2222 results about "Multispectral image" patented technology

The invention provides an on-line quality monitoring method and system based on optical multispectral fusion, and relates to the technical field of multispectral fusion, and the method comprises the steps: collecting visible light and infrared multispectral images of an object to be measured, carrying out the preprocessing and registration, inputting the images into a variational auto-encoder, and carrying out the probability distribution modeling, and obtaining a spectrum basis vector and an initial abundance graph; and constructing a multi-scale morphological feature pyramid, performing graph segmentation in combination with spatial information to obtain a local consistency region, and fusing the local consistency region with the initial abundance graph to obtain an enhanced abundance feature. And fusing the spectrum basis vector and the enhanced abundance feature by adopting a multi-head cross attention mechanism to obtain a multi-spectrum fusion feature. And inputting the fusion features into a dynamic neural network, analyzing a topological connection relationship between nodes, obtaining a structured representation, performing discriminative enhancement, calculating node significance distribution, determining a quality defect area, analyzing a formation mechanism of the quality defect area, and finally generating a quality monitoring report.

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention relates to the technical field of mold detection, and discloses a precision plastic mold detection method, device and equipment and a storage medium, the method comprises the following steps: carrying out multispectral imaging and three-dimensional laser scanning on the surface of a precision plastic mold to obtain multispectral image data and three-dimensional point cloud data; performing multi-scale adaptive threshold segmentation to obtain a first three-dimensional model; performing tensor voting surface reconstruction and texture recovery to obtain a second three-dimensional model; performing global optimization to obtain a target three-dimensional model; extracting multi-view features and carrying out defect classification and positioning to obtain defect category and distribution information; according to the defect category and distribution information and the geometrical characteristics of the precise plastic mold, an optimal scanning detection path is calculated by using a sequence convex optimization algorithm guided by ray tracing, and a self-adaptive mold detection strategy is generated, so that the coverage rate and the precision of detection are improved, the detection time and the resource consumption are balanced, and the detection efficiency is improved. And efficient, comprehensive and accurate mold quality control is realized.

The invention provides a crop whole growth cycle identification method and system based on deep learning, and the method comprises the steps: obtaining a multispectral image sequence of a target crop in a continuous time period through an image collection device, and carrying out the standardized illumination adjustment processing of each image frame in the multispectral image sequence, obtaining a standard illumination image set corresponding to the multispectral image sequence; carrying out crop region segmentation processing on each image, extracting a local feature region related to a target crop, generating a standardized crop image set containing the local feature region, inputting the standardized crop image set into a pre-trained multi-task deep learning model, extracting combined features through parallel convolution branches, and carrying out image segmentation processing on the combined features; and executing cross-stage correlation analysis in the full connection layer, outputting a multi-task classification result corresponding to the target crop growth cycle, and generating a stage identification report corresponding to the target crop full growth cycle. According to the invention, the crop whole growth cycle identification precision and the agricultural management efficiency can be improved.

The invention relates to the technical field of intelligent detection, in particular to a container surface damage detection method and device based on machine vision, and the device consists of a multi-modal data acquisition module, a dynamic compensation processing module, a multi-modal data fusion module and a damage classification and positioning module. The multi-modal data acquisition module generates a high-density three-dimensional point cloud through double-laser line scanning, and acquires a multispectral image, an infrared thermogram and a real-time motion state. The dynamic compensation processing module integrates an optical flow method and acceleration data to realize sub-pixel-level motion compensation, and combines adaptive exposure control to optimize the imaging quality under complex illumination. The multi-modal data fusion module strengthens defect feature expression through space-time alignment and a double-branch collaborative attention mechanism, and model parameters are reduced through lightweight network design. And the damage classification and positioning module adopts an improved deep network to realize defect classification and complete millimeter-level three-dimensional positioning. The automatic detection requirement of a port is met, and the problems of poor container detection precision, poor adaptability and the like are solved.

The invention provides a semiconductor defect detection and process optimization method based on deep learning. The method comprises the following steps: acquiring semiconductor wafer surface multispectral image data, wafer reflectivity distribution data and thickness distribution data through multispectral imaging equipment; performing multi-modal fusion preprocessing to generate a defect enhanced image; extracting micro-nano scale topological features of the surface of the wafer through a lattice structure guided deep learning network; performing frequency domain-space domain joint modeling on the topological features to generate a defect feature energy distribution diagram; fusing real-time process parameter data to dynamically classify defect features and output defect types and confidence coefficients; generating a process equipment control instruction according to the defect classification result; a defect cause knowledge graph is constructed through a causal inference engine, and a process optimization scheme is output; and feeding back the optimized process parameters to the semiconductor manufacturing equipment to form closed-loop control. The semiconductor manufacturing efficiency and the product quality can be improved, and the production cost is reduced.

The invention discloses a panchromatic sharpening method based on multi-resolution panchromatic feature guidance, which comprises the following steps: firstly, designing a multi-resolution feature extraction network based on a spatial frequency Transform module, and respectively extracting multi-scale features from panchromatic and up-sampled multispectral images; secondly, in order to restrain and optimize the extracted detail features, a multi-head self-attention-based texture injection module is adopted to extract a dependency relationship between panchromatic and multispectral features, and more similar detail features are guided to be injected; thirdly, in order to solve information loss caused by multispectral image up-sampling, multi-level detail features are injected into a multispectral reconstruction network, the detail features are learned step by step and reconstructed to a panchromatic image size, and a spectrum fusion module based on adaptive channel attention is utilized to reconstruct a fused image in a high-quality mode; and finally, optimizing the model performance by combining reconstruction loss based on L1 norm, structural similarity constraint and transmission perception loss in training. According to the method, the spectral fidelity and the spatial resolution of the fused image are remarkably improved.

Disclosed in the present invention are a rapid large-range asphalt-pavement quality assessment method and system based on high-resolution remote sensing. The method comprises the following steps: acquiring a high-resolution multispectral image and panchromatic image of a pavement area of a road network area to be subjected to assessment; generating superpixels of the pavement area on the basis of the high-resolution multispectral image and panchromatic image of the pavement area; removing superpixels of a non-asphalt pavement from the superpixels of the pavement area, so as to obtain superpixels of an asphalt pavement in the pavement area; on the basis of the superpixels of the asphalt pavement, performing calculation to obtain a brightness indicator, texture indicator, structure indicator and spectrum indicator of each superpixel of the asphalt pavement; and according to the calculated brightness indicator, texture indicator, structure indicator and spectrum indicator, performing calculation to obtain a remote-sensing quality assessment index corresponding to each superpixel of the asphalt pavement, and then obtaining a quality situation assessment diagram for the asphalt pavement. By means of the present invention, various assessment indexes are comprehensively used, thereby improving the precision and visibility of an assessment result, and reducing the manual intervention.

The invention relates to the field of computer vision, in particular to a crop disease and insect pest real-time identification, prevention and control decision-making method and system based on multi-modal edge calculation. Comprising the following steps: acquiring an unmanned aerial vehicle multispectral image, an infrared thermal image and an NDVI index, performing preprocessing in combination with field Internet of Things sensing data, and generating a multi-modal feature input sequence; constructing a spectrum-environment fusion feature matrix on the basis of a Transform attention mechanism, and performing disease and pest detection by using a YOLOv7-Spectral model to generate a disease and pest distribution heat map; calculating a disease risk index (BRI) and a pesticide application priority map based on historical data; planning a pesticide application path of the unmanned aerial vehicle by adopting an ant colony optimization algorithm, and optimizing a pesticide application scheme in combination with wind speed and humidity parameters; the unmanned aerial vehicle performs precise pesticide application according to the optimized path and monitors the disease change trend; adjusting the model through pesticide application feedback data, and optimizing a disease and pest prevention and control strategy by adopting federal learning. According to the invention, the identification precision is improved, the pesticide use is reduced, and accurate, efficient and intelligent prevention and control are realized.

The invention relates to the technical field of computer vision and three-dimensional reconstruction, and discloses an exhibition hall three-dimensional modeling intelligent optimization system based on multi-modal data fusion, and the system comprises a data collection module which is configured to synchronously obtain laser radar point cloud data, a multispectral image sequence and inertial measurement unit data; the preprocessing module is used for receiving the output of the data acquisition module, aligning a multi-source sensor coordinate system through a space-time calibration algorithm, and separating a static scene from a dynamic interference element by using a dynamic segmentation network; and the multi-modal fusion module is used for receiving the preprocessed data and carrying out adaptive weighted fusion on the geometric features of the laser radar and the visual texture features through a cross-modal attention mechanism. According to the invention, through multi-modal data fusion and a dynamic scene adaptive mechanism, the modeling precision and the real-time updating capability in a complex exhibition hall environment are significantly improved.

The invention provides an industrial production part detection method based on machine vision, and the method comprises the steps: collecting multi-dimensional image data through a multispectral industrial camera when a part passes through a detection region, and carrying out the preprocessing of the multi-dimensional image data; constructing a three-dimensional feature space according to the structured light projection, and mapping the preprocessed multispectral image data into the three-dimensional feature space to carry out space registration operation to obtain a to-be-detected image; extracting composite parameters in the to-be-detected image; performing coarse screening identification on the parts through the geometric parameters and the texture parameters, and removing the parts with appearance defects; and carrying out microcosmic fine judgment identification on the roughly screened parts through material characteristic parameters, and rejecting the parts with quality defects. According to the method, the three-dimensional feature space is constructed through fusion of multispectral imaging and structured light projection, and composite analysis of geometry, texture and material characteristic parameters is combined, so that the problem of single detection dimension of a traditional method is solved, and the recognition accuracy and efficiency of complex defects are improved.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention provides a multi-region independent temperature control intelligent frequency conversion refrigeration and preservation method and system, and relates to the technical field of refrigeration and preservation. A region division module, a temperature control module, an environment monitoring module, a data analysis module, an intelligent frequency conversion control module and a user interface module work cooperatively; fine temperature control management of different areas in the refrigeration equipment is realized, the system adopts three-dimensional laser scanning and multispectral image acquisition technologies to automatically divide temperature areas, a quantum-level precision thermistor array and an intelligent fuzzy PID controller ensure accurate temperature control, and meanwhile, humidity and air flow are monitored to optimize a storage environment, so that the temperature control efficiency is improved. The data analysis module is combined with deep learning and an association rule mining algorithm to provide decision support for frequency conversion control and realize energy consumption optimization, the user interface module provides visual operation experience, and the whole scheme improves the intelligence, accuracy and energy efficiency of refrigeration and preservation, and is suitable for various cold chain scenes.

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

A spatio-temporal variation monitoring method for SPAD of winter wheat, relating to the technical field of crop monitoring. The monitoring method comprises: a multispectral sensor of an unmanned aerial vehicle acquiring canopy images of winter wheat in a heading stage, a flowering stage, and a late grain-filling stage; extracting from multispectral images spectral features based on the canopy reflectance of winter wheat and texture features based on a graylevel co-occurrence matrix; using a feature selection policy to preferably select sensitive remote-sensing features; and applying a feature fusion policy and an SVR algorithm to construct a winter-wheat SPAD estimation model. Spectral features combining a near-infrared band with other bands can fully capture the spectral difference in SPAD of winter wheat in a reproductive growth stage, and texture features in a red-light band and the near-infrared band are relatively sensitive to the SPAD of winter wheat. The stability and estimation accuracy of an SPAD model constructed by applying a feature selection policy and a feature fusion policy are both superior to those of a model applying a feature policy alone and a model not applying any policy.

The invention relates to the technical field of airport safety guarantee, and discloses an FOD recognition system and method based on machine vision. The system comprises a multispectral image acquisition module, a dynamic environment adaptive preprocessing module, a multi-attention feature extraction module, a multi-modal data fusion module, a multi-stage verification decision module and a real-time alarm and closed-loop control module. Pavement data are acquired through multi-modal image acquisition, after preprocessing, feature extraction and fusion, multi-stage verification decision is carried out to detect foreign objects, and finally real-time alarm and elimination are realized. The system can effectively overcome complex environmental interference, accurately identify foreign objects, quickly process and improve the safety and operation efficiency of the airfield pavement, and has the advantages of high identification precision, strong adaptability, good reliability and the like.

The invention relates to the technical field of agricultural science and technology, in particular to a leaf disease and pest identification method and system based on image processing, and the method comprises the following steps: setting multispectral and thermal imaging equipment, collecting multispectral images and thermal imaging data of a target frequency band, deleting images with insufficient definition and data with excessive noise, obtaining optimized multispectral images, and obtaining the leaf disease and pest identification result. And marking and classifying the data according to the collected date to form a time sequence image data set. According to the method, the network parameters are dynamically adjusted to reflect the state of each stage of leaf growth, so that the pest and disease damage prediction is not only based on static data, but also reflects the actual change of plant growth, and the real-time application value and accuracy of the prediction model are improved; the introduction of state transition learning enables the transition from health to each stage of pest and disease damage to be predicted more accurately, carries out early diagnosis and timely intervention, improves the recognition precision of the specific position of the pest and disease damage, can carry out local processing, and effectively improves the pertinence and economic efficiency of the processing.

The invention relates to the technical field of intelligent agriculture, in particular to a mulberry field monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring environment and soil data of a mulberry field through a sensor network to obtain original sensor data; performing time sequence data integration on the original sensor data to obtain an environment parameter data set; the method comprises the following steps: performing multispectral image acquisition on mulberries in a mulberry field through a multispectral camera, and performing image preprocessing to obtain preprocessed image data; estimating the chlorophyll content according to the preprocessed image data to obtain chlorophyll content data; and performing growth health parameter analysis according to the preprocessed image data and the chlorophyll content data to obtain growth health parameters. By combining the sensor technology, the multispectral imaging technology and the intelligent algorithm, comprehensive, accurate and real-time monitoring of the mulberry field is achieved, and an effective technical means is provided for fine management of the mulberry field.

The invention relates to the technical field of photovoltaic power station operation and maintenance, and discloses a photovoltaic power station intelligent operation and maintenance and fault early warning system. A multi-band image fusion algorithm and a temperature gradient enhancement algorithm are applied to identify composite defects; establishing a defect interaction influence model to analyze the synergistic effect of different defects; constructing a defect evolution prediction algorithm to predict a defect development trend; and developing a defect-performance mapping model and a processing priority algorithm to optimize a maintenance decision. According to the method, the defect detection sensitivity and accuracy are improved, accurate identification and evolution prediction of composite defects are realized, a prospective maintenance decision is effectively supported, the operation and maintenance efficiency of a photovoltaic power station is remarkably improved, the service life of the power station is prolonged, and the safety risk is reduced.

The invention belongs to the technical field of image processing, and particularly relates to a cord fabric production defect identification and analysis system based on machine vision, which comprises a multispectral imaging module, an image preprocessing module, a target detection module, a result verification module, a weight correction module and a control response module, the method comprises the following steps: acquiring a multispectral image sequence of each partition of a cord fabric forming surface, combining motion compensation, geometric calibration and noise filtering to improve preprocessing precision, and generating a composite spectral image by adopting adaptive weight fusion on the basis of considering an image signal-to-noise ratio and texture definition; and detecting defects based on frequency division domain texture and morphological gradient parameter bimodal matching, further verifying detection credibility through defect sensitive spectral image feature enhancement degree, dynamically correcting a fusion weight and performing iterative optimization, and finally triggering a production control instruction according to a detection result. And closed-loop management of high-precision defect detection and production control linkage of the cord fabric is realized.

The invention discloses an intelligent pest and disease damage identification and targeted spraying method and system based on machine vision, and the method comprises the steps: inputting a multispectral image sequence and environment sensing data into a processing system, and obtaining a high-quality fusion image through spectrum-space joint correction and environment adaptive compensation; constructing a region growing algorithm based on a topological flow theory, and realizing accurate extraction of a target region in combination with curvature flow boundary optimization; integrating the spatial features, the time sequence features and the context information by adopting a multi-modal feature fusion method, and establishing a pest state evaluation and behavior prediction model; and according to an evaluation result, a spraying track is optimized through an environment constraint field, a self-adaptive parameter control system is adopted to adjust spraying parameters, and closed-loop optimization is realized through real-time monitoring. The technical problems of low pest and disease identification precision, poor spraying effect and the like in a complex environment are solved, and the accuracy and efficiency of prevention and treatment are improved.

The invention discloses a printed circuit board defect detection system. The image acquisition module is used for acquiring a multispectral image of a printed circuit board to be detected, and the image preprocessing module is used for denoising, enhancing and normalizing the acquired image. The feature extraction module extracts geometric and spectral information from the preprocessed image, including the shape and layout structure of the circuit. The deep learning module processes the extracted preliminary geometric and spectral information, and the basic network sub-module performs feature extraction through a pre-trained convolutional neural network to generate high-level features. And the self-adaptive adjustment sub-module carries out fine adjustment on the high-level features through defect sample data with labels, and generates specific features for identifying and classifying various defects of the printed circuit board. The defect identification and classification sub-module combines high-level features and specific features to realize accurate detection of short circuit, open circuit, insufficient etching, welding defects and the like. And the visualization module is used for marking the position and the type of the defect and generating a detection report.

The invention relates to the technical field of crop monitoring, in particular to a multi-spectrum-based crop monitoring method and device, a medium and a product, and the method comprises the steps: carrying out the flight planning of an unmanned plane based on a farmland monitoring area, determining the flight information, and transmitting the flight information to an unmanned plane terminal which carries out the data collection. Then, in order to improve the accuracy and reliability of the multispectral image data, performing preprocessing based on the multispectral image data to obtain target image data; and performing multi-dimensional monitoring analysis based on the target image data, and determining a crop multi-dimensional monitoring result. And finally, performing visual display based on the crop multi-dimensional monitoring result to obtain a multi-dimensional monitoring display map. The multispectral image data can reveal spectral characteristic changes of crops in different growth stages and under different environmental conditions, and the richer and more comprehensive multispectral image data is used for crop monitoring, so that the accuracy of crop monitoring is improved.

The invention relates to an image segmentation method for a burn wound area, and the method comprises the steps: collecting burn wound images under different spectrums through a multispectral imaging device, and enabling the burn wound images to comprise a plurality of wavebands, namely visible light, infrared light and ultraviolet light; for each spectral image, analyzing reflection and absorption characteristics of a burn area under different wavebands by using optical characteristics of biological tissues, and marking a potential burn area; designing a self-adaptive algorithm, and dynamically adjusting parameters, including exposure and light intensity, of imaging equipment based on feedback of different spectral characteristics; the feature contrast of the burn area is enhanced; preliminarily delimiting the boundary of the burn region according to the enhanced spectral image through a threshold segmentation or region growing algorithm; inputting the multispectral image into a hierarchical processing model, and decomposing different tissue levels of the burn area including epidermis and dermis based on different wave band or color information; on each layering result, a feature extraction algorithm including texture analysis and edge detection is used for extracting detail features of each layer.

The invention relates to the technical field of water and fertilizer control, in particular to a water and fertilizer integrated control system and method based on the Internet of Things. The method comprises the following steps of obtaining environment measured data, historical environment data and regional geographic information of a farmland region, performing spatial modeling and time sequence learning on the farmland region, and constructing a farmland digital twinborn model; simulating the state of an area without sensors by using a farmland digital twinborn model to obtain virtual sensing node data; collecting multispectral image data of crops, and extracting physiological feature data of the crops; and constructing a crop physiological state model by utilizing the environment measured data, the virtual sensing node data and the crop physiological feature data, and generating crop physiological state evaluation data. Through multi-source data fusion and three-dimensional visualization integration, data-driven accurate regulation and control and whole-process intelligent management of the water and fertilizer integrated system are realized, and the intelligent level of irrigation and fertilization and the resource utilization efficiency are comprehensively improved.

The invention relates to the technical field of image processing, and discloses a printed matter detection method and device. The method comprises the following steps: acquiring multispectral image data of a printed matter to be detected in different spectral bands; performing spectral feature extraction and multi-level fusion on the multispectral image data to obtain a comprehensive spectral feature map; acquiring sample spectral feature maps of a plurality of sample printed matters to perform frequent feature mode extraction, generating a spectral fingerprint database, and performing region division and matching degree search to obtain a reference spectral fingerprint map; carrying out multi-scale decomposition on the comprehensive spectrum feature map and the reference spectrum fingerprint map to obtain a plurality of scale layers, and carrying out local spectrum similarity calculation and adaptive threshold segmentation to obtain a binary mask map of a defect region; performing spectral feature clustering analysis to obtain defect categories; and performing defect root cause analysis and process adjustment on the printing process parameters to generate a process adjustment scheme, thereby improving the printing quality detection accuracy of the printed matter.

The invention discloses a three-dimensional panoramic intelligent monitoring method and device for a coal conveying gallery, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: obtaining initial point cloud data and an initial multispectral image of the coal conveying gallery through a rotary laser radar, a high-dynamic-range camera and an infrared thermal imager; inertial measurement units are arranged in the devices to collect vibration data, and the vibration data are used for carrying out vibration compensation on point cloud and images to generate target point cloud data and target multispectral images; thirdly, performing three-dimensional modeling and multispectral texture mapping on the coal conveying gallery based on the data to obtain a target three-dimensional model; and finally, mapping equipment, personnel and environment data into the three-dimensional model in real time, displaying each piece of state information, and realizing comprehensive monitoring. According to the application, high-precision three-dimensional panoramic monitoring can be realized in a complex vibration environment of the coal conveying gallery, and the safety management level and the operation and maintenance efficiency of the coal conveying gallery are effectively improved.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.