1195 results about "Macroscopic scale" patented technology

The invention relates to the technical field of oil and gas field development, and discloses an ultra-large type true triaxial hydraulic fracturing fracture evolution path prediction method and system.The prediction method comprises the steps that a coupling geomechanical model integrating microscopic, macroscopic and wellbore flow scales is constructed; initializing the model and performing crack initial expansion simulation; collecting construction data in real time, and dynamically optimizing model parameters through a data assimilation algorithm; performing fracture evolution advanced prediction by using the updated model; and generating an optimization decision based on the prediction result and feeding back to the construction site. According to the method, fusion of a multi-scale physical mechanism and real-time dynamic prediction is realized, and accurate prediction and active control can be performed on crack expansion under the ultra-large true triaxial condition.

The invention relates to a solidified soil proportion and strength prediction method and system based on SEM image analysis, and belongs to the technical field of solidified soil strength testing. Comprising the following steps: S1, measuring physical indexes of disturbed soil; s2, multi-working-condition sample preparation and maintenance; s3, fractal dimension calculation of the SEM image is carried out; s4, unconfined compressive strength testing; s5, establishing a microstructure inversion model and a macroscopic strength prediction model; and S6, proportion optimization and strength prediction: utilizing the microstructure inversion model and the macroscopic strength prediction model to carry out solidified soil proportion optimization or strength prediction. According to the method, the quantitative relation between the microstructure (fractal dimension) and the macroscopic strength is established through SEM image analysis, the defects in the prior art are overcome, and a brand new technical path is provided for resource utilization of disturbed soil.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

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 relates to the technical field of artificial intelligence, and discloses an event prediction method and system based on multi-modal fusion, and the method comprises the steps: constructing a knowledge graph encoder, and converting domain expert knowledge into learnable vector representation; constructing a multi-granularity feature extraction network, and extracting features from different microcosmic, mesoscopic and macroscopic scales; realizing a knowledge-guided attention mechanism, and dynamically adjusting feature scale importance; constructing a prototype learning module, and establishing prototype representation of the abnormal category; a knowledge migration mechanism is constructed, and the generalization ability of the model to novel anomalies is enhanced; and multi-granularity abnormal event detection and early warning are realized, and a detection result and interpretable analysis are output. According to the method, through combination of knowledge guidance and multi-scale feature learning, efficient identification and early warning of social abnormal events under the condition of data scarcity are realized, and the method is suitable for the fields of public place safety monitoring, urban traffic safety management, large-scale activity safety guarantee and the like.

The invention discloses a marine seat framework welding track real-time correction system based on machine vision and mechanical feedback. The marine seat framework welding track real-time correction system comprises a robot control unit, a welding unit, a vision sensing unit, a force sensing unit and a track correction module running on the robot control unit. The visual sensing unit is installed on an end effector of the robot and used for collecting image data including a to-be-welded weld joint and a peripheral area of the to-be-welded weld joint in real time. The force sensing unit is installed between a robot end effector and a welding gun and used for detecting six-dimensional force and torque signals between the welding gun and a workpiece in the welding process in real time. The core of the track correction module is a real-time correction algorithm based on multi-sensor information fusion, and the track correction module receives image data from the visual sensing unit and mechanical data from the force sensing unit. The sensing limitation of a single sensor is overcome, the vision provides macroscopic position deviation, the force sensing provides microcosmic contact and attitude information, and more comprehensive environment sensing is realized through complementation of the vision and the force sensing.

The invention discloses a GFRP durability evaluation method based on a neural network, and belongs to the technical field of composite material performance evaluation. The method comprises the following steps: constructing a training data set containing working condition parameters, macroscopic performance data and microscopic mechanism data; a multi-head physical guidance neural network model is constructed, and the model is provided with a main output head for outputting a macroscopic performance data predicted value and an auxiliary output head which is connected to an internal physical mechanism characterization layer of the model and is used for outputting a microscopic mechanism data predicted value; performing multi-task cooperative training on the model through a composite loss function; and finally, synchronously outputting a macroscopic performance prediction result and a microcosmic mechanism diagnosis result by utilizing the trained model. According to the method, physical mechanism knowledge is fused into the neural network, and a traditional black box model is improved into an interpretable grey box model through middle layer supervision and multi-task cooperative training, so that the accuracy of macroscopic prediction is improved, and quantitative diagnosis of an internal degradation mechanism is realized.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention discloses a low-temperature CMOS carrier MFP and microscopic parameter extraction method and system, which are corresponding schemes, in the scheme, a carrier MFP extraction model for eliminating a microscopic unknown quantity is constructed by utilizing a geometric scaling rule of quasi-ballistic transport and an existing effective mobility extraction technology, and an MFP extraction model for eliminating the microscopic unknown quantity is constructed by utilizing the geometric scaling rule of quasi-ballistic transport and the existing effective mobility extraction technology; the MFP extracted by the carrier MFP extraction model is ensured to be a pure experiment observation value on the basis that effective quality, Fermi velocity and scattering time do not need to be predicted based on the self-consistency of macroscopic current and voltage data along with the change of channel length; meanwhile, on the basis, the microcosmic parameters are decoupled, so that the decoupled microcosmic parameters are extracted one by one; compared with an existing scheme, the method can be directly applied to a standard wafer-level test board in the industry, MFP distribution of hundreds of devices can be rapidly counted, and a feasible engineering scheme is provided for low-temperature process monitoring.

The invention provides a self-adaptive variable-parameter composite swing welding control method and related equipment, and relates to the technical field of welding. The method comprises the steps that real-time multi-physics field sensing data is obtained; inputting the real-time multi-physical field sensing data into a simulation model for predicting the behavior of the molten pool, and outputting a state parameter predicted value of the molten pool in a future preset time period; matching a composite swing track parameter corresponding to the molten pool state parameter predicted value from a preset database; and when the welding gun enters the residence area of the welding seam swing edge according to the macroscopic reference track, the tip of the welding gun is controlled to execute asymmetric spiral oscillation motion in the preset residence area according to the microcosmic modulation track until the tip leaves the preset residence area. By implementing the method, the problems of edge overheating, undercut or insufficient fusion caused by fixed parameters can be relieved, and the welding seam forming quality and the stability of the welding process are improved.

The invention discloses a lithology generality joint feature classification model construction method, which relates to the technical field of intelligent geological prospecting, introduces a cross-scale feature alignment mechanism, and utilizes scale transformation factors to dynamically match the resolutions of macroscopic, mesoscopic and microscopic features, thereby avoiding feature distortion caused by direct fusion; for example, in a volcanic rock and sedimentary rock mixed area, rough texture of a remote sensing image originally conflicts with smooth response of a logging curve, but through alignment processing, the model can automatically balance credibility of different data sources, and misjudgment is reduced; inter-scale dependence modeling is further enhanced through introduction of the feature association graph, vertical sequence association of a thin interbed is captured through a graph structure, the boundary of a millimeter-level thin layer is made clear, and the problem of fuzziness caused by neglecting of interlayer interaction in an existing scheme is solved.

The invention relates to the technical field of computers, and discloses a rock creep model construction method and device and a rock failure mode determination method. The method comprises the steps of obtaining a data set; samples in the data set comprise input data and output data, the input data comprise creep load holding time, sample rock stress data, initial macroscopic damage data, initial microscopic damage data, long-term strength and a creep starting threshold value, and the output data is a strain true value; fitting the initial rock creep model by using the data set to obtain a final rock creep model; wherein the initial rock creep model comprises at least one of a damaged elastomer strain item, a damaged viscoelastomer strain item and a damaged viscoplastic body strain item; each strain item comprises a macro-micro nonlinear coupling damage item, and the macro-micro nonlinear coupling damage item is generated based on the initial micro damage data and the initial macro damage data. The model constructed by the method can truly reflect the nonlinear mechanical response of the rock mass under the combined action of the multi-scale initial defects.

The invention provides a project state summarizing method and device, and belongs to the technical field of DFT (Discrete Fourier Transform), and the project state summarizing method comprises the following steps: distributing a plurality of test tasks to a plurality of predefined categories according to the corresponding test steps according to the mapping relation between the test steps and the predefined categories; and based on the task state of the test task included in each predefined category, determining the category state of the corresponding predefined category. Through a predefined mapping relation, original and scattered test tasks and states thereof are aggregated into classified and macroscopic class states.

The present invention relates to the technical field of fluid mechanics. Disclosed is a variable-density low-speed flow mesoscopic numerical calculation method, comprising: determining a computational domain, and dividing or reading grid information, fluid parameters and control parameters in the computational domain; on the basis of the read information, allocating arrays, initializing corresponding parameters of the computational domain, and entering a time step loop upon completion; setting boundary conditions, and assigning all boundary macroscopic quantities in a flow field; under a finite volume framework, deriving a numerical flux of the flow field by means of a BGK equation to solve a Navier-Stokes equation of the flow field; calculating pressure and velocity fluxes at cell interfaces of the flow field, and updating boundary macroscopic quantity information on the basis of flux information; determining whether there is an external force, and if so, applying the external force by means of splitting, and updating the boundary macroscopic quantities; determining whether a time step meets a loop condition; if so, re-entering setting boundary conditions, and assigning all boundary macroscopic quantities in the flow field; and if not, outputting flow field data, and ending simulation. The present invention has the advantages of high algorithm accuracy and simple programming.

The invention discloses a micro-positive pressure hydrogen environment in-situ stress-strain testing device and method. The device comprises a sealed reaction cavity which can be placed in a scanning electron microscope sample cabin; the loading device is arranged in the cavity and is used for fixing and applying a load; the atmosphere control system is communicated with the cavity and is used for introducing gas and maintaining a micro-positive pressure state; an electron transmission window arranged on the cavity, wherein an electronic transparent film packaged by the electron transmission window can maintain air tightness under micro-positive pressure and allows electron beams to penetrate through; and the heating module is arranged in the cavity. The method comprises the following steps: loading and sealing a sample, establishing a micro-positive pressure atmosphere, heating the sample, starting loading, and synchronously carrying out SEM real-time imaging and data recording. The problem that a traditional SEM in-situ device cannot work in a real micro-positive pressure hydrogen and high-temperature environment is solved, and synchronous visual in-situ observation of material microstructure evolution and macromechanical response under the gas-heat-force multi-physics field coupling condition is achieved.

The invention relates to the technical field of stress measurement, in particular to a form inversion measurement method and system for asymmetric distribution of residual stress of a bent component, and the method comprises the following steps: S1, welding a bent steel component with a box-shaped section, and obtaining the initial curvature of the bent steel component in the length direction of the bent steel component in an unconstrained free state; the method comprises the steps of S1, conducting cutting operation on the middle point in the length direction of the bent steel member or the section with the maximum initial bending rate, releasing the stress of the bent steel member to obtain two symmetrical sections of steel members, S2, conducting form inversion measurement and curvature variation calculation after stress release, S4, conducting residual stress distribution inversion calculation, and S5, conducting deformation calculation. The method is a core method capable of macroscopically and integrally measuring the residual stress distribution of the component and quantitatively verifying the stress asymmetry of the bending concave side and the bending convex side of the component. Meanwhile, a macroscopic measurement method and a local point measurement method can be combined, and mutual verification and correction are carried out, so that more accurate and reliable residual stress field data can be obtained.

The invention discloses multifunctional wafer detection equipment. The equipment comprises an edge detection module, a robot, two sets of macroscopic detection units arranged in parallel and a microcosmic detection assembly. The robot is arranged on the side of the edge detection module to transfer the wafer. Each macroscopic detection unit comprises a transfer mechanism, a wafer rotating and transferring mechanism, an external clamping mechanism, an internal clamping mechanism and a photographing module. Wherein the transfer mechanism is used for receiving wafers from the robot; the working range of the rotary wafer transfer mechanism covers the transfer mechanism, the external clamping mechanism, the internal clamping mechanism and the microscopic detection assembly, and is responsible for efficiently transferring wafers among the transfer mechanism, the external clamping mechanism, the internal clamping mechanism and the microscopic detection assembly; the external clamping mechanism and the internal clamping mechanism which are adjacently arranged can carry out wafer handover; and the photographing module is used for photographing the wafer fixed by the internal clamping mechanism. By optimizing layout and automatic circulation, efficient integration and continuous proceeding of wafer edge detection, macroscopic detection and microcosmic detection are achieved, and the detection efficiency and the automation level are remarkably improved.

The invention discloses a method for predicting mechanical properties of ultra-high performance concrete, particularly relates to the technical field of dynamic property analysis of building materials, and aims to solve the problem that sudden brittle failure of materials cannot be accurately predicted under impact and explosion loads in the conventional method. The method comprises the following steps: calculating a macroscopic stress-strain response based on a continuum mechanical equation by acquiring a material ratio parameter and a dynamic load condition; constructing topological features through the strain energy entropy change rate to locate a damage area; determining an interface debonding energy threshold value and a crack growth rate of the damaged area in combination with interface fracture mechanics; utilizing the law of conservation of energy to establish dynamic correlation between interface debonding energy and strain energy density; when the crack growth rate exceeds an interface debonding energy threshold value, correcting macroscopic response data of a sudden stress drop interval based on the dynamic association relationship; the cross-scale coupling of a microscopic damage mechanism and a macroscopic mechanical behavior is realized, and the prediction precision under a dynamic load is remarkably improved.

The invention provides an out-of-focus lens microlens diopter measurement method, system, medium and device, and relates to the technical field of optical measurement, and the method comprises the steps: placing a to-be-measured out-of-focus lens on a rotary table; controlling a Z-axis motion guide rail to drive a line measurement sensor to move, and determining a path scanning zero point; the rotary table is controlled to rotate at a constant speed, and the X-axis movement guide rail and the Z-axis movement guide rail drive the line measurement sensor to move so as to obtain point cloud data of the upper surface of the to-be-measured out-of-focus lens; performing conversion, fitting and data extraction on the point cloud data to obtain a two-dimensional profile curve of each micro lens unit included in the to-be-measured defocus lens; and calculating the diopter of each micro-lens unit according to the two-dimensional profile curve of each micro-lens unit. According to the method, independent quantitative evaluation of the additional diopter of the microstructure is realized, and the defects that the macroscopic diopter and the microstructure diopter cannot be separated by a lensometer and the macroscopic diopter and the microstructure diopter are difficult to accurately disassemble in an existing non-contact technology are overcome.

The invention discloses a geological structure recognition method based on deep learning, and relates to the technical field of geological recognition, and the method comprises the steps: inputting enhanced multi-modal data into a multi-scale visual Transform network to generate a depth feature map, extracting a local structure feature map through a geological prior function and a pyramid structure, and obtaining a local structure feature map; fusing the local construction feature map with the depth feature map to generate a multi-modal feature map, establishing a spatial attention weight map, calculating the regional correlation between the multi-modal feature map and the spatial attention weight map, and performing regional adaptive weighting on the multi-modal feature map according to the regional correlation to obtain a weighted feature map; according to the method, global feature modeling is realized through the multi-scale visual Transform network, the cross-regional continuity and the wrinkle macroform of the fault are captured, and the processing capability of high-resolution geological data is improved.

The invention provides a traffic accident scene reconstruction method and device, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining a video frame sequence of a traffic accident scene collected by an unmanned plane, carrying out the information gain evaluation of the video frame sequence, and constructing a key frame set; according to the key frame set, macroscopic geometric reconstruction and microscopic normal recovery based on airborne light source dynamic change are carried out on the traffic accident scene, and a macroscopic depth map and a microscopic normal field are obtained; constructing a fusion optimization model with the macroscopic depth map as low-frequency constraint and the microscopic normal field as high-frequency gradient guidance, and fusing the macroscopic depth map and the microscopic normal field by using the fusion optimization model to obtain a fused depth map; and constructing an accident scene reconstruction model according to the fused depth map. By adopting the traffic accident scene reconstruction method and device, key details of the accident scene can be captured, the sensitivity to micromorphology is increased, and the accuracy of traffic accident scene reconstruction is improved.

The invention discloses a cable trench fireproof cover plate performance detection device and method based on multi-band electromagnetic waves. The device comprises a microwave detection module, a terahertz imaging module, a high-precision two-dimensional mechanical scanning platform, a synchronous control and data fusion unit and a comprehensive data analysis platform. The microwave module is used for transmitting and receiving 1-10GHz electromagnetic waves to realize deep structure detection; the terahertz module works at a frequency band of 0.1-3 THz and is used for acquiring near-surface time-domain spectral information. And the scanning platform drives the modules to perform two-dimensional rasterization scanning on the surface of the cover plate. And the synchronous control and data fusion unit coordinates the time sequence action of each module to ensure the time-space consistency of data acquisition. And the comprehensive data analysis platform performs fusion processing on the multi-source data through an inversion and imaging algorithm to generate a comprehensive evaluation report covering internal macroscopic defects, near-surface microdefects and coating thickness. According to the invention, through collaborative integration of microwave and terahertz technologies, multi-scale and integrated nondestructive testing and performance evaluation of the fireproof cover plate from a deep layer to a surface layer are realized.

The invention relates to the technical field of engineering material monitoring, and discloses an SHCC material full-life monitoring system for tunnel engineering and an evaluation method. According to the system, through cooperative work of an initial data acquisition module, a material state modeling module, a service environment simulation module, a damage evolution tracking module, a performance degradation evaluation module and a service life prediction module, the whole life cycle of a material is monitored. The method comprises the following steps: acquiring internal microstructure data of a material, constructing a dynamic response map, tracking a microcrack evolution path in real time by implanting a mark point in a simulated tunnel environment, and finally establishing a performance degradation evaluation matrix to complete life prediction. According to the system, internal correlation analysis from microscopic damage to macroscopic performance is realized through two core technologies of dynamic map construction and microcrack mark tracking, and the accuracy of life prediction and the reliability of engineering durability evaluation are improved.

The invention relates to the field of sensor data processing, and discloses an MEMS drift correction method based on data dynamic sampling and multi-source fusion, and the method comprises the following steps: S1, collecting MEMS three-axis acceleration original data at a first sampling frequency, and synchronously collecting environment temperature, humidity and air pressure data at a second sampling frequency, the first sampling frequency is higher than the second sampling frequency, the basic drift correction model is established by using the low-frequency data to capture the macroscopic and slow-varying relationship between the environmental factors and the drift, then the residual refinement model is established for the high-frequency data, and the high-frequency residual is specially processed; high-frequency drift components related to the environmental change rate are captured, through the two-stage fusion strategy, the accuracy of the model in the macroscopic trend is guaranteed, high-frequency data are fully utilized to improve the instantaneous correction precision, information loss caused by simple downsampling in a traditional method is avoided, and false signals introduced by rough interpolation are also avoided.

The invention provides a wafer defect detection method and device, and the method comprises the steps: obtaining a macroscopic image comprising the global or regional defect information of the surface of a wafer, and generating a rechecking task set based on the abnormal features in the macroscopic image; under the condition that the re-checking task set is not empty, driving the imaging unit and the wafer to generate relative displacement so as to obtain a microscopic feature image of the target area in the re-checking task set; determining the native defect attribute of the wafer according to the microscopic feature image; wherein the optical resolution of the microscopic feature image is higher than that of the macroscopic image.

This invention discloses a method for analyzing the fracture surface of high-Cr steel or alloy steel that cracks in a high-temperature environment. The method involves cutting a metallographic sample at the crack tip of a crack-containing block, polishing it, visually observing the crack morphology, and analyzing the composition of corrosion products on the sample. The sample is then etched with an etchant, and the relationship between the crack direction and the microstructure of the sample is observed under a metallographic microscope to obtain the crack metallographic test results. The remaining crack in the crack-containing block is opened, and the macroscopic features of the fracture surface are visually observed. The microscopic features of different regions of the fracture surface are observed using a scanning electron microscope, and the composition is tested to obtain the micro-region composition. The surface corrosion of the fracture surface is removed using an electrostatic descaling method, and the microscopic features of different regions of the fracture surface are observed under a scanning electron microscope. Based on the crack metallographic test results, the macroscopic and microscopic features of the fracture surface, and the micro-region composition, the cause of the cracking is determined. This invention provides a complete analytical procedure, yields abundant information, and effectively analyzes the causes of component cracking.

The invention relates to the technical field of monitoring operation and maintenance, and discloses a method and system for monitoring operation and maintenance of equipment in a purification machine room, and the method comprises the steps: obtaining a surface deposition image of a related witness surface of a micropore array airflow component, analyzing the arrangement and construction information of a micropore array from the surface deposition image, and calculating the effective permeability of the micropore; positioning the center of the micropore according to the arrangement construction information, performing hole circumference radial statistics on the deposition intensity field, and generating a hole circumference jet flow deposition radial distribution map; within a limited microjet influence domain, calculating a microjet deposition diffusion attenuation factor based on the map; carrying out physical field coupling on the attenuation factor and the effective permeability of the micropore, analyzing a micropore flow field inertia enhancement coefficient and a macroscopic particle deposition mode weight, and further synthesizing an equipment operation comprehensive risk index; and finally, in response to the index and the permeability attenuation degree, generating an operation and maintenance strategy containing the surface clean recovery strength of the microporous panel and the airflow organization current-sharing correction amplitude.

The invention provides a graphical user interface forensic analysis method based on a multi-mode large language model, and belongs to the technical field of man-machine interaction, artificial intelligence safety and digital forensic. The method comprises the steps of evidence capture and distillation, semantic analysis and record generation, storage and indexing, and retrieval and inquiry. Through evidence capture and distillation, on the premise that evidence integrity is guaranteed, the visual data volume needing to be analyzed is greatly reduced, and long-term and efficient evidence obtaining analysis on a mobile terminal becomes possible; by introducing the analysis capability of a multi-modal large language model, original and structurality-free visual evidence is converted into a structuralization intelligent evidence which can be understood and deeply excavated; a designed two-stage natural language evidence obtaining query engine supports a complete analysis process from macroscopic semantic retrieval to microscopic detail inquiry; the problems that a traditional evidence obtaining method depends on manual troubleshooting and is low in efficiency are solved, and unprecedented high efficiency and convenience are provided for backtracking and auditing of mass interaction records.

The invention relates to the technical field of pressure vessel health monitoring and nondestructive testing, in particular to an electromagnetic acoustic surface wave-based pressure vessel intergranular corrosion non-contact detection system, which comprises the following modules: a multi-field coupling electromagnetic acoustic surface wave detection module, which comprises a plurality of detection unit arrays, each detection unit comprises a magnetic biasing module and an impedance matching module and is used for generating a local magnetic field on the surface of the pressure vessel and optimizing signal excitation and receiving; and the signal acquisition and preprocessing module is used for acquiring the surface wave signal of the detection module and carrying out denoising, filtering, curved surface coupling difference correction and lift-off correction on the signal to obtain high-quality signal data. According to the method, non-contact quantitative detection of the intergranular corrosion of the pressure vessel is realized through multi-field coupling electromagnetic acoustic surface wave detection and microscopic and macroscopic mapping and inversion, so that the problem that early corrosion is difficult to quantify due to the fact that contact ultrasound is mostly adopted in traditional pressure vessel detection and grain boundary micro-damage cannot be effectively identified is solved.