68546 results about "Remote sensing" patented technology

The invention provides a news scene three-dimensional reconstruction and visualization method based on multi-source remote sensing data, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: extracting multi-modal data feature points through employing a spatial position weighted iterative nearest point algorithm and a tensor projection algorithm; after bidirectional attention deep neural network processing and local geometric consistency constraint screening, registration multi-source remote sensing data is generated, data enhancement is carried out through a variational auto-encoder network and a generative adversarial network, the enhanced data is used for depth information generation and texture feature migration, an initial three-dimensional grid model is generated based on a Poisson reconstruction algorithm, and the initial three-dimensional grid model is constructed; and carrying out topological optimization by combining multi-view projection consistency constraint and a grid optimization algorithm to obtain an optimized three-dimensional scene model, setting a space division function, carrying out space division, constructing a grade model and a depth region, generating a scene semantic segmentation map, and updating a visual scene.

A method for detecting an infrared ship target based on an improved YOLOv7 is provided, including the following steps: obtaining an infrared maritime ship data set; reforming a YOLOv7 network structure based on an MobileNetv3 network and a bidirectional weighted feature pyramid network, and obtaining an infrared ship target detection model by introducing an attention mechanism and an optimized loss function; training and verifying the infrared ship target detection model based on the infrared maritime ship data set to obtain the infrared ship target detection model trained; and detecting a maritime ship based on the infrared ship target detection model trained.

Disclosed in the present invention are a health monitoring system and monitoring method for wind turbine blades. In the present invention, fault signals are extracted and collected on the basis of multi-sensor monitoring, and the collected signals are amplified and denoised. Preprocessed signal data is then further fused with spectral features of an acoustic emission signal and a vibration signal, self-supervised health representation learning is performed to generate severely scarce negative samples, and classification of measured data collected from healthy and unhealthy blades is performed by means of auxiliary tasks, thereby implementing pseudo-label-based supervised learning. In the present invention, data fusion, EMD denoising, self-supervised learning and LSTM model training technology are used, such that blades of a wind turbine generator can be monitored in real time online, high-precision diagnosis can be performed on faults that have occurred, and inspection and fault type determination for evaluating the state of health of the current blade are performed, thereby meeting the requirements of a wind power market for online monitoring of wind turbine blades.

The invention discloses a multi-source remote sensing image semantic segmentation method based on Transform, Mama and a diffusion model, belongs to the technical field of electric digital data processing, graphic data reading and image data processing, and is used for extracting image information of a remote sensing image. Comprising the following steps: acquiring remote sensing images, constructing an image information extraction network based on the remote sensing images, and respectively inputting the remote sensing images in a training set, a verification set and a test set into the constructed remote sensing image information extraction network based on Transform, Mama and diffusion models. According to the method, the RGB features and the depth features are subjected to Transform feature extraction, and a cross Mama method is provided to perform cross enhancement on the RGB features and the depth features; according to the method, a row and column pooling Transform module is used for replacing a traditional Transform module, and detail texture information and large-scale structure information of a feature map in the vertical direction and the horizontal direction are extracted at the same time; according to the invention, a cross Mama attention feature fusion module is used to replace a traditional Mama module, and the modeling capability for complex scenes and diversified targets is enhanced.

The invention relates to the technical field of camera linkage alarm, and discloses a camera linkage alarm method and system for intelligent environment monitoring, and the method comprises the steps: synchronously collecting the video stream data of a plurality of cameras and the multi-parameter monitoring data of an environment sensor; executing multi-path feature analysis and cross-modal feature fusion to obtain environment-vision joint features; environment scene self-adaptive classification is executed, and environment-vision joint features are analyzed through an anomaly judgment model to obtain an abnormal event judgment result; executing multi-camera collaborative verification and vision-environment data association analysis to obtain multi-dimensional abnormal event description information; and selecting a multi-camera linkage response strategy and coordinating the plurality of cameras to carry out three-dimensional monitoring on the abnormal area to obtain a linkage response execution result, so that a false alarm mode can be identified and an abnormal judgment rule can be dynamically adjusted, continuous optimization of the performance of the plurality of cameras is realized, and the false alarm rate and the missing report rate of the plurality of cameras in long-term operation are reduced.

Systems, methods, and apparatus for detecting UAVs in an RF environment are disclosed. An apparatus is constructed and configured for network communication with at least one camera. The at least one camera captures images of the RF environment and transmits video data to the apparatus. The apparatus receives RF data and generates FFT data based on the RF data, identifies at least one signal based on a first derivative and a second derivative of the FFT data, measures a direction from which the at least one signal is transmitted, analyzes the video data. The apparatus then identifies at least one UAV to which the at least one signal is related based on the analyzed video data, the RF data, and the direction from which the at least one signal is transmitted, and controls the at least one camera based on the analyzed video data.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

The invention belongs to the technical field of data processing, and particularly relates to a well deviation azimuth measurement system based on a high-precision compensation algorithm. The system comprises a reference vector construction part, a compensation part and a Kalman filtering measurement part. The benchmark vector construction part is used for determining a reference benchmark of a geomagnetic field and a gravitational field in the underground environment; the compensation part is used for compensating original sensor data measured by the underground sensor group, and estimating an underground initial attitude angle according to compensated accelerometer data and compensated magnetometer data in the compensated data; calculating attitude change data; according to the attitude change data, constructing a local interference field model to obtain corrected magnetometer data; and the Kalman filtering measurement part is used for modeling all error sources into state vectors, correcting measurement errors in real time through a Kalman filtering algorithm and outputting a final well deviation direction. According to the invention, the precision, the stability and the robustness of well deviation azimuth measurement are obviously improved.

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

The invention discloses a bit timing estimation method and device suitable for satellite communication, and the method comprises the steps: calculating a time correlation parameter and a signal-to-noise ratio parameter of a satellite signal, and determining the size of a time window; constructing a window feature sequence based on a bidirectional recursion processing unit, and performing window segmentation sampling on the received signal; extracting an autocorrelation function of each section of signal to obtain a time domain feature vector, a frequency domain feature vector and a phase feature vector; carrying out three-level nonlinear mapping on the three types of feature vectors to obtain an adaptive weight vector; performing weighted fusion on the features by using a weight vector, executing cubic spline interpolation, and calculating an error compensation value in combination with a channel state parameter to obtain a fine timing estimation value; extracting synchronization features according to the timing estimation value and solving an optimization problem in a segmented manner to obtain a time synchronization parameter; and a fusion feature vector is calculated, a decision result is obtained through pattern recognition and parameter optimization, and bit timing estimation is completed. According to the invention, the timing precision and reliability of the satellite communication system are improved.

The invention provides an inspection data monitoring method and server applied to mountain road and bridge early warning, and the method comprises the steps: firstly obtaining road and bridge remote sensing monitoring data containing current topographic distribution characteristics, road and bridge structure state parameters and historical disaster records, and then, according to the current topographic distribution characteristics, calling a topographic feature extraction model to segment a target mountain road section, generating inspection subareas and topographic feature tags, generating a dynamic flight path containing inspection nodes and flight parameters for the unmanned aerial vehicle through a path planning model according to the topographic feature tags and road and bridge structure state parameters, and then controlling the unmanned aerial vehicle to inspect according to the path. According to the method, multi-dimensional monitoring data such as surface deformation data, road and bridge crack images and slope displacement tracks are collected, finally, the multi-dimensional monitoring data and historical disaster records are input into a risk early warning model, risk level signals and disposal suggestion information are generated, comprehensive intelligent monitoring and early warning of mountainous area roads and bridges are achieved, and the monitoring accuracy and efficiency are improved.

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

Disclosed in the present invention is a method for repairing an RPC of a remote-sensing image. The method comprises: S1, acquiring information such as an image to be repaired and a reference image; S2, performing feature extraction and matching on the image to be repaired and the reference image by using an SIFT algorithm, so as to obtain a matching point pair and corresponding first pixel coordinates; S3, acquiring longitude and latitude coordinates; S4, acquiring geodetic coordinates and a control point; S5, constructing an RFM model, performing RPC positive calculation on an original RPC parameter of the image to be repaired and the geodetic coordinates, so as to obtain corresponding second pixel coordinates of the geodetic coordinates on a remote-sensing image, and separately calculating a mean value of error distances between corresponding pixel coordinates; S6, introducing a constraint condition by using an L1 regularization optimization method, so as to obtain an optimized RFM model; and S7, performing determination according to the mean value of the error distances and a preset threshold value, and selecting different repair policies to recalculate or correct the RPC parameter. In the present invention, an RFM model is optimized by using an L1 regularization method, and different repair policies are selected to perform correction or estimation on an RPC parameter, thereby improving the geometric positioning precision of a satellite image.

The invention provides a power prediction precision improvement method and system for a wind and light station, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the historical power, weather and equipment operation data of the wind and light station through a data preprocessing module, and carrying out the time sequence decomposition, spatial interpolation, abnormal value detection and correction, and obtaining the fusion feature data. And constructing a deep learning prediction model based on the fused feature data, extracting spatial features by using a convolutional neural network, processing the spatial features by using a long-short-term memory network to obtain an initial prediction result, introducing an attention mechanism to perform weight allocation to obtain weighted features, and combining to obtain power prediction data. And judging the weather type according to the real-time meteorological condition, and correcting by adopting the matched correction model when the extreme weather is identified. And finally, an error compensation model is constructed based on historical prediction errors for compensation, a final power prediction result is obtained, error correction is performed according to actual power data, and compensation model parameters are updated.

The invention discloses a remote sensing image semantic segmentation method based on Mama and Transform architecture fusion. The method comprises the following steps: S1, data preprocessing; s2, pre-training the model by using the MAE; s3, preliminary feature extraction: performing preliminary feature extraction on the image subjected to MAE processing through linear projection, a convolutional layer and a nonlinear activation function to obtain a low-dimensional feature map; s4, CFF feature fusion: fusing the low-dimensional feature map and the image after MAE processing by using a CFF module; s5, feature enhancement of a Mamb-T module: inputting the feature map of the fused image into the Mamb-T module for operation to obtain further feature enhancement of the image; s6, multi-scale feature extraction; s7, a segmentation module, wherein the feature map is input to a Mask2Former segmentation module for final image segmentation; and S8, outputting and post-processing. According to the method, local and global features in the image can be captured, and the segmentation problem under a small water body and a complex background is effectively solved through combination of a self-adaptive attention mechanism and convolution operation.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention relates to the technical field of image processing, and particularly discloses a remote sensing image super-resolution reconstruction method and system based on a prior diffusion model, and the method comprises the steps: carrying out the shallow feature information extraction of a low-resolution image, and obtaining a shallow feature map; the method comprises the following steps: performing multi-scale down-sampling and convolution fusion processing on a shallow feature map and a high-resolution image, and performing high-frequency information extraction and adaptive high-frequency enhancement processing on the processed feature map to obtain condition information; according to the method, deep mining of high-frequency information is carried out on a low-resolution image input as condition information, so that the high-frequency information in the low-resolution image gradually approaches a high-resolution image, and the reconstruction precision is improved; the noise prediction network model guides step-by-step noise removal in the back diffusion process, and the detail reduction capability and precision of natural disaster remote sensing image super-resolution reconstruction are improved.

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

The invention provides an unmanned aerial vehicle multi-dimensional information fusion method and system based on acousto-optic-electric composite detection. According to the method, acoustic frequency band signals, visible light image sequences and electromagnetic field intensity change data are collected, acoustic characteristic flow, optical characteristic flow and electrical characteristic flow are generated, and based on the time deviation between a low-frequency vibration mode and transient electromagnetic pulses, multi-channel dynamic calibration is carried out on an image distortion area in the optical characteristic flow. The method comprises the following steps: acquiring an acoustic feature flow, an optical feature flow and an electrical feature flow, correcting space-time coordinate parameters of the acoustic feature flow, the optical feature flow and the electrical feature flow, generating a calibration feature flow, carrying out anti-interference fusion processing on the calibration feature flow, extracting multi-dimensional coupling features, inputting the multi-dimensional coupling features into an unmanned aerial vehicle flight path planning model, and generating a three-dimensional space situation model; according to the invention, the physical space-time consistency alignment and noise suppression of the multi-modal data are realized, and the obstacle positioning precision and the dynamic trajectory prediction real-time performance are significantly improved.

The invention discloses a remote sensing image building extraction method fusing double-space attention features, and belongs to the technical field of image processing. The method comprises the following steps: firstly, inputting a training set of an Inria aviation data set into a ConvNeXt backbone network to extract shallow feature information; then, carrying out feature information enhancement processing on the feature information through a double-space attention feature fusion module (DAFF); and finally, a multi-modal dynamic enhancement module (FAM) is used in the last time of feature information up-sampling operation to carry out channel alignment operation of different levels of features, and feature information fusion errors are reduced. According to the method, the accuracy of building extraction in the remote sensing image is remarkably improved.

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a wind driven generator state remote monitoring system based on the Internet of Things, and relates to the technical field of wind power generation equipment monitoring, and the system comprises a sensing module, a transmission module, a remote processing module and a user terminal module. The sensing module is used for accurately collecting various physical parameters such as vibration, temperature, pressure and rotating speed during operation of the wind driven generator by using various sensors, so that the operation condition of equipment is comprehensively mastered, and a data basis is provided for subsequent analysis and processing; according to the method, intelligent prediction of the future operation state of the wind driven generator is realized through deep learning and time sequence analysis, the fault type and time possibly occurring to the equipment can be predicted based on historical data and real-time data, powerful support is provided for operation and maintenance decision making, an operation and maintenance team can prepare in advance, and the operation and maintenance efficiency is improved. And targeted maintenance measures are taken, so that the maintenance cost is reduced, and the operation efficiency and the safety of the equipment are improved.

The invention provides a terrain surveying method and device based on unmanned aerial vehicle cooperation and electronic equipment, and relates to the technical field of engineering surveying and complex terrain surveying and mapping. The method comprises the following steps: determining a first surveying flight route of an unmanned aerial vehicle in combination with ground elevation data of a to-be-surveyed area, controlling the unmanned aerial vehicle to collect initial area topographic data, identifying and classifying the initial area topographic data, and determining a weak texture false topographic area; re-planning a second survey flight route of the unmanned aerial vehicle according to the weak texture false terrain area, starting the millimeter wave radar, and scanning and collecting supplementary terrain survey data at different angles; fusing the supplementary topographic survey data at different angles to obtain local topographic survey data; and splicing and fusing the initial regional topographic data and the local topographic survey data to obtain real topographic survey data. According to the technical scheme, the measuring precision and the surveying efficiency of the topographic surveying data of the complex topographic region can be improved, and the reliability and the stability of the real topographic surveying data are improved.

The invention discloses a remote sensing image semantic segmentation method based on a double-branch multi-scale fusion network, and relates to the technical field of remote sensing image semantic segmentation. According to the method, a double-encoder and single-decoder structure, a multi-scale feature fusion channel and spatial path information are adopted; an adaptive efficient parameter fine tuning SAM2 large model knowledge is utilized to accelerate new task learning, global feature and local feature information of the SAM2 large model knowledge is effectively captured through a global local window multi-head self-attention module, a mixed Logit adjustment loss function based on mutual information is adopted to adjust category weight aiming at the problem of sample category imbalance of a traditional remote sensing data set, and the new task learning is accelerated. The remote sensing image semantic segmentation effect is improved; according to the method, pixel classification and ground feature correspondence are realized, the segmentation problems of complex background, unbalanced sample category and large scale change are effectively solved, the accuracy and stability of image segmentation are improved, and rapid and accurate segmentation of the remote sensing image is realized.

Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention discloses an inductive sensor calibration method, an inductive sensor calibration system, a medium and a product. The method comprises the following steps: acquiring a measured value data set of an inductive sensor to be calibrated and a measured value data set of a reference inductive sensor under a preset environment condition; performing difference operation on the measured value data set of the inductive sensor to be calibrated and the measured value data set of the reference inductive sensor to obtain a measurement deviation sequence; based on a preset environment condition and the measurement deviation sequence, determining an environment compensation coefficient under the preset environment condition; calculating calibration parameters of the inductive sensor to be calibrated according to the environment compensation coefficient; after the calibration parameters are applied to the inductive sensor to be calibrated, obtaining a standard measurement value of the reference inductive sensor and an actual measurement value of the inductive sensor to be calibrated under a preset environment condition; if the difference value between the standard measurement value and the actual measurement value is smaller than the preset error value, it is determined that calibration succeeds. By implementing the method, the calibration accuracy is improved.

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.