96 results about "Geographical feature" patented technology

The invention discloses a distributed flood forecasting scheduling model construction method based on sub-basins, particularly relates to the technical field of flood forecasting model optimization, and is used for solving the problem of local prediction failure due to spatio-temporal differences caused by neglecting parameter sensitivity of an existing flood forecasting model. A target drainage basin is divided into sub-drainage basins, hydrological and geographic feature parameters are extracted, and a cross-drainage-basin water conservancy facility topology network is constructed to dynamically correct river channel evolution boundary conditions; quantifying parameter sensitivity spatial diversity based on a Sobo index method, identifying a sensitive abnormal response region in combination with historical flood event distribution, and applying spatial lag correction through a river topology network to generate parameter sensitivity grade classification; constructing a dynamic weight matrix, and performing partition parameter calibration by adopting global optimization and local adjustment strategies respectively; and the hydraulic equilibrium constraint of the calibration result is verified based on the river topological relation, and a partition and grading flood discharge scheduling scheme is generated after iterative correction, so that the forecasting capability in a sudden flood scene is remarkably improved.

The invention relates to a chemical plant dangerous area electronic fence generation method and system, and the method comprises the steps: obtaining the positioning data of a target person, the positioning data of production equipment, and the video monitoring data and environment sensing data of a target area, carrying out the recognition based on the video monitoring data, and obtaining the behavior data of the target person, the method comprises the steps of dividing a target area into virtual grids based on a dynamic grid algorithm, determining distribution of geographic features based on GIS geographic information of the target area, filtering impassable areas based on the distribution of the geographic features, aggregating and combining adjacent grids, generating a closed boundary, and establishing a risk level evaluation model to evaluate the risk level of each virtual grid. The boundary adjusting module is used for adjusting the minimum buffer area width of the closed boundary to obtain a final electronic fence boundary, and the electronic fence boundary is used for carrying out risk early warning. According to the invention, dynamic and accurate sensing and protection of the high-risk area in the chemical plant are realized.

The invention discloses a meteorological disaster early warning response method and system based on a meteorological early warning aircraft, and the system comprises a data collection module which is used for constructing a database containing the real-time meteorological data, the terrain data, the historical disaster data and the early warning aircraft operation data; the multi-disaster terrain adaptation analysis module is used for analyzing waterlogging and gale comprehensive risks by using a synchronous superposition model in plains and analyzing landslide and thunder-gale chain risks by using a time sequence chain model in mountainous areas through a double-branch fusion neural network; the early warning response strategy generation module is used for generating differentiation strategies according to regional risks and terrains; and the terminal interaction module pushes information and receives feedback. The system dynamically generates protection guidance for different geographic features such as a plain high-risk area and a mountainous area high-risk area through multi-source meteorological data integration and analysis, and improves the timeliness and coverage of early warning information.

The invention relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographic weighting, and the model comprises a feature decoupling module which is used for constructing a two-channel input structure of geographic features and non-geographic features, and achieves the feature decoupling and fusion through a multi-scale spatial weight matrix; the bandwidth allocation module dynamically determines the bandwidth scale in the XGBoost tree splitting process, and establishes a dynamic weight bound by tree levels; the constraint gain module generates split points with scale marks; a contribution decoupling module extracts split features and associates geographic and non-geographic features to realize salinization prediction and contribution index extraction; and the verification optimization module optimizes model parameters through a multi-scale thermodynamic diagram and spatial autocorrelation analysis. The model can capture geospatial effects of different scales, improves the spatial local prediction precision, and is applied to soil salinization analysis scenes with spatial heterogeneity.

A method and apparatus for assessing the hazard of rainfall-induced landslide clusters, and a computer device. The method comprises: acquiring historical rainfall-induced landslide cluster data from past intense rainfall events to construct landslide sample data; performing analysis on the basis of landslide attribute parameters and rainfall-induced landslide cluster data distribution information of the landslide sample data to obtain landslide development characteristics; on the basis of landslide geographical features and geometric features, using the landslide sample data and a plurality of candidate landslide-influencing indexes to analyze underlying patterns of landslide occurrence, so as to establish a landslide hazard evaluation index system; and performing correlation analysis on the plurality of candidate landslide-influencing indexes to determine a target landslide-influencing index, and combining the target landslide-influencing index with the landslide hazard evaluation index system to obtain a landslide hazard assessment model constructed on the basis of an automated machine learning framework. The efficiency of rainfall-induced landslide cluster hazard assessment can be improved and the accuracy of rainfall-induced landslide cluster hazard assessment can be effectively enhanced.

The invention relates to a three-dimensional sea temperature monitoring method, and the method comprises the steps: predicting a residual error through a sea surface temperature reconstruction model based on low-resolution sea temperature background data, meteorological data and geographic feature data of a to-be-monitored sea area, superposing the residual error with the low-resolution sea temperature background data, and obtaining a high-resolution sea temperature parameter of each region of a sea surface; and the precision and the physical consistency of the sea surface temperature parameters are improved. In deep sea temperature inversion, based on biochemical environment data and high-resolution surface sea temperature parameters, sea temperature parameters of all depth areas of a sea area to be monitored are predicted through a deep sea temperature prediction model, and a heat exchange rule is reflected more accurately. According to the method, surface reconstruction and deep inversion are combined, an association connection mechanism is established, information collaborative integration is achieved, complete and coherent three-dimensional sea temperature data are output, and reliable support is provided for applications such as ocean heat transfer calculation.

The invention provides an engineering investigation surveying and mapping intelligent management method and system based on a geographic feature processing technology. The method comprises the following steps: acquiring building distribution density, ground gradient and underground rock stratum distribution data of a target area, deploying a ground deformation monitoring radar at a selected position based on the building density and the rock stratum data, generating ground deformation data through radar images, and then separating ground subsidence change and original terrain characteristics to form a subsidence map; meanwhile, image reflection characteristics are analyzed to mark dangerous areas. And then integrating the ground gradient, the settlement trend and the rock stratum structure complexity to construct a land bearing evaluation model. The influence weight is further adjusted in combination with engineering requirements, and a bearing force evaluation result is generated after the model is input. Therefore, a scientific three-dimensional development planning scheme is generated, and optimized utilization and risk management and control of land resources are realized. According to the invention, through multi-source data fusion and model calculation, full-process analysis from land deformation monitoring to load bearing evaluation is realized.

The invention provides a high-production planting method and system for realizing agricultural multi-dimensional elements based on geographic information, and relates to the technical field of geographic information, and the method comprises the steps: S1, obtaining multi-source geographic information data of a target planting region, comprising soil type spatial distribution data, climate condition statistical data, landform three-dimensional feature data and pest and disease damage occurrence records; s2, carrying out spatial superposition and correlation analysis on the multi-source geographic information data, and generating a multi-dimensional geographic element comprehensive layer integrating the soil fertility grade, the moisture infiltration capacity and the accumulated temperature illumination adaptation degree; and S3, presetting two target detection points in the multi-dimensional geographic element comprehensive layer, wherein the detection points are respectively located at a geographic feature turning point and a region center point of the planting region boundary. By integrating multi-source geographic information, accurate division of planting areas, intelligent matching of varieties and parameters and dynamic regulation and control of growth cycles are realized.

The invention discloses a wind power plant power prediction method and device based on space-time collaboration and a storage medium, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining historical wind field data and geographic space features of a wind power plant, the wind field data comprising fan operation data and meteorological feature data in the same time period; combining the historical wind field data, the geographic features and the corresponding total power labels of the wind power plant into a sample set, and training a wind power plant power prediction model by using the sample set; wherein the wind power plant power prediction model extracts local and overall double-layer similarity based on historical wind power plant data, obtains a time period cluster with high similarity with meteorological feature data from historical fan operation data according to the double-layer similarity, and performs spatial clustering on the time period cluster based on historical geographic features; and inputting the to-be-measured wind field data and the to-be-measured geographic features into the trained wind power plant power prediction model to obtain the total power prediction value of the wind power plant, thereby improving the wind power prediction precision and efficiency of the complex terrain.

The invention relates to an urban scale remote sensing image building contour extraction method considering geographic features, and the method comprises the steps: obtaining remote sensing image data, optical image data, land utilization data and night light data of a target region, and carrying out the preprocessing; dividing the target area into a plurality of grid units, classifying a building dense area and a building sparse area according to land utilization data, and calculating a noctilucent enhanced built-up area index of each grid unit; constructing a training sample set; the method comprises the following steps: constructing a double-branch encoder comprising a convolutional neural network branch and a Transform branch; a convolution interaction fusion module is embedded in each stage of the encoder, the training sample set is used for training to obtain a building contour extraction model, and the building contour extraction model is applied to contour extraction of urban scale images. The method has the beneficial effects that a new spatial sampling strategy is constructed, so that the training and deployment efficiency of the cross-large geographic region deep learning model is improved, and the data annotation cost is reduced.

The invention belongs to the field of tourism planning, and provides a tourism service method and system integrating local culture and tourism characteristics, and the method comprises the steps: collecting local culture data; collecting tourism characteristic data; defining each cultural activity and scenic spot as a node in the graph; establishing a multi-dimensional edge connection system for the graph; based on the constructed graph structure, fusing the geographic features and the time features into a multi-scale high-dimensional spatial-temporal feature space; constructing a hierarchical reinforcement learning framework; introducing a multi-target reward function into the reinforcement learning framework, wherein the multi-target reward function comprises a culture experience integrity index, a space-time coordination index and a risk avoidance index; and receiving a culture type and a scenic spot type which the user wants to embody, and generating an optimal route and travel time which meet the requirements of the user through the hierarchical reinforcement learning framework in the spatial-temporal feature space. The method is especially suitable for tourist route planning with rich local culture and complex geographical environment in Sichuan and Tibet areas and the like.

The invention provides a coastal zone land coverage type classification method based on a random forest and a topology rule, and belongs to the technical field of coastal zone land coverage type classification. The method comprises the following steps: based on a GEE cloud platform, integrating a multispectral remote sensing image and elevation data, extracting spectral features and geographic features, and realizing pixel-level classification of random forest machine learning; generating a clustering unit through a simple non-iterative clustering method, and performing object-oriented special classification on the water body and the aquaculture pond in combination with spectral and morphological characteristics of the clustering unit; spatial filtering is introduced to remove salt and pepper noise, a coastal zone ground feature topology rule base is established to carry out spatial logic verification, and coastal zone land coverage classification with high precision and reasonable spatial layout is realized. According to the method, the multi-source remote sensing data and the multi-level classification strategy are fused, the problems of coastal zone ground feature spectrum confusion, boundary fuzziness and space logic inconformity are effectively solved, the classification precision and practicability are remarkably improved, and the method is suitable for land coverage monitoring and management of the complex coastal zone environment.

The invention provides a tunnel-slope system state information association mutual feedback model construction method and system, and relates to the technical field of geotechnical engineering monitoring, and the method comprises the steps: constructing a unified database through obtained target monitoring data, and analyzing an influence coefficient of a traffic load on slope stability in combination with a dynamic disturbance factor; on the basis of the coefficient, the geographic features and the stress data, completing unknown region data to form a complete data set; extracting a sensitive feature set, identifying a construction blasting and rainstorm rainfall interference section, and locking a side slope potential sliding surface and tunnel lining deformation collaborative feature interval; constructing a multi-index evaluation matrix, performing normalization and threshold dynamic adjustment, and screening an early warning index set; a dynamic model is established, and a tunnel-slope system state information association mutual feedback model is formed through a bidirectional feedback mechanism optimization model of slope displacement and tunnel excavation data. According to the invention, the accuracy and real-time regulation and control capability of tunnel-slope system state early warning are improved.

The invention provides a high-precision map regionalization updating method and system based on space-time relevance, and relates to the technical field of high-precision maps, and the method comprises the steps: obtaining live-action image data and associated data of a shooting terminal, and binding the live-action image data and the associated data to form an image data unit with a space-time position mark; comparing the data with a pre-stored live-action three-dimensional map, determining satellite positioning information correction coordinates, identifying geographic element features and distributing the geographic element features to corresponding space-time grid units according to the correction coordinates; performing spatial clustering analysis according to space-time grid unit distribution, determining a key area needing to be backtracked, obtaining image data units and geographic element features at different historical times through time retrieval and spatial indexing, and reconstructing a time sequence change state; according to the difference between the time sequence change state and the pre-stored map, regionalized attribute updating is carried out on the live-action three-dimensional map, regionalized attribute updating of the high-precision map can be achieved, and the accuracy and pertinence of map updating are improved.

A method for discovering geofeatures in a subterranean formation includes receiving first input data including training seismic surveys. The method also includes training a model based on the training seismic surveys to produce a pre-trained model using a self-supervised learning (SSL) technique. The method further includes receiving second input data. The second input data includes one or more target seismic surveys associated with the subterranean formation. The method also includes generating geological representations of the subterranean formation using the pre-trained model and based on the one or more target seismic surveys. The method also includes generating a database based on the geological representations. The method also includes generating an output based on the database.

The invention discloses a risk management and control zoning method and system based on multi-medium interactive pollution data, and relates to pollution management and control, and the method comprises the steps: delimiting an initial risk pollution region through random pollution point detection, and determining main pollutants of each medium and the content of the main pollutants; analyzing the content change gradient of the pollution point, and constructing a content reduction vector line; setting a multidirectional edge region, analyzing vector line difference, and if the criterion does not reach the standard, expanding the edge until the criterion is met or crossed; accurately measuring an actual pollution range, extracting geographic features, and establishing a correlation group of pollutants, vector distribution and the geographic features; according to the method, vector distribution, geographic features and pollutants are taken as elements, a multi-medium interactive pollution comparison model is constructed, and real-time data are analyzed, so that the accuracy, dynamic adaptability and real-time response efficiency of multi-medium pollution risk management and control are improved, and efficient partitioning and early warning in a complex terrain scene are supported.

The application discloses a power transmission line fire alarm method and system based on multi-source data fusion, the method comprising: monitoring fire points according to ground monitoring device monitoring data, unmanned aerial vehicle monitoring data and satellite monitoring data of a target area where the power transmission line is located; when it is judged that there is a fire point according to the satellite monitoring data and multiple satellites monitor the fire point, fusing the fire point information and monitoring data of the multiple satellites, generating time series data corresponding to the satellites, then acquiring geographical data and meteorological data of the target area, and respectively extracting geographical features corresponding to the geographical data, time series data and time series features corresponding to the meteorological data, splicing the geographical features and the time series features, obtaining a feature vector, identifying the fire point through a preset classification algorithm, judging whether the fire point monitored by the satellite is a real fire point, and if so, alarming, thereby improving the accuracy of fire point alarm.

The invention discloses a grassland environment monitoring method and system, a storage medium and electronic equipment, relates to the technical field of grassland ecological monitoring, and aims to provide a monitoring method which has the advantages of dynamically adjusting sampling frequency and statistical period to balance monitoring precision and resource consumption, reducing data redundancy and missing, improving monitoring response timeliness and the like. The monitoring method comprises the following steps: determining a grassland type and a monitoring index according to climate conditions and geographic features of a monitoring target area, and setting an initial sampling frequency and an initial statistical period; based on the initial sampling frequency and the initial statistical period, a preset sensor is used for carrying out data collection on the monitored target area, and the collected data are stored; analyzing the collected data to obtain an analysis result; and based on the analysis result, the initial sampling frequency and the initial statistical period are dynamically adjusted to ensure the optimal balance between the monitoring precision and the resources.

The present invention provides a method, system, terminal, and medium for aggregating multi-target REDD project areas, specifically relating to the field of forest carbon sequestration technology. This solution constructs a multidimensional weighted feature set for each coordinate point based on the collected coordinate points within several target REDD project areas and the geographic feature data of the target layer at each coordinate point, as well as preset spatial coordinate aggregation weights and preset geographic feature aggregation weights. A clustering algorithm is used to cluster all the multidimensional weighted feature sets to obtain grid point sets corresponding to the multidimensional weighted feature sets. A convex hull algorithm is used to calculate the convex hull of the coordinate points in each grid point set to obtain the convex hull set of the grid point sets. Based on the target REDD project areas corresponding to all convex hull sets, an aggregated region for all target REDD project areas is obtained. This solution can effectively improve the accuracy of REDD project area boundary identification, thereby facilitating the comprehensive management and assessment of dispersed areas.

This invention proposes a two-stage super-resolution reconstruction method and system for three-dimensional downscaled wind fields in bridge sites with complex terrain. First, the invention utilizes a WRF model and explicitly enables large eddy simulation to construct a high-fidelity training set. To build a spatially three-dimensional reconstruction result, the invention extracts dynamic meteorological sequences and static geographical features for different feature height layers. After filtering out invalid regions using a mountain occlusion mask matrix, three parallel cascaded reconstruction models are independently trained to significantly improve the real-time performance of data acquisition. The model training innovatively integrates terrain-forced physical loss and frequency domain energy constraints. Finally, the two-dimensional reconstruction results from each height layer are combined in vertical space to construct a high-precision three-dimensional microscale meteorological field ensemble, providing reliable technical support for three-dimensional wind energy resource assessment and bridge health monitoring in complex terrain.

The application provides a high-precision map regionalization updating method and system based on space-time correlation, and relates to the technical field of high-precision maps. The application binds real scene image data and associated data of a shooting terminal to form an image data unit with a space-time position marker. The image data unit is compared with a pre-stored real scene three-dimensional map to determine satellite positioning information correction coordinates, identify geographical feature characteristics, and distribute the geographical feature characteristics to corresponding space-time grid units according to the correction coordinates. Spatial clustering analysis is performed according to the distribution of the space-time grid units to determine key areas that need to be traced back. Image data units and geographical feature characteristics at different historical times are obtained through time retrieval and spatial indexing to reconstruct a time sequence change state. According to the difference between the time sequence change state and the pre-stored map, the real scene three-dimensional map is updated in a regionalized attribute, which can realize regionalized attribute updating of a high-precision map and improve the accuracy and pertinence of map updating.

The utility model relates to the technical field of recreation facilities, in particular to a globe-shaped astronomical swivel chair which comprises a base rail, a supporting frame arranged in the middle of the base rail, a rotating frame arranged above the supporting frame through a bearing, a glass ball arranged on the inner side of the rotating frame through a bearing, and rotating frames arranged on two sides of the rotating frame. Rollers are mounted at the bottom of the rotating frame through bearings. According to the astronomical swivel chair in the shape of the globe, tourists can drive the rolling wheels through the pedal plate to freely roll on the base rail so as to control the rotation of the rotating frame and the glass ball body, and the sense of participation and interactivity of the tourists are greatly enhanced through the active operation mode. The map layer installed on the surface of the glass sphere enables tourists to clearly see geographic features such as mountains, rivers, national boundaries and the like on the surface of the earth in the rotating process, teaching is achieved through lively activities, and the knowledge level of the tourists is improved. The seat arranged on the outer side of the rotating frame is reasonable in design and accords with the human engineering principle.

This application discloses a method, apparatus, and equipment for classifying flash flood disasters, relating to the field of disaster monitoring technology. The method includes: acquiring a geographic feature dataset of multiple watershed nodes in a target area, and constructing a feature matrix based on the geographic feature dataset; inputting the feature matrix into a flash flood disaster classification model, and outputting the classification probability of each watershed node belonging to each flash flood disaster category; the flash flood disaster classification model is used to: calculate the edge weight matrix of the graph structure formed by the feature matrix and the watershed nodes in the target area using a multi-head attention mechanism; perform sparsification processing on the edge weight matrix, and perform graph convolution operation based on the feature matrix and the sparsified edge weight matrix to obtain aggregated features; perform global feature enhancement on the aggregated features to obtain enhanced features; and output the classification probability of each watershed node belonging to each flash flood disaster category based on the enhanced features. This application can improve the accuracy of flash flood disaster classification.

The present invention discloses a method and system for monitoring dynamic changes in land use based on multimodal remote sensing data, and relates to the technical field of multimodal remote sensing data analysis. The system of the present invention includes: a data acquisition and preprocessing module, a consistency baseline construction module, a real-time data analysis module, and an anomaly diagnosis and processing module; the data acquisition and preprocessing module realizes the spatiotemporal alignment of multi-source data; the consistency baseline construction module divides land use types based on geographical features and establishes a baseline database; the real-time data analysis module screens suspected anomaly areas through a difference index; the anomaly diagnosis and processing module distinguishes between data quality anomalies and emergency anomalies based on feature correlation relationships, the former triggering data repair and verification, and the latter marking high-priority areas and outputting location information. The system realizes high-precision monitoring of land use changes and intelligent anomaly classification through multimodal data fusion and correlation analysis.

According to the wireless channel error distribution modeling method based on empirical formula and conditional generative adversarial network fusion, topographic features, physical error constraints and a conditional generative adversarial mechanism are introduced, environmental features such as terrains and buildings are used as conditional input of a conditional generative adversarial network (cGAN), adversarial training is carried out in combination with real propagation error data, and the accuracy of confrontation is improved. The model can learn a nonlinear mapping relation between geographic features and propagation errors, and a channel modeling result with physical consistency and terrain correlation is obtained, so that a traditional path loss model or a deep learning prediction model can be effectively assisted to be optimized; and reliable data support is provided for wireless propagation modeling and related model research in a scene with data scarcity or complex environment.

The application discloses an emergency rescue positioning device based on 3D modeling and relates to the technical field of emergency rescue. The emergency rescue positioning device based on 3D modeling comprises a protective bucket, a scanner for scanning 3D information is arranged in the protective bucket through a lifting plate, a lifting component for the movement of the lifting plate is arranged on the surface of the protective bucket, and a closure cover with an opening in the center position is connected to the surface of the protective bucket. The lifting plate, the closure cover and the protective bucket are arranged, the scanner can be lifted through the lifting component, the geographical information near the rescue site can be scanned, 3D information can be obtained, 3D modeling can be carried out based on the obtained 3D information, the geographical features of the rescue site can be more intuitively seen, rescue organization can be facilitated, the positioner can be protected by the protective bucket and the closure cover, the positioner can be prevented from being damaged, the connecting pipe is arranged to facilitate carrying and transfer.

This system provides an optimization method that can expand service area coverage, which is affected by terrain-related interference such as radio wave blockage and diffraction losses, while also reducing the computational load required for that optimization. [Solution] For each of the multiple candidate locations in the sky where multiple aerial PFs can be placed, the system acquires data on the radio wave propagation characteristics of the radio wave propagation path between the candidate location and the service area, based on the geographical characteristics of the service area. Based on the radio wave propagation characteristics data, it determines the locations of the multiple aerial PFs that will form cells toward the service area, or both the number and locations of the multiple aerial PFs. Based on the radio wave propagation characteristics data, it determines the values ​​of the beam parameters to be used to form cells for the multiple aerial PFs.

This invention provides a method, apparatus, device, and medium for merging place name strings based on merging connections, relating to the field of data processing technology. The method includes: acquiring vector place name data corresponding to a study area; extracting place name strings corresponding to multiple vector data points suspected of belonging to the same geographical feature from each vector data point in the vector place name data, obtaining an initial set of place name strings to be merged; generating auxiliary verification markers for the vector data points corresponding to the initial set of place name strings, responding to user verification operations on the initial set of place name strings based on the auxiliary verification markers, obtaining a target set of place name strings and its verification notes; and performing a merging operation on the place name strings contained in the target set of place name strings according to the auxiliary verification markers and verification notes, to obtain the merged place name strings corresponding to the study area. This invention can significantly improve the processing efficiency and data quality of place name data.

A communications system using aircraft flown at high altitude. The aircraft may fly in a pattern providing coverage over a large area on the ground. The aircraft may utilize antennas which may be positioned to provide better coverage in different lateral ranges under the aircraft as the aircraft flies within a flight pattern. The aircraft may use hydrogen powered fuel cells and maintain flight for a significant amount of time. The communications system may be adapted to provide 5G, or higher, wireless communication to areas which may be inadequately served by terrestrial based systems. The beam direction from above avoids signal blockages which may occur due to irregular geographic features, and also allows for service into areas devoid of terrestrial service.

The application provides a tunnel-slope system state information correlation feedback model construction method and system, relates to the geotechnical engineering monitoring technical field, and wherein the method comprises: constructing a unified database through the obtained target monitoring data, combining dynamic disturbance factor analysis, and analyzing the influence coefficient of traffic load on slope stability; based on the coefficient, the geographical features and the stress data, the unknown area data is completed to form a complete data set; the sensitive feature set is extracted therefrom, the construction blasting and the heavy rain interference section are identified, the potential sliding surface of the slope and the deformation coordination feature interval of the tunnel lining are locked; a multi-index evaluation matrix is constructed and normalized and threshold dynamic adjustment is performed, and the early warning index set is screened; a dynamic model is established, the model is optimized through the two-way feedback mechanism of the slope displacement and the tunnel excavation data, and a tunnel-slope system state information correlation feedback model is formed. The application improves the accuracy and real-time control ability of the tunnel-slope system state early warning.