A power distribution network disaster prevention network frame planning method and terminal based on a minimum spanning tree

Abstract

The application discloses a power distribution network disaster prevention network frame planning method and a terminal based on a minimum spanning tree. Key user power supply equipment in a regional power distribution network is determined according to a constructed regional power distribution network frame model. A power distribution network disaster prevention network frame planning model is established based on a constructed regional power distribution network extreme weather prediction model and the key user power supply equipment according to a minimum spanning tree theory. The power distribution network disaster prevention network frame planning model takes the reliability of the key user power supply equipment and the minimum disaster cost as optimization objectives, is solved, and a planning scheme is obtained. The prediction of extreme weather and the determined key user power supply equipment can effectively assist the planning of the power distribution network disaster prevention network frame. The planning scheme obtained can effectively improve the active disaster prevention capability of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network planning, and particularly relates to a power distribution network disaster prevention network architecture planning method based on a minimum spanning tree and a terminal. BACKGROUND

[0002] The rapid development of new active power distribution networks brings many conveniences for relieving power supply pressure, but due to the characteristics of weak and simple network structure of the new active power distribution network, the resistance to extreme natural disasters is poor, and extreme natural weather often leads to power outages of the new active power distribution network. Therefore, it is of great significance to carry out technical research on the power distribution network disaster prevention network architecture planning method. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a power distribution network disaster prevention network architecture planning method based on a minimum spanning tree, which can improve the active disaster prevention capability of the power distribution network.

[0004] In order to solve the above technical problems, the technical scheme adopted by the present application is:

[0005] A power distribution network disaster prevention network architecture planning method based on a minimum spanning tree, comprising the steps of:

[0006] constructing an extreme weather prediction model of a regional power distribution network;

[0007] constructing a network architecture model of the regional power distribution network, and determining key user power supply equipment in the regional power distribution network according to the network architecture model of the regional power distribution network;

[0008] establishing a power distribution network disaster prevention network architecture planning model based on the extreme weather prediction model of the regional power distribution network and the key user power supply equipment according to the minimum spanning tree theory, the power distribution network disaster prevention network architecture planning model taking the reliability of the key user power supply equipment and the minimum disaster cost as the optimization target;

[0009] solving the power distribution network disaster prevention network architecture planning model to obtain a planning scheme.

[0010] In order to solve the above technical problems, another technical scheme adopted by the present application is:

[0011] A power distribution network disaster prevention network architecture planning terminal based on a minimum spanning tree, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0012] constructing an extreme weather prediction model of a regional power distribution network;

[0013] constructing a network architecture model of the regional power distribution network, and determining key user power supply equipment in the regional power distribution network according to the network architecture model of the regional power distribution network;

[0014] establishing a power distribution network disaster prevention network framework planning model according to the minimum spanning tree theory based on the regional power distribution network extreme weather prediction model and the key user power supply equipment, the power distribution network disaster prevention network framework planning model taking guaranteeing the key user power supply equipment reliability and minimizing disaster cost as optimization objectives;

[0015] solving the power distribution network disaster prevention network framework planning model to obtain a planning scheme.

[0016] The present application has the beneficial effects that: the key user power supply equipment in the regional power distribution network is determined according to the constructed regional power distribution network framework model, the power distribution network disaster prevention network framework planning model is established according to the minimum spanning tree theory based on the constructed regional power distribution network extreme weather prediction model and the key user power supply equipment, the power distribution network disaster prevention network framework planning model taking guaranteeing the key user power supply equipment reliability and minimizing disaster cost as optimization objectives, the planning scheme is obtained by solving the power distribution network disaster prevention network framework planning model, and the planning scheme obtained by the prediction of the extreme weather and the determination of the key user power supply equipment can effectively improve the active disaster prevention ability of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 a step flow chart of a power distribution network disaster prevention network framework planning method based on the minimum spanning tree for the embodiment of the present application;

[0018] Figure 2 a structural schematic diagram of a power distribution network disaster prevention network framework planning terminal based on the minimum spanning tree for the embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the technical content, the achieved purposes and effects of the present application clear, the following will be described in detail in combination with the embodiments and the drawings.

[0020] Please refer to Figure 1 a power distribution network disaster prevention network framework planning method based on the minimum spanning tree, including the steps of:

[0021] constructing a regional power distribution network extreme weather prediction model;

[0022] constructing a regional power distribution network framework model and determining the key user power supply equipment in the regional power distribution network according to the regional power distribution network framework model;

[0023] establishing a power distribution network disaster prevention network framework planning model according to the minimum spanning tree theory based on the regional power distribution network extreme weather prediction model and the key user power supply equipment, the power distribution network disaster prevention network framework planning model taking guaranteeing the key user power supply equipment reliability and minimizing disaster cost as optimization objectives;

[0024] Solving the disaster prevention network framework planning model of the power distribution network, a planning scheme is obtained.

[0025] From the above description, the beneficial effects of the present application are that: according to the constructed regional power distribution network framework model, the power supply equipment of the key users in the regional power distribution network is determined, and based on the constructed regional power distribution network extreme weather prediction model and the power supply equipment of the key users, a disaster prevention network framework planning model of the power distribution network is established according to the minimum spanning tree theory, the disaster prevention network framework planning model of the power distribution network takes the reliability of the power supply equipment of the key users and the minimum disaster cost as the optimization target, and the planning scheme is obtained by solving the disaster prevention network framework planning model of the power distribution network. The planning scheme obtained by the prediction of the extreme weather and the determination of the power supply equipment of the key users can effectively assist the planning of the disaster prevention network framework of the power distribution network, so as to effectively improve the active disaster prevention ability of the power distribution network.

[0026] Further, the construction of the regional power distribution network extreme weather prediction model comprises:

[0027] A regional power distribution network geographic and climate digital model is established.

[0028] The regional power distribution network extreme weather prediction model is constructed according to the regional power distribution network geographic and climate digital model.

[0029] From the above description, the regional power distribution network extreme weather prediction model is constructed according to the established regional power distribution network geographic and climate digital model, which ensures that the established extreme weather prediction model is more suitable for the actual situation of the power distribution network and improves the prediction accuracy.

[0030] Further, the regional power distribution network extreme weather prediction model comprises a motion equation, an air continuity equation, a potential energy change equation with time, a thermodynamic equation, a water vapor equation, a statics equation and a gas state equation.

[0031] The motion equation is:

[0032]

[0033]

[0034]

[0035] In the formula, U represents the wind speed component in the east direction, t represents time, m represents mass, x represents the coordinate in the east-west direction, U u represents the wind speed component in the east-west direction, y represents the coordinate in the north-south direction, V v represents the wind speed component in the north-south direction, u represents the first representation of the wind speed component under certain conditions, μ d represents the weight of the air column per unit area under dry air, p represents air pressure, a represents specific volume, a dη represents the specific volume of dry air, η represents the vertical wind speed of the coordinate system, φ represents the potential energy, F V represents the diffusion term, V represents the wind speed component in the north direction, v represents the second representation of the wind speed component under certain conditions, W represents the wind speed component in the vertical direction, U w represents the third representation of the wind speed component under certain conditions, V w represents the fourth representation of the wind speed component under certain conditions, w represents the fifth representation of the wind speed component under certain conditions, g represents the acceleration of gravity, q v represents the mixing ratio of water vapor, q c represents the mixing ratio of cloud water, q r represents the mixing ratio of rainwater, F w represents the external force term, Ω represents a certain rotation term, vorticity or other physical quantity related to fluid motion;

[0036] The air continuity equation is:

[0037]

[0038] In the formula, u d represents the weight of a unit area of air column under dry air;

[0039] The potential energy change equation with time is:

[0040]

[0041] In the formula, φ x represents the potential temperature in the x direction, φ y represents the potential temperature in the y direction, φ η represents the potential temperature in the vertical direction;

[0042] The thermodynamic equation is:

[0043]

[0044] In the formula, Θ represents the temperature rise of a unit area of air column, θ represents the temperature rise;

[0045] The water vapor equation is:

[0046]

[0047] In the formula, Q m represents the amount of water vapor change due to evaporation, condensation or other phase change processes, q m represents the mixing ratio of water vapor and rainwater, F Qm represents the amount of spatial distribution change of water vapor due to wind or other flow processes;

[0048] The hydrostatic equation is:

[0049]

[0050] In the formula, represents the atmospheric pressure;

[0051] The gas state equation is:

[0052]

[0053] In the formula, p0 represents a reference air pressure, R d represents a gas constant of dry air, and theta m represents a gas content of dry air.

[0054] As described above, the regional power distribution network extreme weather prediction model includes a complete atmospheric, hydrological and other coupled calculation model, and the power distribution network extreme weather prediction model can be optimized from a more microscopic perspective of the atmosphere, hydrology and the like, so as to improve the accuracy of extreme weather prediction.

[0055] Further, the construction of the regional power distribution network framework model comprises:

[0056] obtaining current framework status information and future twenty-year framework planning information of the regional power distribution network;

[0057] constructing a regional power distribution network framework model based on the current framework status information and the future twenty-year framework planning information, the regional power distribution network framework model comprising existing important loads, important power sources, user loads to be planned, important power source nodes to be planned and important load nodes to be planned in the regional power distribution network.

[0058] As described above, the regional power distribution network framework model is constructed based on the current framework status information and the future twenty-year framework planning information, and the model comprises existing important loads, important power sources, user loads to be planned, important power source nodes to be planned and important load nodes to be planned in the regional power distribution network, so as to determine the key user power supply equipment of the power distribution network.

[0059] Further, the establishment of the power distribution network disaster prevention framework planning model based on the regional power distribution network extreme weather prediction model and the key user power supply equipment according to the minimum spanning tree theory comprises:

[0060] collecting line historical fault information in the regional power distribution network, the line historical fault information comprising the number of power outages of the line and the duration of each power outage;

[0061] collecting real-time environmental data in the regional power distribution network, and performing extreme weather prediction using the regional power distribution network extreme weather prediction model according to the real-time environmental data to obtain a prediction result;

[0062] determine the annual fault duration of the line according to the number of power outages and the duration of each power outage of the line, and calculate the average failure rate of the line in the regional power distribution network according to the annual fault duration of the line;

[0063] classify the line disaster risk in different weather states according to the average failure rate of the line;

[0064] determine the cumulative failure rate of the line in a preset time interval according to the statistical data, and determine the disaster risk of the line according to the cumulative failure rate and the load capacity of the line;

[0065] establish a power distribution network disaster prevention network framework planning model according to the disaster risk of the line and the construction cost of the line.

[0066] As can be seen from the above description, based on the regional power distribution network extreme weather prediction model and the power supply equipment of the key user, the power distribution network disaster prevention network framework planning model is established according to the minimum spanning tree theory. Compared with other ways of construction, it can avoid the introduction of additional vertices while maintaining connectivity, so as to have higher practicality and efficiency in actual application, and realize more effective active disaster prevention of the power distribution network.

[0067] Further, the determination of the disaster risk of the line according to the cumulative failure rate and the load capacity of the line comprises:

[0068] R k = P k · W k ;

[0069] In the formula, R k represents the disaster risk of line k, P k represents the cumulative failure rate of line k in a preset time interval Δt, and W k represents the load capacity of line k.

[0070] As can be seen from the above description, the disaster risk of the line is determined according to the cumulative failure rate and the load capacity of the line, so as to carry out disaster prevention network framework planning based on the disaster risk and improve the disaster prevention ability of the power distribution network.

[0071] Further, the establishment of the power distribution network disaster prevention network framework planning model according to the disaster risk of the line and the construction cost of the line comprises:

[0072]

[0073] In the formula, n represents the number of all lines in the region, C lk represents the construction cost of line k, R k represents the disaster risk of line k, and K represents the coefficient of balancing the construction cost.

[0074] From the above description, the established power distribution network disaster prevention network framework planning model takes the minimum construction cost and disaster cost as the target, ensures the reliability of power supply equipment of key users and the disaster resistance of the power distribution network.

[0075] Further, after the extreme weather prediction model of the regional power distribution network is constructed, the method further comprises:

[0076] evaluating the omission rate and the accuracy rate of the extreme weather prediction model of the regional power distribution network based on historical operation data;

[0077] determining whether the omission rate and the accuracy rate both reach a preset standard, and if not, returning to execute the step of constructing the extreme weather prediction model of the regional power distribution network.

[0078] From the above description, by evaluating the omission rate and the accuracy rate of the extreme weather prediction model of the regional power distribution network, when the evaluation fails, the model is reconstructed, so as to ensure the prediction accuracy of the constructed extreme weather prediction model of the regional power distribution network.

[0079] Further, the step of evaluating the omission rate and the accuracy rate of the extreme weather prediction model of the regional power distribution network based on historical operation data comprises:

[0080]

[0081]

[0082] In the formula, H f represents the omission rate, T f represents the accuracy rate, N F represents the number of samples of true power failure but predicted power failure, N T represents the number of samples of true power failure but predicted power failure, M F represents the number of samples of true power failure but predicted power failure.

[0083] From the above description, the omission rate and the accuracy rate can intuitively reflect the prediction effect of the extreme weather prediction model of the regional power distribution network.

[0084] Please refer to Figure 2 Another embodiment of the present application provides a power distribution network disaster prevention network framework planning terminal based on a minimum spanning tree, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements each step in the above-mentioned power distribution network disaster prevention network framework planning method based on a minimum spanning tree when executing the computer program.

[0085] The above-mentioned power distribution network disaster prevention network framework planning method and terminal based on a minimum spanning tree of the present application can be applied to the new active power distribution network disaster prevention network framework planning scene, and the following will be described through a specific implementation manner:

[0086] Referring to Figure 1 An embodiment of the present application is:

[0087] A power distribution network disaster prevention network framework planning method based on a minimum spanning tree, comprising the steps of:

[0088] S1, constructing an extreme weather prediction model of the regional power distribution network, specifically comprising S11-S12:

[0089] S11, establishing a regional power distribution network geographic and climate digital model.

[0090] Specifically, the geographic features and climate features of the regional power distribution network are collected, and a regional power distribution network geographic and climate digital model is established based on the geographic features and climate features.

[0091] Among them, the climate features include natural disasters that have occurred in the region.

[0092] S12, constructing an extreme weather prediction model of the regional power distribution network according to the regional power distribution network geographic and climate digital model.

[0093] Among them, the extreme weather prediction model of the regional power distribution network includes motion equation, air continuity equation, potential energy change over time equation, thermodynamic equation, water vapor equation, statics equation and gas state equation;

[0094] The motion equation is:

[0095]

[0096]

[0097]

[0098] In the formula, U represents the eastward wind speed component, V represents the northward wind speed component, W represents the vertical wind speed component, t represents time, m represents mass, x represents the east-west coordinate, y represents the south-north coordinate, U u represents the eastward wind speed component, V v represents the south-north wind speed component, u represents the first representation of the wind speed component under certain coordinate system or conditions, μ d represents the weight of the air column per unit area under dry air, the higher the altitude, the smaller the value, p represents air pressure, a represents specific volume, i.e. the inverse of density, a d represents the specific volume of dry air, η represents the vertical wind speed of the coordinate system, φ represents the potential energy, F V represents the diffusion term, v represents the second representation of the wind speed component under certain conditions, U w represents the third representation of the wind speed component under certain conditions, V ww represents the fourth representation of the wind speed component under certain conditions, w represents the fifth representation of the wind speed component under certain conditions, g represents the acceleration of gravity, q v represents the mixing ratio of water vapor, q c represents the mixing ratio of cloud water, q r represents the mixing ratio of rainwater, i.e. the mass of these components per unit mass of air, F w represents the external force term, Ω represents a certain rotation term, vorticity or other physical quantity related to fluid motion;

[0099] The air continuity equation is:

[0100]

[0101] In the formula, u d represents the weight of a unit area of air column under dry air, and the higher the altitude, the smaller the value;

[0102] The potential energy change equation with time is:

[0103]

[0104] In the formula, φ x represents the potential temperature in the x direction, φ y represents the potential temperature in the y direction, φ η represents the potential temperature in the vertical direction;

[0105] The thermodynamic equation is:

[0106]

[0107] In the formula, Θ represents the temperature rise of a unit area of air column, and θ represents the temperature rise;

[0108] The water vapor equation is:

[0109]

[0110] In the formula, Q m represents the amount of water vapor change due to evaporation, condensation or other phase change processes, q m represents the mixing ratio of water vapor and rainwater, F Qm represents the amount of spatial distribution change of water vapor due to wind or other flow processes;

[0111] The hydrostatic equation is:

[0112]

[0113] In the formula, represents the atmospheric pressure;

[0114] The gas state equation is:

[0115]

[0116] wherein p0 represents a reference air pressure, R d represents a gas constant of dry air, θ m represents a gas content of dry air.

[0117] In this embodiment, p0 = 1000 hPa.

[0118] S2, a regional power distribution network framework model is constructed, and a power supply device for a key user in the regional power distribution network is determined according to the regional power distribution network framework model, and specifically includes S21-S23:

[0119] S21, current framework status information and future twenty-year framework planning information of the regional power distribution network are acquired.

[0120] The current framework status information and the future twenty-year framework planning information include a 35 / 110 kilovolt high-voltage power distribution network backbone framework, a 10 / 20 kilovolt and below medium / low-voltage power distribution network backbone framework, a subordinate framework access position, an important load and power supply access position, a line erection mode, a substation form, and a substation equipment operating condition (including data collected by an online monitoring device in the substation) in the regional power distribution network.

[0121] S22, a regional power distribution network framework model is constructed based on the current framework status information and the future twenty-year framework planning information, and the regional power distribution network framework model includes existing important loads, important power sources, user loads to be planned, important power source nodes to be planned, and important load nodes to be planned in the regional power distribution network.

[0122] S23, a power supply device for a key user in the regional power distribution network is determined according to the regional power distribution network framework model.

[0123] S3, a power distribution network disaster prevention framework planning model is established based on the regional power distribution network extreme weather prediction model and the power supply device for the key user according to a minimum spanning tree theory, and the power distribution network disaster prevention framework planning model takes ensuring reliability of the power supply device for the key user and minimizing a disaster cost as an optimization target, and specifically includes S31-S36:

[0124] S31, line historical fault information in the regional power distribution network is collected, and the line historical fault information includes a number of power outages of a line and a duration of each power outage.

[0125] S32, real-time environmental data in the regional power distribution network is collected, and an extreme weather prediction is performed using the regional power distribution network extreme weather prediction model according to the real-time environmental data to obtain a prediction result.

[0126] S33, determining the annual fault duration of the line according to the number of power outages of the line and the duration of each power outage, and calculating the average failure rate of the line in the regional power distribution network according to the annual fault duration of the line.

[0127] The annual fault duration of the line is determined according to the number of power outages of the line and the duration of each power outage, and the average failure rate of the line in the regional power distribution network is calculated according to the annual fault duration of the line.

[0128] The number of power outages of the line and the duration of each power outage are multiplied to obtain the annual fault duration of the line.

[0129] The annual fault duration of the line is determined according to the number of power outages of the line and the duration of each power outage, and the average failure rate of the line in the regional power distribution network is calculated according to the annual fault duration of the line.

[0130]

[0131] In the formula, λ f represents the average failure rate of the line in the regional power distribution network, T f represents the annual fault duration of the line.

[0132] S34, dividing the line disaster risk under different weather conditions according to the average failure rate of the line, specifically:

[0133] λ n = λ f (1-H b ) / W n ;

[0134] λ a = λ f H b (1-H m ) / W a ;

[0135] λ m = λ f H b H m / W m ;

[0136] In the formula, λ n represents the line disaster risk under normal weather, λ a represents the line disaster risk under severe weather, λ m represents the line disaster risk under extreme weather, W n represents the steady-state probability of normal weather, W a represents the steady-state probability of severe weather, W m represents the steady-state probability of extreme weather, H b represents the proportion of fault occurrence in severe weather, H mPercentage of failures caused by extreme weather in bad weather.

[0137] S35, determining a cumulative failure rate of the line in a preset time interval according to the statistical data, and determining a disaster risk of the line according to the cumulative failure rate and a load capacity of the line, specifically:

[0138]

[0139] In the formula, P k represents the cumulative failure rate of line k in a preset time interval Δt, that is, the disaster risk of the line corresponding to the above division, λ k represents the average failure rate per unit time.

[0140] R k = P k · W k ;

[0141] In the formula, R k represents the disaster risk of line k, W k represents the load capacity of line k.

[0142] S36, establishing a power distribution network disaster prevention network framework planning model according to the disaster risk of the line and the construction cost of the line, specifically:

[0143]

[0144] In the formula, n represents the number of all lines in the region, C lk represents the construction cost of line k, R k represents the disaster risk of line k, and K represents a coefficient for balancing the construction cost.

[0145] S4, evaluating the omission rate and accuracy of the extreme weather prediction model of the regional power distribution network based on historical operation data.

[0146] Specifically, a real power outage and a predicted power outage database of the regional power distribution network under extreme weather are established according to historical operation data, the extreme weather prediction model of the regional power distribution network takes meteorological factors, geographical location factors and user power consumption as main consideration factors, and calculates the omission rate and accuracy of the extreme weather prediction model of the regional power distribution network, specifically:

[0147]

[0148]

[0149] In the formula, H f represents the omission rate, T f represents the accuracy, N F represents the number of samples of real power outage but predicted not to be out of power, and NT the number of samples that are actually not outages but predicted to be outages. F the number of samples that are actually not outages but predicted to be outages.

[0150] S5, judging whether the missing rate and the accuracy rate reach preset standards, if not, returning to execute S1.

[0151] S6, solving the disaster prevention network framework planning model of the power distribution network to obtain a planning scheme.

[0152] Please refer to Figure 2 Embodiment two of the present application is:

[0153] A power distribution network disaster prevention network framework planning terminal based on a minimum spanning tree comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements each step in the power distribution network disaster prevention network framework planning method based on a minimum spanning tree in embodiment one when executing the computer program.

[0154] In summary, the power distribution network disaster prevention network framework planning method and terminal based on a minimum spanning tree provided by the present application can determine key user power supply equipment in a regional power distribution network according to a constructed regional power distribution network framework model, establish a power distribution network disaster prevention network framework planning model based on a constructed regional power distribution network extreme weather prediction model and the key user power supply equipment according to a minimum spanning tree theory, take ensuring the reliability of the key user power supply equipment and minimizing the disaster cost as optimization objectives, solve the power distribution network disaster prevention network framework planning model, and obtain a planning scheme. The prediction of extreme weather and the determination of key user power supply equipment can effectively assist in the planning of a power distribution network disaster prevention network framework, and the planning scheme obtained can effectively improve the active disaster prevention capability of the power distribution network. In addition, the regional power distribution network extreme weather prediction model comprises a complete atmospheric and hydrological coupling calculation model, which can optimize the power distribution network extreme weather prediction model from a more microscopic perspective of atmospheric and hydrological conditions to improve the accuracy of extreme weather prediction. Moreover, the missing rate and the accuracy rate of the regional power distribution network extreme weather prediction model are evaluated, and when the evaluation fails, the model is reconstructed, thereby ensuring the prediction accuracy of the constructed regional power distribution network extreme weather prediction model.

[0155] The above only describes the embodiments of the present application, and does not limit the patent range of the present application. Any equivalent transformation or direct or indirect application in related technical fields based on the content of the specification and drawings of the present application are also included in the patent protection range of the present application.

Claims

1. A power distribution network disaster prevention network framework planning method based on a minimum spanning tree, characterized in that, The method comprises the steps of: constructing an extreme weather prediction model of a regional power distribution network; constructing a network framework model of the regional power distribution network, and determining key user power supply equipment in the regional power distribution network according to the network framework model of the regional power distribution network; establishing a power distribution network disaster prevention network framework planning model based on the extreme weather prediction model of the regional power distribution network and the key user power supply equipment according to a minimum spanning tree theory, the power distribution network disaster prevention network framework planning model taking ensuring reliability of the key user power supply equipment and minimizing disaster cost as an optimization objective; solving the power distribution network disaster prevention network framework planning model to obtain a planning scheme; the step of constructing the extreme weather prediction model of the regional power distribution network comprises: establishing a geographic and climate digital model of the regional power distribution network; constructing an extreme weather prediction model of the regional power distribution network according to the geographic and climate digital model of the regional power distribution network; the step of constructing the network framework model of the regional power distribution network comprises: obtaining current network framework status information and future twenty-year network framework planning information of the regional power distribution network; constructing a network framework model of the regional power distribution network based on the current network framework status information and the future twenty-year network framework planning information, the network framework model of the regional power distribution network comprising existing important loads, important power sources, user loads to be planned, important power source nodes to be planned, and important load nodes to be planned in the regional power distribution network; the step of establishing the power distribution network disaster prevention network framework planning model based on the extreme weather prediction model of the regional power distribution network and the key user power supply equipment according to the minimum spanning tree theory comprises: collecting line historical fault information in the regional power distribution network, the line historical fault information comprising outage times of lines and duration of each outage; collecting real-time environmental data in the regional power distribution network, and performing extreme weather prediction using the extreme weather prediction model of the regional power distribution network according to the real-time environmental data to obtain a prediction result; determining annual fault duration of the lines according to the outage times of the lines and the duration of each outage, and calculating average failure rates of the lines in the regional power distribution network according to the annual fault duration of the lines; dividing line disaster risks in different weather states according to the average failure rates of the lines; determining a cumulative failure rate of the lines in a preset time interval according to statistical data, and determining disaster risks of the lines according to the cumulative failure rate and load capacity of the lines; establishing a power distribution network disaster prevention network framework planning model according to the disaster risks of the lines and construction costs of the lines; after the step of constructing the extreme weather prediction model of the regional power distribution network, the method further comprises: evaluating a missing rate and an accuracy rate of the extreme weather prediction model of the regional power distribution network based on historical operation data; determining whether the missing rate and the accuracy rate both reach a preset standard, and if not, returning to execute the step of constructing the extreme weather prediction model of the regional power distribution network.

2. The power distribution network disaster prevention framework planning method based on minimum spanning tree according to claim 1, characterized in that, The extreme weather prediction model of the regional power distribution network comprises a motion equation, an air continuity equation, a potential energy change over time equation, a thermodynamic equation, a water vapor equation, a statics equation, and a gas state equation; the motion equation is: ； ； ； where U represents the wind speed component in the east direction, t represents time, m represents mass, x represents the coordinate in the east-west direction, U u represents the wind speed component in the east direction, y represents the coordinate in the north-south direction, V v represents the wind speed component in the north-south direction, u represents a first representation of the wind speed component under certain conditions, μ d represents the weight of the air column per unit area under dry air, p represents air pressure, a represents specific volume, a d represents the specific volume of dry air, h represents the vertical wind speed of the coordinate system, φ represents potential energy, F V represents a diffusion term, V represents the wind speed component in the north direction, v represents a second representation of the wind speed component under certain conditions, W represents the wind speed component in the vertical direction, U w represents a third representation of the wind speed component under certain conditions, V w represents a fourth representation of the wind speed component under certain conditions, w represents a fifth representation of the wind speed component under certain conditions, g represents gravitational acceleration, q v represents the mixing ratio of water vapor, q c represents the mixing ratio of cloud water, q r represents the mixing ratio of rainwater, F w represents an external force term, Ω represents a rotation term or a vorticity physical quantity; the air continuity equation is: ； wherein u d represents the weight of the air column per unit area under dry air; the potential energy change over time equation is: ； In the formula, represents the bit temperature in the x direction, represents the bit temperature in the y direction, represents the bit temperature in the vertical direction; the thermodynamic equation is: ； wherein represents the temperature rise of the air column per unit area, represents the temperature rise; the water vapor equation is: ； wherein denotes the amount of water vapor change due to evaporation or condensation phase change process, q m denotes the mixing ratio of water vapor and rainwater, F Qm denotes the amount of spatial distribution change of water vapor due to wind flow process; the statics equation is: ； In the formula, denotes the atmospheric pressure; the gas state equation is: ； where p0represents a reference air pressure, R d represents a gas constant of dry air, represents a gas content of dry air.

3. The power distribution network disaster prevention framework planning method based on minimum spanning tree according to claim 1, characterized in that, The determining the disaster risk of the line according to the accumulated failure rate and the load capacity of the line comprises: ； In the formula, R k represents the risk of disaster of line k, P k represents the cumulative failure rate of line k in the preset time interval Δt, W k represents the load capacity of line k.

4. The power distribution network disaster prevention framework planning method based on minimum spanning tree according to claim 1, characterized in that, The establishing a power distribution network disaster prevention network framework planning model according to the disaster risk of the line and the construction cost of the line comprises: ； where n represents the number of all lines in the region, C lk represents the construction cost of line k, R k represents the disaster risk of line k, and K represents the coefficient of balancing the construction cost.

5. The power distribution network disaster prevention framework planning method based on minimum spanning tree according to claim 1, characterized in that, The evaluating the omission rate and the accuracy rate of the extreme weather prediction model of the regional power distribution network based on historical operation data comprises: ； ； where H f denotes the omission rate, T f denotes the accuracy rate, N F denotes the number of samples in which the power was actually off but was predicted to be on. N T denotes the number of samples in which the power was actually off but was predicted to be on. N F denotes the number of samples in which the power was actually on but was predicted to be off. 6.A power distribution network disaster prevention framework planning terminal based on a minimum spanning tree, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The processor, when executing the computer program, realizes each step in the power distribution network disaster prevention network framework planning method based on the minimum spanning tree in any one of claims 1 to 5.

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