Hydropower engineering extra-high steep slope online dynamic prevention and control method

Abstract

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

Description

Technical Field

[0001] The invention belongs to the technical field of slope prevention and control, and specifically relates to an online dynamic prevention and control method for extremely high and steep slopes in hydropower projects. Background Art

[0002] The stability of extremely high and steep slopes (height greater than 1000m) has always been a major challenge for hydropower engineering construction. Its potential disasters such as dangerous rock collapse and slope instability seriously threaten dam construction and safe operation. Traditional slope prevention and control methods often have problems such as inaccurate data fusion, high false alarm rate of early warning, and lack of real-time decision analysis. For example, data from different sources (such as Beidou, drones, millimeter wave radar, etc.) are difficult to achieve fast and accurate fusion due to differences in format, accuracy and acquisition frequency; the threshold setting of the existing early warning model is not reasonable enough, resulting in frequent false alarms, affecting the reliability and practicality of the early warning; at the same time, facing high and steep slopes with complex characteristics, traditional decision-making methods cannot provide effective reinforcement solutions in a timely and accurate manner.

[0003] Based on this, it is necessary to propose an online prevention and control method for the safety of high and steep slopes in hydropower projects, aiming to solve the above problems and provide reliable technical support for the safety of high and steep slopes. Summary of the invention

[0004] The present invention is proposed to solve the above-mentioned shortcomings, and its purpose is to provide an online dynamic prevention and control method for extremely high and steep slopes in hydropower projects. The method can realize the rapid and accurate fusion of multi-source heterogeneous spatiotemporal data, reduce the false alarm rate of slope warning, and realize the online closed-loop analysis of "automatic identification of dangerous rock hidden dangers - real-time analysis of rockfall trajectory speed - pre-consolidation range and anchoring differentiation decision" for high and steep slopes, providing scientific, accurate and timely decision-making basis for the safe management of high and steep slopes.

[0005] In order to achieve the above purpose, the present invention adopts the following scheme:

[0006] An online dynamic prevention and control method for extremely high and steep slopes in hydropower projects comprises the following steps:

[0007] S1: Construct a data dynamic coupling model based on the interval screening algorithm, perform segmented statistics on multi-source safety monitoring data and dynamically identify abnormal data beyond the confidence interval, and remove the abnormal data to obtain a cleaned valid data sequence;

[0008] S2: Align and synchronize the valid data sequence of multi-source safety monitoring data with the BIM model in time and space;

[0009] S3: Based on the initial functional relationship between the slope geomechanical parameters and the critical value of the slope safety factor, combined with the obtained effective data sequence, the displacement warning value is dynamically corrected by introducing a time optimization factor to obtain the optimized displacement warning value that changes with time;

[0010] S4: Establish a multi-modal algorithm set for slope safety prevention and control, conduct real-time evaluation on the optimized displacement warning value obtained in step S3, and output safety prevention and control decisions including the boundary of dangerous rock mass, the trajectory velocity of falling rocks, the pre-consolidation range, and the differential scheme of bolt anchorage length;

[0011] S5: Return the analysis results in step S4 to the monitoring and control system, continuously monitor and iteratively update the slope safety status, and realize the online closed-loop analysis management of slope safety prevention and control.

[0012] As a preferred implementation manner, in step S1, the data dynamic coupling model constructed based on the interval screening value algorithm is as follows: ; In the formula, μ α(xi) refers to the mean value of the safety monitoring data within the segment; δ α(xi) refers to the standard deviation of the safety monitoring data within the segment; n and m respectively refer to the nth and mth of the safety monitoring data; α(x i ) refers to the safety monitoring data of different categories.

[0013] As a preferred implementation manner, the extra-high and steep slope is a slope with a height greater than 1000m.

[0014] As a preferred implementation manner, in step S1, by dynamically loading multi-source safety monitoring data, segmenting according to the source, characteristics, and time series of the multi-source safety monitoring data, and using the data dynamic coupling model to dynamically eliminate abnormal data.

[0015] As a preferred implementation manner, in step S1, the multi-source safety monitoring data is collected by Beidou, unmanned aerial vehicle, and radar.

[0016] As a preferred implementation manner, in step S3, based on the critical value K 临界 of the slope safety factor, according to the cohesion c and the internal friction angle φ of the slope geomechanical parameters, the displacement warning value x 预警值 is inversely calculated by three-dimensional numerical simulation, and the initial functional relationship K 临界 between the critical value of the slope safety factor and the displacement warning value x 预警值 is determined as K 临界 = f0(x, c, φ). In the formula, c is the cohesion, φ is the internal friction angle, and x is the monitored displacement data obtained in real time.

[0017] As a preferred embodiment, in step S3, based on the monitored displacement data x obtained in real time, the initial function f0() is continuously optimized in combination with the operating state of the slope, and a displacement early warning value optimization algorithm K based on time series is established. 临界 =f 优化 (x, c, φ) = α(t) * f0(x, c, φ), and then the optimized displacement early warning value x varying with time is obtained. 优化 , where α(t) is a time optimization factor and is dynamically optimized according to the displacement data x obtained by real-time monitoring.

[0018] As a preferred embodiment, in step S4, a multi-modal algorithm set S = {s1, s2, s3, s4} for slope safety prevention and control is established, where s1 is a three-dimensional segmentation algorithm for dangerous rock masses, s2 is a falling rock impact fragmentation algorithm, s3 is a plastic zone development calculation algorithm, and s4 is an algorithm for associating slope excavation parameters with the anchorage length.

[0019] As a preferred embodiment, in step S4, the three-dimensional segmentation algorithm for dangerous rock masses extracts geometric characteristic parameters such as automatically delineating the boundary of the dangerous rock mass, calculating the volume, and measuring the dip angle.

[0020] As a preferred embodiment, in step S4, the falling rock impact fragmentation algorithm calculates the impact force of the rolling rock, the velocity of the fragmented body, and the movement distance.

[0021] As a preferred embodiment, in step S4, the plastic zone development calculation algorithm considers slope failure damage to determine the pre-consolidation range; the algorithm for associating slope excavation parameters with the anchorage length performs dynamic analysis on the slope to determine differential support for the anchorage length of the anchor bolts.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] First, the data dynamic coupling model based on the interval screening value algorithm constructed by the present invention realizes dynamic elimination of outliers, greatly improves the credibility of the data, and provides a more accurate basis for subsequent analysis and decision-making; at the same time, multi-source safety monitoring data collected by Beidou, unmanned aerial vehicles, millimeter-wave radars, etc. are collide with the BIM model in time and space to achieve the matching of different data in time and space.

[0024] Second, the slope displacement early warning value optimization algorithm constructed by the present invention can effectively reduce the false alarm rate of the early warning threshold, improve the accuracy and reliability of the early warning, can timely detect potential dangers of the slope, and provide guarantee for the safety of personnel and facilities.

[0025] Thirdly, the multi-modal algorithm set for slope safety prevention and control constructed by the present invention integrates algorithms such as intelligent identification of high-position dangerous rock masses on slopes, calculation of the impact of rolling stones when landing, calculation of the pre-consolidation range, and differential analysis of the anchorage length, realizing the online closed-loop analysis of "automatic identification of hidden dangers of dangerous rock masses - real-time analysis of the trajectory and speed of falling stones - prediction of the range and differential decision-making of anchorage" for slopes.

[0026] Fourthly, the present invention greatly optimizes the slope prevention and control plan for the Wudongde Hydropower Station. Compared with the feasibility study estimate, it saves 1,520 prestressed anchor cables, 3,560 anchor rod piles, 4,250 anchor bolts, and 81,600 m of active protection nets. 2 , and 18,300 m of passive protection nets. 2 . BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic flow chart of an online dynamic prevention and control method for extremely high and steep slopes of hydropower projects according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] This embodiment is specifically described by taking the slope prevention and control plan of the Wudongde Hydropower Station as an example. There are potential hazards such as rock mass weathering and fissure development on the right bank dam shoulder slope of the hydropower station. Using the online dynamic prevention and control method for extremely high and steep slopes of hydropower projects of the present invention, the following steps are included:

[0030] S1: Data dynamic coupling and anomaly elimination

[0031] The fluctuation range of safety monitoring data such as slope displacement is limited within a certain period. Once it exceeds the confidence interval, it can basically be determined that the data is abnormal due to reasons such as hardware acquisition. Based on this, a data dynamic coupling model considering the interval screening value algorithm is proposed, as shown in Equation (1): (Equation 1); In the formula, μ α(xi) refers to the mean value of the safety monitoring data within the segment; δ α(xi) refers to the standard deviation of the safety monitoring data within the segment; n and m respectively refer to the nth and mth of the safety monitoring data; α(x i ) refers to different categories of safety monitoring data.

[0032] Based on the data dynamic coupling model of the interval screening value algorithm, perform segmented statistics and dynamically identify abnormal data beyond the confidence interval, and eliminate the abnormal data to obtain a cleaned effective data sequence; this model dynamically loads data, reasonably segments according to the data source, characteristics, and time series, that is, reasonably sets the values of n and m, and uses the data dynamic coupling model to dynamically eliminate outliers. Since the monitoring data sources are diverse (Beidou, drones, radars, etc.) and the formats and characteristics are different, by using the dynamic coupling model to segment the data according to the source, characteristics, and time series, anomalies can be identified for different data sources respectively.

[0033] S2: Spatiotemporal matching of data

[0034] In this embodiment, methods such as Beidou GNSS, drone oblique photography, and millimeter-wave radar are used to monitor the right-bank dam shoulder slope of the hydropower station to obtain multi-source safety monitoring data, and the multi-source safety monitoring data is aligned and synchronized with the BIM model in terms of time and space; that is, the multi-source safety monitoring data collected by Beidou, drones, millimeter-wave radars, etc. is subjected to spatiotemporal collision with the BIM model to achieve the matching of various data in time and space, so as to ensure that the analysis conclusion is based on data with accurate spatial positions and time synchronization, improving the accuracy of slope analysis.

[0035] S3: Optimization of displacement warning value

[0036] Based on the initial functional relationship between the slope geotechnical mechanics parameters and the critical value of the slope safety factor, combined with the obtained effective data sequence, the displacement warning value is dynamically corrected by introducing a time optimization factor to obtain a displacement warning optimization value that changes with time;

[0037] The specific method is: Based on the critical value K of the slope safety factor 临界 , K can be obtained through three-dimensional numerical simulation and the "Slope Design Code for Water Conservancy and Hydropower Projects SL386 - 2007" 临界 . According to the cohesion c and internal friction angle φ of the slope geotechnical mechanics parameters, the cohesion c and internal friction angle φ can be obtained through on-site direct shear tests, and the displacement warning value x is inversely calculated by three-dimensional numerical simulation 预警值 , that is, the critical value K of the slope safety factor is determined 临界 and the displacement warning value x 预警值 The initial functional relationship K 临界 = f0(x, c, φ), where c is the cohesion, φ is the internal friction angle, and x is the monitored displacement data obtained in real time. Secondly, through the monitored displacement data x obtained in real time, the initial function f0() is continuously optimized in combination with the slope operation state, and a displacement warning value optimization algorithm K based on the time series is established 临界 = f 优化u(x, c, φ) = α(t) * f0(x, c, φ), and then the optimized displacement warning value x that changes with time is obtained. 优化 , where α(t) is the time optimization factor, which is dynamically optimized according to the monitored displacement data x obtained in real time.

[0038] S4: Multi-modal analysis of safety prevention and control

[0039] Establish a multi-modal algorithm set for slope safety prevention and control, and conduct real-time evaluation on the optimized displacement warning value obtained in step S3, and output safety prevention and control decisions including the boundary of dangerous rock mass, the trajectory velocity of falling rocks, the pre-consolidation range, and the differential scheme of bolt anchoring length.

[0040] The specific method is as follows: Establish a multi-modal algorithm set S = {s1, s2, s3, s4} for slope safety prevention and control. Among them, s1: A 3D segmentation algorithm for dangerous rock mass based on improved local pixel growth, which realizes the unmanned extraction of geometric feature parameters such as automatic delineation of the boundary of dangerous rock mass, volume calculation, and dip angle measurement. s2: A falling rock impact fragmentation algorithm based on the non-linear propagation of stress waves, which realizes the calculation of the impact force of rolling rocks, the velocity and movement distance of fragmented bodies. s3: A calculation algorithm for the development of plastic zones, which considers slope failure damage, establishes the calculation of the development of plastic zones at the top of engineering slopes, and realizes precise control of the pre-consolidation range. s4: Based on the "excavation - support" dynamic coupling model, establish an algorithm for associating slope excavation parameters with anchoring length, and realize differential support of bolt anchoring length. By triggering the above multi-modal algorithm set for slope safety prevention and control in real time, online closed-loop analysis of "automatic identification of dangerous rock mass hidden dangers - real-time analysis of falling rock trajectory velocity - differential decision on pre-consolidation range and anchoring" of slopes is realized.

[0041] S5: Result feedback and closed-loop control

[0042] Return the analysis results in step S4 to the monitoring and control system, continuously monitor and iteratively update the safety status of the slope, and realize online closed-loop analysis management of slope safety prevention and control. Whenever there is new monitoring data for the slope, the system will iteratively update the safety status evaluation and prevention and control decisions.

[0043] By optimizing the slope prevention and control plan of Wudongde Hydropower Station through the method of the present invention, compared with the feasibility study estimate, 1520 prestressed anchor cables, 3560 anchor rod piles, 4250 anchor bolts, and 81,600 m of active protection nets are saved. 2 , and 18,300 m of passive protection nets. 2 .

[0044] The above embodiments are merely illustrative examples of the technical solution of the present invention. The present invention is not limited to what is described in the above embodiments, but is subject to the scope defined by the claims. Any modification, supplement or equivalent replacement made by those skilled in the art to which the present invention pertains on the basis of this embodiment is within the scope protected by the claims of the present invention.

Claims

1. An online dynamic prevention and control method for extremely high and steep slopes in hydropower projects, characterized in that: including Construct a data dynamic coupling model based on the interval screening value algorithm, perform segmented statistics on multi-source safety monitoring data and dynamically eliminate abnormal data to obtain an effective data sequence; Align and synchronize the effective data sequence with the BIM model in terms of time and space; Based on the initial functional relationship between slope geomechanical parameters and the critical value of the slope safety factor, combined with the effective data sequence, dynamically correct the displacement warning value by introducing a time optimization factor to obtain the optimized displacement warning value that changes with time; Establish a multi-modal algorithm set for slope safety prevention and control, evaluate the obtained optimized displacement warning value in real time, and output the results of safety prevention and control decision analysis; Return the analysis results to the monitoring and control system to continuously monitor and iteratively update the slope safety status.

2. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 1, wherein: The constructed data dynamic coupling model based on the interval screening value algorithm is as follows: ； where μ α(xi) denotes the mean value of the safety monitoring data within the segment; δ α(xi) denotes the standard deviation of the safety monitoring data within the segment; n and m respectively denote the nth and mth of the safety monitoring data; α(x i ) denotes the safety monitoring data of different categories.

3. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 2, characterized in that: The multi-source safety monitoring data is dynamically loaded, segmented according to the source, characteristics and time series of the multi-source safety monitoring data, and abnormal data beyond the confidence interval is dynamically identified, and the data dynamic coupling model is used to dynamically eliminate the abnormal data.

4. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 3, characterized in that: The multi-source safety monitoring data is collected by Beidou, UAVs and radars.

5. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to any one of claims 1 to 4, characterized in that: According to the cohesion c and internal friction angle φ of the slope rock and soil mechanical parameters, the displacement warning value x is inversely calculated by three-dimensional numerical simulation 预警值 , and the critical value K of the slope safety factor is determined 临界 and the displacement warning value x 预警值 The initial functional relationship K 临界 = f0(x, c, φ), where c is the cohesion, φ is the internal friction angle, and x is the monitored displacement data obtained in real time.

6. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 5, wherein: Based on the monitored displacement data \(x\) obtained in real time, continuously optimize the initial function \(f_0()\) in combination with the operating state of the slope, and establish a displacement early warning value optimization algorithm \(K\) based on time series 临界 =f 优化 (x, c, φ) = α(t) * f0(x, c, φ), and then obtain the optimized displacement early warning value \(x\) that changes with time 优化 , where α(t) is the time optimization factor 7. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 6, wherein: Establish a multi-modal algorithm set S = {s1, s2, s3, s4} for slope safety prevention and control, where s1 is a three-dimensional segmentation algorithm for dangerous rock masses, s2 is a falling rock impact fragmentation algorithm, s3 is a plastic zone development calculation algorithm, and s4 is an algorithm for associating slope excavation parameters with the anchorage length.

8. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 7, characterized in that: The three-dimensional segmentation algorithm for dangerous rock masses extracts the geometric characteristic parameters such as automatically delineating the boundary of the dangerous rock mass, calculating the volume and measuring the dip angle.

9. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 7, characterized in that: The falling rock impact fragmentation algorithm calculates the impact force of the rolling rock, the velocity of the fragmented body and the movement distance.

10. The online dynamic prevention and control method for extremely high and steep slopes in hydropower projects according to claim 7, characterized in that: The plastic zone development calculation algorithm considers slope failure damage to determine the pre-consolidation range; the algorithm for associating slope excavation parameters with the anchorage length dynamically analyzes the slope to determine the differential support of the bolt anchorage length.

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