64 results about "Model building" patented technology

The application relates to the technical field of earthquake prediction, and particularly provides a lightweight on-site ground motion parameter prediction model construction method and an earthquake early warning system. The lightweight on-site ground motion parameter prediction model construction method comprises the following steps: training an initial prediction model according to acceleration waveform data when a P wave of an earthquake arrives, to obtain a pre-training prediction model; taking the pre-training prediction model as a teacher model, and transferring the knowledge of the teacher model to a student model through distillation; and constructing a lightweight on-site ground motion parameter prediction model based on the student model. The method can reduce the complexity and calculation amount of the student model by transferring the knowledge in the pre-training prediction model to the student model through distillation; and the lightweight on-site ground motion parameter prediction model can be constructed based on the student model, so that the demand of the model for hardware resources can be reduced while the on-site ground motion parameter prediction efficiency is ensured, and the deployable scene of the model is expanded.

This invention relates to the field of flood evolution simulation technology, and more particularly to a multi-model fusion flood evolution simulation system, comprising: a model building module; a range determination module for determining the baseline matching setting or direction matching setting for a target viewport based on the linkage viewport fit and the number of linkage viewports; a loading analysis module for determining whether to adjust the loading method of the target viewport's pre-loading range from direct loading to time-domain layered loading based on the network degradation coefficient and viewport feature anomaly degree; a time-domain layering module for determining whether to perform uniform layering based on anomaly evaluation values ​​or clustering layering based on evolution correlation degree in time-domain layered loading based on evolution law coefficients and tile evolution intensity; and an optimization adjustment module for determining whether to perform optimization adjustment based on loading level heterogeneity and multi-viewport loading interference degree. This invention can optimize scene loading strategies to improve the scene loading construction effect of flood evolution digital twins.

This invention relates to the field of safety monitoring technology, and more particularly to an intelligent monitoring system for highway tunnel construction safety. The system includes a data acquisition module, a contour analysis module, a feature fusion module, and a data compensation module. By acquiring images of the tunnel surface, dust concentration within the tunnel, and water vapor humidity, the system divides the tunnel into several sub-image regions. The system determines the unevenness and texture complexity of each sub-image region, calculates region-sensitive feature values, and uses the tunnel dust concentration and water vapor humidity, combined with the region-sensitive feature values, to determine whether each sub-image region meets preset conditions. The system also calculates data missing characterization values ​​to determine the modeling anomaly tendency of each sub-image region. Based on the modeling anomaly tendency and the data missing characterization values, the system determines the data acquisition method for each sub-image region. This invention improves the accuracy and efficiency of model building through comprehensive analysis, data acquisition, and compensation of the tunnel environment, thereby ensuring construction safety.

The application relates to an end-to-end point cloud denoising model construction method and system, and belongs to the technical field of deep learning, and solves the problems of insufficient generalization ability of an existing denoising model and low fidelity of output point cloud geometric features. The method comprises the following steps: after preprocessing clean point clouds collected, point cloud samples are generated by injecting noise to construct a sample set; a deep learning model is constructed, and a combined loss function is constructed by fusing multiple supervision signals; the deep learning model sequentially comprises a feature extraction module, a denoising orientation prediction module and a point cloud attribute prediction module, and is used for outputting predicted coordinates and predicted normal vectors of denoised point clouds; the deep learning model is trained end to end based on the sample set and the combined loss function, and the trained deep learning model is used as a point cloud denoising model. The generalization ability and denoising effect of the denoising model are improved.

The embodiment of the invention provides a thin interlayer and phase control physical property cause sand body configuration model construction method and system, and belongs to the technical field of reservoir modeling. The method comprises the following steps: identifying fluvial facies reservoir distribution characteristics based on exploration data, and constructing a corresponding cause sand body reservoir configuration model; obtaining a corresponding small-layer sand body deterministic model based on the cause sand body reservoir configuration model; on the basis of the deterministic model of the small-layer sand body, performing representation of an internal configuration interface of the sand body based on a lateral lamination model and an adaptive triangular mesh generation algorithm; and generating a distribution model of each lithofacies unit based on representation of a configuration interface in the sand body, and constructing a physical property model for fine modeling and heterogeneity description of high and low permeability sections in the configuration based on a phase control stochastic simulation method. According to the scheme, the limitations of insufficient precision, unreasonable transition and low calculation efficiency in traditional modeling are effectively overcome, and high-precision geologic model support is provided for reservoir management and oil reservoir development.

The application discloses an intelligent power-saving control system based on Internet of Things, which comprises a data acquisition and preprocessing module, a model construction and training module, an energy consumption state recognition module, a control instruction generation module and a power supply strategy execution module; the data acquisition and preprocessing module is used for acquiring multi-time scale power response data of an electric load and performing preprocessing; the model construction and training module is used for constructing a multi-time scale energy consumption response differential model based on the preprocessed multi-time scale power response data; and the application relates to the technical field of energy-saving control. The intelligent power-saving control system based on Internet of Things can accurately recognize redundant energy consumption types in the load, including unnecessary standby energy consumption, repeated start-up energy consumption and inertia delay energy consumption, by constructing a multi-time scale energy consumption response differential model and combining a deep learning algorithm, and can dynamically adjust according to the actual use of the electric load, thereby improving the recognition accuracy, as compared with a traditional system which relies on fixed rules for recognition.

A computer-implemented method for model building with explainability is provided. The method includes receiving, by a hardware processor, a first metric and a second metric of minimum model performance. The first metric relates to data modeling quality and the second metric relates to model to business rule correlations. The method further includes performing, by the hardware processor, auto Artificial Intelligence model generation responsive to training data and a combination of the first and the second metrics of minimum model performance to obtain a model that is trained and meets model prediction accuracy and model prediction explainability requirements represented by the combination of the first and the second metrics of minimum model performance.

This application relates to digital twin 3D modeling, specifically to a method and device for constructing a digital twin waterway scene using electronic navigation charts. The method includes: analyzing the historical trend of water depth values ​​at each depth point to determine the inherent terrain feature values ​​of each depth point set at the current time; determining the terrain ruggedness feature value by fitting the last data point in the temporal sequence of the trend components of all depth points in each depth point set at the current time with its fitted value and the fitting error; and determining interpolation feature values ​​by combining the inherent terrain feature values ​​to classify all depth points into critical and non-critical points; and performing interpolation processing on the water depth values ​​of all critical and non-critical points using different interpolation methods respectively. This application solves the problem that a single interpolation method cannot simultaneously ensure the realism and real-time performance of the model construction, thus improving the realism and real-time performance of the digital twin waterway scene model.

The application discloses a greenhouse gas point source emission satellite hyperspectral inversion model construction method and system. The method constructs a multi-branch deep neural network including a shared feature extraction layer, a physical characterization auxiliary branch and a main task predictor, jointly trains based on data alignment loss and physical residual loss, so that the network directly regresses the emission from the hyperspectral image block end to end; the physical residual loss substitutes the predicted concentration, the mask, the wind field and the emission into a preset physical equation structure, and constrains the network to meet the fluid transport law. The system includes a model construction module and an inversion module. The application avoids the error cascade amplification of the traditional serial inversion, reduces the dependence on external meteorological data, and realizes automatic inversion with high precision and high physical consistency.

The application relates to the technical field of scaffold scheme checking, and particularly discloses a scaffold scheme checking system, which comprises a model construction module, a parameter input module and a checking module. The model construction module is used for setting a structure parameter design table and a design standard value corresponding to a scaffold building type under different scaffold safety levels, inputting steel pipe sizes and corresponding characteristic parameters, and inputting a calculation formula of the related parameters. The parameter input module is used for obtaining the scaffold building type, the structure parameters and the steel pipe sizes of the scaffold. The checking module is used for calculating the design value of the scaffold according to the scaffold building type, the structure parameters and the steel pipe sizes of the steel pipes used, comparing and analyzing the design value with the design standard value, and outputting a scaffold checking result. The application realizes checking of a scheme before scaffold building, safety level and risk assessment of the scaffold after building, and makes the operation personnel accurately and timely know the construction risk of the scaffold, so that the construction safety is ensured.

The invention provides an online high-precision map construction method and a model construction method thereof, and the model construction method comprises the steps: sequentially constructing a backbone network, a view conversion network, a hierarchical query embedded network and a model optimization network based on a MapTr model architecture, and obtaining an initial model; training the initial model to obtain a map construction model; and carrying out model integration on the plurality of map construction models to obtain an online high-precision map construction model. According to the method, efficient and accurate high-precision map construction can be realized through a stronger backbone network, an effective view conversion and data enhancement technology and a model integration strategy. The method has excellent performance in the aspect of multi-category semantic information extraction, and is suitable for real-time map construction tasks in an automatic driving system.

This invention relates to the field of data processing, and more particularly to a control method and system for constructing a visual behavior simulation model. The method includes: acquiring real-time business event streams, performing protocol decoding and field parsing to obtain structured behavior data; constructing a behavior dependency graph structure based on the structured behavior data and determining edge weights; extracting the current behavior path based on the behavior dependency graph structure; evaluating the path legality and fit of the current behavior path based on the edge weights to obtain an average path fit; and judging the legality of the current behavior path based on the average path fit, correcting and optimizing invalid current behavior paths to obtain an optimized current behavior path, thus achieving process control. This solves the problem that traditional simulation model construction methods lack automated structure recognition and process rationality judgment mechanisms when facing complex business scenarios.

The application discloses the field of dizziness model construction equipment, and discloses a three-axis rotation multi-animal model construction device.

This invention relates to the field of BIM model building technology, and more particularly to a collaborative construction management system for a water plant expansion project based on BIM and the Internet of Things. The system includes: a data processing module for processing multi-source construction data in the water plant expansion project; a model updating module, including a model building unit for constructing a BIM model of the water plant expansion project; a collaborative management module for collaboratively managing the construction process of the water plant expansion project based on the BIM model; an attenuation coefficient adjustment module for determining the attenuation coefficient of redundant data in the multi-source construction data based on the accuracy of the association between standardized data and the BIM model; a mapping correction adjustment module for determining the model coordinate mapping correction coefficient based on the spatial deviation rate of the standardized data; and an attribute matching adjustment module for determining the attribute matching similarity threshold based on the BIM model comparison difference rate. This invention improves the management stability of the water plant expansion project construction.

A model construction method of a photovoltaic power generation unit, characterized in that the method comprises the following steps: step 1, model simulation is performed on a grid-connected inverter of the photovoltaic power generation unit, the simulation model comprises a basic model and a simplified model, wherein the simplified model is used to simplify a half-bridge structure of the grid-connected inverter into a three-phase power supply in the simulation process; step 2, the simulation model of the grid-connected inverter is input into power system simulation software, and a running disturbance is added to the simulation model to obtain an adjustment effect of the simulation model on the running disturbance; and step 3, based on the adjustment effect of the simulation model and the network scale to be analyzed, the basic model or the simplified model is selected to be used. The method is simple, the improvement of the simulation model is accurate and effective, and fully meets the demand of model calculation efficiency under large-scale simulation.

The application discloses a model construction method and system based on digital twinning, and the method comprises the following steps: collecting multi-source heterogeneous data of a physical entity, wherein the multi-source heterogeneous data comprises running data, environment data and state data; based on data collection errors, transmission delays and environmental interference factors, the multi-source heterogeneous data is dynamically corrected in credibility; based on the corrected credibility result, the sensitivity parameters output by the digital twinning model in combination with the multi-source heterogeneous data and the real-time requirement parameters of the model application scene corresponding to the multi-source heterogeneous data are used for dynamic weight distribution; based on the allocated dynamic weight, the core parameters of the digital twinning model are updated by using a lightweight iteration strategy to construct the digital twinning model. The system corresponds to realize the above method. The application solves the problems of multi-source data distortion, fixed weight and iteration calculation power consumption, realizes accurate model construction, and adapts to dynamic scene requirements.

This invention relates to the field of electronic packaging technology and discloses a multi-physics coupled packaging simulation method, including S1, macroscopic equivalent model construction, which establishes a macroscopic packaging model containing multi-physics loads and performs equivalent processing on the microscopic complex structure to efficiently obtain the overall temperature, displacement, and stress distribution; S2, key region identification, which combines macroscopic equivalent modeling with microscopic fine modeling and establishes a macro-micro bidirectional coupling mechanism to enable the macroscopic structural response and microscopic local behavior to be solved collaboratively under the same analysis framework; at the same time, combined with an adaptive iteration strategy, the microscopic model is dynamically introduced only in key high-risk areas to avoid the degree-of-freedom explosion problem caused by global fine modeling, thereby significantly reducing the overall computational scale while ensuring the accuracy of stress and strain prediction in key areas, effectively solving the problem of "difficulty in balancing accuracy and efficiency" in the prior art, and achieving a unity of high efficiency and high accuracy in multi-scale simulation.

This invention discloses a low-cost, accurate flood inundation model construction method based on low-precision hydrodynamics and data-driven principles, comprising the following steps: S1: Constructing a low-fidelity hydrodynamic model; S2: Establishing a mapping module to estimate flood inundation in the model domain based on two relationships; S3: Inputting boundary conditions into the low-fidelity hydrodynamic model to obtain a low-fidelity estimate of the river channel depth, wherein the boundary conditions include inflow, outflow, and initial water level; S4: Processing the low-fidelity estimate of the river channel depth using the mapping module constructed in S2, and outputting the flood inundation range and depth of the entire model domain. The advantages of this invention compared to existing technologies are: providing a low-cost, accurate flood inundation model construction method based on low-precision hydrodynamics and data-driven principles for efficient and accurate simulation of flood inundation range and depth.

This specification provides one or more embodiments of a method, apparatus, and electronic device for modeling building eaves. The method includes: receiving a two-dimensional outline of a floor and a two-dimensional outline of an eave from a server; the eave is to be placed on the edge of the floor, and the two-dimensional outline of the eave represents the cross-sectional outline of the eave; splitting the two-dimensional outlines of the floor and the eave according to the normal direction of the vertices to obtain a set of vertices on the two-dimensional outlines, and generating a normal corresponding to each vertex in the vertex set; based on the vertex set corresponding to the floor and the eave and the normal, converting the two-dimensional outline of the eave to a three-dimensional coordinate system to obtain rendering data of the three-dimensional shape of the eave; rendering according to the rendering data, and displaying the three-dimensional shape of the eave on the floor; the floor is rendered based on the two-dimensional outline of the floor. This specification embodiment reduces the downlink traffic sent from the server to the client.

This invention relates to the field of digital twin technology, and more particularly to a predictive maintenance platform for the entire lifecycle of a heat dissipation system based on digital twins. The platform includes: a data acquisition module; a model building module; a block partitioning module for determining whether to perform direct partitioning based on regional coupling strength or related partitioning based on heat exchange correlation, according to the fault propagation coefficient; a category analysis module for determining block categories based on temperature deviation and determining whether to adjust the block categories of the second-class blocks based on the contaminant deposition impact and fluid pressure anomaly of the second-class blocks, according to the maximum fluctuation cycle decay rate; an update analysis module for determining the update frequency of each updated block based on a comprehensive evaluation value; and an update optimization module for determining whether to perform optimization adjustments based on the block change coefficient. This invention can improve model update efficiency.

The application discloses a kind of based on spatial data mining and geological constraint seismic activity fault model construction method, system, equipment and medium.The method is first arranged and analyzed to seismic data, extracts minimum complete subdirectory;Using improved mean shift algorithm for spatial clustering, identify each active fault corresponding small earthquake cluster;Subsequently, each fault cluster is three-dimensional slice, and least square method fitting is generated fault interpretation line;With interpretation line as foundation, initial seismic activity fault three-dimensional model is constructed, and is optimized by multi-source geological constraint, and active fault three-dimensional fine model is obtained;Finally, the model is integrated with digital earth spatial registration.This application can realize the whole process automation and quantization from seismic data analysis to active fault three-dimensional fine model construction and application, improve the objectivity, efficiency, precision and reliability of model construction, provide important technical support for seismic disaster risk assessment, active fault detection.

This invention provides a method for constructing a simulation model of the impedance characteristics of a circuit breaker closing resistor under damage conditions. It relates to the field of simulation model construction technology. First, a bidirectional electromagnetic-thermal-mechanical multiphysics coupled simulation model is established, including a resistor element, a clamping spring, connecting conductors, and an insulating medium. Nonlinear temperature-varying resistivity and contact resistance related to clamping force are set. The steady-state field distribution is first solved at different DC operating points, and then a swept-frequency small-signal excitation is superimposed to generate an impedance-frequency characteristic curve. Finally, the model parameters are calibrated by comparing with measured data from a healthy sample of the same model. This invention implements a joint solution strategy of DC bias steady-state and AC small-signal swept-frequency in the simulation, ensuring both the convergence stability of the strongly nonlinear electro-thermal coupling problem and realistically simulating the impedance response mechanism of the closing resistor under actual load conditions.

The application discloses a kind of multi-objective mathematical calculation model construction method and application system, it is related to mathematical modeling technical field, including the following steps: S1, real-time data acquisition;S2, data preprocessing;S3, dynamic weight adjustment;S4, model construction and solution.The multi-objective mathematical calculation model construction method and application system, through the dynamic frequency mechanism of basic frequency and trigger type frequency increase, according to the parameter fluctuation characteristics flexible adjustment acquisition strategy, avoid the key fluctuation information missing caused by fixed low-frequency acquisition, reduce the resource waste caused by fixed high-frequency acquisition, different change characteristics parameters can be realized efficient data capture, and rely on the linkage mechanism of real-time data acquisition and dynamic weight adjustment, can quickly respond to parameter change, combined with efficient model solution architecture, substantially shorten the period from parameter change to output optimal decision scheme, effectively avoid the decision lag problem of traditional fixed weight model.

This invention provides an intelligent modeling method and system for steel performance prediction based on LLM (Liquidity Management Model). The system includes: receiving and parsing user instructions through a natural language understanding module to generate a standardized task description; automatically generating or matching an automated model building process based on the task description through a planning and execution module; providing decision support based on a knowledge base through a knowledge recommendation module during task planning or execution; and automatically scheduling functional modules such as data access, feature engineering, and model building to complete the modeling task and output the results, based on the recommended information, through the planning and execution module. The system accordingly includes various functional modules implementing the above method. This application achieves end-to-end automated modeling through natural language interaction, automated task planning, and intelligent knowledge recommendation, significantly improving modeling efficiency, lowering the technical threshold, and enhancing model quality and versatility.

The application provides a knowledge and data double-driven broken mill equipment health management industry model construction method, and belongs to the technical field of artificial intelligence.In the application, firstly, broken mill equipment multi-source knowledge and operation and maintenance data are acquired, knowledge extraction, sequence reconstruction and unified vectorization processing are performed, a mixed vector with semantic alignment is formed, differences of heterogeneous data are eliminated, and standardized input is provided for a model;then, a neural engine is constructed based on a Transformer, a symbolic reasoning engine is constructed in combination with an expert rule base and forward reasoning, an interactive checkable double-engine collaborative reasoning architecture is formed, data generalization and mechanism constraint are considered;next, real-time operation and maintenance data and query input architecture are constructed, a result is generated by the neural engine, and the symbolic engine is checked, iteratively modified, and a reliable reasoning sample is obtained;finally, a health management instruction set is constructed, a low-rank adaptive fine-tuning model is adopted, RAG retrieval and thinking chain prompting are fused, and finally integrated and highly reliable broken mill equipment health management industry model construction is completed.

The application relates to the field of model construction, in particular to a construction method of an ultra-short-term wind farm power online prediction model, which comprises the following steps: performing unit-level data acquisition based on a wind farm data platform, performing data preprocessing, and obtaining a quality-optimized data sequence; performing short-sequence data correction on the quality-optimized data sequence to obtain a continuous wind farm data sequence; performing data breakpoint elimination on the continuous wind farm data sequence to obtain a second training data sample; performing tensorization processing on the second training data sample to construct a unified space-time feature tensor; performing data training and space-time modeling based on the unified space-time feature tensor to output a wind power prediction model; performing error optimization constraint and model performance authentication on the wind power prediction model; and when the performance authentication passes, the model construction is completed. The application realizes the prediction accuracy and stability of the ultra-short-term wind farm online power prediction.

The present disclosure provides a model construction method and an aging performance prediction method, an electronic device and a storage medium, and relates to the field of biomass composite material performance prediction. The model construction method comprises: obtaining multi-scale aging characteristic data samples under a target service climate condition, including environmental meteorological data samples, and micro-chemical characteristic data samples and macro-mechanical performance data samples of biomass composite materials under the action of the environmental meteorological data samples; constructing a micro-macro correlation model based on the micro-chemical characteristic data samples and the macro-mechanical performance data samples; screening key micro-feature samples from the micro-chemical characteristic data samples based on an explainability analysis method; for each key micro-feature sample, a time series prediction model is constructed respectively with the environmental meteorological data samples as exogenous variables; and cascading the time series prediction model corresponding to each key micro-feature sample and the micro-macro correlation model to obtain an aging performance prediction model of the biomass composite material.