1762 results about "Spatial distribution" patented technology

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention relates to the technical field of flood early warning, and discloses an urban flood disaster early warning method and system based on artificial intelligence, and the method comprises the steps: collecting five types of information, i.e., meteorological perception, hydrological monitoring, geographic space, urban operation and social perception in real time, and obtaining multi-source data with precise space-time coordinates; through preprocessing, gridding space-time alignment and key feature screening, rainfall accumulation and confluence evolution related features are extracted; constructing a physically constrained space-time fusion deep learning model, and outputting a future ponding depth prediction result in combination with a multi-head attention mechanism; environmental changes such as urban terrains and drainage facilities are adapted through incremental updating and transfer learning; and fusing the ponding depth, the influence range and the regional vulnerability characteristics to generate multi-level early warning, and synchronously outputting a spatial distribution map, a time evolution trend and affected object evaluation information. According to the invention, urban flood control and disaster reduction decision making and public accurate risk avoiding can be effectively supported.

The embodiment of the invention provides a mineral target area intelligent delineation method and system based on multi-source heterogeneous geoscience data, and relates to the technical field of mineral resource exploration, and the method comprises the steps: dividing a multi-source heterogeneous geoscience data set comprising geological structure, geochemistry, geophysics and remote sensing image data into a plurality of data modules according to a geological action influence range; each data module records spatial distribution and attribute information, then a dynamic mapping relation between the data modules and a mineral product mineralization mechanism is constructed, a mineralization association chain between the data modules is generated based on the dynamic mapping relation, a cooperative change rule of nodes and connection paths is defined, then a target region feature set conforming to the mineralization association chain is screened, and a target region feature set is obtained; and finally, according to a collaborative change rule, judging the integrating degree of a target region feature set space unit and a mineralization condition, dynamically adjusting a space boundary range, and generating a mineral target region delineation result, thereby comprehensively utilizing multi-source data, deeply mining a relationship with a mineralization mechanism, and improving the accuracy and reliability of mineral target region delineation.

Disclosed in the present invention is a three-dimensional Gaussian splatting optimization method for an unposed input. The method comprises: for an input image, predicting a ray bundle distribution by using a ray prediction model, to obtain distribution features in the form of ray bundles; on the basis of the distribution features in the form of ray bundles, calculating a camera pose; for the ray bundle distribution, performing sampling on the basis of the volumetric density of light rays, to obtain an initial spatial distribution of a three-dimensional Gaussian point cloud focused on a visual center area; for the input image, obtaining a visible shell by means of view frustum projection and object-mask computation; on the basis of the initial spatial distribution of the three-dimensional Gaussian point cloud and the visible shell, performing three-dimensional Gaussian splatting scene training, to obtain a three-dimensional scene reconstruction model satisfying a preset loss function criterion, the loss function comprising a training regularization term for a camera pose parameter. The present invention provides important scene initialization information for three-dimensional Gaussian splatting training, and significantly improves the quality and the richness of detail of the final three-dimensional structure.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The invention discloses a regional scale crop nitrogen dynamic regulation and control method and device. The method comprises the following steps of obtaining multi-source data and constructing a space-time database; selecting a crop growth model, and calibrating and verifying the crop growth model based on the space-time database to obtain the crop growth model and generate a localized model parameter set; dividing a target area into space units, inputting soil data, meteorological data and a localized model parameter set of each space unit to a crop growth model, simulating crop growth processes of different nitrogen gradients, extracting optimal biomass and critical nitrogen concentration data point pairs of each space unit, and performing fitting to generate a spatialized pCNDC parameter map layer; inverting an actual biomass and nitrogen concentration spatial distribution map based on remote sensing data, calculating critical nitrogen concentration and nitrogen nutrition index of each spatial unit in combination with a pCNDC parameter map layer, and generating a spatial distribution map; when the nitrogen nutrition index is lower than a preset threshold value, the nitrogen deficiency amount is calculated, and a variable fertilization prescription map is generated.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention discloses a prediction method for quantifying the development degree of undermining diseases of rammed earth cultural relics, and relates to the field of cultural relic protection. The method comprises the steps of obtaining three-dimensional laser point cloud data and close photography three-dimensional model data of the rammed earth cultural relics; fusion of the three-dimensional laser point cloud model and the close photography three-dimensional model is realized, and a rammed earth cultural relic undermining disease three-dimensional model is constructed; based on the rammed earth cultural relic undermining disease stereoscopic model, quantitative indexes of undermining disease development are extracted, and an undermining disease development prediction model is constructed and optimized; and generating and outputting a quantitative report of the development degree of the undermining disease of the rammed earth cultural relics. According to the method, three-dimensional laser scanning and close photogrammetry are combined, differentiated routes are designed for areas with different heights, alignment of cross section contours of different models is realized based on Fourier description, a Poisson curved surface reconstruction technology is combined with a hole filling algorithm to complement missing parts of the models, spatial distribution and morphological characteristics of undercut diseases are intuitively presented, and the method is suitable for large-scale popularization and application. The change rule of undercut diseases along with time is quantified, and protection work is promoted to be changed from passive repair to active prevention.

The invention discloses a GIS interactive visualization processing method and system for geographic coordinate data, and relates to the field of GIS and the technical field of computers. The method comprises the following steps: collecting multi-source geographic coordinate data, preprocessing the multi-source geographic coordinate data, and standardizing the preprocessed multi-source geographic coordinate data to obtain target multi-source geographic coordinate data; loading the target multi-source geographic coordinate data to a GIS map, and displaying the target multi-source geographic coordinate data through different layers; automatically detecting and marking data quality based on a display interface corresponding to the GIS map; and correcting the detected problem data based on a GIS map by adopting an interactive data correction mode. According to the method and the device, the technical problem of low data processing efficiency caused by difficulty in intuitively reflecting the spatial distribution characteristics and the association relationship of the data because the current geographic coordinate data treatment method mostly depends on the traditional data processing means and presents the data in the forms of texts and tables is solved.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention discloses a rock damage assessment method fusing wave velocity and microscopic fracture, and belongs to the technical field of rock damage assessment, and the rock damage assessment method comprises the following steps: arranging a wave velocity testing device on a rock sample, and respectively making an unloaded rock sample and a damaged snapshot rock sample under different stress loading; microcosmic fracture information of the damaged snapshot rock sample is identified, a three-dimensional wave velocity model of the damaged rock sample under different stress loads based on a wave velocity test is constructed, and then a macro-micro scale fusion multi-dimensional data set is constructed; based on the macro-micro scale fusion multi-dimensional data set, constructing and training a rock damage identification model; and inputting the characteristic parameters of the rock sample to be evaluated into the rock damage identification model, identifying the damage distribution characteristics of the rock sample on the spatial scale, and evaluating the rock damage degree. According to the method, accurate prediction and evolution process evaluation of the rock damage degree and spatial distribution can be realized, and a reliable technical means is provided for disaster early warning, stability evaluation and risk prevention and control in complex rock mass projects such as deep mines and tunnels.

The embodiment of the invention discloses a mineral resource exploration data analysis method and device based on three-dimensional space modeling. The method comprises the steps that back-flushing pushing-covering structure feature information is extracted through structure interface occurrence measurement, lithology combination analysis and structure movement trace recognition and converted into geologic structure feature parameters; stratigraphic age data and lithology distribution information are integrated, and a three-dimensional stratigraphic model is constructed with a stratigraphic superposition relation as a constraint; geophysical data are imported to extract geophysical response characteristics of stratums with different depths, and lithologic boundary correction is combined to obtain deep fracture characteristic parameters; sedimentary system division is carried out on lithofacies paleogeographic information, and deep fracture characteristic parameters are fused to establish a fracture system three-dimensional model; the multi-feature association relationship is identified, the ore body prediction model is established, the spatial data is input to obtain the three-dimensional visual prediction result of the hidden ore body spatial distribution, and the exploration efficiency and accuracy in the deep phosphorite exploration process are improved.

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention provides a water quality inversion method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring wide-area hyperspectral image data, regional multispectral image data and actually measured water quality parameters; performing correction and space-time registration processing on the wide-area hyperspectral image data and the regional multispectral image data, and performing feature fusion to generate a fusion feature vector; based on the fusion feature vector, performing inversion through a first inversion model to obtain a first type of water quality parameters; based on the first type of water quality parameters and the actually measured water quality parameters, performing inversion through a second inversion model to obtain second type of water quality parameters; a water quality parameter spatial distribution diagram is obtained based on inversion of the first type of water quality parameters and the second type of water quality parameters; and according to the water quality parameter spatial distribution map, when the actually measured water quality parameter is greater than a preset threshold value, generating an early warning signal. Based on the scheme of the invention, the precision, timeliness and automation level of water quality parameter inversion in a complex water area environment can be improved.

The invention provides an AGV navigation texture feature extraction and identification method, which comprises the following steps: analyzing the spatial distribution position of a plurality of extracted matching peak values according to the matching peak values, counting the number of the peak values and the repeated spacing between the peak values, and identifying a positioning fuzzy region caused by texture cycle repetition; scanning a local aperiodic mark position according to the identified positioning fuzzy region, and performing uniqueness verification based on the geometric shape feature and color attribute of the mark to obtain a unique anchor point coordinate and a mark type; according to the unique anchor point coordinate, combining with the tile texture data to extract a texture repetition interval, analyzing the size of the texture repetition interval and a preset threshold value, and determining a periodic intensity value; dynamically adjusting a position screening range according to the periodic intensity value, establishing a screening constraint condition, and applying the screening constraint condition to a phase correlation method to obtain a preliminary positioning candidate point set;

The invention discloses a poplar degenerated forest stand accurate restoration method, and relates to the technical field of poplar degenerated forest stand restoration, and the method comprises the steps: obtaining a multi-temporal satellite remote sensing image and an unmanned plane multi-spectral image of a target area; in combination with soil moisture content, rhizosphere respiration rate and micrometeorological data collected by ground Internet of Things sensing nodes arranged in a forest stand, geographic coordinate registration and time sequence alignment are performed on the data to form a multi-source observation data set; the method comprises the following steps: extracting vegetation indexes, short-wave infrared reflectivity, soil moisture and rhizosphere physiological parameters based on a multi-source observation data set, constructing a degradation diagnosis index fusing spectral change, moisture stress and root activity, calculating the degradation degree of each pixel, and generating a degradation level spatial distribution diagram; the area with the degradation degree reaching a set threshold value is determined as a to-be-repaired area; and selecting representative poplar individuals in the to-be-repaired area, measuring the photosynthetic ability of the individuals, and calculating the individual competition intensity in combination with the three-dimensional point cloud data.

The invention relates to a multi-dimensional high-resolution turbine blade heat-flow coupling field synchronous measurement method and system, and the method comprises the steps: obtaining a target spot image of known space distribution in a measurement space, and building a three-dimensional space mapping function; carrying out particle matching on the velocity field measurement original image based on a three-dimensional space mapping function, and reconstructing a three-dimensional particle trajectory by adopting a self-adaptive grid algorithm combined with machine learning to obtain a high-resolution three-dimensional velocity vector field; acquiring a temperature field measurement original image, performing three-dimensional space registration according to a three-dimensional space mapping function, further calculating voxel-level temperature distribution, and inverting a three-dimensional temperature field; and carrying out coupling analysis on the three-dimensional velocity vector field and the three-dimensional temperature field, and outputting a three-dimensional heat-flow coupling cloud picture. According to the invention, multi-dimensional and high-precision synchronous measurement of the temperature field and the velocity field in the complex flow field of the turbine blade is realized, the spatial resolution and dynamic response capabilities are remarkably improved, and a reliable experimental basis is provided for analysis and optimization of the cooling performance of the blade.

The invention provides a geological disaster intelligent early warning method based on fusion of a physical information neural network and space-air-ground monitoring, and the method comprises the following steps: S10, obtaining geological structure features of different depths under the ground of a city, and recognizing the spatial distribution and thickness of an underground abnormal body; acquiring a surface deformation time sequence, crack and landform information, a three-dimensional point cloud and a continuous vibration signal of a surface-shallow stratum; s20, constructing a three-dimensional twin substrate, performing space-time alignment and resampling on multi-source heterogeneous data, mapping the data to a three-dimensional grid of a unified coordinate system, and extracting and fusing geological disaster precursor features; s30, taking the generated fusion feature field as an input training neural network model, carrying out geological disaster forward prediction and parameter inversion, and outputting future stability probability distribution, a potential slip plane and key parameter evolution; and S40, adaptively adjusting a risk threshold and an early warning rule, constructing an incremental data set, and carrying out periodic incremental training and parameter optimization on the physical information neural network in the step S30.

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 a stacked cargo positioning method and device. The method comprises the steps of identifying acquired image data, constructing a dynamic grid based on a spatial distribution rule of an initial mask, and performing local fine segmentation on missing grids in the dynamic grid to obtain a total mask set; performing mask quality optimization and anomaly elimination on the total mask set based on the multi-dimensional matching degree of the dynamic grid to obtain a correct mask set; performing coordinate system reverse transformation on the correct mask set based on the original coordinate system of the image data to obtain a correct binary mask, and positioning the stacked goods; according to the method, the acquired image data is identified, a dynamic grid is constructed, and a mask quality optimization and anomaly elimination mechanism is combined, so that the problem of segmentation noise caused by factors such as non-uniform illumination and cargo shielding in a warehouse environment is effectively solved, high-precision positioning on low-computing-power equipment is realized, and the method is suitable for large-scale popularization and application. The problem that the positioning precision of stacked goods on low-computing-power equipment such as an embedded platform is insufficient is solved.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention discloses a hydraulic engineering monitoring and early warning system and method based on artificial intelligence, and belongs to the technical field of hydraulic engineering. Gate operation data are collected through a gate sensor group to form a gate state data set; floating object data are collected through a high-definition camera and an image recognition technology, and the floating object data are marked as floating substance points containing volume, form and coordinates; dividing a flood discharge area into grid units, and recording floating object information in each grid to form a spatial distribution data set; predicting a migration path and duration based on the water flow data and the floating object parameters, marking a grid overlapped with the gate area as a core grid, and identifying a high-risk grid; dynamically associating floating object parameters and gate state parameters in the core grid, calculating an association coefficient, and positioning a potential contact risk grid; according to the floating object accumulation rate and the correlation coefficient, early warning grades are divided, and gate blockage early warning is generated; meanwhile, high-risk floating objects passing through the gate are tracked, and the path of the high-risk floating objects is predicted to generate salvage early warning reminding.

The invention provides a multi-modal hydrological data and underlying surface element data processing method and a computer system, and the method comprises the steps: collecting multi-source observation data in a hydrological cycle process through distributed observation nodes, and carrying out the spatial-temporal feature dynamic decoupling operation of the multi-source observation data, separating to obtain a hydrological dynamic feature set with a time sequence characteristic and an underlying surface static feature set with a spatial distribution characteristic; calling a preset underlying surface parameter adjustment model, performing parameter dynamic optimization on the underlying surface static feature set in combination with the hydrological dynamic feature set, and generating a dynamic parameter configuration set containing a geographic response coefficient; hydrological-underlying surface coupling correction processing is carried out based on the dynamic parameter configuration set, and a hydrological variable set is obtained through optimization; and performing spatio-temporal evolution law analysis according to the hydrological variable set, and outputting a hydrological system evolution trend map and a disaster risk early warning index. According to the invention, a more reliable basis can be provided for hydrological system analysis and disaster risk management.

The invention relates to the technical field of automatic control, in particular to a detection equipment attitude real-time correction system which comprises an attitude trajectory extraction module, a trajectory stability evaluation module, an interference area recognition module, a light spot trajectory reconstruction module and an attitude correction control module. According to the invention, through comprehensive extraction of the direction change trend, the angle difference and the trajectory overlapping condition in the light spot image sequence, the trajectory trend of the attitude change between continuous frames is established, so that the identification of the attitude offset evolution process is more detailed, the attitude change stability analysis is realized in the time sequence dimension, and the accuracy of the attitude change is improved. Interference area identification is carried out by means of luminance continuity and spatial distribution consistency of light-emitting pixels, real light spots and lagging residual signals can be effectively distinguished, a light spot track path is reconstructed on the basis of interference removal, and real-time calculation of a track dynamic offset direction and an angle change amplitude is combined with track correction rule setting, so that the accuracy of track correction is improved. And the specific correction action matching of the deviation state is realized.

The invention discloses a layered densification Gaussian sputtering method based on visibility, and relates to the technical field of artificial intelligence and computer vision. According to the scheme, an initial three-dimensional Gaussian primitive set is generated based on sparse multi-view observation data, and initial scene representation is established by extracting spatial distribution parameters, morphological parameters and radiation parameters; performing fusion analysis on the Gaussian primitives based on the multi-dimensional observability parameter set to obtain comprehensive observability index data; executing hierarchical clustering according to the index data, and constructing a multi-layer Gaussian structure of a significant layer, a transition layer and a background layer; performing density enhancement, geometric continuity constraint and parameter update processing on different levels of Gaussian structures, and generating a rendered image of a target view angle under a volume light traveling and transparency hybrid mechanism; according to the method, continuous reconstruction of a scene structure and accurate expression of radiation characteristics can be realized under the sparse view condition, and the geometric fidelity and rendering consistency of new view angle synthesis are improved.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention relates to the technical field of machine vision and intelligent quality inspection, in particular to a bolt surface tiny defect feature extraction method based on an image enhancement technology, which comprises the following steps: acquiring an original two-dimensional image sequence of a bolt surface acquired at the same imaging view angle and a preset multi-light-source angle, and calculating a surface normal vector field and a surface albedo; obtaining basic decoupling feature data including two-dimensional coordinates, a gray value, a surface albedo and a normal vector; reconstructing three-dimensional point cloud data of the bolt surface by using the surface normal vector field and a preset integral boundary condition, and determining a target topological region set and a corresponding spatial enhancement weight; enhancing the basic decoupling feature data based on a spatial enhancement weight to generate a defect risk feature map; calculating a defect risk value based on the feature intensity value, the spatial distribution, the connected region area of the high-risk feature and the spatial aggregation degree, and respectively generating an alarm instruction, a recheck instruction or a qualification instruction; according to the method, the sensitivity to hidden cracks in a weak area is greatly improved.

The invention relates to the technical field of image recognition, in particular to a plate defect recognition and detection method based on an image recognition model. The method comprises the following steps: aiming at the technical problems of insufficient texture direction identification precision and difficulty in defect type distinguishing in a traditional plate surface detection method, the invention provides an intelligent detection solution based on polarization imaging. A multi-angle polarization measurement system is constructed through a fixed polarizer and a rotatable analyzer, and omnibearing polarization characteristic collection of the surface of a steel plate is achieved. A polarization intensity change model is established based on the Malus law, the polarization degree and polarization angle parameters of each pixel point are extracted, and a polarization parameter spatial distribution diagram reflecting the surface microstructure is constructed; through statistical analysis of polarization angle distribution characteristics, a rolling direction dominant angle is identified by adopting a peak detection algorithm, and accurate classification and severity evaluation of defects are realized in combination with anisotropy characteristic analysis.

The invention relates to a subtropical forest aboveground carbon reserve estimation method fused with a multi-modal remote sensing technology. The method comprises the following steps: acquiring and preprocessing multi-modal remote sensing data of a research area; feature extraction is carried out based on the preprocessed multi-modal remote sensing data; adopting a multi-scale feature fusion method to perform complementary fusion on the extracted feature data; based on the fused feature data, performing model training by adopting a hybrid machine learning method to obtain a carbon reserve estimation model; and estimating the carbon reserve by using the carbon reserve estimation model. According to the invention, subtropical forest aboveground carbon reserves can be accurately estimated, and spatial distribution characteristics and main influence factors of the subtropical forest aboveground carbon reserves can be systematically analyzed.

The invention belongs to the technical field of environment monitoring, and discloses an ecological environment anomaly detection and early warning method based on artificial intelligence. The emission characteristic fingerprint database is constructed according to enterprise production process characteristics by acquiring multi-point pollutant concentration, meteorological parameters and enterprise space distribution data in a park. By analyzing the space-time diffusion trajectory of pollutants, identifying abnormal areas and calculating reverse traceability parameters, a space-time superposition inversion model of multi-source emission is constructed. A multi-enterprise contribution degree separation matrix is generated based on fingerprint matching degree weighted calculation, and accurate separation of emission contributions of all enterprises in the mixed pollution field is achieved. By monitoring pollutant concentration fluctuation characteristics in real time, contribution degree weight distribution is dynamically adjusted, and the accuracy of a monitoring result is ensured. And accurately determining a responsibility subject with excessive emission and generating an early warning signal. According to the invention, accurate identification and responsibility determination of the pollution source can be realized in a complex multi-source emission environment, and the accuracy and fairness of industrial park environment supervision are effectively improved.