Method, device, equipment, and medium for evaluating power grid damage under compound chain disasters

Abstract

The present application provides a method, device, equipment, and medium for evaluating the damage of a power grid under compound chain disasters. The method includes: establishing a physical model of the power grid; establishing an evolution model of compound chain disasters based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; fusing historical ground motion records and the physical model of the power grid to evaluate the probability of power grid damage caused by a single disaster; based on the Copula function and the evolution model of compound chain disasters, evaluating the joint damage probability of the power grid caused by compound disasters; evaluating the difference between the probability of power grid damage caused by a single disaster and the sum of the joint damage probabilities of the power grid caused by compound disasters under compound chain disasters. The method provided by the present application evaluates the damage of the power grid under compound chain disasters through the probability of power grid damage caused by a single disaster and the joint damage probability of the power grid caused by compound disasters, achieving accurate and rapid evaluation of the damage of the power grid under compound chain disasters.

Description

Technical Field

[0001] The present application relates to the technical field of power grid damage assessment, and particularly to a method, device, equipment, and medium for power grid damage assessment under compound chain disasters. Background Art

[0002] With the increase in global climate change and extreme weather events, the power grid is facing increasingly complex threats of compound chain disasters. A compound chain disaster refers to the interaction and mutual influence of multiple disasters, forming a disaster chain that causes serious damage to the power grid. For example, an earthquake may trigger a landslide, which in turn damages the tower foundation of a transmission line, resulting in a power outage; a flood may cause the substation equipment to be flooded, triggering equipment failures, etc.

[0003] Traditional power grid damage assessment methods often only consider single disaster factors or insufficiently consider the coupling effects of compound disasters. These methods are mainly based on physical models, and the damage is evaluated by analyzing the mechanical and electrical characteristics of power grid components. However, in the scenario of compound chain disasters, it is difficult for physical models to comprehensively consider the complex disaster evolution process and the interaction of multiple factors, resulting in inaccurate assessment results.

[0004] At the same time, with the improvement of the digitalization level of the power grid, a large amount of operation data has been collected, but these data have not been fully utilized in traditional assessment methods. How to combine data-driven methods with physical models to achieve accurate and rapid assessment of power grid damage under compound chain disasters is an urgent problem to be solved in the current power grid security field. Summary of the Invention

[0005] To solve one of the above technical defects, the present application provides a method, device, equipment, and medium for power grid damage assessment under compound chain disasters.

[0006] In the first aspect of the present application, a method for power grid damage assessment under compound chain disasters is provided. The method includes:

[0007] Establish a power grid physical model; wherein, the power grid physical model includes a power grid topological structure, equipment parameters, and operating status;

[0008] Establish a compound chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data;

[0009] Fuse historical ground motion records and the power grid physical model to evaluate the probability of power grid damage caused by a single disaster; wherein, the probability of power grid damage caused by a single disaster includes: the probability of power grid damage caused by an earthquake disaster 、the probability of power grid damage caused by a landslide 、the probability of power grid damage caused by a debris flow ;

[0010] Based on the Copula function and the compound chain disaster evolution model, evaluate the probability of combined damage to the power grid caused by compound disasters; among them, the probability of combined damage to the power grid caused by compound disasters includes: the probability of combined damage to the power grid caused by earthquake and landslide and the probability of combined damage to the power grid caused by mountain and landslide debris flow and the probability of combined damage to the power grid caused by earthquake and debris flow ;

[0011] Evaluate the difference between the sum of the probabilities of power grid damage caused by single disasters and the sum of the probabilities of combined damage to the power grid caused by compound disasters under compound chain disasters; among them, the sum of the probabilities of power grid damage caused by single disasters is and the sum of the probabilities of combined damage to the power grid caused by compound disasters is .

[0012] Optionally, the compound chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-debris flow disaster time evolution model, earthquake-landslide-debris flow disaster time evolution model;

[0013] Establish a compound chain disaster evolution model based on Bayesian network according to sensor data, meteorological data, geographic information data and historical disaster data, including:

[0014] According to sensor data, meteorological data, geographic information data and historical disaster data, determine the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, lithology;

[0015] Construct an earthquake disaster Bayesian network model with the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, and the geological structure as inputs and the earthquake intensity as the output;

[0016] Construct a landslide disaster Bayesian network model with earthquake intensity, slope, altitude, and rock and soil type as inputs and landslide volume and landslide speed as outputs;

[0017] Construct a debris flow disaster Bayesian network model with 24-hour precipitation, landslide volume, landslide speed, and vegetation coverage as inputs and the total volume of a single debris flow deposit and the peak debris flow discharge as outputs;

[0018] Construct an aftershock time evolution Bayesian network model with earthquake intensity, distance from the fault zone, geological structure, and lithology as inputs and the earthquake magnitude of aftershocks, the focal depth of aftershocks, and the occurrence time of aftershocks as outputs;

[0019] Establish a time evolution model for earthquake-landslide disasters based on the Bayesian network model for earthquake disasters, the Bayesian network model for landslide disasters, and the Bayesian network model for aftershock time evolution;

[0020] Construct a time evolution model for landslide-debris flow disasters based on the Bayesian network model for landslide disasters, the Bayesian network model for debris flow disasters, and the Bayesian network model for aftershock time evolution;

[0021] Stitch together the time evolution model for earthquake-landslide disasters and the time evolution model for landslide-debris flow disasters to obtain the time evolution model for earthquake-landslide-debris flow disasters.

[0022] Optionally, the earthquake magnitude is a discrete node, and the values of the earthquake magnitude include: below magnitude 4.5, 4.5 - 6, 6 - 7, 7 - 8, above magnitude 8. The earthquake magnitude is determined according to the energy released by the earthquake source in the evaluation area;

[0023] The focal depth is a discrete node, and the values of the focal depth include: below 5 km, 5 - 10 km, 10 - 15 km, 15 - 20 km, 20 - 25 km, 25 - 30 km, above 30 km. The focal depth is determined according to the vertical distance from the earthquake source to the ground in the evaluation area;

[0024] The epicentral distance is a discrete node, and the values of the epicentral distance include: below 0.2L, 0.2L - 0.4L, 0.4L - 0.6L, 0.6L - 0.8L, 0.8L - L, above L. The epicentral distance is determined according to the straight-line distance from any point on the ground in the evaluation area to the epicenter, where L is the length of the distance window;

[0025] The geological structure is a discrete node, and the values of the geological structure include: monoclinic structure, fold structure, fault structure, block structure. The geological structure is determined according to the shape and arrangement of rock layers in the crust of the evaluation area;

[0026] The slope is a discrete node, and the values of the slope include: below 10 degrees, 10 - 45 degrees, 45 - 90 degrees. The slope is determined according to the slope degree of the landform in the evaluation area;

[0027] The altitude is a discrete node, and the values of the altitude include: below 500 m, 500 - 1000 m, 1000 - 1500 m, 1500 - 2000 m, above 2000 m. The altitude is determined according to the vertical distance between the ground and the sea level in the evaluation area;

[0028] The rock and soil type is a discrete node, and the values of the rock and soil type include: cohesive soil, loess, filled soil, accumulated soil, broken rock, rock. The rock and soil type is determined according to the material composition of the sliding mass in the evaluation area;

[0029] The 24-hour precipitation is a discrete node, and the values of the 24-hour precipitation include: below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm. The 24-hour precipitation is determined based on the continuous rainfall in the 12 hours before and 12 hours after the landslide in the evaluation area;

[0030] The vegetation coverage is a discrete node, and the values of the vegetation coverage include: below 45%, 45% - 60%, 60% - 75%, 75% - 100%. The vegetation coverage is determined based on the percentage of the vertical projection area of the ground in the evaluation area to the total area of the evaluation area;

[0031] The distance from the fault zone is a discrete node, and the values of the distance from the fault zone include: below 10 km, 10 - 20 km, 20 - 30 km, 30 - 40 km, 40 - 50 km, above 50 km. The distance from the fault zone is determined based on the spatial distance to the nearest active fault zone from the current location;

[0032] The lithology is a discrete node, and the values of the lithology include sedimentary rock, igneous rock, and metamorphic rock. The lithology is determined based on the physical and chemical properties of the rocks in the evaluation area.

[0033] Optionally, by integrating historical ground motion records and the power grid physical model, evaluate the probability of power grid damage caused by a single disaster, including:

[0034] Integrate historical ground motion records and the power grid physical model to evaluate the probability of each line segment of the power grid failing, and determine based on the probability of each line segment of the power grid failing ; where the probability of the th line segment of the power grid failing ; is the power grid line segment identifier, is the total number of utility poles in the th line segment, is the utility pole identifier in the th line segment, is the earthquake intensity, is the identifier of the earthquake involved in the evaluation, is the earthquake intensity of the earthquake involved in the evaluation, is the fault state, is the th in the th utility pole in the condition with the fault state being probability; , is the standard normal distribution function, To evaluate the peak ground acceleration value of the earthquake involved; is the median collapse intensity, is the standard deviation of the ground motion intensity index, , ; is the total number of historical ground motion records, is the ground motion record identifier, is the peak ground acceleration value of the th record in the historical ground motion records;

[0035] Fuse historical ground motion records and the power grid physical model to evaluate ; Among them, is the total number of times to simulate the probability of pole damage under the impact of landslides through the Monte Carlo simulation method, is the landslide-induced disaster intensity obtained by simulation under the impact of landslides greater than the disaster resistance ability of transmission poles; , is the landslide volume, is the expected landslide speed, , is the landslide speed, is the probability of landslide occurrence, , is the factor identifier affecting the occurrence of landslides, is the regression coefficient of the th factor affecting the occurrence of landslides, is the value of the th factor affecting the occurrence of landslides;

[0036] Fuse historical ground motion records and the power grid physical model to evaluate ; Among them, is the impact pressure of debris flow on the structure, , is the building shape coefficient, is the debris flow unit weight, is the acceleration due to gravity, is the average cross-sectional velocity of the debris flow, is the angle between the force-bearing surface of the building and the direction of the debris flow impact pressure, is the impact energy resistance of the transmission pole.

[0037] Optionally, for any earthquake , ;

[0038] Among them, is the earthquake identifier, earthquake The earthquake is any one of the historical earthquake ground motion records, or the earthquake involved in the assessment; is the earthquake 's peak ground acceleration value, is the earthquake 's earthquake magnitude, is the distance from the assessment area to the epicenter of the earthquake ; is the earthquake 's epicentral distance.

[0039] Optionally, the probability density of the Copula function ; or,

[0040] The probability density of the Copula function ; or,

[0041] The probability density of the Copula function ; or,

[0042] The probability density of the Copula function ;

[0043] wherein, and are random variables, is the Gaussion distribution function, is the standard normal distribution function, is 's inverse function, is the parameter of the Copula function.

[0044] Optionally, the parameters of the Copula function are estimated by maximizing the likelihood function of the observed data;

[0045] wherein, the likelihood function of maximizing the observed data is ;

[0046] is the observed data identifier, is the total amount of observed data, and are the th random variables of the observed data, is the probability density of the Copula function for the th observed data.

[0047] In the second aspect of the present application, a power grid damage assessment device under compound chain disasters is provided, and the device includes:

[0048] A first model establishment module, configured to establish a power grid physical model; wherein, the power grid physical model includes a power grid topological structure, equipment parameters, and operating status;

[0049] A second model establishment module, configured to establish a compound chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data;

[0050] A first evaluation module, configured to fuse historical ground motion records and the power grid physical model established by the first model establishment module to evaluate the power grid damage probability caused by a single disaster; wherein, the power grid damage probability caused by a single disaster includes: the power grid damage probability caused by an earthquake disaster and the power grid damage probability caused by a landslide and the power grid damage probability caused by a debris flow ;

[0051] A second evaluation module, configured to evaluate the power grid combined damage probability caused by a compound disaster based on the Copula function and the compound chain disaster evolution model established by the second model establishment module; wherein, the power grid combined damage probability caused by a compound disaster includes: the power grid combined damage probability caused by an earthquake and a landslide and the power grid combined damage probability caused by a mountain and landslide debris flow and the power grid combined damage probability caused by an earthquake and a debris flow ;

[0052] A third evaluation module, configured to evaluate the difference between the sum of the power grid damage probabilities caused by single disasters and the sum of the power grid combined damage probabilities caused by compound disasters under compound chain disasters; wherein, the sum of the power grid damage probabilities caused by single disasters is and the sum of the power grid combined damage probabilities caused by compound disasters is .

[0053] In a third aspect of the present application, an electronic device is provided, including:

[0054] A memory;

[0055] A processor; and

[0056] A computer program;

[0057] wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in the first aspect above.

[0058] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the method as described in the first aspect above.

[0059] The present application provides a method, device, equipment, and medium for evaluating the damage of a power grid under compound chain disasters. The method includes: establishing a physical model of the power grid; establishing an evolution model of compound chain disasters based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; fusing historical ground motion records and the physical model of the power grid to evaluate the probability of power grid damage caused by a single disaster; based on the Copula function and the evolution model of compound chain disasters, evaluating the combined probability of power grid damage caused by compound disasters; and evaluating the difference between the probability of power grid damage caused by a single disaster and the sum of the combined probability of power grid damage caused by compound disasters under compound chain disasters. The method provided by the present application establishes an evolution model of compound chain disasters based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; and then evaluates the damage of the power grid under compound chain disasters based on the probability of power grid damage caused by a single disaster evaluated by the physical model of the power grid and the combined probability of power grid damage caused by compound disasters evaluated by the evolution model of compound chain disasters, realizing accurate and rapid evaluation of the damage of the power grid under compound chain disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0061] Figure 1 is a schematic flow chart of a method for evaluating the damage of a power grid under compound chain disasters provided by an embodiment of the present application;

[0062] Figure 2 is a schematic diagram of a time evolution model of earthquake-landslide-debris flow disasters provided by an embodiment of the present application;

[0063] Figure 3 is a schematic diagram of the result of evaluating the damage of a power grid under compound chain disasters provided by an embodiment of the present application;

[0064] Figure 4 is a schematic structural diagram of a device for evaluating the damage of a power grid under compound chain disasters provided by an embodiment of the present application;

[0065] Figure 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] In order to make the technical solutions and advantages in the embodiments of the present application clearer and more understandable, the exemplary embodiments of the present application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0067] In the process of implementing the present application, the inventors found that traditional power grid damage assessment methods often only consider a single disaster factor, or insufficiently consider the coupling effect of compound disasters. These methods are mainly based on physical models, and evaluate damage by analyzing the mechanical and electrical characteristics of power grid components. However, in the scenario of compound chain-generated disasters, it is difficult for physical models to comprehensively consider the complex disaster evolution process and the interaction of multiple factors, resulting in inaccurate assessment results.

[0068] In view of the above problems, the embodiments of the present application provide a power grid damage assessment method, device, equipment, and medium under compound chain-generated disasters. The method includes: establishing a power grid physical model; establishing a compound chain-generated disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; fusing historical ground motion records and the power grid physical model to evaluate the probability of power grid damage caused by a single disaster; based on the Copula function and the compound chain-generated disaster evolution model, evaluating the joint probability of power grid damage caused by compound disasters; evaluating the difference between the probability of power grid damage caused by a single disaster and the sum of the joint probability of power grid damage caused by compound disasters under compound chain-generated disasters. The method provided by the present application establishes a compound chain-generated disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; and then evaluates the power grid damage under compound chain-generated disasters based on the probability of power grid damage caused by a single disaster evaluated by the power grid physical model and the joint probability of power grid damage caused by compound disasters evaluated by the compound chain-generated disaster evolution model, realizing accurate and rapid assessment of power grid damage under compound chain-generated disasters.

[0069] See Figure 1 , the implementation process of the power grid damage assessment method under compound chain-generated disasters provided in this embodiment is as follows:

[0070] S101, establish a power grid physical model.

[0071] Among them, the power grid physical model includes the power grid topology structure, equipment parameters, and operating status.

[0072] Among them, the power grid topology structure can reflect the load information on network nodes, the connection relationship of the network, etc.

[0073] For example, the power grid topology is the power system topology with 33 nodes. Among them, a certain node is a substation node, the voltage is 1.05Un, and the active power of the network is 6.76MW.

[0074] S102. Establish a compound chain disaster evolution model based on Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data.

[0075] Among them, the compound chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-debris flow disaster time evolution model, earthquake-landslide-debris flow disaster time evolution model.

[0076] The implementation process of step S102 is as follows:

[0077] S102-1. According to sensor data, meteorological data, geographic information data, and historical disaster data, determine the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology.

[0078] Among them, the earthquake magnitude is a discrete node, and the values of the earthquake magnitude include: below 4.5 magnitude, 4.5 - 6 magnitude (i.e., moderately strong earthquake), 6 - 7 magnitude (i.e., strong earthquake), 7 - 8 magnitude (i.e., great earthquake), above 8 magnitude (i.e., giant earthquake). The earthquake magnitude is determined according to the energy released by the earthquake source in the evaluation area.

[0079] The focal depth is a discrete node, and the values of the focal depth include: below 5 km, 5 - 10 km, 10 - 15 km, 15 - 20 km, 20 - 25 km, 25 - 30 km, above 30 km. The focal depth is determined according to the vertical distance from the earthquake source to the ground in the evaluation area.

[0080] The epicentral distance is a discrete node, and the values of the epicentral distance include: below 0.2L, 0.2L - 0.4L, 0.4L - 0.6L, 0.6L - 0.8L, 0.8L - L, above L. The epicentral distance is determined according to the straight-line distance from any point on the ground in the evaluation area to the epicenter, where L is the length of the distance window.

[0081] The geological structure is a discrete node, and the values of the geological structure include: monoclinic structure, fold structure, fault structure, block structure. The geological structure is determined according to the form and arrangement of rock layers in the crust of the evaluation area.

[0082] The slope is a discrete node, and the values of the slope include: below 10 degrees, 10 - 45 degrees, 45 - 90 degrees. The slope is determined according to the slope degree of the landform in the evaluation area.

[0083] The altitude is a discrete node, and the values of altitude include: below 500 meters, 500 - 1000 meters, 1000 - 1500 meters, 1500 - 2000 meters, above 2000 meters. The altitude is determined according to the vertical distance between the ground and the sea level within the evaluation area.

[0084] The geotechnical type is a discrete node, and the values of the geotechnical type include: cohesive soil, loess, filled soil, accumulated soil, fractured rock, rock. The geotechnical type is determined according to the material composition of the landslide body within the evaluation area.

[0085] The 24 - hour precipitation is a discrete node, and the values of the 24 - hour precipitation include: below 0.1 mm (i.e., trace rainfall), 0.1 - 9.9 mm (i.e., light rain), 10.0 - 24.9 mm (i.e., moderate rain), 25.0 - 49.9 mm (i.e., heavy rain), 50.0 - 99.9 mm (i.e., rainstorm), 100.0 - 249.9 mm (i.e., heavy rainstorm), above 250.0 mm (i.e., extremely heavy rainstorm). The 24 - hour precipitation is determined according to the continuous rainfall in the 12 hours before and 12 hours after the landslide occurred within the evaluation area (i.e., the precipitation within 24 hours in total, 12 hours before and 12 hours after the landslide disaster occurred).

[0086] The vegetation coverage is a discrete node, and the values of the vegetation coverage include: below 45% (i.e., low coverage), 45% - 60% (i.e., medium coverage), 60% - 75% (i.e., medium - high coverage), 75% - 100% (i.e., high coverage). The vegetation coverage is determined according to the percentage of the vertical projection area of the ground within the evaluation area to the total area of the evaluation area.

[0087] The distance from the fault zone is a discrete node, and the values of the distance from the fault zone include: below 10 km, 10 - 20 km, 20 - 30 km, 30 - 40 km, 40 - 50 km, above 50 km. The distance from the fault zone is determined according to the spatial distance to the nearest active fault zone from the current location.

[0088] The lithology is a discrete node, and the values of the lithology include sedimentary rock, igneous rock, metamorphic rock. The lithology is determined according to the physical and chemical properties of the rocks within the evaluation area.

[0089] S102 - 2. Construct a Bayesian network model for earthquake disasters with the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, and the geological structure as inputs and the seismic intensity as the output.

[0090] The nodes of the Bayesian network model for earthquake disasters constructed in step S102 - 2 can be as shown in Table 1.

[0091] Table 1

[0092]

[0093] S102-3, construct a Bayesian network model for landslide disasters with seismic intensity, slope, elevation, and rock and soil type as inputs and landslide volume and landslide speed as outputs.

[0094] The nodes of the Bayesian network model for landslide disasters constructed in step S102-3 can be shown in Table 2.

[0095] Table 2

[0096]

[0097] S102-4, construct a Bayesian network model for debris flow disasters with 24-hour precipitation, landslide volume, landslide speed, and vegetation coverage as inputs and total volume of a single debris flow deposit and peak debris flow discharge as outputs.

[0098] The nodes of the Bayesian network model for debris flow disasters constructed in step S102-4 can be shown in Table 3.

[0099] Table 3

[0100]

[0101] S102-5, construct a Bayesian network model for aftershock time evolution with seismic intensity, distance from the fault zone, geological structure, and lithology as inputs and earthquake magnitude, focal depth, and occurrence time of aftershocks as outputs.

[0102] The Bayesian network model for aftershock time evolution considers the influence of aftershocks triggered by an earthquake in various locations. According to the influence relationship between the main shock and aftershocks, time factors are introduced. The input nodes are determined to be seismic intensity, distance from the fault zone, geological structure, and lithology, and the output nodes are earthquake magnitude, focal depth, and occurrence time. The nodes of the Bayesian network model for aftershock time evolution constructed in step 102-5 can be shown in Table 4.

[0103] Table 4

[0104]

[0105] S102-6, establish a time evolution model for earthquake-landslide disasters based on the Bayesian network model for earthquake disasters, the Bayesian network model for landslide disasters, and the Bayesian network model for aftershock time evolution.

[0106] The Bayesian network models for earthquake disasters, landslide disasters, and aftershock time evolution have common variables, namely, the output node "earthquake intensity" of the Bayesian network model for earthquake disasters, the input node "earthquake intensity" of the Bayesian network model for landslide disasters, and the input node "earthquake intensity" of the Bayesian network model for aftershock time evolution. Through the operation of merging common variables, an earthquake-landslide disaster time evolution model can be constructed.

[0107] Landslide disasters triggered by earthquakes can be divided into two types. On the one hand, due to the triggering effect of earthquakes, a large number of landslides occur during the earthquake, which are called coseismic landslides. On the other hand, earthquakes cause new damage to slopes, prompting landslides to occur successively after the earthquake, which are called post-seismic landslides. Evolution models are established for the two types of landslides respectively. Since coseismic landslides occur simultaneously with earthquakes, their occurrence time is the earthquake occurrence time. For post-seismic landslides, their occurrence time is the time interval from the earthquake occurrence time. Therefore, the evolution time in the earthquake-landslide disaster time evolution model represents the duration of the landslide disaster.

[0108] S102-7, construct a landslide-debris flow disaster time evolution model according to the Bayesian network model for landslide disasters, the Bayesian network model for debris flow disasters, and the Bayesian network model for aftershock time evolution.

[0109] The Bayesian network models for landslide disasters, debris flow disasters, and aftershock time evolution have common variables, namely, the output nodes "landslide volume" and "landslide speed" of the Bayesian network model for landslide disasters, the input nodes "landslide volume" and "landslide speed" of the Bayesian network model for debris flow disasters, the input node "earthquake intensity" of the Bayesian network model for landslide disasters, and the input node "earthquake intensity" of the Bayesian network model for aftershock time evolution. Through the operation of merging common variables, a landslide-debris flow disaster time evolution model can be constructed.

[0110] S102-8, splice the earthquake-landslide disaster time evolution model and the landslide-debris flow disaster time evolution model to obtain an earthquake-landslide-debris flow disaster time evolution model.

[0111] The earthquake-landslide-debris flow disaster time evolution model can be as Figure 2 shown.

[0112] S103, fuse historical ground motion records and the power grid physical model to evaluate the probability of power grid damage caused by a single disaster.

[0113] Among them, the probability of power grid damage caused by a single disaster includes: the probability of power grid damage caused by earthquake disasters and the probability of power grid damage caused by landslides , Probability of power grid damage caused by debris flow .

[0114] The implementation process of step S103 is as follows:

[0115] S103-1, Integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and determine according to the probability of failure of each line segment of the power grid .

[0116] For example, integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and determine the maximum value among the probabilities of failure of each line segment of the power grid as . Or, integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and determine the average value of the probabilities of failure of each line segment of the power grid as . Or, integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and determine the median value (or the value of a preset position, or the mode value, etc.) among the probabilities of failure of each line segment of the power grid as . Or, integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and determine the minimum value among the probabilities of failure of each line segment of the power grid as . Or, integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and determine the average value of multiple maximum probabilities among the probabilities of failure of each line segment of the power grid as . Or, integrate historical ground motion records and the power grid physical model to evaluate the probability of failure of each line segment of the power grid, and use other existing feasible solutions to determine the maximum value among the probabilities of failure of each line segment of the power grid as .

[0117] Among them, the necessary condition for the normal operation of the line is that all the telegraph poles on the line segment are in good condition. Therefore, the probability of failure of the th line segment of the power grid .

[0118] is the power grid line segment identifier, is the total number of telegraph poles in the th line segment, is the telegraph pole identifier in the th line segment, is the earthquake intensity, is the identifier of the earthquake involved in the evaluation, is the fault state, is the The probability that the th utility pole in the line segment fails under the condition.

[0119] The damage of the distribution network line is mainly determined by the pole collapse rate, that is, the conditional probability that the bearing capacity of the utility pole reaches or exceeds the collapse limit value during an earthquake. Therefore , is the standard normal distribution function, is the peak ground acceleration value of the earthquake involved in the assessment. is the median collapse intensity (i.e., the ground motion intensity corresponding to a 50% probability of structural collapse), is the standard deviation of the ground motion intensity index (this value reflects the discreteness of the calculation results using different ground motion records).

[0120] , .

[0121] is the total number of historical ground motion records, is the ground motion record identifier, is the th peak ground acceleration value of the historical ground motion record.

[0122] For the measurement of earthquake disasters, radiating outward from the epicenter, for the divided regions, the average distance from all points in the region to the epicenter is selected as the basis, and the calculated ground motion parameters are used as the reference values for the entire region. For example, among them, for any earthquake , .

[0123] Among them, is the earthquake identifier, and the earthquake is any earthquake in the historical ground motion records, or the earthquake involved in the assessment. is the earthquake 's peak ground acceleration value, is the earthquake 's earthquake magnitude, is the distance from the assessment region to the epicenter of the earthquake , is the epicentral distance of the earthquake .

[0124] S103 - 2, integrating historical ground motion records and the power grid physical model, evaluates .

[0125] The probability of power grid damage caused by landslides can be calculated using the Monte Carlo method. Assume that the total number of times of simulating the probability of pole tower damage under the impact of landslides by the Monte Carlo simulation method is and the disaster-causing intensity of the landslide under the impact of the landslide is simulated The number of times greater than the disaster resistance ability of the transmission pole is Then .

[0126] Among them, is the total number of times of simulating the probability of pole tower damage under the impact of landslides by the Monte Carlo simulation method, is the number of times that the disaster-causing intensity of the landslide under the impact of the landslide is greater than the disaster resistance ability of the transmission pole.

[0127] , is the landslide volume, is the expected landslide speed, , is the landslide speed, is the probability of the occurrence of the landslide.

[0128] The impact and damage process of the landslide body on the transmission tower is actually a contest between the disaster-causing intensity of the landslide body and the disaster resistance performance of the transmission tower. Based on historical data, the probability of a landslide caused by an earthquake can be: , is the factor identifier affecting the occurrence of the landslide, is the regression coefficient of the th factor affecting the occurrence of the landslide, is the value of the th factor affecting the occurrence of the landslide.

[0129] Among them, the factors affecting the occurrence of the landslide include elevation, slope, aspect, distance from the transmission tower pole, etc.

[0130] S103-3, integrating historical ground motion records and the power grid physical model, evaluate .

[0131] Among them, is the impact pressure of the debris flow on the structure (unit: Pa).

[0132] The positional relationship between the pole tower and the debris flow ditch is mainly divided into the following two types: ① The pole tower is located on the debris flow accumulation fan; ② The pole tower is located on both sides of the debris flow channel. The damage methods of the debris flow to the building are generally mainly impact and siltation. Based on the above relationship, the impact pressure of the debris flow on the structure .

[0133] is the shape factor of the building, and for the transmission tower foundation, it can be taken as 1.33. is the density of debris flow (unit: t / m 3 ), is the acceleration due to gravity (take m / s 2 ), is the average flow velocity of the debris flow cross-section (unit: m / s), is the included angle between the force-bearing surface of the building and the direction of the impact pressure of the debris flow (such as taking 85°), is the impact resistance energy of the transmission tower.

[0134] The impact damage process of the debris flow on the transmission tower is actually a competition between the disaster-causing intensity of the debris flow and the disaster-resistant performance of the transmission tower. Based on the functional relationship, the probability of power grid damage caused by the debris flow is expressed as the ratio of the impact pressure to the impact resistance energy. When the impact pressure of the debris flow is too large and exceeds the impact resistance energy of the transmission tower itself, the transmission tower is damaged, that is .

[0135] It should be noted that the implementation processes of the above steps S103-1, S103-2, and S103-3 are only examples. In specific implementation, steps S103-1, S103-2, and S103-3 can be carried out simultaneously, or S103-2 can be executed first, then S103-1, and finally S103-3, etc. This embodiment does not limit the execution order of steps S103-1, S103-2, and S103-3.

[0136] S104, based on the Copula function and the compound chain disaster evolution model, evaluate the joint damage probability of the power grid caused by the compound disaster.

[0137] Among them, the joint damage probability of the power grid caused by the compound disaster includes: the joint damage probability of the power grid caused by the earthquake and landslide , the joint damage probability of the power grid caused by the mountain and landslide debris flow , the joint damage probability of the power grid caused by the earthquake and debris flow .

[0138] In addition, the probability density of the Copula function . Or, the probability density of the Copula function . Or, the probability density of the Copula function . Or, the probability density of the Copula function .

[0139] Among them, and are random variables, is the Gaussion distribution function, is the standard normal distribution function, is the inverse function of is the parameter of the Copula function.

[0140] In specific implementation, according to historical data (such as historical ground motion records), the physical model of the power grid, and the Akaike Information Criterion (AIC), a suitable Copula function can be selected from various models of the Copula function shown in Table 5.

[0141] Table 5

[0142]

[0143] In addition, the parameters of the Copula function can be estimated by maximizing the likelihood function of the observed data.

[0144] Among them, the likelihood function of maximizing the observed data is .

[0145] is the observed data identifier, is the total amount of observed data, and are the random variables of the th observed data, is the probability density of the Copula function for the th observed data.

[0146] S105, evaluate the difference between the probability sum of the power grid damage caused by a single disaster and the probability sum of the combined power grid damage caused by the compound disaster under the compound chain disaster.

[0147] Among them, the probability sum of the power grid damage caused by a single disaster is , and the probability sum of the combined power grid damage caused by the compound disaster is .

[0148] That is, the power grid damage under the compound chain disaster .

[0149] A possible exemplary evaluation result of the power grid damage under the compound chain disaster can be as Figure 3 shown.

[0150] The method for evaluating the power grid damage under the compound chain disaster provided in this embodiment is a data and physics co-driven method for evaluating the power grid damage under the compound chain disaster, which can make full use of the advantages of data and physical models, improve the accuracy and efficiency of the power grid damage evaluation, and solve the problems of inaccurate and incomplete evaluation in the case of compound chain disasters.

[0151] This embodiment provides a method for evaluating the damage of a power grid under compound chain disasters, which includes establishing a physical model of the power grid; establishing an evolution model of compound chain disasters based on a Bayesian network according to sensor data, meteorological data, geographical information data, and historical disaster data; fusing historical ground motion records and the physical model of the power grid to evaluate the probability of power grid damage caused by a single disaster; evaluating the probability of combined power grid damage caused by compound disasters based on the Copula function and the evolution model of compound chain disasters; and evaluating the difference between the probability of power grid damage caused by a single disaster and the sum of the probability of combined power grid damage caused by compound disasters under compound chain disasters. The method provided in this embodiment establishes an evolution model of compound chain disasters based on a Bayesian network according to sensor data, meteorological data, geographical information data, and historical disaster data; and then evaluates the damage of the power grid under compound chain disasters based on the probability of power grid damage caused by a single disaster evaluated according to the physical model of the power grid and the probability of combined power grid damage caused by compound disasters evaluated according to the evolution model of compound chain disasters, realizing accurate and rapid evaluation of the damage of the power grid under compound chain disasters.

[0152] Based on the same inventive concept of the method for evaluating the damage of a power grid under compound chain disasters, this embodiment provides a device for evaluating the damage of a power grid under compound chain disasters. Refer to Figure 4 and the device includes:

[0153] The first model establishment module 401 is used to establish a physical model of the power grid. Among them, the physical model of the power grid includes the power grid topology structure, equipment parameters, and operating status.

[0154] The second model establishment module 402 is used to establish an evolution model of compound chain disasters based on a Bayesian network according to sensor data, meteorological data, geographical information data, and historical disaster data.

[0155] The first evaluation module 403 is used to fuse historical ground motion records and the physical model of the power grid established by the first model establishment module 401 to evaluate the probability of power grid damage caused by a single disaster. Among them, the probability of power grid damage caused by a single disaster includes: the probability of power grid damage caused by an earthquake disaster , the probability of power grid damage caused by a landslide , the probability of power grid damage caused by a debris flow .

[0156] The second evaluation module 404 is used to evaluate the probability of combined power grid damage caused by compound disasters based on the Copula function and the evolution model of compound chain disasters established by the second model establishment module 402. Among them, the probability of combined power grid damage caused by compound disasters includes: the probability of combined power grid damage caused by an earthquake and a landslide , the probability of combined power grid damage caused by a landslide and a debris flow , the probability of combined power grid damage caused by an earthquake and a debris flow 。

[0157] The third evaluation module 405 is used to evaluate the difference between the probability sum of power grid damage caused by single disasters and the probability sum of combined power grid damage caused by compound disasters under compound chain disasters. Among them, the probability sum of power grid damage caused by single disasters is , and the probability sum of combined power grid damage caused by compound disasters is 。

[0158] Among them, the compound chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-debris flow disaster time evolution model, earthquake-landslide-debris flow disaster time evolution model.

[0159] The second model establishment module 402 is used to determine the earthquake magnitude of the main earthquake, the focal depth of the main earthquake, the epicentral distance of the main earthquake, geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology according to sensor data, meteorological data, geographic information data, and historical disaster data.

[0160] Construct an earthquake disaster Bayesian network model with the earthquake magnitude of the main earthquake, the focal depth of the main earthquake, the epicentral distance of the main earthquake, and the geological structure as inputs and the earthquake intensity as the output.

[0161] Construct a landslide disaster Bayesian network model with the earthquake intensity, slope, altitude, and rock and soil type as inputs and the landslide volume and landslide speed as the outputs.

[0162] Construct a debris flow disaster Bayesian network model with the 24-hour precipitation, landslide volume, landslide speed, and vegetation coverage as inputs and the total volume of a single debris flow deposit and the peak debris flow discharge as the outputs.

[0163] Construct an aftershock time evolution Bayesian network model with the earthquake intensity, distance from the fault zone, geological structure, and lithology as inputs and the earthquake magnitude of the aftershock, the focal depth of the aftershock, and the occurrence time of the aftershock as the outputs.

[0164] Establish an earthquake-landslide disaster time evolution model according to the earthquake disaster Bayesian network model, the landslide disaster Bayesian network model, and the aftershock time evolution Bayesian network model.

[0165] Construct a landslide-debris flow disaster time evolution model according to the landslide disaster Bayesian network model, the debris flow disaster Bayesian network model, and the aftershock time evolution Bayesian network model.

[0166] Stitch together the earthquake-landslide disaster time evolution model and the landslide-debris flow disaster time evolution model to obtain the earthquake-landslide-debris flow disaster time evolution model.

[0167] Among them, the earthquake magnitude is a discrete node, and the values of the earthquake magnitude include: below magnitude 4.5, 4.5 - 6, 6 - 7, 7 - 8, above magnitude 8. The earthquake magnitude is determined according to the energy released by the earthquake source in the evaluation area.

[0168] The focal depth is a discrete node, and the values of the focal depth include: below 5 km, 5 - 10 km, 10 - 15 km, 15 - 20 km, 20 - 25 km, 25 - 30 km, above 30 km. The focal depth is determined according to the vertical distance from the earthquake source to the ground in the evaluation area.

[0169] The epicentral distance is a discrete node, and the values of the epicentral distance include: below 0.2L, 0.2L - 0.4L, 0.4L - 0.6L, 0.6L - 0.8L, 0.8L - L, above L. The epicentral distance is determined according to the straight-line distance from any point on the ground to the epicenter in the evaluation area, where L is the length of the distance window.

[0170] The geological structure is a discrete node, and the values of the geological structure include: monoclinic structure, fold structure, fault structure, block structure. The geological structure is determined according to the shape and arrangement of rock layers in the crust in the evaluation area.

[0171] The slope is a discrete node, and the values of the slope include: below 10 degrees, 10 - 45 degrees, 45 - 90 degrees. The slope is determined according to the slope degree of the landform in the evaluation area.

[0172] The altitude is a discrete node, and the values of the altitude include: below 500 m, 500 - 1000 m, 1000 - 1500 m, 1500 - 2000 m, above 2000 m. The altitude is determined according to the vertical distance between the ground and the sea level in the evaluation area.

[0173] The rock and soil type is a discrete node, and the values of the rock and soil type include: cohesive soil, loess, filled soil, accumulated soil, fractured rock, rock. The rock and soil type is determined according to the material composition of the landslide mass in the evaluation area.

[0174] The 24-hour precipitation is a discrete node, and the values of the 24-hour precipitation include: below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm. The 24-hour precipitation is determined according to the continuous rainfall in the 12 hours before and 12 hours after the landslide occurs in the evaluation area.

[0175] The vegetation coverage is a discrete node, and the values of the vegetation coverage include: below 45%, 45% - 60%, 60% - 75%, 75% - 100%. The vegetation coverage is determined according to the percentage of the vertical projection area of the ground within the evaluation area to the total area of the evaluation area.

[0176] The distance from the fault zone is a discrete node, and the values of the distance from the fault zone include: below 10 km, 10 - 20 km, 20 - 30 km, 30 - 40 km, 40 - 50 km, above 50 km. The distance from the fault zone is determined according to the spatial distance from the active fault zone closest to the current location.

[0177] The lithology is a discrete node, and the values of the lithology include sedimentary rock, igneous rock, and metamorphic rock. The lithology is determined according to the physical and chemical properties of the rocks within the evaluation area.

[0178] Among them, the first evaluation module 403 is used to fuse historical ground motion records and the power grid physical model, evaluate the probability of failure of each line segment of the power grid, and determine according to the probability of failure of each line segment of the power grid . Among them, the probability of failure of the th line segment of the power grid is the power grid line segment identifier, is the total number of electric poles in the th line segment, is the identifier of the electric pole in the th line segment, is the earthquake intensity, is the identifier of the earthquake involved in the evaluation, is the fault state, is the th line segment, and the th electric pole in the condition has a probability of failure state . , is the standard normal distribution function, is the peak ground acceleration value of the earthquake involved in the evaluation. is the median collapse intensity, is the standard deviation of the ground motion intensity index, , is the total number of historical ground motion records, is the ground motion record identifier, is the peak ground acceleration value of the th record in the historical ground motion records.

[0179] Fuse historical ground motion records and the power grid physical model to evaluate , where is the total number of times to simulate the damage probability of the pole tower under the impact of landslide by the Monte Carlo simulation method, is the disaster-causing intensity of the landslide obtained by simulation under the impact of the landslide greater than the disaster resistance ability of the transmission pole. , is the landslide volume, is the expected landslide speed, , is the landslide speed, is the probability of the landslide occurring, , is the factor identifier affecting the occurrence of the landslide, is the th regression coefficient of the factor affecting the occurrence of the landslide, is the th value of the factor affecting the occurrence of the landslide.

[0180] Fusing historical ground motion records and the power grid physical model to evaluate . Where is the impact pressure of the debris flow on the structure, , is the shape coefficient of the building, is the unit weight of the debris flow, is the acceleration of gravity, is the average cross-sectional flow velocity of the debris flow, is the angle between the force-bearing surface of the building and the direction of the impact pressure of the debris flow, is the impact resistance energy of the transmission pole tower.

[0181] Optionally, for any earthquake , .

[0182] Where is the earthquake identifier, and earthquake is any earthquake record in the historical ground motion records, or the earthquake involved in the evaluation. is the earthquake 's peak ground acceleration value, is the earthquake 's earthquake magnitude, is the distance from the evaluation area to the epicenter of the earthquake , is the earthquake 's epicentral distance.

[0183] Where the probability density of the Copula function . Or,

[0184] Probability Density of Copula Function Or

[0185] Probability Density of Copula Function Or

[0186] Probability Density of Copula Function .

[0187] Wherein and are random variables is the Gaussion distribution function is the standard normal distribution function is the inverse function of is the parameter of the Copula function

[0188] Wherein, the parameters of the Copula function are estimated by maximizing the likelihood function of the observed data

[0189] Wherein, the likelihood function of maximizing the observed data is .

[0190] is the observed data identifier is the total amount of observed data and are the th random variables of the observed data is the probability density of the Copula function for the th observed data

[0191] The device provided in this embodiment establishes a compound chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; and then evaluates the power grid damage under compound chain disasters based on the power grid damage probability caused by a single disaster evaluated by the power grid physical model and the power grid joint damage probability caused by compound disasters evaluated by the compound chain disaster evolution model, realizing accurate and rapid evaluation of the power grid damage under compound chain disasters

[0192] Based on the same inventive concept of the power grid damage assessment method under compound chain disasters, this embodiment provides an electronic device, as shown in Figure 5 including: a memory 501, a processor 502, and a computer program

[0193] Wherein, the computer program is stored in the memory 501 and is configured to be executed by the processor 502 to implement the power grid damage assessment method under compound chain disasters

[0194] Specifically

[0195] Build a physical model of the power grid. Among them, the physical model of the power grid includes the power grid topology structure, equipment parameters, and operating status.

[0196] Establish a compound chain disaster evolution model based on the Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data.

[0197] Fuse historical ground motion records and the physical model of the power grid to evaluate the probability of power grid damage caused by a single disaster. Among them, the probability of power grid damage caused by a single disaster includes: the probability of power grid damage caused by an earthquake disaster , the probability of power grid damage caused by a landslide , the probability of power grid damage caused by a debris flow .

[0198] Based on the Copula function and the compound chain disaster evolution model, evaluate the probability of combined power grid damage caused by compound disasters. Among them, the probability of combined power grid damage caused by compound disasters includes: the probability of combined power grid damage caused by an earthquake and a landslide , the probability of combined power grid damage caused by a mountain and a landslide-debris flow , the probability of combined power grid damage caused by an earthquake and a debris flow .

[0199] Evaluate the difference between the sum of the probabilities of power grid damage caused by a single disaster and the sum of the probabilities of combined power grid damage caused by compound disasters under compound chain disasters. Among them, the sum of the probabilities of power grid damage caused by a single disaster is , and the sum of the probabilities of combined power grid damage caused by compound disasters is .

[0200] Optionally, the compound chain disaster evolution model includes: an earthquake-landslide disaster time evolution model, a landslide-debris flow disaster time evolution model, and an earthquake-landslide-debris flow disaster time evolution model.

[0201] Establish a compound chain disaster evolution model based on the Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data, including:

[0202] According to sensor data, meteorological data, geographic information data, and historical disaster data, determine the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, the geological structure, the slope, the altitude, the rock and soil type, the 24-hour precipitation, the vegetation coverage, the distance from the fault zone, and the lithology.

[0203] Construct a Bayesian network model for earthquake disasters with the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, and the geological structure as inputs and the earthquake intensity as the output.

[0204] Construct a Bayesian network model for landslide disasters with seismic intensity, slope, altitude, and rock and soil type as inputs and landslide volume and landslide speed as outputs.

[0205] Construct a Bayesian network model for debris flow disasters with 24-hour precipitation, landslide volume, landslide speed, and vegetation coverage as inputs and total volume of a single debris flow deposit and peak debris flow discharge as outputs.

[0206] Construct a Bayesian network model for aftershock time evolution with seismic intensity, distance from the fault zone, geological structure, and lithology as inputs and earthquake magnitude of aftershocks, focal depth of aftershocks, and occurrence time of aftershocks as outputs.

[0207] Establish a time evolution model for earthquake-landslide disasters based on the Bayesian network model for earthquake disasters, the Bayesian network model for landslide disasters, and the Bayesian network model for aftershock time evolution.

[0208] Construct a time evolution model for landslide-debris flow disasters based on the Bayesian network model for landslide disasters, the Bayesian network model for debris flow disasters, and the Bayesian network model for aftershock time evolution.

[0209] Stitch together the time evolution model for earthquake-landslide disasters and the time evolution model for landslide-debris flow disasters to obtain the time evolution model for earthquake-landslide-debris flow disasters.

[0210] Optionally, the earthquake magnitude is a discrete node, and the values of the earthquake magnitude include: below magnitude 4.5, 4.5 - 6, 6 - 7, 7 - 8, above magnitude 8. The earthquake magnitude is determined according to the energy released by the earthquake source in the evaluation area.

[0211] The focal depth is a discrete node, and the values of the focal depth include: below 5 km, 5 - 10 km, 10 - 15 km, 15 - 20 km, 20 - 25 km, 25 - 30 km, above 30 km. The focal depth is determined according to the vertical distance from the earthquake source to the ground in the evaluation area.

[0212] The epicentral distance is a discrete node, and the values of the epicentral distance include: below 0.2L, 0.2L - 0.4L, 0.4L - 0.6L, 0.6L - 0.8L, 0.8L - L, above L. The epicentral distance is determined according to the straight-line distance from any point on the ground in the evaluation area to the epicenter, where L is the length of the distance window.

[0213] The geological structure is a discrete node, and the values of the geological structure include: monoclinic structure, fold structure, fault structure, block structure. The geological structure is determined according to the shape and arrangement of rock layers in the crust of the evaluation area.

[0214] The slope is a discrete node, and the values of the slope include: below 10 degrees, 10 - 45 degrees, 45 - 90 degrees. The slope is determined according to the slope degree of the landform in the evaluation area.

[0215] The altitude is a discrete node, and the values of the altitude include: below 500 meters, 500 - 1000 meters, 1000 - 1500 meters, 1500 - 2000 meters, above 2000 meters. The altitude is determined according to the vertical distance between the ground in the evaluation area and the sea level.

[0216] The rock and soil type is a discrete node, and the values of the rock and soil type include: cohesive soil, loess, filled soil, accumulated soil, fractured rock, rock. The rock and soil type is determined according to the material composition of the sliding mass in the evaluation area.

[0217] The 24 - hour precipitation is a discrete node, and the values of the 24 - hour precipitation include: below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm. The 24 - hour precipitation is determined according to the continuous rainfall in the 12 hours before and 12 hours after the landslide occurs in the evaluation area.

[0218] The vegetation coverage is a discrete node, and the values of the vegetation coverage include: below 45%, 45% - 60%, 60% - 75%, 75% - 100%. The vegetation coverage is determined according to the percentage of the vertical projection area of the ground in the evaluation area to the total area of the evaluation area.

[0219] The distance from the fault zone is a discrete node, and the values of the distance from the fault zone include: below 10 km, 10 - 20 km, 20 - 30 km, 30 - 40 km, 40 - 50 km, above 50 km. The distance from the fault zone is determined according to the spatial distance from the nearest active fault zone to the current location.

[0220] The lithology is a discrete node, and the values of the lithology include sedimentary rock, igneous rock, metamorphic rock. The lithology is determined according to the physical and chemical properties of the rocks in the evaluation area.

[0221] Optionally, by integrating historical ground motion records and the power grid physical model, evaluate the probability of power grid damage caused by a single disaster, including:

[0222] Integrate historical ground motion records and the power grid physical model to evaluate the probability of each line segment of the power grid failing, and determine according to the probability of each line segment of the power grid failing . Among them, the probability of the th line segment of the power grid failing is the power grid line segment identifier, is the Total number of utility poles in a line segment, is the identification of the utility pole in the th line segment, is the earthquake intensity, is the identification of the earthquake involved in the assessment, is the earthquake intensity of the earthquake involved in the assessment, is the failure state, is the th utility pole in the th line segment, and the probability that the failure state is under the condition. , is the standard normal distribution function, is the peak ground acceleration value of the earthquake involved in the assessment. is the median of the collapse intensity, is the standard deviation of the ground motion intensity index, , is the total number of historical ground motion records, is the identification of the ground motion record, is the peak ground acceleration value of the th record in the historical ground motion record.

[0223] Fusing historical ground motion records and the power grid physical model to evaluate . Among them, is the total number of times to simulate the probability of tower damage under the impact of landslides through the Monte Carlo simulation method, is the landslide-induced disaster intensity obtained by simulation under the impact of landslides greater than the disaster resistance ability of transmission poles. , is the landslide volume, is the expected landslide speed, , is the landslide speed, is the probability of landslide occurrence, , is the identification of the factors affecting landslide occurrence, is the regression coefficient of the th factor affecting landslide occurrence, is the value of the th factor affecting landslide occurrence.

[0224] Fusing historical ground motion records and the power grid physical model to evaluate . Among them, is the impact pressure of debris flow on structures, , is the shape coefficient of the building, is the debris flow unit weight, is the acceleration due to gravity, is the average velocity of the debris flow cross-section, is the angle between the force-bearing surface of the building and the direction of the debris flow impact pressure, is the impact resistance energy of the transmission tower.

[0225] Optionally, for any earthquake , .

[0226] Wherein, is the earthquake identifier, and the earthquake is any earthquake record in the historical ground motion records, or the earthquake involved in the assessment. is the earthquake peak ground acceleration value, is the earthquake magnitude, is the distance from the assessment area to the epicenter of the earthquake , is the earthquake epicentral distance.

[0227] Optionally, the probability density of the Copula function . Or,

[0228] The probability density of the Copula function . Or,

[0229] The probability density of the Copula function . Or,

[0230] The probability density of the Copula function .

[0231] Wherein, and are random variables, is the Gaussion distribution function, is the standard normal distribution function, is inverse function, is the parameter of the Copula function.

[0232] Optionally, the parameters of the Copula function are estimated by maximizing the likelihood function of the observed data.

[0233] Wherein, the likelihood function of maximizing the observed data is .

[0234] is the observed data identifier, is the total amount of observed data, and is the random variable of the th observed data, is the probability density of the Copula function for the th observed data.

[0235] The electronic device provided in this embodiment has a computer program executed by a processor to establish a compound chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; and then evaluate the power grid damage under compound chain disasters based on the power grid damage probability caused by a single disaster evaluated according to the power grid physical model and the power grid combined damage probability caused by compound disasters evaluated according to the compound chain disaster evolution model, realizing accurate and rapid evaluation of the power grid damage under compound chain disasters.

[0236] Based on the same inventive concept of the power grid damage evaluation method under compound chain disasters, this embodiment provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to implement the above-mentioned power grid damage evaluation method under compound chain disasters.

[0237] Specifically,

[0238] Establish a power grid physical model. Among them, the power grid physical model includes the power grid topology structure, equipment parameters, and operating status.

[0239] Establish a compound chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data.

[0240] Fuse historical ground motion records and the power grid physical model to evaluate the power grid damage probability caused by a single disaster. Among them, the power grid damage probability caused by a single disaster includes: the power grid damage probability caused by earthquake disasters , the power grid damage probability caused by landslides , the power grid damage probability caused by debris flows .

[0241] Based on the Copula function and the compound chain disaster evolution model, evaluate the power grid combined damage probability caused by compound disasters. Among them, the power grid combined damage probability caused by compound disasters includes: the power grid combined damage probability caused by earthquakes and landslides , the power grid combined damage probability caused by landslides and debris flows , the power grid combined damage probability caused by earthquakes and debris flows .

[0242] Evaluate the difference between the sum of the probabilities of power grid damage caused by single disasters and the sum of the probabilities of combined power grid damage caused by compound disasters in the compound chain disaster. Among them, the sum of the probabilities of power grid damage caused by single disasters is , and the sum of the probabilities of combined power grid damage caused by compound disasters is .

[0243] Optionally, the compound chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-debris flow disaster time evolution model, earthquake-landslide-debris flow disaster time evolution model.

[0244] Establish a compound chain disaster evolution model based on Bayesian network according to sensor data, meteorological data, geographical information data and historical disaster data, including:

[0245] According to sensor data, meteorological data, geographical information data and historical disaster data, determine the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, lithology.

[0246] Construct an earthquake disaster Bayesian network model with the earthquake magnitude of the main shock, the focal depth of the main shock, the epicentral distance of the main shock, and the geological structure as inputs and the earthquake intensity as the output.

[0247] Construct a landslide disaster Bayesian network model with earthquake intensity, slope, altitude, and rock and soil type as inputs and landslide volume and landslide speed as the outputs.

[0248] Construct a debris flow disaster Bayesian network model with 24-hour precipitation, landslide volume, landslide speed, and vegetation coverage as inputs and the total volume of a single debris flow deposit and the peak debris flow discharge as the outputs.

[0249] Construct an aftershock time evolution Bayesian network model with earthquake intensity, distance from the fault zone, geological structure, and lithology as inputs and the earthquake magnitude of the aftershock, the focal depth of the aftershock, and the occurrence time of the aftershock as the outputs.

[0250] Establish an earthquake-landslide disaster time evolution model according to the earthquake disaster Bayesian network model, the landslide disaster Bayesian network model and the aftershock time evolution Bayesian network model.

[0251] Construct a landslide-debris flow disaster time evolution model according to the landslide disaster Bayesian network model, the debris flow disaster Bayesian network model and the aftershock time evolution Bayesian network model.

[0252] Stitch together the earthquake-landslide disaster time evolution model and the landslide-debris flow disaster time evolution model to obtain the earthquake-landslide-debris flow disaster time evolution model.

[0253] Optionally, the earthquake magnitude is a discrete node, and the values of the earthquake magnitude include: below magnitude 4.5, magnitude 4.5 - 6, magnitude 6 - 7, magnitude 7 - 8, above magnitude 8. The earthquake magnitude is determined according to the energy released by the earthquake source in the evaluation area.

[0254] The focal depth is a discrete node, and the values of the focal depth include: below 5 km, 5 - 10 km, 10 - 15 km, 15 - 20 km, 20 - 25 km, 25 - 30 km, above 30 km. The focal depth is determined according to the vertical distance from the earthquake source to the ground in the evaluation area.

[0255] The epicentral distance is a discrete node, and the values of the epicentral distance include: below 0.2L, 0.2L - 0.4L, 0.4L - 0.6L, 0.6L - 0.8L, 0.8L - L, above L. The epicentral distance is determined according to the straight-line distance from any point on the ground in the evaluation area to the epicenter, where L is the length of the distance window.

[0256] The geological structure is a discrete node, and the values of the geological structure include: monoclinic structure, fold structure, fault structure, block structure. The geological structure is determined according to the shape and arrangement of the rock strata in the crust of the evaluation area.

[0257] The slope is a discrete node, and the values of the slope include: below 10 degrees, 10 - 45 degrees, 45 - 90 degrees. The slope is determined according to the slope degree of the landform in the evaluation area.

[0258] The altitude is a discrete node, and the values of the altitude include: below 500 m, 500 - 1000 m, 1000 - 1500 m, 1500 - 2000 m, above 2000 m. The altitude is determined according to the vertical distance between the ground and the sea level in the evaluation area.

[0259] The type of rock and soil is a discrete node, and the values of the type of rock and soil include: cohesive soil, loess, filled soil, accumulated soil, broken rock, rock. The type of rock and soil is determined according to the material composition of the sliding mass in the evaluation area.

[0260] The precipitation in 24 hours is a discrete node, and the values of the precipitation in 24 hours include: below 0.1 mm, 0.1 - 9.9 mm, 10.0 - 24.9 mm, 25.0 - 49.9 mm, 50.0 - 99.9 mm, 100.0 - 249.9 mm, above 250.0 mm. The precipitation in 24 hours is determined according to the continuous rainfall in the 12 hours before and 12 hours after the landslide occurs in the evaluation area.

[0261] The vegetation coverage is a discrete node, and the values of the vegetation coverage include: below 45%, 45% - 60%, 60% - 75%, 75% - 100%. The vegetation coverage is determined according to the percentage of the vertical projection area of the ground in the evaluation area to the total area of the evaluation area.

[0262] The distance from the fault zone is a discrete node, and the values of the distance from the fault zone include: below 10 km, 10 - 20 km, 20 - 30 km, 30 - 40 km, 40 - 50 km, above 50 km. The distance from the fault zone is determined according to the spatial distance from the nearest active fault zone to the current position.

[0263] The lithology is a discrete node, and the values of the lithology include sedimentary rock, igneous rock, and metamorphic rock. The lithology is determined according to the physical and chemical properties of the rocks in the evaluation area.

[0264] Optionally, by integrating historical ground motion records and the physical model of the power grid, the probability of power grid damage caused by a single disaster is evaluated, including:

[0265] By integrating historical ground motion records and the physical model of the power grid, the probability of each line segment of the power grid failing is evaluated, and it is determined according to the probability of each line segment of the power grid failing . Among them, the probability of the th line segment of the power grid failing is the power grid line segment identifier, is the total number of electric poles in the th line segment, is the identifier of the electric pole in the th line segment, is the earthquake intensity, is the identifier of the earthquake involved in the evaluation, is the fault status, is the th line segment, and the th electric pole in the condition has a probability of the fault status being . , is the standard normal distribution function, is the peak ground acceleration value of the earthquake involved in the evaluation. is the median of the collapse intensity, is the standard deviation of the ground motion intensity index, , is the total number of historical ground motion records, is the ground motion record identifier, is the peak ground acceleration value of the th record in the historical ground motion records.

[0266] Fusing historical ground motion records and power grid physical models for assessment . Among them, is the total number of times for simulating the probability of pole damage under the impact of landslides through the Monte Carlo simulation method, is the intensity of landslide disasters obtained by simulation under the impact of landslides greater than the disaster resistance ability of transmission poles. , is the landslide volume, is the expected landslide speed, , is the landslide speed, is the probability of landslide occurrence, , is the factor identifier affecting the occurrence of landslides, is the th regression coefficient of the factor affecting the occurrence of landslides, is the th value of the factor affecting the occurrence of landslides.

[0267] Fusing historical ground motion records and power grid physical models for assessment . Among them, is the impact pressure of debris flow on structures, , is the shape coefficient of the building, is the unit weight of debris flow, is the acceleration of gravity, is the average flow velocity of the debris flow cross-section, is the included angle between the force-bearing surface of the building and the direction of the debris flow impact pressure, is the impact resistance energy of the transmission tower.

[0268] Optionally, for any earthquake , .

[0269] Among them, is the earthquake identifier, and the earthquake is any earthquake record in the historical ground motion records, or the earthquake involved in the assessment. is the earthquake 's peak ground acceleration value, is the earthquake 's earthquake magnitude, is the distance from the assessment area to the epicenter of the earthquake , is the epicentral distance of the earthquake .

[0270] Optionally, the probability density of the Copula function . Or,

[0271] the probability density of the Copula function . Or,

[0272] the probability density of the Copula function . Or,

[0273] the probability density of the Copula function .

[0274] Wherein, and are random variables, is the Gaussion distribution function, is the standard normal distribution function, is 's inverse function, is the parameter of the Copula function.

[0275] Optionally, the parameters of the Copula function are estimated by maximizing the likelihood function of the observed data.

[0276] Wherein, the likelihood function of maximizing the observed data is .

[0277] is the observed data identifier, is the total amount of observed data, and are the random variables of the th observed data, is the probability density of the Copula function for the th observed data.

[0278] The computer-readable storage medium provided in this embodiment, on which the computer program is executed by a processor to establish a compound chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographical information data, and historical disaster data; and then evaluate the power grid damage under compound chain disasters based on the power grid damage probability caused by a single disaster evaluated by the power grid physical model and the power grid joint damage probability caused by compound disasters evaluated by the compound chain disaster evolution model, realizing accurate and rapid evaluation of the power grid damage under compound chain disasters.

[0279] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0280] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0281] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0282] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0283] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0284] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for evaluating power grid damage under complex chain disasters, characterized in that: The method comprises: Establishing a physical model of the power grid; wherein the physical model of the power grid includes the power grid topology, equipment parameters and operating status; A composite chain disaster evolution model based on Bayesian network is established according to sensor data, meteorological data, geographic information data and historical disaster data; Integrate historical earthquake records and power grid physical models to assess the probability of power grid damage caused by a single disaster; the probability of power grid damage caused by a single disaster includes: the probability of power grid damage caused by earthquake disasters , Probability of power grid damage caused by landslide , Probability of power grid damage caused by debris flow ; Based on the Copula function and the compound chain disaster evolution model, the probability of joint damage to the power grid caused by compound disasters is evaluated; the probability of joint damage to the power grid caused by compound disasters includes: the probability of joint damage to the power grid caused by earthquakes and landslides , Joint damage probability of power grid caused by landslide and debris flow , Joint damage probability of power grid caused by earthquake and debris flow ; The power grid damage under composite chain disasters is evaluated as the difference between the power grid damage probability sum caused by a single disaster and the power grid joint damage probability sum caused by composite disasters; among them, the power grid damage probability sum caused by a single disaster is , the probability of combined power grid damage caused by compound disasters is .

2. The method according to claim 1, characterized in that The composite chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-mud-rock flow disaster time evolution model, earthquake-landslide-mud-rock flow disaster time evolution model; The composite chain disaster evolution model based on Bayesian network is established according to sensor data, meteorological data, geographic information data and historical disaster data, including: Based on sensor data, meteorological data, geographic information data and historical disaster data, determine the earthquake magnitude of the main shock, the focal depth of the main shock, the distance from the epicenter of the main shock, geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology; Constructing a Bayesian network model of earthquake disasters with the earthquake magnitude of the main shock, the focal depth of the main shock, the epicenter distance of the main shock, and the geological structure as inputs and the earthquake intensity as output; Construct a Bayesian network model for landslide hazards with earthquake intensity, slope, altitude, rock and soil type as input and landslide volume and landslide velocity as output; A Bayesian network model of debris flow disaster was constructed with 24-hour precipitation, landslide volume, landslide velocity, and vegetation coverage as inputs, and total volume of debris flow accumulation and debris flow peak volume as outputs. Construct a Bayesian network model for aftershock time evolution with earthquake intensity, distance from the fault zone, geological structure, and lithology as inputs, and aftershock magnitude, focal depth, and occurrence time of aftershocks as outputs; Based on the Bayesian network model of earthquake disaster, the Bayesian network model of landslide disaster and the Bayesian network model of aftershock time evolution, an earthquake-landslide disaster time evolution model was established; According to the Bayesian network model of landslide disaster, the Bayesian network model of debris flow disaster and the Bayesian network model of aftershock time evolution, a landslide-debris flow disaster time evolution model was constructed; The earthquake-landslide-mud-flow disaster time evolution model is spliced ​​together with the landslide-mud-flow disaster time evolution model to obtain the earthquake-landslide-mud-flow disaster time evolution model.

3. The method according to claim 2, characterized in that The earthquake magnitude is a discrete node. The values ​​of the earthquake magnitude include: below 4.5, 4.5-6, 6-7, 7-8, and above 8. The earthquake magnitude is determined according to the energy released by the earthquake source in the assessment area. The focal depth is a discrete node. The focal depth values ​​include: below 5 km, 5-10 km, 10-15 km, 15-20 km, 20-25 km, 25-30 km, and above 30 km. The focal depth is determined based on the vertical distance from the focal point to the ground in the assessment area. The epicentral distance is a discrete node. The values ​​of the epicentral distance include: below 0.2L, 0.2L~0.4L, 0.4L~0.6L, 0.6L~0.8L, 0.8L~L, and above L. The epicentral distance is determined based on the straight-line distance from any point on the ground in the assessment area to the epicenter, where L is the distance window length; The geological structure is a discrete node. The values ​​of the geological structure include: monocline structure, fold structure, fault structure, block structure. The geological structure is determined according to the morphology and arrangement of the rock layers in the crust in the assessment area. The slope is a discrete node, and the slope values ​​include: less than 10 degrees, 10-45 degrees, and 45-90 degrees. The slope is determined according to the slope degree of the landform in the assessment area; Altitude is a discrete node. The values ​​of altitude include: below 500 meters, 500-1000 meters, 1000-1500 meters, 1500-2000 meters, and above 2000 meters. The altitude is determined based on the vertical distance between the ground and sea level in the assessment area. The geotechnical type is a discrete node. The values ​​of the geotechnical type include: clay, loess, fill soil, piled soil, broken rock, and rock. The geotechnical type is determined according to the material composition of the sliding body in the assessment area. The 24-hour precipitation is a discrete node. The values ​​of the 24-hour precipitation include: less than 0.1 mm, 0.1-9.9 mm, 10.0-24.9 mm, 25.0-49.9 mm, 50.0-99.9 mm, 100.0-249.9 mm, and more than 250.0 mm. The 24-hour precipitation is determined based on the continuous rainfall 12 hours before and 12 hours after the landslide occurs in the assessment area. Vegetation coverage is a discrete node. The values ​​of vegetation coverage include: below 45%, 45% to 60%, 60% to 75%, and 75% to 100%. Vegetation coverage is determined based on the percentage of the vertical projection area of ​​the ground in the assessment area to the total area of ​​the assessment area. The distance to the fault zone is a discrete node. The values ​​of the distance to the fault zone include: less than 10 kilometers, 10-20 kilometers, 20-30 kilometers, 30-40 kilometers, 40-50 kilometers, and more than 50 kilometers. The distance to the fault zone is determined based on the spatial distance to the active fault zone closest to the current location; Lithology is a discrete node. The lithology values ​​include sedimentary rock, igneous rock, and metamorphic rock. The lithology is determined based on the physical and chemical properties of the rocks in the assessment area.

4. The method according to claim 1, characterized in that The method of integrating historical earthquake records and power grid physical models to assess the probability of power grid damage caused by a single disaster includes: The historical earthquake records and the physical model of the power grid are integrated to evaluate the probability of failure of each line segment of the power grid, and the probability of failure of each line segment of the power grid is determined according to the probability of failure of each line segment of the power grid. Among them, the power grid The probability of a line segment failure ; It is the line segment identifier of the power grid. For the The total number of poles in the line segment, For the The pole markings in the line section. is the earthquake intensity, To assess the identity of the earthquake involved, To assess the seismic intensity of the earthquake in question, In fault state, For the The line segment Telegraph poles in The fault state occurs under the condition probability; , is the standard normal distribution function, To assess the peak acceleration value of the earthquake involved; is the median collapse strength, is the standard deviation of the earthquake intensity index, , ; is the total number of historical earthquake records, It is the seismic record mark. The first earthquake in the history of the record The peak acceleration value of the vibration recorded for the first time; Integrate historical earthquake records and power grid physical models to evaluate ;in, In order to simulate the total number of tower damage probabilities under the impact of landslides by Monte Carlo simulation method, To simulate the landslide disaster intensity under the impact of landslide The number of times is greater than the disaster resistance capacity of the transmission pole; , is the landslide volume, is the expected landslide velocity, , is the landslide velocity, is the probability of landslide occurrence, , To identify the factors that affect landslides, To influence the occurrence of landslides The regression coefficients of the factors, To influence the occurrence of landslides The value of the factor; Integrate historical earthquake records and power grid physical models to evaluate ;in, is the impact pressure of debris flow on the structure, , is the building shape factor, is the bulk density of debris flow, is the acceleration due to gravity, is the average flow velocity of the debris flow section, is the angle between the building's stress surface and the direction of debris flow impact pressure, It is the impact resistance of transmission towers.

5. The method according to claim 4, characterized in that For any earthquake , ; in, Earthquake sign, earthquake Any earthquake recorded in the historical seismic record, or the earthquake to which the assessment relates; For earthquake The peak acceleration value of vibration, For earthquake The magnitude of the earthquake, To assess the area to earthquake The distance from the epicenter, For earthquake The epicenter distance.

6. The method according to claim 1, characterized in that The probability density of the Copula function ; or, Probability Density of Copula Function ; or, Probability Density of Copula Function ; or, Probability Density of Copula Function ; in, and is a random variable, is the Gaussion distribution function, is the standard normal distribution function, for The inverse function of It is the parameter of the Copula function.

7. The method according to claim 6, characterized in that Estimate the parameters of the Copula function by maximizing the likelihood function of the observed data; The likelihood function that maximizes the observed data is ; is the observation data identifier, is the total amount of observation data, and For the A random variable with observations, For the The probability density of the Copula function of the observed data.

8. A power grid damage assessment device under complex chain disasters, characterized in that: The device comprises: The first model building module is used to build a physical model of the power grid; wherein the physical model of the power grid includes a power grid topology, equipment parameters and operating status; The second model building module is used to build a composite chain disaster evolution model based on Bayesian network according to sensor data, meteorological data, geographic information data and historical disaster data; The first evaluation module is used to integrate the historical earthquake records and the physical model of the power grid established by the first model establishment module to evaluate the probability of power grid damage caused by a single disaster; wherein the probability of power grid damage caused by a single disaster includes: the probability of power grid damage caused by an earthquake disaster , Probability of power grid damage caused by landslide , Probability of power grid damage caused by debris flow ; The second evaluation module is used to evaluate the probability of joint damage to the power grid caused by the composite disaster based on the Copula function and the composite chain disaster evolution model established by the second model establishment module; wherein the probability of joint damage to the power grid caused by the composite disaster includes: the probability of joint damage to the power grid caused by earthquakes and landslides , Joint damage probability of power grid caused by landslide and debris flow , Joint damage probability of power grid caused by earthquake and debris flow ; The third evaluation module is used to evaluate the difference between the power grid damage probability sum caused by a single disaster and the power grid joint damage probability sum caused by a composite disaster under a compound chain disaster; where the power grid damage probability sum caused by a single disaster is , the probability of combined power grid damage caused by compound disasters is .

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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