Dangerous rock collapse disaster monitoring and early warning method and device based on axial force-video cooperative monitoring and storage medium

Abstract

The invention provides a dangerous rock collapse disaster monitoring and early warning method and device based on axial force-video cooperative monitoring and a storage medium. The method comprises the steps of obtaining axial force measurement data of a nearest axial force monitoring period based on an axial force sensor at each potential collapse body; calculating an axial force reference value and an axial force stable interval of each time window; judging whether the monitoring points where the potential collapse bodies are located are abnormal point locations or not; acquiring monitoring video data of the collapse and rolling processes of the potential collapse body in the target risk source area; acquiring speed item and displacement item data of a potential collapse body in each image frame of the monitoring video data in the collapse and rolling process of the target risk source area; acquiring a comprehensive risk index of the potential collapse body in each image frame in the collapse and rolling process of the target risk source area; and obtaining the probability that the collapse risk occurs in the monitoring area of the target risk source area where the abnormal point location is located. By adopting the early warning method provided by the invention, more accurate monitoring and early warning of dangerous rock collapse disasters can be realized.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning technology, and in particular to a method, equipment and storage medium for monitoring and early warning of dangerous rockfall disasters based on axial force-video collaborative monitoring. Background Technology

[0002] Rockfalls, as a typical geological hazard, are characterized by their high incidence and destructive power in canyon areas with active tectonic movements and significant topographic elevation differences. Under the coupled effects of long-term weathering, unloading, and seismic disturbances, the internal structure of the risk source area undergoes progressive damage accumulation. When this damage exceeds the critical stability threshold, a rockfall disaster occurs. These disasters exhibit a significant chain-like evolutionary characteristic; the destructive process is often accompanied by high-level rock mass collapse, debris flow transport, and shock wave effects, posing a significant threat to residential areas and transportation infrastructure below.

[0003] A risk source area refers to a mountain or slope that has not yet collapsed but possesses the main conditions for a potential collapse. Risk source areas often exhibit some precursory phenomena, such as rocks or soil blocks on steep slopes suddenly separating from the mountain or slope under the influence of gravity and collapsing or rolling. These rocks (or soil blocks) are called landslide masses; they vary in size, are disorderly, and often accumulate in a cone-shaped form at the foot of the slope. These accumulations are called colluvial deposits, or rock piles or boulders.

[0004] Based on the material composition of the landslide, we can divide them into two main categories: one occurs in soil, called a soil landslide; the other occurs in rock, called a rockfall. If the scale of the landslide is quite large, even involving an entire mountain or slope, then this type of landslide is usually called a mountain landslide. When a landslide occurs along a river, lake, or coastline, it is called a bank landslide. Furthermore, based on the movement pattern of the landslide, we can further subdivide it into several types, such as toppling, falling, and collapse.

[0005] In the process of realizing this invention, the inventors discovered at least the following problems in the prior art:

[0006] Current methods for monitoring and early warning of rockfall disasters suffer from several limitations: manual inspections are restricted by low observation frequency and poor accessibility of hazardous areas, making it difficult to promptly capture precursory features of instability disasters such as crack expansion and structural surface displacement in the risk source area. Conventional instrument monitoring relies on single-point sensors, such as displacement gauges and inclinometers, whose data is often affected by environmental temperature and humidity changes and the instantaneous release of local stress in the rock mass, resulting in abnormal fluctuations in the monitoring data. This highly random signal characteristic makes it difficult for the data to accurately reflect the mechanical evolution process within the rock mass, easily triggering false alarms. Furthermore, the discrete monitoring points cannot comprehensively reflect the disaster risk in the risk source area. While some studies have attempted to introduce novel sensing technologies such as fiber optic gratings and acoustic emission, their high deployment costs and complex signal interpretation requirements severely limit their applicability. Therefore, achieving comprehensive monitoring of rockfall stability and highly accurate early warning of instability risks while reducing the complexity and economic cost of monitoring deployment is a pressing technical problem to be solved in the field of rockfall disasters.

[0007] Therefore, there is a need for a method, equipment, and storage medium for monitoring and early warning of rockfall disasters based on axial force-video collaborative monitoring. Summary of the Invention

[0008] In view of this, embodiments of the present invention provide a method, device and storage medium for monitoring and early warning of rockfall disasters based on axial force-video collaborative monitoring, so as to at least solve one of the problems in the prior art.

[0009] In a first aspect, embodiments of the present invention provide a method for monitoring and early warning of rockfall disasters based on axial force-video collaborative monitoring, the early warning method comprising:

[0010] Axial force measurement data for the most recent axial force monitoring cycle is obtained from axial force sensors located at predetermined positions of potential collapse bodies in different monitoring areas of the target risk source area.

[0011] The most recent axial force monitoring cycle is divided into multiple time windows. The axial force reference value for each time window is calculated, and the axial force stable range for each time window is determined.

[0012] Based on the axial force measurement data of the most recent axial force monitoring cycle, retrieve the axial force measurement data of each time window and compare it with the axial force stable interval of each time window to determine whether the monitoring point where each potential collapse body is located is an abnormal point.

[0013] By activating the cameras pre-installed at each monitoring point that are identified as abnormal locations, monitoring video data of the corresponding potential landslide bodies' collapse and rolling process in the target risk source area is obtained; cameras not identified as abnormal locations are not activated.

[0014] Based on the monitoring video data of the collapse and rolling process of the potential collapse body in the target risk source area, the velocity and displacement data of the potential collapse body in the target risk source area are obtained in each image frame of the monitoring video data.

[0015] Based on the velocity and displacement data of each image frame, obtain the comprehensive risk index of the potential collapse body in the target risk source area during the collapse and rolling process in each image frame;

[0016] Based on the comparison between the comprehensive risk index and the risk threshold of the collapse and rolling process of potential landslide bodies in the target risk source area in each image frame, the probability of landslide risk in the monitoring area of ​​the target risk source area where the abnormal point is located is obtained, and an alarm is triggered when it exceeds the set threshold.

[0017] Secondly, embodiments of the present invention also provide a monitoring and early warning device for rockfall disasters based on axial force-video collaborative monitoring, the early warning device comprising:

[0018] Memory is used to store executable instructions for a computer;

[0019] A processor, used to execute computer-executable instructions stored in the memory, implements the early warning method of the above-mentioned technical solution.

[0020] Thirdly, embodiments of the present invention also provide a storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the early warning method of the above-described technical solution.

[0021] According to the early warning method of the present invention, while reducing deployment complexity and monitoring costs, axial force sensing and video surveillance are deeply integrated to achieve more accurate monitoring and early warning of the mechanical stability state and collapse motion state of the risk source area.

[0022] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0023] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. The components in the drawings are not drawn to scale but are merely illustrative of the principles of the invention. For ease of illustration and description of certain parts of the invention, corresponding portions in the drawings may be enlarged, i.e., may appear larger relative to other components in an exemplary device actually manufactured according to the invention. In the drawings:

[0025] Figure 1 This is a flowchart of a rockfall disaster monitoring and early warning method based on axial force-video collaborative monitoring according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram illustrating the positional relationship between the axial force sensor, camera device, potential landslide body, and target risk source area in a rockfall disaster monitoring and early warning method based on axial force-video collaborative monitoring according to an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram of the collapse and rolling process of a potential landslide body in a target risk source area in an image frame of the monitoring video data in a rockfall disaster monitoring and early warning method based on axial force-video collaborative monitoring according to an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of an early warning device according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of an early warning system according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0031] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0032] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0033] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0034] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0035] First, refer to Figure 1 This application describes a method 100 for monitoring and early warning of rockfall disasters based on axial force-video collaborative monitoring, according to an embodiment of this application. For example... Figure 1 As shown, the early warning method 100 may include steps S110 to S170, as detailed below:

[0036] In step S110, axial force measurement data for the most recent axial force monitoring cycle is acquired based on axial force sensors located at the set positions of potential collapse bodies in different monitoring areas of the target risk source area.

[0037] In step S120, the most recent axial force monitoring cycle is divided into multiple time windows, the axial force reference value for each time window is calculated, and the axial force stable range for each time window is determined.

[0038] In step S130, based on the axial force measurement data of the most recent axial force monitoring cycle, the axial force measurement data of each time window is retrieved and compared with the axial force stable interval of each corresponding time window to determine whether the monitoring point where each potential collapse body is located is an abnormal point.

[0039] In step S140, the camera devices that have been pre-installed at each monitoring point and identified as abnormal locations are activated to acquire monitoring video data of the collapse and rolling process of the corresponding potential landslide body in the target risk source area; camera devices that have not been identified as abnormal locations are not activated.

[0040] In step S150, based on the monitoring video data of the potential collapse body's collapse and rolling process in the target risk source area, the velocity and displacement data of the potential collapse body in the target risk source area are obtained in each image frame of the monitoring video data.

[0041] In step S160, based on the velocity and displacement data of each image frame, the comprehensive risk index of the potential collapse body in the target risk source area in each image frame is obtained.

[0042] In step S170, based on the comparison between the comprehensive risk index and the risk threshold of the collapse and rolling process of potential collapse bodies in the target risk source area in each image frame, the probability of collapse risk in the monitoring area of ​​the target risk source area where the abnormal point is located is obtained, and an alarm is triggered when it is greater than the set threshold.

[0043] In the embodiments of this application, firstly, based on axial force sensors located at predetermined positions of potential landslide bodies in different monitoring areas of the target risk source area, axial force measurement data for the most recent axial force monitoring cycle is acquired; the most recent axial force monitoring cycle is divided into multiple time windows, the axial force reference value for each time window is calculated, and the axial force stability interval for each time window is determined; then, based on the axial force measurement data of the most recent axial force monitoring cycle, the axial force measurement data for each time window is retrieved and compared with the corresponding axial force stability interval for each time window to determine whether the monitoring point where each potential landslide body is located is an abnormal point; by activating the camera devices pre-installed at each monitoring point that are determined to be abnormal points, the corresponding potential landslide body is acquired. The system first collects monitoring video data of the potential collapse body's collapse and rolling process within the target risk source area. Then, based on this data, it acquires the velocity and displacement data of the potential collapse body in each image frame of the monitoring video data. Based on the velocity and displacement data of each image frame, it obtains the comprehensive risk index of the potential collapse body in the target risk source area for that image frame. Finally, based on the comparison between the comprehensive risk index of the potential collapse body in the target risk source area for each image frame and a risk threshold, it obtains the probability of a collapse risk occurring in the monitoring area of ​​the target risk source area where the abnormal point is located, and triggers an alarm when the probability exceeds a set threshold.

[0044] As can be seen from the above description, the early warning method 100 according to the embodiment of this application deeply integrates axial force sensing and video monitoring to achieve more accurate monitoring and early warning of the mechanical stability state and collapse motion state of the risk source area.

[0045] The following will describe in detail the contents of the above steps of the early warning method 100 according to the embodiments of this application.

[0046] In the embodiments of this application, in step S110, axial force measurement data of the most recent axial force monitoring cycle is obtained based on the axial force sensors at the set locations of each potential collapse body in different monitoring areas of the target risk source area.

[0047] As needed, based on the actual coverage area of ​​the target risk source area, it can be divided into a certain number of monitoring areas, such as one, two, or more monitoring areas, to enable more precise monitoring of the target risk source area. For each monitoring area, a potential landslide body is selected, and axial force measurement data for the most recent axial force monitoring cycle is acquired using axial force sensors located at designated positions on the potential landslide body.

[0048] Therefore, refer to Figure 2Before proceeding to step S110, axial force sensors can be pre-positioned at designated locations corresponding to potential collapse bodies in different monitoring areas of the target risk source zone. These potential collapse body locations refer to the connection areas between the target risk source zone and the potential collapse body, such as narrow rock mass "bottlenecks" between them. For example, axial force sensors are embedded at contact points of structural surfaces, potential sliding surfaces, and key connection points of the rock mass. These locations are critical nodes for force transmission; when the risk source zone begins to destabilize due to factors such as weathering, rainfall, and earthquakes, the axial force sensor readings will change drastically. By monitoring the axial force changes at these critical locations in real time, early signals of rock instability can be captured promptly, providing continuous data support for monitoring and early warning of the early stages of rockfall disaster instability.

[0049] The axial force sensor can be a MEMS miniature load sensor, model FC22, which is about one-third the size of a traditional sensor and weighs more than 50% less. To improve the long-term stability and accuracy of the axial force sensor, it integrates a self-calibration function, reducing the need for manual maintenance. The sensor uses special packaging, which has strong corrosion resistance and weather resistance, ensuring the accuracy of the measurement data.

[0050] Simultaneously, during the placement of the axial force sensors, monitoring points are first selected based on geological analysis, the rock surface is cleaned, and the installation positions are marked. Then, a high-strength magnetic base is attached to the marked points, adjusted to a horizontal state using a level, and flexible shims are used to compensate for unevenness in the rock surface, ensuring the stability of the temporary positioning. Vertical holes are drilled along the pre-set guide holes of the magnetic base to a depth sufficient for anchoring adhesive filling. After cleaning the holes, fast-curing anchoring adhesive is injected to an appropriate volume. The base is rotated to allow the adhesive to evenly penetrate the rock pores, forming a composite anchoring layer with both bonding strength and shear strength. After the adhesive has initially cured, the sensor is embedded in the magnetic slot and rotated to lock, completing physical fixation and attitude calibration. After initial installation and fixation, the sensor is calibrated using a high-precision standard force source. A standard force is applied according to the procedure, and the output signal is compared with the standard force value to correct the measurement data. The sealing of the adhesive and the bonding state of the base are checked regularly. If point adjustments are needed, the anchoring adhesive can be softened by local heating for non-destructive disassembly. Residual adhesive can be cleaned with solvent and reused, improving the flexibility of the mesh layout and allowing for repeated disassembly and reuse. The axial force sensor can be powered by solar energy, ensuring continuous data transmission.

[0051] To obtain the latest axial force changes in potential landslide bodies in different monitoring areas of the target risk source zone, axial force measurement data from the most recent axial force monitoring period is required. The axial force monitoring period can be set reasonably as needed, such as 1 day, 2 days, or other reasonable timeframes.

[0052] In the embodiments of this application, in step S120, the most recent axial force monitoring cycle is divided into multiple time windows, the axial force reference value of each time window is calculated, and the axial force stable range of each time window is determined.

[0053] To more accurately determine whether a location is an anomaly in subsequent step S130, the most recent axial force monitoring cycle needs to be divided into multiple time windows. Taking a one-day axial force monitoring cycle as an example, this can be divided into 12 time windows, each lasting two hours. Then, the baseline axial force value for each time window is calculated, and the stable axial force range for each time window is determined. Because the most recent axial force monitoring cycle is divided into multiple time windows, the obtained baseline axial force value and stable axial force range are dynamically adjusted over time.

[0054] Specifically, the axial force reference value for each time window is calculated to dynamically reflect the long-term evolution trend of stress within the risk source area.

[0055]

[0056] Among them, F b (i) represents the reference value of the axial force in the i-th time window in sequence. The starting time of the i-th time window is t. 1,i The end time is t 2,i α is a smoothing factor, ranging from 0 to 1, used to control the decay rate of historical data. A larger α value indicates a higher weight for recent data; for example, a value of 0.2 is acceptable. F(t,i) is the axial force measurement value at time t in the i-th time window. b (1) is the axial force reference value for the first time window. t0 is the end time of the first time window (the start time is 0). The duration of each time window is Δt. M is the total number of time windows.

[0057] Determine the axial force plateau range for each time window.

[0058] Γ s,i =[F b (i)-βσ F,i F b (i)+βσ F,i ]

[0059]

[0060] Among them, Γ s,i Let σ be the stationary interval of the axial force in the i-th time window. β is the interval coefficient. F,i Let be the standard deviation of all axial force measurements within the i-th time window.

[0061] The axial force benchmark value is dynamically adjusted based on new data. Compared to the traditional fixed average value, this method better adapts to long-term trends such as seasonal temperature changes and rock mass creep. From an engineering perspective, when α is 0.2, the latest data accounts for 20% and historical data accounts for 80%, which strikes a good balance between the system's stability and sensitivity. Regarding the determination of the stable interval, the standard deviation of axial force σ is used. F,i The axial force steady-state interval Γ is calculated in real time using the interval coefficient β (ranging from 0.5 to 1.8). s,i Γ s,i As a boundary for judging whether axial force is abnormal, its determination is based on the assumption of normal distribution and can cover approximately 70% to 95% of the data. The specific coverage depends on the value of β. Axial force standard deviation σ F,i It will be dynamically updated to quantify the dispersion of historical data, while the interval coefficient β controls the interval width. The larger the β value, the higher the system's fault tolerance.

[0062] In the embodiments of this application, in step S130, the axial force measurement data of each time window is retrieved based on the axial force measurement data of the most recent axial force monitoring cycle, and compared with the axial force stable interval of each corresponding time window to determine whether the monitoring point where each potential collapse body is located is an abnormal point.

[0063] Specifically, determining whether the monitoring points where each potential landslide body is located are abnormal locations refers to:

[0064] If the axial force measurement data retrieved for each time window contains values ​​greater than the corresponding stable axial force range for that time window, i.e., F(t, i) > F... s,i If any data in the axial force measurement data of each time window is greater than the corresponding stable axial force range of each time window, then the monitoring point where the potential collapse body is located is an abnormal point; otherwise, it is a normal point.

[0065] In the embodiments of this application, in step S140, the camera devices that are identified as abnormal points in the camera devices that have been pre-installed at each monitoring point are activated to obtain monitoring video data of the collapse and rolling process of the corresponding potential collapse body in the target risk source area; wherein, the camera devices that are not identified as abnormal points are not activated.

[0066] Specifically, before proceeding to step S140, camera devices need to be installed at monitoring points located at all potential landslide sites. Based on the terrain and surrounding environment of the monitoring points, locations with open and unobstructed views should be selected for camera installation. Cameras should be securely mounted using tripods or fixed supports, and the shooting angle should be finely adjusted using the equipment to ensure clear capture of the movement of potential landslides. Camera parameters, including resolution, frame rate, and shooting mode, should be set to meet the requirements of image recognition. The video analysis server performs real-time analysis of the video data transmitted from the cameras, using video image motion detection algorithms to identify whether there is object movement. After installation, on-site debugging should be conducted to optimize image quality and tracking effects.

[0067] The daily video data collected by the process monitoring module is packaged, processed, and transmitted to the data processing and analysis module via wireless communication technology. 4G / 5G wireless communication technology ensures real-time and reliable data delivery. Redundant communication links are designed to guarantee the reliability of data transmission.

[0068] When a monitoring point where a potential landslide body is located is identified as an abnormal point, it indicates that the potential landslide body may collapse or roll from the target risk source area. Only when a monitoring point where a potential landslide body is located is identified as an abnormal point will the camera device at that abnormal point be activated to obtain monitoring video data of the collapse or rolling process of the corresponding potential landslide body in the target risk source area.

[0069] It should be noted that cameras not identified as abnormal locations will not be activated, thus greatly reducing resource consumption and avoiding bandwidth usage.

[0070] The collapse and rolling process of a potential landslide body in the target risk source area refers to the process by which the potential landslide body collapses from its initial position and then rolls along the slope of the target risk source area until it leaves the slope. For risk source areas at abnormal locations, camera devices can, for example, adjust the shooting position and image size using a wide-angle lens to ensure that monitoring video data of the collapse and rolling process of the potential landslide body in the target risk source area is obtained.

[0071] In the embodiments of this application, in step S150, based on the monitoring video data of the potential collapse body's collapse and rolling process in the target risk source area, the velocity and displacement data of the potential collapse body in the target risk source area are obtained in each image frame of the monitoring video data.

[0072] Specifically, the velocity term E1 of the potential landslide body in the target risk source area is acquired from each image frame of the monitoring video data during the landslide and rolling process.

[0073]

[0074] Where V(t') is the real-time velocity of the potential collapse body at the video monitoring time t' of the current image frame. a ΔW(t') represents the critical velocity value of the potential collapse body. ΔW(t') is the area of ​​the newly added pixel motion region at time t'. b The baseline area is denoted as F′. F′ is the axial force adjustment coefficient, which controls the weight of the influence of axial force anomalies on the collapse motion velocity term, thus increasing the sensitivity of collapse motion to axial force measurements. τ is the axial force adjustment factor, determined through historical data evaluation, and can be set to 1.1.

[0075] refer to Figure 3 The real-time velocity V(t') is the movement velocity of the potential collapse body at the video monitoring time t' of the current image frame, calculated by the pixel offset of the centroid of the potential collapse body between adjacent frames. (The relationship between pixels and actual distance needs to be calibrated). Critical velocity value V a Based on historical collapse data, this is the minimum velocity threshold that triggers a collapse. The newly added pixel motion region area ΔW(t') is the difference between the motion pixel area in the current frame and the motion pixel area in the previous frame, i.e., the newly added motion pixel region in the current frame, extracted using background subtraction. The baseline background area W... b This is the reference area area for the static background, initialized to the number of pixels in the background area when there is no movement, and can be dynamically updated to adapt to changes in lighting.

[0076] According to the energy-damage equivalence principle, the greater the collapse kinetic energy and the wider the expansion area in the risk source zone, the greater the product of the velocity square term (kinetic energy) and the area change term (damage expansion), and the higher the risk of rockfall disaster. From E=mv 2 Thus, this term quantifies the degree to which kinetic energy approaches the critical state of collapse by using the square ratio of the real-time velocity to the critical velocity value. Drawing on the concept of strain in crack propagation during material failure, it reflects the dynamic rate of change of the area of ​​the collapse zone, based on the relationship between area propagation and energy release rate in fracture mechanics.

[0077] Acquire the displacement term E2 data of the potential landslide body in the target risk source area during the collapse and rolling process in each image frame of the monitoring video data.

[0078]

[0079] Where, S(t') max S represents the maximum single-frame displacement at time t'. c ρ is the critical displacement value. ρ is the displacement exponential gain coefficient, which is fitted based on historical data of rockfalls.

[0080] The displacement term E2 is derived from the phase transition model in nonlinear dynamics. When the collapse displacement in the risk source region approaches the critical value, the exponential term... The accelerated mutation behavior before instability in the risk source region was simulated, which is similar to the creep-fracture process of materials.

[0081] Maximum single-frame displacement S(t') max This refers to the maximum offset among all moving pixels of a potential collapse within a single frame, calculated by multiplying the horizontal displacement dx of all pixels based on adjacent frames (e.g., frame m and frame n). m,n and vertical displacement dy m,n Calculate the Euclidean distance of all pixels and obtain the maximum Euclidean distance at time t'. S c This is the displacement threshold, which is the maximum displacement threshold that triggers a collapse. This value is set based on historical collapse data.

[0082] In the embodiments of this application, step S160 obtains the comprehensive risk index of the collapse and rolling process of the potential collapse body in the target risk source area in each image frame based on the velocity and displacement data of each image frame.

[0083] Specifically, based on the calculation results of the velocity and displacement terms, the comprehensive risk index R(t') of the collapse motion in the risk source area at time t' is determined through a composite parameter dynamic weighting calculation method.

[0084] R(t')=E1+E2

[0085] In the formula, the velocity term E1 reflects the "energy accumulation" of the video frame, and the displacement term E2 captures the sudden "critical failure" of the video frame. The combined risk index R(t') formed by the sum of the two can reflect the superposition of gradual risk and instantaneous risk.

[0086] In the embodiments of this application, in step S170, based on the comparison between the comprehensive risk index and the risk threshold of the collapse and rolling process of potential collapse bodies in the target risk source area in each image frame, the probability of collapse risk occurring in the monitoring area of ​​the target risk source area where the abnormal point is located is obtained, and an alarm is activated when it is greater than the set threshold.

[0087] Specifically, it obtains the probability of a landslide risk occurring in the monitored area of ​​the target risk source zone where the anomaly point is located.

[0088]

[0089] Where P is the probability of a collapse. T is the video monitoring duration of the monitoring video data. L is the risk threshold, which can be calibrated based on historical values. ω(t') is the comparison result of the comprehensive risk index of the potential collapse body in the target risk source area during the collapse and rolling process in each image frame and the risk threshold.

[0090] Furthermore, according to the obtained collapse risk probability P, different warning levels can be set based on P. When 10% < P < 35%, it is the blue warning level; when 35% < P < 65%, it is the yellow warning level; when P > 65%, it is the red warning level, so as to take targeted emergency measures. When the unstable rock shows an unstable trend and reaches the preset warning level, a warning signal of the corresponding level shall be immediately issued. The warning forms include sound and light alarms, text message notifications, APP push, etc., to ensure that relevant personnel can obtain information in a timely manner.

[0091] In addition, the GIS technology can be used to construct a regional monitoring network. By utilizing the powerful spatial analysis and data management capabilities of the GIS technology, the geographical information of the risk source area and the data of the monitoring equipment are integrated. By establishing a three-dimensional geographical information model, the monitoring data of the axial force sensors and camera devices at each monitoring point are associated with specific geographical locations, forming a comprehensive regional monitoring network to achieve single-point dual-modal and regional full-coverage monitoring. A high-precision GPS positioning device is combined to obtain accurate coordinates. In terms of hardware, a wireless network communication device with stable performance and long transmission distance is selected. By optimizing the GIS algorithm, the data processing time is reduced, and the efficiency of positioning and data integration is improved, thereby constructing a stable and efficient monitoring network.

[0092] Based on the above description, according to the warning method of the embodiment of the present application, the axial force sensing and video monitoring are deeply integrated to achieve more accurate monitoring and warning of the mechanical stability state of the risk source area and the collapse motion state of the risk source area.

[0093] Reference Figure 4 , the embodiment of the present application further provides a warning device 200 for implementing the warning method 100 according to the embodiment of the present application. The warning device 200 includes a processor 210 and a memory 220. The warning device 200 may include one or more processors 210 and one or more memories 220. The memory 220 stores an executable program run by the processor 210. When the executable program is run by the processor 210, the processor 210 is caused to execute the warning method 100 according to the embodiment of the present application described above.

[0094] The processor 210 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities.

[0095] The memory 220 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 210 may execute the program instructions to implement the client functions (implemented by the processor) in the embodiments of this application described herein, and / or other desired functions. Various applications and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the applications.

[0096] The early warning device 200 may also include input and output devices, which are interconnected via a bus system and / or other forms of connection mechanisms. It should be noted that... Figure 4 The components and structure of the warning device 200 shown are merely exemplary and not limiting; the warning device 200 may also have other components and structures as needed.

[0097] The input device can be a device used by a user to input commands, and can include one or more of a keyboard, mouse, microphone, and touchscreen. Furthermore, the input device can also be any interface for receiving information.

[0098] The output device can output various information (e.g., images or sounds) to the outside (e.g., a user), and may include one or more of a display, speaker, etc. Furthermore, the output device can also be any other device with output functionality.

[0099] For example, the example warning device 200 for implementing the warning method 100 according to the embodiments of this application can be applied to terminal devices (such as mobile phones), tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smartwatches, smart glasses, or smart helmets), augmented reality (AR) devices, virtual reality (VR) devices, smart home devices, in-vehicle computers, and other electronic devices. The embodiments of this application do not impose any limitations on this.

[0100] Those skilled in the art can understand the specific operation of the warning device 200 for implementing the warning method 100 according to the embodiments of this application in conjunction with the content described above. For the sake of brevity, the specific details will not be repeated here, but only some main operations of the processor 210 will be described.

[0101] In one embodiment of this application, when the executable program is run by the processor 210, the processor 210 performs the following steps: Based on axial force sensors located at predetermined positions of potential landslide bodies in different monitoring areas of the target risk source area, axial force measurement data for the most recent axial force monitoring cycle is acquired. The most recent axial force monitoring cycle is divided into multiple time windows, an axial force reference value for each time window is calculated, and a stable axial force interval for each time window is determined. Based on the axial force measurement data of the most recent axial force monitoring cycle, the axial force measurement data for each time window is retrieved and compared with the corresponding stable axial force interval for each time window to determine whether the monitoring point where each potential landslide body is located is an abnormal point. By activating the cameras pre-installed at each monitoring point that are determined to be abnormal points, monitoring video data of the collapse and rolling process of the corresponding potential landslide body in the target risk source area is acquired; wherein, cameras not determined to be abnormal points are not activated. Based on monitoring video data of the potential landslide and rolling process in the target risk source area, the velocity and displacement data of the potential landslide in the target risk source area are obtained for each image frame of the monitoring video data. Based on the velocity and displacement data of each image frame, a comprehensive risk index for the potential landslide in the target risk source area in that image frame is obtained. By comparing the comprehensive risk index of the potential landslide in the target risk source area in each image frame with a risk threshold, the probability of a landslide risk occurring in the monitored area of ​​the target risk source area where the abnormal point is located is obtained, and an alarm is triggered when the probability exceeds a set threshold.

[0102] The above exemplarily illustrates a warning method 100 according to an embodiment of this application. The following, in conjunction with... Figure 5 This application describes an early warning system 300 provided in another aspect of its embodiments.

[0103] Reference Figure 5 This document describes an example early warning system 300 used to implement the early warning method of the embodiments of this application. The early warning system 300 may include an axial force data acquisition module 310, a calculation module 320, a judgment module 330, a video data acquisition module 340, a velocity and displacement acquisition module 350, a comprehensive risk index acquisition module 360, and an alarm module 370. Wherein:

[0104] The sampling point meteorological data acquisition module 310 is used to: acquire axial force measurement data for the most recent axial force monitoring cycle based on axial force sensors located at the set positions of potential landslide bodies in different monitoring areas of the target risk source area.

[0105] The calculation module 320 is used to: divide the most recent axial force monitoring cycle into multiple time windows, calculate the axial force reference value for each time window, and determine the axial force stable range for each time window.

[0106] The judgment module 330 is used to: retrieve the axial force measurement data of each time window based on the axial force measurement data of the most recent axial force monitoring cycle, and compare it with the axial force stable interval of each time window to determine whether the monitoring point where each potential collapse body is located is an abnormal point.

[0107] The video data acquisition module 340 is used to: acquire monitoring video data of the collapse and rolling process of the corresponding potential landslide body in the target risk source area by activating the camera devices that have been pre-installed at each monitoring point and identified as abnormal points; wherein, the camera devices that have not been identified as abnormal points are not activated.

[0108] The velocity and displacement acquisition module 350 is used to: acquire the velocity and displacement data of the potential collapse body in the target risk source area in each image frame of the monitoring video data based on the collapse and rolling process of the potential collapse body in the target risk source area.

[0109] The comprehensive risk index acquisition module 360 ​​is used to: acquire the comprehensive risk index of the potential collapse body in the target risk source area in each image frame based on the velocity and displacement data of each image frame.

[0110] The alarm module 370 is used to: obtain the probability of a collapse risk in the monitoring area of ​​the target risk source area where the abnormal point is located by comparing the comprehensive risk index of the collapse and rolling process of the potential collapse body in the target risk source area in each image frame with the risk threshold, and activate the alarm when it is greater than the set threshold.

[0111] The early warning system 300 proposed in this embodiment of the invention can achieve more accurate monitoring and early warning of the mechanical stability state and collapse motion state of the risk source area.

[0112] Furthermore, according to embodiments of this application, this application also provides a storage medium on which a computer program is stored. When the computer program is run by a processor, it is used to execute corresponding steps of the warning method 100 of this application. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0113] Furthermore, according to embodiments of this application, this application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the early warning method 100 of embodiments of this application.

[0114] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0117] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0118] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0119] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for monitoring and early warning of rockfall disasters based on axial force-video collaborative monitoring, characterized in that, The early warning method includes: Axial force measurement data for the most recent axial force monitoring cycle is obtained from axial force sensors located at predetermined positions of potential collapse bodies in different monitoring areas of the target risk source area. The most recent axial force monitoring cycle is divided into multiple time windows. The axial force reference value for each time window is calculated, and the axial force stable range for each time window is determined. Based on the axial force measurement data of the most recent axial force monitoring cycle, retrieve the axial force measurement data of each time window and compare it with the axial force stable interval of each time window to determine whether the monitoring point where each potential collapse body is located is an abnormal point. By activating the cameras pre-installed at each monitoring point that are identified as abnormal locations, monitoring video data of the corresponding potential landslide bodies' collapse and rolling process in the target risk source area is obtained; cameras not identified as abnormal locations are not activated. Based on the monitoring video data of the collapse and rolling process of the potential collapse body in the target risk source area, the velocity and displacement data of the potential collapse body in the target risk source area are obtained in each image frame of the monitoring video data. Based on the velocity and displacement data of each image frame, obtain the comprehensive risk index of the potential collapse body in the target risk source area during the collapse and rolling process in each image frame; Based on the comparison between the comprehensive risk index and the risk threshold of the collapse and rolling process of potential landslide bodies in the target risk source area in each image frame, the probability of landslide risk in the monitoring area of ​​the target risk source area where the abnormal point is located is obtained, and an alarm is triggered when it exceeds the set threshold.

2. The early warning method according to claim 1, characterized in that, The calculation of the axial force reference value for each time window and the determination of the axial force stability range for each time window include: 2≤i≤M; Among them, F b (i) represents the axial force reference value for the i-th time window in sequence, with the starting time of the i-th time window being t. 1,i The end time is t 2,i α is the smoothing factor, and F(t,i) is the axial force measurement at time t in the i-th time window; F b (1) is the axial force reference value for the first time window, t0 is the end time of the first time window; the duration of each time window is Δt; M is the total number of time windows; C s,i =[F b (i)-bs F,i ,F b (i)+bs F,i ] Among them, Γ s,i Let β be the stationary interval of axial force in the i-th time window, and σ be the interval coefficient. F,i Let be the standard deviation of all axial force measurements within the i-th time window.

3. The early warning method according to claim 1, characterized in that, The determination of whether the monitoring points where each potential landslide body is located are abnormal locations specifically refers to: If the axial force measurement data retrieved for each time window contains a value greater than the corresponding stable axial force range for that time window, then the monitoring point where the potential collapse body is located is an abnormal point; otherwise, it is a normal point.

4. The early warning method according to claim 1, characterized in that, The velocity term E1 and displacement term E2 of the potential landslide body in the target risk source area are acquired from each image frame of the monitoring video data, including: Where V(t') is the real-time velocity of the potential collapse body at the video monitoring time t' of the current image frame. a Let W be the critical velocity value of the potential collapse body, and ΔW(t') be the area of ​​the newly added pixel motion region at time t'. b The reference background area is F′, the axial force adjustment coefficient is F′, and the axial force adjustment factor is τ. Where, S(t') max S is the maximum single-frame displacement at time t'. c ρ is the critical displacement value, and ρ is the displacement exponential gain coefficient.

5. The early warning method according to claim 1, characterized in that, The method of obtaining a comprehensive risk index for the collapse and rolling process of potential landslide bodies in the target risk source area based on velocity and displacement data of each image frame includes: R(t')=E1+E Where R(t') is the comprehensive risk index of the collapse and rolling process of potential collapse bodies in the target risk source area in each image frame.

6. The early warning method according to claim 1, characterized in that, The comparison between the comprehensive risk index and the risk threshold based on the collapse and rolling process of potential landslide bodies in the target risk source area of ​​each image frame is used to obtain the probability of landslide risk in the monitored area of ​​the target risk source area where the abnormal point is located, including: Where P is the probability of a collapse, T is the video monitoring duration of the monitoring video data, L is the risk threshold, and ω(t') is the comparison result of the comprehensive risk index of the potential collapse body in the target risk source area and the risk threshold in each image frame.

7. The early warning method according to claim 1, characterized in that, It also includes using GIS technology to associate stress data inside the target risk source area collected by axial force sensors and monitoring video data of potential collapse bodies in the target risk source area, as well as the collapse and rolling process of the potential collapse body detected by camera devices, with geospatial location. By locating the geographic coordinates of abnormal points, a three-dimensional geographic model is constructed to intuitively display the spatial distribution and changes of monitoring data.

8. The early warning method according to claim 1, characterized in that, It also includes pre-positioning axial force sensors at the corresponding potential collapse locations in different monitoring areas of the target risk source area; where the potential collapse location refers to the connection area between the target risk source area and the potential collapse.

9. A monitoring and early warning device for rockfall disasters based on axial force-video collaborative monitoring, characterized in that, The early warning device includes: Memory is used to store executable instructions for a computer; A processor, when executing computer-executable instructions stored in the memory, implements the early warning method according to any one of claims 1 to 8.

10. A storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the early warning method according to any one of claims 1 to 8.

Images