810 results about "Mudflow" patented technology

The invention, which relates to the technical field of disaster monitoring and early warning, discloses a landslide and debris flow disaster monitoring and early warning system comprising a multi-source data acquisition module, an intelligent processing module, an analysis decision module and an early warning response module. The space-time reference unified framework is constructed, the high-precision time synchronization and space projection conversion technology is adopted, the space-time matching problem of the satellite-ground heterogeneous data in the prior art is solved, the multi-source parameter fusion proportion is adaptively adjusted according to indexes such as signal quality and environment interference degree through a dynamic weight distribution mechanism based on data reliability, and the space-time matching precision of the satellite-ground heterogeneous data is improved. In addition, a multi-model collaborative decision-making framework is established, the advantages of three models of mechanism driving, probability statistics and intelligent learning are fused, a comprehensive research and judgment result is generated through a confidence quantitative evaluation and conflict resolution algorithm, an early warning response mechanism is matched, a pre-arranged plan library is automatically matched according to a risk level, and an external control system is linked. And a closed-loop management chain of monitoring early warning, analysis decision and emergency disposal is formed.

The invention discloses a debris flow disaster early warning method and an emergency response system based on big data analysis, and belongs to the technical field of geological monitoring, and the method specifically comprises the steps: collecting and preprocessing watershed terrain, weather, geology, vegetation coverage and historical debris flow event record data, and obtaining image data through combining a remote sensing technology; extracting and integrating the data and image features to form a debris flow feature data set; a debris flow early warning model is constructed by using a finite volume model, a deep learning algorithm and an Internet of Things detection technology in combination with an integrated learning method, and training and verification are performed through historical data; when the model predicts a risk, issuing early warning information and starting a dynamic response mechanism; and finally, the early warning model is optimized and adjusted according to response effect feedback. According to the invention, the accuracy and timeliness of debris flow early warning are improved, and support is provided for disaster prevention and reduction.

The invention relates to the technical field of geological disaster monitoring, and discloses a debris flow monitoring and early warning system based on Internet of Things, comprising a multi-source data acquisition module used for acquiring environmental data related to debris flow; the data transmission module is used for transmitting the environment data to a data center or a cloud end from a collection point; the data preprocessing module is used for carrying out abnormal value cleaning, time synchronization and normalization processing on the environment data; the deep learning analysis module is used for analyzing the preprocessed data so as to predict the occurrence risk of debris flow; and the dynamic early warning and publishing module is used for generating a multi-stage early warning signal according to an output result of the deep learning analysis module and publishing early warning information to a user in multiple modes. The accuracy, the real-time performance and the adaptability of the debris flow early warning system are improved through multi-source data fusion, edge calculation and a dynamic learning mechanism so as to cope with debris flow disasters in a complex geological environment.

The invention relates to the field of highway engineering safety monitoring, in particular to a highway slope stability image monitoring and risk early warning system which comprises an image acquisition module, a slope parameterization representation module, a multi-scale curvature flow analysis module, a Riemannian manifold learning module and a risk early warning module. Differential features are extracted by calculating a measurement tensor and a curvature tensor of the curved surface; constructing multi-scale representation of the slope curved surface by applying a curvature flow theory, and identifying deformation characteristics under different scales; according to the method, riemannian manifold learning and a geodesic convolutional network are utilized to extract advanced features, geological disaster precursors such as landslide, collapse and debris flow are accurately identified, the system has a self-adaptive monitoring frequency adjustment function, monitoring modes are automatically switched according to risk levels, the slope disaster risk is greatly reduced, and a powerful guarantee is provided for safe operation of roads.

The invention provides an intelligent debris flow early warning method and system based on a physical model and an algorithm, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: S1, carrying out an indoor soil physical mechanics test, building a physical model, and carrying out the parameter calibration of the physical model through combining a stability criterion equation of a rainfall infiltration-matrix suction coupling effect; s2, simulating different conditions, and generating and outputting debris flow occurrence process data under different working conditions by the physical model; s3, taking the generation process data and the real-time monitoring data as input, constructing a two-channel input network, and training a correction model to obtain corrected response data; s4, on the basis of an SHAP interpretability framework, constructing a Bayesian probability early warning model driven by multi-source heterogeneous data; and S5, triggering real-time early warning of the early warning platform according to the debris flow real-time occurrence probability of the monitoring point, and constructing a mapping relation between soil deformation and critical parameters through dynamic simulation of a physical model on a soil mechanical behavior.

The invention relates to the technical field of geological disaster monitoring, and discloses a point-like landslide and debris flow potential degree adaptive monitoring and early warning method. According to the method, a dot matrix monitoring equipment array is arranged, and displacement data and environmental parameters of multiple monitoring points are collected; analyzing the displacement data timestamp through a timeline analysis module, generating a dynamic timeline, and marking geological event nodes; a geographic information system is combined to carry out spatial mapping on displacement data, and environmental parameters are fused to obtain a preliminary potential degree evaluation result. The pattern recognition engine classifies displacement speed and deformation characteristics in the preliminary result to generate a deformation characteristic spectrum with a space-time label; calculating a dynamic potential degree evaluation map according to the multi-factor weighted evaluation model; and adjusting an early warning threshold according to a user instruction, generating a personalized early warning rule set, and scanning the atlas to output a multi-stage early warning response scheme. On the basis of an early warning feedback iteration updating model and an engine, monitoring data are newly added to trigger and re-analyze in real time, a related scheme is updated in a linkage mode, and different scene requirements are met.

The invention discloses a mountain torrent debris flow early warning method and system based on multi-source data comprehensive analysis. And the monitoring system carries out multi-source monitoring on rainfall, material source and mud level change characteristics of the ditch domain. The method is based on double-threshold rainfall intensity parameters, a quantitative analysis model of channel mud level and flow, and a blocking and collapsing mechanism of object source deformation. The threshold value of each element is determined according to the disaster history, a disaster outbreak probability formula is established, and key factor weights are screened through a random forest algorithm. And data preprocessing is carried out through cross validation and dynamic adjustment. And substituting monitoring data into the analysis model, and comprehensively judging the early warning level of the mountain torrent debris flow. And issuing an early warning signal of a corresponding level to the object in the threatened area through the early warning system. After operation, an incremental random forest method is adopted to update the model through online learning, data redisk is carried out, and model threshold correction and parameter inversion analysis are carried out. According to the pre-warning system, a multi-index combined action comprehensive monitoring and pre-warning evaluation system for quantitative analysis is established.

The invention provides a drainage basin mountain torrent debris flow physical process simulation method for risk early warning, which comprises specific research and development contents and corresponding problems and achieves the following purposes: constructing a drainage basin unit database, clarifying drainage basin and channel characteristics, and providing data support based on physical process refined simulation; and carrying out basin hydrological process simulation research on the basis of the SWAT distributed model for improving glacier melt water simulation. A mountain torrent and debris flow disaster whole-process coupling model is constructed, a dynamic data chain is used for connecting watershed hydrology and a channel water and sediment dynamic process, full-link simulation from rainfall to disaster modeling is achieved, a disaster forming mechanism is accurately revealed, a visualization system is integrated through the model, channel disaster dynamic evolution evaluation and risk zoning functions are provided, and the reliability of channel disaster dynamic evolution evaluation is improved. A forecasting system designed in an object-oriented development mode can be seamlessly accessed to a provincial early warning platform, and a systematic solution is provided for early warning of watershed or regional disasters.

The invention discloses a grating dam safety evaluation and design method and structure based on debris flow blocking degree. Wherein the safety assessment method comprises the following steps: S100, calculating the safety of the debris flow on the basis of the solid matter concentration of a debris flow susceptible area, the particle size value corresponding to the accumulated content of 95% in a full particle size grading curve, the channel average gradient, the channel average width and the primary debris flow scale in the section range of the grating dam, the minimum particle size value of the maximum boulder in the drainage basin and the opening width of the grating dam; calculating a critical blocking judgment coefficient according to the correlation value of the height of the grating dam, the angle of the wedge-shaped structure and 60 degrees and the correlation value of the total amount of debris flow solid substances and the reservoir capacity of the wedge-shaped beam type grating dam; and S200, evaluating the safety of the grating dam based on the critical blocking discrimination coefficient. The evaluation and design method and structure incorporate the relative relation between the debris flow scale and the dam body storage capacity, meanwhile, the influence on active structure parameters such as wedge-shaped flow guide is quantified, and the blocking state prediction accuracy of the grating dam in actual operation is improved to a certain degree.

A method for reducing the danger of a dammed lake formed by a debris flow blocking a river, comprising: first predicting the accumulation range of a river-blocking debris dam during occurrence of a debris flow, dividing a river boundary into multiple areas on the basis of the elevation, and then performing roughness reduction treatment on the areas of the river boundary during a dry season. The present method reduces the roughness of a river boundary within the accumulation range of a river-blocking debris dam, so as to reduce the opposite bank dam height of the river-blocking debris dam and the storage capacity of the dammed lake, thereby achieving the purpose of reducing the danger of dammed lakes formed by debris flows and reducing the impact of debris flow disasters.

The invention discloses a power distribution network intelligent planning method, system and device and a storage medium, and the method comprises the steps: obtaining point cloud data, carrying out the topographic feature information extraction of the point cloud data, and obtaining a topographic data set; acquiring remote sensing data, and performing earth surface change information extraction on the remote sensing data to obtain an earth surface change data set; fusing the topographic data set and the surface change data set to obtain a topographic point cloud data set; according to the terrain point cloud data set, a debris flow scouring path of the gully edge collapse area is analyzed, and a first terrain model is constructed; acquiring unmanned aerial vehicle monitoring data, simulating a line cross-trench path in combination with the first terrain model, optimizing cross-trench points and tower footing coordinates, and obtaining a power grid planning scheme; and the stability of the power grid planning scheme is evaluated, tower footing reinforcement parameters are adjusted, a final power grid planning scheme is obtained, and technical support is provided for power grid construction in the desert area.

The invention discloses a debris flow geological disaster monitoring and early warning method, and belongs to the field of intelligent early warning, and the method comprises the following steps: carrying out the preprocessing of debris flow disaster impact factor data collected by a multi-source heterogeneous sensor, and obtaining a fusion data set under a unified spatial-temporal scale; performing abnormal value detection and data filling on the fused data set to obtain a high-quality data set; performing feature extraction on the high-quality data set by adopting a multi-scale convolutional neural network to obtain multi-scale features; establishing a debris flow disaster occurrence probability prediction model; and inputting the multi-scale features into the debris flow disaster occurrence probability prediction model to obtain debris flow geological disaster early warning. According to the invention, accurate early warning of debris flow geological disasters is realized, and powerful support is provided for disaster prevention and reduction.

The invention provides a typhoon rainstorm debris flow susceptibility evaluation method based on an ensemble learning algorithm, and the method comprises the steps: selecting an impact factor for a research region, building an evaluation index system, and obtaining related data according to an index demand; then extracting factor data by using ArcGIS and preprocessing the factor data, screening irrelevant characteristic factors by using a factor correlation analysis method, quantitatively measuring the colinearity among multivariate factors by using a multi-colinearity analysis method and eliminating the correlation among the factors by using a KPCA (Kernel Principal Component Analysis) method; then performing oversampling on debris flow occurrence samples by using an SMOTE algorithm; then optimizing parameters of the CatBoost regression model through an integrated optimization algorithm based on WOA, ISO, CSA and IGA optimization models; k-fold cross validation is carried out through Skleen to evaluate the stability of the model, and an optimal model is identified; and finally, drawing a debris flow susceptibility map by adopting the optimal model, and analyzing the influence of rainfall data in different time ranges on debris flow susceptibility prediction accuracy.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The buried person search and rescue device comprises a portable drilling machine, the portable drilling machine is provided with a plurality of hollow drill rod sleeves, a gas conveying channel used for conveying gas to the ground is arranged in a drill rod, and a gas outlet of the gas conveying channel is connected with a sampling gas inlet of a gas component analysis device through a gas conveying pipe. A gas purifying and filtering device and a gas extracting pump for extracting gas are connected to the gas conveying pipe in series; a plurality of air inlets communicated with the air conveying channel are formed in the side wall of each drill rod sleeve in a surrounding mode, a jet flow nozzle is arranged at each air inlet in the drill rod sleeve, each jet flow nozzle is installed at an outlet of an injection pipe, each injection pipe is connected with an electromagnetic valve in series, and an inlet of each injection pipe is connected with the jet flow pipe. The invention aims to provide the device for searching and rescuing the buried persons, which can remarkably improve the search and rescue efficiency and effect in a complex geological disaster scene, and further realize the rapid search and rescue of the trapped and buried persons in earthquake, landslide and debris flow disasters and the search and rescue of the remains of the victims.

The invention relates to a debris flow low-frequency evaluation method and system based on flow velocity calculation, and the method comprises the following steps: collecting historical large low-frequency debris flow disaster cases, analyzing the valley terrain of the historical large low-frequency debris flow disaster, and determining the terrain features of the large low-frequency debris flow disaster; collecting to-be-recognized valley terrain data, and recognizing whether the to-be-recognized valley terrain is a large low-frequency debris flow disaster valley terrain or not according to the terrain features; according to the flow velocity, calculating and identifying the recurrence period of the large low-frequency debris flow disaster hidden danger gully; and according to the recurrence period of the large-scale low-frequency debris flow disaster hidden danger gully, combined with the weather forecast before the recurrence period, sending out a corresponding large-scale low-frequency debris flow disaster early warning.

The invention relates to a debris flow and landslide positioning and classifying method based on natural images. The method comprises the following steps: firstly, acquiring natural image data of debris flow and landslide, and preprocessing the natural image data of debris flow and landslide to obtain disaster data; then, a disaster range positioning model is built, and the disaster range positioning model comprises a U-Net network module and an SE module; then, inputting the disaster data into the disaster range positioning model to obtain a disaster range image; and finally, performing feature extraction and iterative feature selection based on the disaster range image, and performing classification by adopting a machine learning model to obtain a target debris flow and landslide classification result.

The invention belongs to the technical field of geological disaster monitoring and early warning, and relates to a rainfall type landslide-debris flow chain disaster intelligent dynamic monitoring and early warning method, which comprises the following steps: data acquisition: generating multi-source data including an optical image, an SAR image, an unmanned aerial vehicle image and ground data; preprocessing the multi-source data; performing space-time registration, data format and organization on the multi-source data after data processing; key characteristic parameters are calculated, and data fusion is carried out; constructing a multi-dimensional feature vector, and generating a multi-dimensional feature image; constructing a risk assessment model by using a deep learning model, and performing real-time prediction; dynamically determining the total risk level of each area according to a preset comprehensive research and judgment rule by integrating the real-time prediction result; and when the total risk level reaches a'high risk 'or'extremely high risk' threshold value, generating early warning information, and performing early warning release. According to the invention, comprehensive, whole-process three-dimensional perception, dynamic and self-adaptive intelligent evaluation and disaster prediction are realized.

An early warning method for debris flow disaster in a small watershed, and a disaster reduction method for debris flows in a small watershed is disclosed. An early warning method for debris flow disaster in a small watershed is provided, in which parameters of a hydrological model are calibrated by rainfall, evaporation and runoff data of the watershed based on water balance of the watershed, and an early monitoring and warning scheme for debris flows, which takes water storage in the watershed as a core monitoring and evaluation indicator, is established. The GR4J hydrological model is improved by taking into account a watershed area, specific yield of an aquifer and several geological parameters, as well as an evapotranspiration effect of vegetation. A disaster reduction method by means of regulation of a drainage channel can provide a basis for mathematical model simulation studies of disaster reduction in small watersheds.

The invention discloses a spatial-temporal probability-based regional debris flow risk dynamic assessment method, and belongs to the technical field of geological disaster prediction.The spatial-temporal probability-based regional debris flow risk dynamic assessment method comprises the steps of establishing a dynamic probability model of a time dimension and a distributed probability model of a spatial dimension by integrating data such as meteorological data and historical disaster records; in particular, the method can solve the problem of result deviation caused by neglecting time dynamic characteristics in a traditional method, achieves the accurate prediction of the debris flow risk, breaks through the limitation of the traditional method in the aspect of data utilization, and improves the accuracy of debris flow risk prediction. Through fusion of multi-source information, the precision and applicability of the model are improved, and the assessment and decision support capability for complex natural disasters is significantly enhanced.

The invention discloses a debris flow intelligent monitoring and early warning system. The system comprises a sensing layer which is responsible for deploying a multi-parameter sensor matrix, constructing a space-air-ground integrated monitoring system, collecting debris flow formation environment key parameters in real time, performing preliminary data preprocessing, generating an encrypted compressed data packet and sending the encrypted compressed data packet to a transmission layer; the transmission layer is used for receiving the encrypted and compressed data packet through a 5G and Beidou short message dual-channel redundancy transmission technology and forwarding the encrypted and compressed data packet to the analysis layer; and the analysis layer is responsible for constructing a hybrid early warning model driven by a physical mechanism and data, fusing decrypted and decompressed data, dynamically capturing a coupling relationship among rainfall, soil humidity and terrain gradient parameters, learning a multi-factor evolution mode of a historical debris flow event, generating a dynamically adjusted critical threshold curved surface, and performing multi-parameter joint early warning. The early warning information is output to the application layer; and the application layer is responsible for triggering graded early warning and driving multiple departments to cooperatively respond based on the early warning information.

The invention relates to a geological disaster change detection method and system based on an improved twin U-Net and a central surrounding double-flow network. According to the method, an improved twin U-Net network is constructed as a feature extraction trunk, and a deformable convolution module is integrated in an encoder to adaptively adjust a receptive field; a central surrounding double-flow network is embedded in a decoding path, detail features are extracted through a central flow path, and a global context is obtained through a surrounding flow path; a feature fusion module is designed to realize double-path feature deep fusion, and a gating attention unit is adopted to calibrate feature response; introducing a contrast feature learning mechanism to reinforce feature space clustering characteristics; multi-modal degradation enhancement training is implemented to improve the robustness of the model; and an objective function containing difference perception loss and feature comparison loss is minimized through end-to-end joint optimization. The method can accurately identify geological disaster change areas such as landslide, debris flow and land subsidence, and has the advantages of high detection precision and strong anti-interference capability.

The invention discloses a data processing method for geological disasters, and particularly relates to the technical field of geological disaster monitoring and data processing. Collecting and normalizing multi-source monitoring data, and constructing a unified time sequence fusion vector; identifying a weak abnormal signal based on the disturbance change rate and a sensitivity model; high-disturbance feature points are extracted through sliding window clustering, and a dynamic risk factor matrix is constructed in combination with a remote sensing image; extracting dominant risk factor feature vectors by using principal component analysis, inputting the dominant risk factor feature vectors into a disaster evolution simulation model, and predicting a future high-risk area in combination with a historical path library; and finally, potential micro-disaster trigger points and risk levels are output, and graded early warning processing is realized. The system has the advantages of high disturbance identification precision, strong simulation prediction capability, automatic early warning response and the like, and is suitable for an intelligent early warning system for sudden geological disasters such as landslide and debris flow.

The invention discloses a geological risk identification method based on multi-source data fusion, belongs to the technical field of geological risk monitoring and analysis, and can solve the problems that the existing address risk identification mode is single, the identification precision is poor, and the identification efficiency is low due to the lack of dynamic monitoring means. The method comprises the following steps: S1, collecting geological data of a target area, and constructing a three-dimensional geological risk dynamic model according to the geological data; s2, determining risk index calculation data of the target area according to debris flow source data and rainfall data of the target area and the three-dimensional geological risk dynamic model; s3, determining a risk evaluation coefficient of the target area according to the risk index calculation data and risk index reference data of the target area, and generating a dynamic risk partition map according to the risk evaluation coefficient; and S4, generating early warning information of the target area according to the dynamic risk partition map, and performing geological risk identification according to the early warning information. The method is used for geological risk identification.

The invention discloses a drainage basin unit debris flow susceptibility evaluation method based on multi-model Stacking integration. The method comprises the following steps: constructing a debris flow disaster data set; constructing a Stacking ensemble learning model which comprises four base models, a feature stacking layer, an attention layer, a mixed feature layer, a feature splicing layer and a meta-model; a Stacking integrated learning model is trained; predicting the debris flow disaster probability of the drainage basin unit to be identified; and performing debris flow susceptibility evaluation. According to the method, multi-source data can be efficiently fused, and the mixed probability vector and the original sample are creatively spliced, so that the meta-model can simultaneously utilize the intrinsic characteristics of the original data and the prediction information of the base model in the training process; interference of irrelevant information and dependence on the quality of a base model can be reduced, key information in data can be captured more comprehensively, and the explanation capacity and prediction precision of the model on complex data are enhanced.

The invention discloses a mountain torrent disaster grading early warning method and system based on multi-source data analysis, and particularly relates to the field of mountain torrent disaster grading early warning, through establishing a mountain torrent disaster risk area soil anti-erosion capability evaluation model, the soil anti-erosion capability of each monitoring early warning area is evaluated, and the mountain torrent disaster risk area soil anti-erosion capability evaluation model is established. The water flow diffusivity of the mountain torrents is evaluated based on the water flow monitoring network data of the mountain torrent disaster early-warning area, and the debris flow transformation tropism of the mountain torrent disaster early-warning area is evaluated according to the real-time soil erosion resistance of the mountain torrent disaster early-warning area in combination with the regional topographic data. And comprehensively evaluating an expected disaster influence level based on debris flow transformation tropism and water flow diffusivity, setting a disaster influence graded early warning gradient interval, and performing graded early warning of disaster influence expectation on a monitoring area.

The invention discloses a debris flow monitoring and early warning method and system. The method comprises the following steps: constructing a debris flow susceptible area judgment model, and calculating a debris flow susceptible area judgment value based on rainfall prediction data, debris flow distribution historical data, human production data and the debris flow susceptible area judgment model so as to determine a key area of the debris flow susceptible area; arranging a sensor network, and obtaining multi-index sensor data of a monitoring area; obtaining a deformation value of the monitoring area based on the high-resolution satellite remote sensing image data of the monitoring area; and constructing a prediction model of the debris flow in the monitoring area, and obtaining a prediction result of the debris flow in the monitoring area so as to execute a corresponding early warning operation on the monitoring area. According to the invention, the monitoring range of the debris flow area can be narrowed, the debris flow monitoring cost is reduced, the deformation value of the monitoring area and the multi-index sensor data are fused, the accuracy of the prediction result can be effectively improved, and the early warning operation is more practical.

The invention discloses a debris flow susceptibility evaluation method based on a progressive optimization framework. The debris flow susceptibility evaluation method comprises the steps of collecting and sorting basic data of a research area; selecting evaluation factors, and performing correlation analysis on the evaluation factors; dividing drainage basin units, constructing a frequency ratio model to carry out susceptibility evaluation, and selecting an optimal drainage basin unit; adopting semi-supervised learning to select negative samples, and constructing a machine learning data set; through comparison of different Boosting algorithms and hyper-parameter optimization algorithms, an evaluation model with the highest precision under a village and town scale is constructed through multiple evaluation indexes, and an SHAP technology is adopted, so that contribution of different evaluation factors to an evaluation model decision process is quantitatively analyzed. According to the method, accurate and explainable theoretical support can be provided for risk assessment of the geological disasters in the village and town areas through a progressive optimization framework method, and important practical reference can be provided for optimization of disaster management and prevention and control strategies, so that potential risks and economic losses are effectively reduced.

The invention relates to the technical field of navigation systems, and particularly discloses a road geological disaster real-time judgment and early warning method and system based on elevation displacement track variation of navigation software and a storage medium. Acquiring vehicle driving elevation data and displacement real-time trajectory data in real time; based on the obtained vehicle driving elevation data and displacement real-time trajectory data, when it is judged that the vehicle elevation displacement trajectory changes, the type of the occurring geological disaster is judged through a collision trajectory model and a parabolic trajectory model; and transmitting the vehicle elevation displacement track change condition, the geological disaster type and the corresponding early warning information to a rear vehicle of the vehicle in real time. The problem that after natural geological disasters such as landslide, collapse, debris flow and pavement collapse occur in a front road section, a rear vehicle navigation system has no related geological disaster early warning, and continuous safety accidents of mass death and mass injury will occur is solved.

The invention discloses a mountainous area debris flow dynamic early warning method based on deep learning, and relates to the technical field of geological disaster early warning, and the method comprises the steps: S1, marking a rainwater collection conventional region covered by a rainfall region as a rainwater collection risk region; s2, recording a real-time influence factor set of potential debris flow in each rainwater harvesting risk area; s3, inputting the real-time influence factor set as an iteration input value into the deep learning network for iteration, and setting an iteration output value as a prediction influence factor set; and S4, carrying out risk assessment on the recorded data of the prediction influence factor set by using an abnormal assessment strategy, screening out all rainwater collection risk areas meeting the risk assessment level, and marking the rainwater collection risk areas as debris flow risk areas. Compared with the prior art, the method utilizes the deep learning network to process the complex data set, covers more potential risk areas, and has the advantages of improving the accuracy of mountain debris flow early warning and recognizing the debris flow risk mountain areas more accurately.