1082 results about "Physical property" patented technology

The invention provides a tunnel disaster source multi-attribute joint inversion method and system based on physical property constraint, and relates to the technical field of tunnel engineering and joint inversion, and the method comprises the steps: constructing a tunnel face three-dimensional geologic model, applying controlled disturbance to surrounding rock physical property parameters, analyzing the change characteristics of earthquake and electromagnetic observation response, and obtaining a tunnel face three-dimensional geologic model; establishing a quantitative mapping relation between the surrounding rock physical property parameter disturbance quantity and the multi-field response signal variable quantity; constructing a multi-dimensional sensitivity evaluation index, and generating a joint sensitivity result; the surrounding rock units are identified and divided, differentiated partition rock physical relationship constraints are constructed, and a physical multi-attribute inversion target of joint inversion is determined based on the partition rock physical relationship constraints; and on the basis of a physical multi-attribute inversion target, a multi-source joint inversion target function is constructed in combination with the joint sensitivity result, joint solving and iterative updating are carried out, and a tunnel face advanced inversion result is output. According to the invention, calamity source fine identification and risk quantitative evaluation in front of the tunnel face during the tunnel construction period are realized.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention discloses a deep resource fluidized mining disturbance effect evaluation model under a multi-field coupling effect, and the method specifically comprises the following steps: constructing a multi-field coupling parameter system: collecting geologic body basic parameters, covering rock mass mechanical parameters, thermal physical parameters, fluid parameters and chemical parameters in deep three-high, high ground stress, high ground temperature and high karst water pressure environments, and establishing a multi-field coupling parameter system; the method comprises the following steps: establishing a dynamic parameter database, realizing real-time updating of parameters along with a mining process, establishing a heat-flow-force-chemical full-coupling mathematical model, introducing the influence of a rock plastic damage correction term quantification temperature on heat conduction efficiency, associating a dynamic mapping relation between mineral reaction and rock physical property parameters, and establishing a dynamic parameter database. Simplification errors of a traditional model on multi-field effects are thoroughly eliminated, chained interaction of temperature, fluid, stress and chemical processes in a deep three-high environment is accurately described, a model prediction result better fits an actual mining working condition, and prediction deviation is greatly reduced.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention belongs to the technical field of geometric pretreatment in computer aided engineering (CAE), and particularly relates to a dirty geometry restoration method for a complex three-dimensional geometric model, which comprises a dirty geometry detection and restoration module based on heuristic rules and geometric topology analysis. By means of a multi-model collaborative alignment and bonding mechanism, high-precision assembly between parts is achieved through geometric feature matching and adjacent domain self-adaptive adsorption; acquiring material attributes provided by the simulation platform, and performing accurate cutting and boundary reconstruction on an intersection domain based on an intersection domain intelligent segmentation algorithm of the material attributes to ensure correct mapping of boundary conditions of a physical field; a geometric Boolean processing engine iterative learning mechanism of a general merging algorithm is integrated, and a high-quality model for simulating a grid is generated through multi-round iterative optimization. According to the method, double challenges of internal dirty geometry of a single model and derivative dirty geometry of intersection / adjacent areas among multiple bodies are solved, and key physical attributes and topological continuity of electromagnetic signal links are ensured and are completely reserved during repair.

The invention belongs to the technical field of tunnel engineering, and provides a multi-physics field real-time assimilation simulation and regulation method and system in a tunnel grouting process in order to solve the problem that real-time dynamic simulation and automatic regulation are lacked in existing tunnel construction, and the real-time assimilation simulation and regulation method and system in the tunnel grouting process are provided by utilizing ensemble Kalman filtering and combining real-time monitoring data in the tunnel grouting process. Dynamically correcting parameters of the multi-physical model; time correlation in the slurry condensation process is considered, a time-varying condensation model depicting physical property changes of slurry evolving along with time is integrated, the time-varying condensation model serves as an external function in the time step length to be embedded into the multi-physical field model in correction, and the slurry flowing state is adjusted in a self-adaptive mode through numerical simulation; and generating control parameters of tunnel grouting according to a dynamic simulation result, and realizing closed-loop regulation and control of tunnel grouting. Synchronous linkage of numerical simulation and on-site working conditions is realized.

The invention relates to the field of digital core modeling, in particular to a stomatal basalt three-dimensional digital core reconstruction method, which comprises the following steps: extracting target characteristic parameters from CT (Computed Tomography) scanning gray volume data of a stomatal basalt sample, the target characteristic parameters comprising porosity, cluster number, cluster size statistics, spatial uniformity index and simplified compactness; generating an initial three-dimensional digital core model according to the target characteristic parameters; carrying out iterative optimization on the initial three-dimensional digital core model by utilizing a self-adaptive Markov chain-Monte Carlo algorithm so as to enable cluster features of the optimized three-dimensional digital core model to approach target feature parameters; and performing curvature smoothing post-processing on the optimized three-dimensional digital core model to obtain the pore basalt three-dimensional digital core model. The model not only is highly matched with real pore basalt in the aspect of macroscopic statistical characteristics, but also shows natural and smooth curved surface characteristics in the aspect of microscopic pore boundary morphology, and provides a reliable digital basis for subsequent rock physical property analysis.

The invention provides a deep energy reservoir multi-scale heat-fluid-solid coupling simulation method and system, and belongs to the field of deep energy reservoir numerical simulation. Acquiring spatial distribution characteristics and physical property parameters of different lithologic reservoir media under a micro-scale, and constructing a digital structure model of the reservoir media; a micro-observation action mechanism and a flow mechanism are embedded in the model to obtain a micro-observation numerical model; establishing a mapping relation by solving model structure parameters and physical property parameters of the microscopic numerical model under different thermal-fluid-solid coupling conditions; and on the basis of the mapping relationship, in combination with the macrogeological and engineering data of the target reservoir, establishing a macroscale heat-fluid-solid multi-field coupling control equation and solving, and outputting the multi-physics field response of the target reservoir under the given working condition. According to the method, fine simulation of physical property parameter dynamic transmission and a multi-field coupling evolution process from a micro view to a macro view is realized, and the accuracy and reliability of deep complex reservoir engineering behavior prediction are remarkably improved.

The invention discloses a tunnel dew formation and icing rapid monitoring method and system based on a space-time phase change cooperation mechanism, and relates to the technical field of tunnel engineering safety monitoring and freeze injury prevention and control. Establishing a tunnel geometric model and a heat-humidity-turbulence multi-field coupling physical model; synchronously acquiring heat and humidity physical property parameters and outdoor hourly meteorological parameters; taking the outdoor hourly meteorological parameters as Dirichlet boundary conditions and initial conditions of a heat-humidity-turbulence multi-field coupling physical model; determining a heat and humidity exchange coupling process of the tunnel wall surface and the internal environment, and defining heat and humidity transfer parameters under an unsteady state boundary; performing discretization processing on the tunnel geometric model by adopting a grid division strategy; by setting a dew point temperature threshold value and an icing temperature threshold value, a condensation risk area is judged, dynamic changes of the thickness of an ice layer are quantified, and monitoring of tunnel condensation and icing is achieved. The method can effectively reflect the distribution rule of multiple physical fields on the surface and in the tunnel in any time, accurately judge the risk area and quantify the thickness of the ice layer.

The invention relates to the technical field of electronic component testing for investigating or analyzing internal microstructures and physical properties of materials by utilizing an electrical means, and discloses an online testing and analyzing method for physical properties of electronic component materials, which comprises the following steps of: constructing a composite excitation signal formed by linearly superposing a main carrier voltage and a perturbation wave voltage; applying the signal to a to-be-tested electronic component and acquiring response current; and extracting an intermodulation current amplitude at an intermodulation frequency point in the frequency spectrum of which the linear response interval is excluded, calculating a nonlinear response coefficient of the material in combination with the excitation amplitude, and comparing the nonlinear response coefficient with a preset degradation threshold so as to judge whether the nonlinear response coefficient is qualified or not. By extracting intermodulation components which cannot be generated by linear contact resistance, physical isolation of external contact noise in a frequency domain is realized, and the problem that contact resistance fluctuation and material intrinsic aging cannot be distinguished in a traditional linear impedance test is solved.

The invention provides a power equipment three-dimensional dynamic updating method and system for virtual reality training, and the method comprises the steps: collecting the working state data of power equipment in real time through a sensor; processing the working state data based on a preset function model, and calculating the physical state change of the power equipment; according to the physical state change, adjusting physical attributes of the three-dimensional model of the power equipment in real time so as to simulate physical effects of thermal expansion, stress concentration or structural deformation; loading the adjusted three-dimensional model into a virtual reality environment, and performing real-time display through a virtual reality rendering platform; and after the user executes the interactive operation through the virtual reality equipment, the working state of the power equipment is updated, and the influence of the operation on the three-dimensional model of the equipment is dynamically fed back, so that a virtual-real linkage closed-loop training process is formed. The virtual model can change in real time along with the collected data, and the technical verisimilitude and operability of virtual training are remarkably improved.

The invention discloses a physical attribute inversion and three-dimensional reconstruction method based on Gaussian splashing and micro-rendering, and belongs to the technical field of computer vision, and the method comprises the steps: generating a sparse point cloud based on a multi-view image to initialize a Gaussian ball; determining a Gaussian pivot point based on a Gaussian ball, constructing a self-adaptive tetrahedral mesh in combination with gradient optimization, extracting an explicit surface from the self-adaptive tetrahedral mesh, and sampling to obtain geometric information; predicting the physical attribute of each sampling point through an implicit texture network, and providing ambient light through an HDR ambient light map; geometric information, physical attributes and ambient light are used as input, a differentiable PBR renderer is adopted to generate a physical rendering image, multi-term loss function reverse optimization is constructed, and collaborative optimization and inversion of three-dimensional geometry and surface physical attributes are realized. According to the method, the problems of texture dislocation, thin-wall structure disappearance and high rendering calculation cost under dynamic topology are effectively solved, and a digital model with fine geometry and a realistic material can be efficiently recovered from a multi-view image.

The invention discloses an underground physical characteristic prediction method and system combining seismic waves and drilling data, and relates to the technical field of geophysical exploration, and the method comprises the steps: collecting original seismic records, carrying out the preprocessing, obtaining a three-dimensional seismic data body, carrying out the complex channel analysis of the three-dimensional seismic data body, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes, and carrying out the calculation of the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes. Fault features are identified through a coherent body algorithm, an earthquake feature matrix is obtained, depth alignment and correction are carried out on density logging, interval transit time and neutron porosity curves of drill holes, and a standardized drill hole data table is generated; and based on the seismic feature matrix and the standardized drilling data table. According to the method, deep complementation of the earthquake macroscopic information and the borehole local truth value is realized through the geometric perception multi-modal graph neural network and the dynamic gating fusion mechanism, the problems of insufficient cross-modal feature fusion and misalignment of physical rules in the prior art are solved, and the prediction precision and reliability under complex geological conditions are remarkably improved.

The invention relates to the technical field of intelligent control, in particular to a breeding feed mixing control method and mixing equipment, and solves the technical problem of poor mixing quality caused by physical property difference of raw materials in the prior art. The method comprises the following steps: monitoring component composition and particle physical form of a mixed material in a mixing process and stirring parameters of mixing equipment; the mixing process comprises the following steps: stirring the dry materials in the mixing equipment, and adding liquid by spraying; the components are obtained through near infrared spectroscopic analysis; the physical form of the particulate matter is obtained through image analysis; analyzing the influence of the stirring parameters on the mixing quality based on the component composition, and determining a correction coefficient of the operation parameters of the mixing equipment in combination with the physical form of the particles; and regulating and controlling the current operation parameter of the mixing equipment according to the correction coefficient, so that the mixing equipment operates according to the regulated and controlled target operation parameter.

Provided are computer-implemented methods and quantum computing systems for preparing computational states representing quantum states of a physical system, including performing a computational evolution of the state and then determining physical properties of the system using the time-evolved computational state. Hamiltonian dynamics of observables are computed on a quantum computer to provide information about the physical system represented by the Hamiltonian. Low energy electronic structure states of a physical system are prepared using adiabatic evolution. The energy of an equilibrium quantum state of a physical system is determined using a time-evolution operator. The energy of an eigenstate of a physical system is indirectly determined by evaluating expectation values of a time-evolution operator averaged across multiple shots for randomly-generated quantum circuits. Example methods enable calculation of the energy of an evolved computational state with chemical accuracy, due to avoiding discretization errors, with a smaller circuit depth than known alternatives.

The invention relates to a method and system for determining the water content of a safe shutdown boundary of a submarine mixed transportation pipeline, and the method comprises the steps: carrying out a physical property parameter test, and taking the physical property parameter as a multiphase flow simulation input parameter; carrying out submarine pipeline shutdown simulation on the basis of the physical property parameter test data, and obtaining oil section temperatures and spatial distribution characteristics at different water depth positions in the pipeline; carrying out an oil-water layering characteristic experiment under a temperature control condition to obtain an oil-water emulsion layering characteristic; sampling oil sections with different layer heights in the oil-water layering characteristic experiment, and testing the water content and simulating the yield / slip characteristic at the oil section temperature based on the temperature distribution of the oil sections; according to the yield / slip stress of the oil sections with different layer heights, carrying out segmented calculation and weighted accumulation to obtain an overall starting stress; on the basis of the overall starting stress, the safe shutdown boundary moisture content is determined in combination with pressure boundary conditions. The method can effectively guide the safe transportation stopping and restarting of the submarine pipeline, optimizes the replacement operation after the transportation stopping of the submarine pipeline, and improves the operation safety and economy.

The invention discloses a formula optimization method and device of an insulating material, equipment and a storage medium, and relates to the technical field of chemical materials.The method comprises the steps that initial formula information corresponding to the current insulating material is obtained, and a physical constraint model is constructed according to the initial formula information; the physical constraint model is composed of a component compatibility constraint condition, a process feasibility constraint condition and a material performance threshold condition; generating an initial candidate formula according to a physical constraint model based on the initial formula information; performing iterative optimization on the initial candidate formula by adopting a gradient response optimization algorithm to obtain a current candidate formula; and performing physical property prediction on the current candidate formula, and determining a target optimization formula according to a physical property prediction result. The optimization process is based on a scientific model and algorithm, the limitation of traditional experience can be broken through, the relation between different performance indexes is balanced, the formula combination which may be ignored is explored, and a new possibility is provided for performance improvement of the insulating material.

The invention relates to the technical field of numerical control machine tool monitoring, and discloses a numerical control machine tool intelligent monitoring system based on digital twinning. A digital twin modeling module of the system obtains geometric parameters, physical attributes and historical operation data of a numerical control machine tool in real time, and constructs a dynamically updated virtual machine tool model; the working condition sensing module collects vibration, temperature and current data through a multi-source sensor, and generates a running state feature set through time sequence alignment processing. The exception response module is used for matching an exception mode library according to the feature set and outputting an exception type identifier and an influence range parameter; the decision optimization module generates an optimization instruction set containing maintenance priority ranking based on the parameters; and the strategy iteration module monitors an instruction execution result, calculates a deviation value between an actual effect and a virtual model prediction effect, and dynamically updates the abnormal mode library. According to the system, accurate sensing of the operation state of the numerical control machine tool, quick response to abnormity and dynamic optimization of a maintenance strategy are realized, and the intelligent level of monitoring is improved.

The invention discloses a supercritical carbon dioxide pipeline transportation simulation method and device. The method comprises the following steps: acquiring physical property data of the supercritical carbon dioxide pipeline; correcting the unit of the physical property data; obtaining a preset steady-state calculation formula; correcting a partial derivative formula in the preset steady-state calculation formula to obtain a corrected steady-state calculation formula; and inputting the physical property data after unit correction into the corrected steady-state calculation formula, and carrying out steady-state calculation so as to realize static simulation of supercritical carbon dioxide pipeline transportation. According to the scheme provided by the invention, by correcting the physical property unit in the steady-state calculation formula and correcting the partial derivative formula of the unit mass flow rate of the energy equation part in the steady-state calculation formula on the fixed distance, the correctness of steady-state data of supercritical carbon dioxide during pipeline transportation is ensured.

The invention provides a tap density prediction modeling method for a prebaked anode formula, which comprises the following steps: screening calcined coke into multiple particle sizes, measuring the tap density of each particle size, and obtaining physical property parameters; performing high-precision three-dimensional scanning on the representative particles, and constructing a digital model retaining geometric features such as edges and corners and textures; then, by adjusting the particle surface friction coefficient and the corner angle parameter, the deviation between the simulated porosity and an experimental value is within a preset range, and the feasibility of the model is verified; then, performing three-dimensional equal-proportion scaling on the reference fraction model, correcting physical parameters, and then, importing the reference fraction model into a discrete element system to simulate multi-fraction accumulation; and finally, based on the real-time porosity data, establishing a tap density prediction model of the irregular particle system by utilizing the negative correlation between the porosity and the tap density, and realizing the accurate prediction of the tap density under different proportions. The prediction accuracy of the tap density of the prebaked anode can be improved.

The invention discloses a three-dimensional packaging modeling method based on NeRF and a diffusion model, and the method comprises the steps: firstly receiving demand description information, fusing packaging physical constraint conditions for joint coding, and forming a comprehensive condition vector; secondly, a cross-path dynamic information exchange mechanism is established through a geometric generation path and a texture optimization path, an initial three-dimensional packaging model is generated, physical consistency constraints are added, and NeRF is used for conducting volume rendering reconstruction on the initial three-dimensional packaging model; and then introducing an observation distance adaptive dynamic scale perception neural radiation field optimization mechanism, and generating a multi-scale NeRF model after scale perception optimization. And finally, establishing mapping of physical attributes and optical characteristics of the packaging materials, and generating a final three-dimensional packaging model through independent optical calculation and physical synthesis processes of the multi-layer materials. According to the method, the semantic understanding deviation of CLIP coding in vertical field application is solved, and the material reality representation of industrial-grade three-dimensional packaging modeling is realized.

The invention aims to serve the national strategic emerging industry, and discloses a deterministic material synthesis method and a deterministic material synthesis system for pre-nucleation chiral intervention (PNChI for short), which are suitable for one-dimensional or quasi-one-dimensional material systems (such as carbon nanotubes, biomacromolecules and the like) with formalized chiral tags. According to the method, before irreversible closed nucleation of a precursor is completed, deterministic authorization of a target chiral index (n, m) is realized in a controllable time window through at least one engineering intervention mechanism (such as physical field regulation, catalytic interface design, information coding guidance and the like) based on distinguishable physical properties, and consistency is kept in subsequent growth. The system comprises a programmable intervention unit and an intelligent sequence control module, and does not depend on a separation or screening step after nucleation. The single chiral index abundance in the obtained material is greater than or equal to 95%, preferably greater than or equal to 98%. The statistical limitation of traditional chiral control is broken through, a general synthesis normal form from'instruction input 'to'structure output' is constructed, and the method is suitable for high-consistency application scenes such as semiconductors, quantum devices, catalysts and biological recognition.

The invention relates to a spherical wave oscillation-slow nuclear high-order decoupling method and device for physical property inversion, and belongs to the field of geophysical detection and acoustic detection. The method comprises the following steps: obtaining and inputting observation data, and initializing seabed elastic parameters; determining an integral interval according to the observation data, decomposing the integral interval into a plurality of sub-intervals, and setting a decoupling order; calculating an oscillation nuclear local integral matrix of each order corresponding to the observation data incident angle; calculating the angle deviation guiding quantity of each order of the slowly-varying kernel; calculating a reflection coefficient vector of each order of spherical wave; calculating a spherical wave reflection coefficient of high-order decoupling; and solving a subsequent inversion problem by using the obtained high-order decoupled spherical wave reflection coefficient. According to the method, the calculation efficiency of the seabed spherical wave reflection coefficient in the inversion process is effectively improved; the decoupling order is improved, the calculation precision is improved, or the number of the subintervals can be reduced by improving the decoupling order under the condition that the calculation precision is certain.

The invention discloses a real-time software deformation interaction method based on a physical engine in an environment. The real-time software deformation interaction method comprises the following steps: S1, establishing a virtual environment and a software model; s2, constructing an interactive perception and collision detection network; s3, receiving and analyzing a multi-modal interaction instruction; s4, real-time deformation calculation based on a physical engine; s5, performing multi-user collaborative state synchronization and rendering feedback; and S6, carrying out fault state simulation on the software object. According to the method, real-time and high-precision simulation of software deformation is realized through deep fusion of software dynamics calculation of a physical engine and an interaction instruction stream, and through hierarchical physical attribute binding and client prediction, the network synchronization data volume is greatly reduced while the millimeter-level deformation precision is ensured, so that the network synchronization efficiency is improved. And low-delay and high-consistency experience under multi-person cooperative operation is ensured.

The application provides a low-grade oil reservoir water injection development effect evaluation method, comprising the following steps: S1, collecting core scale limit oil displacement efficiency data; S2, collecting well pattern controlled reserve degree data; S3, calculating sweep efficiency of the low-grade water drive oil reservoir; S4, correcting the sweep efficiency according to the changes of reservoir physical property parameters caused by near-well pollution and reservoir energy decline; and S5, calculating the reference recovery rate and evaluating the development effect. The application can effectively avoid collecting and arranging a large number of cumbersome production dynamic data, fully utilize the flow characteristics and dynamic physical property parameter evolution of the low-grade oil reservoir, accurately evaluate the development effect of the low-grade oil reservoir, and be beneficial to efficient development of the low-grade oil reservoir.

The invention discloses a joint object attitude generation method based on physical perception and graph diffusion, and relates to the field of computer vision. The method comprises the following steps: firstly, reconstructing componentized geometric and physical attributes of an object from an image through a double-branch neural implicit network, and initializing a component connection relationship; then, a component relation graph is refined through kinematics fitting and time sequence consistency test, and attitude prior distribution of the component on the SE (3) manifold is inferred by utilizing a physically enhanced graph diffusion process; and finally, constructing a conditional diffusion model on the SE (3) manifold under the condition of prior and reconstruction information, and performing reverse sampling through a physically guided two-step reverse sampling framework to generate a group of diversified and physically reasonable joint object attitude hypothesis sets. According to the method, efficient generation of diversified and high-physical-rationality postures of a joint object is realized through tight coupling of physical rules and data driving generation.

The invention discloses an intelligent prediction method for the stirring power consumption of a reaction kettle, and the method comprises the steps: collecting and calibrating the fluid physical property and process operation parameters in the reaction kettle in real time through a high-precision sensor array and an automatic system, carrying out the multi-stage data cleaning, denoising, normalization and time alignment, generating standardized high-quality data, and carrying out the intelligent prediction of the stirring power consumption of the reaction kettle. Based on physical property mapping, dimension reduction and feature fusion, extracting a low-dimensional fluid physical property descriptor, inputting the low-dimensional fluid physical property descriptor into a deep regression network in combination with process parameters, carrying out stirring power consumption modeling and dynamic prediction, and carrying out dynamic prediction by introducing a self-adaptive physical property embedding mechanism and distribution alignment training. The self-adaption and generalization ability of the model to different and unseen fluid physical property distributions is improved, and the accuracy, the stability and the engineering decision support ability of energy consumption prediction under complex working conditions are remarkably enhanced.

The invention relates to the technical field of material surface quality nondestructive testing, and discloses a rapid identification method for surface microdefects of a copper alloy strip, which comprises the following steps: firstly, applying a specific time sequence thermal excitation sequence to a surface to be detected, the sequence comprising a state pre-excitation pulse for inducing nonlinear response of thermal physical properties of a material; secondly, synchronously acquiring whole-process dynamic thermal response under the action of thermal excitation, and constructing a thermal response data cube; thirdly, extracting a time-temperature response curve for each pixel point in the data cube, and calculating a multi-dimensional dynamic thermodynamic feature vector; and finally, inputting the feature vector into a pre-trained supervised learning classification model to obtain a pixel-level classification label, and performing post-processing to determine the type, position and size of the defect. According to the method, the nonlinear and spectral response difference of the material is deeply excavated, the feature vector with high discrimination degree is constructed, and the detection sensitivity and classification accuracy of micron-sized physical and chemical defects can be effectively improved.

The invention relates to the technical field of vulcanized rubber processing, and discloses an intelligent vulcanized rubber processing system and method. According to the method, a physical property parameter set of a rubber raw material and real-time working condition data of vulcanization equipment are obtained, the physical property parameter set comprises the rubber content, the filler distribution density and the heat conduction coefficient, and the real-time working condition data comprises a vulcanization temperature curve, a pressure gradient sequence and a mold state identifier; multi-modal feature fusion processing is carried out on the physical property parameter set and the real-time working condition data, a feature fusion atlas containing thermodynamic response features and mechanical stress distribution is generated, and the atlas is associated with a phase change critical point and a material deformation track in the vulcanization process; and finally, a process parameter optimization model is called to perform dynamic scheduling analysis on the feature fusion atlas, and a vulcanization process control instruction set containing a time sequence operation sequence based on the temperature compensation parameters and the pressure adjustment step length is generated.

The invention relates to the technical field of hydraulic building material analysis, in particular to a material performance analysis method based on dam material physical properties and a related product, and the method comprises the following steps: applying multi-physical field composite loading including water pressure and temperature change to a sample of a dam material, collecting multiple performance data of the test sample in the composite loading process; key indexes are extracted according to the performance data; a comprehensive performance evaluation model is constructed based on the key indexes, an evaluation value representing the comprehensive performance of the dam material is obtained through calculation, and quantitative comprehensive analysis on the performance of the dam material is completed; according to the method, the performance comprehensive evaluation model integrating the core indexes is constructed, a principal component analysis and self-learning mechanism is introduced, and automatic extraction of main factors, reasonable weight distribution and dynamic adaptation of the regional environment are realized on the basis of original performance data; and the problems of single-point data isolation, weak coupling capability, poor structure specificity and the like in a traditional evaluation method are obviously solved.