2063 results about "Physical property" patented technology

The invention discloses a building energy consumption dynamic optimization method and system based on BIM and reinforcement learning, and belongs to the technical field of building energy management and intelligent control, and the method comprises the steps: building a BIM containing building component physical attribute parameters, and generating a building digital twinborn body with dynamic thermal attribute evolution; extracting the spatial topological relation and the physical property parameters of the components, and constructing a multi-dimensional state space of a preset reinforcement learning model; embedding physical constraint conditions, and training the reinforcement learning model to generate a multi-objective optimization strategy of the energy equipment; and analyzing the multi-objective optimization strategy into an equipment control instruction set, and feeding back the equipment control instruction set to the building digital twin for real-time physical attribute simulation. According to the method, the physical accuracy of the BIM and the self-adaptive decision-making ability of reinforcement learning are combined, adversarial training under physical constraints is introduced, an energy consumption optimization strategy which conforms to actual operation limitation and dynamically adapts to environmental changes can be generated, and the energy utilization efficiency and the system response speed are remarkably improved.

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

A method for performing an automated additive manufacturing (AM) process comprises obtaining a current state that includes a set of first AM factors and a set of first characteristics, which are geometric or physical properties of a first AM output are generated using the first AM factors. The current state is provided to a reinforcement learning (RL) framework designed to adjust welding parameters in real-time based on the current state and reward feedback. The RL framework includes an actor network and a critic network, and safety limits are enforced through a custom loss function. The first AM factors are updated based on the critic network's evaluation, resulting in second AM factors used to perform the second AM process and generate a second AM output.

According to the goaf detection method based on transient electromagnetic and seismic wave field joint inversion, data acquisition and joint inversion are carried out on the goaf through the combination of the transient electromagnetic detection system and the reflection seismic detection system, the complementary advantages of the two detection methods are played, the bottlenecks of a single method in the aspects of resolution and signal-to-noise ratio are broken through, and the goaf detection method based on transient electromagnetic and seismic wave field joint inversion is realized. Fine characterization of the structure, the physical property and the stability of the goaf is realized; a machine learning classification algorithm is introduced to analyze a goaf multi-attribute feature data set, and intelligent discrimination of the type, scale and stability of the goaf under complex geological conditions is realized; establishing a quantitative response relationship between goaf physical property parameters and goaf electrical and elastic difference characteristic parameters, constructing a goaf transient electromagnetic and seismic wave field joint forward modeling numerical model, and constructing an electromagnetic-seismic joint inversion objective function; and calculating and solving to obtain goaf resistivity, seismic wave velocity three-dimensional distribution and uncertainty quantitative evaluation results, and reducing blind areas of goaf judgment.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention belongs to the technical field of rock freeze-thaw damage prediction, and relates to a deep learning rock freeze-thaw damage prediction method based on physical constraints, which is used for improving the physical consistency and prediction precision of a rock freeze-thaw damage prediction model. According to the method, a physical constraint formula is introduced into a data-driven deep learning model, and organic unification of data fitting and physical rationality is realized by using an adaptive weight strategy. The prediction method comprises the steps of data acquisition, data set generation, network construction, physical constraint mechanism formula design, rock freezing and thawing experiment observation or theoretical formula derivation, design of a formula reflecting evolution of various physical properties, model training and prediction output. The deep learning rock freeze-thaw damage prediction method based on the physical constraint has the advantages that generalization ability is remarkably improved, physical continuity and consistency are higher, multi-parameter and multi-source data are comprehensively applied, a staged self-adaptive training strategy is adopted, cost and resources are saved, operation is easy and convenient, and application and popularization are convenient.

Methods, apparatus, and systems for generating a price of a target device. An evaluator device obtains technical properties associated with the target device. The technical properties include a make and a model of the target device. Physical properties associated with the target device are obtained. The physical properties include information related to wear and tear of the target device. Obtaining the physical properties includes indicating to a user that the user should position the target device in multiple predetermined positions and that the evaluator device records an image of the target device in each of the multiple predetermined positions. A video of the target device is recorded while the target device is positioned in the multiple predetermined positions. The obtained physical properties are evaluated to generate a condition metric value of the target device. Based on the generated condition metric value, the price of the target device is determined.

The invention discloses a construction method and system for a coal mine geological transparent working face, and relates to the technical field of intelligent mine and geological information, and the method comprises the following steps: collecting and preprocessing visible light, near-infrared images and point cloud data, extracting texture, reflectivity, curvature and density features, and calculating the texture, reflectivity, curvature and density features; the method comprises the following steps: constructing a three-dimensional structure reconstruction model, a rock stratum physical property inversion model and a geological stress prediction model, generating a three-dimensional geometric structure coordinate set, a rock stratum moisture content matrix and a vertical stress distribution matrix of each region, setting an anomaly judgment condition, outputting three-dimensional coordinates of an abnormal region, carrying out graded early warning, synthesizing a three-dimensional visual image, and predicting the geological stress. And holographic transparent expression of the working face geological state is realized. According to the method, centimeter-level three-dimensional reconstruction of the geological transparent working face of the coal mine, precise positioning of a hidden water-containing area and a stress abnormal area and construction of a full-attribute dynamic visual scene are achieved, the timeliness and accuracy of early warning of disasters such as water inrush and rockburst are remarkably improved, real-time geological decision support is provided for intelligent mining, and safe and efficient production is guaranteed.

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 plastic mold parameterized simulation, in particular to a parameterized simulation model design method and system for a plastic mold. The method comprises the following steps: carrying out raw material fine preparation on a target plastic raw material to obtain a homogeneous dry material; performing multi-field rheological characteristic scanning on the homogenized and dried material, and extracting viscosity model parameters to obtain viscosity model initial parameters; precisely measuring thermodynamic characteristics of the plastic material on the homogenized and dried material, and analyzing crystallization kinetic behaviors to obtain crystallization kinetic model parameters; and carrying out material gene spectrum integration according to the initial parameters of the viscosity model and the parameters of the crystallization kinetic model to obtain a material gene spectrum. According to the method, through performance-driven parameter optimization and intelligent mold blueprint generation based on the material physical property and the microenvironment, the mold manufacturing efficiency and quality are remarkably improved, and the manufacturing cost is reduced.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

The invention relates to a plastic part forming process parameter optimization regulation and control system, in particular to the field of plastic part forming, through the synergistic effect of multiple modules, the intelligent level and stability of a forming process are remarkably improved, and the system can quickly respond to the physical property change of a new material or a new batch; intelligently generating high-adaptability initial parameter suggestions based on historical data; by fusing physical simulation and real-time sensor data, dynamic behavior characteristics of a material are accurately captured, and an enhanced characteristic vector is constructed; on the premise of strictly following the thermodynamic safety boundary, the process parameter combination is efficiently optimized to meet the complex rheological constraint; and meanwhile, closed-loop precipitation and self-improvement of process knowledge are realized according to a continuous evolution anti-disturbance control strategy of production data, rapid and accurate regulation and control in a high-frequency switching scene are finally achieved, and the consistency of forming quality and production stability of plastic accessories are greatly improved.

The invention provides a rock mass high-temperature deformation simulation method and system based on deep learning near-field dynamics, and relates to the field of rock mass thermal-mechanical coupling analysis and research, and the method comprises the steps: obtaining the parameter information of a to-be-simulated target rock mass, including the geometric shape of the rock mass and the physical attribute of the rock mass; dispersing the target rock mass into a plurality of material points according to the parameter information, and constructing a thermal-mechanical coupling model of the target rock mass; based on the initialized thermal-mechanical coupling model, time step iterative simulation of a solid mechanical field is conducted, and finally the crack propagation process and temperature distribution of the target rock mass are obtained; according to the method, a solid field near-field dynamics theory based on strain energy density is introduced, a fluid field near-field dynamics equation is combined, temperature distribution in a rock medium is predicted through a physical information neural network, a deep learning near-field dynamics framework of rock mass high-temperature deformation damage is formed, and therefore rock mass high-temperature deformation is efficiently and accurately simulated.

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 provides a dry-type transformer turn-to-turn short circuit multi-physical field simulation analysis method considering insulation aging and harmonic influence, and relates to the technical field of simulation analysis, and the method comprises the following steps: constructing a physical simulation model of a to-be-tested dry-type transformer; setting physical parameters and multi-physical field operation conditions of the physical simulation model; calculating a corresponding physical field operation calculation result under each physical field operation condition; performing post-processing on the physical field operation calculation result; by establishing a detailed simulation model and accurately assigning physical property parameters of each component, physical property changes of the turn-to-turn insulating material at different aging stages and under different temperature and frequency conditions are emphatically considered; turn-to-turn short circuit fault modeling is further introduced, magnetic-thermal-flow coupling simulation calculation is carried out under multi-harmonic excitation, the multi-physical field distribution characteristics of the dry-type transformer during different turn-to-turn short circuit faults are explored, and a reliable basis is provided for design optimization and safety evaluation.

The invention provides a heat exchanger intelligent design method based on deep reinforcement learning, and relates to the technical field of data processing. Establishing a flow model for simulating the flow path, speed distribution and pressure change of the fluid in the heat exchanger; according to the geometrical shape of the heat exchanger, the physical property of a heat exchanger material and fluid flowing in the heat exchanger, a heat exchange model is established by combining the thermodynamic principle, and the heat exchange efficiency under different fluid flowing conditions is calculated; the flow model and the heat exchange model are coupled, so that a coupling performance evaluation model for evaluating the overall performance of the heat exchanger under the specific working condition is obtained; and determining a state space, an action space and a reward function according to the flow heat exchange intensity coupling performance evaluation model. The design efficiency is improved.

The invention relates to a machine learning-based scouring damage bridge buckling load prediction method and system, and belongs to the technical field of machine learning and structural engineering. The prediction method comprises the steps of obtaining original data, preprocessing the original data, constructing a LightGBM machine learning model, and evaluating and verifying the constructed model by adopting multiple performance evaluation indexes. According to the method, key variables such as bridge structure parameters and foundation soil physical property indexes are comprehensively considered, a large sample database covering a large number of scouring combinations is constructed, and the critical buckling load of the structure is accurately calculated as an output target by using a pier-bearing platform-pile structure system buckling analysis method after scouring damage based on an energy method. A multivariable nonlinear mapping relation is established in combination with an ensemble learning algorithm, traditional complex calculation is replaced, and the method has good engineering adaptability and universality and can be suitable for rapid prediction of the buckling bearing capacity under different parameter bridges, different foundation forms and complex scouring conditions.

The invention relates to the technical field of oil and gas well drilling, in particular to an experimental system for measuring the dielectric constant of drilling fluid, which comprises a reaction kettle body, a pressure unit, a temperature unit and a dielectric constant sensor. In the oil-gas well drilling operation process, whether the overflow phenomenon occurs in an oil-gas well or not is judged by monitoring the fluctuation of the dielectric constant of drilling fluid in real time, but the fluctuation of the dielectric constant can also be caused by changes of physical conditions such as pressure and temperature of the drilling fluid, and if the fluctuation of the dielectric constant caused by the changes of the physical conditions is not eliminated, the overflow phenomenon is judged. If so, the accuracy of overflow monitoring is influenced. In the application, the pressure unit and the temperature unit are used for respectively changing the pressure and the temperature of the drilling fluid so as to obtain the change relationship between the pressure and the temperature of the drilling fluid and the dielectric constant; therefore, whether the fluctuation of the dielectric constant of the drilling fluid in the oil and gas well is caused by the change of physical property conditions such as pressure and temperature or not is judged according to the change relation, and the accuracy of overflow monitoring is further improved.

The invention relates to the technical field of data, and discloses a logging data intelligent processing method. The method comprises the following steps: acquiring data through a multi-source logging device and preprocessing the data to form a result set; identifying and classifying stratum features according to the result set; analyzing oil and gas parameters based on the stratigraphic map and the result set; identifying risks by using the oil-gas evaluation table and the result set and establishing an early warning mechanism; the reservoir physical property is evaluated according to the oil and gas evaluation table and the early warning mechanism; and integrating the stratigraphic map, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure prediction enrichment area. According to the method, full-process intelligent processing from acquisition and preprocessing of multi-source heterogeneous logging data to stratum identification, oil and gas evaluation, drilling risk early warning, reservoir assessment and oil and gas reservoir description is realized, and the problems of data space-time mismatching, parameter isolated analysis, high evaluation subjectivity, early warning lag and the like in a traditional method are solved; and the efficiency, accuracy and systematicness of logging data processing are improved.

The invention provides a reservoir physical property parameter pre-stack seismic inversion method based on resistivity logging constraint. The method comprises the following steps: cutting observation data and depth; normalizing the seismic data, the resistivity data and the travel time to form network input; constructing a neural network framework, substituting the obtained porosity and saturation into an elastic rock physical model, and calculating to obtain an elastic parameter; the elastic parameters are substituted into a zoeppritz equation, and an underground reflection coefficient is obtained through calculation; generating a Ricker wavelet, and performing convolution calculation on the Ricker wavelet and the reflection coefficient to obtain predicted pre-stack seismic data; the porosity and the saturation are substituted into an Archie formula, and the predicted resistivity is obtained through calculation; constructing a target function; automatic differentiation is carried out, network parameters are updated, and then network output porosity and saturation are optimized. Reservoir parameter inversion driven by physical information is realized, and an intelligent scheme is provided for reservoir parameter prediction and evaluation in oil-gas exploration and development.

The invention discloses a three-dimensional model intelligent generation method and system based on a natural language, and the method comprises the steps: receiving and describing the natural language inputted by a user in real time, based on the three-dimensional model data structure, the de-noising symbol set and the stop word list, key information data are extracted according to natural language description and mapped into a three-dimensional model parameter set; according to the scene type described by the natural language and the three-dimensional model parameter set, calling a three-dimensional model generation network corresponding to the scene type described by the natural language to generate an initial three-dimensional model; and receiving and analyzing a model optimization instruction input by a user in real time, updating the geometric structure, detail features and physical attributes of the initial three-dimensional model according to an analysis result, generating a dynamic interaction model, and outputting a three-dimensional model file of the dynamic interaction model. By applying the method and the system provided by the invention, the efficiency, the accuracy, the flexibility, the usability and the user experience of three-dimensional model modeling 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 provides a coal seam hydraulic fracturing parameter dynamic optimization method based on deep learning, and belongs to the technical field of coal seam parameter optimization based on computer data processing. The method comprises the following steps: firstly, acquiring a coal seam slice CT image with spatial continuity; secondly, designing a coal seam structure-physical parameter intelligent sensing model, and performing joint prediction on coal seam image semantic recognition and physical property estimation; then, performing coal seam three-dimensional structure reconstruction and regional layering modeling by utilizing a prediction output result, and performing finite element mesh division and material attribute accurate mapping on the basis to construct a coal seam three-dimensional finite element model; and finally, a coal seam hydraulic fracturing parameter multi-objective optimization problem is constructed, a coal seam physical information embedded agent model is designed, the optimization problem is dynamically solved in combination with a multi-objective evolutionary algorithm, and optimal hydraulic fracturing parameters are obtained. On the basis of deep learning, multi-source information, structural parameters and material attributes are fused, and intelligent development of coal seam hydraulic fracturing design is promoted.

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 provides a building structure monitoring method and system based on multi-source data fusion, and the method comprises the steps: collecting the initial data of a building structure, employing the initial data to drive a relation logic model for the physical attributes of the building structure through a digital twin technology, and carrying out the calculation of the initial variable data, according to the method, the state of the building structure is deduced by using the digital twinning technology by obtaining the safety data in the real-time data of the building structure, and the related parameters related to the abnormal state data can be determined by analyzing the abnormal state data of the building structure, so that the safety data in the real-time data of the building structure can be obtained. According to the method, the correlation parameters and the abnormal parameters are analyzed in a combined mode, so that the cause of abnormity is determined, the monitoring model is established, the abnormity possibly occurring in the building structure can be predicted in advance through the detection model, maintenance personnel are helped to determine the maintenance direction and carry out maintenance in advance, and the building structure can be analyzed more comprehensively.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source geological data processing method and system of a three-dimensional geological model.The method comprises the following steps that mountain landform and river valley images are obtained, gray frequency characteristics are extracted, a frequency energy gradient layer is constructed, a frequency continuous response area is screened to generate a structure boundary set, and a structure boundary set is constructed; the method comprises the following steps of: extracting a boundary normal vector by utilizing principal component analysis, identifying boundary sections with consistent directions, estimating a physical property parameter gradient direction, judging an included angle screening blocking region, generating a space attribute limiting layer, carrying out space alignment analysis on an overlapping region vector included angle, updating a boundary label, and generating an available attribute path structure set in three-dimensional geological modeling through a Dijkstra algorithm. According to the method, a conduction model is constructed through frequency domain decomposition and logarithmic transformation enhanced recognition, frequency window analysis noise reduction, principal component extraction vector analysis direction and center difference estimation, dynamic matching is promoted through alignment, a Dijkstra algorithm optimizes a path, and the geological model bedding characterization and conduction simulation precision is improved through cooperation of a multi-dimensional technology.

According to the spatial configuration bidirectional fluid-solid coupling analysis method for the deep-sea mining hose, an internal flow coupling equation is constructed based on internal fluid parameters and hose physical property parameters, an external flow coupling equation is constructed based on external fluid parameters and hose physical property parameters, boundary conditions are obtained based on near-wall fluid parameters and hose physical property parameters, and the deep-sea mining hose is obtained. Coupling the external flow coupling equation and the internal flow coupling equation through boundary conditions to construct a bidirectional coupling dynamic equation set; an undamped free vibration equation and a hose balance equation are constructed based on hose physical property parameters, the undamped free vibration equation and the hose balance equation are combined, and an additional fluid mass matrix is introduced to establish a wet modal analysis model; and high-precision simulation and efficient optimization of the deep-sea mining hose spatial configuration are realized.