Dam and high slope structure health intelligent sensing and early warning system

Abstract

The invention relates to a dam and high slope structure health intelligent sensing and early warning system, and relates to the technical field of intelligent water conservancy, slope surface displacement, deep deformation, pore water pressure, dam body displacement, dam foundation uplift pressure and seepage flow data are obtained in real time through a distributed sensing network, and multi-source parameters such as reservoir water level and rainfall are combined; performing spatio-temporal interpolation repair and noise filtering by using an edge calculation unit, and constructing an analytic hierarchy process weight matrix and a dynamic risk mapping model; a displacement thermodynamic diagram and an osmotic pressure contour surface cloud picture are dynamically rendered in a digital twin platform, a CRI curve and a reservoir water level process line are synchronously compared, and a grading response mechanism is triggered based on a CRI threshold value.

Description

Technical Field

[0001] This invention relates to the field of smart water conservancy technology, specifically to a smart sensing and early warning system for the health of dam and high slope structures. Background Technology

[0002] Current safety monitoring technologies for dams and high slopes primarily rely on independently deployed sensing systems. While these systems possess basic reliability in monitoring single structures, they suffer from significant drawbacks, leading to low early warning accuracy and delayed response. Traditional methods typically employ equipment such as GNSS displacement sensors, piezometers, and weirs, setting fixed thresholds for single-point early warning based on industry standards (such as the "Design Code for Slopes of Water Conservancy and Hydropower Projects" SL386-2007). However, this fragmented architecture causes multiple problems: severe data silos, the inability to link dam and high slope data for analysis, resulting in a persistently high rate of missed detections for coupled risks.

[0003] The publicly disclosed CN 119559757 A, a slope structure safety monitoring method and system based on distributed sensing technology, and CN 114707227 B, a dam safety early warning and fire protection method and system based on digital twins, achieve high-fidelity simulation of the entire life cycle of the dam through high-precision model construction and multi-source data fusion, and utilize adaptive numerical models and XR technology. This solves the problem in traditional monitoring where "single-point data is difficult to map the overall safety status". Combined with dam break flood analysis and disaster chain extrapolation models (such as flood inundation analysis + loss assessment), it enables early prediction of the consequences of failure and alleviates the shortcomings of "low early warning timeliness" of manual inspection or automated systems.

[0004] However, there are serious drawbacks: 1. Data silos lead to missed risks due to coupling: Dam and high slope data are analyzed independently, making it impossible to quantify the dynamic impact of sudden drops in reservoir water level on slope stability, such as sudden changes in seepage pressure. Statistics from the Ministry of Water Resources show that the missed rate for such scenarios is high; 2. Fragmented analysis models: The dam uses a finite element stress model, while the slope uses the limit equilibrium method. Incompatible algorithm architectures lead to conflicting early warning conclusions, such as the dam model still showing safety when slope displacement accelerates; 3. Rigid threshold mechanism: Fixed thresholds cannot adapt to complex working conditions such as continuous heavy rainfall superimposed with seismic disturbances.

[0005] Therefore, a smart sensing and early warning system for the health of dam and high slope structures is needed to solve the above problems. Summary of the Invention

[0006] To address the problems of existing technologies, this invention provides an intelligent sensing and early warning system for the health of dam and high slope structures.

[0007] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: Firstly, a dam and high slope structure health intelligent sensing and early warning system includes: a data acquisition module configured to acquire in real time GNSS surface displacement, inclinometer deep displacement, piezometer pore water pressure data of the high slope, dam body GNSS displacement, dam foundation uplift pressure, water measuring weir seepage flow data of the dam, as well as external reservoir water level and rainfall data; The data preprocessing module is configured to perform missing value repair, timestamp alignment, and spatial coordinate unification on the data. The comprehensive risk analysis engine is configured to execute: (a) Generating index weights based on the Analytic Hierarchy Process (AHP): Construct an importance judgment matrix among displacement, seepage pressure, and rainfall indicators for high slopes, and calculate the weight values ​​from the eigenvectors. ; Construct an importance judgment matrix among dam displacement, seepage, and uplift pressure indicators, and calculate the weight values ​​from the eigenvectors. ; All weight values ​​must meet the consistency test (CR<0.1); (b) Risk score mapping: Calculate the risk score for each indicator using a piecewise function. ( (For any indicator) in As a safety threshold, This is the danger threshold; (c) Calculation of the Comprehensive Risk Index (CRI): in The impact coefficient of high slope instability on the dam body (value ranges from 1.0 to 1.5). This is the dam safety level coefficient (values ​​range from 1.0 to 1.3). The intelligent early warning decision module is configured to: output a normal signal when CRI < 25, trigger a concern warning when 25 ≤ CRI < 50, trigger an action warning when 50 ≤ CRI < 75, and activate an emergency alarm when CRI ≥ 75. The 3D visualization module is configured to dynamically render CRI values ​​and warning statuses within the digital twin model.

[0008] In one specific embodiment of the first aspect, the threshold for the high slope is set as: deep displacement rate: =2.0mm / day, =10.0mm / day Osmotic pressure change rate: =0 kPa (historical average) =+15kPa (maximum allowable fluctuation).

[0009] In one specific implementation of the first aspect, the threshold value of the dam is set as: seepage rate of change: =0% (baseline value) =+20% (abnormal threshold); Uplift pressure at dam foundation: =60% (design value) =90% (safety limit).

[0010] In one specific implementation of the first aspect, the construction of the importance judgment matrix satisfies: Scale of relative importance among indicators for high slopes: displacement > seepage pressure > rainfall (scale value ≥ 3); Scale of relative importance among dam indicators: seepage flow > uplift pressure > displacement (scale value ≥ 2); The scale value is converted into a judgment matrix element according to the 1-9 scale method, and the matrix element value corresponding to scale 3 is 3.

[0011] In one specific embodiment of the first aspect, the early warning module performs the following operations: Pay attention to early warnings (25≤CRI<50): Automatically generate inspection work orders and push them to mobile terminals. Action alert (50≤CRI<75): Trigger drone automatic patrol and photography of potential hazard areas. Emergency Alarm (CRI≥75): Makes a voice alarm call to a preset contact person.

[0012] In one specific implementation of the first aspect, the three-dimensional visualization module dynamically displays: The locations where the displacement rate exceeds the limit are rendered using a heat map. Areas with abnormal osmotic pressure are represented by overlaid contour maps. CRI change curve and reservoir water level process line are compared synchronously on both axes.

[0013] In one specific embodiment of the first aspect, the impact coefficient The value selection logic is as follows: When the distance between the slope and the dam body is ≤50 meters, =1.5 When the distance is >50 meters and ≤200 meters, =1.2 When the distance is greater than 200 meters, =1.0.

[0014] In one specific implementation of the first aspect, when the system receives three consecutive false alarms, the following is automatically executed: Weighting of indicators that trigger false alarms = ×0.8; Reset the threshold of this metric = ×1.2.

[0015] Secondly, a method for intelligent sensing and early warning of the structural health of dams and high slopes includes the following steps: S10 Data Acquisition: Real-time acquisition of GNSS surface displacement, inclinometer deep displacement, and piezometer pore water pressure data of high slopes; Real-time acquisition of GNSS displacement of the dam body, uplift pressure of the dam foundation, and seepage flow of the measuring weir; Simultaneous collection of reservoir water level and rainfall data; S20 Data Preprocessing: Missing data was repaired using spatiotemporal interpolation. Unify the timestamps of all monitoring data to the UTC standard time zone; Transform the spatial coordinates to the engineering independent coordinate system; S30 Comprehensive Risk Analysis: S31 Weight Calculation: Construct a high slope index judgment matrix (displacement / seepage pressure / rainfall), and solve for the weights by solving the eigenvectors. (Satisfying CR<0.1); Construct a dam index judgment matrix (seepage / uplift pressure / displacement), and solve for the weights by solving the eigenvectors. ; S32 Risk Score Mapping: For each indicator value calculate: ; S33 CRI calculation: in ∈[1.0,1.5], ∈[1.0,1.3]; S40 Early Warning Decision: When CRI < 25, the status is marked as normal; When 25≤CRI<50, push the inspection work order to the mobile terminal; When 50≤CRI<75, initiate automatic drone cruise; When CRI ≥ 75, a voice alarm and emergency communication are triggered. S50 3D Visualization: Heatmaps are used to render points where displacement exceeds limits in a digital twin model; Overlay contour maps of areas with abnormal seepage pressure; The CRI curve is dynamically displayed, showing a comparison with the reservoir water level process line.

[0016] Thirdly, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the functions of an intelligent sensing and early warning system for the health of dam and high slope structures.

[0017] The beneficial effects of this invention are as follows: 1. By employing computer-aided modeling and simulation technology, the accuracy and timeliness of safety early warning for water conservancy projects are significantly improved. Based on multi-source heterogeneous data, the system utilizes a parametric modeling engine to render the linked risk status of the dam and slope in a digital twin environment in real time. The system automatically generates indicator weights and a CRI risk index calculation model using the AHP algorithm, achieving closed-loop design verification from physical monitoring to the virtual model. When the deep slope displacement reaches 7.8 mm / day (not exceeding the traditional threshold), the system extrapolates the CRI to 106.95 (far exceeding the 75 emergency threshold) using the coupling coefficient and seepage pressure weight, triggering a voice alarm two hours in advance. This multi-physics coupled simulation capability solves the problem of missed reports caused by data silos in traditional methods, effectively improving the disaster identification rate. 2. The system deeply integrates an automated design correction mechanism. When false alarms occur, it autonomously optimizes model parameters by dynamically adjusting indicator weights and danger thresholds. For example, in a rainstorm interference scenario, after learning from three false alarms, the system iterates the seepage pressure threshold from 15 kPa to 25.9 kPa, reducing the CRI (Critical Identification Rate) of the subsequent measured value +17.3 kPa from 74 to 39, thus preventing the drone from being falsely activated. This process relies on a computer-aided decision-making architecture, increasing the response speed of manual operation and maintenance by 20 times and reducing the false alarm rate from 100% to zero. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system framework of the present invention.

[0019] Figure 2 This is a schematic diagram of the present invention.

[0020] Figure 3 This is a schematic diagram of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figures 1 to 3 The diagram shows an intelligent sensing and early warning system for the structural health of dams and high slopes.

[0023] Example 1: Slope-dam chain reaction scenario.

[0024] The hydropower station was hit by continuous heavy rain (120 mm of rainfall per day), and the reservoir water level rose by 8 meters in 3 hours. At the same time, deep landslides were observed on the high slope.

[0025] Traditional system performance Independent analysis of slope and dam data Slope warning: Deep displacement 7.8 mm / day (not exceeding the 10 mm threshold), marked as low risk. Dam warning: Seepage flow +22% (exceeding the 20% threshold), marked as medium risk. Actual result: The slope landslide impacted the dam body, causing localized seepage (it was later found that the actual CRI value was 79). This system is implemented as follows: Key coupling parameters: CRI calculation: (0.5×72.5+0.3×88)×1.5+(0.3×100)×1.0=106.95; Output: CRI=106.95 → Trigger emergency alarm (voice call alarm); Technological advantages 1. 2-hour advance warning: The system uses coupling coefficients to provide early warnings. The CRI was increased from 68 (calculated independently) to 106.95. 2. Avoiding losses: Evacuation of personnel and flood discharge scheduling reduced direct economic losses by approximately ¥12 million; 3. Comparison of false negative rates: The false negative rate of the traditional system is 100%, while the false negative rate of this system is 0%.

[0026] Example 2: Rainstorm Interference Scenario.

[0027] The reservoir experienced three short-duration heavy rainfalls during the rainy season. The piezometer generated abnormal data (+14.8 kPa) due to air bubble interference, triggering a false alarm.

[0028] Defects of traditional systems: Each time, an action warning was triggered (CRI≈68); The threshold needs to be manually reset, resulting in a 40% decrease in operation and maintenance efficiency.

[0029] The self-learning process of this system: Data during the 4th rainstorm: Measured seepage pressure: +17.3 kPa; Traditional system: Sk=100 (because 17.3>15) → CRI=74 → Accidentally triggering drone This system: (Because 17.3 < 25.9 kPa is the new threshold); The CRI dropped from 74 to 39, and only inspection work orders were pushed out.

[0030] Technological advantages 1. False alarm rate decreased: from 100% to 0% (starting from the 4th time); 2. Operation and maintenance costs: Reduced number of drone malfunctions lowers costs; 3. Adaptive capability: The threshold dynamic adjustment speed is 20 times faster than manual adjustment.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart sensing and early warning system for the structural health of dams and high slopes, characterized in that, include: The data acquisition module is configured to acquire in real time GNSS surface displacement, inclinometer deep displacement, piezometer pore water pressure data of high slopes, dam body GNSS displacement, dam foundation uplift pressure, weir seepage flow data of dam, as well as external reservoir water level and rainfall data. The data preprocessing module is configured to perform missing value repair, timestamp alignment, and spatial coordinate unification on the data. The comprehensive risk analysis engine is configured to execute: (a) Generating index weights based on the Analytic Hierarchy Process (AHP): Construct an importance judgment matrix among displacement, seepage pressure, and rainfall indicators for high slopes, and calculate the weight values ​​from the eigenvectors. ; Construct an importance judgment matrix among dam displacement, seepage, and uplift pressure indicators, and calculate the weight values ​​from the eigenvectors. ; All weight values ​​must meet the consistency test (CR<0.1); (b) Risk score mapping: Calculate the risk score for each indicator using a piecewise function. ( (For any indicator) in As a safety threshold, This is the danger threshold; (c) Calculation of the Comprehensive Risk Index (CRI): in The impact coefficient of high slope instability on the dam body (value ranges from 1.0 to 1.5). This is the dam safety level coefficient (values ​​range from 1.0 to 1.3). The intelligent early warning decision module is configured to: output a normal signal when CRI < 25, trigger a concern warning when 25 ≤ CRI < 50, trigger an action warning when 50 ≤ CRI < 75, and activate an emergency alarm when CRI ≥ 75. The 3D visualization module is configured to dynamically render CRI values ​​and warning statuses within the digital twin model.

2. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: The threshold for the high slope is set as follows: deep displacement rate: =2.0mm / day, =10.0mm / day Osmotic pressure change rate: =0 kPa (historical average), =+15kPa (maximum allowable fluctuation).

3. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: The threshold value for the dam is set as: seepage rate of change: =0% (baseline value) =+20% (abnormal threshold); Uplift pressure at dam foundation: =60% (design value) =90% (safety limit).

4. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: The construction of the importance judgment matrix satisfies: Scale of relative importance among indicators for high slopes: displacement > seepage pressure > rainfall (scale value ≥ 3); Scale of relative importance among dam indicators: seepage flow > uplift pressure > displacement (scale value ≥ 2); The scale value is converted into a judgment matrix element according to the 1-9 scale method, and the matrix element value corresponding to scale 3 is 3.

5. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: The early warning module performs the following operations: Pay attention to early warnings (25≤CRI<50): Automatically generate inspection work orders and push them to mobile terminals. Action alert (50≤CRI<75): Trigger drone automatic patrol and photography of potential hazard areas. Emergency Alarm (CRI≥75): Makes a voice alarm call to a preset contact person.

6. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: The 3D visualization module displays dynamically: The locations where the displacement rate exceeds the limit are rendered using a heat map. Areas with abnormal osmotic pressure are represented by overlaid contour maps. CRI change curve and reservoir water level process line are compared synchronously on both axes.

7. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: The impact coefficient The value selection logic is as follows: When the distance between the slope and the dam body is ≤50 meters, =1.5 When the distance is >50 meters and ≤200 meters, =1.2 When the distance is >200 meters, =1.

0.

8. The intelligent sensing and early warning system for the structural health of dams and high slopes according to claim 1, characterized in that: When the system receives three consecutive false alarms, the following will be executed automatically: Weighting of indicators that trigger false alarms = ×0.8; Reset the threshold of this metric = ×1.

2.

9. A method for intelligent sensing and early warning of the structural health of dams and high slopes, characterized in that: Includes the following steps: S10 Data Acquisition: Real-time acquisition of GNSS surface displacement, inclinometer deep displacement, and piezometer pore water pressure data of high slopes; Real-time acquisition of GNSS displacement of the dam body, uplift pressure of the dam foundation, and seepage flow of the measuring weir; Simultaneous collection of reservoir water level and rainfall data; S20 Data Preprocessing: Missing data was repaired using spatiotemporal interpolation. Unify the timestamps of all monitoring data to the UTC standard time zone; Transform the spatial coordinates to the engineering independent coordinate system; S30 Comprehensive Risk Analysis: S31 Weight Calculation: Construct a high slope index judgment matrix (displacement / seepage pressure / rainfall), and solve for the weights by solving the eigenvectors. (Satisfying CR<0.1); Construct a dam index judgment matrix (seepage / uplift pressure / displacement), and solve for the weights by solving the eigenvectors. ; S32 Risk Score Mapping: For each indicator value calculate: ； S33 CRI calculation: in ∈[1.0,1.5], ∈[1.0,1.3]; S40 Early Warning Decision: When CRI < 25, the status is marked as normal; When 25≤CRI<50, push the inspection work order to the mobile terminal; When 50≤CRI<75, initiate automatic drone cruise; When CRI ≥ 75, a voice alarm and emergency communication are triggered. S50 3D Visualization: Heatmaps are used to render points where displacement exceeds limits in a digital twin model; Overlay contour maps of areas with abnormal seepage pressure; The CRI curve is dynamically displayed, showing a comparison with the reservoir water level process line.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the functionality of the system as described in any one of claims 1 to 7.

Images