Real-time prevention and control method for disaster chain of bank slope

Abstract

The present application belongs to the technical field of geotechnical engineering and artificial intelligence, and specifically discloses a real-time prevention and control method for reservoir bank slope disaster chain, which comprises determining a plurality of disaster nodes of the reservoir bank slope, and constructing a graph model of the reservoir bank slope disaster chain according to the plurality of disaster nodes; determining the correlation degree and the mechanical transmission coefficient between two disaster nodes corresponding to each edge in the graph model, and determining the coupling weight of each edge according to the correlation degree and the mechanical transmission coefficient; presetting the starting disaster nodes and the ending disaster nodes of a plurality of disaster chains in the graph model, and determining the shortest path between each pair of starting disaster nodes and ending disaster nodes and the path betweenness centrality of the shortest path according to the coupling weight; and determining the criticality score of each shortest path according to the path betweenness centrality, the expected loss value and the dynamic propagation risk probability. The present application can intervene in the triggering and propagation links of the disaster chain, weaken the disaster propagation energy, improve the global resilience of the reservoir bank slope, and realize real-time prevention and control.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of geotechnical engineering and artificial intelligence, and discloses a reservoir bank slope disaster chain real-time prevention and control method, in particular to a reservoir bank slope disaster chain real-time prevention and control method based on dynamic coupling network and multi-level blocking, which is used for real-time prevention and control and management of disaster chains in reservoir bank slope engineering. BACKGROUND

[0002] The reservoir bank slope is prone to landslides and other geological disasters due to long-term immersion in reservoir water, water level fluctuations, and various geological structures. Once a landslide occurs, it may trigger a surge, and the impact of the surge on the dam may lead to dam failure, forming a complex disaster chain. The reservoir bank slope disaster chain (such as "rainfall infiltration → seepage-stress coupling → local slip → overall instability → surge secondary disaster") has characteristics such as multi-factor dynamic coupling, nonlinear cascade propagation, and time-sensitive blocking.

[0003] Domestic and foreign researches often use static network theory to construct disaster chain models, abstracting landslide, surge, dam failure and other disaster events as nodes, and historical correlation as edge weight. However, the edge weight is fixed based on historical data, which cannot dynamically integrate real-time monitoring data (such as rainfall intensity, displacement rate) and geotechnical mechanics parameters (such as elastic modulus, permeability coefficient). The constructed network topology does not reflect the physical mechanisms of stress transmission and hydraulic gradient changes in rock and soil bodies, resulting in the mechanical driving process of the propagation path being simplified as a probabilistic relationship. In addition, traditional reservoir bank slope disaster researches often focus on the mechanism analysis of single disaster species (such as landslide or dam failure) or a single mechanical model, while ignoring the dynamic propagation law of multi-disaster species cascade effect (such as landslide → surge → dam failure chain reaction). The traditional single disaster species research paradigm cannot fully reveal the cascade effect and propagation law among disaster species in the disaster chain, leading to the prevention and control strategy focusing on reinforcing a single disaster node (such as landslide grouting), which cannot meet the demand for effective prevention and control of reservoir bank slope disasters.

[0004] In summary, the existing reservoir bank slope disaster chain prevention and control methods have the following problems:

[0005] (1) The existing disaster chain models (such as event tree, Bayesian network) are mostly based on historical data to construct static topological structures, with fixed edge weights, which cannot dynamically integrate dynamic changes such as hydrological fluctuations (such as water level drop rate > 0.5 m / d) and rock and soil parameter degradation (such as cohesion attenuation).

[0006] (2) The dynamic coupling of real-time monitoring data (such as Beidou displacement meter, osmometer) and network models is insufficient. In the key path calculation process, the traditional path search algorithm (such as Dijkstra algorithm) needs to update the network globally, which has high time complexity and cannot meet the real-time prevention and control demand.

[0007] (3) The existing blocking methods (such as anti-slide pile reinforcement and grouting leakage) are mostly single-point prevention and control, ignore the cascading propagation characteristics of the disaster chain, the propagation path is insufficiently inhibited, and the blocking strength is not dynamically adjusted for high-weight edges (such as weak interlayer seepage paths). SUMMARY

[0008] The purpose of the present application is to provide a reservoir bank slope disaster chain real-time prevention and control method to solve the technical problem that the existing reservoir bank slope disaster prevention and control method cannot combine real-time data for real-time disaster chain prevention and control.

[0009] The present application provides a reservoir bank slope disaster chain real-time prevention and control method, comprising:

[0010] Step 1, determining a plurality of disaster nodes of the reservoir bank slope, and constructing a graph model of the reservoir bank slope disaster chain according to the plurality of disaster nodes;

[0011] Step 2, determining the correlation degree and mechanical transmission coefficient between two disaster nodes corresponding to each edge in the graph model, and determining the coupling weight of each edge according to the correlation degree and the mechanical transmission coefficient;

[0012] Step 3, presetting a plurality of starting disaster nodes and ending disaster nodes of the disaster chain in the graph model, and determining the shortest path and the path betweenness centrality of the shortest path between each pair of starting disaster nodes and ending disaster nodes according to the coupling weight;

[0013] Step 4, determining the criticality score of each shortest path according to the path betweenness centrality, the expected loss value and the dynamic propagation risk probability.

[0014] Preferably, after step 4, it further comprises:

[0015] determining a blocking threshold according to the criticality score of each shortest path, and determining the shortest path with a criticality score greater than the blocking threshold as a critical path;

[0016] risk blocking the critical path.

[0017] Preferably, the blocking threshold is determined according to the criticality score of each shortest path, specifically:

[0018] obtaining the maximum criticality score and determining the mean of all criticality scores;

[0019] determining the blocking threshold according to the maximum criticality score and the mean of all criticality scores.

[0020] Preferably, after the risk blocking of the critical path, it further comprises:

[0021] determining the pre-blocking resilience index of the critical path according to the criticality score of the critical path before risk blocking.

[0022] determining a post-blocking resilience index of the critical path according to the criticality score of the critical path after the risk blocking;

[0023] determining effectiveness of the risk blocking according to the pre-blocking resilience index and the post-blocking resilience index.

[0024] Preferably, a mechanical transmission coefficient between two disaster nodes corresponding to each edge in the graph model is determined, specifically:

[0025] determining an elastic modulus at two disaster nodes corresponding to each edge in the graph model;

[0026] determining a tangent modulus of a stress-strain curve of the bank slope rock mass, and determining a mechanical transmission coefficient between two disaster nodes corresponding to each edge in the graph model according to the elastic modulus and the tangent modulus.

[0027] Preferably, a coupling weight of each edge is determined according to the correlation degree and the mechanical transmission coefficient, specifically:

[0028] determining a rock mass bearing force according to a critical load of the bank slope rock mass and a maximum load currently borne;

[0029] determining a coupling weight of each edge according to a product of the correlation degree, the mechanical transmission coefficient and the rock mass bearing force.

[0030] Preferably, a shortest path between each pair of starting disaster node and ending disaster node is determined according to the coupling weight, specifically:

[0031] determining an initial shortest path between each pair of starting disaster node and ending disaster node according to the coupling weight;

[0032] when a variation of the coupling weight in the initial shortest path is greater than a preset threshold, determining disaster nodes corresponding to adjacent edges of the coupling weight;

[0033] updating the initial shortest path according to the disaster nodes corresponding to the adjacent edges of the coupling weight to obtain a shortest path between each pair of starting disaster node and ending disaster node.

[0034] Preferably, an initial shortest path between each pair of starting disaster node and ending disaster node is determined according to the coupling weight, specifically:

[0035] determining an inverse of a rock mass bearing force according to a critical load of the bank slope rock mass and a maximum load currently borne;

[0036] determining an activation probability of a disaster node corresponding to the coupling weight;

[0037] Determine the initial shortest path between each pair of starting disaster node and ending disaster node according to the coupling weight, the reciprocal of the rock mass bearing force and the activation probability.

[0038] Preferably, the criticality score of each shortest path is determined according to the path betweenness centrality, the loss expectation value and the dynamic propagation risk probability, specifically:

[0039] Determine the inhibition parameter of each shortest path according to the length of each shortest path and the mean value of the length of all shortest paths;

[0040] Determine the criticality score of each shortest path according to the inhibition parameter of each shortest path, the path betweenness centrality, the loss expectation value and the dynamic propagation risk probability.

[0041] Preferably, the determination method of the dynamic propagation risk probability is:

[0042] Determine the path correlation strength according to the connected product of multiple coupling weights on each shortest path;

[0043] Determine the probability activation superposition amount according to the activation probability of multiple disaster nodes on each shortest path;

[0044] Determine the dynamic propagation risk probability of each shortest path according to the path correlation strength and the probability activation superposition amount.

[0045] The bank slope disaster chain real-time prevention and control method of the present application has the following beneficial effects compared with the prior art:

[0046] (1) The present application combines the mechanical transmission coefficient with the correlation degree, real-time corrects the coupling weight of each edge, realizes the real-time update of the coupling weight with the dynamic monitoring data, and solves the problem that the edge weight of the existing static network cannot reflect the dynamic evolution.

[0047] (2) The criticality score of the shortest path of the present application combines the path betweenness centrality, the loss expectation value and the dynamic propagation risk probability, and solves the decision bias problem of a single index.

[0048] (3) After determining the criticality score of each shortest path, the present application determines the critical path according to the criticality score and blocks the risk of the critical path, and proposes a three-level blocking rule when blocking the risk, which directly intervenes in the triggering and propagation link of the disaster chain through three mechanisms of hydraulic regulation (reducing the penetration pressure), mechanical reinforcement (improving the critical load) and energy dissipation (constructing redundant paths), weakens the disaster propagation energy, improves the global resilience of the system and realizes real-time prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The figure is a flowchart of the bank slope disaster chain real-time prevention and control method in the embodiments of the present application.

[0050] Figure 2 A comparison chart of the key scores and the change rates of the post-blocking resilience indexes in Embodiment 1 to Embodiment 3 of the present application. DETAILED DESCRIPTION

[0051] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced without these specific details. In other instances, well-known structures, devices, circuits, and methods have not been described in detail in order to avoid obscuring the present application.

[0052] The embodiment of the present application provides a reservoir bank slope disaster chain real-time prevention and control method, as shown in the figure, which comprises the following steps. Figure 1 As shown in the figure, the method comprises the following steps.

[0053] Step 1, determining a plurality of disaster nodes of the reservoir bank slope, and constructing a graph model of the reservoir bank slope disaster chain according to the plurality of disaster nodes.

[0054] The embodiment of the present application determines the disaster nodes and constructs a disaster node set according to the triggering-spreading-amplification process of the reservoir bank slope disaster chain. Wherein represents the total number of disaster nodes, and realizes full-chain coverage from a disaster-causing factor (C) to a carrier medium (M) to a disaster entity (E) to a prevention and control feedback (P). As shown in Table 1.

[0055] Table 1: Disaster node division

[0056]

[0057] According to the above disaster node, a graph model of the reservoir bank slope disaster chain is constructed, specifically:

[0058] If the occurrence of the disaster node C causes the occurrence of the disaster node M, a directed edge E is constructed between the two disaster nodes, the set of edges is denoted as E, and the constructed graph model (namely, the complex network model) is denoted as G. .

[0059] The embodiment of the present application carries out non-dimensionalization on the data of the 12 disaster nodes in Table 1, and obtains non-dimensionalized data , as shown in formula (1).

[0060] ​​​​​​​ (1)

[0061] wherein, is a disaster node at time , is a disaster node at time , is a disaster node , is a disaster node , is a disaster node , is a time decay factor at time and , is a decay coefficient, taken as 0.05~0.2 / day, is a current time,

[0062] is a time when data is actually collected. .

[0063] Step 2, determine the correlation degree and mechanical transmission coefficient between two disaster nodes corresponding to each edge in the graph model, and determine the coupling weight of each edge according to the correlation degree and the mechanical transmission coefficient, comprising:

[0064] Step 2.1, determine the correlation degree between two disaster nodes corresponding to each edge in the graph model, specifically:

[0065] Step 2.1.1, determine the difference value of monitoring data between two disaster nodes corresponding to each edge in the graph model .

[0066] wherein is a difference value between disaster node and disaster node at time , , are monitoring data of disaster node and disaster node at time .

[0067] Step 2.1.2, determine the correlation degree between two disaster nodes corresponding to each edge in the graph model according to the time decay factor and the difference value .

[0068] The embodiment of the application constructs a correlation degree based on machine learning for quantifying disaster node Dynamic correlation strength between disaster nodes The greater the value represents the more similar the behavior of two disaster nodes in the process of disaster dynamic evolution. The greater the value represents the more similar the behavior of two disaster nodes in the process of disaster dynamic evolution.

[0069] (2)

[0070] In the formula, Indicates the length of the time window, that is, the total number of time points participating in the calculation; The difference value between disaster nodes The difference value between disaster nodes At The difference value between disaster nodes , The monitoring data of disaster nodes The monitoring data of disaster nodes The monitoring data of disaster nodes The monitoring data of disaster nodes The global minimum difference value between disaster nodes The global minimum difference value between disaster nodes The global maximum difference value between disaster nodes The global maximum difference value between disaster nodes The global maximum difference value between disaster nodes The global maximum difference value between disaster nodes The resolution coefficient, usually Balancing sensitivity and stability; The dynamic weight at Reflects the importance of different time points to the correlation degree, .

[0071] Step 2.2, determine the mechanical transmission coefficient between the two disaster nodes corresponding to each edge in the graph model, specifically:

[0072] Step 2.2.1, determine the elastic modulus at the two disaster nodes corresponding to each edge in the graph model.

[0073] Exemplarily, the elastic modulus at the disaster node and the elastic modulus at the disaster node are determined.

[0074] Step 2.2.2, determine the tangent modulus of the stress-strain curve of the rock mass of the bank slope, and determine the mechanical transmission coefficient between the two disaster nodes corresponding to each edge in the graph model according to the elastic modulus and the tangent modulus .

[0075] The embodiment of the application introduces the geotechnical mechanical transmission coefficient between the disaster node and the disaster node ​ Quantified disaster nodes Disaster Node The mechanical correlation strength between them is shown in formula (3). The mechanical transmission efficiency between disaster nodes is modified by the material mechanical properties (elastic modulus, stress-strain relationship) on the integral path. The mechanical response of the geological structure is embedded in the graphical model, so that the cascade process of "landslide → surge → dam break" not only depends on the event correlation degree, but is also controlled by the actual mechanical behavior of the rock and soil:

[0076] (3)

[0077] Where, Disaster Node Disaster Node The effective transfer path length between them; Disaster nodes and disaster nodes The elastic modulus at ; is the tangent modulus of the stress-strain curve of the reservoir bank slope rock mass, is stress, For strain; is the length of the infinitesimal element on the integration path.

[0078] Step 2.3: Determine the coupling weight of each edge based on the correlation degree and mechanical transfer coefficient, specifically:

[0079] Step 2.3.1: Based on the critical load of the reservoir bank slope rock mass The maximum load at the current moment The quotient of determines the bearing capacity of the rock mass.

[0080] Step 2.3.2: According to the correlation , mechanical transfer coefficient The coupling weight of each edge is determined by the continuous product of the rock mass bearing capacity , the coupling weight is the dynamic coupling weight, as shown in formula (4).

[0081] (4)

[0082] Where, Disaster Node Disaster Node The coupling weight between When the value is high, it indicates that the disaster node Disaster Node The correlation is strong and may be the key path for the disaster chain transmission; When the value is low, it indicates that the disaster node Disaster Node The correlation is weak and can serve as a secondary path for disaster transmission.

[0083] Step 3: Preset the starting disaster nodes and ending disaster nodes of multiple disaster chains in the graph model, and determine the shortest path between each pair of starting disaster nodes and ending disaster nodes and the path betweenness centrality of the shortest path based on the coupling weight, where the shortest path is the determined disaster chain.

[0084] Step 3.1: Preset the starting disaster nodes and ending disaster nodes of multiple disaster chains in the graph model, and determine the shortest path between each pair of starting disaster nodes and ending disaster nodes based on the coupling weight, specifically:

[0085] Step 3.1.1: Preset the starting disaster node and ending disaster node of multiple disaster chains in the graph model. For example, the starting disaster node of one of the preset disaster chains is , the termination disaster node is .

[0086] Step 3.1.2: Determine the shortest path between each pair of starting disaster nodes and ending disaster nodes based on the coupling weights, specifically:

[0087] A. Determine the initial shortest path between each pair of starting disaster nodes and ending disaster nodes based on the coupling weight.

[0088] For example, the starting disaster node is determined to be and terminate disaster nodes The method of initial shortest path between the two paths is as follows: according to the critical load of the reservoir bank slope rock mass The maximum load at the current moment Determine the inverse of the rock mass bearing capacity; then determine the activation probability of the disaster node corresponding to the coupling weight Finally, the starting disaster node is determined according to the coupling weight, the inverse of the rock mass bearing capacity and the activation probability. and terminate disaster nodes The initial shortest path between is shown in formula (5).

[0089] (5)

[0090] Where, for The disaster node at the time of starting is and terminate disaster nodes The initial shortest path; for The disaster node at the time of starting is and terminate disaster nodes The coupling weight between them; The starting disaster node is exist The activation probability at the moment, which ranges from 0 to 1; The critical load of the reservoir bank slope rock mass is and Maximum load at any time The reciprocal of the rock mass bearing capacity determined by the quotient reflects the stability of the node; 、 Represents the adjustment coefficient, which is defaulted based on engineering experience 、 To balance the risk of propagation and the risk of mechanical failure.

[0091] The method for determining the initial shortest path in the embodiment of the present invention introduces the activation probability, the adjustment coefficient, and the ratio of the maximum load to the critical load, and comprehensively calculates the corresponding initial shortest path, resulting in a more accurate result.

[0092] B. When the change in the coupling weight in the initial shortest path is greater than a preset threshold, the disaster node corresponding to the adjacent edge of the coupling weight is determined.

[0093] Since the shortest path changes dynamically, the existing artificial intelligence-based path search algorithm Dijkstra needs to globally recalculate all paths. The embodiment of the present invention modifies the Dijkstra algorithm. In the graph model, only the disaster nodes whose coupling weights change are locally updated to improve computational efficiency.

[0094] The modified Dijkstra algorithm of the embodiment of the present invention realizes incremental calculation by the following improvements: when the initial shortest path consists of a disaster node To the disaster node The change in the coupling weight of the directed edges When The other connected edges are recorded as adjacent edges, and the adjacent edges are marked as "need to be updated". The disaster nodes on their adjacent edges form the affected disaster node set. is the preset threshold, and .

[0095] C. Update the initial shortest path according to the disaster nodes corresponding to the adjacent edges of the coupling weights to obtain the shortest path between each pair of starting disaster nodes and ending disaster nodes.

[0096] set up For a disaster node in the affected disaster node set, all disaster nodes in the disaster node set are traversed to find a shorter path, and the initial shortest path is updated to obtain the shortest path between each pair of starting disaster nodes and ending disaster nodes.

[0097] Exemplarily, the initial shortest path is updated according to formula (6).

[0098] (6)

[0099] Where, for Time disaster node Disaster Node The shortest path between for Time disaster node Disaster Node The initial shortest path; for Time disaster node Disaster Node The shortest path between for Time disaster node Disaster Node The shortest path between and Determined according to formula (5).

[0100] The embodiment of the present invention is based on a sliding time window (i.e. Only the paths whose coupling weight changes are greater than the preset threshold are updated, which reduces the computational complexity, improves the updating efficiency of the paths in the graph model, and significantly improves the real-time and accuracy of disaster chain path identification.

[0101] Step 3.2: Determine the path betweenness centrality of the shortest path.

[0102] Assume the shortest path is , the embodiment of the present invention introduces a time attenuation factor , calculate the dynamic betweenness centrality of disaster nodes and path betweenness centrality ,Since the historical contribution of the disaster nodes in the ,path gradually weakens over time, the embodiment of the present ,presentation focuses more on the recent activities of the ,disaster chain.

[0103] (7)

[0104] Where, is the shortest path Path betweenness centrality of ; is the shortest path Total number of medium disaster nodes; is the shortest path The disaster node in ; is the shortest path Medium disaster node Dynamic betweenness centrality of is the time attenuation factor of the current time, and wherein is an attenuation coefficient, taking 0.05-0.2 / day, is the current time, is the time when the data is actually collected; is the number of shortest paths passing through the disaster node . is the number of shortest paths between the disaster node and the disaster node .

[0105] The shortest path with high path betweenness centrality is the "main road" of disaster transmission, and blocking such shortest path can most effectively slow down the spread of disaster.

[0106] Step 4, determining the criticality score of each path according to the path betweenness centrality, the loss expectation value and the dynamic propagation risk probability.

[0107] The loss expectation value and the dynamic propagation risk probability need to be determined before step 4.

[0108] The embodiment of the present application constructs a loss expectation function to determine the loss expectation value of the shortest path , which is used to quantify the upper limit of direct economic loss and indirect instability diffusion risk of the shortest path.

[0109] (8)

[0110] In the formula, represents the direct economic loss caused by the failure of the disaster node , which is valued by expert evaluation; is the direct economic loss of the most vulnerable disaster node in the shortest path, reflecting the single-point failure risk in the worst case; is the penalty coefficient of load instability (default ); is the current load of the disaster node (such as the stress of slope rock mass, seepage pressure); is the critical load (instability threshold) of the disaster node .

[0111] The loss expectation value of the embodiment of the present application takes into account the direct economic loss of the most serious single point and the systemic instability risk, and adjusts the prevention and control priority. The path with the obtained numerical value needs to be reinforced at the end disaster node and the load pressure needs to be reduced.

[0112] ​The shortest path in the embodiment of the present invention Dynamic transmission risk probability The risk is determined by the path association strength (structural attribute) and the probability activation superposition effect (dynamic state), and the probability of dynamic transmission risk The determination process is as follows: the path association strength is determined by the continuous product of multiple coupling weights on the shortest path; the probability activation superposition is determined according to the activation probability of multiple disaster nodes on the shortest path; the dynamic propagation risk probability of the shortest path is determined according to the path association strength and the probability activation superposition. , as shown in formula (9).

[0113] (9)

[0114] In the formula for Time disaster node Disaster Node The coupling weight of the edges between them; is the path association strength, indicating the shortest path The overall propagation risk is the product of the coupling weights of each edge. If any coupling weight is reduced, the entire shortest path will be significantly weakened. the ability to spread; is the probability activation superposition, represents the natural exponential function, Disaster Node exist The activation probability at the moment represents the disaster node exist The activation status at the moment (such as the probability of landslide triggering). High The shortest path needs to be monitored first, as it may cause chain disasters due to the instability of a single node.

[0115] Step 4.1: Determine the suppression parameter of each shortest path based on the length of each shortest path and the average of all shortest path lengths.

[0116] Step 4.2: Determine the criticality score of each shortest path based on its suppression parameter, path betweenness centrality, loss expectation value, and dynamic propagation risk probability. For example, its criticality score is shown in formula (10).

[0117] (10)

[0118] Where, is the shortest path Criticality rating; is the indicator weight, the default , can be adjusted dynamically; the length of the shortest path ; the average length of all shortest paths is a suppression parameter used to suppress excessively long shortest paths is a control suppression strength, and the default .

[0119] The criticality score of the embodiment of the present application , the criticality of the shortest path is comprehensively evaluated from three dimensions of propagation ability (dynamic propagation risk probability), topological importance (path betweenness centrality) and consequence severity (expected loss value), avoiding single index deviation, and real-time fusion of mechanical state, topology and time sequence data, adapting to disaster evolution.

[0120] The embodiment of the present application further comprises the following steps after step 4:

[0121] Step 5, determining a blocking threshold according to the criticality score of each shortest path, and determining the shortest path with a criticality score greater than the blocking threshold as a critical path.

[0122] Wherein, the blocking threshold is determined according to the criticality score of each shortest path, specifically: obtaining the maximum criticality score and determining the average of the criticality scores of all shortest paths; then determining the blocking threshold according to the maximum criticality score and the average of all criticality scores, as shown in formula (11).

[0123] (11)

[0124] In the formula, is the blocking threshold, which is essentially an adaptive dynamic blocking threshold; is the maximum criticality score; is the total number of all shortest paths; is the average of the criticality scores of all shortest paths; when , it is determined that is a critical path and triggers blocking, ensuring that only paths that are significantly higher than the current average risk and have a very high criticality score are blocked, optimizing resource allocation. The design of both prevents insufficient response to a single extreme risk and avoids frequent triggering of blocking due to local fluctuations, and can dynamically adjust with the real-time state of the graph model.

[0125] Step 6, risk blocking of the critical path.

[0126] The risk blocking strategy of the embodiment of the present application is shown in Table 2.

[0127] Table 2 Risk blocking strategy of bank slope disaster chain

[0128]

[0129] The embodiment of the present application further comprises, after the risk blocking of the critical path:

[0130] Step 7, determining the pre-blocking resilience index of the critical path according to the criticality score of the critical path before the risk blocking; and determining the post-blocking resilience index of the critical path according to the criticality score of the critical path after the risk blocking.

[0131] The pre-blocking resilience index and the post-blocking resilience index are determined according to formula (12) as follows.

[0132] (12)

[0133] In the formula, is the resilience index; is the criticality score of the shortest path; is the maximum criticality score; is the total number of all shortest paths. The higher the value is, the stronger the resilience of the graph model is.

[0134] Step 8, determining the effectiveness of the risk blocking according to the pre-blocking resilience index and the post-blocking resilience index, as shown in formula (13).

[0135] (13)

[0136] In the formula, is the pre-blocking resilience index, is the post-blocking resilience index. When the blocking measure is effective, the system resilience is improved, which can significantly reduce disaster losses; when the blocking effect is insufficient, the strategy needs to be adjusted or the blocking strength needs to be enhanced.

[0137] The present application constructs a multi-level adaptive risk blocking strategy, realizes intelligent prevention and control of the disaster chain of the bank slope and improvement of the system resilience through the closed-loop mechanism of multi-level blocking, resilience optimization and parameter self-learning, and exhibits significant advantages in disaster suppression efficiency, engineering cost optimization and interdisciplinary integration, and provides an innovative solution for engineering safety under complex geological environment, and the specific effects are as follows:

[0138] (1) The present application realizes dynamic adjustment of the coupling weight with data update by real-time fusion of dynamic monitoring data such as hydrological fluctuation (e.g. water level drop rate > 0.5 m / d) and rock-soil parameter deterioration (e.g. cohesion attenuation). ​

[0139] (2) The present invention introduces a critical score that integrates path betweenness centrality, loss expectation value, and dynamic propagation risk probability to evaluate the disaster situation of the shortest path, solving the decision-making bias problem of a single indicator.

[0140] (3) The present invention uses the collaborative optimization of multi-level risk blocking strategies to solve the problem that traditional blocking strategies are mostly single-point reinforcement (such as anti-slide piles) with poor blocking effects. The present invention proposes a three-level adaptive risk blocking strategy. Through the three mechanisms of hydraulic regulation (reducing seepage pressure), mechanical reinforcement (increasing critical load), and energy dissipation (constructing redundant paths), it directly intervenes in the triggering and propagation links of the disaster chain, weakens the disaster propagation energy, improves the overall resilience of the system, and realizes real-time prevention and control.

[0141] The effectiveness of the method of the present invention will be verified with more specific examples below.

[0142] Example 1

[0143] Taking the disaster chain of landslide-wave surge-dam failure on the bank of a certain reservoir in China as an example, the critical path of this disaster chain is The calculation results of disaster nodes and coupling weights are as follows, as shown in Table 3.

[0144] Table 3 Definition of disaster nodes and coupling weights in Example 1

[0145]

[0146] Calculate the criticality score based on the above results , as shown in Table 4.

[0147] Table 4 Calculation results of criticality score and blocking threshold in Example 1

[0148]

[0149] From Table 4, we can see that and , triggering the first-level blocking condition, the measures taken are grouting to reduce the seepage pressure. The blocking effect after the implementation of the blocking strategy was reduced from 0.83 to 0.3, as shown in Table 5. The resilience index increased by 94.3%, proving that the blocking measures are effective and can significantly reduce disaster losses.

[0150] Table 5 Blocking effect verification of Example 1

[0151]

[0152] Example 2

[0153] Taking the rainfall-seepage-landslide disaster chain of a domestic reservoir bank slope as an example, the critical path of this disaster chain is The disaster nodes and coupling weight calculation results of the key path are shown in Table 6.

[0154] Table 6 Disaster node and coupling weight definition of Example 2

[0155]

[0156] The criticality score is calculated according to the above results , as shown in Table 7.

[0157] Table 7 Criticality score and blocking threshold calculation results of Example 2

[0158]

[0159] From Table 7, we can see that and , triggering a secondary blocking condition, taking measures to reinforce the disaster node , the critical load is increased by 20%, and the blocking effect after implementing the blocking strategy is shown in Table 8, and the resilience index is increased by 140%, which proves that the blocking measures are effective.

[0160] Table 8 Blocking effect verification of Example 2

[0161]

[0162] Example 3

[0163] Taking a reservoir bank slope earthquake-fault activation-dam failure disaster chain of a domestic hydropower project as an example, the key path of the disaster chain and the coupling weight calculation results are shown in Table 9.

[0164] Table 9 Disaster node and coupling weight definition of Example 3

[0165]

[0166] The criticality score is calculated according to the above results , as shown in Table 10.

[0167] Table 10 Criticality score and blocking threshold calculation results of Example 3

[0168]

[0169] From Table 10, we can see that and , triggering a tertiary blocking condition, taking measures to add anti-seismic buttress nodes , for real-time monitoring of the mechanical response of the buttress, thereby adding a new path to indicate that the seismic energy bypasses the fault fracture zone ), directly through the pier to the dam, reducing the load of the original path. The blocking effect after the implementation of the blocking strategy is shown in Table 11, and the toughness index is increased by 130%, which proves that the blocking measure is effective, and the disaster loss can be significantly reduced after blocking.

[0170] Table 11: Blocking effect verification of example 3

[0171]

[0172] The comparison chart of the key score and the change rate of the toughness index after blocking in the above examples 1 to 3 is shown in Figure 2 .

[0173] The toughness of the above three examples after blocking is increased by more than 90%, meeting the target of The key score is significantly reduced (average reduction of -56.8%) by the dynamic blocking threshold and multi-level blocking strategy. The blocking measure directly intervenes in the triggering and propagation links of the disaster chain through the three mechanisms of hydraulic regulation (reducing seepage pressure), mechanical reinforcement (increasing critical load) and energy dissipation (constructing redundant path), weakens the disaster propagation energy, and improves the overall system toughness. The multi-scenario disaster chain data verifies the scientificity and practicality of the multi-level blocking strategy in complex engineering scenarios.

[0174] The above is only a few embodiments of the present application, not any form of limitation on the present application. Although the present application is disclosed as above with the preferred embodiments, it is not intended to limit the present application. Any skilled person in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the technical solution of the present application, which is equivalent to the equivalent implementation cases, and belongs to the scope of the technical solution.

Claims

1. A real-time prevention and control method for reservoir bank slope disaster chain, characterized in that: include: Step 1: determine multiple disaster nodes of the reservoir bank slope, and construct a graphical model of the reservoir bank slope disaster chain based on the multiple disaster nodes; Step 2: determining the correlation degree and mechanical transfer coefficient between two disaster nodes corresponding to each edge in the graph model, and determining the coupling weight of each edge according to the correlation degree and mechanical transfer coefficient; Determining the correlation between two disaster nodes corresponding to each edge in the graph model, specifically: determining a difference value of monitoring data between two disaster nodes corresponding to each edge in the graph model; determining the correlation between two disaster nodes corresponding to each edge in the graph model according to a time decay factor and the difference value; Determining a mechanical transfer coefficient between two disaster nodes corresponding to each edge in the graphical model, specifically: determining the elastic modulus at the two disaster nodes corresponding to each edge in the graphical model; determining the tangent modulus of the stress-strain curve of the reservoir bank slope rock mass, and determining the mechanical transfer coefficient between the two disaster nodes corresponding to each edge in the graphical model based on the elastic modulus and the tangent modulus; Determining the coupling weight of each edge based on the correlation degree and the mechanical transfer coefficient, specifically: determining the rock mass bearing capacity based on the quotient of the critical load of the reservoir bank slope rock mass and the maximum load it bears at the current moment; determining the coupling weight of each edge based on the product of the correlation degree, the mechanical transfer coefficient and the rock mass bearing capacity; Step 3: Preset the starting disaster nodes and ending disaster nodes of multiple disaster chains in the graph model, and determine the shortest path between each pair of starting disaster nodes and ending disaster nodes and the path betweenness centrality of the shortest path according to the coupling weight; Step 4: Determine the criticality score of each shortest path based on the path betweenness centrality, loss expectation value, and dynamic propagation risk probability; The shortest path The expected value of loss Determined according to the following formula: ； Where, Disaster node Direct economic losses caused by failure; is the penalty coefficient for load instability; Disaster Node The current load; Disaster Node Critical load of Shortest Path Dynamic transmission risk probability Determined according to the following formula: ； In the formula for Time disaster node Disaster Node The coupling weight of the edges between them; represents the natural exponential function, Disaster Node exist The activation probability at the moment; After step 4, it also includes: Determining a blocking threshold according to the criticality score of each shortest path, and determining the shortest path having a criticality score greater than the blocking threshold as a critical path; Risk blocking is performed on the critical path.

2. The real-time prevention and control method for reservoir bank slope disaster chain according to claim 1 is characterized in that: The blocking threshold is determined based on the criticality score of each shortest path, specifically: Get the maximum criticality score and determine the mean of all criticality scores; The blocking threshold is determined based on the maximum criticality score and the mean of all criticality scores.

3. The real-time prevention and control method for reservoir bank slope disaster chain according to claim 1 is characterized in that: After risk blocking of the critical path, the following steps are also included: Determine a resilience index before blocking of the critical path according to the criticality score of the critical path before risk blocking; Determine a post-blocking resilience index of the critical path according to the criticality score of the critical path after the risk is blocked; The effectiveness of risk blocking is determined based on the pre-blocking resilience index and the post-blocking resilience index.

4. The real-time prevention and control method for reservoir bank slope disaster chain according to claim 1 is characterized in that: The shortest path between each pair of starting disaster nodes and ending disaster nodes is determined according to the coupling weights, specifically: determining an initial shortest path between each pair of a starting disaster node and an ending disaster node according to the coupling weight; When the change in the coupling weight in the initial shortest path is greater than a preset threshold, determining the disaster node corresponding to the adjacent edge of the coupling weight; The initial shortest path is updated according to the disaster nodes corresponding to the adjacent edges of the coupling weights to obtain the shortest path between each pair of the starting disaster node and the ending disaster node.

5. The real-time prevention and control method for reservoir bank slope disaster chain according to claim 4 is characterized in that: The initial shortest path between each pair of starting disaster nodes and ending disaster nodes is determined according to the coupling weights, specifically: Determine the inverse of the rock mass bearing capacity based on the critical load of the reservoir bank slope rock mass and the maximum load it bears at the current moment; Determining the activation probability of the disaster node corresponding to the coupling weight; An initial shortest path between each pair of a starting disaster node and an ending disaster node is determined according to the coupling weight, the inverse of the rock mass bearing capacity and the activation probability.

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