Power grid damage assessment method, device, equipment and medium under composite chain disaster

Abstract

The invention provides a power grid damage assessment method, device, equipment and medium under a composite chain disaster, and the method comprises the steps: building a power grid physical model; establishing a composite chain disaster evolution model based on the Bayesian network according to the sensor data, the meteorological data, the geographic information data and the historical disaster data; historical seismic oscillation records and a power grid physical model are fused, and the power grid damage probability caused by a single disaster is evaluated; based on a Copula function and a composite chain disaster evolution model, evaluating the probability of power grid joint damage caused by the composite disaster; and evaluating the difference between the probability that the power grid damage under the composite chain disaster is the power grid damage caused by the single disaster and the probability sum of the power grid joint damage caused by the composite disaster. According to the method provided by the invention, the power grid damage under the composite chain disaster is evaluated through the power grid damage probability caused by the single disaster and the power grid joint damage probability caused by the composite disaster, and accurate and rapid evaluation of the power grid damage under the composite chain disaster is realized.

Description

Technical Field

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

[0002] With global climate change and the increase in extreme weather events, the power grid is facing increasingly complex threats of compound chain disasters. Compound chain disasters refer to the interaction and influence of multiple disasters, forming a disaster chain that causes serious damage to the power grid. For example, earthquakes trigger landslides, which in turn damage the tower foundation of transmission lines, causing power outages; floods may cause substation equipment to be flooded, causing equipment failures, etc.

[0003] Traditional power grid damage assessment methods often only consider a single disaster factor, or do not adequately consider the coupling effects of compound disasters. These methods are mainly based on physical models, which assess damage by analyzing the mechanical and electrical characteristics of power grid components. However, in the case of compound chain disasters, physical models are difficult to fully 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 of power grids, a large amount of operating data is 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] In order to solve one of the above-mentioned technical defects, the present application provides a method, device, equipment and medium for evaluating power grid damage under compound chain-generated disasters.

[0006] In a first aspect, the present application provides a method for evaluating power grid damage under a compound chain disaster, the method comprising: 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 , the probability of joint damage to the power grid caused by mountain and landslide 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 .

[0007] Optionally, the composite chain disaster evolution model includes: an earthquake-landslide disaster time evolution model, a landslide-mud-rock flow disaster time evolution model, and an earthquake-landslide-mud-rock flow disaster time evolution model; A composite chain disaster evolution model based on Bayesian network is established based on 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; Construct 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.

[0008] Optionally, the earthquake magnitude is a discrete node, and 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.

[0009] Optionally, historical earthquake records and power grid physical models are integrated to assess the probability of power grid damage caused by a single disaster, including: Integrate historical earthquake records and the physical model of the power grid to evaluate the probability of failure of each line segment of the power grid and determine the 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 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 debris flow impact pressure direction, It is the impact resistance of transmission towers.

[0010] Optionally, 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.

[0011] Optionally, 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.

[0012] Optionally, the parameters of the copula function are estimated 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.

[0013] In a second aspect of the present application, a device for evaluating power grid damage under a composite chain disaster is provided, the device comprising: 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 compound disasters based on the Copula function and the compound chain disaster evolution model established by the second model establishment module; wherein 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 , the probability of joint damage to the power grid caused by mountain and landslide 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 .

[0014] In a third aspect of the present application, an electronic device is provided, including: Memory; Processor; and Computer programs; 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.

[0015] 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 described in the first aspect above.

[0016] The present application provides a method, device, equipment, and medium for evaluating power grid damage 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; integrating historical earthquake records and the power grid physical model to evaluate the probability of power grid damage caused by a single disaster; evaluating the probability of joint power grid damage caused by compound disasters based on Copula functions and the compound chain-generated disaster evolution model; evaluating the power grid damage under compound chain-generated disasters as the difference between the probability of power grid damage caused by a single disaster and the probability of joint power grid damage caused by compound 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 evaluating the power grid damage under compound chain-generated disasters according to the probability of power grid damage caused by a single disaster evaluated by the power grid physical model and the probability of joint power grid damage caused by compound disasters evaluated according to the compound chain-generated disaster evolution model, thereby achieving accurate and rapid evaluation of power grid damage under compound chain-generated disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for evaluating power grid damage under a composite chain-generated disaster provided in an embodiment of the present application; Figure 2A schematic diagram of a time evolution model of earthquake-landslide-mud flow disaster provided in an embodiment of the present application; Figure 3 A schematic diagram of evaluating power grid damage results under a composite chain disaster provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a power grid damage assessment device under a composite chain-generated disaster provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0019] In the process of implementing this application, the inventors found that traditional power grid damage assessment methods often only consider a single disaster factor, or do not consider the coupling effect of compound disasters enough. These methods are mainly based on physical models, and assess damage by analyzing the mechanical and electrical characteristics of power grid components. However, in the case of compound chain disasters, it is difficult for physical models to fully consider the complex disaster evolution process and the interaction of multiple factors, resulting in inaccurate assessment results.

[0020] In view of the above problems, the embodiment of the present application provides a method, device, equipment, and medium for evaluating power grid damage under composite chain-generated disasters. The method includes: establishing a physical model of the power grid; establishing a composite chain-generated disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data, and historical disaster data; integrating historical earthquake records and the physical model of the power grid to evaluate the probability of power grid damage caused by a single disaster; based on Copula functions and the composite chain-generated disaster evolution model, evaluating the probability of joint power grid damage caused by composite disasters; evaluating the power grid damage under composite chain-generated disasters as the difference between the probability of power grid damage caused by a single disaster and the probability of joint power grid damage caused by composite disasters. The method provided in the present application establishes a composite 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 evaluating the power grid damage under composite chain-generated disasters according to the probability of power grid damage caused by a single disaster evaluated by the physical model of the power grid and the probability of joint power grid damage caused by composite disasters evaluated according to the composite chain-generated disaster evolution model, thereby achieving accurate and rapid evaluation of power grid damage under composite chain-generated disasters.

[0021] See also Figure 1The implementation process of the power grid damage assessment method under the composite chain disaster provided in this embodiment is as follows: S101, establishing a physical model of the power grid.

[0022] Among them, the physical model of the power grid includes the power grid topology, equipment parameters and operating status.

[0023] Among them, the power grid topology structure can reflect the load information on the network nodes, the connection relationship of the network, etc.

[0024] For example, the grid topology is a power system topology with 33 nodes, in which a certain node is a substation node, the voltage is 1.05Un, and the active power of the network is 6.76MW.

[0025] S102, establishing a composite chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data and historical disaster data.

[0026] Among them, the composite chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-mud flow disaster time evolution model, earthquake-landslide-mud flow disaster time evolution model.

[0027] The implementation process of step S102 is as follows: S102-1, 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, the geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology.

[0028] Among them, the earthquake magnitude is a discrete node, and the values ​​of the earthquake magnitude include: below 4.5, 4.5~6 (i.e. moderate-strong earthquake), 6~7 (i.e. strong earthquake), 7~8 (i.e. major earthquake), and above 8 (i.e. huge earthquake). The earthquake magnitude is determined according to the energy released by the epicenter in the assessment area.

[0029] The focal depth (Focal Depth) is a discrete node. The values ​​of the focal depth include: below 5 kilometers, 5-10 kilometers, 10-15 kilometers, 15-20 kilometers, 20-25 kilometers, 25-30 kilometers, and above 30 kilometers. The focal depth is determined based on the vertical distance from the focal point to the ground in the assessment area.

[0030] 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.

[0031] The geological structure is a discrete node. The values ​​of the geological structure include: monocline structure, fold structure, fault structure, and block structure. The geological structure is determined based on the morphology and arrangement of the rock layers in the crust in the assessment area.

[0032] The slope is a discrete node. The slope values ​​include: less than 10 degrees, 10 to 45 degrees, and 45 to 90 degrees. The slope is determined based on the slope degree of the landform in the assessment area.

[0033] 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.

[0034] The geotechnical type is a discrete node. The values ​​of the geotechnical type include: clay, loess, fill, piled soil, broken rock, and rock. The geotechnical type is determined based on the material composition of the sliding body in the assessment area.

[0035] The 24-hour precipitation is a discrete node. The values ​​of the 24-hour precipitation include: less than 0.1 mm (i.e. trace rainfall), 0.1~9.9 mm (i.e. light rain), 10.0~24.9 (millimeter, 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), and more than 250.0 mm (i.e. extremely heavy rainstorm). The 24-hour precipitation is determined based on the continuous rainfall in the assessment area 12 hours before and 12 hours after the landslide occurs (i.e. the precipitation in the 24 hours from 12 hours before and after the landslide disaster occurs).

[0036] Vegetation coverage is a discrete node. The values ​​of vegetation coverage include: below 45% (i.e. low coverage), 45%~60% (i.e. medium coverage), 60%~75% (i.e. medium-high coverage), and 75%~100% (i.e. high coverage). 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.

[0037] 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 between the active fault zone closest to the current location.

[0038] 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.

[0039] 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 epicenter distance of the main shock, and the geological structure as input and the seismic intensity as output.

[0040] The nodes of the earthquake disaster Bayesian network model constructed in step S102-2 may be as shown in Table 1.

[0041] Table 1

[0042] S102-3, construct a Bayesian network model for landslide disasters with earthquake intensity, slope, altitude, rock and soil type as input and landslide volume and landslide velocity as output.

[0043] The nodes of the Bayesian network model for landslide disaster constructed in step S102-3 may be as shown in Table 2.

[0044] Table 2

[0045] S102-4, construct a Bayesian network model for debris flow disasters 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.

[0046] The nodes of the debris flow disaster Bayesian network model constructed in step S102-4 may be as shown in Table 3.

[0047] Table 3

[0048] S102-5, construct an aftershock time evolution Bayesian network model 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.

[0049] The aftershock time evolution Bayesian network model considers the impact of aftershocks caused by earthquakes in various places, introduces the time factor according to the influence relationship between the main shocks and aftershocks, determines the input nodes as earthquake intensity, distance from the fault zone, geological structure, and lithology, and the output nodes as earthquake magnitude, focal depth, and occurrence time. The nodes of the aftershock time evolution Bayesian network model constructed in step 102-5 can be shown in Table 4.

[0050] Table 4

[0051] S102-6. 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, a time evolution model of earthquake-landslide disaster is established.

[0052] There are common variables in the Bayesian network model of earthquake disaster, Bayesian network model of landslide disaster and Bayesian network model of aftershock time evolution, namely, the output node "earthquake intensity" of the Bayesian network model of earthquake disaster, the input node "earthquake intensity" of the Bayesian network model of landslide disaster and the input node "earthquake intensity" of the Bayesian network model of aftershock time evolution. By merging common variables, the earthquake-landslide disaster time evolution model can be constructed.

[0053] Landslide disasters caused 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 co-seismic landslides. On the other hand, earthquakes cause new damage to slopes, prompting landslides to occur one after another after the earthquake, which are called post-seismic landslides. Evolution models are established for the two types of landslides. Since co-seismic landslides occur simultaneously with earthquakes, their occurrence time is the time when the earthquake occurs. For post-seismic landslides, their occurrence time is the interval time with the earthquake. Therefore, the evolution time in the earthquake-landslide disaster time evolution model represents the duration of the landslide disaster.

[0054] S102-7, based on 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 is constructed.

[0055] The Bayesian network model of landslide disaster, the Bayesian network model of debris flow disaster and the Bayesian network model of aftershock time evolution have common variables, namely, the output nodes "landslide volume" and "landslide velocity" of the Bayesian network model of landslide disaster, the input nodes "landslide volume" and "landslide velocity" of the Bayesian network model of debris flow disaster, the input node "earthquake intensity" of the Bayesian network model of landslide disaster and the input node "earthquake intensity" of the Bayesian network model of aftershock time evolution. By merging common variables, the time evolution model of landslide-debris flow disaster can be constructed.

[0056] S102-8, splicing the earthquake-landslide disaster time evolution model and the landslide-mud flow disaster time evolution model to obtain the earthquake-landslide-mud flow disaster time evolution model.

[0057] The time evolution model of earthquake-landslide-debris flow disaster can be expressed as Figure 2 shown.

[0058] S103, integrates historical earthquake records and power grid physical models to assess the probability of power grid damage caused by a single disaster.

[0059] Among them, 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 .

[0060] The implementation process of step S103 is: S103-1, integrate historical earthquake records and the physical model of the power grid to evaluate the probability of failure of each line segment of the power grid, and determine the probability of failure of each line segment of the power grid according to the probability of failure of each line segment of the power grid. .

[0061] For example, by integrating historical earthquake records and the physical model of the power grid, the probability of failure of each line segment of the power grid is evaluated, and the line segment with the highest probability of failure among the lines of the power grid is determined as Alternatively, 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 average probability of failure of each line segment of the power grid is determined as Alternatively, 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 median value (or preset value, or mode value, etc.) of the probability of failure of each line segment of the power grid is determined as Alternatively, 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 line segment with the smallest probability of failure among the lines of the power grid is determined as Alternatively, 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 average of the largest multiple probabilities of failure of each line segment of the power grid is determined as Alternatively, 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 other existing feasible solutions are used to determine the line segment with the highest probability of failure of each line segment of the power grid as .

[0062] Among them, the necessary condition for the normal operation of the line is that the poles on the line section are in good condition. The probability of a line segment failure .

[0063] 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.

[0064] The damage of distribution network lines is mainly determined by the collapse rate of power poles, that is, the conditional probability that the bearing capacity of power poles reaches or exceeds the collapse limit value in an earthquake. , is the standard normal distribution function, To assess the peak acceleration value of the earthquake involved. is the median collapse intensity (i.e., the earthquake intensity corresponding to a 50% probability of structural collapse), is the standard deviation of the seismic intensity index (this value reflects the discreteness of the calculation results using different seismic records).

[0065] , .

[0066] 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.

[0067] For the measurement of earthquake disasters, the average distance from all points in the area to the epicenter is taken as the basis for radiating outward from the epicenter. The calculated ground motion parameters are used as the reference values ​​for the entire area. , .

[0068] in, Earthquake sign, earthquake Any recorded earthquake 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.

[0069] S103-2, Integration of historical ground motion records and power grid physical models, evaluation .

[0070] The probability of power grid damage caused by landslide can be calculated by Monte Carlo method. Assume that the total number of tower damage probabilities under the impact of landslide simulated by Monte Carlo simulation method is , the landslide disaster intensity under the impact of landslide was simulated The number of times greater than the disaster resistance capacity of the transmission pole is ,but .

[0071] 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.

[0072] , is the landslide volume, is the expected landslide velocity, , is the landslide velocity, is the probability of landslide occurrence.

[0073] The impact and damage process of landslide on transmission tower is actually a contest between the disaster-causing strength of landslide and the disaster resistance of transmission tower. Based on historical data, the probability of landslide caused by earthquake can be: , To identify 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 a factor.

[0074] Among them, factors that affect the occurrence of landslides include elevation, slope, slope direction, and distance from transmission towers.

[0075] S103-3, Integration of historical ground motion records and power grid physical models, evaluation .

[0076] in, is the impact pressure of debris flow on the structure (in Pa).

[0077] The position relationship between the tower and the debris flow ditch can be divided into the following two types: ① The tower is located in the debris flow accumulation fan, ② The tower is located on both sides of the debris flow ditch. The main ways of damage to buildings by debris flow are impact and siltation. Based on the above relationship, the impact pressure of debris flow on the structure .

[0078] is the building shape coefficient, which can be taken as 1.33 for the transmission tower foundation. is the bulk density of debris flow (in t / m 3 ), is the gravitational acceleration (take m / s 2 ), is the average flow velocity of the debris flow section (in m / s), is the angle between the bearing surface of the building and the direction of the impact pressure of the debris flow (e.g. 85°), It is the impact resistance of transmission towers.

[0079] The impact and damage process of debris flow on transmission towers is actually a contest between the disaster intensity of debris flow and the disaster resistance performance of transmission towers. Based on the functional relationship, the probability of power grid damage caused by debris flow is expressed as the ratio of impact pressure and impact energy. When the impact pressure of debris flow is too large and exceeds the impact energy of the transmission tower itself, the transmission tower will be damaged, that is, .

[0080] It should be noted that the implementation process of the above steps S103-1, S103-2 and S103-3 is only an example. In the specific implementation, steps S103-1, S103-2 and S103-3 can be performed simultaneously, or S103-2 can be performed 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.

[0081] S104, based on the Copula function and the compound chain disaster evolution model, evaluates the probability of joint damage to the power grid caused by compound disasters.

[0082] Among them, the combined damage probability of the power grid caused by compound disasters includes: the combined damage probability of the power grid caused by earthquakes and landslides , the probability of joint damage to the power grid caused by mountain and landslide debris flow , Joint damage probability of power grid caused by earthquake and debris flow .

[0083] In addition, the probability density of the Copula function Or, the probability density of the Copula function is Or, the probability density of the Copula function is Or, the probability density of the Copula function is .

[0084] 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.

[0085] In specific implementation, a suitable Copula function can be selected from the various models of Copula functions shown in Table 5 according to historical data (such as historical earthquake records) and the physical model of the power grid, as well as the Akaike Information Criterion (AIC).

[0086] Table 5

[0087] In addition, the parameters of the Copula function can be estimated by maximizing the likelihood function of the observed data.

[0088] The likelihood function that maximizes the observed data is .

[0089] 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.

[0090] S105, evaluating the power grid damage under the composite chain disaster 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 the composite disaster.

[0091] Among them, the probability of power grid damage caused by a single disaster is , the probability of combined power grid damage caused by compound disasters is .

[0092] That is, power grid damage under compound chain disasters .

[0093] A possible exemplary evaluation result of power grid damage under compound chain disasters can be as follows: Figure 3 shown.

[0094] The power grid damage assessment method under compound chain-generated disasters provided in this embodiment is a data and physics co-driven power grid damage assessment method under compound chain-generated disasters, which can fully utilize the advantages of data and physical models, improve the accuracy and efficiency of power grid damage assessment, and solve the problem of inaccurate and incomplete assessment during compound chain-generated disasters.

[0095] The present embodiment provides a method for evaluating power grid damage under compound chain-generated disasters, establishes a power grid physical model; 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; integrates historical earthquake 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, evaluates the probability of joint power grid damage caused by compound disasters; evaluates the power grid damage under compound chain-generated disasters as the difference between the sum of the probability of power grid damage caused by a single disaster and the sum of the probability of joint power grid damage caused by compound disasters. The method provided in the present embodiment 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 according to the probability of power grid damage caused by a single disaster evaluated by the power grid physical model and the probability of joint power grid damage caused by compound disasters evaluated according to the compound chain-generated disaster evolution model, thereby achieving accurate and rapid evaluation of power grid damage under compound chain-generated disasters.

[0096] Based on the same inventive concept of the power grid damage assessment method under compound chain-generated disasters, this embodiment provides a power grid damage assessment device under compound chain-generated disasters, see Figure 4 , the device comprises: The first model building module 401 is used to build 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.

[0097] The second model building module 402 is used to build a composite chain disaster evolution model based on a Bayesian network according to sensor data, meteorological data, geographic information data and historical disaster data.

[0098] The first evaluation module 403 is used to integrate the historical earthquake 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. 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 .

[0099] The second evaluation module 404 is used to evaluate the probability of joint damage to the power grid caused by the compound disaster based on the Copula function and the compound chain disaster evolution model established by the second model establishment module 402. The probability of joint damage to the power grid caused by the compound disaster includes: the probability of joint damage to the power grid caused by earthquake and landslide , the probability of joint damage to the power grid caused by mountain and landslide debris flow , Joint damage probability of power grid caused by earthquake and debris flow .

[0100] The third evaluation module 405 is used to evaluate the difference between the probability of power grid damage caused by a single disaster and the probability of combined power grid damage caused by a composite disaster. , the probability of combined power grid damage caused by compound disasters is .

[0101] Among them, the composite chain disaster evolution model includes: earthquake-landslide disaster time evolution model, landslide-mud flow disaster time evolution model, earthquake-landslide-mud flow disaster time evolution model.

[0102] The second model building module 402 is used to determine the earthquake magnitude of the main shock, the focal depth of the main shock, the distance from the epicenter of the main shock, the geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology based on sensor data, meteorological data, geographic information data and historical disaster data.

[0103] A Bayesian network model for earthquake disasters was constructed 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.

[0104] A Bayesian network model for landslide disasters was constructed with earthquake intensity, slope, altitude, rock and soil type as input and landslide volume and landslide velocity as output.

[0105] A Bayesian network model of debris flow disaster is constructed with 24-hour precipitation, landslide volume, landslide velocity and vegetation coverage as input, and the total volume of debris flow accumulation and debris flow peak volume as output.

[0106] A Bayesian network model for aftershock time evolution is constructed, which takes earthquake intensity, distance from the fault zone, geological structure, and lithology as input, and takes the earthquake magnitude, focal depth, and occurrence time of aftershocks as output.

[0107] According to 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 is established.

[0108] 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 is constructed.

[0109] 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.

[0110] Among them, the earthquake magnitude is a discrete node, and the values ​​of the earthquake magnitude include: below 4.5, 4.5 to 6, 6 to 7, 7 to 8, and above 8. The earthquake magnitude is determined according to the energy released by the epicenter in the assessment area.

[0111] The focal depth is a discrete node. The values ​​of the focal depth include: below 5 kilometers, 5-10 kilometers, 10-15 kilometers, 15-20 kilometers, 20-25 kilometers, 25-30 kilometers, and above 30 kilometers. The focal depth is determined based on the vertical distance from the focal point to the ground in the assessment area.

[0112] 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.

[0113] The geological structure is a discrete node. The values ​​of the geological structure include: monocline structure, fold structure, fault structure, and block structure. The geological structure is determined based on the morphology and arrangement of the rock layers in the crust in the assessment area.

[0114] The slope is a discrete node. The slope values ​​include: less than 10 degrees, 10 to 45 degrees, and 45 to 90 degrees. The slope is determined based on the slope degree of the landform in the assessment area.

[0115] 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.

[0116] The geotechnical type is a discrete node. The values ​​of the geotechnical type include: clay, loess, fill, piled soil, broken rock, and rock. The geotechnical type is determined based on the material composition of the sliding body in the assessment area.

[0117] 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 above 250.0 mm. The 24-hour precipitation is determined based on the continuous rainfall in the assessment area 12 hours before and 12 hours after the landslide occurs.

[0118] Vegetation coverage is a discrete node. The values ​​of vegetation coverage include: below 45%, 45%~60%, 60%~75%, and 75%~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.

[0119] 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 between the active fault zone closest to the current location.

[0120] 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.

[0121] The first evaluation module 403 is used to integrate historical earthquake records and the physical model of the power grid to evaluate the probability of failure of each line segment of the power grid, and determine the probability of failure of each line segment of the power grid 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.

[0122] 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 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 a factor.

[0123] 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 debris flow impact pressure direction, It is the impact resistance of transmission towers.

[0124] Optionally, for any earthquake , .

[0125] in, Earthquake sign, earthquake Any recorded earthquake 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.

[0126] Among them, 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 .

[0127] 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.

[0128] The parameters of the Copula function are estimated by maximizing the likelihood function of the observed data.

[0129] The likelihood function that maximizes the observed data is .

[0130] 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.

[0131] The device provided in this embodiment establishes a composite 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 composite chain-generated disasters according to the probability of power grid damage caused by a single disaster evaluated by the power grid physical model and the probability of joint power grid damage caused by composite disasters evaluated according to the composite chain-generated disaster evolution model, thereby achieving accurate and rapid evaluation of power grid damage under composite chain-generated disasters.

[0132] Based on the same inventive concept of a method for evaluating power grid damage under a composite chain disaster, this embodiment provides an electronic device such as Figure 5As shown, it includes: a memory 501, a processor 502, and a computer program.

[0133] The computer program is stored in the memory 501 and is configured to be executed by the processor 502 to implement a method for evaluating power grid damage under compound chain disasters.

[0134] Specifically, Establish a physical model of the power grid, which includes the power grid topology, equipment parameters and operating status.

[0135] 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.

[0136] The historical earthquake records and the physical model of the power grid are integrated 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 earthquake disasters , Probability of power grid damage caused by landslide , Probability of power grid damage caused by debris flow .

[0137] Based on the Copula function and the compound chain disaster evolution model, the combined damage probability of the power grid caused by compound disasters is evaluated. Among them, the combined damage probability of the power grid caused by compound disasters includes: the combined damage probability of the power grid caused by earthquakes and landslides , the probability of joint damage to the power grid caused by mountain and landslide debris flow , Joint damage probability of power grid caused by earthquake and debris flow .

[0138] 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 .

[0139] Optionally, the composite chain disaster evolution model includes: an earthquake-landslide disaster time evolution model, a landslide-mud flow disaster time evolution model, and an earthquake-landslide-mud flow disaster time evolution model.

[0140] A composite chain disaster evolution model based on Bayesian network is established based on 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, the earthquake magnitude of the main shock, the focal depth of the main shock, the distance from the epicenter of the main shock, the geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology are determined.

[0141] A Bayesian network model for earthquake disasters was constructed 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.

[0142] A Bayesian network model for landslide disasters was constructed with earthquake intensity, slope, altitude, rock and soil type as input and landslide volume and landslide velocity as output.

[0143] A Bayesian network model of debris flow disaster is constructed with 24-hour precipitation, landslide volume, landslide velocity and vegetation coverage as input, and the total volume of debris flow accumulation and debris flow peak volume as output.

[0144] A Bayesian network model for aftershock time evolution is constructed, which takes earthquake intensity, distance from the fault zone, geological structure, and lithology as input, and takes the earthquake magnitude, focal depth, and occurrence time of aftershocks as output.

[0145] According to 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 is established.

[0146] 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 is constructed.

[0147] 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.

[0148] Optionally, the earthquake magnitude is a discrete node, and 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.

[0149] The focal depth is a discrete node. The values ​​of the focal depth include: below 5 kilometers, 5-10 kilometers, 10-15 kilometers, 15-20 kilometers, 20-25 kilometers, 25-30 kilometers, and above 30 kilometers. The focal depth is determined based on the vertical distance from the focal point to the ground in the assessment area.

[0150] 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.

[0151] The geological structure is a discrete node. The values ​​of the geological structure include: monocline structure, fold structure, fault structure, and block structure. The geological structure is determined based on the morphology and arrangement of the rock layers in the crust in the assessment area.

[0152] The slope is a discrete node. The slope values ​​include: less than 10 degrees, 10 to 45 degrees, and 45 to 90 degrees. The slope is determined based on the slope degree of the landform in the assessment area.

[0153] 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.

[0154] The geotechnical type is a discrete node. The values ​​of the geotechnical type include: clay, loess, fill, piled soil, broken rock, and rock. The geotechnical type is determined based on the material composition of the sliding body in the assessment area.

[0155] 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 above 250.0 mm. The 24-hour precipitation is determined based on the continuous rainfall in the assessment area 12 hours before and 12 hours after the landslide occurs.

[0156] Vegetation coverage is a discrete node. The values ​​of vegetation coverage include: below 45%, 45%~60%, 60%~75%, and 75%~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.

[0157] 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 between the active fault zone closest to the current location.

[0158] 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.

[0159] Optionally, historical earthquake records and power grid physical models are integrated to assess the probability of power grid damage caused by a single disaster, including: Integrate historical earthquake records and the physical model of the power grid to evaluate the probability of failure of each line segment of the power grid and determine the 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.

[0160] 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 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 a factor.

[0161] 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.

[0162] Optionally, for any earthquake , .

[0163] in, Earthquake sign, earthquake Any recorded earthquake 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.

[0164] Optionally, 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 .

[0165] 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.

[0166] Optionally, the parameters of the copula function are estimated by maximizing the likelihood function of the observed data.

[0167] The likelihood function that maximizes the observed data is .

[0168] 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.

[0169] The electronic device provided in this embodiment has a computer program executed by a processor to establish a composite 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 the grid damage under composite chain-generated disasters is evaluated according to the probability of grid damage caused by a single disaster evaluated by the grid physical model and the probability of combined grid damage caused by composite disasters evaluated according to the composite chain-generated disaster evolution model, thereby achieving accurate and rapid evaluation of grid damage under composite chain-generated disasters.

[0170] Based on the same inventive concept of the method for evaluating power grid damage under compound chain-generated 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 method for evaluating power grid damage under compound chain-generated disasters.

[0171] Specifically, Establish a physical model of the power grid, which includes the power grid topology, equipment parameters and operating status.

[0172] 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.

[0173] The historical earthquake records and the physical model of the power grid are integrated 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 earthquake disasters , Probability of power grid damage caused by landslide , Probability of power grid damage caused by debris flow .

[0174] Based on the Copula function and the compound chain disaster evolution model, the combined damage probability of the power grid caused by compound disasters is evaluated. Among them, the combined damage probability of the power grid caused by compound disasters includes: the combined damage probability of the power grid caused by earthquakes and landslides , the probability of joint damage to the power grid caused by mountain and landslide debris flow , Joint damage probability of power grid caused by earthquake and debris flow .

[0175] 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 .

[0176] Optionally, the composite chain disaster evolution model includes: an earthquake-landslide disaster time evolution model, a landslide-mud flow disaster time evolution model, and an earthquake-landslide-mud flow disaster time evolution model.

[0177] A composite chain disaster evolution model based on Bayesian network is established based on 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, the earthquake magnitude of the main shock, the focal depth of the main shock, the distance from the epicenter of the main shock, the geological structure, slope, altitude, rock and soil type, 24-hour precipitation, vegetation coverage, distance from the fault zone, and lithology are determined.

[0178] A Bayesian network model for earthquake disasters was constructed 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.

[0179] A Bayesian network model for landslide disasters was constructed with earthquake intensity, slope, altitude, rock and soil type as input and landslide volume and landslide velocity as output.

[0180] A Bayesian network model of debris flow disaster is constructed with 24-hour precipitation, landslide volume, landslide velocity and vegetation coverage as input, and the total volume of debris flow accumulation and debris flow peak volume as output.

[0181] A Bayesian network model for aftershock time evolution is constructed, which takes earthquake intensity, distance from the fault zone, geological structure, and lithology as input, and takes the earthquake magnitude, focal depth, and occurrence time of aftershocks as output.

[0182] According to 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 is established.

[0183] 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 is constructed.

[0184] 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.

[0185] Optionally, the earthquake magnitude is a discrete node, and 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.

[0186] The focal depth is a discrete node. The values ​​of the focal depth include: below 5 kilometers, 5-10 kilometers, 10-15 kilometers, 15-20 kilometers, 20-25 kilometers, 25-30 kilometers, and above 30 kilometers. The focal depth is determined based on the vertical distance from the focal point to the ground in the assessment area.

[0187] 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.

[0188] The geological structure is a discrete node. The values ​​of the geological structure include: monocline structure, fold structure, fault structure, and block structure. The geological structure is determined based on the morphology and arrangement of the rock layers in the crust in the assessment area.

[0189] The slope is a discrete node. The slope values ​​include: less than 10 degrees, 10 to 45 degrees, and 45 to 90 degrees. The slope is determined based on the slope degree of the landform in the assessment area.

[0190] 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.

[0191] The geotechnical type is a discrete node. The values ​​of the geotechnical type include: clay, loess, fill, piled soil, broken rock, and rock. The geotechnical type is determined based on the material composition of the sliding body in the assessment area.

[0192] 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 above 250.0 mm. The 24-hour precipitation is determined based on the continuous rainfall in the assessment area 12 hours before and 12 hours after the landslide occurs.

[0193] Vegetation coverage is a discrete node. The values ​​of vegetation coverage include: below 45%, 45%~60%, 60%~75%, and 75%~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.

[0194] 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 between the active fault zone closest to the current location.

[0195] 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.

[0196] Optionally, historical earthquake records and power grid physical models are integrated to assess the probability of power grid damage caused by a single disaster, including: Integrate historical earthquake records and the physical model of the power grid to evaluate the probability of failure of each line segment of the power grid and determine the 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.

[0197] 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 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 a factor.

[0198] 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 debris flow impact pressure direction, It is the impact resistance of transmission towers.

[0199] Optionally, for any earthquake , .

[0200] in, Earthquake sign, earthquake Any recorded earthquake 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.

[0201] Optionally, 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 .

[0202] 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.

[0203] Optionally, the parameters of the copula function are estimated by maximizing the likelihood function of the observed data.

[0204] The likelihood function that maximizes the observed data is .

[0205] 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.

[0206] The computer-readable storage medium provided in this embodiment has a computer program executed by a processor to establish a composite 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 the grid damage under composite chain-generated disasters is evaluated according to the probability of grid damage caused by a single disaster evaluated by the physical model of the grid and the probability of combined grid damage caused by composite disasters evaluated according to the composite chain-generated disaster evolution model, thereby achieving accurate and rapid evaluation of grid damage under composite chain-generated disasters.

[0207] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CDROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0208] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0212] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present 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 , the probability of joint damage to the power grid caused by mountain and landslide 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 , the probability of joint damage to the power grid caused by mountain and landslide 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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