76results about How to "Improve modeling efficiency" patented technology

The embodiment of the invention provides a modeling method and device for an airborne system function architecture model, computer equipment and a medium, and relates to the technical field of aviation architecture design, the method comprises the following steps: inputting a design requirement document into an airborne large language model, and in the large language model, under the structural constraint of an airborne system knowledge graph, establishing a design requirement document; the method comprises the following steps: extracting a logical relationship between entities required by airborne system modeling from a design demand document, screening a corresponding airborne meta-model according to the extracted logical relationship between the entities, and constructing a demand graph; constructing and outputting a text model of a concept scene, a text model of a task scene, a text model of a system function, a text model of a logic architecture and a text model of a physical architecture of the airborne system based on the demand graph; and inputting each text model into a system modeling language modeling terminal for visualization to obtain a multi-level functional architecture model of the airborne system. According to the scheme, the modeling efficiency, consistency and normalization are improved.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention relates to a method for calculating equivalent parameters of a data-driven permanent magnet direct drive wind power plant, which belongs to the technical field of analog computation of a power system and comprises the following steps of: identifying a wind turbine generator group with similar dynamic behaviors by carrying out clustering analysis on a transient current time sequence of an outlet of a wind turbine generator; a neural network model is constructed, key electrical parameters such as operation condition parameters and voltage drop serve as input, a clustering result serves as output, and automatic classification and dynamic response feature extraction of the wind turbine generator are achieved by training a neural network; calculating unit equivalent parameters and current collection line parameters to obtain an equivalent model; and multi-operation scene simulation verification of the large-scale permanent magnet direct-driven wind power plant is passed. According to the method, equivalent modeling of the large-scale permanent-magnet direct-drive wind power plant is realized, and the modeling efficiency and the adaptability to complex operation scenes are remarkably improved.

The invention provides an OPC UA information model automatic construction method based on a knowledge graph and a large language model. The method comprises the steps of obtaining an unstructured industrial text, performing semantic analysis on the unstructured industrial text, and extracting a main body entity, an object entity, a method entity and attribute information thereof according to a predefined hierarchical tuple structure; calling an industrial field knowledge graph in real time to perform semantic enhancement on an extraction result; performing similarity calculation and evaluation on an enhanced result, and performing information fusion and duplicate removal based on a similarity evaluation result to obtain an entity-attribute set; generating a tree-shaped hierarchical structure framework by using a large language model, and calling a knowledge graph to perform structural integrity verification; semantic association between the entity-attribute set and the tree structure framework is analyzed, attributes and methods are mapped to corresponding structure nodes, and a mapping reasoning process is output in a natural language form; converting the mapped model structure into a format of XML (Extensive Makeup Language) grammar, and executing automatic compliance check and repair; by designing a progressive task chain of information extraction, semantic fusion, model construction, interpretable mapping and standardized output, a set of complete understanding-enhancement-construction-verification automation system is constructed.

The application discloses a hub node modeling and analysis data generation method and equipment of a single-layer net shell structure, and relates to the technical field of computer-aided design and finite element analysis of building structures. The method comprises the following steps: reading and initializing engineering parameters of multi-source pre-processing data; standardizing and cleaning section data in the multi-source pre-processing data and constructing a local reference system in a three-dimensional space; generating an initial three-dimensional envelope entity of a hub body and an intersecting branch pipe; performing a cascaded intelligent Boolean topology clipping on the initial three-dimensional envelope entity of the intersecting branch pipe based on a perimeter descending order sorting; automatically detecting interference in the three-dimensional space and adaptively performing secondary clipping on a plate; generating a hub inner and branch pipe stiffening plate based on engineering parameters; binding and integrating the above geometric models and non-geometric attribute data; and bidirectionally outputting the geometric models and analysis data and docking downstream. The application can automatically generate a high-precision node entity model and finite element analysis data from pre-processing data, and simultaneously improve generation efficiency and precision.

The embodiment of the application provides a network quality evaluation method, device and equipment and computer storage medium, and relates to the technical field of low-altitude communication. The method comprises the following steps: obtaining static evaluation indexes and dynamic evaluation indexes of a space to be evaluated stored in different storage spaces. Based on the static evaluation indexes and the dynamic evaluation indexes, a static model and a dynamic model are constructed, and then a fusion model is obtained. Based on the fusion model and a preset evaluation method, the network quality evaluation result of the space to be evaluated is determined. The static evaluation indexes and the dynamic evaluation indexes stored in different storage spaces are obtained, and the static and dynamic indexes are differentiated, processed and stored, so as to adapt to the stability of the static evaluation indexes and the periodic change law of the dynamic evaluation indexes, improve the modeling efficiency and the prediction accuracy, and then improve the evaluation accuracy. Through fusing indexes of multiple dimensions, a multidimensional model is obtained, and multiple dimensions are comprehensively covered, so that the evaluation is close to the actual business, and the evaluation accuracy is improved.

The invention provides a parameterization calculation method for a top cover modal and a pitch diameter of a water turbine, and belongs to the technical field of hydraulic mechanical structure dynamics and simulation automation. The method comprises the steps of parameter input, parametric modeling, automatic simulation control, pitch diameter identification and analysis and result output: after a parameter set is input, constructing a parametric modeling module based on Python and ANSYS Spaceclaim, and automatically generating a three-dimensional geometric model containing a basic panel, a rib plate and a connecting hole; an automatic simulation control module is constructed based on Python and ANSYS Workbench, and full-process automatic simulation is completed to obtain a modal analysis result; pitch diameter characteristic parameters are automatically recognized through a least square discrete Fourier transform algorithm of non-equiangular sampling and confidence degree judgment; and outputting results to form a standardized database. According to the method, automation and quantification of the whole process are achieved, modeling and simulation efficiency is improved, manual misjudgment is avoided, the method is suitable for different types of water turbine top covers, and reliable data support is provided for structural optimization and resonance risk assessment.

The present disclosure provides an electromagnetic modeling method of a quantum chip packaging substrate, which comprises the following steps: S1, obtaining a plurality of influencing factors affecting signal transmission in the quantum chip packaging substrate; S2, determining a parameter range of the plurality of influencing factors at a specific working temperature of the quantum chip; S3, performing sensitivity analysis of the influencing factors on the quantum chip packaging substrate within the parameter range of the influencing factors to determine a proportion factor of the influence of each influencing factor on signal transmission; and S4, performing electromagnetic modeling based on the proportion factor of the influencing factors and an adjustment rule. The present disclosure considers the influence of a plurality of influencing factors on signal transmission of the quantum chip packaging substrate in an extremely low temperature environment, so that the modeling method is more accurate, the complexity of the optimized model is reduced, the modeling efficiency is improved, and a reasonable design threshold is provided, thereby saving material and design costs.

The invention discloses a variable topology system dynamics recursion grouping method based on graph theory description, and belongs to the field of spacecraft dynamics. The implementation method comprises the following steps: automatically calculating a cyclic dimension based on a graph theory algorithm, matching non-tree edges, generating pseudo nodes, and converting a ring-containing structure into a connected ring-free tree structure; multi-module topology automatic continuation: through a node number continuation method, automatically identifying repeated nodes, deleting redundant description and splicing new module topology, and realizing topology unified description of multi-module assembly; according to the method, a dynamic model is automatically grouped, manual derivation is not needed, a constraint equation is described by a body-pseudo body recursive relationship, a unified recursive relationship of rigid body-rigid body, flexible body-flexible body and rigid body-flexible body connection is combined, modal parameters are automatically called, a grouping matrix is generated, and the dynamic equation is output, so that automatic modeling updating can be realized. According to the method, automatic dynamic grouping of the variable topology system can be realized, dynamic reconstruction is avoided, and characteristic analysis and attitude control of an on-orbit assembly system are facilitated.

The invention discloses a Modelica model automatic generation Agent development method based on SysPhS, and belongs to the field of system engineering and AI based on models. Aiming at each core component in the electric vehicle system, extracting each component, a corresponding port and a connection relation through an analysis interface by utilizing a SysMLv2 system model; secondly, aiming at the analyzed component S, retrieving each component model matched with the information of the component S in a Modelica model library through an RAG algorithm; according to the RAG semantic similarity threshold value, sorting is carried out from high to low; selecting the component model with the highest sequence, mapping the component model with the highest sequence to a port consistent with the SysMLv2 system model in connection relation through a SysPhS standard specification, and forming a Modelica system model by using all the component models with the highest sequence and the mapped connection ports; finally, inputting the Modelica system model into a model checking Agent for lexical, syntactic and semantic checking, and if an error prompt exists in a checking result, automatically correcting the Modelica system model and re-checking the Modelica system model; and otherwise, outputting the final Modelica system model. According to the invention, the accuracy, consistency and efficiency of model generation are improved.

The application discloses a LIBS element quantitative analysis method based on double-branch feature fusion and electronic equipment, the method comprises the following steps: preprocessing the collected LIBS spectrum signal, including baseline correction and spectrum resampling; inputting the spectrum signal data after preprocessing into a double-branch feature extraction network, extracting local features through a CNN branch, and extracting global features through an MLP branch in parallel; performing adaptive weighted fusion on the local features and the global features through a gating fusion module to obtain fusion features; inputting the fusion features into a WMA-MLP model, the WMA-MLP model is based on MLP, integrates a multi-head self-attention mechanism and a residual module, is used for modeling the global dependency relationship between features, and outputs a final element quantitative analysis result. Through the parallelly arranged CNN and MLP branch feature extraction structures, more comprehensive spectrum feature representation can be obtained, and the accuracy and robustness of the spectrum analysis model are improved.

This application discloses a method, apparatus, equipment, and medium for flutter analysis of UAV wings during the design phase, relating to the field of aircraft design. The method includes: generating a first component by calling a corresponding structural component simulation model from a finite element structure module library based on the shape parameters of the wing to be analyzed; constructing a skin finite element mesh model based on the first component; determining the installation position of a second component at the node positions of the skin finite element mesh model; generating the second component by calling a corresponding structural component simulation model from the finite element structure module library; installing the second component at the corresponding node positions of the skin finite element mesh model based on the installation position to obtain a target wing model; performing aerodynamic modeling using the skin finite element mesh model to obtain a flutter calculation file; performing flutter analysis based on the flutter calculation file and the target wing model; and optimizing the wing to be analyzed that has a flutter risk based on the flutter analysis results. This application shortens the UAV design cycle.

The application is suitable for the technical field of distributed power supply, and provides a district and county-oriented distributed power supply carrying capacity rapid evaluation model construction method and device, which comprises the following steps: constructing a model initial structure comprising a target area substructure and an equivalent area substructure, obtaining multi-source data of the target area substructure and the equivalent area substructure according to the definition result of the model initial structure, loading the multi-source data into the model initial structure, and generating a full-voltage level evaluation intermediate model comprising a main network model and a distribution network model; performing time sequence power flow calculation verification on the full-voltage level evaluation intermediate model, judging whether the power flow section of the full-voltage level evaluation intermediate model converges in all time periods, and determining the full-voltage level evaluation intermediate model after correction and full-time period convergence as a full-voltage level evaluation target structure as a district and county-oriented distributed power supply carrying capacity rapid evaluation model. The application effectively improves the modeling efficiency and the model reliability.

The invention relates to the technical field of coal mine tunnel reconstruction, in particular to a tunneling tunnel digital modeling method based on a millimeter wave radar array. The millimeter wave radar array technology is adopted, high-precision point cloud data can be obtained through roadway data collection, data preprocessing, point cloud data processing and point cloud three-dimensional reconstruction, and accurate digital modeling and measurement of a tunneling roadway can be achieved; point cloud data of multiple angles can be acquired at the same time, the data acquisition and processing speed is accelerated, and the modeling efficiency is improved. The device can penetrate through obstacles such as dust for measurement, is not influenced by factors such as illumination, and improves the reliability and stability of data acquisition.

The application discloses a kind of banking data processing method, system, terminal equipment and readable storage medium, it is related to banking data processing field, the banking data processing method of the present application includes: by uniform syntax specification, generate the database modeling requirement information of multiple different databases;According to the database modeling requirement information of each database, and in combination with uniform logic modeling structure, establish banking professional knowledge base;Acquire the banking data to be processed, by banking professional knowledge base, the data logic model corresponding to banking data is translated into the data physical model corresponding to banking data;According to data physical model, generate the modeling statement adapted to each database;Modeling statement is input to the database environment corresponding to each database and is executed, obtains the result of analyzing and processing banking data.Processing analysis efficiency of banking data can be improved and cost is reduced by using the present application.

The application discloses a continuous flow reaction process rapid quantitative analysis system and method. The application combines standard substance mixing design, FTIR spectrum real-time acquisition and deep convolution modeling, realizes accurate prediction of the concentrations of reactants and products in continuous flow, and has good promotion adaptability and industrial application prospect. The application forms a dynamic concentration gradient by adjusting the flow rate ratio of a multi-component solution, generates multi-concentration samples in the whole process under continuous flow state, and significantly improves the modeling efficiency and real-time monitoring capability. In addition, the application uses a self-designed detection structure to optimize the spectrum acquisition module, combines constant temperature control and degassing treatment, effectively reduces the influence of flow fluctuation, temperature interference and bubbles on the signal, enhances the system stability and integration, realizes efficient in-situ spectrum detection, can be expanded to various continuous reaction monitoring scenes, and has actual deployment and industrial promotion value.

This invention belongs to the field of cross-industry information model construction technology, and relates to a method and system for semantic construction and automatic generation of multi-industry information models. The method includes: generating standard format requirement parameters for cross-industry models based on cross-industry model construction requests; decomposing the standard format requirement parameters into industry requirement parameter sets; generating customized meta-models by combining the industry requirement parameter sets, a meta-model base library, and a cross-industry feature library; semantically aligning the customized meta-models based on an industry terminology list and a cross-industry semantic rule library to obtain a cross-industry model element semantic alignment table; constructing lightweight model structures based on the cross-industry model element semantic alignment table; performing syntax validation on each lightweight model structure; and generating standardized model files corresponding to each target industry. This invention achieves automated construction of cross-industry models from scratch, improves model adaptation success rate and semantic accuracy, and avoids the problem of cross-industry models being unable to communicate with each other.

This invention proposes a secondary channel modeling method for a vibration active control system based on a top-mounted rotor hub. The method includes: activating the actuator of the vibration active control system when the rotor hub is not rotating, outputting actuating forces in both directions. These forces serve as load inputs to the test bench. The actuating forces are transmitted via a path that sequentially passes through the rotor hub actuator mounting location, the collector ring, the shaft, the spring, and the base, ultimately reaching the vibration sensor mounting location. The vibration sensor collects the vibration response at this location, identifying the secondary channel transfer function matrix in the fixed state. The secondary channel transfer function matrix is ​​then substituted into the output force transmission relationship formulas for the fixed and rotating states to obtain the initial value of the secondary channel model in the rotating state. Using this initial value, an adaptive algorithm is used as the physical model for online identification of the secondary channel, enabling online modeling of the system's secondary channels under rotor hub rotation conditions. This method allows for online iterative construction of time-varying secondary channel models, resulting in high modeling efficiency.

The invention relates to the field of air conditioner design, and provides a room wind speed field simulation method for an air conditioner indoor unit in order to realize simple and efficient simulation and ensure the accuracy, and the method comprises the following steps: obtaining an accurate wind speed vector of an air outlet by two-dimensionally refining an internal structure; the three-dimensional simulation model neglects structures such as an evaporator and an electric heater in an air duct, and only an air outlet molded line and a swing blade structure are reserved, so that the modeling efficiency is improved; the wind speed vector is given to an air inlet of the three-dimensional air duct model, other corresponding boundary conditions and algorithms are given, and three-dimensional numerical calculation is carried out; through two-dimensional and three-dimensional combined simulation, the room wind speed field simulation method which is simple and efficient and can ensure the simulation accuracy is realized.

The application discloses an IGBT residual life prediction method based on multi-feature fusion and KPCA optimization. The method first collects IGBT collector current and voltage and other multi-source signals, extracts time domain, frequency domain, time-frequency domain and derived statistical domain features after pretreatment; then adopts a two-stage strategy of comprehensive evaluation index preliminary screening and mutual information regression fine screening to eliminate redundant features and retain high correlation features. On this basis, nonlinear dimension reduction is carried out by using kernel principal component analysis to construct a high-robustness health index, and the exponential weighted moving average and adaptive gradient detection are combined to accurately divide the degradation into three stages. Finally, the CNN-BiLSTM model is used to deeply mine the time sequence degradation features, and the MC Dropout algorithm is introduced to realize the accurate prediction and uncertainty quantification of the residual life. The application effectively solves the one-sidedness of single-source signal representation and the feature redundancy interference problem, and significantly improves the prediction accuracy and reliability.

This invention discloses a method and system for modeling vehicle door structures, relating to the field of automotive design technology. The method includes: determining the large glass surface based on the glass boundary of the door window frame; calling a library of standard door cross-sections with predefined standard cross-sectional references based on the door structure; determining design cross-sectional references for each standard cross-section based on the door structure design parameters; adjusting the standard cross-sectional references to the design cross-sectional references to determine the design position of each cross-section; and generating a door structure model based on all determined cross-sectional positions. This invention achieves rapid and standardized modeling of vehicle door structures through parametric and automated cross-sectional reference alignment and positioning, reducing repetitive manual work, improving design efficiency and consistency, and enabling early verification and optimization of key designs such as hinge arrangement. The system includes corresponding functional modules to implement the method.

This invention discloses an automatic finite element modeling method and system based on construction drawings. The method includes: automatically outputting unannotated construction drawings as annotated ones using a computer vision-based construction drawing recognition module; assigning horizontal and vertical axis numbers and 3D coordinate information to each building component on the annotated construction drawings using a grid dimension recognition module; automatically recognizing and fusing the dimensional information of each building component on the annotated construction drawings with their assigned horizontal and vertical axis numbers and 3D coordinate information using a large language model as an automatic modeling module; naming and assigning material properties to each building component in the 3D geometric model; and automatically creating a visualized finite element model based on the construction drawings by assigning support constraint information and load information to the 3D geometric model. This invention has the advantages of high modeling efficiency, high quality, and replacement of manual labor.

The invention discloses a clone selection-based mourhua surface modeling method and related equipment, and the method constructs an'antibody-parameter 'iterative optimization framework by simulating the clone selection principle of an immune system, achieves the automatic and intelligent global search of key parameters, and improves the modeling efficiency. The defect that parameters are set by depending on artificial experience in a traditional method can be effectively overcome, and then the objectivity and repeatability of the model are ensured; through loop iteration of a series of collaborative operations such as initialization, evaluation, cloning, variation, supplementation and reselection, convergence from a preset parameter space to an optimal solution can be efficiently achieved, when a precision threshold value is met, stopping and outputting an optimal antibody are achieved, and it can be ensured that a high-precision mourhua face model is obtained in limited computing resources. According to the method, full-process automation from parameter initialization to optimal model output is realized, manual participation can be remarkably reduced, the modeling efficiency is improved, the calculation process is more stable and robust, and the method can be widely applied to the technical field of data processing.

The invention discloses a Grasshopper-based parameterized modeling method for a polygon projection space distorted surface reticulated shell, which comprises the following steps of: S1, setting geometric parameters of a reticulated shell model in a Grasshopper platform through an input plug-in, and obtaining a corresponding projection surface of a space curved surface reticulated shell surface; s2, connecting vertexes of the projection surface to generate ridge lines forming a reticulated shell curved surface, and constructing a polygonal projection space distorted surface through curved surface generation operation based on the ridge lines; s3, performing grid division on the spatial warping surface by using an isoparametric method or a mapping method to generate grid points; and S4, based on the grid points, generating a single-layer polygonal projection space distorted surface latticed shell structure through a polyline connection plug-in. The method is used for solving the technical problems that an existing spatial reticulated shell structure with a complex shape is difficult to model, the modeling efficiency is low, and the repeated utilization rate is low.

The invention discloses an intelligent building structure construction method based on point cloud data and digital twinning, and the method comprises the steps: obtaining a building point cloud through three-dimensional laser scanning, carrying out the high-precision registration and preprocessing, carrying out the component feature extraction and parameterized reverse modeling through an RANSAC algorithm, and generating a high-fidelity BIM model; then, the model is deeply fused with GIS, finite element and Internet of Things data under a unified space-time reference, and a dynamically updated digital twinning scene is constructed. Finally, a virtual simulation platform is developed based on the scene, and real-time simulation, visualization and intelligent decision support of the construction progress and mechanical response are achieved. According to the method, the problems of insufficient precision and difficulty in multi-source data fusion of a traditional modeling method are solved, accurate mapping and interaction of a building structure from a physical entity to a digital virtual body are realized, and the intelligent level and decision scientificity of a construction process are remarkably improved.

The invention relates to a crop phenotype information generation system for unmanned aerial vehicle multispectral remote sensing images. The method comprises the following steps: acquiring remote sensing images by using an unmanned aerial vehicle with a multispectral sensor and a visible light sensor; constructing and using a user-defined vegetation index calculation module, and extracting characteristic variables with biological significance from the data; performing preliminary screening on all vegetation indexes; carrying out model training, and carrying out system evaluation on a training result by adopting multiple performance evaluation indexes; loading the trained model to carry out field prediction; predicting and generating a two-dimensional prediction image consistent with the original image in size; the topdressing amount is calculated, and a fertilization prescription map suitable for guiding agricultural machinery operation is generated in combination with the unmanned aerial vehicle remote sensing image and the grid width input by the user. According to the method, full-chain visualization from remote sensing data preprocessing to prediction result output, crop parameter inversion estimation and farmland management prescription map automatic generation functions are realized, and an intuitive decision support tool is provided for precision agricultural practice.

The application discloses a method for intelligently modeling rock mechanics properties of a tight sandstone reservoir, comprising the following steps: obtaining three-dimensional seismic interpretation data, well logging data and rock mechanics experiment data; establishing a three-dimensional geological structure model of the tight sandstone reservoir which fuses a fault structure; calculating a quantitative fitting relationship between static rock mechanics parameters and a sand ratio; extracting a lithology factor data body from the three-dimensional seismic interpretation data, discriminating the lithology factor data body, generating a three-dimensional spatial distribution of sandstone and mudstone, and thus obtaining a three-dimensional sand ratio distribution model; and based on the three-dimensional sand ratio distribution model and the quantitative fitting relationship, calculating initial values of rock mechanics parameters of each grid unit, and assigning values to the three-dimensional geological structure model and correcting the fault, so as to obtain a final three-dimensional rock mechanics property model. The application realizes multi-source data fusion and quantitative modeling of fault disturbance, and solves the problems of insufficient description of reservoir heterogeneity, lack of physical basis of the model and low reliability of modeling of a fracture zone in the prior art.

The application discloses a kind of plaque three-dimensional parameter atlas reconstruction evaluation methods based on coronary center line constraint, comprising: obtaining and preprocessing coronary CCTA volume data;Using the continuous action deep reinforcement learning model based on Actor-Critic architecture to extract center line and radius value, generate tubular region of interest;Orthogonal section resampling is carried out, section sequence is constructed, and double-coordinate joint representation is formed;Input VM-UNet segmentation network, obtain the segmentation result of lumen, vessel wall and plaque component;Introduce pixel-level contrast learning strategy in the training of segmentation network;From the segmentation result, plaque three-dimensional parameter atlas is reconstructed by back projection;Construct plaque component relationship graph;Input uncertainty perception multimodal fusion network, obtain risk result and corresponding confidence;When confidence result exceeds preset threshold, resample and reconstruct plaque component relationship graph, obtain corrected result.

This invention provides a complex multi-condition alcohol distillation soft measurement method and system based on neural networks, belonging to the field of industrial process soft measurement technology. The method includes the following steps: acquiring time-series variable data of the multi-condition alcohol distillation process and preprocessing it; dividing the preprocessed time-series data into source condition datasets and target condition datasets according to the operating conditions; inputting the source condition dataset into a preset time-series structured echo network for pre-training to obtain a source condition pre-trained model; inputting the target condition dataset into the source condition pre-trained model and obtaining a target condition soft measurement model through transfer learning; acquiring real-time variable data under the operating condition to be predicted and inputting the real-time variable data into the target condition soft measurement model to obtain the target measurement variable prediction result; outputting and storing the target measurement variable prediction result to complete the alcohol distillation soft measurement. This invention effectively improves the adaptability and prediction accuracy of alcohol distillation soft measurement under complex operating conditions.

According to the three-dimensional modeling method and system based on the oblique photography technology, by means of a three-dimensional modeling scheme based on the oblique photography technology, an oblique image, POS and ground image control data of a specified measurement area are collected firstly, and then a real scene three-dimensional model is generated according to the process operation of aerial triangulation measurement, multi-view image dense matching, TIN structure construction and texture combined generation. Manual intervention is reduced, the modeling period is shortened, and scenes with high timeliness requirements are met; accurate image exterior orientation elements and ground point coordinates provided by aerial triangulation provide geometric constraints for multi-view image matching, so that the matching is efficient and accurate, a large amount of accurate three-dimensional point cloud data is quickly generated, and the overall efficiency is improved; the automatic process reduces the dependence on professionals and saves the labor cost; the TIN structure can more accurately describe the surface shape of the ground feature, and after the texture is combined, the generated real scene three-dimensional model can better present the geometric features and appearance details of a real scene, and the reality sense and the detail effect are better.