the differentiable world model simulation system comprises a differentiable sensing module, a dynamic model and a strategy network; freezing the strategy network, and pre-training the differentiable world model simulation system by using the first training data; unfreezing the strategy network, and performing end-to-end joint optimization on the differentiable world model simulation system by utilizing the second training data and adopting a course learning strategy; and carrying out safety strengthening training on the differentiable world model simulation system by utilizing the third training data. According to the method, the full link is automatically driven, namely, perception, prediction and planning end-to-end differential is achieved, the training efficiency is improved, and the generalization of a long-tail scene is improved.

The invention discloses a landscaping landscape design method based on big data, and belongs to the technical field of landscape simulation design, and the method specifically comprises the steps: obtaining microclimate data of a target area and surrounding building environment data, and inputting the data into a dynamic microclimate simulation model to obtain a microclimate dynamic distribution diagram; identifying the position and the type of a microclimate discomfort area based on the distribution map, and generating a landscape design element set; carrying out overlay analysis on the element set and the distribution map, and adjusting the attribute and position of each element to form a preliminary design scheme; the preliminary scheme is input into a model to simulate microclimate distribution after implementation, and the difference degree of new and old distribution diagrams is calculated; and when the difference degree exceeds a preset range, iteratively adjusting the design elements and repeating the optimization process, and outputting a final scheme until the difference degree reaches the standard. According to the invention, dynamic coordination of landscape design and microclimate environment is realized.

The invention relates to a BIM model-based maintenance method and system for a split-phase vertical laminated GIS bus cylinder. The method comprises the following steps: collecting point cloud data through laser scanning to construct a three-dimensional model; key feature points are extracted, and displacement reference vectors are determined; constructing a displacement constraint matrix and optimizing a path; generating an interactive three-dimensional space model; simulating a dismounting sequence and evaluating the risk; generating an emergency instruction and a tool track; and the maintenance process is optimized by feeding back the cycle correction model. Visual simulation, precise control and dynamic optimization of the whole overhaul process are achieved, and the overhaul operation precision, efficiency and safety are remarkably improved.

The invention relates to an unmanned ship dynamic channel modeling method in an offshore water surface environment, and the method comprises the steps: constructing a three-dimensional offshore water surface simulation scene which comprises a sea surface, a wharf facility, an unmanned ship cluster and a communication node; establishing a wireless channel propagation model based on a ray tracing method, and simulating a propagation mechanism of direct, reflection and diffraction paths; establishing a motion characteristic model of the unmanned ship under the action of waves, and obtaining six-degree-of-freedom pose data of the unmanned ship; generating multiple frames of scene copies in batches by adopting an intelligent batch processing dynamic reconstruction method; dynamically correcting an antenna pattern in each frame of scene copy according to the pose data so as to couple the influence of hull motion on antenna radiation characteristics; importing the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response; simulation is executed, and channel data obtained through simulation are collected and analyzed. According to the method, the motion characteristics of the unmanned ship in waves and the wireless channel propagation mechanism can be accurately coupled, and the accuracy of dynamic channel prediction is improved.

The invention discloses a multi-energy coupling temperature field control method and system based on intelligent optimization, and relates to the technical field of intelligent control, and the method comprises the steps: extracting temperature field space-time features through an ST-CNN space-time convolution network in the aspect of energy efficiency optimization, solving a Pareto leading edge of a multi-objective optimization model in combination with an NSGA-III algorithm, and solving the Pareto leading edge of a multi-objective optimization model; the system achieves the optimal balance among the electro-thermal conversion efficiency, the refrigeration coefficient and the human body comfort level; secondly, in the aspect of control precision, a 3D convolution kernel and an LSTM module are adopted to cooperatively analyze the dynamic law of a temperature field, and in cooperation with CFD turbulence model simulation verification, the temperature control error can be reduced to a small range, and the space temperature uniformity is obviously improved; finally, in the aspect of system intellectualization, autonomous iterative optimization of control parameters is achieved through a near-end strategy optimization algorithm, a closed-loop control system of perception-decision-execution-feedback is formed, and the manual intervention requirement is reduced or eliminated.

The invention provides a partition metering leakage monitoring management method and system based on the Internet of Things, and belongs to the technical field of water supply pipe network leakage monitoring, and the method comprises the steps: carrying out the metering partition division of a water supply pipe network, and deploying the Internet of Things monitoring equipment in the metering partition; acquiring position information of the Internet of Things monitoring equipment in the metering subarea to which the Internet of Things monitoring equipment belongs; acquiring geographic information data of a water supply pipe network in the metering partition, identifying the shortest path between adjacent Internet of Things monitoring equipment through a pipe network topological relation based on the geographic information data, and calculating the shortest pipeline length; constructing a pipe network hydraulic model, and simulating simulation parameters of the Internet of Things monitoring equipment under a normal water supply working condition and a pipe explosion working condition; acquiring actual parameters monitored by the Internet of Things monitoring equipment in each metering partition; and calculating the deviation between the normal simulation parameters and the actual parameters, and positioning and correcting the leakage position in combination with a leakage positioning algorithm and the abnormal simulation parameters. The leakage monitoring efficiency of the water supply network is improved, and the leakage point is accurately positioned.

The invention provides a vaccine target screening system based on calculation model simulation. The vaccine target screening system comprises a multi-source heterogeneous database, wherein the multi-source heterogeneous database integrates and standardizes pathogenic genes, protein structures, literatures and experimental data; the feature calculation module calls a calculation biological model to carry out structural analysis, immunogenicity simulation and stability prediction; the intelligent screening and sorting module applies a multi-objective optimization algorithm to perform parallel evaluation and outputs optimal target spots; a structure iteration optimizer automatically iteratively corrects the optimized target spots to generate a high-potential variant library; and the process suitability simulation module couples the variants with the preparation formula and the process parameters to simulate production storage behaviors and feeds back an optimization target. According to the invention, efficient screening and optimization of vaccine targets can be realized, the accuracy and efficiency of target screening are improved, the research and development cost is reduced, and the research and development process of vaccines is accelerated.

The invention discloses a dam interior and exterior deformation monitoring method and system based on deep fusion of finite element and GNSS (Global Navigation Satellite System) monitoring. According to the method, surface displacement observation data are obtained through a GNSS receiver, a finite element model is constructed, and the mechanical response of the finite element model under hydrostatic pressure, temperature and seismic load is simulated. According to the first layer, parallel primary fusion is conducted on GNSS data and finite element output through Kalman filtering, particle filtering, a neural network and a Bayesian algorithm; in the second layer, weights are dynamically distributed on the basis of mean square errors of all algorithm results in a sliding window and reference data, and a high-precision displacement estimation value is generated through weighted integration. The system evaluates data quality and model confidence in real time, dynamically optimizes weight distribution, performs early warning based on multistage thresholds of displacement, speed and acceleration, and finally studies and judges structural risks through time sequence decomposition and abnormal mode recognition. According to the invention, the overall precision and reliability of dam deformation monitoring are significantly improved.

The invention discloses an AGC information physical system reliability verification method and system, and relates to the technical field of power dispatching automation. The problems that an existing AGC system only adopts a static operation mode and an ideal communication condition in a simulation test, and coupling response of continuous operation and sudden disturbance of a power grid under a long time scale is difficult to reproduce truly are solved. The state evolution under event driving is realized by constructing three types of simulation event sources of a time sequence operation sample, a master station control instruction and external disturbance and setting a priority mechanism of external disturbance gt, master station instruction gt and time sequence propulsion; an uplink channel fault model is introduced to simulate on-off, delay and errors of measurement data, and a downlink channel fault model is introduced to simulate transmission abnormity of a control instruction; closed-loop simulation of frequency response and electrical quantity updating is realized by combining promotion of a rotor motion equation and dynamic load flow calculation; and finally, by analyzing indexes such as frequency deviation, tie line deviation and control response delay, comprehensive reliability evaluation of the AGC system in a complex information physical environment is completed.

The invention discloses an air inlet casing full-process simulation closed-loop optimization method, which relates to the technical field of mechanical manufacturing and processing, and comprises the following steps: simplifying an air inlet casing model; selecting a plurality of key procedures from the whole procedures; according to process parameters and boundary conditions of the key process, grid division is carried out on the simplified model, and a corresponding finite element simulation model is constructed; sequentially simulating the finite element simulation model according to a key process sequence, correcting a simulation result according to deformation data of actual processing, transmitting the corrected simulation result to a next key process for model simulation until simulation and correction of all key processes are completed, and constructing a full-process simulation prediction model; performing iterative optimization on multiple parameters in the whole-process simulation prediction model to obtain an optimal parameter combination; and correcting the simulation model according to the deviation between the actual processing data and the simulation result under the control of the optimal parameter combination. According to the method, high-precision prediction of various complex manufacturing processes can be realized, and the manufacturing precision is improved.