624 results about "Rockfall" patented technology

The invention discloses a high and steep slope dangerous rock falling intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, collecting and preprocessing real-time monitoring data, and generating a standardized input sample set; s2, constructing a long-term prediction network model, and outputting a state prediction sequence of multiple time steps in the future; s3, optimizing structure parameters and training parameters of the prediction network model based on a bald eagle search algorithm; s4, executing multi-step state prediction by using the optimized model, and outputting a crack trend, a displacement trend and an abnormal probability value; s5, constructing an early warning risk scoring function, and fusing multiple prediction indexes to generate a risk scoring value; and S6, setting a multi-level early warning threshold value, outputting an early warning level, and issuing the early warning level in linkage with voice, a terminal and a platform. According to the invention, through constructing the intelligent monitoring and early warning method fusing the long-term prediction network model and the bald eagle search algorithm, accurate prediction and multi-stage linkage early warning of the high and steep slope dangerous rock falling risk are realized.

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

The invention discloses a dangerous rock falling motion feature prediction method based on multi-source data fusion, and belongs to the technical field of engineering geology. Comprising the steps of collecting dangerous rock multi-source data to construct a basic information base; processing dangerous rock multi-source data based on a CNN algorithm, identifying a dangerous rock contour and calculating geometric features; the dangerous rock multi-source data is processed through multiple algorithms, and the terrain is reconstructed to generate a digital elevation and terrain model; analyzing a vibration signal frequency spectrum, quantifying dangerous rock multi-source data features and determining a fusion weight; fusing the multi-source data of the dangerous rock to construct a three-dimensional real scene model and construct a physical and mechanical model; establishing a historical database, selecting a machine learning algorithm to construct a data driving model, and adopting an optimization algorithm to predict a rockfall movement track and state; and determining a two-dimensional simulation key section, establishing a two-dimensional numerical model, and dynamically simulating and calculating rockfall movement and rock-soil body response. According to the scheme, the defects in the aspects of dangerous rock falling motion feature prediction and protection design in the prior art are overcome, and the prediction accuracy and efficiency are improved.

The invention relates to the technical field of geological disaster risk evaluation research, in particular to a collapse geological disaster risk evaluation method and system of a transformer substation structure damage model. The collapsed dangerous rock mass is investigated in detail, the stability is evaluated through a stereographic projection method, and the instability probability is calculated through a Monte Carlo method; fLAC3D and PFC3D are coupled to simulate a collapse dynamic process, and risk evaluation is completed in combination with ArcGIS; establishing a transformer substation electrical facility physical model and a structural damage model; establishing a vulnerability evaluation model based on the collapse rockfall impact strength and the electric facility structure resistance; and performing risk evaluation by adopting a matrix analysis method, and estimating economic loss. The invention further comprises corresponding system implementation. According to the method, by combining geological survey, numerical simulation and structural analysis, comprehensive quantitative evaluation of the collapse disaster risk of the transformer substation is achieved, the accuracy, reliability and pertinence of evaluation are improved, and a scientific basis is provided for disaster prevention and reduction of the transformer substation in the mountainous area.

The invention discloses a slope collapse rockfall monitoring method, and relates to the technical field of slope rockfall monitoring, and the method comprises the following steps: arranging multiple types of monitoring sensors in a slope region, and covering a key monitoring region in combination with a camera to form a multi-source data collection network; according to the method, false alarms caused by natural interference and non-dangerous interference are effectively reduced through real-time monitoring of a multi-source sensor and a signal filtering technology based on an environmental noise model, and the authenticity of risk signals is verified from multiple dimensions by combining sensor data with camera images through a multi-modal data fusion algorithm, so that the reliability of the system is improved. In combination with a dynamically updated slope collapse feature library and an intelligent algorithm, the system can accurately identify key parameters of slope instability and predict risks, flexibly responds based on a graded early warning mechanism, improves the disaster prevention and control capability, reduces resource waste caused by false alarm, reduces threats of disasters to life and property, and improves the safety and reliability of the system. And sufficient time is provided for managers to take measures.

The invention provides an anchor rod fixed type collapse rockfall monitoring and early warning method based on vibration induction, and belongs to the field of geological disaster prevention and early warning. The detection system comprises a plurality of vibration sensors fixed on a slope through anchor rods, a solar panel for supplying power to the sensors, an intermediate data processing platform, early warning channels and early warning modes corresponding to different early warning channels, for a dangerous area where rockfall disasters are prone to happening, n vibration sensors fixed by anchor rods are arranged to form a dot matrix network, the coverage of the monitored area is increased, the vibration sensors are powered by solar panels, the dot matrix network is connected with an intermediate data processing platform after data fusion, and the intermediate processing platform is connected with an early warning channel. The early warning information is transmitted out in different police modes; the system has applicability for early warning of mountain slope rockfall disasters, can realize large-range, full-angle and large-area monitoring and early warning, and is powered by solar energy, so that power consumption is reduced, and cost is saved.

The invention relates to the technical field of intelligent monitoring, in particular to a road monitoring device based on multi-source data fusion, which comprises a distributed fiber bragg grating sensing module for collecting various data of each monitoring area of a roadbed and a road surface; the optical signal processing module is used for analyzing the data acquired by the distributed fiber grating sensing module and judging whether the data exceed respective threshold values or not; the imaging radar and the low-illumination camera are used for acquiring radar point cloud images and visible light images of a road and a slope area; the image processing module is used for fusing the radar point cloud image and the visible light image of the same monitoring area and identifying whether rockfall, fracture or collapse conditions exist in each monitoring area on the road or not; and the alarm module is used for sending out a corresponding alarm signal when any one of the data acquired by the distributed fiber grating sensing module exceeds a threshold value or rockfall, fracture or collapse conditions exist in a certain monitoring area. According to the invention, the monitoring precision of road abnormal conditions can be improved.

The invention relates to a rockfall detection method and system based on a YOLOv9 improved algorithm, and belongs to the technical field of image processing. The invention aims to solve the problems of rockfall form diversity and complex environment which are difficult to identify. According to the method, firstly, image data of a rockfall area are obtained, then an improved YOLOv9 algorithm is used for carrying out target detection on an image, and improvement comprises the step of introducing an effective channel attention ECA mechanism and a Wise-IoU loss function. And finally, the detection result of the rockfall target is output to a monitoring platform or other equipment, and tracking or early warning is carried out. The method can effectively improve the precision and efficiency of rockfall detection, has good generalization performance, and can be applied to the fields of slope rockfall monitoring, traffic early warning and the like.

The invention relates to the technical field of intelligent traffic and road safety monitoring, and discloses a 4D imaging millimeter wave radar-based slope deformation monitoring and identification method, which comprises the following steps of: acquiring a continuous point cloud; carrying out voxel modeling on the point cloud in a three-dimensional space; selecting three continuous frames of point clouds, and identifying mutant voxels by calculating the point number variation of each voxel in adjacent frames; performing spatial clustering on the mutant voxels to obtain an obstacle region; performing feature extraction on the obstacle area, wherein the extracted features comprise the height difference, the average reflection intensity and the centroid drift of the obstacle area; carrying out linear weighted fusion on the extracted features, and constructing a landslide mutation index; and judging whether an obstacle target exists in an obstacle area or not according to a preset condition, and performing risk grading identification according to the landslide sudden change index. According to the invention, all-weather, structured and high-reliability perception of major traffic potential safety hazards such as landslide / rockfall is realized, and a new basic capability is provided for a traffic safety system.

A rock mass informatization model establishment method based on laser point cloud relates to the field of model identification and analysis, and is characterized by comprising the following steps: unmanned aerial vehicle laser data acquisition; establishing an inclination model; re-matching the laser point cloud flight strip; carrying out refined point cloud classification; establishing a triangulation network model; establishing a rock form model; establishing a rock mass information model; establishing a rock mass analysis system; and collapse simulation. The method provided by the embodiment of the invention has the advantages that the unmanned aerial vehicle can be used for carrying the laser radar to carry out close-to-land imitation flight, structures such as collapse, cracks and rockfall of the mountain under the dense forest can be directly measured by using the good vegetation penetrating ability of the laser point cloud, and compared with the original modes such as field investigation and photogrammetry, the method has the advantages that the measurement accuracy is greatly improved. The information obtained by the laser point cloud is more accurate and comprehensive, and the geometric dimension, the steep cliff section shape, the structural surface feature, the lithology, the boundary condition and other features of the dangerous rock mass can be directly confirmed.

The invention discloses a rockfall motion tracking method and system based on computer vision. The method comprises the steps that an improved Gaussian mixture model (PGMM) is adopted to monitor and detect a real-time video stream, and a moving target is extracted; classifying the detected target by using a YOLOv5 deep learning model, and identifying whether the target is rockfall; in combination with the output of the PGMM and the YOLOv5 model, screening and accurately identifying a rockfall target; calculating and predicting a motion track of the rockfall based on the rockfall motion model and a free falling body formula; and outputting the motion trail and the predicted position of the rockfall, generating early warning information and displaying the early warning information in real time. The method can effectively improve the precision and real-time performance of rockfall motion monitoring, is suitable for complex environments such as high and steep mountainous areas and construction sites, provides important rockfall disaster early warning and prevention and control support, and has high adaptability and wide application prospects.

The invention relates to the technical field of slope disaster early warning, in particular to a mine slope landslide disaster early warning method and system based on multi-modal data, and the method comprises the following steps: monitoring a surface mine slope through radar equipment, and obtaining a radar data sequence corresponding to slope rockfall and rock-soil loosening conditions; extracting a meteorological data sequence at the same moment as the radar data sequence from meteorological equipment; and calling a pre-established slope disaster early warning model through the cloud server, and predicting a landslide occurrence area and landslide occurrence time of the surface mine slope by using the radar data sequence and the meteorological data sequence. According to the method, radar data and meteorological data of rockfall and rock-soil loosening in the initial stage of the slope disaster are utilized to predict the occurrence area and occurrence time of the landslide in the outbreak period of the slope disaster, the early warning timeliness is met, and the neural network structure model is utilized to predict the occurrence area and occurrence time of the landslide on the basis of multi-modal data. And the prediction accuracy of the landslide disaster is improved.

The invention relates to the technical field of slope simulation tests, in particular to a high and steep embankment slope soil engineering simulation test device and method.The high and steep embankment slope soil engineering simulation test device comprises a multi-degree-of-freedom vibration table, a slope assembly, an airflow assembly and a driving assembly, the slope assembly is installed on the multi-degree-of-freedom vibration table, and the airflow assembly is installed on the driving assembly; the airflow assembly is located above the side slope assembly, and the driving assembly is connected with the multi-degree-of-freedom vibration table, the side slope assembly and the airflow assembly; the shifting piece is connected with the frame unit and is used for driving the inclination of the frame unit to change; the data acquisition system is used for monitoring the motion characteristics and collapse deformation of the slope model in the simulation process in real time and collecting data information generated in the test process; the problems that a traditional slope test device cannot dynamically simulate gradient gradual change caused by earthquake collapse, and the multi-disaster coupling effect of rockfall impact and hydrological airflow disturbance is difficult to reproduce are solved.

The invention discloses a reinforcing device before dangerous rock removal and treatment, which comprises at least two pairs of fixed anchor rods, the two pairs of fixed anchor rods are arranged in a matrix, the two pairs of fixed anchor rods are embedded in dangerous rock, the two pairs of fixed anchor rods are externally connected with a protective net, and the reinforcing device further comprises a pair of traction anchor rods arranged between the two pairs of fixed anchor rods. The invention relates to the technical field of dangerous rock reinforcement, the drilling, cooling and grouting functions are integrated through the hollow anchor rod, and the rock mass damage risk is reduced through single-point construction; the flexible connection system can effectively resolve the rock mass displacement stress; a gapless interception system is formed by the double-layer structure of the protective net and the blocking net, and falling stone impact energy is dissipated by the hydraulic buffer device in a grading manner; according to the design of the split type positioning hanging rings and the quick-release pins, modularized quick replacement of the protective net is achieved, the construction complexity is reduced through the standardized anchor rod assembly, the requirement for the number of anchoring points is reduced through cooperative bearing of the traction anchor rods and the fixed anchor rods, and the dangerous rock reinforcing safety and the construction efficiency are remarkably improved.

The utility model relates to the technical field of landslide collapse protection, and discloses a landslide collapse protection device which comprises a protection net. The supporting part is arranged on one side of the protective net, the supporting part is connected with the protective net, and a pressure sensor is arranged in the supporting part; the fixing part comprises a plurality of air cylinders and fixing seats, the fixing seats are arranged on the lower portions of the protective net, the supporting part and the air cylinders correspondingly, and the ends, away from the fixing seats, of the air cylinders are fixed to the sides, away from the protective net, of the supporting part; the controller is arranged on the supporting part, and the controller is electrically connected with the pressure sensor and the air cylinder. The supporting part is arranged on one side of the protective net, and meanwhile the fixing part is arranged, so that the supporting force of the device on a slope body can be improved, and high-strength impact can be well resisted; and secondly, a pressure sensor is arranged in the supporting part, pressure data of the slope body on the protective net are monitored in real time, when the pressure data exceed a preset pressure value, a controller controls an air cylinder to extend so as to enhance falling stone blocking, and practicability is high.

The invention discloses a dangerous rock falling motion feature analysis method based on discrete element simulation, and belongs to the technical field of engineering geology. Comprising the steps that dangerous rock data are collected through multiple means and preprocessed, and a three-dimensional visual dangerous rock mass initial model is constructed; based on the three-dimensional visual dangerous rock mass initial model, constructing a discrete element model, defining contact types and parameters among joint units by adopting a Hertz-mindlin contact theory, and determining normal contact force; based on the discrete element model and the normal contact force, a discrete element simulation technology is adopted, multi-physical field factors are considered to simulate dangerous rock falling movement, a discrete element model simulation result is obtained, a dynamic updating method is adopted for joint failure judgment, and the discrete element model is verified through an indoor physical model test and field monitoring data. And designing and implementing a prevention project according to a simulation result, thereby effectively preventing dangerous rock falling disasters and ensuring project safety.

The invention discloses a mountain state real-time monitoring and identification method and system based on deep learning. The method comprises the following steps: performing multi-angle dynamic image acquisition through a PTZ camera; training a dual-target detection network based on a YOLOv7 framework, constructing a rockfall-crack detection model, and synchronously outputting bounding box coordinates of a rockfall target and position information of a crack region; constructing a rockfall trajectory prediction model through a DeepSORT multi-target tracking algorithm, and obtaining rockfall movement trajectory data; pixel-level semantic segmentation is carried out on the crack area, a crack mask graph is generated, and a crack main direction vector is extracted; carrying out registration and difference calculation on the image to obtain a crack width variation and an extension length, identifying a newly-added crack region and marking spatial distribution of the newly-added crack region; and constructing a multistage alarm mechanism which comprises multistage alarm signals of an on-site audible and visual alarm device and a cloud platform. The mountain disaster monitoring and early warning system has the advantages that efficient and real-time monitoring and early warning of mountain disasters are achieved through rockfall detection and tracking and crack analysis and prediction.

The present application discloses a method for measuring impact force of debris flow and application, and a design method for barrier energy dissipation pile array. The method for measuring vertical impact force of debris flow measures the tangential velocity of debris flow at the impact point to measure the vertical impact force distribution of debris flow on the impact point. Further, the design method for pile array simplifies the free section of energy dissipation pile as a cantilever beam, which generates a bending resistance when impacted by debris flow, and the bending resistance continuously works to dissipate the impact kinetic energy of debris flow. According to the motion characteristics of debris flow, the impact time and distance parameters are further derived to solve the problems of measuring and designing the stability parameters of single energy dissipation pile, the energy dissipation parameters of barrier energy dissipation pile array, and the number parameters of barrier energy dissipation pile array. The present application also provides the application of each measuring and designing method. The present application provides a more scientific and perfect solution for the energy dissipation design of barrier energy dissipation pile array to resist the impact damage of rockfall and debris flow caused by landslide disaster.

The invention discloses a hydropower engineering extra-high steep slope online dynamic prevention and control method. The method comprises the steps that S1, data dynamic coupling and abnormity elimination are carried out; s2, carrying out space-time matching on the data; s3, optimizing a displacement early warning value; s4, carrying out safety prevention and control multi-modal analysis; and S5, performing result feedback and closed-loop control. According to the online dynamic prevention and control method for the ultra-high and steep slope in the hydropower engineering, rapid and accurate fusion of multi-source heterogeneous spatio-temporal data can be realized, the false alarm rate of slope early warning is reduced, and online closed-loop analysis of automatic identification of dangerous rock body hidden danger, real-time analysis of rockfall trajectory speed and pre-consolidation range and anchoring differentiation decision of the high and steep slope is realized; and a scientific, accurate and timely decision basis is provided for safety management of the high and steep slope.

The invention discloses a reservoir side slope protection device, and relates to the technical field of side slope protection, the reservoir side slope protection device comprises a slope protection main body, the upper part of the slope protection main body is rotatably connected with a falling stone baffle plate, and damping mechanisms for damping falling stone impact are symmetrically arranged between the slope protection main body and the falling stone baffle plate, and each damping mechanism comprises a connecting column arranged at the upper part of the slope protection main body; the connecting column is rotatably connected with the slope protection main body, a fixing sleeve is fixedly connected to the connecting column, second connecting rods are rotatably connected to the two sides of the fixing sleeve, a first connecting plate is rotatably connected to the side, away from the fixing sleeve, of each second connecting rod, and a first connecting rod is rotatably connected to the side, away from the corresponding second connecting rod, of each first connecting plate; and when falling rocks fall on the falling rock baffles, the impact force of the falling rocks on the whole slope protection device can be reduced through the damping mechanisms symmetrically arranged between the falling rock baffles and the slope protection main body, the falling rocks are prevented from damaging the slope protection device, and the service life of the slope protection device is prolonged.

The invention relates to the technical field of video image intelligent analysis, in particular to a rockfall detection and early warning method and system based on videos. According to the method, a suspected rockfall target is recognized and judged by collecting a road or side slope monitoring video and combining target detection and motion trail analysis; and after the rockfall is judged, generating early warning information containing the position, time and danger level, storing the early warning information in a database, and triggering an early warning prompt of a user side at the same time. The system is composed of a video acquisition module, a detection and judgment module, an early warning generation module, a log storage module and a front-end interaction module, and all the modules achieve cooperation through data interfaces. Compared with the prior art, the method can still keep high detection accuracy and real-time performance in a complex environment, has the advantages of high automation degree and high expandability, can be widely applied to scenes such as highways, railways and mining areas, and remarkably improves the traffic and engineering safety level.

The invention provides a high and steep slope dangerous rock mass instability mode judgment method and system, and belongs to the field of rock mass engineering, dangerous rock mass point cloud data of a target area is obtained, neighborhood point searching is performed on point clouds at different positions of a slope according to coordinate information in the dangerous rock mass point cloud data, plane fitting and coplanarity testing are performed on neighborhood points, and a dangerous rock mass instability mode judgment result is obtained. And merging the coplanar point sets, and calculating to obtain the inclination and the inclination angle of the structural plane according to the point sets. And clustering and grouping the structural surfaces, fitting the structural surfaces and performing intersection calculation. Point-surface calculation and analysis are carried out by combining topographic features of the high and steep slope, geometric morphology of the rock mass and development conditions of structural surfaces, the dangerous rock mass is positioned, the damage mode of the dangerous rock mass is identified, the judgment accuracy is improved, the damage mode of the slope rock mass is finely divided, and disaster prevention and reduction work of rockfall disasters of the high and steep slope is facilitated.

The invention discloses a design method of a slope flexible protection system in an extra-high and steep environment. The number of flexible protection nets is determined based on terrain parameters of a high and steep slope and parameters of the flexible protection nets; the arrangement position of each flexible protective net is determined according to the movement track of the rolling rocks and the falling rocks; determining the height of the flexible protective net based on the arrangement position of the flexible protective net; the length of each flexible protective net is determined according to the envelope line of the projection range of the rolling and falling rock bouncing trajectory; and based on the number of the flexible protection nets and the arrangement position, height and length of each flexible protection net, the ultra-high steep environment slope flexible protection system is designed. According to the method, the number of tracks is determined by dynamically matching the kinetic energy of rolling stones and falling stones and the energy level of the protective net, the protective net is arranged by combining the vertex of the bouncing track, the length of the protective net is determined based on the track projection range, the problems that a traditional method depends on experience, a protective blind area exists and resources are wasted are solved, and the method has the advantages of scientific quantification and accurate protection.

The invention discloses a valley-type ice rock collapse clastic flow disaster chain danger range prediction method, system and device and a storage medium, and belongs to the field of clastic flow disaster prevention and control, and the method comprises the steps: obtaining the average width of a valley; determining the volume and the ice content of the hidden danger body; the ratio H / L of the predicted vertical height difference H to the horizontal distance L in the whole movement process of the ice rock collapse clastic flow is obtained; the ratio Ht / Lt of the vertical height difference Ht and the horizontal distance Lt from the position of the hidden danger body to the position of each point of the gully is obtained, and when the first value smaller than H / L appears in the Ht / Lt, the position is determined to be the maximum danger range boundary of the ice rock collapse clastic flow disaster in the gully. The invention provides a set of efficient, quantitative and spatialized scheme, the static distribution information of the ice rock clastic flow hidden danger can be accurately quantified into visual and available geographic space information of the maximum danger range in combination with the dynamic influence of the valley terrain, and the risk assessment and emergency decision of ice rock collapse clastic flow disasters can be more scientifically supported.

The invention relates to the technical field of slope monitoring, in particular to a mine field slope instability trend-based radar monitoring and early warning method and system, and the method comprises the following steps: recognizing a slope region with an instability risk on a geological image through a pre-trained Fast R CNN model, and taking the slope region as an instability risk region; scanning the instability risk area in real time through a slope radar to obtain a radar image sequence of the instability risk area; and acquiring a geological image sequence of the instability risk area with the same time sequence as the radar image sequence, determining an instability trend of the instability risk area based on the radar image sequence and the geological image sequence, and then allocating an early warning level according to the instability trend. According to the method, locking of instability factors in advance is realized on the basis of the Fast R CNN model, falling rock and soil loosening sliding track trends are quickly analyzed in real time on the actual falling rock and soil loosening track obtained through multi-modal data solution on the basis of the LSTM network, and thus integrated display, focusing monitoring and graded early warning of mining area risk points are realized.

The invention provides a dangerous rock body track prediction method and device based on fusion of visible light and infrared thermal imaging, and belongs to the technical field of computers, and the method comprises the steps: obtaining an infrared thermal imaging video and a visible light video of a dangerous rock region in a first time period, and obtaining a fusion video based on the infrared thermal imaging video and the visible light video; inputting the fused video into a trained dangerous rock mass collapse and sliding identification model, determining rockfall position data corresponding to a current image frame based on the current image frame and a previous image frame in the fused video, obtaining a first feature vector based on the current image frame, carrying out an embedding operation on the first feature vector to obtain an embedded vector, and carrying out the recognition of the collapse and sliding of the dangerous rock mass. And inputting the embedded vector into a down-sampling module to obtain an image feature vector corresponding to the current image frame, and inputting the image feature vector corresponding to the current image frame and the rockfall position data into an up-sampling module to predict rockfall position data corresponding to the next image frame. The method can accurately identify the rockfall and can improve the prediction accuracy of the rockfall trajectory.

The invention discloses measuring equipment for a tunnel construction site, and relates to the field of construction measuring equipment, the measuring equipment comprises a measuring vehicle, a driving arm is rotatably mounted on the measuring vehicle, and two measuring instruments are movably mounted on the driving arm; the device also comprises an impact rebound protection device, and the impact rebound protection device is disposed on the measuring vehicle. It needs to be explained that falling rocks can be effectively bounced off through the arrangement of the side impact plates and the front impact plate; if the falling rockfall is too large, the measuring instrument can be stored in the measuring vehicle to be protected, the rotary storage shaft drives the synchronous supporting rotating frame to rotate, the measuring vehicle is lifted, the rockfall slides away, safety of equipment on the measuring vehicle is further guaranteed, the synchronous supporting rotating frame can be pushed to move, and then the supporting area of the measuring vehicle is enlarged; in addition, when falling rocks fall on the front impact plate, the active cleaning strip moves transversely to pull away the falling rocks on the front side of the measuring vehicle, and cleanliness of the advancing path of the measuring vehicle is guaranteed.

The invention discloses a rockfall real-time monitoring and tracking method based on an improved YOLOv8 model. The method comprises the following steps in a model training stage: collecting stone data; the structure of the YOLOv8 model is improved, images in the stone data set are preprocessed, and a stone recognition model is trained based on the improved YOLOv8 model; the rockfall identification and tracking stage comprises the following steps: receiving mountain and highway video information collected by a front end in real time, identifying stones in the video in real time, and recording position information of the stones; judging whether the stone is falling stone or not according to the change condition of the stone position, and calculating impact force; and judging whether to give an alarm and record according to whether the rockfall stays on the road or the value of the impact force of the rockfall. According to the method, the detection head of the YOLOv8 model is improved, and the stone image data set is collected to train the stone recognition model, so that a rock falling event possibly occurring in a mountain or slope area can be effectively monitored, measures can be taken in time to protect personnel and property safety, and reliable support is provided for disaster early warning and risk management.

The invention relates to the technical field of high and steep slope dangerous rock mass exploration, in particular to a dangerous rock risk rapid quantitative evaluation method based on probability calculation. Comprising the following steps: S1, constructing a multi-level evaluation factor system of dangerous rock dangerousness, including three factor types of a basic geological environment factor, a dangerous rock falling rock collapse development factor and an induction factor; s2, performing quantitative analysis or qualitative analysis on the multi-level evaluation factor indexes according to the evaluation factor types, establishing a comprehensive evaluation system with subjective and objective fusion based on a system interaction analysis method, and calculating the weight of each evaluation factor; s3, constructing a dangerous rock danger calculation model based on a risk triangle theory, and inputting the weight of each evaluation factor into the dangerous rock danger calculation model to calculate a danger value; and S4, dividing danger levels of the dangerous rock mass according to the danger values, and outputting a visual evaluation result. According to the quantitative evaluation method provided by the invention, the danger of the high and steep slope can be quantified more accurately, and a basis is provided for engineering construction safety.

The present invention relates to the field of slope monitoring technology, and more specifically to a radar monitoring and early warning method and system based on the instability trend of mine slopes. The method comprises the following steps: using a pre-trained Fast R-CNN model to identify slope areas at risk of instability on a geological image as instability risk areas; using a slope radar to scan the instability risk areas in real time to obtain a radar image sequence of the instability risk areas; obtaining a geological image sequence of the instability risk areas that is co-sequential with the radar image sequence, and determining the instability trend of the instability risk areas based on the radar image sequence and the geological image sequence, and then assigning an early warning level based on the instability trend. The present invention uses the Fast R-CNN model to achieve early locking of instability factors, and based on the LSTM network, quickly and in real time analyzes the sliding trajectory trends of falling rocks and loose soil based on the actual trajectory of falling rocks and loose soil obtained by solving multimodal data, thereby achieving integrated display of risk points in the mining area, focused monitoring, and graded early warning.