Slope disaster real-time monitoring and early warning method and system

Abstract

The invention relates to the field of disaster monitoring, and provides a slope disaster real-time monitoring and early warning method and system, and the method comprises the steps: laying a distributed optical fiber sensing network, a Beidou GNSS receiver, a miniature pore water pressure gauge and an inclinometer at a target slope earth surface position and a deep position, and obtaining internal and external cooperative monitoring data; extracting and calculating a plurality of branch characterization values in the internal and external cooperative monitoring data; calculating and generating multi-source fusion feature data based on the plurality of branch representation values; establishing a dynamic threshold monitoring model, and calculating and generating a real-time early warning threshold; establishing a multi-level linkage early warning mechanism based on the real-time early warning threshold value; and internal and external cooperative monitoring is realized through a multi-mode sensor network, and slope state information is comprehensively captured. Multi-source data fusion processing improves data quality and reliability, a dynamic threshold model accurately adapts to different working conditions, and a multi-level linkage early warning mechanism ensures timely response. The accuracy and timeliness of slope disaster early warning are greatly improved on the whole, and disaster loss is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the field of disaster monitoring, and in particular relates to a real-time monitoring and early warning method and system for slope disasters. Background Art

[0002] Existing slope hazard monitoring technologies face significant bottlenecks in deep deformation sensing and multi-factor coupled early warning. On the one hand, traditional monitoring methods are generally limited in depth and struggle to adapt to complex geological environments, resulting in insufficient capture of deep rock deformation trends. On the other hand, landslide precursors are influenced by the coupling of multiple factors, such as rainfall, earthquakes, and groundwater. Existing early warning systems often rely on fixed thresholds or static machine learning models, unable to dynamically adapt to environmental changes and prone to false alarms or missed alerts.

[0003] To address these technical challenges, a real-time slope disaster monitoring and early warning method and system is urgently needed that can achieve coordinated surface-deep monitoring and dynamically adapt to the influence of multiple coupled factors. This would address the adaptability and early warning reliability issues of traditional technologies in complex geological conditions and provide key technical support for proactive prevention and control of slope disasters. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time monitoring and early warning method and system for slope disasters, aiming to solve the problems raised in the above background technology.

[0005] The present invention is implemented as follows: on the one hand, a real-time monitoring and early warning method for slope disasters, the method comprising: Deploy distributed fiber optic sensor networks, BeiDou GNSS receivers, micro pore water pressure gauges, and inclinometers at targeted slope surface and deep locations to obtain internal and external collaborative monitoring data; Extract and calculate several branch representation values from internal and external collaborative monitoring data; Based on several branch representation values, multi-source fusion feature data is calculated and generated; Establish a dynamic threshold monitoring model to calculate and generate real-time warning thresholds; Based on real-time warning thresholds, a multi-level linkage warning mechanism is established.

[0006] As a further solution of the present invention, the extraction and calculation of several branch characterization values from the internal and external collaborative monitoring data specifically includes: Extracting the original measured strain value of the optical fiber sensing network from the internal and external collaborative monitoring data ; Based on the original measurement strain value of optical fiber , calculate the real-time strain value of soil ; The real-time strain value of the soil The calculation process is: ; Where, is the deformation coordination coefficient, is the thermal expansion coefficient of the optical fiber, is the temperature change; Extracting the local coordinate system displacement of BeiDou GNSS receivers from internal and external collaborative monitoring data , rotation matrix and translation vectors ; Displacement based on local coordinate system , rotation matrix and translation vectors , calculate the three-dimensional displacement in the global coordinate system ; The three-dimensional displacement in the global coordinate system The calculation process is: ; Three-dimensional displacement based on the global coordinate system , calculate the displacement rate ; The displacement rate The calculation process is: ; Where, is the time interval; Extracting real-time pore water pressure from micro pore water pressure gauges in internal and external collaborative monitoring data ; Based on real-time pore water pressure , calculate the pore water pressure change rate ; The pore water pressure change rate The calculation process is: ; Extract the real-time tilt angle of the inclinometer from the internal and external collaborative monitoring data ; Based on real-time tilt angle , calculate the rate of change of the tilt angle ; The tilt angle change rate The calculation process is: .

[0007] As a further solution of the present invention, the calculation and generation of multi-source fusion feature data based on a plurality of branch representation values specifically includes: Obtaining real-time soil strain values , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values ; Based on real-time soil strain value , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , calculate and generate the representation fusion vector ; The representation fusion vector The calculation process is: ; Where, 、 、 、 、 is the corresponding weight coefficient, and .

[0008] As a further solution of the present invention, the establishment of a dynamic threshold monitoring model and the calculation and generation of a real-time warning threshold specifically include: Based on the representation fusion vector , calculate and generate the displacement rate threshold ; The displacement rate threshold The calculation process is: ; Where, is the baseline threshold, are the pore water pressure coefficient and rainfall sensitivity coefficient respectively.

[0009] As a further solution of the present invention, the establishment of a multi-level linkage warning mechanism based on the real-time warning threshold specifically includes: Get real-time displacement rate ; when ≥0.8 When the alarm is triggered, the first level warning is triggered, and local sound and light alarm instructions are generated and sent; when ≥ When a second-level warning is triggered, a text message push instruction is generated and sent to the targeted responsible person; when ≥1.2 When the vehicle is in the vicinity of the designated area, a level 3 warning is triggered, generating and sending a preset built-in anchor cable tensioning system start-up instruction and a slope range traffic blocking instruction; when ≥0.8 When the risk is initially apparent, the first-level warning is triggered, and a local sound and light alarm command is generated and sent. This setting is intended to promptly remind on-site personnel to pay attention to slope changes and buy time for subsequent handling; if ≥ When the slope is abnormal, a secondary warning is triggered and a text message push instruction is sent to the target responsible person. This ensures that the relevant responsible personnel are aware of the slope abnormality information in a timely manner, quickly organize forces to conduct assessments and prepare further measures; when ≥1.2 When the third-level warning is triggered, a preset built-in anchor tensioning system start-up instruction and a slope range traffic blocking instruction are generated and sent. At this time, the system determines that the risk is highly urgent, and starting the anchor tensioning can actively reinforce the slope. Traffic blocking can effectively prevent people and vehicles from entering the dangerous area, minimizing the disaster losses. The built-in anchor tensioning system is the core execution module for active slope reinforcement, and is composed of high-strength, low-relaxation anchor cables, high-precision hydraulic jacks, intelligent control terminals and pressure sensing components. When the third-level warning start-up instruction is issued, the system responds immediately: the hydraulic jacks tension the anchor cables at a uniform speed, and accurately apply prestress to the stable rock mass near the deep slip surface of the slope. The anti-slip force of the sliding body is improved through the bonding force between the anchor section and the rock mass. The pressure sensor collects the tensioning force data in real time and feeds it back to the intelligent control terminal to ensure that the tensioning force strictly matches the design value to avoid stress concentration or insufficient reinforcement.

[0010] As a further embodiment of the present invention, in another aspect, a real-time monitoring and early warning system for slope disasters is provided, the system comprising: The acquisition module is used to deploy distributed fiber optic sensor networks, Beidou GNSS receivers, micro pore water pressure gauges, and inclinometers at the surface and deep locations of the targeted slope to obtain internal and external collaborative monitoring data; An extraction and calculation module is used to extract and calculate several branch representation values from the internal and external collaborative monitoring data; A first calculation and generation module is used to calculate and generate multi-source fusion feature data based on a plurality of branch representation values; The second calculation and generation module is used to establish a dynamic threshold monitoring model, calculate and generate real-time warning thresholds; The early warning module is used to establish a multi-level linkage early warning mechanism based on real-time early warning thresholds.

[0011] As a further solution of the present invention, the extraction calculation module specifically includes: The first extraction unit is used to extract the original measured strain value of the optical fiber sensor network from the internal and external collaborative monitoring data ; The first calculation unit is used to calculate the strain value of the optical fiber based on the original measurement , calculate the real-time strain value of soil ; The second extraction unit is used to extract the local coordinate system displacement of the Beidou GNSS receiver from the internal and external collaborative monitoring data , rotation matrix and translation vectors ; The second calculation unit is used for displacement based on the local coordinate system , rotation matrix and translation vectors , calculate the three-dimensional displacement in the global coordinate system ; The third calculation unit is used for three-dimensional displacement based on the global coordinate system , calculate the displacement rate ; The third extraction unit is used to extract the real-time pore water pressure of the micro pore water pressure gauge from the internal and external collaborative monitoring data. ; The fourth calculation unit is used to calculate the pore water pressure based on real-time , calculate the pore water pressure change rate ; The fourth extraction unit is used to extract the real-time tilt angle of the inclinometer from the internal and external collaborative monitoring data ; The fifth calculation unit is used to calculate the real-time tilt angle , calculate the rate of change of the tilt angle .

[0012] As a further solution of the present invention, the first calculation generation module specifically includes: Acquisition unit, used to obtain real-time soil strain value , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values ; The first calculation generation unit is used to calculate the real-time strain value of the soil , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , calculate and generate the representation fusion vector .

[0013] This invention provides a real-time slope disaster monitoring and early warning method and system. This system utilizes a multimodal sensor network to achieve coordinated internal and external monitoring, comprehensively capturing slope status information. Multi-source data fusion improves data quality and reliability, a dynamic threshold model accurately adapts to different operating conditions, and a multi-level linkage early warning mechanism ensures timely response. This significantly improves the accuracy and timeliness of slope disaster early warnings, effectively reducing disaster losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a main flow chart of a real-time monitoring and early warning method for slope disasters.

[0015] Figure 2 The invention relates to a flow chart for extracting and calculating several branch representation values from internal and external coordinated monitoring data in a real-time monitoring and early warning method for slope disasters.

[0016] Figure 3 The present invention is a flow chart for calculating and generating multi-source fusion feature data based on several branch characterization values in a real-time monitoring and early warning method for slope disasters.

[0017] Figure 4 It is the main structure diagram of a real-time monitoring and early warning system for slope disasters.

[0018] Figure 5 The present invention is a structural block diagram of the calculation module extracted from the real-time monitoring and early warning system for slope disasters.

[0019] Figure 6 The present invention is a structural block diagram of the first calculation generation module in a real-time monitoring and early warning system for slope disasters. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0022] The present invention provides a real-time monitoring and early warning method and system for slope disasters, which solves the technical problems in the background technology.

[0023] like Figure 1 FIG. 1 is a main flow chart of a method for real-time monitoring and early warning of slope disasters provided by an embodiment of the present invention. The method for real-time monitoring and early warning of slope disasters includes: Step S100: deploying a distributed fiber optic sensor network, BeiDou GNSS receivers, micro pore water pressure gauges, and inclinometers at the surface and deep locations of the targeted slope to obtain internal and external collaborative monitoring data; Step S200: extracting and calculating a plurality of branch representation values from the internal and external collaborative monitoring data; Step S300: Calculate and generate multi-source fusion feature data based on a number of branch representation values; Step S400: Establish a dynamic threshold monitoring model to calculate and generate a real-time warning threshold; Step S500: Establish a multi-level linkage warning mechanism based on the real-time warning threshold.

[0024] In this implementation, differentiated sensor networks are first constructed on the slope surface and deep within (depth ≥ 50m). Deeply, a distributed fiber optic sensor network (BOTDR / OFDR, spatial resolution ≤ 0.5m) is employed, combined with armored fiber optic cables and steel pipe fixing technology. Deformation coordination coefficient correction is used to achieve millimeter-level monitoring of deep soil strain. On the surface, Beidou GNSS receivers (positioning accuracy ≤ 1mm), micro pore water pressure gauges (range 0-10MPa, accuracy ±0.1%FS), and inclinometers (resolution 0.001°) are deployed, forming a comprehensive monitoring system covering shallow surface displacement, groundwater pressure, and tilt angle. Correction formulas are used to eliminate temperature effects in fiber optic strain to obtain true strain. Beidou displacement is converted and rate-calculated to accurately quantify displacement. Pore water pressure and inclinometer data are used to calculate the rate and amount of change. These branch representation values are then merged to form a fused dataset, generating multi-source fused feature data. Deep reinforcement learning is then introduced to establish a dynamic threshold warning model. Based on multi-source fusion feature data, considering the impact of factors such as pore water pressure and rainfall on slope stability, the warning threshold is dynamically optimized to generate a real-time and practical displacement rate threshold. Finally, a multi-level linkage warning mechanism is established based on the real-time warning threshold, and graded responses are given to different levels of danger, realizing full-process automation from data collection to warning response.

[0025] like Figure 2 As shown, as a preferred embodiment of the present invention, the extraction and calculation of several branch characterization values in the internal and external collaborative monitoring data specifically includes: Step S201: Extracting the original measured strain value of the optical fiber sensor network from the internal and external collaborative monitoring data ; Step S202: Based on the original measured strain value of the optical fiber , calculate the real-time strain value of soil ; The real-time strain value of the soil The calculation process is: ; Where, is the deformation coordination coefficient, is the thermal expansion coefficient of the optical fiber, is the temperature change; Step S203: Extract the local coordinate system displacement of the BeiDou GNSS receiver from the internal and external collaborative monitoring data , rotation matrix and translation vectors ; Step S204: Displacement based on the local coordinate system , rotation matrix and translation vectors , calculate the three-dimensional displacement in the global coordinate system ; The three-dimensional displacement in the global coordinate system The calculation process is: ; Step S205: Three-dimensional displacement based on the global coordinate system , calculate the displacement rate ; The displacement rate The calculation process is: ; Where, is the time interval; Step S206: Extract the real-time pore water pressure of the micro pore water pressure gauge from the internal and external collaborative monitoring data ; Step S207: Based on real-time pore water pressure , calculate the pore water pressure change rate ; The pore water pressure change rate The calculation process is: ; Step S208: Extracting the real-time tilt angle of the inclinometer from the internal and external collaborative monitoring data ; Step S209: Based on the real-time tilt angle , calculate the rate of change of the tilt angle ; The tilt angle change rate The calculation process is: ; In this embodiment, the fiber optic sensor network first extracts its raw measured strain values. The real-time soil strain values are then calculated by comprehensively considering the deformation coordination coefficient, the fiber thermal expansion coefficient, and the temperature change. This process effectively eliminates interference from factors such as temperature on fiber optic measurements, thereby accurately reflecting the true deformation state of the soil. For Beidou GNSS receiver data, the local coordinate system displacement, rotation matrix, and translation vector are first acquired. A specific calculation transforms the three-dimensional displacement in the global coordinate system. The displacement rate is then calculated based on the time interval to accurately characterize the dynamic displacement changes of the slope surface. A micro-pore water pressure gauge measures pore water pressure in real time and calculates the rate of change using pressure values at adjacent moments. This clearly illustrates the dynamic evolution of groundwater pressure and provides key data for analyzing the hydraulic state of the slope. An inclinometer monitors the slope inclination angle in real time and calculates the rate of change of the inclination angle using the angle difference between adjacent moments, capturing subtle changes in the slope's inclination state. Targeted calculations are performed based on the characteristics of different sensor data to extract slope state characteristics from multiple dimensions, including strain, displacement, water pressure, and inclination angle. This provides an effective data foundation for subsequent multi-source data fusion and slope stability analysis and early warning.

[0026] like Figure 3 As shown, as a preferred embodiment of the present invention, the calculation and generation of multi-source fusion feature data based on a plurality of branch representation values specifically includes: Step S301: Obtaining real-time soil strain values , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values ; Step S302: Based on the real-time strain value of the soil , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , calculate and generate the representation fusion vector ; The representation fusion vector The calculation process is: ; Where, 、 、 、 、 is the corresponding weight coefficient, and ; When this embodiment is applied, the real-time strain value of the soil is obtained. , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , introduce real-time rainfall intensity value , calculate and generate the representation fusion vector The process assigns a specific weight to each data item and converts the real-time strain value of the soil into , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values Weighted fusion is performed. Domain experts can set weighting coefficients based on soil mechanics and engineering experience. This is based on professional analysis of the impact of various factors on slope stability, reflecting the importance of different data in the comprehensive assessment. This approach integrates dispersed multi-source data into a comprehensive vector that comprehensively represents the real-time state of the slope and provides structured input information for subsequent dynamic threshold warning models.

[0027] As a preferred embodiment of the present invention, the establishment of a dynamic threshold monitoring model and the calculation and generation of a real-time warning threshold specifically include: Based on the representation fusion vector , calculate and generate the displacement rate threshold ; The displacement rate threshold The calculation process is: ; Where, is the baseline threshold, are the pore water pressure coefficient and the rainfall sensitivity coefficient respectively; It should be understood that the baseline threshold The determination of the displacement rate of the slope under normal stability needs to rely on long-term monitoring and geological analysis. By collecting the displacement rate data of the slope under stable state, combined with geological conditions such as rock and soil type and structural surface characteristics, the displacement rate critical value representing the normal stability of the slope is set through calculation and analysis; the pore water pressure sensitivity coefficient The acquisition of pore water pressure depends on indoor and outdoor tests and data inversion. Indoors, triaxial permeability tests are used to simulate different pore water pressure change scenarios, monitor the mechanical response and displacement rate fluctuations of the slope model, and establish the pore water pressure change rate. Displacement rate threshold The historical monitoring data were analyzed on site, and the parameter inversion algorithm was used to quantify the influence of pore water pressure on slope stability. , so that it can accurately reflect the regulatory effect of pore water pressure changes on the threshold; rainfall sensitivity coefficient Obtained through historical data mining and regression analysis. Collect rainfall events of different intensities in the area and corresponding slope displacement rate data to establish rainfall intensity values Displacement rate threshold The mathematical model, after fitting and verification of multiple groups of data, clearly The value of reflects the superposition effect of rainfall on slope weight and water pressure; When the pore water pressure change rate The increase indicates that groundwater seepage intensifies and slope stability decreases. The threshold value will be adjusted accordingly to make it more stringent to provide early warning; the rainfall intensity value When increasing, The threshold value will be dynamically modified according to historical laws to reflect the additional load and softening effect of rainfall on the slope. It can respond to changes in the internal state of the slope and the external environment in real time. Compared with fixed thresholds, it greatly improves the accuracy and timeliness of early warnings, effectively avoids omissions or false alarms caused by sudden changes in a single factor, and builds a more reliable line of defense for slope safety monitoring.

[0028] As a preferred embodiment of the present invention, the establishment of a multi-level linkage warning mechanism based on the real-time warning threshold specifically includes: Get real-time displacement rate ; when ≥0.8 When the alarm is triggered, the first level warning is triggered, and local sound and light alarm instructions are generated and sent; when ≥ When a second-level warning is triggered, a text message push instruction is generated and sent to the targeted responsible person; when ≥1.2 When the vehicle is in the vicinity of the designated area, a level 3 warning is triggered, generating and sending a preset built-in anchor cable tensioning system start-up instruction and a slope range traffic blocking instruction; When this embodiment is applied, ≥0.8 When the risk is initially apparent, the first-level warning is triggered, and a local sound and light alarm command is generated and sent. This setting is intended to promptly remind on-site personnel to pay attention to slope changes and buy time for subsequent handling; if ≥ When the slope is abnormal, a secondary warning is triggered and a text message push instruction is sent to the target responsible person. This ensures that the relevant responsible personnel are aware of the slope abnormality information in a timely manner, quickly organize forces to conduct assessments and prepare further measures; when ≥1.2 When the third-level warning is triggered, a preset built-in anchor tensioning system start-up instruction and a slope range traffic blocking instruction are generated and sent. At this time, the system determines that the risk is highly urgent, and starting the anchor tensioning can actively reinforce the slope. Traffic blocking can effectively prevent people and vehicles from entering the dangerous area, minimizing the disaster losses. The built-in anchor tensioning system is the core execution module for active slope reinforcement, and is composed of high-strength, low-relaxation anchor cables, high-precision hydraulic jacks, intelligent control terminals and pressure sensing components. When the third-level warning start-up instruction is issued, the system responds immediately: the hydraulic jacks tension the anchor cables at a uniform speed, and accurately apply prestress to the stable rock mass near the deep slip surface of the slope. The anti-slip force of the sliding body is improved through the bonding force between the anchor section and the rock mass. The pressure sensor collects the tensioning force data in real time and feeds it back to the intelligent control terminal to ensure that the tensioning force strictly matches the design value to avoid stress concentration or insufficient reinforcement.

[0029] like Figure 4 As shown, as another preferred embodiment of the present invention, on the other hand, a real-time monitoring and early warning system for slope disasters includes: Acquisition module 100 is used to deploy distributed fiber optic sensing networks, Beidou GNSS receivers, micro pore water pressure gauges and inclinometers at the surface and deep locations of the targeted slope to obtain internal and external coordinated monitoring data; Extraction and calculation module 200, used to extract and calculate several branch representation values from the internal and external collaborative monitoring data; A first calculation and generation module 300 is used to calculate and generate multi-source fusion feature data based on a plurality of branch representation values; The second calculation and generation module 400 is used to establish a dynamic threshold monitoring model, calculate and generate a real-time warning threshold; The early warning module 500 is used to establish a multi-level linkage early warning mechanism based on real-time early warning thresholds.

[0030] When this embodiment is applied, a distributed fiber optic sensing network, Beidou GNSS receivers, micro pore water pressure gauges and inclinometers are deployed at the surface and deep locations of the targeted slope. The acquisition module 100 obtains internal and external collaborative monitoring data; the extraction and calculation module 200 extracts and calculates several branch characterization values in the internal and external collaborative monitoring data. Based on the several branch characterization values, the first calculation and generation module 300 calculates and generates multi-source fusion feature data. The second calculation and generation module 400 establishes a dynamic threshold monitoring model, calculates and generates a real-time warning threshold; based on the real-time warning threshold, the warning module 500 establishes a multi-level linkage warning mechanism.

[0031] like Figure 5 As shown, as another preferred embodiment of the present invention, the extraction calculation module 200 specifically includes: The first extraction unit 201 is used to extract the original measured strain value of the optical fiber sensor network from the internal and external collaborative monitoring data. ; The first calculation unit 202 is used to calculate the strain value of the optical fiber based on the original measurement , calculate the real-time strain value of soil ; The second extraction unit 203 is used to extract the local coordinate system displacement of the Beidou GNSS receiver from the internal and external collaborative monitoring data. , rotation matrix and translation vectors ; The second calculation unit 204 is used to calculate the displacement based on the local coordinate system , rotation matrix and translation vectors , calculate the three-dimensional displacement in the global coordinate system ; The third calculation unit 205 is used to calculate the three-dimensional displacement based on the global coordinate system. , calculate the displacement rate ; The third extraction unit 206 is used to extract the real-time pore water pressure of the micro pore water pressure gauge from the internal and external collaborative monitoring data. ; The fourth calculation unit 207 is used to calculate the real-time pore water pressure , calculate the pore water pressure change rate ; The fourth extraction unit 208 is used to extract the real-time tilt angle of the inclinometer from the internal and external collaborative monitoring data. ; The fifth calculation unit 209 is used to calculate the real-time tilt angle , calculate the rate of change of the tilt angle .

[0032] When this embodiment is applied, the first extraction unit 201 extracts the original measured strain value of the optical fiber sensor network from the internal and external collaborative monitoring data. , based on the original measured strain value of the optical fiber The first calculation unit 202 calculates the real-time strain value of the soil The second extraction unit 203 extracts the local coordinate system displacement of the BeiDou GNSS receiver in the internal and external collaborative monitoring data. , rotation matrix and translation vectors , based on the local coordinate system displacement , rotation matrix and translation vectors The second calculation unit 204 calculates the three-dimensional displacement in the global coordinate system , based on the three-dimensional displacement in the global coordinate system , the third calculation unit 205 calculates the displacement rate The third extraction unit 206 extracts the real-time pore water pressure of the micro pore water pressure gauge from the internal and external collaborative monitoring data. , based on real-time pore water pressure The fourth calculation unit 207 calculates the pore water pressure change rate The fourth extraction unit 208 extracts the real-time tilt angle of the inclinometer in the internal and external collaborative monitoring data. , based on real-time tilt angle The fifth calculation unit 209 calculates the tilt angle change rate .

[0033] like Figure 6 As shown, as another preferred embodiment of the present invention, the first calculation generation module 300 specifically includes: Acquisition unit 301, used to obtain the real-time strain value of the soil , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values ; The first calculation generating unit 302 is used to calculate the soil based on the real-time strain value , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , calculate and generate the representation fusion vector .

[0034] When this embodiment is applied, the acquisition unit 301 acquires the real-time strain value of the soil. , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , based on the real-time strain value of soil , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , the first calculation and generation unit 302 calculates and generates the representation fusion vector .

[0035] The above-described embodiments of the present invention provide a real-time slope disaster monitoring and early warning method and system. First, differentiated sensor networks are constructed on the surface and deep within the slope (depth ≥ 50 m). A distributed fiber optic sensor network (BOTDR / OFDR, spatial resolution ≤ 0.5 m) is used in the deep layer. Armored fiber optic cables and steel pipes are used to fix these sensors, and deformation coordination coefficient correction is used to achieve millimeter-level monitoring of deep soil strain. Beidou GNSS receivers (positioning accuracy ≤ 1 mm), micro pore water pressure gauges (range 0-10 MPa, accuracy ±0.1% FS), and inclinometers (resolution 0.001°) are deployed on the surface, forming a comprehensive monitoring system covering shallow surface displacement, groundwater pressure, and tilt angle. Correction formulas are used to eliminate temperature effects in optical fiber strain to obtain true strain. Beidou displacement is converted and rate-calculated to accurately quantify displacement. Pore water pressure and inclinometer data are used to calculate the rate and amount of change. These branch representations are then merged to form a fused dataset, forming multi-source fused feature data. Deep reinforcement learning is then introduced to establish a dynamic threshold early warning model. Based on multi-source fusion feature data, and considering the impact of factors such as pore water pressure and rainfall on slope stability, the early warning threshold is dynamically optimized to generate a real-time and realistic displacement rate threshold. Finally, a multi-level linkage early warning mechanism is established based on the real-time early warning threshold, with graded responses tailored to different levels of danger, automating the entire process from data collection to early warning response. This method and system achieves coordinated internal and external monitoring through a multimodal sensor network, comprehensively capturing slope status information. Multi-source data fusion processing improves data quality and reliability, the dynamic threshold model accurately adapts to different working conditions, and the multi-level linkage early warning mechanism ensures timely response. Overall, this significantly improves the accuracy and timeliness of slope disaster early warnings, effectively reducing disaster losses.

[0036] In order to enable the above-mentioned method and system to be loaded and run smoothly, in addition to the various modules mentioned above, the system may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0037] The processor may be a central processing unit, other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the system, connecting various components using various interfaces and lines.

[0038] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0039] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A real-time monitoring and early warning method for slope disasters, characterized in that: The method comprises: Deploy distributed fiber optic sensor networks, BeiDou GNSS receivers, micro pore water pressure gauges, and inclinometers at targeted slope surface and deep locations to obtain internal and external collaborative monitoring data; Extract and calculate several branch representation values from internal and external collaborative monitoring data; Based on several branch representation values, multi-source fusion feature data is calculated and generated; Establish a dynamic threshold monitoring model to calculate and generate real-time warning thresholds; Based on real-time warning thresholds, a multi-level linkage warning mechanism is established.

2. The method for real-time monitoring and early warning of slope disasters according to claim 1, characterized in that: The extraction and calculation of several branch representation values from the internal and external collaborative monitoring data specifically includes: Extracting the original measured strain value of the optical fiber sensing network from the internal and external collaborative monitoring data ; Based on the original measurement strain value of optical fiber , calculate the real-time strain value of soil ; The real-time strain value of the soil The calculation process is: ； Where, is the deformation coordination coefficient, is the thermal expansion coefficient of the optical fiber, is the temperature change; Extracting the local coordinate system displacement of BeiDou GNSS receivers from internal and external collaborative monitoring data , rotation matrix and translation vectors ; Displacement based on local coordinate system , rotation matrix and translation vectors , calculate the three-dimensional displacement in the global coordinate system ; The three-dimensional displacement in the global coordinate system The calculation process is: ； Three-dimensional displacement based on the global coordinate system , calculate the displacement rate ; The displacement rate The calculation process is: ； Where, is the time interval; Extracting real-time pore water pressure from micro pore water pressure gauges in internal and external collaborative monitoring data ; Based on real-time pore water pressure , calculate the pore water pressure change rate ; The pore water pressure change rate The calculation process is: ； Extract the real-time tilt angle of the inclinometer from the internal and external collaborative monitoring data ; Based on real-time tilt angle , calculate the rate of change of the tilt angle ; The tilt angle change rate The calculation process is: 。 3. The method for real-time monitoring and early warning of slope disasters according to claim 1, characterized in that: The calculation and generation of multi-source fusion feature data based on a plurality of branch representation values specifically includes: Obtaining real-time soil strain values , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values ; Based on real-time soil strain value , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , calculate and generate the representation fusion vector ; The representation fusion vector The calculation process is: ； Where, 、 、 、 、 is the corresponding weight coefficient, and .

4. The method for real-time monitoring and early warning of slope disasters according to claim 1, characterized in that: The establishment of a dynamic threshold monitoring model and calculation and generation of a real-time warning threshold specifically include: Based on the representation fusion vector , calculate and generate the displacement rate threshold ; The displacement rate threshold The calculation process is: ； Where, is the baseline threshold, are the pore water pressure coefficient and rainfall sensitivity coefficient respectively.

5. The method for real-time monitoring and early warning of slope disasters according to claim 1, characterized in that: The establishment of a multi-level linkage early warning mechanism based on real-time early warning thresholds specifically includes: Get real-time displacement rate ; when ≥0.8 When the alarm is triggered, the first level warning is triggered, and local sound and light alarm instructions are generated and sent; when ≥ When a second-level warning is triggered, a text message push instruction is generated and sent to the targeted responsible person; when ≥1.2 When the warning is triggered, the third level warning is generated and sent, and the preset built-in anchor cable tensioning system start-up instruction and the slope range traffic blocking instruction are generated and sent.

6. A real-time monitoring and early warning system for slope disasters, characterized in that: The method for real-time monitoring and early warning of slope disasters according to any one of claims 1 to 5 is applied, wherein the system comprises: The acquisition module is used to deploy distributed fiber optic sensor networks, Beidou GNSS receivers, micro pore water pressure gauges, and inclinometers at the surface and deep locations of the targeted slope to obtain internal and external collaborative monitoring data; An extraction and calculation module is used to extract and calculate several branch representation values from the internal and external collaborative monitoring data; A first calculation and generation module is used to calculate and generate multi-source fusion feature data based on a plurality of branch representation values; The second calculation and generation module is used to establish a dynamic threshold monitoring model, calculate and generate real-time warning thresholds; The early warning module is used to establish a multi-level linkage early warning mechanism based on real-time early warning thresholds.

7. The real-time monitoring and early warning system for slope disasters according to claim 6 is characterized in that: The extraction calculation module specifically includes: The first extraction unit is used to extract the original measured strain value of the optical fiber sensor network from the internal and external collaborative monitoring data ; The first calculation unit is used to calculate the strain value of the optical fiber based on the original measurement , calculate the real-time strain value of soil ; The second extraction unit is used to extract the local coordinate system displacement of the Beidou GNSS receiver from the internal and external collaborative monitoring data , rotation matrix and translation vectors ; The second calculation unit is used for displacement based on the local coordinate system , rotation matrix and translation vectors , calculate the three-dimensional displacement in the global coordinate system ; The third calculation unit is used for three-dimensional displacement based on the global coordinate system , calculate the displacement rate ; The third extraction unit is used to extract the real-time pore water pressure of the micro pore water pressure gauge from the internal and external collaborative monitoring data. ; The fourth calculation unit is used to calculate the pore water pressure based on real-time , calculate the pore water pressure change rate ; The fourth extraction unit is used to extract the real-time tilt angle of the inclinometer from the internal and external collaborative monitoring data ; The fifth calculation unit is used to calculate the real-time tilt angle , calculate the rate of change of the tilt angle .

8. The real-time monitoring and early warning system for slope disasters according to claim 6 is characterized in that: The first calculation generation module specifically includes: Acquisition unit, used to obtain real-time soil strain value , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values ; The first calculation generation unit is used to calculate the real-time strain value of the soil , three-dimensional displacement in the global coordinate system , displacement rate , pore water pressure change rate and real-time rainfall intensity values , calculate and generate the representation fusion vector .

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