498223 results about "Environmental geology" patented technology

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

Disclosed in the present invention is a three-dimensional scene point cloud data extraction and recognition system based on a power distribution network. The system comprises: a data collection module used for collecting data for multiple times and fusing same to acquire a complete power distribution network scene; a data processing module used for using the least square method to calibrate closest point matching and perform point cloud registration; a data extraction and segmentation module used for using principal component analysis to calculate a normal vector of the points in a neighborhood of each point cloud data point, measuring the curvature change near each point of a curved surface of a three-dimensional scene of a power distribution network by means of Gaussian curvature, and segmenting and classifying point cloud data by means of a random sampling fitting method and a clustering method; a data recognition and monitoring module used for using PointNet to perform local and global feature learning on the features of each point cloud data point, and acquiring global features by means of a maximum pooling operation for coding and recognition; and a data visual analysis module used for connecting to an open-source point cloud data processing library PCL and a hardware device for visual interaction. The efficiency and sustainability of the power supply system are improved.

The present application relates to an on-line evaluation layered model including a monitoring data layer, a diagnosis method layer, an evaluation indicator layer, a monitored item layer, a key part layer, and an overall project layer. The method further includes collecting same-type multi-measuring-point monitoring data and multi-type multi-measuring-point monitoring data of a concrete dam, determining various indicator analysis results of the concrete dam based on the same-type multi-measuring-point monitoring data, determining various indicator operation scoring results of the concrete dam based on the multi-type multi-measuring-point monitoring data. In some embodiments, the method includes performing, based on the various indicator analysis results and the various indicator operation scoring results, analysis of an impact effect of external special loads on the dam caused by one or more risk events, and giving operation decision support suggestions of the dam in combination with overall performance graded evaluation of the dam.

A decentralized event detection and geolocation system is disclosed, utilizing a dynamically adaptable Spontaneously Emergent Geolocation Network (SEGNet) comprising mobile, stationary, and other devices equipped with multi-sensor capabilities. The system operates by obtaining sensor data from distributed devices, applying trained models to detect potential events of interest (EOIs), and dynamically forming device networks for collaborative geolocation and event validation. The system employs synchronization mechanisms and geolocation techniques, including Time Difference of Arrival (TDOA) and / or Angle of Arrival (AoA), to determine event locations through multimodal data fusion and iterative validation processes. The architecture supports multiple operational modes, including decentralized, centralized, and hybrid configurations, enabling operation in connectivity-limited environments and enhanced processing when server access is available. The system is designed to accommodate dynamic task delegation and real-time threshold adjustments based on environmental conditions, providing reliable event detection and geolocation across diverse scenarios.

The invention discloses a tunnel multi-source fusion dynamic twin surrounding rock intelligent prediction and control method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through intelligent drilling equipment, a distributed optical fiber sensing network and a mobile scanning device which are disposed on a tunnel construction surface; inputting the multi-source heterogeneous data into a space-time diagram convolutional network, constructing a space-time correlation model of geological features and construction disturbance parameters, and outputting a coupling feature matrix; based on the coupling characteristic matrix, simulating the dynamic response of the surrounding rock-support system through a streaming voxelization engine and a multi-physics coupling engine, and generating a risk evolution three-dimensional thermodynamic diagram; and inputting the risk evolution three-dimensional thermodynamic diagram into the deep reinforcement learning model, and dynamically optimizing to generate a construction parameter regulation and control instruction meeting a safety constraint condition. According to the technology, a full-link channel of'data perception-mechanical analysis-decision execution 'is opened in a closed-loop mode, and therefore the double risks of'data distortion misjudgment' and'response lag out-of-control 'of a traditional method are avoided.

The invention discloses a territorial space planning environment influence monitoring method and device. The method comprises the following steps: collecting and preprocessing multi-source environment monitoring data; performing spatio-temporal feature extraction on the preprocessed multi-source environment monitoring data by using a self-supervised spatio-temporal diagram mask transfer attention network to obtain initial spatio-temporal features; performing fusion processing on the initial spatial-temporal characteristics based on a multi-mode diffusion converter to obtain fusion characteristics; calculating an environmental impact evaluation index based on the fusion feature; and based on a preset visualization template and an early warning rule, generating a dynamic monitoring result including a space-time evolution diagram, a trend analysis diagram and early warning information according to the environmental influence evaluation index. Through deep learning and multi-modal data fusion technologies, the precision and efficiency of environmental impact monitoring are improved, and accurate evaluation and timely early warning of environmental impact in the land space planning implementation process are realized.

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention relates to the technical field of data processing, and discloses an intelligent water conservancy inspection method, device and equipment and a storage medium. The method comprises the following steps: performing dynamic fusion processing on acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twinborn body; performing agent collaborative analysis on the digital twinborn body to obtain a self-adaptive inspection strategy; performing multi-modal sensing data acquisition on the target area according to the self-adaptive inspection strategy to obtain multi-modal data; performing federated learning processing on the multi-modal data to obtain holographic situation awareness data; performing causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map; and inputting the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report. According to the invention, the efficiency and accuracy of intelligent water conservancy inspection are improved.

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention discloses an underground pipe network leakage detection method fusing big data analysis and machine learning. The underground pipe network leakage detection method comprises the step of deploying an acoustic sensor, a pressure sensor and a flow sensor at preset positions of an underground pipe network. Performing noise reduction and standardization processing on the acquired operation data, extracting frequency domain, energy and time sequence characteristics of acoustic signals to generate acoustic characteristic vectors, and calculating initial space coordinates of a leakage position based on time difference of arrival of the acoustic signals and sensor arrangement; and generating a pressure feature vector, and analyzing the spatial-temporal correlation of the flow data to generate a flow feature vector. And fusing acoustic, pressure and flow feature vectors to form a multi-dimensional feature vector, inputting the multi-dimensional feature vector and the initial space coordinates into a pre-trained cascade deep learning model, and outputting the leakage probability and the leakage position of underground pipe network leakage. And when the leakage probability exceeds a preset threshold value, triggering an alarm mechanism. According to the invention, efficient detection and accurate positioning of underground pipe network leakage are realized, and high real-time performance and positioning accuracy are achieved.

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

The invention provides a meteorological metadata storage method and system based on machine learning, and the method comprises the steps: obtaining a meteorological metadata set of a target region, inputting the meteorological metadata set into a pre-trained time sequence feature extraction model, generating the time sequence distribution features of all meteorological file files, and inputting a spatial topology mapping model, generating spatial correlation topological features of the meteorological elements; based on a cross fusion result of the time sequence distribution features and the space correlation topological features, a hierarchical index structure of meteorological metadata is constructed, a meteorological storage optimization model is called to generate a storage node distribution strategy of a meteorological file, and the meteorological file is stored according to the storage node distribution strategy. And writing each meteorological file file in the meteorological metadata set into the corresponding distributed storage node, and dynamically adjusting the mapping relationship between the physical position and the index of the stored file based on a real-time updating mechanism of the hierarchical index. The meteorological metadata storage efficiency and retrieval accuracy can be improved, and storage resource waste and operation and maintenance cost are reduced.

In some embodiments, a computer system facilitates movement, including rotation, of a virtual object in a three-dimensional environment. In some embodiments, a computer system facilitates movement of a virtual object in a three-dimensional environment toward a movement boundary. In some embodiments, a computer system facilitates dynamic scaling of a virtual object in a three-dimensional environment based on movement of the virtual object in the three-dimensional environment. In some embodiments, a computer system facilitates inertial movement of a virtual object in a three-dimensional environment. In some embodiments, a computer system facilitates converging offsets between a portion of a user and a virtual object. In some embodiments, a computer system facilitates rotation of a volumetric virtual object in a three-dimensional environment.

Various implementations disclosed herein include devices, systems, and methods that associate user activities with user interface (UI) elements within a 3D environment. Some implementations do so in ways that account for inaccuracies (i.e., fuzziness) in sensor-based detection of the user activities, e.g., inaccuracy in sensor data-based gaze tracking or sensor data-based hand / joint position. Some implementations use a sampling technique to associate user activity in a 3D environment with an appropriate portion of a UI positioned within a 3D environment. For example, a sampling technique may be used to identify sample locations within the 3D environment (e.g., sample locations around a gaze direction) to evaluate and ultimately select from to associate with a user activity, e.g., associating a particular UI button with a gaze direction.

The invention provides a flowmeter online calibration system and method, and the method comprises the steps: firstly collecting a real-time operation data set of a target flowmeter in a pipeline operation environment, and covering dynamic monitoring data and environment state data; preprocessing the data set to obtain an operation data sequence capable of describing the change trend of the operation state of the flowmeter at multiple continuous time nodes, inputting the operation data sequence into a pre-trained calibration parameter generation model, and generating a calibration parameter set comprising a flow correction coefficient, an environment compensation factor and an error adjustment threshold value; and adjusting and measuring output data in real time based on the calibration parameter set, generating a calibrated flow measurement result, comparing the calibrated flow measurement result with a standard flow reference value for verification, and if the deviation is greater than an error adjustment threshold value, regenerating the calibration parameter set and performing iterative adjustment until the deviation is within a threshold value range, thereby realizing high-precision and self-adaptive online calibration of the flowmeter.

In some cases, flood risk quantification computing systems and methods are computationally intensive and in some cases are inaccurate. A flood risk computing system and method are provided that quantifies a flood vulnerability within a specified area of interest, irrespective of flood event characteristics; estimates and maps a flood hazard probability; integrates the flood vulnerability and the flood hazard probability to quantify a flood risk; and develops a rapid flood risk software tool, stored in the memory, to directly quantify flood risk characteristics using deep learning. The rapid flood risk took using a plurality of hierarchical deep neural networks.

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention discloses a time sequence marine ecological environment early warning monitoring data abnormal point detection method, and relates to the technical field of marine ecological environment analysis, and the method comprises the steps: collecting a plurality of types of marine monitoring indexes, constructing a multi-source and multi-dimensional time sequence data set, and carrying out the preprocessing and unification of the data into a standard input format. And based on the causal relationship and co-evolution characteristics between the factors, constructing a graph structure model, and depicting a coupling propagation path between the factors. Generating a local time sequence model by adopting a sliding window, constructing an anti-fact path, comparing the propagation difference of an original path in a map structure, and identifying a key disturbance moment; and in combination with historical event similarity and space consistency information, the early warning credibility of abnormal points is evaluated, and efficient and interpretable graded early warning response is realized. According to the method, accurate identification and credible early warning of abnormal points are realized, causal modeling, anti-factual reasoning and hierarchical response capabilities are realized, and the intelligence and interpretability of marine ecological monitoring are improved.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

A method for reacting one or more hydrocarbons by steam cracking including conducting one or more input streams containing the one or more hydrocarbons, obtaining one or more product streams, through one or more radiation zones of one or more cracker furnaces, in which the one or more radiation zones are heated by firing heating gas with combustion air, in which at least a portion of the combustion air is subjected to combustion air preheating in which steam is produced from feed water, and in which the feed water is subjected to feed water preheating in one or more convection zones of the one or more cracker furnaces. The combustion air preheating is carried out at least in part and / or at least temporarily using heat withdrawn from at least part of the feed water upstream of the feed water preheating.

The invention provides an intelligent fire-fighting equipment self-inspection and fault early warning system based on an AI algorithm. The intelligent fire-fighting equipment self-inspection and fault early warning system comprises a sensing layer used for integrating a multi-source heterogeneous sensing unit; the edge layer is used for deploying intelligent preprocessing nodes; the transmission layer is used for constructing a safe and reliable transmission channel; the platform layer is used for establishing an intelligent analysis center; the application layer is used for providing intelligent decision service; and the five layers of architectures work cooperatively. According to the invention, full-dimension monitoring of fire-fighting equipment is realized through a multi-physics field cooperative sensing framework of a sensing layer, and the fault detection capability is remarkably improved. The multi-source heterogeneous sensing unit integrated in the sensing layer can capture various physical quantity information such as acoustics, vibration and thermal fields at the same time, information of different physical fields is complementarily enhanced through a cross-domain feature complementation enhancement mechanism, and more comprehensive equipment state features are obtained. In particular, the sensor self-adaptive excitation technology dynamically adjusts the excitation energy of the sensor according to the environmental interference degree, and ensures that high-quality signals can be obtained under different working conditions.

The invention provides a marine ranch water quality parameter real-time correction and compensation method and system for a multi-source sensor, and relates to the technical field of multi-source sensors, and the method comprises the steps: constructing a double-layer edge computing network, and connecting a sensor through a micro-service architecture to collect water quality data. And carrying out data preprocessing in combination with wavelet transform. And establishing a sensor digital twinborn model, and calculating the real-time credibility. Establishing a multi-dimensional sensor association network, optimizing a weight coefficient by adopting federal learning, and establishing a self-evolution correction parameter matrix; and fusing the sensor data by using a multi-task deep learning model to generate an initial correction value. And calculating a theoretical reference value through a space-time sequence prediction model. A compensation coefficient is adaptively adjusted by adopting a fuzzy decision tree, hierarchical water quality parameter correction is realized, and a closed-loop self-optimization intelligent correction system is formed through verification of a digital twin model. The accuracy and reliability of marine ranch water quality monitoring data are effectively improved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering dam safety monitoring and early warning method and system, which realize comprehensive perception and accurate early warning of the health state of a dam structure through a composite sensing technology and an intelligent analysis algorithm. A micro-mechanical resonance sensor and a distributed optical fiber sensor are cooperatively deployed, and an interface and structure integrated three-dimensional monitoring network is constructed; a three-dimensional interface stripping characteristic spectrum is constructed based on a time-frequency conjoint analysis technology, and the bonding degradation state between the sensor and the dam body is accurately identified; a strain field anomaly distribution matrix is established through spatial correlation modeling, precise positioning of internal damage is realized, a dual-channel feature fusion network and a deep neural network evaluator based on an attention mechanism are designed, and multi-dimensional correlation analysis is performed on an interface state and structural damage features; and finally, realizing progressive response from data verification and multi-source verification to emergency linkage through a three-level linkage early warning decision tree.

The invention provides a crop yield prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining continuous historical meteorological data, soil data, crop planting data and remote sensing image data of a designated region for many years, and carrying out the alignment according to a time sequence, and constructing a multi-dimensional spatio-temporal data cube; a fusion prediction model is trained, the fusion prediction model is used for extracting growth feature vectors in the crop growth and development process and carrying out yield prediction, and model parameters of the fusion prediction model are dynamically adjusted through an adaptive learning rate optimization algorithm; the method comprises the following steps: acquiring real-time meteorological data, soil data and remote sensing image data of a specified area, inputting the data into a pre-constructed fusion prediction model, introducing an agricultural knowledge graph into the fusion prediction model to restrain and correct a prediction result of the fusion prediction model, and reasoning to obtain crop growth and estimated yield of each grid of the specified area. And performing spatial interpolation on the estimated yield of each grid to generate a yield distribution diagram of the specified area.

In an intelligent pollution and carbon reduction method based on combustion control and load distribution, a data processing layer, a multi-unit load distribution and operation optimization layer and a single-unit boiler multi-objective combustion optimization layer are used. The data processing layer, the multi-unit load distribution and operation optimization layer and the single-unit boiler multi-objective combustion optimization layer are embedded in a power plant information system in the form of modules. A load distribution and operation optimization method for a multi-source fuel blending combustion unit with economy as an objective is provided to overcome the operation optimization difficulty of the key production process of a multi-source fuel blending combustion cogeneration unit, such as sludge drying-steam distribution.

The invention discloses a multi-sensor fusion unmanned equipment agricultural monitoring method and system, and belongs to the technical field of agricultural monitoring, and the method specifically comprises the steps: obtaining a farmland image and multispectral data through an image shooting device and a multispectral sensor carried by an unmanned aerial vehicle, and recognizing the features of diseases and pests through a deep learning model, generating a distribution diagram and statistical data, and performing early warning and risk assessment in combination with the multispectral data; formulating a pesticide management strategy according to an evaluation result, and pushing the pesticide management strategy to an unmanned equipment control system in real time; meanwhile, according to actual and operation requirements of the farmland, generating an optimal operation path by using a path planning algorithm, setting operation parameters, and allocating tasks by using a task allocation algorithm; in the operation process, environment changes are monitored in real time, an edge computing device is used for finding out abnormity in time and adjusting operation instructions, and efficient and accurate monitoring and intelligent operation management of farmland diseases and insect pests are achieved.

An agricultural vehicle includes cameras and radars operably coupled to the agricultural vehicle, and an imaging controller operably coupled to the radars and the cameras. The imaging controller includes at least one processor, and instructions that cause the processor to receive image data from the cameras, receive radar data from the radars, combine the image data from a first camera with the image data from a second camera having a different field of view than the first camera to generate combined image data, and fuse the combined image data with the radar data to generate fused data. Related agricultural vehicles and methods are also disclosed.

The invention provides a fine three-dimensional modeling method and system based on air-ground multi-source data fusion, and the method comprises the steps: collecting data from three data sources: unmanned plane oblique photography, scanning point cloud data and panoramic video data, and carrying out the preprocessing of the data; according to the method, the advantages of different data sources are comprehensively utilized, the refined and full-coverage three-dimensional reconstruction of the target environment is realized, the multi-source data fusion is realized by utilizing the improved ICP algorithm to carry out precise registration, and finally, the modeling is carried out by utilizing the improved Poisson surface reconstruction algorithm. The problems of detail missing and incomplete coverage caused by a single data source in a complex building modeling process are solved; according to the invention, the multi-source data fusion technology can comprehensively capture the geometric morphology and detail features of the building in a complex scene, is suitable for complex scenes such as villages in cities and old buildings, and achieves all-around and detailed three-dimensional modeling.