Methods, software products and electronic equipment for analyzing the propagation path of slope instability risk

Abstract

This disclosure provides a method, program product, and electronic equipment for analyzing the propagation path of slope instability risks, relating to the field of hydraulic engineering analysis technology. The method includes: acquiring first monitoring data from multiple slopes in a hybrid pumped storage power station reservoir area; if the first slope experiences instability, acquiring second monitoring data related to the instability on a second slope, and determining the instability transmission intensity based on the second monitoring data; determining the hybrid field coupling intensity of different slopes based on the correlation and spatial structure relationship of the first monitoring data; using the slopes as nodes, determining the comprehensive edge weights between different nodes based on the instability transmission intensity and the hybrid field coupling intensity, and constructing a directed supernetwork; determining the cumulative risk transmission value between nodes in the directed supernetwork, and identifying key risk propagation paths in the directed supernetwork. This disclosure improves the accuracy of identifying key risk propagation paths in a hybrid pumped storage power station slope group, providing a reliable basis for risk prevention and control.

Description

Technical Field

[0001] This disclosure relates to the field of hydraulic engineering analysis technology, and more specifically, to methods, program products, and electronic equipment for analyzing the propagation path of slope instability risks. Background Technology

[0002] Hybrid pumped storage power stations combine the runoff power generation function of conventional hydropower stations with the peak-shaving and valley-filling function of pumped storage power stations. Due to frequent water level regulation in the upper and lower reservoirs, the intensity of hydraulic disturbance on the reservoir bank slopes is significantly increased, and the risk of slope instability and its propagation among slope groups are becoming increasingly prominent.

[0003] In related technologies, the analysis methods for risk propagation in cascade reservoir slope groups mostly follow the cascading failure model with the dam as the basic node. However, the slopes of hybrid pumped storage power stations are distributed along the entire reservoir bank between the upper and lower reservoirs, are numerous, and have complex spatial relationships. Models with the dam as the node cannot accurately reflect this distribution characteristic and interaction relationship of the slope group, resulting in a misalignment at the research object level. In characterizing risk propagation, related technologies often focus on a single physical process, lacking means to systematically quantify the instability transmission intensity between slopes, making it difficult to accurately reflect the risk transmission capacity from upstream to downstream slopes. Furthermore, the unique tightly coupled hydraulic regulation of the upper and lower reservoirs in hybrid pumped storage power stations causes synchronous rise and fall of water levels, resulting in a strong hydraulic linkage between slopes. This linkage significantly enhances the propagation of risk between slopes. Currently, no parameters have been introduced to characterize the degree of this hydraulic linkage, leading to a significant deviation between the risk propagation paths identified in the special scenario of hybrid pumped storage and the actual engineering situation. Summary of the Invention

[0004] This disclosure provides methods, software products, and electronic devices for analyzing the propagation paths of slope instability risks, in order to at least partially address related technical issues.

[0005] According to a first aspect of this disclosure, a method for analyzing the propagation path of slope instability risk is provided. The method includes: acquiring first monitoring data of multiple slopes in a mixed pumped storage power station reservoir area; if the first slope becomes unstable, acquiring second monitoring data of a second slope related to the instability, and determining the instability transmission intensity from the first slope to the second slope based on the second monitoring data; the second slope being located downstream of the first slope; determining the mixed field coupling intensity of different slopes based on the correlation and spatial structure relationship of the first monitoring data of different slopes; using the slopes as nodes, determining the comprehensive edge weights between different nodes based on the instability transmission intensity and the mixed field coupling intensity between different slopes, and constructing a directed supernetwork; determining the cumulative risk transmission value between two nodes in the directed supernetwork, and identifying the key risk propagation path in the directed supernetwork based on the cumulative risk transmission value.

[0006] According to a second aspect of this disclosure, a slope instability risk propagation path analysis device is provided. The device includes: a first monitoring data acquisition module configured to acquire first monitoring data from multiple slopes in a mixed pumped storage power station reservoir area; an instability transmission intensity determination module configured to, if the first slope experiences instability, acquire second monitoring data related to the instability of a second slope, and determine the instability transmission intensity from the first slope to the second slope based on the second monitoring data; the second slope is located downstream of the first slope; a mixed field coupling intensity determination module configured to determine the mixed field coupling intensity of different slopes based on the correlation and spatial structure relationship of the first monitoring data of different slopes; a directed supernetwork construction module configured to construct a directed supernetwork by using the slopes as nodes and determining the comprehensive edge weights between different nodes based on the instability transmission intensity and the mixed field coupling intensity between different slopes; and a key risk propagation path determination module configured to determine the cumulative risk transmission value between two nodes in the directed supernetwork and identify the key risk propagation path in the directed supernetwork based on the cumulative risk transmission value.

[0007] According to a third aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method of the first aspect described above and possible implementations thereof.

[0008] According to a fourth aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of the first aspect and possible implementations thereof by executing the executable instructions.

[0009] The technical solution disclosed herein has the following beneficial effects: First monitoring data of multiple slopes in the reservoir area of ​​a hybrid pumped storage power station were acquired, and second monitoring data of the second slope were acquired when the first slope became unstable to determine the instability transmission intensity from the first to the second slope. The coupling intensity of the mixed field was determined based on the correlation and spatial structure relationship of the first monitoring data of different slopes. Using the slopes as nodes, the comprehensive edge weights between nodes were determined by integrating the instability transmission intensity and the mixed field coupling intensity, and a directed supernetwork was constructed. Then, key risk propagation paths were identified based on the cumulative risk transmission value. By introducing the instability transmission intensity, the risk transmission capacity between slopes was quantified, and the influence of the hydraulic linkage unique to hybrid pumped storage power stations on propagation was characterized by combining the mixed field coupling intensity. This enabled the constructed directed supernetwork to more realistically reflect the risk propagation relationship between the slope group. The ultimately identified key risk propagation paths are more consistent with actual working conditions, improving the accuracy and engineering practicality of the identification results, and providing a reliable basis for targeted risk prevention and control of slope groups in hybrid pumped storage power stations. Attached Figure Description

[0010] Figure 1 A flowchart is shown showing a slope instability risk propagation path analysis method according to one embodiment of the present disclosure; Figure 2 This diagram illustrates a flowchart of determining key risk propagation paths in one embodiment of the present disclosure; Figure 3 This diagram illustrates a method architecture in one embodiment of the present disclosure. Figure 4 A schematic diagram showing some slope parameters in one embodiment of this disclosure is provided. Figure 5 A schematic diagram illustrating various instability transmission strengths in one embodiment of this disclosure is shown; Figure 6 A schematic diagram illustrating the instantaneous instability probability and cumulative risk value in one embodiment of this disclosure is shown. Figure 7 This diagram illustrates a slope instability risk propagation path analysis device according to one embodiment of the present disclosure; Figure 8 A schematic diagram of an electronic device according to one embodiment of the present disclosure is shown. Detailed Implementation

[0011] Exemplary embodiments of this disclosure will be described more fully below with reference to the accompanying drawings.

[0012] The accompanying drawings are schematic illustrations of this disclosure and are not necessarily drawn to scale. Some block diagrams shown in the drawings may be functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in hardware modules or integrated circuits, or in networks, processors, or microcontrollers. Implementations can be carried out in various forms and should not be construed as limited to the examples set forth herein. The features, structures, or characteristics described in this disclosure can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full description of the technical solutions of this disclosure. However, those skilled in the art will recognize that one or more specific details may be omitted when implementing the technical solutions provided in this disclosure, or other methods, components, apparatuses, steps, etc., may be used to replace one or more specific details.

[0013] In hybrid pumped storage power stations, multiple slopes with potential instability risks are typically distributed along the banks between the upper and lower reservoirs. These slopes are spatially tiered; if an upstream slope becomes unstable, it not only directly threatens its own safety but also exerts varying degrees of risk on the downstream slopes through various means, including surges, seepage disturbances, landslide debris migration, and long-term circulating hydraulic disturbances, thus propagating risk among the slope group. However, different propagation paths have different mechanical mechanisms and propagation intensities, and the unique hydraulic regulation coupling effect between the upper and lower reservoirs in hybrid pumped storage power stations further exacerbates this propagation. Most related technologies treat the dam as a risk analysis node, making it difficult to reflect the complex risk propagation network of multiple nodes and multiple paths in the slope group. Furthermore, these technologies rarely unify the modeling of transmission mechanisms such as hydraulic fluctuations, sedimentation, and cumulative damage with surge propagation, and even fewer introduce characteristic parameters that reflect the hydraulic linkage between the upper and lower reservoirs.

[0014] In view of one or more of the above-mentioned problems, this disclosure provides a method for analyzing the propagation path of slope instability risk. Figure 1 An exemplary flow of the method is shown, including the following steps: S110, acquire the first monitoring data of multiple slopes in the reservoir area of ​​the hybrid pumped storage power station; S120, if the first slope becomes unstable, obtain the second monitoring data related to the instability of the second slope, and determine the instability transmission intensity from the first slope to the second slope based on the second monitoring data; the second slope is located downstream of the first slope. S130, based on the correlation and spatial structure relationship of the first monitoring data of different slopes, determine the coupling intensity of the mixed field of different slopes; S140 uses slopes as nodes and determines the comprehensive edge weights between different nodes based on the instability transmission strength and the coupling strength of the mixed field between different slopes to construct a directed supernetwork. S150, determine the cumulative risk propagation value between two nodes in the directed hypernetwork, and identify the key risk propagation path in the directed hypernetwork based on the cumulative risk propagation value.

[0015] Based on the above method, first monitoring data of multiple slopes in the reservoir area of ​​a hybrid pumped storage power station were obtained. Second monitoring data of the second slope were obtained when the first slope became unstable to determine the instability transmission intensity from the first to the second slope. The coupling intensity of the mixed field was determined based on the correlation and spatial structure relationship of the first monitoring data of different slopes. Using the slopes as nodes, the comprehensive edge weights between nodes were determined by integrating the instability transmission intensity and the mixed field coupling intensity, and a directed supernetwork was constructed. Then, key risk propagation paths were identified based on the cumulative risk transmission value. By introducing the instability transmission intensity, the risk transmission capacity between slopes was quantified. Combined with the mixed field coupling intensity, the impact of the unique hydraulic linkage of the hybrid pumped storage power station on propagation was characterized, enabling the constructed directed supernetwork to more realistically reflect the risk propagation relationship between the slope group. The ultimately identified key risk propagation paths are more consistent with actual working conditions, improving the accuracy and engineering practicality of the identification results, and providing a reliable basis for targeted risk prevention and control of slope groups in hybrid pumped storage power stations.

[0016] The following describes, in conjunction with one or more embodiments and related accompanying drawings, Figure 1 Each step in the process will be explained in detail.

[0017] refer to Figure 1 In step S110, the first monitoring data of multiple slopes in the reservoir area of ​​the hybrid pumped storage power station are obtained.

[0018] Hybrid pumped storage power station reservoirs typically comprise multiple slopes distributed along the banks between the upper and lower reservoirs. Due to differences in topography and geological conditions, these slopes exhibit varying stability states under frequent reservoir water level fluctuations. Primary monitoring data refers to various data used to characterize the basic features and real-time status of each slope; its acquisition forms the data foundation for subsequent risk propagation analysis.

[0019] In one implementation, the first monitoring data may include slope geometric parameters, geotechnical parameters, structural surface development characteristics, hydrogeological parameters, and historical deformation parameters. Each type of parameter is described in detail below.

[0020] Slope geometric parameters include: slope i i (Unit: °), Slope Height h i (Unit: m), Slope area S i (unit: m) 2 ); Geotechnical parameters include: cohesion c i (Unit: kPa), internal friction angle i (unit: °), severe c i(Unit: kN / m) 3 ); Structural development features include: attitude α i (Unit: °), Spacing d i (Unit: m), connectivity k i (dimensionless); Hydrogeological parameters: permeability coefficient k p,i (Unit: m / day) Pore water pressure u i (Unit: kPa) Groundwater level H w,i (Unit: m); Historical deformation parameters (which can be obtained from historical monitoring data) include: displacement rate. v d,i (Unit: mm / d) Deformation trend x i (Dimensionless, value range 0~1, larger value indicates more significant deformation).

[0021] The aforementioned parameters can be collected through various means. For example, slope geometric parameters can be extracted after obtaining high-precision topographic data through UAV mapping, lidar scanning, or total station measurements. Geotechnical parameters can be obtained through field sampling combined with indoor direct shear tests and triaxial compression tests. Structural surface development characteristics can be obtained through geological sketching, borehole photography, or ground-penetrating radar detection. Hydrogeological parameters can be monitored long-term by deploying piezometers and water level observation wells. Historical deformation parameters can be obtained through technologies such as global navigation satellite systems, total stations, and radar to obtain the time series of slope surface displacement. Standardized collection of these parameters provides comprehensive and accurate basic data support for subsequent quantitative analysis of risk propagation paths, ensuring the completeness and accuracy of slope characteristic characterization.

[0022] Continue to refer to Figure 1 In step S120, if the first slope becomes unstable, second monitoring data related to the instability of the second slope is obtained, and the instability transmission intensity from the first slope to the second slope is determined based on the second monitoring data; the second slope is located downstream of the first slope.

[0023] The second monitoring data refers to the monitoring data related to the instability of the downstream slope when the upstream slope becomes unstable. In the cascade slope group of a hybrid pumped storage power station, when an upstream slope (i.e., the first slope) becomes unstable, its direct impact will propagate to a downstream slope (i.e., the second slope) through various pathways. The instability transmission intensity can be used to quantify the degree of risk hazard along these pathways. Instability transmission intensity refers to the normalized measure of the risk hazard capacity along a certain risk propagation path from the first slope to the second slope; its magnitude determines the importance of that propagation path in the cascading instability process. The second monitoring data is specifically collected for this propagation process and is related to the response of the downstream slope. It may be part of the first monitoring data or data collected after the instability event. For example, changes in pore water pressure, surge wave height, increase in the volume of deposits at the slope toe, and amplitude of cyclic disturbance stress on the second slope caused by the instability of the first slope all fall within the scope of the second monitoring data.

[0024] To comprehensively capture the different types of risk propagation mechanisms, instability propagation intensity can be categorized into one or more of the following: hydraulic wave propagation intensity, surge impact propagation intensity, sedimentation propagation intensity, and cumulative damage propagation intensity. These intensities correspond to different physical mechanisms and each has its own independent quantitative model. The following section explains the calculation method for each instability propagation mode and its corresponding instability propagation intensity.

[0025] I. Hydraulic fluctuation transmission mode, used to describe the deformation and instability of the upstream slope, through the changes in the seepage field caused by the rapid rise and fall of the reservoir water level, to the downstream slope. v j The pathway of risk propagation. Its main mechanical mechanism is as follows: upstream slope deformation and instability lead to sudden changes in local water level in the reservoir area, triggering seepage field disturbances. These disturbances spread along the reservoir bank soil and rock mass, causing abnormal changes in pore water pressure on the downstream slope, thereby reducing its shear strength and increasing the risk of instability. The high-frequency circulating water level change rate of hybrid pumped storage power stations can reach 27 m / day, significantly higher than that of conventional hydropower stations. This high-frequency, large-amplitude water level fluctuation exacerbates the propagation effect of seepage field disturbances, causing a delayed response in the matrix suction of the downstream slope, further amplifying the intensity of risk propagation.

[0026] In one implementation, the hydraulic wave transmission intensity can be defined. (Dimensionless, value range can be 0~1), used to quantify the risk transmission capability of this transmission method, its calculation formula is as follows: (1) in, i , j These represent the first slope. v i Second slopev j , v j In v i Downstream; Δ u ij The first slope v i Downstream second slope caused by instability v j Change in pore water pressure at location (unit: kPa). u j0 The second slope v j The initial pore water pressure (unit: kPa). The larger the value, the stronger the risk propagation capability of the hydraulic fluctuation transmission path.

[0027] II. The surge impact transmission mechanism describes the path of a surge triggered when the soil and rock mass slides into the water after the upstream slope becomes unstable. This surge propagates downstream along the reservoir's water surface, impacting the downstream slope and ultimately spreading the risk of instability. The main mechanical mechanism is as follows: When the upstream slope becomes unstable and enters the water, it releases a large amount of potential energy, which is converted into the kinetic energy of the water, forming a high-speed surge. During its propagation, the surge carries energy, generating impact loads on the toe and surface of the downstream slope, disrupting the mechanical equilibrium of the soil and rock mass and inducing downstream slope instability. The generation and propagation of the surge are modulated by various factors such as reservoir topography, reservoir width, water depth, and the scale of slope instability. Combining surge propagation theory, it is necessary to consider both two-dimensional and three-dimensional propagation conditions, including amplitude attenuation and run-up effects, to ensure the accuracy of wave height calculations when the surge reaches the downstream slope. Furthermore, after the slope becomes unstable and enters the water, the thickness of the landslide decreases exponentially, indirectly affecting the initial energy and propagation characteristics of the surge.

[0028] In one implementation, surge impact transmission intensity can be defined. (Dimensionless, value range can be 0~1), used to quantify the risk transmission capability of this transmission method, its calculation formula is as follows: (2) in, For the surge from the first slope v i Spread to the second slope v j Wave height at time (in meters); The second slope v j The critical safety wave height threshold (in meters) can be determined based on the strength of the slope soil and rock, slope height, and slope protection conditions. The larger the value, the stronger the risk propagation capability of the surge impact transmission path. At that time, the second slope v j The risk of instability increases significantly after being impacted by swells.

[0029] In one implementation, the result is calculated using a surge propagation attenuation model. This surge propagation attenuation model integrates parameters such as reservoir topography, water depth, and propagation distance, and can be referenced by the following formula: (3) in, The first slope upstream v i The initial surge height triggered by instability, ac This is the surge attenuation coefficient. L ij The propagation distance, i.e., the first slope. v i Second slope v j Spatial distance.

[0030] III. The depositional and siltation transmission mechanism describes the path of landslide deposits generated by upstream slope instability, which slide into the river channel and migrate downstream along the direction of water flow, eventually silting up and covering the toe of the downstream slope, thus propagating the risk of instability. Its main mechanical mechanism is as follows: When the volume of landslide deposits generated by upstream slope instability is large, it will form a deposit at the toe of the downstream slope, increasing the load on the toe. Simultaneously, silting up the river channel causes a rise in water level, increasing the hydraulic pressure on the downstream slope, disrupting the slope's mechanical balance, and inducing instability. During the downstream migration of the deposits, the migration attenuation effect is significantly modulated by factors such as river gradient, water flow sediment carrying capacity, riverbed morphology, and particle size distribution of the deposits. Some deposits will be deposited during migration, resulting in a reduction in the volume of deposits reaching the toe of the downstream slope. The shape coefficient of the landslide body affects the diffusion capacity of the deposits, thus influencing the degree of migration attenuation.

[0031] In one implementation, the clogging transmission strength can be defined. (Dimensionless, value range can be 0~1), used to quantify the risk transmission capability of this transmission method, its calculation formula is as follows: (4) in, The first slope v i Volume of landslide deposits resulting from instability (in m³) 3 This can be calculated from the slope geometric parameters and the properties of the soil and rock mass; The second slope v jMaximum accumulation volume that can be borne at the toe of the slope (unit: The specific value can be determined based on the bearing capacity of the soil and rock mass at the toe of the slope and the requirements for slope stability. α ij The migration attenuation coefficient (dimensionless, ranging from 0 to 1) can be determined based on riverbed slope, water flow velocity, and sediment particle size distribution, reflecting the degree of attenuation of sediment during migration. α ij The smaller the value, the more significant the attenuation during the migration of the deposits. The higher the value, the stronger the risk propagation ability of the accumulation and blockage transmission path.

[0032] IV. Cumulative Damage Transmission Mode: This describes the path by which long-term hydraulic disturbances (including changes in water level fluctuation frequency, alterations in seepage paths, and periodic fluctuations in pore water pressure) triggered by upstream slope instability cause cumulative fatigue damage to the downstream slope's soil and rock mass, ultimately propagating the risk of instability. The main mechanical mechanism is as follows: long-term hydraulic disturbances lead to the formation of microcracks within the downstream slope's soil and rock mass. These microcracks continuously expand and connect, causing a gradual decrease in the strength of the soil and rock mass, ultimately inducing slope instability. This process conforms to the fatigue damage accumulation theory.

[0033] In one implementation, the cumulative damage transmission intensity can be defined. (Dimensionless, value range can be 0~1), used to quantify the risk transmission capability of this transmission method, its calculation formula is as follows: (5) Where, Δ t ijk The first slope v i After instability, the first k The secondary hydraulic disturbance affects the downstream second slope. v j Shear stress amplitude generated by the soil and rock mass (unit: kPa). t f,j The second slope v j The fatigue limit strength (in kPa) of soil and rock mass can be determined through laboratory tests. m This is a material constant (dimensionless), which is related to the type and structural characteristics of the soil and rock mass, and its value can range from 2 to 5. N The effective number of cycles of disturbance (dimensionless) can be determined based on the frequency of water level fluctuations and the duration of the disturbance. The higher the value, the stronger the risk propagation capability of the cumulative damage transmission path. At that time, it indicates the second slope v jThe soil and rock mass has reached its fatigue damage limit, and the risk of instability is extremely high.

[0034] In one embodiment, the instability transmission intensity includes one or more of the following: hydraulic wave transmission intensity, surge impact transmission intensity, siltation transmission intensity, and cumulative damage transmission intensity. The hydraulic wave transmission intensity, surge impact transmission intensity, siltation transmission intensity, and cumulative damage transmission intensity from the first slope to the second slope can be calculated using one or more of the above formulas (1), (2), (4), and (5).

[0035] Continue to refer to Figure 1 In step S130, the coupling intensity of the mixed field of different slopes is determined based on the correlation and spatial structure relationship of the first monitoring data of different slopes.

[0036] A key characteristic of hybrid pumped-storage power stations is the water recycling process between the upper and lower reservoirs through pumping and power generation, resulting in synchronized rises and falls in water levels and creating a hybrid hydraulic environment. This operating condition means that the hydraulic effects on different slopes are not isolated but rather closely interconnected. For example, large-scale pumping upstream leads to a rapid drop in water level downstream, while releasing water upstream during power generation causes a rise in downstream water level, creating highly correlated fluctuations. This hydraulic interconnection significantly enhances the correlation of risk propagation between slopes. Therefore, the hybrid field coupling strength index is introduced to quantify the degree of hydraulic interconnection between any two slopes due to hydraulic gradient driving, synchronized water level fluctuations, and pumping / releasing flow regulation.

[0037] In one implementation, the coupling strength of the mixed field for different slopes is calculated using the following formula: (6) in, i , j Indicates two different slopes v i , v j slope v j Located on the slope v i Downstream; C ij Indicates slope v i and slope v j The coupling strength of the mixed field; Δ H ij Indicates slope v i With slope v j The water level difference at the location;L ij Indicates slope v i With slope v j Spatial distance between them; Indicates slope v i With slope v j Correlation coefficient of water level change time series; Indicates slope v i Pumping flow rate in the area; Indicates slope v j The power generation flow rate in the area; α , β , c These are the weighting coefficients.

[0038] Formula (6) integrates the coupling effect across three dimensions. The first term This reflects the coupling strength driven by the hydraulic gradient, the water level difference Δ H ij Larger, spatial distance L ij The smaller the gradient, the greater the hydraulic gradient per unit distance, meaning a stronger seepage driving force between the two slopes and a closer hydraulic linkage. (Second item) This reflects the coupling caused by the synchronicity of water level fluctuations. Water level correlation coefficient. The correlation coefficient is obtained by calculating the time series of water level changes at two slope locations. The value can range from -1 to 1. The closer the absolute value is to 1, the more synchronized the rise and fall of water levels on the two slopes are. This value is often higher under the unique synchronous fluctuation condition of the upper and lower reservoirs in pumped storage power stations. (Third item) This reflects the coupling relationship between pumping and power generation flow regulation. Upstream region pumping flow rate. With downstream regional power generation flow The relative magnitude of these values ​​reflects the intensity of the mixed field operation and is directly related to the degree of hydraulic disturbance experienced by the slope. The weighting coefficients α, β, and γ sum to 1, and their specific values ​​can be determined using the analytic hierarchy process (AHP) or expert scoring method based on the actual pumping and power generation ratio of the power station, to reflect the importance of different dimensions of influence.

[0039] The resulting hybrid field coupling strength C ij The value can range from 0 to 1, with a larger value indicating stronger hydraulic coupling between the two slopes. C ijWhen a certain threshold is exceeded, risk propagation will be significantly amplified. The introduction of the hybrid field coupling strength index quantifies the unique hydraulic environment of hybrid pumped storage power stations, which differs from that of conventional hydropower stations, as a network parameter, overcoming the shortcomings of related technologies that cannot reflect this difference in operating conditions.

[0040] Continue to refer to Figure 1 In step S140, the slope is used as a node, and the comprehensive edge weight between different nodes is determined according to the instability transmission strength and the coupling strength of the mixed field between different slopes, so as to construct a directed super network.

[0041] Each slope with potential instability risk is abstracted as a node in a network, forming a set of nodes. V ={ v 1, v 2,…, v n},in n This refers to the number of slopes (i.e., the number of nodes). v i ( i =1,2,…, n ) represents the first i The slope or the corresponding first slope i There are nodes. At any two slope nodes that have an upstream-downstream relationship... v i and v j Between these points, based on one or more previously calculated instability propagation strengths, the range from the upstream node can be defined. v i Pointing to downstream nodes v j One or more transmission edges (such as hydraulic wave transmission edges) , surge impact transmission edge Accumulation and siltation on the edge Cumulative damage transmission edge Therefore, from the set of nodes V Transitive edge set E and the overall weight of the edges W Together, they form a directed supernetwork. This directed supernetwork can systematically characterize the complex, multifaceted, and directed risk propagation relationships among slope groups.

[0042] In one implementation, the edge set is passed. E It can be the union of the above four types of transitive edges, i.e. E = E H ∪ E W ∪ E D ∪ EC ,in , , , .

[0043] In one implementation, a comprehensive directed edge from the upstream node to the downstream node can be defined by combining the mixed field coupling strength between the two slopes. The comprehensive directed edge has a comprehensive edge weight, which comprehensively reflects the strength of various risk propagation paths and the enhancing effect of hydraulic coupling on propagation.

[0044] In one implementation, the combined edge weights between different nodes are calculated using the following formula: (7) (8) in, i , j Indicates two different nodes v i , v j , for slope v i , v j The corresponding nodes; w ij Represents a node v i To the node v j The overall edge weights; or b Indicates the first b The weighting coefficients corresponding to the instability propagation strength can satisfy the following conditions: or H + or W + or D + or C =1, or H , or W , or D , or C These represent the weighting coefficients corresponding to the transmission intensity of hydraulic fluctuations, the transmission intensity of surge impacts, the transmission intensity of sedimentation, and the transmission intensity of cumulative damage, respectively. Each weighting coefficient reflects the relative importance of its instability risk transmission mode in the process of cascading instability of the slope group, and can be determined according to the slope type, geological conditions, etc. of the hybrid pumped storage power station. Represents a node v i To the nodev j The b Instability propagation strength, as defined above , , , ; C ij Indicates slope v i and slope v j The coupling strength of the mixed field; ( C ij ) is the coupling strength modulation function, which is used to reflect the enhancement effect of the coupling strength of the mixed field on risk propagation. For details, refer to formula (8). C 0 is the preset coupling strength threshold, which is dimensionless and its value can be determined according to the actual engineering situation. For example, the value range can be 0.3~0.5. a This is the coupling strength enhancement coefficient, dimensionless, and its value can be determined based on experience or specific needs; for example, its range can be 1 to 3. C ij > C When the value is 0, the coupling strength modulation function amplifies the overall edge weight, reflecting the enhanced effect of hydraulic coupling on risk propagation.

[0045] By calculating multiple instability propagation intensities and comprehensive edge weights, and constructing a directed supernetwork containing comprehensive edge weights, the system integration of multiple risk propagation paths was achieved, solving the technical defects of path segmentation processing in related technologies. Furthermore, by modulating the coupling intensity of the hybrid field, the adaptability of the calculation model to the hybrid pumped storage scenario was ensured.

[0046] In one implementation, the method further includes the following steps: For a node in a directed hypernetwork, the cumulative risk value of the node is obtained by calculating the propagation of the instability risk of other nodes to that node by comprehensively considering the edge weights.

[0047] In one implementation, the cumulative risk value of a node in a directed hypernetwork is calculated using the following formula: (9) in, i Represents nodes in a directed hypernetwork v i , for slope v i The corresponding node; Represents a node v i exist t Accumulated risk value over time; Pre( i ) represents a nodev i The set of precursor nodes, where precursor nodes are those capable of transmitting instability risks to nodes through risk propagation paths. v i Other nodes; l ji Indicating instability risk from nodes v j propagation to nodes v i The risk propagation delay coefficient (dimensionless, ranging from 0 to 1) reflects the risk from node to node. v j propagation to nodes v i The delay effect is related to the propagation path type and propagation distance, and can be equal to the product of the inverse square of the propagation distance and the quantization value of the propagation path type (the easier the path type propagates, the larger its quantization value); Δ t ji Indicating instability risk from nodes v j propagation to nodes v i The time delay can be calculated based on the propagation path type (such as the propagation speed of surge, the migration speed of deposits, etc.); P j ( t -Δ t ji ) represents a node v j exist t -Δ t ji The probability of instantaneous instability at any given moment; w ji Represents a node v j To node v i The comprehensive edge weight can be calculated using the comprehensive edge weight formula above, reflecting the risk propagation capability.

[0048] In one implementation, the instantaneous instability probability is calculated based on the limit equilibrium method (such as Bishop's method of segments) or the strength reduction method, for example, referring to the following formula: (10) in, i Represents nodes in a directed hypernetwork v i , for slope v i The corresponding node; P i ( t ) represents a nodev i exist t The probability of instantaneous instability at any given moment; F s,i ( t ) is a slope v i exist t The safety factor at any given time can be calculated using the limit equilibrium method or the strength reduction method. F s,min slope v i The minimum safety factor (dimensionless) corresponds to the critical state of slope instability; F s,max slope v i The maximum safety factor (dimensionless) corresponds to the slope stability state. P i ( t The larger the value, the stronger the slope. v i At any moment t The higher the risk of instantaneous instability.

[0049] Cumulative risk value It characterizes the cumulative effect of slope instability risk; the larger the value, the stronger the slope instability risk. v i The higher the overall instability risk after being affected by the instability of the upstream slope, the more quantitative basis is provided for subsequent analysis.

[0050] Continue to refer to Figure 1 In step S150, the cumulative risk transmission value between two nodes in the directed hypernetwork is determined, and the key risk propagation path in the directed hypernetwork is identified based on the cumulative risk transmission value.

[0051] In a constructed directed hypernetwork, risk can propagate from one node through a series of intermediate nodes along directed edges to another distant node, forming a risk propagation path. The combined weights of each edge segment on each path accumulate continuously, and this cumulative effect can be measured by the cumulative risk propagation value. The cumulative risk propagation value refers to the comprehensive index obtained by accumulating the combined weights of all directed edges along a specific path from the starting node to the ending node according to a certain accumulation rule (such as multiplication), and is used to measure the total risk propagation capacity of the entire path. By finding the path with the largest cumulative risk propagation value between two nodes, the critical risk propagation path can be determined, which is the chain where risk is most easily propagated and most likely to trigger continuous instability during a cascading instability process.

[0052] In one implementation, the cumulative risk transfer value is calculated using the following formula: (11) in, i , j Indicates two different nodes v i , v j , for slope v i , v j The corresponding nodes; Path i→j Indicates from node v i To the node v j Any risk transmission path, e pq Path i→j Represents any associated edge on any risk propagation path. w pq Indicates associated edges e pq The comprehensive edge weight. Formula (11) means: in the slave node v i To the node v j Among all possible paths, find a path such that the product of the combined edge weights of all edges on that path is maximized; this maximum value is the node. v i arrive v j The cumulative risk transmission value corresponds to the path of the critical risk propagation. Using a product form aligns with the characteristics of risk transmission at each level and its cumulative nature based on conditional probability.

[0053] In one implementation, reference Figure 2 As shown, the above-described method for determining the cumulative risk propagation value between two nodes in a directed hypernetwork and identifying key risk propagation paths in the directed hypernetwork based on the cumulative risk propagation value includes the following steps: S210, Initialize the cumulative risk transmission value for all nodes; S220: Select the node with the largest cumulative risk transmission value among the untraversed nodes as the current node. Update the cumulative risk transmission value of the adjacent nodes based on the cumulative risk transmission value of the current node and the combined edge weight from the current node to its adjacent nodes. S230: After traversing all nodes, extract the path with the largest cumulative risk transmission value between two nodes as the key risk propagation path.

[0054] This implementation employs an improved shortest path algorithm (such as Dijkstra's algorithm), changing its optimization objective from minimizing the sum of path edge weights to maximizing the product of path edge weights. For example, the cumulative risk propagation value of all nodes is initialized. For instance, the starting node... v i The cumulative risk transmission value is set to 1 (multiplicative unit), and the remaining nodes are set to 0; or, the cumulative risk transmission value of all nodes is initialized to the cumulative risk value, which can be calculated using formula (9). Then, the node with the largest cumulative risk transmission value among the untraversed nodes is selected as the current node and marked as traversed. For each outgoing edge of the current node, the temporary cumulative risk transmission value of its adjacent nodes is calculated, which is equal to the cumulative risk transmission value of the current node multiplied by the comprehensive edge weight of the outgoing edge. If the temporary value is greater than the existing cumulative risk transmission value of the adjacent node, the cumulative risk transmission value of the adjacent node is updated, and its predecessor node is recorded as the current node. This process is repeated until all reachable nodes are visited. After completion, the previous node can be obtained by backtracking the recorded predecessor node. v i To any v j The path with the highest cumulative risk transmission value is the key risk propagation path.

[0055] In one implementation, after determining the critical risk propagation path, the critical instability node can be identified based on the cumulative risk value of each node in the critical risk propagation path. For example, the node with the highest cumulative risk value can be identified as the critical instability node, or the node with a cumulative risk value exceeding a certain threshold (which can be set based on experience or specific needs) can be identified as the critical instability node.

[0056] In one implementation, the method further includes the following steps: For a node in a directed hypernetwork, the betweenness centrality of the node is determined by the number of critical risk propagation paths passing through the node, and whether the node is an unstable critical node is determined by the betweenness centrality, out-degree, and in-degree of the node.

[0057] After identifying key risk propagation paths, further risk assessments can be conducted on individual nodes within the network to identify critical instability nodes (i.e., weak nodes) that play a crucial role in the entire slope group. Critical instability nodes are those slope nodes whose failure would significantly impact the overall network connectivity and exacerbate cascading instability effects. Identifying critical instability nodes can guide the implementation of more targeted reinforcement and monitoring measures.

[0058] In one implementation, the betweenness centrality of a node is calculated using the following formula: (12) in,B ( v ) represents a node v Betweenness centrality (dimensionless, ranging from 0 to 1) is used to evaluate nodes. v The degree of criticality of a node in the entire risk propagation network; a higher betweenness centrality indicates a more critical node. v The more critical risk propagation paths there are, the greater the impact on overall network connectivity after a failure. s xy For the node x To the node y The number of key risk transmission paths s xy ( v ) is a slave node x Passing through the node v To the node y The number of key risk transmission paths n This represents the number of nodes in the directed hypernetwork.

[0059] Out-degree of a node out ( v (Dimensionless) represents the number of associated edges emitted by this node, reflecting its risk output capability. Nodes with high out-degrees are high-risk source nodes; their instability will propagate risk downstream through multiple paths. Node in-degree in ( v (dimensionless) represents the number of associated edges received by the node, reflecting the node's risk-receiving capacity. Nodes with high in-degree are high-risk convergence nodes, which are susceptible to the instability of multiple upstream slopes and have a high cumulative risk value.

[0060] By combining the analysis results of node betweenness centrality, in-degree, and out-degree, nodes with high betweenness centrality, high in-degree, or high out-degree can be selected as key nodes for the chain instability of slope groups, providing key targets for risk prevention and control.

[0061] In one implementation, a directed graph can be used to visualize the slope nodes (critical instability nodes can be distinguished by special markings, such as red nodes representing high-risk source nodes and blue nodes representing high-risk convergence nodes), four types of associated edges (distinguished by different colors or line types), and comprehensive edge weights (marked next to the corresponding edges). This forms a map of the risk propagation path of the cascading instability of the slope group, clearly showing the risk propagation relationship between each slope node, the distribution of critical paths, and the location of critical instability nodes, providing engineering technicians with an intuitive risk reference.

[0062] In one implementation, the early warning level and corresponding prevention and control measures for key risk transmission paths can be determined based on the cumulative risk transmission value of each key risk transmission path. For example, based on the cumulative risk transmission value of each key risk transmission path...R i→j , combined with the risk prevention and control requirements of the engineering practice, three risk levels are divided, specifically including: R i→j When it is ≥ TH1, it is a first-level warning (high risk), indicating that the risk propagation ability of this critical risk propagation path is extremely strong, and it is easy to cause chain instability. Immediate emergency prevention and control measures need to be taken (such as slope toe reinforcement, drainage pressure reduction, real-time monitoring densification, etc.); when TH2 ≤ R i→j < TH1, it is a second-level warning (medium risk), indicating that the risk propagation ability of this critical risk propagation path is relatively strong, and there is a possibility of causing chain instability. Targeted prevention and control measures need to be taken (such as regular monitoring, slope protection, etc.); R i→j When it is < TH2, it is a third-level warning (low risk), indicating that the risk propagation ability of this critical risk propagation path is relatively weak, and the possibility of causing chain instability is relatively low. Regular monitoring and maintenance need to be carried out. TH1 and TH2 are the first warning threshold and the second warning threshold respectively, which can be determined according to the scale of the hybrid pumped-storage power station, the number of slopes, the geological structure characteristics and combined with specific requirements. For example, TH1 = 0.8 and TH2 = 0.5.

[0063] For each critical risk propagation path, generate hierarchical warning information, clarify the warning scope and prevention and control measures, support the disaster prevention, mitigation and emergency decision-making of the cascade slope group of the hybrid pumped-storage power station, and improve the safe operation level.

[0064] In one implementation, the method structure can refer to Figure 3As shown in the diagram. In the first stage, the spatial topology of multiple slopes in the reservoir area of ​​the hybrid pumped storage power station is constructed. Specifically, slope nodes are defined, a set of slope nodes is constructed, and a database of slope node attributes (including slope geometric parameters, geotechnical parameters, structural surface development characteristics, hydrogeological parameters, historical deformation parameters, etc.) is established. In the second stage, multiple associated edges are defined, and four basic propagation path models are constructed. Specifically, hydraulic fluctuation propagation edges, surge impact propagation edges, sedimentation propagation edges, and cumulative damage propagation edges can be constructed. In the third stage, a hybrid field coupling strength index is introduced. Specifically, the hybrid field coupling strength index is obtained by weighting the hydraulic gradient drive, water level fluctuation synchronicity (i.e., the correlation between water level changes of upstream and downstream slopes), and pumping flow regulation (i.e., the ratio between upstream pumping flow and downstream power generation flow). In the fourth stage, a directed supernetwork for the propagation of cascading instability risks of the slope group is constructed. Specifically, a set of directed associated edges is constructed, the comprehensive edge weights are calculated, and a directed supernetwork is constructed. In the fifth stage, the instantaneous instability probability and cumulative risk value of the slope are calculated. Specifically, the instantaneous instability probability and the cumulative risk value of each node are calculated. In the sixth stage, key risk propagation paths and critical instability nodes are identified. Specifically, an improved shortest path algorithm is constructed to find the path with the largest cumulative risk transmission value, which is then used as the key risk propagation path. Critical instability nodes are determined based on node topological indicators (such as betweenness centrality, out-degree, and in-degree). In the seventh stage, a risk propagation path map and tiered early warning information are output. Specifically, different paths and nodes are classified into risk levels, tiered early warning information is generated, and resource optimization allocation schemes are provided.

[0065] To verify the feasibility, accuracy, and advancement of this implementation method, a cascade slope group of a hybrid pumped storage power station was selected as the test object. By substituting actual engineering data into the calculation, the effectiveness of this implementation method in identifying key risk propagation paths, locating critical instability nodes, and providing graded early warning was verified. At the same time, it was compared with related technical methods.

[0066] The hybrid pumped storage power station has a head difference of 320m between its upper and lower reservoirs, and there are a total of 8 slopes with potential instability risks distributed in the cascade reservoir area (numbered...). v 1~ v 8) It is distributed in a tiered manner, among which v 1~ v 4 is the upstream slope. v 5~ v 8 represents the downstream slope. The slope type is mainly rock slope, with some areas being mixed soil-rock slope. This power station operates under both runoff power generation and pumped storage conditions, with water level regulation frequency of 1-2 times / day for both upper and lower reservoirs, and a maximum water level regulation range of 15m. The intensity of hydraulic disturbance is significantly higher than that of conventional pumped storage power stations. The slope group is affected by multiple risks such as hydraulic fluctuations and surge impacts, posing a potential risk of cascading instability, which aligns with the applicable scenario of this implementation method.

[0067] Complete initial monitoring data for eight slope nodes was obtained through on-site investigation, UAV mapping, and ground-penetrating radar detection. Some data can be used for reference. Figure 4 As shown, data such as the spatial distance between slopes, water level change sequence, pumping flow rate, and power generation flow rate were collected to provide basic data support for subsequent calculations.

[0068] A set of slope nodes is constructed using 8 slope locations as nodes. V ={ v 1, v 2, v 3, v 4, v 5, v 6, v 7, v 8}, n =8. Based on the four instability transmission modes defined above, and combined with the collected second monitoring data, calculate the four instability transmission intensities between any two slope nodes. , , , Some calculation results are for reference. Figure 5 As shown (only listed) v 1~ v 4. Instability transfer strength to downstream slopes.

[0069] Based on the formula for calculating the coupling strength of the mixed field and considering the pumping / power generation conditions of the test power station, the weighting coefficients are determined. α =0.35、 β =0.40、 c =0.25 (determined using the analytic hierarchy process), preset coupling strength threshold. C 0 = 0.4. Substituting data such as slope water level difference, spatial distance, correlation coefficient of water level change, pumping flow rate, and power generation flow rate, the mixed field coupling strength between any two slope nodes is calculated. C ij The values ​​are shown in Table 1 for reference.

[0070] Table 1. Calculation results of the coupling strength of the mixed field

[0071] Integrating four types of associative edges to construct a directed hypernetwork G =( V , E , W Determine the weighting coefficients for the four instability propagation mechanisms. or H =0.3, or W=0.35, or D =0.2, or C =0.15 (which can be determined by expert scoring), coupling strength enhancement coefficient a =2. Based on the formula for calculating the comprehensive edge weight, the comprehensive edge weight between different nodes is calculated, and some results are shown in Table 2.

[0072] Table 2 Calculation results of the combined edge weights

[0073] Based on historical monitoring data, determine the safety factor range for each slope. F s,min =1.05, F s,max =1.35), the instantaneous instability probability of each node was calculated. P i ( t Then introduce the risk propagation delay coefficient. l ji (Value range 0.7~0.9, determined according to the propagation path type), time delay Δ t ji (Values ​​range from 2 to 8h), substitute them into the cumulative risk transfer function to calculate the cumulative risk value of each node. Some calculation results can be used as a reference. Figure 6 As shown.

[0074] An improved Dijkstra algorithm (with the objective of maximizing the cumulative risk transfer value) is used to calculate the cumulative risk transfer value between all slope node pairs. R i→j It identifies key risk propagation paths; at the same time, it calculates the betweenness centrality, in-degree, and out-degree of each node to identify key unstable nodes.

[0075] The results of identifying key risk propagation paths are as follows: A total of 3 key risk propagation paths were identified, and they are sorted from largest to smallest by cumulative risk transmission value: v 2→ v 6→ v 8. Cumulative risk transfer value R =0.45 × 0.38 = 0.171; v 1→ v 5→ v 8. Cumulative risk transfer value R =0.36 × 0.32 = 0.115; v 4→ v 7→v 6. Cumulative risk transfer value R =0.38×0.33=0.125.

[0076] Among them, path v 2→ v 6→ v The cumulative risk transmission value of 8 is the highest, making it the most critical risk propagation path. v 2 is a high-risk source node upstream. v 6 is the intermediate hub node. v 8 is a high-risk downstream aggregation node.

[0077] Based on betweenness centrality, in-degree, and out-degree analysis, three key instability nodes were identified: High-risk source nodes v 2: With a betweenness centrality of 0.32, an out-degree of 4, and a transient instability probability of 0.32, it is the node with the strongest risk output capability; Weak nodes of the hub v 6: Betweenness centrality 0.45 (highest), in-degree 3, out-degree 2, cumulative risk value 0.53 (highest), is the core hub of risk propagation; High-risk convergence nodes v 8: With a betweenness centrality of 0.28, in-degree of 3, and cumulative risk value of 0.44, it is the node with the most significant risk accumulation in the downstream slope.

[0078] Key risk propagation paths and critical instability points are visualized and drawn to form a risk propagation path map (omitted). v 2. Marked in red (high-risk source nodes). v 6. Marked in yellow (weak nodes in the hub). v 8 are marked in blue (high-risk convergence nodes), and the critical path is marked with a bold line.

[0079] The preset first-level warning threshold TH1=0.15 and the second-level warning threshold TH2=0.12 are used to classify risk levels based on the cumulative risk transmission value, and the warning information is generated as follows: Level 1 Warning (High Risk): Path v 2→ v 6→ v 8 ( R =0.171≥0.15), requires immediate action. v 2. Reinforce the slope toe. v 6. Perform drainage and pressure reduction. v 8. Real-time monitoring and encryption are implemented; Level 2 Warning (Medium Risk): Path v 4→ v 7→ v 6 ( R=0.125≥0.12), needs to be adjusted. v 4. v 7. Conduct regular monitoring. v 6. Implement slope protection; Level 3 Warning (Low Risk): Path v 1→ v 5→ v 8 ( R =0.115<0.12), needs to be adjusted. v 1. v 5. v 8. Conduct routine monitoring and maintenance.

[0080] In the above example, the calculations of each physical quantity were rigorous and the data were reasonable. Key risk propagation paths and critical instability nodes were successfully identified, and tiered early warning information was output. Furthermore, the early warning information was consistent with the actual slope monitoring data of the power station. v 2. v 6. v The fact that the displacement rates of nodes 8 are all higher than those of other nodes shows a high degree of consistency, indicating that this implementation method has good feasibility and can be adapted to the actual engineering scenario of the cascade slope group of the hybrid pumped storage power station.

[0081] The identification results of this implementation method are compared with those of related technologies (traditional Dijkstra algorithm with single-path analysis), and the comparison results are shown in Table 3. This implementation method identifies more comprehensive key risk propagation paths and more accurate instability key nodes, with a 27.2% higher degree of agreement with actual monitoring data compared to related technologies. This confirms that this implementation method is more accurate and can precisely characterize the multiple risk propagation characteristics of slope groups.

[0082] Table 3 Comparison of this implementation method with related technologies

[0083] The above examples demonstrate the advantages of this implementation method: Introducing the hybrid field coupling strength index makes the calculation of the comprehensive edge weights more closely aligned with the operating characteristics of hybrid pumped storage power stations, avoiding the shortcomings of related technologies that neglect hydraulic linkages. The integrated analysis of the four propagation paths solves the problem of single-path approaches in related technologies, enabling a comprehensive capture of the risk propagation patterns of slope groups. The improved Dijkstra algorithm can accurately locate the most critical risk propagation paths, providing clear targets for risk prevention and control. This implementation method can accurately identify key risk propagation paths and weak points, outputting scientifically sound and reasonable hierarchical early warning information. It possesses good feasibility, accuracy, and advancement, effectively addressing the shortcomings of related technologies and providing reliable technical support for disaster prevention, mitigation, and emergency decision-making for cascade slope groups in hybrid pumped storage power stations. It can be widely applied to slope safety control projects for various types of hybrid pumped storage power stations.

[0084] In summary, compared with related technologies, this disclosure has the following significant advantages: This research addresses the technical shortcomings of current studies that focus on misaligned research objects. Taking the cascade slope group of a hybrid pumped storage power station as the core research object, it constructs a three-layer progressive model consisting of slope nodes, multiple associated edges, and a hybrid field hypernetwork. This model accurately maps the complex spatial distribution and mechanical relationships of the slope group, adapts to the risk prevention and control needs of hybrid pumped storage power stations, and fills the technical gap in the analysis of the cascading instability risk propagation of slope groups in hybrid pumped storage scenarios.

[0085] Breaking through the limitations of related technologies that only focus on a single risk propagation path, this study clearly defines four basic risk propagation paths: hydraulic fluctuations, surge impacts, sedimentation, and cumulative damage. It establishes the mechanical mechanisms and quantitative models for each path, achieving systematic integration and precise characterization of multiple risk propagation paths and solving the problem of missing dimensions in the path characterization of related technologies.

[0086] By introducing a hybrid field coupling strength index, and comprehensively considering three dimensions—hydraulic gradient driving, water level fluctuation synchronization, and pumping flow regulation—the hydraulic linkage between slopes of hybrid pumped storage power stations is accurately characterized. This fully reflects the special characteristics of hybrid pumped storage scenarios, improves the accuracy and pertinence of risk propagation path identification, and is significantly different from conventional hydropower station slope risk identification methods.

[0087] By employing an improved shortest path algorithm combined with complex network topology indicators, the system accurately identifies key risk propagation paths and weak nodes, while outputting visualized maps and hierarchical early warning information. This provides intuitive and operable technical support for engineering practice, effectively enhancing the risk prevention and control capabilities of cascade slope groups in hybrid pumped storage power stations, reducing the probability of cascading instability disasters, and ensuring the safe and stable operation of the power station. It has significant engineering application value.

[0088] This disclosure also provides a device for analyzing the propagation path of slope instability risk. (Reference) Figure 7 As shown, the slope instability risk propagation path analysis device 700 includes: The first monitoring data acquisition module 710 is configured to acquire first monitoring data from multiple slopes in the reservoir area of ​​the hybrid pumped storage power station. The instability transmission intensity determination module 720 is configured to, if the first slope becomes unstable, acquire second monitoring data related to the instability of the second slope, and determine the instability transmission intensity from the first slope to the second slope based on the second monitoring data; the second slope is located downstream of the first slope. The mixed field coupling intensity determination module 730 is configured to determine the mixed field coupling intensity of different slopes based on the correlation and spatial structure relationship of the first monitoring data of different slopes. The directed supernetwork construction module 740 is configured to use the slope as a node, determine the comprehensive edge weight between different nodes based on the instability transmission strength and the coupling strength of the mixed field between different slopes, and construct a directed supernetwork. The critical risk propagation path determination module 750 is configured to determine the cumulative risk transmission value between two nodes in the directed hypernetwork, and identify the critical risk propagation path in the directed hypernetwork based on the cumulative risk transmission value.

[0089] In one embodiment, the instability transmission intensity includes one or more of the following: hydraulic wave transmission intensity, surge impact transmission intensity, siltation transmission intensity, and cumulative damage transmission intensity; determining the instability transmission intensity from the first slope to the second slope based on the second monitoring data includes: Calculate one or more of the following formulas for the hydraulic wave transmission intensity, surge impact transmission intensity, sedimentation transmission intensity, and cumulative damage transmission intensity from the first slope to the second slope: ; ; ; ; in, Indicates the intensity of hydraulic wave transmission. Indicates the intensity of surge impact transmission. Indicates the intensity of blockage transmission. Indicates the cumulative damage transmission intensity; Δ u ij The first slope v i Instability caused the second slope v j Change in pore water pressure at location u j0 The second slope v j The initial pore water pressure; For the surge from the first slope v i Spread to the second slope v j Wave height at that time; The second slope v j The critical safety wave high threshold; The first slope v i The volume of landslide debris resulting from instability; The second slope v jThe maximum accumulation volume that can be borne at the toe of the slope; α ij Δ is the migration attenuation coefficient. t ijk The first slope v i After instability, the first k The secondary circulation hydraulic disturbance affects the second slope v j The amplitude of shear stress generated in the soil and rock mass; t f,j The second slope v j Fatigue limit strength of soil and rock mass; m These are material constants; N The effective number of cyclic perturbations.

[0090] In one embodiment, determining the mixed-field coupling intensity of different slopes based on the correlation and spatial structure relationship of the first monitoring data of different slopes includes: The coupling intensity of the mixed field on different slopes is calculated using the following formula: ; in, i , j Indicates two different slopes v i , v j slope v j Located on the slope v i Downstream; C ij Indicates slope v i and slope v j The coupling strength of the mixed field; Δ H ij Indicates slope v i With slope v j The water level difference at the location; L ij Indicates slope v i With slope v j Spatial distance between them; Indicates slope v i With slope v j Correlation coefficient of water level change time series; Indicates slope vi Pumping flow rate in the area; Indicates slope v j The power generation flow rate in the area; α , β , c These are the weighting coefficients.

[0091] In one embodiment, determining the comprehensive edge weights between different nodes based on the instability transmission strength and the coupling strength of the mixed field between different slopes includes: The combined edge weights between different nodes are calculated using the following formula: ; ; in, i , j Indicates two different nodes v i , v j , for slope v i , v j The corresponding nodes; w ij Represents a node v i To the node v j The overall edge weights; or b Indicates the first b Weighting coefficients corresponding to the instability propagation strength; Represents a node v i To the node v j The b Instability propagation strength; C ij Indicates slope v i and slope v j The coupling strength of the mixed field; ( C ij ) is the coupling strength modulation function; C 0 represents the preset coupling strength threshold. a This is the coupling strength enhancement factor.

[0092] In one implementation, determining the cumulative risk propagation value between two nodes in the directed hypernetwork, and identifying key risk propagation paths in the directed hypernetwork based on the cumulative risk propagation value, includes: Initialize the cumulative risk propagation value for all nodes; Select the node with the largest cumulative risk transmission value from the untraversed nodes as the current node. Update the cumulative risk transmission value of the adjacent nodes based on the cumulative risk transmission value of the current node and the combined edge weight from the current node to its adjacent nodes. After traversing all nodes, the path with the largest cumulative risk transmission value between two nodes is extracted as the key risk propagation path.

[0093] In one implementation, the cumulative risk transfer value is calculated using the following formula: ; in, i , j Indicates two different nodes v i , v j , for slope v i , v j The corresponding nodes; Path i→j Indicates from node v i To the node v j Any risk transmission path, e pq Path i→j Represents any associated edge on any risk propagation path. w pq Indicates associated edges e pq The overall edge weights.

[0094] In one embodiment, the device is further configured to: For a node in the directed hypernetwork, the cumulative risk value of the node is obtained by calculating the propagation of the instability risk of other nodes to that node through the comprehensive edge weights.

[0095] In one implementation, the step of calculating the propagation of the instability risk of other nodes to a node in the directed hypernetwork using the comprehensive edge weights to obtain the cumulative risk value of that node includes: The cumulative risk value of a node in the directed hypernetwork is calculated using the following formula: ; in, i Represents the nodes in the directed hypernetwork. v i , for slope vi The corresponding node; Represents a node v i exist t Accumulated risk value over time; Pre( i ) represents a node v i The set of predecessor nodes, wherein the predecessor node refers to the node that can transmit instability risk to the node through the risk propagation path. v i Other nodes; l ji Indicating instability risk from nodes v j propagation to nodes v i The risk propagation delay coefficient; Δ t ji Indicating instability risk from nodes v j propagation to nodes v i Time delay; P j ( t -Δ t ji ) represents a node v j exist t -Δ t ji The probability of instantaneous instability at any given moment; w ji Represents a node v j To node v i The overall edge weights.

[0096] In one implementation, the instantaneous instability probability is calculated using the following formula: ; in, i Represents the nodes in the directed hypernetwork. v i , for slope v i The corresponding node; P i ( t ) represents a node v i exist t The probability of instantaneous instability at any given moment; F s,i ( t ) is a slope v i exist tSafety factor at any time; F s,min slope v i The minimum safety factor; F s,max slope v i The maximum safety factor.

[0097] In one embodiment, the device is further configured to: For a node in the directed hypernetwork, the betweenness centrality of the node is determined based on the number of critical risk propagation paths passing through the node, and whether the node is an unstable critical node is determined based on the betweenness centrality, out-degree, and in-degree of the node.

[0098] In one implementation, determining the betweenness centrality of a node based on the number of critical risk propagation paths passing through the node includes: The betweenness centrality of a node is calculated using the following formula: ; in, B ( v ) represents a node v betweenness centrality; s xy For the node x To the node y The number of key risk transmission paths s xy ( v ) is a slave node x Passing through the node v To the node y The number of key risk transmission paths n The number of nodes in the directed hypernetwork.

[0099] In one embodiment, the device is further configured to: Based on the cumulative risk transmission value of the key risk transmission path, the early warning level of the key risk transmission path and the corresponding prevention and control measures are determined.

[0100] In one embodiment, the first monitoring data includes slope geometric parameters, geotechnical parameters, structural surface development characteristics, hydrogeological parameters, and historical deformation parameters; the slope geometric parameters include slope, slope height, and slope area; the geotechnical parameters include cohesion, internal friction angle, and unit weight; the structural surface development characteristics include attitude, spacing, and connectivity; the hydrogeological parameters include permeability coefficient, pore water pressure, and groundwater level; and the historical deformation parameters include displacement rate and deformation trend.

[0101] The specific details of each part of the above-mentioned device have been described in detail in the method section of the implementation plan. For any undisclosed details, please refer to the implementation plan of the method section, and therefore will not be repeated here.

[0102] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0103] This disclosure also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the method steps of various exemplary embodiments of this disclosure.

[0104] In one implementation, the computer program product can be a tangible product, such as a computer-readable storage medium storing a computer program. The readable storage medium can be based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, and includes, but is not limited to: Random Access Memory (RAM), Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, Hard Disk Drive (HDD), Solid State Disk (SSD), etc. For example, the computer program product can be a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0105] In one implementation, the computer program product can be an intangible product. For example, the computer program product can be a virtual digital product, such as an executable file or installation package containing a computer program.

[0106] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0107] Computer programs can be carried or transmitted via signals such as electrical, magnetic, optical, electromagnetic, and infrared rays. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to be executed by the processor of the electronic device) the method steps of various embodiments of this disclosure, such as... Figure 1 The method and steps.

[0108] Implementing the above method steps through a computer program achieves the following technical effects: First monitoring data of multiple slopes in the reservoir area of ​​a hybrid pumped storage power station is acquired; second monitoring data of the second slope is acquired when the first slope becomes unstable to determine the instability transmission intensity from the first to the second slope; and the coupling intensity of the hybrid field is determined based on the correlation and spatial structure relationship of the first monitoring data of different slopes. Using slopes as nodes, the comprehensive edge weights between nodes are determined by integrating the instability transmission intensity and the hybrid field coupling intensity, and a directed supernetwork is constructed. Then, key risk propagation paths are identified based on the cumulative risk transmission value. By introducing the instability transmission intensity, the risk transmission capacity between slopes is quantified, and the hybrid field coupling intensity is combined to characterize the impact of the unique hydraulic linkage of the hybrid pumped storage power station on propagation, enabling the constructed directed supernetwork to more realistically reflect the risk propagation relationship between the slope group. The ultimately identified key risk propagation paths are more consistent with actual working conditions, improving the accuracy and engineering practicality of the identification results, and providing a reliable basis for targeted risk prevention and control of slope groups in hybrid pumped storage power stations.

[0109] This disclosure also provides an electronic device. The electronic device includes a processor and a memory. The memory stores executable instructions for the processor, such as computer programs. The processor executes the executable instructions to perform the method steps of various exemplary embodiments of this disclosure.

[0110] The following is for reference. Figure 8The electronic device is illustrated by way of a general-purpose computing device. It should be understood that... Figure 8 The electronic device 800 shown is merely an example and should not be construed as limiting the functionality or scope of this disclosure.

[0111] like Figure 8 As shown, the electronic device 800 may include: a processor 810, a memory 820, a bus 830, an I / O (input / output) interface 840, and a network adapter 850.

[0112] The memory 820 may include volatile memory, such as RAM 821 and cache unit 822, and may also include non-volatile memory, such as ROM 823. The memory 820 may also include one or more program modules 824, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. For example, program module 824 may include the modules described above.

[0113] The processor 810 may include one or more processing units, such as an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit).

[0114] The processor 810 can be used to execute executable instructions stored in the memory 820 to perform method steps of various embodiments of this disclosure, such as... Figure 1 The method and steps.

[0115] By executing the above method steps through processor 810, the following technical effects are achieved: First monitoring data of multiple slopes in the reservoir area of ​​the hybrid pumped storage power station are acquired; second monitoring data of the second slope is acquired when the first slope becomes unstable to determine the instability transmission intensity from the first slope to the second slope; and the coupling intensity of the mixed field is determined based on the correlation and spatial structure relationship of the first monitoring data of different slopes. Using slopes as nodes, the comprehensive edge weights between nodes are determined by integrating the instability transmission intensity and the coupling intensity of the mixed field, and a directed supernetwork is constructed. Then, key risk propagation paths are identified based on the cumulative risk transmission value. By introducing the instability transmission intensity, the risk transmission capacity between slopes is quantified, and the influence of the unique hydraulic linkage of the hybrid pumped storage power station on propagation is characterized by combining the coupling intensity of the mixed field, enabling the constructed directed supernetwork to more realistically reflect the risk propagation relationship between the slope group. The ultimately identified key risk propagation paths are more consistent with actual working conditions, improving the accuracy and engineering practicality of the identification results, and providing a reliable basis for targeted risk prevention and control of the slope group of the hybrid pumped storage power station.

[0116] Bus 830 is used to connect different components of electronic device 800 and may include data bus, address bus and control bus.

[0117] Electronic device 800 can communicate with one or more external devices 900 (such as keyboard, mouse, external controller, etc.) through I / O interface 840.

[0118] Electronic device 800 can communicate with one or more networks via network adapter 850. For example, network adapter 850 can provide mobile communication solutions such as 3G / 4G / 5G, or wireless communication solutions such as wireless LAN, Bluetooth, and near-field communication. Network adapter 850 can communicate with other modules of electronic device 800 via bus 830.

[0119] In one embodiment, the electronic device 800 further includes a display for displaying a graphical user interface.

[0120] although Figure 8 As not shown in the diagram, other hardware and / or software modules may also be configured in the electronic device 800, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems.

[0121] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects. Exemplarily, these three forms can be referred to as "circuit," "module," and "system," respectively.

[0122] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A method for analyzing the propagation path of slope instability risk, characterized in that, The method includes: Acquire the first monitoring data from multiple slopes in the reservoir area of ​​the hybrid pumped storage power station; If the first slope becomes unstable, second monitoring data related to the instability is obtained from the second slope, and the instability transmission intensity from the first slope to the second slope is determined based on the second monitoring data; the second slope is located downstream of the first slope. Based on the correlation and spatial structure relationship of the first monitoring data of different slopes, the coupling intensity of the mixed field of different slopes is determined; Using the slopes as nodes, the comprehensive edge weights between different nodes are determined based on the instability transmission strength and the coupling strength of the mixed field between different slopes, and a directed supernetwork is constructed. Determine the cumulative risk propagation value between two nodes in the directed hypernetwork, and identify the key risk propagation path in the directed hypernetwork based on the cumulative risk propagation value.

2. The method according to claim 1, characterized in that, The instability transmission intensity includes one or more of the following: hydraulic wave transmission intensity, surge impact transmission intensity, sedimentation transmission intensity, and cumulative damage transmission intensity; determining the instability transmission intensity from the first slope to the second slope based on the second monitoring data includes: Calculate one or more of the following formulas for the hydraulic wave transmission intensity, surge impact transmission intensity, sedimentation transmission intensity, and cumulative damage transmission intensity from the first slope to the second slope: ； ； ； ； in, Indicates the intensity of hydraulic wave transmission. Indicates the intensity of surge impact transmission. Indicates the intensity of blockage transmission. Indicates the cumulative damage transmission intensity; Δ u ij The first slope v i Instability caused the second slope v j Change in pore water pressure at location u j0 The second slope v j The initial pore water pressure; For the surge from the first slope v i Spread to the second slope v j Wave height at that time; The second slope v j The critical safety wave high threshold; The first slope v i The volume of landslide debris resulting from instability; The second slope v j The maximum accumulation volume that can be borne at the toe of the slope; α ij Δ is the migration attenuation coefficient. τ ijk The first slope v i After instability, the first k The secondary circulation hydraulic disturbance affects the second slope v j The amplitude of shear stress generated in the soil and rock mass; τ f,j The second slope v j Fatigue limit strength of soil and rock mass; m These are material constants; N The effective number of cyclic perturbations.

3. The method according to claim 1, characterized in that, The determination of the mixed field coupling intensity of different slopes based on the correlation and spatial structure relationship of the first monitoring data of different slopes includes: The coupling intensity of the mixed field on different slopes is calculated using the following formula: ； in, i , j Indicates two different slopes v i , v j slope v j Located on the slope v i Downstream; C ij Indicates slope v i and slope v j The coupling strength of the mixed field; Δ H ij Indicates slope v i With slope v j The water level difference at the location; L ij Indicates slope v i With slope v j Spatial distance between them; Indicates slope v i With slope v j Correlation coefficient of water level change time series; Indicates slope v i Pumping flow rate in the area; Indicates slope v j The power generation flow rate in the area; α , β , γ These are the weighting coefficients.

4. The method according to claim 1, characterized in that, The determination of the comprehensive edge weights between different nodes based on the instability transmission strength and the coupling strength of the mixed field between different slopes includes: The combined edge weights between different nodes are calculated using the following formula: ； ； in, i , j Indicates two different nodes v i , v j , for slope v i , v j The corresponding nodes; w ij Represents a node v i To the node v j The overall edge weights; η b Indicates the first b Weighting coefficients corresponding to the instability propagation strength; Represents a node v i To the node v j The b Instability propagation strength; C ij Indicates slope v i and slope v j The coupling strength of the mixed field; ( C ij ) is the coupling strength modulation function; C 0 represents the preset coupling strength threshold. a This is the coupling strength enhancement factor.

5. The method according to claim 1, characterized in that, Determining the cumulative risk propagation value between two nodes in the directed hypernetwork, and identifying the critical risk propagation path in the directed hypernetwork based on the cumulative risk propagation value, includes: Initialize the cumulative risk propagation value for all nodes; Select the node with the largest cumulative risk transmission value from the untraversed nodes as the current node. Update the cumulative risk transmission value of the adjacent nodes based on the cumulative risk transmission value of the current node and the combined edge weight from the current node to its adjacent nodes. After traversing all nodes, the path with the largest cumulative risk transmission value between two nodes is extracted as the key risk propagation path.

6. The method according to claim 1, characterized in that, The cumulative risk transfer value is calculated using the following formula: ； in, i , j Indicates two different nodes v i , v j , for slope v i , v j The corresponding nodes; Path i→j Indicates from node v i To the node v j Any risk transmission path, e pq Path i→j Represents any associated edge on any risk propagation path. w pq Indicates associated edges e pq The overall edge weights.

7. The method according to claim 1, characterized in that, The method further includes: For a node in the directed hypernetwork, the cumulative risk value of the node is obtained by calculating the propagation of the instability risk of other nodes to that node through the comprehensive edge weights.

8. The method according to claim 7, characterized in that, For a node in the directed hypernetwork, the cumulative risk value of that node is obtained by calculating the propagation of the instability risk of other nodes to that node through the comprehensive edge weights, including: The cumulative risk value of a node in the directed hypernetwork is calculated using the following formula: ； in, i Represents the nodes in the directed hypernetwork. v i , for slope v i The corresponding node; Represents a node v i exist t Accumulated risk value over time; Pre( i ) represents a node v i The set of predecessor nodes, wherein the predecessor node refers to the node that can transmit instability risk to the node through the risk propagation path. v i Other nodes; λ ji Indicating instability risk from nodes v j propagation to nodes v i The risk propagation delay coefficient; Δ t ji Indicating instability risk from nodes v j propagation to nodes v i Time delay; P j ( t -Δ t ji ) represents a node v j exist t -Δ t ji The probability of instantaneous instability at any given moment; w ji Represents a node v j To node v i The overall edge weights.

9. The method according to claim 8, characterized in that, The instantaneous instability probability is calculated using the following formula: ； in, i Represents the nodes in the directed hypernetwork. v i , for slope v i The corresponding node; P i ( t ) represents a node v i exist t The probability of instantaneous instability at any given moment; F s,i ( t ) is a slope v i exist t Safety factor at any time; F s,min For slope v i The minimum safety factor; F s,max For slope v i The maximum safety factor.

10. The method according to claim 1, characterized in that, The method further includes: For a node in the directed hypernetwork, the betweenness centrality of the node is determined based on the number of critical risk propagation paths passing through the node, and whether the node is an unstable critical node is determined based on the betweenness centrality, out-degree, and in-degree of the node.

11. The method according to claim 10, characterized in that, The determination of the betweenness centrality of a node based on the number of critical risk propagation paths passing through it includes: The betweenness centrality of a node is calculated using the following formula: ； in, B ( v ) represents a node v betweenness centrality; σ xy For the node x To the node y The number of key risk transmission paths σ xy ( v ) is a slave node x Passing through the node v To the node y The number of key risk transmission paths n The number of nodes in the directed hypernetwork.

12. The method according to claim 1, characterized in that, The method further includes: Based on the cumulative risk transmission value of the key risk transmission path, the early warning level of the key risk transmission path and the corresponding prevention and control measures are determined.

13. The method according to any one of claims 1 to 12, characterized in that, The first monitoring data includes slope geometric parameters, geotechnical parameters, structural surface development characteristics, hydrogeological parameters, and historical deformation parameters; the slope geometric parameters include slope, slope height, and slope area; the geotechnical parameters include cohesion, internal friction angle, and unit weight; the structural surface development characteristics include attitude, spacing, and connectivity; the hydrogeological parameters include permeability coefficient, pore water pressure, and groundwater level; and the historical deformation parameters include displacement rate and deformation trend.

14. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 13.

15. An electronic device, characterized in that, include: Processor and memory; The memory is used to store executable instructions of the processor; the processor is configured to implement the method of any one of claims 1 to 13 by executing the executable instructions.

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