A Method and System for Determining the Seismic Resilience of Mains Power Systems Based on Multidimensional Data Analysis

Abstract

The present application belongs to the field of urban power system operation and maintenance technology, and discloses a city power system earthquake disaster resilience determination method and system based on multi-dimensional data analysis. The present application obtains the earthquake parameters, power facility information, geographic data and social economic data of each county in the city, and constructs a multi-source heterogeneous evaluation database. Based on the seismic motion parameters and the power facility vulnerability function, the damage probability and the expected loss rate of the function of different voltage level substations and transmission lines are calculated, and the residual function matrix of regional power supply is generated. Combined with the real-time power demand model and the power supply capacity recovery curve, the post-earthquake power supply gap rate is calculated, and the influence of the terrain characteristics on the transmission line repair cost is evaluated to quantify the influence of the earthquake on the economic loss, the affected population and the social stability. The present application can effectively improve the resilience and risk management capability of the power system in the earthquake disaster, and provide technical support for the post-earthquake power rapid recovery and resource optimization allocation.

Description

Technical Field

[0001] This invention belongs to the field of urban power system operation and maintenance technology, and in particular relates to a method and system for determining the seismic resilience of a municipal power system based on multidimensional data analysis. Background Technology

[0002] Earthquakes, as highly destructive natural disasters, pose a serious threat to the safety of power systems, especially in high-intensity areas where they can easily cause widespread power outages, resulting in significant socio-economic losses. With the increasing frequency of earthquakes and the acceleration of urbanization worldwide, the importance of seismic risk assessment for power systems is becoming increasingly prominent. However, existing technologies have significant shortcomings: traditional methods are limited to assessments of single equipment or local areas, lacking comprehensive analysis of power facilities across different counties and districts nationwide, at multiple voltage levels (35kV, 110kV, 220kV), and considering geographical differences (plains, hills, and mountains), making it difficult to support national-level post-disaster decision-making; they focus only on the physical vulnerability of facilities, neglecting the coupled impact of geographical environment (slope, altitude, terrain type) and socio-economic conditions (population density) on the degree of earthquake damage and repair efficiency, resulting in insufficient regional adaptability; they emphasize the analysis of physical damage to facilities, lacking dynamic quantification of the affected population size, economic losses, and social stability risks caused by power outages, and failing to consider the impact of the spatiotemporal heterogeneity of electricity demand on the supply-demand gap, leading to a one-sided social impact assessment; they rely on static repair models, which cannot dynamically simulate the power restoration process under different resource inputs, and lack comparative analysis of multiple repair scenarios, resulting in a lack of scientific basis for resource optimization and affecting the efficiency and economy of post-disaster recovery. In summary, the existing technologies have significant shortcomings in multi-dimensional comprehensive assessment, multi-factor coupling analysis, quantification of social impact, and optimization of dynamic repair strategies. Therefore, developing a method and system for assessing the seismic resilience of urban force systems based on multi-dimensional data analysis, which can effectively overcome the deficiencies of the aforementioned technologies, has become an urgent technical problem to be solved in the industry.

[0003] Currently, the resilience assessment of municipal power systems under earthquake disasters faces three major bottlenecks: First, the assessment models rely on single-parameter drivers and fail to effectively integrate ground motion, facility vulnerability, topographic conditions, and socio-economic factors; second, post-earthquake power restoration models generally adopt static estimation methods and lack the ability to dynamically model resource input, load time-varying characteristics, and multi-scenario recovery processes; third, existing methods generally ignore the secondary social impacts caused by earthquake damage, making it difficult to quantify economic losses and social stability risks from the perspective of power outages, thus limiting their practical value in emergency dispatch and resilience planning.

[0004] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0005] (1) The assessment model relies on a single parameter and fails to effectively integrate ground motion, facility vulnerability, topographic conditions and socioeconomic factors.

[0006] (2) Post-earthquake power supply recovery models generally adopt static estimation methods and lack the ability to dynamically model resource input, load time-varying nature and multi-scenario recovery process.

[0007] (3) Existing methods generally ignore the secondary social impacts caused by earthquakes and are difficult to quantify economic losses and social stability risks from the perspective of power outages, which restricts their practical value in emergency dispatch and resilience planning. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a method and system for determining the seismic resilience of mains power systems based on multidimensional data analysis.

[0009] This invention is implemented as follows: A method for determining the seismic resilience of mains power systems based on multidimensional data analysis includes:

[0010] Step 1: Collect seismic motion parameters, power facility structure data, geographic topographic data, and socio-economic data for each county and district of the target city to construct a seismic damage assessment database;

[0011] Step 2: Based on the ground motion parameters and the vulnerability function of power facilities, assess the probability of damage to substations and transmission lines at different voltage levels and the corresponding expected functional loss rate, and construct the regional power supply residual function matrix.

[0012] Step 3: Combine the time sequence characteristics of electricity load with the post-earthquake power supply capacity recovery curve to dynamically calculate the power supply gap rate of each county and district;

[0013] Step 4: Use the terrain elevation model to quantify the slope and divide the terrain into zones, and calculate the transmission line repair cost based on various terrain and line route data;

[0014] Step 5: Take into account the power supply shortage rate, terrain restoration costs, and socio-economic factors to quantify the direct economic losses, affected population size, and social stability risks caused by post-earthquake power outages.

[0015] Step 6: Based on the multi-gradient resource input scenario, simulate the power supply capacity recovery process under different repair paths after the earthquake, form multi-scenario comparison data, and then construct a power system earthquake damage risk zoning map and a repair strategy recommendation scheme.

[0016] Furthermore, the power facility vulnerability function is used to assess the probability that the facility will reach or exceed a specific damage level under earthquakes of different intensities. The probability is described by an error function, in which the difference between the logarithm of the peak ground acceleration and the logarithm of the median ground motion intensity corresponding to the damage level is used as input, and the standard deviation is used as an adjustment factor, which is calculated by a log-normal distribution function.

[0017] Furthermore, the terrain classification is based on a standard scale digital elevation model, extracting the terrain elevation undulation and converting it into a slope value. The slope is determined by the ratio of the square root of the sum of the squares of the elevation change rates in two vertical directions to the horizontal distance. Based on the converted slope, the terrain is divided into five types: plains, hills, mountains, high mountains, and steep ridges. The proportion of each terrain type in the county is used to calculate the resources and costs required for route repair.

[0018] Furthermore, the method for calculating the expected functional loss rate is as follows: For substations, based on the probability-loss accumulation model, the functional loss rate under each damage state is combined with its corresponding exceedance probability, and then the cumulative probability of all damage states not occurring is calculated using the product, thereby obtaining the expected functional loss rate of substations of various voltage levels; For transmission lines, a piecewise continuous function model is used to weight and superimpose the damage states of different length segments to comprehensively reflect the overall functional attenuation of the line.

[0019] Furthermore, the power supply capacity recovery curve is modeled using an exponentially growing time function, where the power supply capacity increases over time. The growth rate of this trend is determined by the repair response time constant. The smaller the constant, the greater the intensity of emergency repair resource investment and the faster the power supply capacity is restored.

[0020] Furthermore, the social stability risk assessment model is based on the ratio of the exposure rate per unit population to the social stability threshold, and is adjusted in combination with the degree of dispersion of population geographical distribution in each region. The risk value is affected by the ratio of the affected population size to the total population of the county / district, and the maximum risk value is limited to not exceeding a set upper limit by a truncation function.

[0021] Another objective of this invention is to provide a system for determining the seismic resilience of mains power systems based on multidimensional data analysis, comprising:

[0022] The data acquisition module is used to acquire seismic motion parameters, power facility information, geographic topographic data, and socio-economic data for various counties and districts of the city.

[0023] A database construction module is used to integrate the multi-source data to form a seismic damage assessment database.

[0024] The seismic damage assessment module is used to calculate the probability of damage and the expected loss rate of function of substations and transmission lines at each voltage level based on seismic parameters and power facility vulnerability functions, and to generate a power supply remaining function matrix.

[0025] The power supply gap calculation module is used to dynamically assess the power supply gap rate in various regions after the earthquake by combining the power load model with the power supply capacity recovery curve.

[0026] The risk analysis module is used to quantify the economic losses, population impacts and social stability risks caused by earthquakes based on terrain division and repair cost models.

[0027] The resilience assessment module is used to simulate repair paths and resource inputs under multiple scenarios, and output post-earthquake power system risk zoning and repair optimization strategies.

[0028] The earthquake damage assessment module includes:

[0029] The vulnerability function calculation unit is used to calculate the probability that a facility will reach or exceed a specific damage state under different earthquake intensities, based on the peak ground acceleration and facility type, combined with the median ground motion intensity and logarithmic standard deviation of each damage level.

[0030] The functional loss rate calculation unit is used to apply a probability-weighted cumulative model to substations and a multi-segment nonlinear attenuation model to transmission lines, and output the expected functional loss rate of different facilities.

[0031] The risk analysis module includes:

[0032] The terrain processing unit is used to calculate the topographic relief of a county based on a digital elevation model and convert it into a slope value. According to the set classification, it is divided into five terrain types: plains, hills, mountains, high mountains and steep ridges.

[0033] The repair cost assessment unit is used to estimate the impact of various terrain types on the repair cost of transmission lines by combining the line route with the terrain distribution ratio.

[0034] The social stability assessment unit is used to calculate the post-earthquake social stability risk value based on the affected population size, geographical distribution coefficient, and social stability threshold.

[0035] The resilience assessment module includes:

[0036] The resource allocation unit is used to simulate the emergency repair response process under different input intensities.

[0037] The power supply recovery curve generation unit is used to output the dynamic changes in power supply capacity at each moment based on the exponential recovery function.

[0038] The strategy output unit is used to generate the optimal repair path and emergency strategy under different repair durations and damage rates.

[0039] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for determining the seismic resilience of a mains power system based on multidimensional data analysis.

[0040] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for determining the seismic resilience of a mains power system based on multidimensional data analysis.

[0041] Another objective of this invention is to provide an information data processing terminal for implementing the system for determining the seismic resilience of a mains power system based on multidimensional data analysis.

[0042] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0043] This invention proposes a method and apparatus for determining the seismic resilience of municipal power systems based on multidimensional data analysis. It systematically constructs a comprehensive database integrating seismic motion parameters, power facility status, topographic relief, and socioeconomic exposure factors. It employs probability-loss functions and piecewise failure functions to accurately model different facility damage states and assesses repair costs through topographic slope mapping and line path correlation analysis. Furthermore, it introduces dynamic power load functions and power supply capacity recovery functions to construct time-series power supply gap curves. Combined with population distribution and social stability models, it achieves a quantitative expression of multi-level seismic damage consequences and generates resilience optimization strategies through multi-gradient resource allocation scenario simulation.

[0044] Compared with existing technologies, this invention significantly improves the depth of earthquake damage resilience modeling and system adaptability: First, it enhances the accuracy and spatial adaptability of substation and line damage assessment through a ground motion-functional degradation linkage modeling mechanism; second, it innovatively achieves dynamic mapping of transmission line repair costs based on terrain classification and slope quantification methods; third, it establishes a triple impact assessment model of power supply gap, economic impact, and social stability, filling the gap in extending resilience assessment to the social level; finally, multi-repair scenario simulation supports a closed-loop support from strategy formulation to recovery path optimization, significantly enhancing the recovery resilience and disaster response capability of the post-earthquake power system, and possessing good engineering promotion prospects and industrial transformation value.

[0045] The method and system for determining the seismic resilience of power systems based on multidimensional data analysis provided in this invention significantly improve the comprehensiveness and accuracy of power system seismic damage assessment through multidimensional data integration and dynamic modeling: It constructs a comprehensive assessment system covering the entire county and multiple voltage levels, overcoming the limitations of local assessments; it quantifies the impact of terrain complexity and socioeconomic factors on seismic damage and repair, improving regional adaptability; it establishes a social impact assessment model for power outages, dynamically quantifying economic losses and social stability risks; it supports multi-repair scenario simulation, optimizes resource scheduling strategies, and achieves closed-loop support from seismic damage assessment to repair decision-making, providing a scientific basis for rapid post-disaster recovery, risk zoning, and emergency management, effectively enhancing the seismic resilience and risk management capabilities of power systems. Attached Figure Description

[0046] Figure 1 This is a flowchart of a method for determining the seismic resilience of a mains power system based on multidimensional data analysis, provided in an embodiment of the present invention.

[0047] Figure 2 This is a block diagram of a system for determining the seismic resilience of a mains power system based on multidimensional data analysis, provided in an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of the structure of the device for determining the seismic resilience of a mains power system based on multidimensional data analysis, provided in an embodiment of the present invention.

[0049] Figure 4 This is a schematic diagram of the physical structure of the electronic device provided in an embodiment of the present invention.

[0050] Figure 5 This is a schematic diagram of the system structure for determining the seismic resilience of a mains power system based on multidimensional data analysis, provided in an embodiment of the present invention.

[0051] Figure 6 This is a map showing the area ratio of different terrains in various counties and districts of China provided in this embodiment of the invention;

[0052] Figure 7 This refers to the economic simulation error of power system earthquake disasters provided in the embodiments of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] like Figure 1 As shown in the figure, the method for determining the seismic resilience of a mains power system based on multidimensional data analysis provided by an embodiment of the present invention includes the following steps:

[0055] S101: Collect seismic motion parameters, power facility structure data, geographic topographic data, and socio-economic data of each county and district of the target city to construct a seismic damage assessment database;

[0056] S102, based on ground motion parameters and power facility vulnerability functions, assess the probability of damage to substations and transmission lines at different voltage levels and the corresponding expected functional loss rate, and construct the regional power supply residual function matrix.

[0057] S103, combining the time sequence characteristics of electricity load with the post-earthquake power supply capacity recovery curve, dynamically calculates the power supply gap rate of each county and district;

[0058] S104 uses a terrain elevation model to quantify slope and divide terrain, and calculates the cost of power transmission line repair based on various terrain and line path data.

[0059] S105, taking into account the power supply shortage rate, terrain restoration costs and socio-economic factors, quantifies the direct economic losses, affected population size and social stability risks caused by post-earthquake power outages.

[0060] S106, based on multi-gradient resource input scenarios, simulates the power supply capacity recovery process under different repair paths after an earthquake, generates multi-scenario comparison data, and then constructs a power system earthquake damage risk zoning map and a recommended repair strategy.

[0061] The power facility vulnerability function provided in this embodiment of the invention is used to assess the probability that the facility will reach or exceed a specific damage level under earthquakes of different intensities. The probability is described by an error function, wherein the difference between the logarithm of the peak ground acceleration and the logarithm of the median ground motion intensity corresponding to the damage level is used as input, and the standard deviation is used as an adjustment factor, which is calculated by a log-normal distribution function.

[0062] The terrain classification provided in this embodiment of the invention is based on a standard scale digital elevation model. It extracts the terrain elevation undulation and converts it into a slope value. The slope is determined by the ratio of the square root of the sum of the squares of the elevation change rates in two vertical directions to the horizontal distance. Based on the converted slope, the terrain is divided into five types: plains, hills, mountains, high mountains, and steep ridges. The proportion of each terrain type in the county is used to calculate the resources and costs required for line repair.

[0063] The method for calculating the expected functional loss rate provided in this embodiment of the invention is as follows: For substations, based on the probability-loss accumulation model, the functional loss rate under each damage state is combined with its corresponding exceedance probability, and then the cumulative probability of all damage states not occurring is calculated using the product, thereby obtaining the expected functional loss rate of substations of various voltage levels; For transmission lines, a piecewise continuous function model is used to weight and superimpose the damage states of different length segments to comprehensively reflect the overall functional attenuation of the line.

[0064] The power supply recovery curve provided in this embodiment of the invention is modeled by an exponentially growing time function, wherein the power supply capacity increases over time. The growth rate of this trend is determined by the repair response time constant. The smaller the constant, the greater the intensity of emergency repair resource investment and the faster the power supply capacity is restored.

[0065] The social stability risk assessment model provided in this embodiment of the invention is based on the ratio of the exposure rate per unit population to the social stability threshold, and is adjusted in combination with the degree of dispersion of population geographical distribution in each region. The risk value is affected by the ratio of the affected population size to the total population of the county / district, and the maximum risk value is limited to not exceeding a set upper limit by a truncation function.

[0066] like Figure 2 As shown in the figure, an embodiment of the present invention provides a system for determining the seismic resilience of a mains power system based on multidimensional data analysis, comprising:

[0067] The data acquisition module is used to acquire seismic motion parameters, power facility information, geographic topographic data, and socio-economic data for various counties and districts of the city.

[0068] A database construction module is used to integrate the multi-source data to form a seismic damage assessment database.

[0069] The seismic damage assessment module is used to calculate the probability of damage and the expected loss rate of function of substations and transmission lines at each voltage level based on seismic parameters and power facility vulnerability functions, and to generate a power supply remaining function matrix.

[0070] The power supply gap calculation module is used to dynamically assess the power supply gap rate in various regions after the earthquake by combining the power load model with the power supply capacity recovery curve.

[0071] The risk analysis module is used to quantify the economic losses, population impacts and social stability risks caused by earthquakes based on terrain division and repair cost models.

[0072] The resilience assessment module is used to simulate repair paths and resource inputs under multiple scenarios, and output post-earthquake power system risk zoning and repair optimization strategies.

[0073] The earthquake damage assessment module includes:

[0074] The vulnerability function calculation unit is used to calculate the probability that a facility will reach or exceed a specific damage state under different earthquake intensities, based on the peak ground acceleration and facility type, combined with the median ground motion intensity and logarithmic standard deviation of each damage level.

[0075] The functional loss rate calculation unit is used to apply a probability-weighted cumulative model to substations and a multi-segment nonlinear attenuation model to transmission lines, and output the expected functional loss rate of different facilities.

[0076] The risk analysis module includes:

[0077] The terrain processing unit is used to calculate the topographic relief of a county based on a digital elevation model and convert it into a slope value. According to the set classification, it is divided into five terrain types: plains, hills, mountains, high mountains and steep ridges.

[0078] The repair cost assessment unit is used to estimate the impact of various terrain types on the repair cost of transmission lines by combining the line route with the terrain distribution ratio.

[0079] The social stability assessment unit is used to calculate the post-earthquake social stability risk value based on the affected population size, geographical distribution coefficient, and social stability threshold.

[0080] The resilience assessment module includes:

[0081] The resource allocation unit is used to simulate the emergency repair response process under different input intensities.

[0082] The power supply recovery curve generation unit is used to output the dynamic changes in power supply capacity at each moment based on the exponential recovery function.

[0083] The strategy output unit is used to generate the optimal repair path and emergency strategy under different repair durations and damage rates.

[0084] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for determining the seismic resilience of a mains power system based on multidimensional data analysis.

[0085] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for determining the seismic resilience of a mains power system based on multidimensional data analysis.

[0086] Another objective of this invention is to provide an information data processing terminal for implementing the system for determining the seismic resilience of a mains power system based on multidimensional data analysis.

[0087] Specific implementation of the present invention:

[0088] This invention provides a method for determining the seismic resilience of mains power systems based on multidimensional data analysis. (See also...) Figure 1The method includes: acquiring seismic parameters, power facility information, geographic topographic data, and socioeconomic data for each county and district of the city to construct an assessment database; calculating the probability of damage and expected functional loss rate of substations and transmission lines at different voltage levels based on seismic parameters and power facility vulnerability functions, and generating a regional power supply remaining function matrix; calculating the power supply gap rate based on real-time electricity demand models and power supply capacity recovery curves, calculating the repair cost of transmission lines in combination with different geographic topographic conditions and data, and quantifying the impact of post-earthquake power outages on economic losses, the scale of affected population, and social stability risks; defining multi-gradient repair scenarios, simulating the power supply recovery process under different resource inputs, and generating seismic damage risk zoning and repair strategy schemes for the urban power system based on seismic damage rate, repair time, and cumulative losses.

[0089] Specifically, a national power system seismic resilience assessment database is established by collecting power facility information (including the number of substations, voltage levels, and transmission line lengths) from multiple data sources (such as earthquake monitoring systems, power facility databases, geographic information systems (GIS), and socio-economic databases) from various counties and districts, geographical and topographic data (such as slope, terrain type, and geographic coordinates) from various cities, and socio-economic data (such as population and electricity consumption), ensuring the comprehensiveness and accuracy of the system's data.

[0090] Based on the above method embodiments, as an optional embodiment, the method for determining the seismic resilience of mains power systems based on multidimensional data analysis provided in this embodiment of the invention includes a power facility vulnerability function comprising:

[0091] Where P represents the peak ground acceleration (PGA) when the earthquake reaches x, and the degree of damage D to the power facilities reaches or exceeds the i-th level of damage. i The probability of; x is the peak ground acceleration (PGA) value; M i β represents the median ground motion intensity for damage state i; i The mean peak ground acceleration (PGA) M for damage level i i Logarithmic standard deviation; erf() is the error function. Specifically, by combining the epicenter location, earthquake intensity, and regional distribution of power facilities, the seismic damage rate of substations and transmission lines in different regions is calculated. Based on different geographical and topographical data of counties and districts across the country, the system assesses the damage status of substations and transmission lines in each county and district, obtaining an accurate seismic damage assessment. The specific modeling method for seismic damage assessment includes: inputting the epicenter location (latitude and longitude coordinates) and peak ground acceleration (PGA), with earthquake intensity represented by PGA. The vulnerability function describes the probability of damage to power facilities under different earthquake intensities; the vulnerability function is shown in P. D iFor damage classification, D1-D4 represent "minor damage", "moderate damage", "severe damage", and "complete damage", respectively. The damage level classification of power facilities and the vulnerability function parameters M and β are shown in Table 1.

[0092] Table 1

[0093]

[0094] Based on the above method embodiments, as an optional embodiment, the present invention provides a method for determining the seismic resilience of mains power systems based on multidimensional data analysis.

[0095] Topography is a crucial factor influencing the construction and repair costs of power transmission lines. Establishing a quantitative calculation scheme for topographic classification is of great significance for the construction and repair of power transmission lines. Therefore, the system performs quantitative analysis of topography based on 1:250,000 scale DEM data to classify the topography of each county / district.

[0096] The system first calculates the topographic relief of each county / district, dividing the relief data into four categories: 0-30m, 30-200m, 200-500m, 500-1000m, and 1000-2500m. The relief is then converted into slope using the following formula. The converted slope classifications are shown in Table 2 below.

[0097]

[0098] In the formula, α represents the slope; dZ represents the height difference; dX and dY represent the distance increments in the orthogonal directions, respectively; and dL represents the distance increments on the horizontal plane.

[0099] Table 2:

[0100]

[0101] The area ratio of different terrain categories in a county is calculated by using the total area S of the county, which provides support for subsequent assessment of the direct economic losses of transmission lines after an earthquake.

[0102]

[0103] The area of ​​each terrain type within the county is represented by S. n S1-S5 represent plains, hills, mountains, high mountains, and steep ridges, respectively. R n This indicates the proportion of each type of terrain within the total area of ​​the county.

[0104] Based on combined seismic parameters and vulnerability functions, the probability of damage to substations and transmission lines within each county is calculated. The calculation formulas include:

[0105] P(D=D i|PGA)=P(D≥D i |PGA)-P(D≥D i+1 |PGA)

[0106] This formula comprehensively considers earthquake intensity and topographical factors to calculate the probability of a substation being in each damage state. For transmission lines, the probability P(D) for each damage state is... i |PGA) is equivalent to the proportion of the total length of the line.

[0107] Based on the above method embodiments, as an optional embodiment, the method for determining the seismic resilience of a mains power system based on multidimensional data analysis provided in this embodiment of the invention includes, as an example, the expected functional loss rate, which comprises: applying a probability-loss accumulation model to the substation.

[0108]

[0109] in, Let be the expected functional loss rate of substation type k, where k∈{1,2,3}, representing 35 kV, 110 kV, and 220 kV substation types respectively. For damage state D at level i i The functional loss rate is shown below; ε is the smoothing coefficient, specifically taking a value of 10. -6 ;P(D i When |PGA) is the peak ground acceleration of the earthquake, and PGA = x, the degree of damage D to the power facilities reaches or exceeds the i-th level of damage. i The probability of.

[0110] Specifically, the expected functional loss rate of power facilities is the degree of functional loss of the facility after an earthquake. Facilities experience different degrees of damage, and the functional loss rate varies under each condition. The functional loss rate of a substation reflects the proportion of its capacity lost due to damage. Therefore, a probability-loss cumulative effect method is used to quantify the cumulative impact of multi-state damage. The expected functional loss rate of a substation is as follows: As shown. When the facility's damage status is D1, D2, D3, D4 in sequence, the corresponding functional loss rate of the facility is... The values ​​are 0, 0.4, 1.0, and 1.0 respectively (the substation and transmission line operate normally when they are basically intact or slightly damaged; they cannot operate when they are severely damaged or completely damaged). This indicates that if the damaged state D1 has a probability P(D i If |PGA) occurs, the remaining functionality is Because D1 corresponds The base is 0, but exponentiation requires avoiding zero bases, so smoothing is performed in practical applications by adding ε = 10. -6 Ensure the formula is valid.

[0111] The functional loss of transmission lines is related to their length and segmental damage; therefore, a segmented Sigmoid failure model is used to quantify the relationship between line length and damage probability.

[0112]

[0113] This represents the overall functional loss rate of a type K transmission line; This represents the expected loss rate per unit length of line. Indicates the total length of the transmission line. This indicates the offset, meaning that when the damaged length exceeds half of the total length, the functional loss rate of the transmission line increases significantly.

[0114] Remaining power supply function Q p This indicator is used to assess the power supply capacity that a region can maintain after an earthquake. It considers the expected functional loss rate of each power facility within the region and the importance of different types of power facilities (represented by capacity or capacity per unit length). The functional loss rates of individual power facilities are aggregated to calculate the remaining power supply capacity of a region, including:

[0115]

[0116] in, This indicates the substation capacity (MVA). N represents the capacity per unit length of the transmission line (MVA / km). k This indicates the number of type k substations in the region. Formula Q p By integrating the functional loss rate of each power facility to assess the power supply capacity of the entire county, this result parameter quantifies the remaining power supply capacity of a regional power system after an earthquake.

[0117] Residual power supply capacity Q calculated for each county / district p A regional power supply residual function matrix is ​​constructed to facilitate subsequent analysis and decision-making. The matrix formula includes:

[0118] Q p matrix = [Q] p(1) Q p(2) ,...,Q p(m) ]

[0119] Among them, Q p(m) This matrix represents the remaining power supply capacity for m counties / districts. It can be used to store the remaining power supply capacity data for each county / district, enabling rapid identification of severely affected areas (Q). p <0.4), prioritize the allocation of emergency repair resources. This facilitates subsequent repair assessment and resilience analysis.

[0120] Based on the above method embodiments, as an optional embodiment, the method for determining the seismic resilience of the mains power system based on multidimensional data analysis provided in this embodiment of the invention includes a time correction factor and an intraday fluctuation function in the real-time electricity demand model, comprising:

[0121]

[0122] Where D(t) represents the actual electricity demand at time t; D base The average daily electricity consumption is calibrated based on the pre-earthquake electricity consumption data of each county and district; η(t) is the duration correction factor; sin is the sign of the sine function.

[0123] Specifically, after the earthquake, the system assesses the impact of power outages on various aspects of society based on the earthquake damage assessment results of each county and district, combined with local electricity consumption and social needs, and by setting different gradients of repair duration. Electricity demand exhibits significant spatiotemporal heterogeneity. After an earthquake, electricity demand is not only affected by damage to power facilities but also closely related to population activity patterns (such as daytime industrial electricity peaks and nighttime residential electricity peaks). By introducing a duration correction factor and a diurnal fluctuation function, real-time electricity demand is dynamically simulated to avoid overestimating or underestimating the supply-demand gap in static models. The impact quantification formula is shown in D(t). Calibration is performed based on pre-earthquake electricity consumption data for each county and district; a sine function is used to simulate diurnal electricity consumption fluctuations (such as midday peaks and late-night troughs); the specific values ​​of the duration correction factor are shown in η(t).

[0124] Based on the above method embodiments, as an optional embodiment, the method for determining the seismic resilience of a mains power system based on multidimensional data analysis provided in this embodiment of the invention includes a power supply capacity recovery curve comprising:

[0125]

[0126] Among them, Q p G(t) represents the remaining power supply capacity of the county / district; G(t) represents the power supply capacity at time t; τ is the repair response time constant, which represents the intensity of emergency repair resource input, and the smaller the value, the faster the repair; Δ(t) represents the power supply gap rate; max represents the maximum value sign.

[0127] Specifically, power supply capacity is gradually restored over time, but the restoration rate is constrained by factors such as the investment of emergency repair resources and the complexity of the terrain. The system describes the gradual nature of the restoration process using an exponential function and defines the power supply gap rate as the core driving factor for social impact assessment, as shown in G(t) and Δ(t). G(t) represents the power supply capacity at time t; for example, if t = 0, Q... p =0.7, then the initial power supply capacity is 70% of the average daily electricity demand. Post-earthquake damage to power facilities will result in high repair costs, leading to an economic loss E for the system. directTo reflect the cost assessment of post-earthquake power system repair, including:

[0128]

[0129] In the formula, This represents the actual construction cost of type k transmission lines. R represents the baseline construction cost of type k transmission lines, expressed in km / ten thousand yuan. n This represents the proportion of each type of terrain within the total area of ​​the county. ξ n The coefficients representing the impact of different terrain conditions on the cost of transmission lines are compared. By comparing the general cost schemes for transmission lines of various voltage levels, the cost per unit path length of transmission line projects in hilly terrain is 5% higher than that in flat terrain; in mountainous terrain, it is 19% higher; in high mountains, it is 26% higher; and in steep ridges, it is 46% higher. Therefore, the coefficients ξ1-ξ5 are 1.0, 1.05, 1.19, 1.26, and 1.46, respectively.

[0130]

[0131] In the formula, This represents the construction cost of type k substations and transmission lines; substation costs are expressed in units per 10,000 yuan. and These represent the damaged state D respectively. i Structural damage rate of substations and transmission lines, N k This indicates the number of type k substations in the region. This indicates the total length of the transmission line. Specific values ​​for the relevant parameters are shown in Table 3.

[0132] Table 3

[0133]

[0134] Based on the above method embodiments, as an optional embodiment, the method for determining the seismic resilience of mains power systems based on multidimensional data analysis provided in this embodiment of the invention includes the following social stability risks:

[0135]

[0136] Where S(t) is used to quantify the social stability of the county / district; Δ critical N represents the critical threshold for social stability. affectted denoted as the affected population size; N represents the total population of the county / district; min indicates the minimum value; μ represents the degree of geographical dispersion.

[0137] Specifically, power outages cause inconvenience to residents and can lead to social instability. This model combines population distribution with power shortages to quantify the affected population size. It then uses a logistic function to map the social stability risk, specifically N. affectted As shown. When the electricity demand gap rate Δ(t) < 0.5, S(t) ≈ 1 (stable); when Δ(t) > 0.5, S(t) ≈ 0 (high risk). critical =0.5, representing the critical threshold for social stability. The system simulates the power supply recovery curves under different emergency resource input intensities by adjusting the repair response time constant τ, and introduces a loss conversion method to convert social impact into a proportion of economic loss, thereby calculating the cumulative economic loss. Specific formulas include:

[0138]

[0139] ε represents the social stability weighted integral coefficient, with the integration interval from the moment of the earthquake to the moment of complete repair. This correction ensures that the social impact assessment results accurately reflect the post-earthquake state evolution of the power system, providing a reliable basis for emergency decision-making.

[0140] The system comprehensively analyzes the seismic damage assessment, repair time and cost, and cumulative losses of power facilities in different counties and districts to evaluate the resilience of the power system under seismic damage of varying intensities. Ultimately, the system zons the seismic damage risk of the power system at the county level, providing a scientific basis for post-disaster recovery and reconstruction in each region.

[0141] The method for determining the seismic resilience of power systems based on multidimensional data analysis provided in this invention significantly improves the comprehensiveness and accuracy of power system seismic damage assessment through multidimensional data integration and dynamic modeling: it constructs a comprehensive assessment system covering the entire county and multiple voltage levels, overcoming the limitations of local assessments; it quantifies the impact of terrain complexity and socioeconomic factors on seismic damage and repair, improving regional adaptability; it establishes a social impact assessment model for power outages, dynamically quantifying economic losses and social stability risks; it supports multi-repair scenario simulation, optimizes resource scheduling strategies, and achieves closed-loop support from seismic damage assessment to repair decision-making, providing a scientific basis for rapid post-disaster recovery, risk zoning, and emergency management, effectively enhancing the seismic resilience and risk management capabilities of power systems.

[0142] The various embodiments of this invention are implemented through programmed processing using a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of this invention can be encapsulated into various modules. Based on this reality, and building upon the above embodiments, this invention provides a device for determining the seismic resilience of a mains power system based on multidimensional data analysis. This device is used to execute the method for determining the seismic resilience of a mains power system based on multidimensional data analysis in the above method embodiments. See also... Figure 3The device comprises: a first main module for acquiring seismic parameters, power facility information, geographic topography data, and socioeconomic data for various counties and districts within the city, and constructing an assessment database; a second main module for calculating the probability of damage and expected functional loss rate of substations and transmission lines at different voltage levels based on seismic parameters and power facility vulnerability functions, and generating a regional power supply remaining function matrix; a third main module for calculating the power supply gap rate based on real-time electricity demand models and power supply capacity recovery curves, calculating the repair cost of transmission lines in conjunction with different geographic topography conditions and data, and quantifying the impact of post-earthquake power outages on economic losses, the scale of affected population, and social stability risks; and a fourth main module for defining multi-gradient repair scenarios, simulating the power supply recovery process under different resource inputs, and generating seismic damage risk zoning and repair strategy schemes for the urban power system based on seismic damage rate, repair time, and cumulative losses.

[0143] The system consists of core modules, each working collaboratively based on data flow and functional logic:

[0144] Multi-source data integration and management module: Responsible for integrating, storing, and managing earthquake monitoring data (such as epicenter location, peak ground acceleration (PGA), power facility information (substation type, voltage level, transmission line parameters), geographic topographic data (slope, altitude, terrain type), and socioeconomic data (population, electricity consumption). Supports dynamic data updates and calibration, and provides a unified data interface for subsequent modules.

[0145] Power Facility Seismic Damage Assessment Module: Based on seismic parameters and power facility vulnerability models, calculates the probability of damage to substations and transmission lines at different voltage levels. It also calculates the seismic damage rate and expected functional loss rate of power facilities in each county / district by analyzing geographical and topographical data (considering the amplification effect of mountainous and hilly terrain on transmission lines and repair costs).

[0146] Dynamic power supply capacity assessment module: Based on the earthquake damage assessment results, calculate the remaining power supply capacity of the area, simulate the recovery process of power supply capacity after the earthquake, and generate a dynamic curve of power supply capacity changing with time.

[0147] Power supply gap and social impact assessment module: Combining daytime / nighttime electricity consumption patterns and intraday fluctuations, dynamically predicting electricity demand at different times; comparing real-time demand with power supply capacity to quantify the power supply gap rate; and analyzing the impact of power outages on economic losses, residents' lives, and social stability risks.

[0148] Resilience assessment module: Defines three scenarios: fast repair, standard repair, and delayed repair. Simulates power restoration efficiency and cumulative losses under different resource scheduling strategies. Generates power system risk zoning map based on indicators such as seismic damage rate, number of affected people, repair time, and social impact.

[0149] The multi-source data integration module receives seismic parameters, power facility data, and geographical and socioeconomic data, which are then cleaned, standardized, and stored in a database. The power facility seismic damage assessment module retrieves seismic parameters and facility vulnerability models from the database, outputting the damage probability to the dynamic power supply capacity assessment module to generate remaining power supply capacity and recovery curves. The results from the dynamic power supply capacity assessment module are input to the power supply gap and social impact assessment module, which, combined with a real-time electricity demand model, calculates the power supply gap rate and assesses economic losses, impacts on residents' lives, and social risks. The resilience assessment module integrates power supply gap data, social impact results, and repair scenario parameters, simulating the recovery process under different resource inputs to generate a risk zoning map.

[0150] The seismic resilience determination device for mains power systems based on multidimensional data analysis provided in this embodiment of the invention employs... Figure 3 Several modules within the system significantly enhance the comprehensiveness and accuracy of power system earthquake damage assessment through multi-dimensional data integration and dynamic modeling: They construct a comprehensive assessment system covering the entire county and multiple voltage levels, overcoming the limitations of local assessments; quantify the impact of terrain complexity and socio-economic factors on earthquake damage and repair, improving regional adaptability; establish a social impact assessment model for power outages, dynamically quantifying economic losses and social stability risks; support multi-repair scenario simulations, optimize resource scheduling strategies, and achieve closed-loop support from earthquake damage assessment to repair decision-making, providing a scientific basis for rapid post-disaster recovery, risk zoning, and emergency management, effectively enhancing the earthquake resilience and risk management capabilities of the power system.

[0151] It should be noted that the apparatus in the device embodiments provided by the present invention can be used not only to implement the methods in the above method embodiments, but also to implement the methods in other method embodiments provided by the present invention. The difference lies only in the setting of corresponding functional modules. Its principle is basically the same as that of the above device embodiments provided by the present invention. As long as those skilled in the art, based on the above device embodiments and referring to the specific technical solutions in other method embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and improve the apparatus in the above device embodiments while ensuring the practicality of the technical solutions, they can obtain corresponding device-type embodiments for implementing the methods in other method-type embodiments. For example:

[0152] Based on the above-described device embodiments, as an optional embodiment, the mains power system seismic resilience determination device based on multidimensional data analysis provided in this embodiment of the invention further includes: a first submodule, used to implement the power facility vulnerability function, including:

[0153]

[0154] Where P represents the peak ground acceleration (PGA) when the earthquake reaches x, and the degree of damage D to the power facilities reaches or exceeds the i-th level of damage. i The probability of; x is the peak ground acceleration (PGA) value; M i β represents the median ground motion intensity for damage state i; i The mean peak ground acceleration (PGA) M for damage level i i Logarithmic standard deviation; erf() is the error function.

[0155] Based on the above-described device embodiments, as an optional embodiment, the device for determining the seismic resilience of a mains power system based on multidimensional data analysis provided in this embodiment of the invention further includes: establishing a terrain division quantitative calculation scheme, performing quantitative analysis of terrain based on 1:250,000 scale DEM data, and realizing the division of terrain for each county / district.

[0156] The system first calculates the topographic relief of each county / district, classifying the relief data into 0-30m, 30-200m, 200-500m, 500-1000m, and 1000-2500m, and then converts the relief into slope using the following formula. The converted slope classification is shown in Table 4 below:

[0157]

[0158] In the formula, α represents the slope; dZ represents the height difference; dX and dY represent the distance increments in the orthogonal directions, respectively; and dL represents the distance increments on the horizontal plane.

[0159] Table 4:

[0160]

[0161] The area ratio of different terrain categories in a county is calculated by using the total area S of the county, which provides support for subsequent assessment of the direct economic losses of transmission lines after an earthquake.

[0162]

[0163] The area of ​​each terrain type within the county is represented by S. n S1-S5 represent plains, hills, mountains, high mountains, and steep ridges, respectively. R n This indicates the proportion of each type of terrain within the total area of ​​the county.

[0164] Based on the above-described device embodiments, as an optional embodiment, the mains power system seismic resilience determination device based on multidimensional data analysis provided in this embodiment of the invention further includes: a third submodule, used to achieve the desired loss rate, including: applying a probability-loss accumulation model to the substation:

[0165]

[0166] in, Let be the expected functional loss rate of substation type k, where k∈{1,2,3}, representing 35 kV, 110 kV, and 220 kV substation types respectively. For damage state D at level i i The functional loss rate is shown below; ε is the smoothing coefficient, specifically taking a value of 10. -6 ;P(D i When |PGA) is the peak ground acceleration of the earthquake, and PGA = x, the degree of damage D to the power facilities reaches or exceeds the i-th level of damage. i The probability of.

[0167] Based on the above-described device embodiments, as an optional embodiment, the mains power system seismic resilience determination device based on multidimensional data analysis provided in this embodiment of the invention further includes: a fourth sub-module, used to implement the introduction of a time correction factor and intraday fluctuation function into the real-time electricity demand model, including:

[0168]

[0169] Where D(t) represents the actual electricity demand at time t; D base The average daily electricity consumption is calibrated based on the pre-earthquake electricity consumption data of each county and district; η(t) is the duration correction factor; sin is the sign of the sine function.

[0170] Based on the above-described device embodiments, as an optional embodiment, the mains power system seismic resilience determination device based on multidimensional data analysis provided in this embodiment of the invention further includes: a fifth sub-module, used to implement the power supply capacity recovery curve, including:

[0171]

[0172] Among them, Q p G(t) represents the remaining power supply capacity of the county / district; G(t) represents the power supply capacity at time t; τ is the repair response time constant, which represents the intensity of emergency repair resource input, and the smaller the value, the faster the repair; Δ(t) represents the power supply gap rate; max represents the maximum value sign.

[0173] Based on the above-described device embodiments, as an optional embodiment, the mains power system seismic resilience determination device based on multidimensional data analysis provided in this embodiment of the invention further includes: a sixth sub-module, used to implement the aforementioned social stability risk, including:

[0174]

[0175] Where S(t) is used to quantify the social stability of the county / district; Δcritical N represents the critical threshold for social stability. affectted denoted as the affected population size; N represents the total population of the county / district; min indicates the minimum value; μ represents the degree of geographical dispersion.

[0176] See details Figure 5 The system includes: a multi-source data integration and management module, which integrates earthquake parameters (source location, PGA), power facility information (substation type, transmission line parameters), geographic topographic data (slope, terrain type), and socioeconomic data (population, electricity consumption); a power facility seismic damage assessment module, which calculates the probability of damage and expected functional loss rate of substations and transmission lines based on earthquake parameters and a vulnerability function (log-normal distribution model); a dynamic power supply capacity assessment module, which generates remaining power supply capacity and repair curves based on seismic damage results, simulating recovery efficiency under different repair scenarios (rapid / standard / delayed repair); a power supply gap and social impact assessment module, which dynamically calculates real-time electricity demand, compares the generated power supply capacity gap rate, and assesses its impact on the economy, public services, and residents' lives (affected population, social stability index); and a resilience assessment module, which simulates cumulative losses through time-gradient repair scenarios and generates repair priorities and resource allocation plans. The input layer integrates earthquake data, power facility information, and geographic and socioeconomic data through a database module. The processing layer transmits seismic damage assessment results to the power supply dynamic assessment module, while power supply capacity and gap rate are input to the social impact assessment module. Output layer: Social impact data is fed back to the resilience assessment module, which outputs the power system risk zoning results.

[0177] The method in this embodiment of the invention is implemented using an electronic device; therefore, it is necessary to introduce the relevant electronic device. For this purpose, this embodiment of the invention provides an electronic device, such as... Figure 4 As shown, the electronic device includes: at least one processor, a communication interface, at least one memory, and a communication bus, wherein the at least one processor, the communication interface, and the at least one memory communicate with each other via the communication bus. The at least one processor can invoke logical instructions stored in the at least one memory to execute all or part of the steps of the methods provided in the foregoing method embodiments.

[0178] The system provided in this embodiment of the invention integrates multi-source heterogeneous data through a data acquisition module, including seismic intensity parameters (such as PGA), power facility structural configuration (such as substation voltage level, number of substations, and line length), topographic factors (undulation based on 1:250,000 DEM elevation data), and socioeconomic indicators (including electricity load, population density, and electricity consumption). After structured processing, the data is uniformly stored in the assessment database, realizing a three-dimensional fusion model of seismic field characteristics, power system vulnerability, and social exposure factors, providing spatiotemporal matching data support for subsequent assessment modules.

[0179] The seismic damage assessment module uses a facility vulnerability function to probabilistically model the damage states of different types of power facilities. Specifically, a generalized error function is used to perform a log-normal mapping of the peak ground acceleration (PGA) to obtain the cumulative distribution probability of the facility at each damage level. For substations, the expected functional loss rate is output using a probability-loss accumulation model, taking into account the reduction ratio of system function by each damage level. For transmission lines, considering their spatial extension characteristics, a nonlinear attenuation model is constructed using a sigmoid function based on path segment length to quantify the cumulative impact of multi-state damage on their transmission capacity.

[0180] The system uses a power supply gap calculation module to model the post-earthquake power supply and demand imbalance by incorporating time-varying load functions and time response recovery curves. The electricity demand model incorporates intraday periodic functions and time correction factors to dynamically fit the changing trends of electricity demand after the earthquake. Power supply recovery capability is characterized by an exponential growth model, with the recovery rate adjusted by the repair response time constant. Combined with the residual function matrix, the system accumulates calculations in the time domain, outputting the power supply gap rate at each moment, providing a basis for decision-making in post-earthquake emergency power dispatch.

[0181] The system integrates a slope estimation mechanism into its risk analysis module, converting elevation differences in DEM data into slope indices and establishing a terrain influence factor system based on a five-level terrain classification standard (flatland, hills, mountains, high mountains, and steep ridges). The transmission line route is superimposed with the proportion of each terrain type as a weighted factor for repair costs. Further, combined with engineering unit price parameters and line length, the system outputs quantitative estimates of line repair costs for each county / district, addressing the problem of existing methods neglecting the impact of terrain heterogeneity on repair difficulty.

[0182] The social stability assessment unit constructs a risk determination model based on the proportion of the affected population after an earthquake and a set social stability threshold, combined with the dispersion coefficient of the population's geographical distribution within the county. By using a piecewise linear function to weight the disaster rate and distribution concentration, the potential fluctuations in post-earthquake social stability are accurately assessed. This model introduces a spatial exposure heterogeneity compensation mechanism, which can distinguish different forms of social system stability risk responses under the same population size, thus providing a quantitative reference for public safety intervention measures.

[0183] The resilience assessment module simulates power restoration paths under different levels of emergency repair input based on multi-scenario resource allocation input. The repair resource allocation unit quantifies the capacity of nodes or lines that can be repaired per unit time; the power supply restoration curve generation unit updates the system power supply status in real time and feeds it back to the risk analysis module; the strategy output unit generates post-earthquake phased repair strategy suggestions by constructing objective functions (such as minimum cumulative power supply gap, minimum economic loss, maximum population benefit, etc.) and integrating heuristic optimization algorithms, providing high-precision auxiliary decision support for the power grid control and emergency command system.

[0184] I. Evidence related to the technical effects obtained by the embodiments of the present invention.

[0185] To fully verify the technical effectiveness of the proposed method and system for determining the seismic resilience of mains power systems based on multidimensional data analysis, we comprehensively evaluated the model's prediction accuracy and adaptability through terrain restoration cost mapping, historical earthquake data comparison, and quantitative analysis of simulated cumulative loss errors. The following discussion focuses on three aspects: terrain restoration cost mapping heatmap analysis, economic loss simulation reliability verification, and error comparison with existing technologies. We also use charts and data to demonstrate its innovation and reliability.

[0186] (1) Heatmap analysis of terrain restoration cost mapping

[0187] This invention integrates digital elevation model (DEM) data to classify the terrain of each county into five categories based on slope: flat land (slope < 5°), hills (5°–15°), mountains (15°–25°), high mountains (25°–35°), and steep ridges (slope > 35°), and generates a terrain area percentage heatmap (see...). Figure 6 ).

[0188] Heatmaps visually illustrate the impact of different terrain types on the cost of power transmission line repair. For example, in southwestern regions such as Sichuan and Yunnan, mountainous areas, high mountains, and steep ridges account for over 60% of the total area, with a benchmark replacement cost of 1.0233 million yuan / km for transmission lines. However, actual engineering data exceeds 1.4 million yuan / km. In contrast, traditional methods that estimate repair costs based on elevation intervals (e.g., 0-500m, 500-1000m) have significantly lower accuracy than the actual data. Heatmap analysis shows that this invention, through slope quantification and path correlation models, can accurately reflect the impact of terrain complexity on repair costs, providing a scientific basis for optimal resource allocation.

[0189] (2) Verification of economic losses from earthquakes in historical power systems

[0190] To verify the model's ability to predict economic losses in the power system, we collected earthquake case data from 22 counties in China over the past 20 years (see Table 5), covering different magnitudes (5.3–7.1), peak ground accelerations (0.05–0.4g), and terrain types. By inputting earthquake parameters, terrain data, and socioeconomic factors, the model simulated the cumulative economic losses for each case and compared the results with actual values. Figure 7 ).

[0191] Table 5. Historical Economic Losses from Earthquake Disasters on Power Systems

[0192]

[0193] The results show that the root mean square error (RMSE) between the simulated and actual values ​​is 25.6. While this error indicates a slight deviation, it can be attributed to the differences between historical data from the year of the earthquake and the current number, cost, and electricity consumption of power facilities. According to relevant literature, the annual growth rate of regional power facility data is approximately 8%, and the annual growth rate of substation and transmission line costs is approximately 7%. These differences in annual growth rates affect the model's output. Nevertheless, the overall error is within an acceptable range, and the assessment accuracy is significantly higher than other models, indicating that the model can accurately reflect the losses of power facilities during earthquakes. To further eliminate the influence of data range on error analysis, we also calculated the normalized root mean square error (NRMSE), which was 0.0004. This value indicates that even with a large range of actual loss data, ranging from 8.15 million yuan to 620 million yuan, the model's assessment results still maintain a high degree of consistency with the actual loss situation. This validation process shows that the model can not only adapt to the loss assessment of power facilities in different regions but also maintain a certain level of accuracy when dealing with data with large differences. This indicates that the assessment results are consistent with the actual losses, thus partially verifying the rationality and reliability of the model.

[0194] (3) Comprehensive comparison with existing technologies

[0195] To highlight the inventiveness of this invention, we compared it with two mainstream models: traditional static models (based solely on seismic motion parameters) and single-parameter models (ignoring topography and socioeconomic factors). The results show that the simulation error of this invention is reduced by 62.5% compared to traditional models and by 72.3% compared to single-parameter models. Furthermore, in terms of topographic restoration cost mapping, this invention quantifies the impact of slope using heatmaps, achieving an error rate of <5%, significantly superior to the traditional elevation classification method (error rate >15%). These data fully demonstrate the technological breakthroughs of this invention in multi-dimensional data integration, dynamic correction, and topographic adaptability.

[0196] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0197] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for determining the resilience of a power system to seismic hazards based on multi-dimensional data analysis, characterized by, The method comprises the following steps: Step 1, collecting the ground motion parameters, power facility structure data, geographic and topographic data and social and economic data of each county in the target city, constructing a seismic damage assessment database, and the social and economic data including population and power consumption; Step 2, based on the ground motion parameters and the power facility vulnerability function, the damage state probability of substations and transmission lines of different voltage levels is calculated, and the damage state probability and the corresponding functional expected loss rate of substations and transmission lines of different voltage levels are evaluated by calculating the geographic and topographic data, considering the functional expected loss rate of each power facility in the region and the importance of different types of power facilities, the functional loss rate of a single power facility is aggregated, and the remaining power supply function of a region is calculated, the power supply function of the whole county is evaluated by integrating the functional loss rate of each power facility, and a regional power supply remaining function matrix is constructed based on the calculated power supply remaining function of each county; Step 3, based on the seismic damage assessment results, the regional remaining power supply capacity is calculated, and the recovery process of the post-earthquake power supply capacity is simulated to generate a post-earthquake power supply capacity recovery curve, and the power supply gap rate of each county is dynamically calculated by combining the power load time sequence characteristics and the post-earthquake power supply capacity recovery curve; Step 4, using the terrain elevation model to quantify the slope and divide the terrain, and calculating the repair cost of the transmission line based on the terrain and line path data; The terrain division and repair cost model are used to quantify the economic loss, population influence and social stability risk caused by the earthquake; Step 5, integrating the power supply gap rate, terrain repair cost and social and economic factors to quantify the direct economic loss, affected population size and social stability risk caused by the post-earthquake power supply interruption; Step 6, based on the multi-gradient resource input situation, the power supply capacity recovery process under different repair paths after the earthquake is simulated to form multi-scenario comparison data, and then an electric power system seismic hazard risk zoning map and a repair strategy recommendation scheme are constructed.

2. The method of claim 1, wherein the method is characterized by, The power facility vulnerability function is used to evaluate the probability of a facility reaching or exceeding a certain damage level under the action of different intensity earthquakes, and the probability is described by an error function, wherein the difference between the logarithmic value of the peak ground acceleration and the logarithmic value of the median ground motion intensity corresponding to the damage level is taken as the input, and the standard deviation is taken as the adjustment factor, and the logarithmic normal distribution function is used to calculate the probability.

3. The method of claim 1, wherein the method is characterized by: The terrain classification is based on a standard scale digital elevation model, the terrain elevation relief is extracted and converted into a slope value, the slope is determined by the square root of the ratio of the square sum of the elevation change rate in two perpendicular directions to the horizontal distance, and the terrain is divided into five types of flat land, hilly land, mountainous land, high mountainous land and steep land according to the converted slope, and the proportion of each terrain type in the county is used to calculate the resources and cost required for line repair.

4. The method of claim 1, wherein the method is characterized by: The calculation method of the function expected loss rate is: for a substation, the function loss rate under each damage state is combined with the corresponding exceedance probability based on a probability-loss cumulative model, and then the cumulative probability of all damage states not occurring is calculated using a continuous product, and then the function expected loss rate of each voltage level substation is obtained; for a transmission line, the damage states of different length sections are weighted and superimposed using a segmented continuous function model to comprehensively reflect the overall functional attenuation degree of the line.

5. The method of claim 1, wherein the method is characterized by: The power supply capacity recovery curve is modeled by an exponential growth type time function, wherein the power supply capacity shows an increasing trend over time, and the growth rate of the trend is determined by a repair response time constant. The smaller the constant, the greater the intensity of repair resources, and the faster the recovery of power supply capacity.

6. The method of claim 1, wherein the method is characterized by: The social stability risk assessment model is based on the ratio of unit population exposure rate to social stability critical value, and adjusts the population geographical distribution dispersion degree of each region, wherein the risk value is affected by the ratio of the affected population to the total population of the county, and the maximum risk value is limited by a truncation function not to exceed the set upper limit.

7. A multi-dimensional data analysis-based power system seismic disaster resilience determination system for implementing the multi-dimensional data analysis-based power system seismic disaster resilience determination method according to any one of claims 1 to 6, characterized by, The power system seismic damage resilience determination system based on multi-dimensional data analysis comprises: a data acquisition module for acquiring seismic ground motion parameters, power facility information, geographic and topographic data, and social and economic data of each county in the city; a database construction module for integrating multi-source data to form a seismic damage assessment database; a seismic damage assessment module for calculating the damage state probability of different voltage level substations and transmission lines based on seismic parameters and power facility vulnerability functions, and calculating the damage probability and function expected loss rate of each voltage level substation and transmission line by calculating the geographic and topographic data, considering the function expected loss rate of each power facility in the region and the importance of different types of power facilities, aggregating the function loss rate of a single power facility, calculating the remaining power supply function of a region, evaluating the power supply function of the entire county by integrating the function loss rate of each power facility, and generating a power supply remaining function matrix based on the calculated power supply remaining function of each county; a power supply gap calculation module for calculating the remaining power supply capacity of the region based on the seismic damage assessment results, simulating the recovery process of the power supply capacity after the earthquake, generating a power supply capacity recovery curve after the earthquake, and dynamically evaluating the power supply gap rate of each region by combining the power load model and the power supply capacity recovery curve; a risk analysis module for quantifying the economic loss, population impact and social stability risk caused by the earthquake based on the terrain division and repair cost model; a resilience assessment module for simulating repair paths and resource inputs under multiple scenarios, outputting post-earthquake power system risk zoning and repair optimization strategies; The seismic damage assessment module comprises: a vulnerability function calculation unit for calculating the probability of a facility reaching or exceeding a specific damage state under different seismic intensities according to the peak ground acceleration and the facility type, combining the median seismic intensity and the logarithmic standard deviation of each damage level; a function loss rate calculation unit for calculating the function expected loss rate of different facilities by using a probability weighted cumulative model for substations and a multi-segment nonlinear attenuation model for transmission lines. The risk analysis module comprises: a terrain processing unit for calculating county terrain undulation based on a digital elevation model and converting the terrain undulation into slope values, and dividing the terrain into five types of flat land, hilly land, mountainous land, high mountainous land and steep land according to a set classification; a repair cost evaluation unit for estimating the influence of each type of terrain on the repair cost of the power transmission line in combination with the line path and the proportion of terrain distribution; a social stability evaluation unit for calculating a post-earthquake social stability risk value according to the affected population size, the geographical distribution coefficient and a social stability threshold value; The resilience evaluation module comprises: a repair resource allocation unit for simulating a repair response process under different input intensities; a power supply recovery curve generation unit for outputting the dynamic change of power supply capacity at each time based on an exponential recovery function; a strategy output unit for generating an optimal repair path and emergency strategy under different repair durations and damage rates.

8. A computer device, comprising: The computer device comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the method for determining the resilience of a power system to earthquake damage based on multi-dimensional data analysis according to any one of claims 1-6. 9.A computer readable storage medium storing a computer program, the computer program being executed by a processor to make the processor execute the steps of the method for determining the resilience of a power system to earthquake damage based on multi-dimensional data analysis according to any one of claims 1-6.

10. An information data processing terminal, characterized by The information data processing terminal is used to implement the system for determining the resilience of a power system to earthquake damage based on multi-dimensional data analysis according to claim 7.

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