559 results about "Field monitoring" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention provides a frozen soil roadbed thaw collapse prediction method based on multi-source data and deep learning driving, and relates to the field of cold region engineering. The method comprises the steps that mathematical description is conducted on the thermal-mechanical coupling process of the frozen soil roadbed, and a frozen soil roadbed thermal-mechanical coupling physical model is obtained; the method comprises the following steps: acquiring frozen soil roadbed field monitoring data, remote sensing data, indoor test data and engineering environment data, and performing cleaning and normalization processing on multi-source data to obtain a multi-source data set for constructing and training a physical information neural network model; constructing a physical information neural network based on the frozen soil roadbed physical model and the multi-source data set; constructing a physical residual error of the physical information neural network model through a differential equation, and constructing a loss function based on a residual error item; and training the physical information neural network model to obtain a deep learning model for predicting thaw collapse deformation of the frozen soil roadbed. According to the method, the accuracy and reliability of thaw collapse deformation prediction of the frozen soil roadbed can be improved.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The invention discloses a valve vibration impact test method and system based on digital twinning, and belongs to the technical field of industrial equipment test and evaluation. The method comprises the following steps: firstly, deploying a multi-source sensor network, constructing a physical entity layer of a valve vibration impact test, collecting and preprocessing various physical monitoring data, and constructing a digital twinborn body layer; secondly, real-time data assimilation with a physical test bed is realized through parameter calibration and updating of a digital twinborn model based on various physical monitoring data and the digital twinborn layer; and finally, executing a virtual vibration impact test, realizing prediction and evaluation of global response and key part information of the physical entity according to a test result, and finally realizing interaction and performance prediction. And the valve vibration impact test is realized through the valve vibration impact test system. According to the method, the fault diagnosis capability, the early damage identification capability and the valve state prediction and evaluation capability can be remarkably improved, and the defect of insufficient field monitoring information is overcome.

The invention discloses an atmospheric ecological environment data synchronous monitoring method and system, and relates to the technical field of atmospheric environment monitoring, and the method comprises the steps: building a sky-air-ground global monitoring network, and achieving the unification of global data collection and space-time reference; on the basis of a hybrid interpolation model, performing hybrid interpolation and residual analysis on the atmospheric ecological environment monitoring data collected in a global manner, identifying a monitoring weak region, and performing positioning and labeling; based on monitoring weak area position distribution, starting an unmanned aerial vehicle to execute a weak area inspection task, and collecting weak area monitoring data through field monitoring to realize monitoring-prediction closed loop verification; actual measurement and prediction data are fused to reconstruct a global monitoring network, a digital twin platform is constructed through three-dimensional situation mapping, and global sensing and synchronous monitoring of the atmospheric ecological environment are achieved. According to the invention, by constructing a space-air-ground collaborative three-dimensional monitoring network, total element synchronous acquisition and dynamic integration of atmospheric pollution data are realized, and a seamlessly connected data base is provided for pollution traceability and trend prediction.

The invention relates to the field of industrial automation, in particular to an industrial robot glass processing system based on large model intelligent decision, and relates to an industrial robot key technology based on multi-source information integration and intelligent decision. Glass cutting is optimized through multi-physics field monitoring, the edge defects are reduced by 70%, and the rejection rate is reduced by 50%. And the edge grinding of the special-shaped glass is assisted by three-dimensional reconstruction, and the precision deviation is controlled within 0.1 mm. And multi-modal data improves the coating uniformity, the thickness deviation is within 5 [mu] m, and the film layer shedding is reduced by 80%. Multi-robot cooperation is achieved through reinforcement learning, the machining efficiency is improved by 60%, and the cost is reduced by 40%. And a digital twinning optimization process is adopted, so that the equipment failure rate is reduced by 60%.

The invention relates to the technical field of material processing control, and discloses a method for efficiently cutting a vacuum brazing soldering lug. The method comprises the following steps: firstly, receiving material parameters and target geometrical shape data of a to-be-cut soldering lug, and generating and optimizing an initial cutting track based on a preset algorithm; in combination with real-time temperature field monitoring data, thermal stress distribution is calculated through a finite element analysis method, and the cutting speed and laser power parameters are dynamically adjusted; and then the cutting precision and the equipment energy consumption are synchronously optimized through a multi-objective optimization algorithm, and final cutting parameters are output to a vacuum brazing control system. In addition, signals are collected in real time during cutting to recognize abnormity, and algorithm weights are calibrated by comparing geometric parameters after cutting. The cutting precision and efficiency can be effectively improved, equipment energy consumption is reduced, cutting abnormity is treated in time, self-optimization of the cutting process is achieved, and the method is suitable for the production field with the high requirement for vacuum brazing piece cutting.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention discloses an open pit coal mine slope monitoring and early warning method and system based on multi-modal satellite fusion AI, and belongs to the technical field of geological disaster monitoring. The method comprises the following steps: S1, collecting multi-source data; s2, transmitting data in real time; s3, data preprocessing and space-time registration; s4, multi-band InSAR phase reconstruction and deformation field extraction are carried out; s5, deeply fusing the multi-source data; and S6, AI intelligent prediction and grading early warning. According to the open pit coal mine slope monitoring and early warning method and system based on multi-modal satellite fusion AI, slope global millimeter-level precision continuous deformation field monitoring is achieved, vegetation terrain shielding interference is reduced, the monitoring range is expanded, meanwhile, monitoring blind areas are eliminated, data processing efficiency is improved, a high-reliability deformation field is constructed, and the early warning error and missing report rate is reduced.

The invention discloses a shield muck modified slurry grouting reinforcement effect evaluation method based on multi-data fusion, and belongs to the field of shield construction grouting. The method comprises the steps that during field monitoring, a monitoring section is selected according to tunnel geology and structure characteristics, and a sensor is installed; acquiring data of soil pressure, displacement and pore water pressure, calculating corresponding change rate and gradient, and analyzing change trend. According to the numerical simulation, a three-dimensional model is established based on geological exploration and the like to determine parameters, shield propulsion and grouting reinforcement are simulated, and stratum deformation, soil stress and slurry diffusion and permeation data are obtained. And in the data fusion link, the two types of data are subjected to standardization processing, grouting reinforcement effect grades are divided to determine a prior probability, a probability distribution model is established for monitoring indexes, and a posterior probability is calculated through a Bayesian formula to determine an evaluation result. According to the method, various data are integrated, the reinforcement effect can be comprehensively and accurately evaluated, a scientific decision basis is provided for shield tunnel construction, the construction quality and safety are improved, and waste soil resource utilization is optimized.

The invention provides a coal mine goaf early warning method and system, equipment and a medium, and belongs to the technical field of coal mine monitoring. The method comprises the steps that multi-field monitoring data of a target goaf at the current moment are acquired; constructing a multi-field coupling relationship based on the multi-field monitoring data, each coupling relationship being a coupling relationship formed by every two fields of monitoring data and used for representing a mutual influence relationship between the two fields of monitoring data; performing prediction based on the multi-field monitoring data and the multi-field coupling relationship to obtain target multi-field monitoring data and a target early warning state; the target multi-field monitoring data and the target early warning state are the multi-field monitoring data and the early warning state corresponding to the future target moment. According to the coal mine goaf early warning method, system and equipment and the medium provided by the invention, accurate prediction of future multi-field data of the goaf and accurate early warning of the future state of the goaf can be realized.

The invention relates to the technical field of geotechnical engineering disaster monitoring and early warning, and discloses a stratum rainfall seepage deformation coupling numerical simulation method and system and a storage medium, and the method comprises the steps: building a three-dimensional geologic model reflecting soft and hard rock interbed characteristics; converting the on-site rainfall data into hydraulic boundary conditions in real time; calculating a rock mass shear expansion volume change rate, determining a chemical damage acceleration factor, and updating a total damage variable; reconstructing a permeation tensor representing the dominant flow channel according to the space gradient of the damage variable; solving a fluid-solid coupling equation based on the permeability tensor, obtaining pore water pressure and calculating a displacement field; building a likelihood function by using field monitoring data to perform inversion correction on model parameters; and evaluating the stability based on the corrected calculation result and outputting an early warning signal. By constructing a mechanical coupling mechanism and a dominant flow channel model, precise simulation of a landslide evolution process in a complex geological environment is realized.

A temperature-rainfall-reservoir water combined testing system for a permafrost landslide model includes a tempered glass case with a permafrost landslide model and reservoir water provided therein; a temperature control system provided on the tempered glass case and including an incubator temperature control module, a landslide mass rear edge water tank temperature control module and an air circulation temperature control module; a seepage control system configured to realize seepage of the permafrost landslide model, and form the reservoir water in front of a landslide mass; a rainfall control system including a water tube, a flow controller and a nozzle, where the nozzle is provided in the tempered glass case, and located above the permafrost landslide model; and a multi-field monitoring system including a temperature sensor, a pore water pressure gauge, a moisture meter and a grating strain gauge that are provided in the permafrost landslide model.

The invention relates to the technical field of tunnel defect identification, in particular to a tunnel defect identification method based on a dielectric distribution diagram. According to the method, on the basis of AI rock-soil perspective radar field monitoring, gprMax simulation and laboratory electromagnetic data, a two-dimensional dielectric distribution diagram is generated through variational Bayesian inversion fusion. Through multi-scale spectral clustering and expert knowledge, a potential abnormal region is automatically identified, and then accurate classification and identification of a defect region are realized by adopting graph form constraint propagation and integrating a Transform-graph neural network model. And finally, projecting an identification result to an original image, and generating a visual defect labeling layer and a structured report. According to the invention, high-precision, automatic, visual and structured detection of tunnel structure defects is realized, the intelligent level and risk early warning capability of tunnel safety operation and maintenance are significantly improved, and the digitization and intelligent process of tunnel operation and maintenance management is promoted.

A multi-source spatio-temporal data fusion signal processing system based on a high-elasticity transmission conductor comprises a high-elasticity intelligent transmission conductor body and a low-noise light amplification system, and a multi-source data real-time fusion and intelligent processing method is used in the multi-source spatio-temporal data fusion signal processing system. The high-elasticity intelligent power transmission conductor body is a heat-resistant aluminum alloy stranded wire; the high-elasticity intelligent power transmission conductor body is combined with an optical fiber sensing technology and a meteorological technology to form a point, line and surface combined low-cost wide-area multi-element sensing technology, and a signal processing technology for mining wind dance characteristic parameters by virtue of a deep network and realizing multi-source spatio-temporal data fusion by virtue of big data association analysis is adopted, so that the understanding degree of field monitoring data is improved; the value of the multi-element sensing big data is excavated, and objective scientific basis and theoretical basis are provided for safe operation, efficient management and intelligent decision making of the ultra / extra-high voltage transmission line. The device is good in stability, large in bearing current, high in safety, low in cost and capable of reducing carbon emission, and has remarkable economic benefits and social benefits.

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, device and equipment based on field monitoring and real-time modelling, and the method comprises the steps: collecting a topographic data file and an earth surface data text of a target area to construct a sub-meter DEM digital elevation model; radar real-time base data, radar and ground rainfall historical observation data, brightness temperature data and geographical coordinate information of a target area are obtained to solve actual radar real-time base data, rain short-term forecast data and soil moisture data, and then the data are utilized to calculate a preset hydrological model to obtain a mountain torrent gully basin distributed hydrological model result; and establishing a hydrological-hydrodynamic coupling model according to the sub-meter DEM digital elevation model and the torrential flood gully basin distributed hydrological model so as to forecast the current flood state and the future flood state. Therefore, the problems that an existing mountainous area hydrological model cannot completely reflect the current state of a drainage basin, and the simulation error of a hydrodynamic model is large are solved.

The invention provides a hydraulic fracturing fracture net distribution form near-field monitoring method, which comprises the following steps of: arranging a plurality of roof monitoring holes in roadways on two sides of an underground coal mine working surface, installing electromagnetic signal sensors in each roof monitoring hole and on anchor rods of roadway stoping sides, and constructing a full-space monitoring network covering a hydraulic fracturing layer; a data interpretation processing module of the electromagnetic signal time-shifting imaging analysis system comprises a multi-field coupling mathematical model for analyzing and deducing real-time imaging of electromagnetic signals; in the hydraulic fracturing process, an electromagnetic signal sensor is used for monitoring a current field, an electromagnetic field and an ion diffusion field in the rock mass fracture development and expansion process, a monitoring result is substituted into an electromagnetic signal time-shifting imaging analysis system for calculation and feature value extraction, and the fracture net distribution form is deduced through the feature value. According to the invention, the expansion path of the fracturing fluid in the rock stratum can be monitored in real time, the three-dimensional distribution form of the hydraulic fracturing fracture network can be inverted, and a new technical support is provided for the inspection of the hydraulic fracturing pressure relief effect of the underground coal mine.

The invention belongs to the technical field of geological engineering monitoring and multi-physics field intelligent sensing, and discloses a separation grouting slurry migration monitoring method and system, and the method comprises the steps: constructing an acoustics-optics-electricity-electromagnetism multi-physics field coupling monitoring system; a grouting hole and a monitoring drilling hole array are arranged in a target stratum, a distributed optical fiber sensor, a drilling hole ultrasonic imager, a resistivity measuring device and a transient electromagnetic detection device are installed, and a three-dimensional monitoring network is formed; synchronously acquiring sound field, light field, electric field and magnetic field data; four-field monitoring data and overlying strata multi-parameter observation data are fused, a coal seam mining three-dimensional overlying strata separation model is constructed, and a slurry diffusion three-dimensional visualization model is generated through numerical simulation; and based on the four-field monitoring response difference of the separation space before and after grouting, in combination with a monitoring frequency domain correction model of multiple indexes of grout diffusion, the diffusion range, compactness and filling effect of the grouting grout are evaluated, and abnormal early warning is triggered.

The invention is suitable for the technical field of concrete pouring, and provides a temperature real-time monitoring method and system for fan foundation concrete pouring, and the method comprises the steps: obtaining the temperature field monitoring data of concrete, building a mapping relation between a plurality of measurement points and a finite difference grid through a space coordinate mapping method according to the space coordinates of the measurement points, and obtaining the temperature field monitoring data of the concrete; generating a mapping table; calculating a temperature difference value between adjacent measuring points in the mapping table, and distributing the temperature difference value to a corresponding three-dimensional grid by using a grid interpolation method to obtain initial temperature difference value distribution data; constructing a three-dimensional temperature gradient tensor field based on the initial temperature difference distribution data by using a high-order difference method, and determining a temperature gradient change trend according to the three-dimensional temperature gradient tensor field; if the temperature gradient change trend is larger than or equal to a preset change trend threshold value, internal thermal stress distribution of the concrete is calculated through a thermal-mechanical coupling model, thermal stress distribution data are obtained, and the internal temperature of the concrete can be accurately monitored in real time.

The invention relates to an underground cavern surrounding rock three-dimensional vibration measurement method and device, and the method comprises the steps: generating a light spot meeting a preset clear condition through beam splitting laser; acquiring the vibration condition of the laser source and the vibration condition of the photographic lens; cutting the tracking record image of the light spot, and respectively calculating the relative displacement time history of the feature points and the boundary of the light spot; based on curvature, length and area changes of the circular light spot in different directions, a vertical relative displacement time travel curve of the light spot perpendicular to the surface of the surrounding rock of the target underground cavern is calculated; and differentiating the obtained plane and vertical displacement time histories to obtain corresponding vibration velocity time histories, and combining to construct the actual three-dimensional vibration velocity of each light spot. According to the embodiment of the invention, the monitoring cost is reduced, the field monitoring efficiency and the monitoring data volume are improved, the applicability in the construction environment with poor light of the underground cavern is higher, the defects of a high-speed photographing single lens in three-dimensional data representation are overcome, and effective monitoring and evaluation of three-dimensional vibration of the underground cavern are realized.

The invention relates to the field of underground multi-physics field monitoring, in particular to an intelligent well completion underground multi-parameter fusion monitoring system, which comprises a functional casing acquisition module, an acoustic-electric-magnetic excitation module, an acoustic-electric-magnetic excitation module and a multi-parameter fusion monitoring module, wherein the functional casing acquisition module is integrated with a strain sensing element, a temperature sensing element, a stress sensing element and a resistivity sensing element; a double-domain inversion fusion module based on a multi-physics field equation and a graph time sequence causal network; the edge closed-loop regulation and control module adopts entropy modulation dual strategy reinforcement learning; and the communication interface has time slot encryption and version verification functions. According to the method, second-level data alignment, minute-level adaptive regulation and control and hour-level global optimization are realized, the crack conductivity is improved, the energy consumption is reduced, and yield increase and well completion integrity are considered.

The invention provides a cabinet three-dimensional temperature field monitoring method and device, and the method comprises the steps: arranging four temperature sensors at the positions of left and right columns at the rear part of a cabinet and the left and right middle positions of the rear side of the top of the cabinet respectively, and forming vertical and horizontal coordinate distribution points covering a key heat distribution region of the cabinet; collecting temperature data of the four temperature sensors at preset coordinate positions, and inputting the temperature data into a three-dimensional temperature field reconstruction algorithm model; processing the temperature data by adopting a bilinear interpolation method, and calculating the temperature value of any position in the cabinet; and according to a hot air convection rule, introducing height direction temperature gradient correction into the three-dimensional temperature field reconstruction algorithm model so as to eliminate the influence of heat convection on a temperature monitoring result. The three-dimensional temperature distribution in the cabinet can be accurately reflected, the temperature monitoring precision and real-time performance are improved, and the installation and maintenance cost is reduced.

The invention discloses an offshore wind power cable line fault early warning method and system, and relates to the technical field of cable line fault early warning, and the method comprises the following steps: laying a three-dimensional distributed optical fiber temperature array along an offshore wind power cable, and monitoring the cable thermal field space distribution in combination with an active acoustic dispersion imaging technology; acquiring initial evolution trajectory data of the edge high-temperature region; and on the basis of the initial evolution trajectory data, constructing a coupling relationship among the ocean current parameter, the sediment disturbance characteristic and the cable heat conduction parameter, forming an ocean current-sediment-heat conduction coupled digital twin model, and dynamically correcting a virtual scene of the cable burial depth in the model through a recursive filtering algorithm. Through combination of thermal field monitoring, digital twin modeling and intelligent intervention, accurate early warning and dynamic response of offshore wind power cable faults are realized, the operation and maintenance efficiency is improved, system self-optimization and cross-scene migration are supported, and the safety and intelligent level of cable operation are significantly enhanced.

The invention discloses a temperature field real-time monitoring method and system based on special ceramic sintering, and relates to the field of temperature monitoring of special ceramic sintering, and the method specifically comprises the following steps: S1, processing a sensor array; s2, construction of multi-parameter data acquisition; s3, a temperature field reconstruction and prediction model based on multi-physics field coupling; and S4, temperature field real-time monitoring and abnormity early warning are carried out. According to the temperature field real-time monitoring method and system based on special ceramic sintering, the temperature sensor is preset in the green body, a three-dimensional temperature field reconstruction function is combined, discrete sensor node real-time temperature data can be converted into a continuous three-dimensional temperature field cloud picture, temperature distribution of the surface of the green body can be clearly presented, and the temperature field can be monitored in real time. And the temperature gradient change from the surface layer to the core can be more accurately restored, so that an operator can intuitively master the temperature state of the whole green body, the situation that the whole process is misjudged due to local data is avoided, and the comprehensiveness and accuracy of temperature field monitoring are fundamentally improved.

The invention provides a temperature rise test system, and relates to the field of heating characteristic testing of power equipment. The system comprises a power supply control system, an electric field regulation and control system, an environment simulation system, a discharge detection system, a three-dimensional temperature field monitoring system and a processing system. The power supply control system simulates equipment operation voltage; the electric field regulation and control system simulates a non-uniform electric field in the equipment; the environment simulation system simulates external environment conditions; the discharge detection system detects the partial discharge condition of the equipment under the simulation condition, obtains discharge parameters and transmits the discharge parameters to the processing system; the three-dimensional temperature field monitoring system monitors temperature distribution on the surface and in the equipment and transmits the temperature distribution to the processing system; and the processing system determines an association relationship between the partial discharge condition and the temperature rise based on the temperature distribution and the discharge parameters. Through cooperation of multiple systems, the influence of the external environment and internal electrical parameters on heating of the equipment is comprehensively simulated, and the heating behavior of the equipment under the actual working condition is reflected more truly, so that the accuracy of temperature rise simulation is improved.

The invention discloses a self-adaptive optimization control system and method for the combustion process of a biomass generator set. The self-adaptive optimization control system comprises a multi-dimensional sensing array module, an edge calculation module, a self-adaptive decision server, an execution mechanism cluster and a closed-loop verification terminal. The multi-dimensional sensing array module comprises a temperature field monitoring unit, a flue gas component analysis unit and a fuel characteristic detection unit; a data fusion processor is arranged in the edge calculation module; the self-adaptive decision-making server carries a dual-module decision-making engine; the executing mechanism cluster comprises a frequency conversion fan, a precise feeding machine and an adjustable fire grate. Through multi-source sensing data fusion and a self-adaptive decision-making mechanism, real-time response to biomass component fluctuation and load change is achieved, and the combustion efficiency is improved; a deep learning prediction model is combined, the air-coal ratio and the hearth temperature set value are adjusted in advance, and CO emission is reduced; the manual intervention frequency is reduced through an optimization mechanism, and the equipment maintenance period is prolonged.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention discloses a dangerous rock falling motion feature analysis method based on discrete element simulation, and belongs to the technical field of engineering geology. Comprising the steps that dangerous rock data are collected through multiple means and preprocessed, and a three-dimensional visual dangerous rock mass initial model is constructed; based on the three-dimensional visual dangerous rock mass initial model, constructing a discrete element model, defining contact types and parameters among joint units by adopting a Hertz-mindlin contact theory, and determining normal contact force; based on the discrete element model and the normal contact force, a discrete element simulation technology is adopted, multi-physical field factors are considered to simulate dangerous rock falling movement, a discrete element model simulation result is obtained, a dynamic updating method is adopted for joint failure judgment, and the discrete element model is verified through an indoor physical model test and field monitoring data. And designing and implementing a prevention project according to a simulation result, thereby effectively preventing dangerous rock falling disasters and ensuring project safety.

The invention provides an online monitoring method for a curing process of a resin-based composite material, and belongs to the technical field of curing monitoring of composite materials. In the curing process of the resin-based composite material, a conductive patch is placed on the surface of the resin-based composite material to form a laminated structure consisting of the conductive patch, a dielectric layer containing resin and a high-reflection substrate, and the curing process of the resin-based composite material is deduced in real time by using a microwave signal to radiate the laminated structure and monitoring peak valley characteristics and temperature changes of a reflection signal. According to the method, a microwave signal is utilized to irradiate a laminated structure, and the curing process of the resin-based composite material is judged by monitoring the peak valley characteristics and temperature change of a reflected signal in real time. The in-situ online curing process monitoring method based on the microwave signal and the conductive patch film sensor not only can realize real-time online monitoring of the resin curing key point and the curing degree, but also has the remarkable advantages of being capable of adjusting the monitoring position in real time, realizing curing degree field monitoring and the like.

The invention discloses a foundation pit excavation and support cooperative control method based on finite element dynamic feedback, which comprises the following steps: S1, establishing a foundation pit support finite element model, defining constitutive parameters of a soil body small strain hardened soil model HSS and support structure rigidity, and obtaining an initial displacement field and a stress field through step-by-step excavation simulation; and S2, iteratively calibrating parameters based on the simulation result and the field monitoring data, and constructing a construction stage dynamic adjustment threshold library which comprises a displacement warning value (UXmax), a stress safety coefficient (SF is greater than or equal to 1.3) and a support activation time sequence. According to the method, dynamic parameter iteration is achieved, a finite element model is reversely optimized through monitoring data, and the parameter solidification problem of a traditional method is solved; automatic closed-loop control is achieved, manual intervention is reduced, and the response speed is increased to the minute level; and economical efficiency is improved, waste of materials such as steel is reduced through accurate control, and the comprehensive cost is reduced by 15%-25%.