Fault probability determination method and device of power distribution network equipment, electronic equipment and storage medium

Abstract

The invention discloses a fault probability determination method and device of power distribution network equipment, electronic equipment and a storage medium. The method comprises the following steps: acquiring a health value of power distribution network equipment and first data corresponding to the power distribution network equipment in a rainstorm environment; predicting a first probability based on the first data, wherein the first probability is used for representing the probability of a fault caused by immersion of the power distribution network equipment in a rainstorm environment; acquiring second data corresponding to the power distribution network equipment in a rainstorm environment; predicting a second probability corresponding to the power distribution network equipment based on the health value and the second data, wherein the second probability is used for representing the probability of a line fault of the power distribution network equipment in the rainstorm environment; acquiring third data corresponding to the power distribution network equipment in a rainstorm environment; predicting a third probability corresponding to the power distribution network equipment based on the health value and the third data, wherein the third probability is used for representing the probability of an insulation fault of the power distribution network equipment in the rainstorm environment; and determining the fault probability of the power distribution network equipment based on the first probability, the second probability and the third probability.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, electronic device, and storage medium for determining the failure probability of distribution network equipment. Background Art

[0002] In recent years, the frequency of extreme weather disasters, such as rainstorms, has been on the rise. The complex structure of distribution networks poses a significant challenge to their stable operation. Once a low-probability, high-risk event caused by an extreme weather disaster occurs, it will cause considerable damage to the power supply network. As the most vulnerable component of the power system, the distribution network's performance during extreme weather disasters directly affects the speed and scope of disaster recovery. With climate change and accelerated urbanization, the frequency and intensity of extreme weather disasters are increasing, and rainstorms and flooding have become a regular threat. In this context, determining the failure probability of distribution network equipment, especially during extreme disasters, has become key to improving urban emergency management capabilities and reducing disaster losses. Summary of the Invention

[0003] The present application provides a method, device, electronic device and storage medium for determining the failure probability of distribution network equipment. The method comprehensively considers the health value of the distribution network equipment, the probability of failure of the distribution network equipment due to water immersion, the probability of line failure of the distribution network equipment, and the probability of insulation failure of the distribution network equipment to determine the failure probability of the distribution network equipment. The method has higher accuracy, thereby improving the city's emergency management capabilities and reducing disaster losses.

[0004] In a first aspect, the present application provides a method for determining a failure probability of a distribution network device, the method comprising:

[0005] Obtaining the health value of the distribution network equipment;

[0006] Acquire first data corresponding to the distribution network equipment in a rainstorm environment;

[0007] Predicting a first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to represent a probability of failure of the distribution network equipment due to water immersion in a rainstorm environment;

[0008] Acquire second data corresponding to the distribution network equipment in a rainstorm environment;

[0009] Predicting a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment;

[0010] Acquire third data corresponding to the distribution network equipment in a rainstorm environment;

[0011] Based on the health value and the third data, predict a third probability corresponding to the distribution network equipment, wherein the third probability is used to characterize the probability of insulation failure of the distribution network equipment in a rainstorm environment;

[0012] A failure probability of the power distribution network equipment is determined based on the first probability, the second probability, and the third probability.

[0013] In a second aspect, the present application provides a device for determining a failure probability of a distribution network device, the device comprising: an acquisition unit and a processing unit;

[0014] An acquiring unit, configured to acquire a health value of the distribution network equipment; and acquire first data corresponding to the distribution network equipment in a rainstorm environment;

[0015] a processing unit, configured to predict, based on the first data, a first probability corresponding to the distribution network equipment, wherein the first probability is used to represent a probability of failure of the distribution network equipment due to water immersion in a rainstorm environment;

[0016] The acquiring unit is further configured to acquire second data corresponding to the distribution network equipment in a rainstorm environment;

[0017] The processing unit is further configured to predict a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment;

[0018] The acquiring unit is further configured to acquire third data corresponding to the distribution network equipment in a rainstorm environment;

[0019] The processing unit is further configured to predict a third probability corresponding to the distribution network equipment based on the health value and the third data, wherein the third probability is used to represent a probability of insulation failure of the distribution network equipment in a rainstorm environment;

[0020] The processing unit is further configured to determine the failure probability of the distribution network equipment based on the first probability, the second probability, and the third probability.

[0021] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, the processor being connected to the memory, the memory being used to store computer programs, and the processor being used to execute the computer programs stored in the memory, so that the electronic device performs the method of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of the first aspect is performed.

[0023] In a fifth aspect, the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, performs the method of the first aspect.

[0024] The implementation of this application has the following beneficial effects:

[0025] By obtaining the health value of the distribution network equipment; obtaining first data corresponding to the distribution network equipment in a rainstorm environment; and then predicting a first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to characterize the probability of the distribution network equipment failing due to water immersion in a rainstorm environment; and obtaining second data corresponding to the distribution network equipment in a rainstorm environment; and then predicting a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment; and obtaining third data corresponding to the distribution network equipment in a rainstorm environment; and then predicting a third probability corresponding to the distribution network equipment based on the health value and the third data, wherein the third probability is used to characterize the probability of an insulation failure of the distribution network equipment in a rainstorm environment. In other words, by calculating the probability of water immersion failure, distribution line failure probability and insulation failure probability of distribution network equipment under the influence of extreme weather such as heavy rain, the failure probability of distribution network equipment can be comprehensively determined, providing a reference for the power system to prevent and respond to disasters such as extreme heavy rain. This will help improve the resilience of the distribution network, enable it to operate more stably and reliably in the face of extreme weather events, improve the city's emergency management capabilities and reduce disaster losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flowchart of a method for determining the failure probability of distribution network equipment provided in an embodiment of the present application;

[0028] Figure 2 A flowchart of a method for predicting a first probability corresponding to the distribution network equipment based on the first data provided in an embodiment of the present application;

[0029] Figure 3 A flowchart of a method for predicting a second probability corresponding to the distribution network equipment based on the health value and the second data provided in an embodiment of the present application;

[0030] Figure 4 A flowchart of a method for predicting a third probability corresponding to the distribution network equipment based on the health value and the third data provided in an embodiment of the present application;

[0031] Figure 5 A schematic diagram of a system for determining the failure probability of distribution network equipment provided in an embodiment of the present application;

[0032] Figure 6 A block diagram of the functional units of a device for determining the failure probability of distribution network equipment provided in an embodiment of the present application;

[0033] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] The terms "first," "second," "third," and "fourth," etc., in the specification, claims, and drawings of this application are used to distinguish between different objects, not to describe a particular order. In addition, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0036] References herein to "embodiments" mean that a particular feature, result, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] See Figure 1 , Figure 1 This is a flow chart of a method for determining the failure probability of distribution network equipment provided in an embodiment of the present application. The method is applied to a device for determining the failure probability of distribution network equipment, and the method includes but is not limited to steps S101-S108:

[0038] S101. Obtain health values ​​of distribution network equipment.

[0039] The health value of the distribution network equipment obtained in the embodiment of the present application corresponds to the first time period, i.e., the current time period. The health value of the distribution network equipment can be pre-set; or optionally, it can also be obtained in the following way: obtain the maximum service life and current service life of the distribution network equipment, and obtain the number of failures of the distribution network equipment in the second time period, wherein the second time period is a historical time period before the current time period, and the present application does not specifically limit the length of the second time period; then determine the health value of the distribution network equipment based on the maximum service life, current service life and number of failures in the second time period of the distribution network equipment. For example, the health value of the distribution network equipment can be obtained by formula (1):

[0040]

[0041] Among them, E represents the health value of the distribution network equipment, A max Represents the maximum service life of the distribution network equipment, A represents the current service life of the distribution network equipment, γ represents the first parameter factor, S represents the second parameter factor, α represents the third parameter factor, β represents the fourth parameter factor, and exp() represents the exponential function; wherein the first parameter factor is used to characterize the efficiency of the maintenance of the distribution network equipment, the second parameter factor is used to characterize the size of the failure probability corresponding to the type of distribution network equipment, and the third parameter factor and the fourth parameter factor are used to characterize the correction factor of the distribution network equipment; the values ​​of the first parameter factor, the second parameter factor and the third parameter factor can be preset. Or optionally, the first parameter factor can also be obtained by evaluating based on the historical maintenance data and maintenance frequency of the distribution network equipment in combination with a data analysis algorithm (such as data fitting, regression analysis, machine learning, etc., which are not limited in this application), which can reflect the adequacy and timeliness of the maintenance of the distribution network equipment; the second parameter factor can be obtained by evaluating based on the complexity of the equipment corresponding to the type of distribution network equipment and the historical fault data in combination with a data analysis algorithm, which can reflect the inherent reliability differences of different types of distribution network equipment; the third parameter factor and the fourth parameter factor can be obtained by evaluating based on the historical usage data of the distribution network equipment in combination with a data analysis algorithm. It can be seen that the health value of the distribution network equipment is used to reflect the health status of the distribution network equipment. The larger the health value, the better the operating status of the distribution network equipment and the closer it is to the optimal working state; the smaller the health value, the worse the operating status of the distribution network equipment, and it may need to be repaired or replaced.

[0042] S102: Acquire first data corresponding to distribution network equipment in a rainstorm environment.

[0043] S103: Based on the first data, predict a first probability corresponding to the distribution network equipment, where the first probability is used to represent the probability of failure of the distribution network equipment due to water immersion in a rainstorm environment.

[0044] In an embodiment of the present application, the first probability corresponds to the first time period, i.e., the current time period, and is used to characterize the probability of a failure of the distribution network equipment due to water immersion within the first time period. The duration of the first time period is the first duration, which is not limited in this application; the first data is the data within the first time period, for example, the starting time of the first time period is the first time and the ending time is the second time.

[0045] Because distribution network equipment such as transformers, switchgear, and distribution boxes may be flooded during flooding, which can degrade insulation performance, cause short circuits, or damage the equipment, the first data may include at least one of the following: a first submerged height of the distribution network equipment at a first moment, a rainfall rate at each moment in the first period, a drainage rate at each moment in the first period, drainage channel dimensions, and a terrain slope corresponding to the distribution network equipment.

[0046] Therefore, for example, when predicting the first probability corresponding to the distribution network equipment based on the first data, refer to Figure 2 , Figure 2 A flowchart of a method for predicting a first probability corresponding to the distribution network equipment based on the first data provided in an embodiment of the present application includes but is not limited to the following steps S201-203:

[0047] S201. Determine the second flooding height corresponding to each moment in the first time period of the distribution network equipment based on the first flooding height, the rainfall rate corresponding to each moment in the first time period, the drainage rate corresponding to each moment in the first time period, the size of the drainage channel, and the terrain slope corresponding to the distribution network equipment.

[0048] For example, taking time t in the first period as an example, the second immersion height corresponding to time t can be obtained by the following formula (2):

[0049]

[0050] Among them, H W (t) represents the second flooding height corresponding to time t, H0 represents the first flooding height of the distribution network equipment at the first time, i.e., time t0, V BR (τ) represents the rainfall rate corresponding to the τth moment in the first period, V DP(τ) represents the drainage rate corresponding to the τth moment in the first period, S1 represents the size of the drainage channel, θ represents the terrain slope corresponding to the distribution network equipment, z, u, and v are all preset weight coefficients, and their values ​​are not limited here; then, similarly, according to the principle of formula (2), the second flooding height corresponding to each moment in the first period of the distribution network equipment is determined, which will not be repeated here.

[0051] S202: Determine a fourth probability corresponding to each moment in the first time period based on the second flooding height corresponding to each moment in the first time period, the ground height of the cable connector of the distribution network equipment, and the flood prevention height of the distribution network equipment.

[0052] For example, if the second flooding height corresponding to each moment in the first period is less than or equal to the height of the cable joint of the distribution network equipment to the ground, the fourth probability corresponding to each moment in the first period is determined to be a first preset value (for example, 0). If the second flooding height corresponding to each moment in the first period is greater than the height of the cable joint of the distribution network equipment to the ground and less than the first height, where the first height is the sum of the height of the cable joint of the distribution network equipment to the ground and the flood prevention height of the distribution network equipment, then based on the second flooding height corresponding to each moment in the first period, the health value of the distribution network equipment, the height of the cable joint of the distribution network equipment to the ground, and the flood prevention height of the distribution network equipment, the fourth probability corresponding to each moment in the first period is determined. For example, when the second flooding height corresponding to each moment in the first period is greater than the height of the cable joint of the distribution network equipment to the ground and less than the first height, taking the moment t in the first period as an example, the fourth probability corresponding to the moment t can be obtained by the following formula (3):

[0053]

[0054] Among them, λ J (t) represents the fourth probability corresponding to time t, λ J0 Indicates the preset initial water immersion failure rate, H D Indicates the height of the cable connector of the distribution network equipment from the ground, H B It represents the flood-proof height of the distribution network equipment, b is the preset first coefficient, and this application does not impose any specific limitation on its value.

[0055] Therefore, for example, taking the first preset value equal to 0 as an example, in step S202, based on the second flooding height corresponding to each moment in the first time period, the height of the cable connector of the distribution network equipment to the ground, and the flood prevention height of the distribution network equipment, the principle of determining the fourth probability corresponding to each moment in the first time period can be summarized as the following formula (4):

[0056]

[0057] It should be noted that the interpretation of each parameter in formula (4) refers to the existing interpretation of the above parameters and will not be repeated here.

[0058] S203: Determine a first probability corresponding to the distribution network equipment based on a fourth probability corresponding to each moment in the first time period.

[0059] That is, to determine the probability of waterlogging failure of the distribution network equipment in the first time period, for example, the sixth probability corresponding to each moment can be averaged or weighted averaged to obtain the first probability; or optionally, the first probability corresponding to the distribution network equipment in the first time period can also be obtained by formula (5):

[0060]

[0061] Among them, P J represents the first probability, represents the fourth probability corresponding to the τth moment in the first period (any moment in the first period), ε J Represents the preset second coefficient, and this application does not impose any specific limitation on its value.

[0062] S104: Acquire second data corresponding to the distribution network equipment in a rainstorm environment.

[0063] S105 . Predict a second probability corresponding to the distribution network equipment based on the health value and the second data, where the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment.

[0064] In an embodiment of the present application, the second probability corresponds to the first time period and is used to characterize the probability of a line failure in the distribution network equipment during the first time period. Because heavy rain accompanied by flooding or loose soil may cause the collapse of distribution network towers, resulting in line failure or power outage, the second data is data from the first time period and may include at least one of the following: wind speed, rainfall intensity, and soil moisture at each moment in the first time period.

[0065] Therefore, illustratively, when predicting the second probability corresponding to the distribution network device based on the health value and the second data, refer to Figure 3 , Figure 3 A flowchart of a method for predicting a second probability corresponding to the distribution network device based on the health value and the second data provided in an embodiment of the present application includes but is not limited to the following steps S301-306:

[0066] S301. Obtain preset maximum wind speed, maximum rainfall intensity, maximum soil moisture, minimum wind speed, minimum rainfall intensity, and minimum soil moisture.

[0067] S302: Determine a first value corresponding to the wind speed at each moment based on the maximum wind speed, the minimum wind speed, and the wind speed corresponding to each moment.

[0068] Exemplarily, a first difference between the wind speed corresponding to each moment and the minimum wind speed is determined, and a second difference between the maximum wind speed and the minimum wind speed is determined, and then the first value is obtained based on the ratio of the first difference to the second difference.

[0069] S303: Determine a second value corresponding to the rainfall intensity at each moment based on the maximum rainfall intensity, the minimum rainfall intensity, and the rainfall intensity corresponding to each moment.

[0070] Exemplarily, a third difference between the rainfall intensity corresponding to each moment and the minimum rainfall intensity is determined, and a fourth difference between the maximum rainfall intensity and the minimum rainfall intensity is determined, and then the second value is obtained based on the ratio of the third difference to the fourth difference.

[0071] S304: Determine a third value corresponding to the soil moisture at each moment based on the maximum soil moisture, the minimum soil moisture, and the soil moisture corresponding to each moment.

[0072] Exemplarily, a fifth difference between the soil moisture corresponding to each moment and the minimum soil moisture is determined, and a sixth difference between the maximum soil moisture and the minimum soil moisture is determined, and then the third value is obtained based on the ratio of the fifth difference to the sixth difference.

[0073] S305 : Determine a fifth probability corresponding to the distribution network equipment at each moment based on the first value, the second value, the third value, and the health value of the distribution network equipment corresponding to each moment.

[0074] For example, based on the first weight coefficient corresponding to the wind speed, the second weight coefficient corresponding to the rainfall intensity, the third weight coefficient corresponding to the soil moisture, and the fourth weight coefficient corresponding to the health value, the first value, the second value, the third value, and the health value corresponding to each moment are weighted and summed to obtain the fourth value corresponding to each moment; then, based on the fourth value corresponding to each moment, the first adjustment coefficient corresponding to each moment is determined, such as performing an exponential function operation on the fourth value to obtain the first adjustment coefficient, which is not limited in this application; then, based on the first adjustment coefficient corresponding to each moment and the preset initial line failure rate, the fifth probability corresponding to each moment is obtained.

[0075] For example, taking time t in the first period as an example, the fifth probability corresponding to time t can be obtained by the following formula (6):

[0076]

[0077] Among them, λ C (t) represents the fifth probability corresponding to time t, λ C0represents the preset initial line failure rate; X1(t) represents the wind speed corresponding to time t, Indicates the maximum wind speed, represents the minimum wind speed; X2(t) represents the rainfall intensity corresponding to time t, Indicates the maximum rainfall intensity, represents the minimum rainfall intensity; X3(t) represents the soil moisture corresponding to time t, represents the maximum soil moisture, represents the minimum soil moisture; β1, β2, β3, and c represent the first weight coefficient, the second weight coefficient, the third weight coefficient, and the fourth weight coefficient, respectively.

[0078] S306: Determine a second probability corresponding to the distribution network equipment based on the fifth probability corresponding to the distribution network equipment at each moment.

[0079] That is, to determine the line failure probability of the distribution network equipment in the first time period, for example, the fifth probability corresponding to each moment can be averaged or weighted averaged to obtain the second probability; or optionally, the second probability corresponding to the distribution network equipment in the first time period can also be obtained by formula (7):

[0080]

[0081] Among them, P C represents the second probability, represents the fifth probability corresponding to the τth moment in the first period (any moment in the first period), ε C It represents the preset third coefficient, and this application does not impose any specific limitation on its value.

[0082] S106. Acquire third data corresponding to the distribution network equipment in a rainstorm environment.

[0083] S107. Predict a third probability corresponding to the distribution network equipment based on the health value and the third data, where the third probability is used to characterize the probability of insulation failure of the distribution network equipment in a rainstorm environment.

[0084] In an embodiment of the present application, the third probability corresponds to the first time period and is used to characterize the probability of insulation failure of the distribution network equipment within the first time period; the third data is data within the first time period; due to the increase in humidity caused by heavy rain, the grounding and insulation systems of the distribution network equipment may be affected, increasing the risk of grounding failure and insulation breakdown. Therefore, the third data can include at least one of the following data: the ambient humidity corresponding to each moment in the first time period, the withstand voltage of the insulation material of the distribution network equipment, etc.

[0085] Therefore, for example, when predicting the third probability corresponding to the distribution network equipment based on the health value and the third data, refer to Figure 4 , Figure 4 A flowchart of a method for predicting a third probability corresponding to the distribution network device based on the health value and the third data provided in an embodiment of the present application includes but is not limited to the following steps S401-403:

[0086] S401: Obtain a first weighting factor corresponding to a health value of a distribution network device, a second weighting factor corresponding to ambient humidity, and a third weighting factor corresponding to a withstand voltage of an insulating material.

[0087] S402 : Based on the first weight factor, the second weight factor, and the third weight factor, the health value, the ambient humidity corresponding to each moment, and the withstand voltage of the insulation material are processed to obtain a sixth probability corresponding to the distribution network equipment at each moment.

[0088] For example, based on the first weight factor, the second weight factor and the third weight factor, the health value, the ambient humidity corresponding to each moment and the withstand voltage of the insulating material are weighted and summed to obtain the fifth value corresponding to each moment; then based on the fifth value corresponding to each moment, the second adjustment coefficient corresponding to each moment is determined, for example, an exponential function operation or a normalization operation is performed on the fifth value corresponding to each moment to obtain the second adjustment coefficient corresponding to each moment, which is not limited in this application; then based on the second adjustment coefficient corresponding to each moment and the preset initial insulation failure rate, the sixth probability corresponding to each moment is obtained.

[0089] For example, taking time t in the first period as an example, the sixth probability corresponding to time t can be obtained by the following formula (8):

[0090] λ I (t) = λ I0 exp(qE+glnh(t)+kL)(8)

[0091] Among them, λ I (t) represents the sixth probability corresponding to time t, λ I0 represents the preset initial insulation failure rate; h(t) represents the ambient humidity corresponding to time t; L represents the withstand voltage of the insulating material; q, g, and k represent the first weight factor, the second weight factor, and the third weight factor, respectively.

[0092] S403: Determine a third probability corresponding to the distribution network equipment based on the sixth probability corresponding to each moment.

[0093] That is, to determine the insulation fault probability of the distribution network equipment in the first time period, for example, the sixth probability corresponding to each moment can be averaged or weighted averaged to obtain the third probability; or optionally, the third probability corresponding to the distribution network equipment in the first time period can also be obtained by formula (9):

[0094]

[0095] Among them, P I represents the third probability, represents the sixth probability corresponding to the τth moment in the first period (any moment in the first period), ε I It represents the preset fourth coefficient, and this application does not impose any specific limitation on its value.

[0096] S108. Determine the failure probability of the distribution network equipment based on the first probability, the second probability, and the third probability.

[0097] For example, the first probability, the second probability, and the third probability can be averaged or weighted averaged to obtain the failure probability of the distribution network equipment. Alternatively, if the number of devices included in the distribution network equipment is N, then the failure probability is the overall failure probability of the N devices. In this case, the failure probability can be obtained by the following formula (10):

[0098]

[0099] Among them, P F represents the failure probability, N represents the number of distribution network equipment, P i J represents the first probability corresponding to the i-th distribution network equipment, P i C represents the second probability corresponding to the i-th distribution network equipment, P i I represents the third probability corresponding to the i-th distribution network equipment.

[0100] It can be seen that in an embodiment of the present application, by obtaining the health value of the distribution network equipment; obtaining first data corresponding to the distribution network equipment in a rainstorm environment; and then predicting a first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to characterize the probability of the distribution network equipment failing due to water immersion in a rainstorm environment; and obtaining second data corresponding to the distribution network equipment in a rainstorm environment; and then predicting a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment; and obtaining third data corresponding to the distribution network equipment in a rainstorm environment; and then predicting a third probability corresponding to the distribution network equipment based on the health value and the third data, wherein the third probability is used to characterize the probability of an insulation failure of the distribution network equipment in a rainstorm environment. In other words, by calculating the probability of water immersion failure, distribution line failure probability and insulation failure probability of distribution network equipment under the influence of extreme weather such as heavy rain, the failure probability of distribution network equipment can be comprehensively determined, providing a reference for the power system to prevent and respond to disasters such as extreme heavy rain. This will help improve the resilience of the distribution network, enable it to operate more stably and reliably in the face of extreme weather events, improve the city's emergency management capabilities and reduce disaster losses.

[0101] Furthermore, the failure probability determination device of the distribution network equipment can be a terminal device or a server, wherein the terminal device can be a smart phone, tablet computer, laptop computer, desktop computer, smart TV, desktop computer, smart watch, smart car and other smart terminals, but is not limited to this, and this application does not make any limitation; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, as well as basic cloud computing services such as big data and artificial intelligence platforms, and this application does not make any specific limitations.

[0102] The following takes the failure probability determination device of the distribution network equipment as an example, see Figure 5 , Figure 5 A schematic diagram of a system for determining the failure probability of distribution network equipment provided in an embodiment of the present application. The system includes a device for determining the failure probability of distribution network equipment and a terminal device; the device for determining the failure probability of distribution network equipment can be a server, and the number of servers can be one or more. Figure 5 The embodiment is mainly described by taking one example.

[0103] Therefore, the failure probability determination device of the distribution network equipment obtains the health value of the distribution network equipment; and obtains first data corresponding to the distribution network equipment in a rainstorm environment; then the failure probability determination device of the distribution network equipment predicts the first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to characterize the probability of the distribution network equipment causing a failure due to water immersion in a rainstorm environment; then the failure probability determination device of the distribution network equipment obtains second data corresponding to the distribution network equipment in a rainstorm environment; and based on the health value and the second data, predicts the second probability corresponding to the distribution network equipment, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment; then the third data corresponding to the distribution network equipment in a rainstorm environment is obtained; and based on the health value and the third data, predicts the third probability corresponding to the distribution network equipment, wherein the third probability is used to characterize the probability of an insulation failure of the distribution network equipment in a rainstorm environment; then the failure probability determination device of the distribution network equipment determines the failure probability of the distribution network equipment based on the first probability, the second probability and the third probability. Furthermore, the failure probability determination device of the distribution network equipment can also send the failure probability of the distribution network equipment to the terminal device so that relevant personnel can take other corresponding measures, which are not listed here one by one.

[0104] It should be noted that Figure 5 The device for determining the failure probability of distribution network equipment in the embodiment can also execute other steps corresponding to the above embodiment and achieve the same technical effects, which will not be described in detail here.

[0105] See Figure 6 , Figure 6 This is a block diagram of the functional units of a device for determining the failure probability of a distribution network device provided in an embodiment of the present application. The device 600 for determining the failure probability of a distribution network device includes: an acquisition unit 601 and a processing unit 602;

[0106] An acquiring unit 601 is configured to acquire a health value of the distribution network device; and acquire first data corresponding to the distribution network device in a rainstorm environment.

[0107] A processing unit 602 is configured to predict a first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to represent a probability of failure of the distribution network equipment due to water immersion in a rainstorm environment;

[0108] The acquiring unit 601 is further configured to acquire second data corresponding to the distribution network equipment in a rainstorm environment;

[0109] The processing unit 602 is further configured to predict a second probability corresponding to the distribution network device based on the health value and the second data, wherein the second probability is used to represent a probability of a line failure of the distribution network device in a rainstorm environment;

[0110] The acquiring unit 601 is further configured to acquire third data corresponding to the distribution network equipment in a rainstorm environment;

[0111] The processing unit 602 is further configured to predict a third probability corresponding to the distribution network equipment based on the health value and the third data, wherein the third probability is used to represent a probability of insulation failure of the distribution network equipment in a rainstorm environment;

[0112] The processing unit 602 is further configured to determine a failure probability of the distribution network equipment based on the first probability, the second probability, and the third probability.

[0113] In one embodiment of the present application, the first probability corresponds to a first time period, the starting time of the first time period being the first time period; the first data includes at least one of the following data: a first submerged height of the distribution network equipment at the first time period, a rainfall rate corresponding to each time period in the first time period, a drainage rate corresponding to each time period in the first time period, a size of a drainage channel, and a terrain slope; in predicting the first probability corresponding to the distribution network equipment based on the first data, the processing unit 602 is specifically configured to:

[0114] Determining a second submerged height of the distribution network equipment corresponding to each moment in the first time period based on the first submerged height, the rainfall rate corresponding to each moment, the drainage rate corresponding to each moment, the size of the drainage channel, and the terrain slope;

[0115] Determining a fourth probability corresponding to each moment in the first time period based on the second flooding height corresponding to each moment, the height of the cable connector of the distribution network equipment to the ground, and the flood prevention height of the distribution network equipment;

[0116] The first probability is determined based on the fourth probability corresponding to each moment in the first time period.

[0117] In one embodiment of the present application, in determining the second submergence height of the distribution network equipment corresponding to each moment in the first time period based on the first submergence height, the rainfall rate corresponding to each moment, the drainage rate corresponding to each moment, the size of the drainage channel, and the terrain slope, the processing unit 602 is specifically configured to:

[0118] If the second water immersion height corresponding to each moment is less than or equal to the height of the cable joint to the ground, determining the fourth probability corresponding to each moment to be the first preset value;

[0119] If the second water immersion height corresponding to each moment is greater than the height of the cable joint to the ground and less than the first height, the fourth probability corresponding to each moment is determined based on the second water immersion height corresponding to each moment, the health value of the distribution network equipment, the height of the cable joint to the ground and the flood prevention height, wherein the first height is the sum of the height of the cable joint to the ground and the flood prevention height.

[0120] In one embodiment of the present application, the second probability corresponds to a first time period, and the second data includes at least one of the following data: wind speed, rainfall intensity, and soil moisture corresponding to each moment in the first time period; in predicting the second probability corresponding to the distribution network equipment based on the health value and the second data, the processing unit 602 is specifically configured to:

[0121] Get the preset maximum wind speed, maximum rainfall intensity, maximum soil moisture, minimum wind speed, minimum rainfall intensity and minimum soil moisture;

[0122] Determining a first value corresponding to the wind speed at each moment based on the maximum wind speed, the minimum wind speed, and the wind speed corresponding to each moment;

[0123] Determining a second value corresponding to the rainfall intensity at each moment based on the maximum rainfall intensity, the minimum rainfall intensity, and the rainfall intensity corresponding to each moment;

[0124] determining a third value corresponding to the soil moisture at each moment based on the maximum soil moisture, the minimum soil moisture, and the soil moisture corresponding to each moment;

[0125] Determine a fifth probability corresponding to the distribution network equipment at each moment based on the first value, the second value, the third value, and the health value of the distribution network equipment corresponding to each moment;

[0126] The second probability is determined based on the fifth probability corresponding to the power distribution network equipment at each moment.

[0127] In one embodiment of the present application, the third probability corresponds to the first time period, and the third data includes at least one of the following data: the ambient humidity corresponding to each moment in the first time period, and the withstand voltage of the insulating material of the distribution network equipment. In predicting the third probability corresponding to the distribution network equipment based on the health value and the third data, the processing unit 602 is specifically configured to:

[0128] Obtaining a first weighting factor corresponding to the health value of the distribution network equipment, a second weighting factor corresponding to the ambient humidity, and a third weighting factor corresponding to the withstand voltage of the insulation material;

[0129] Processing the health value, the ambient humidity corresponding to each moment, and the withstand voltage of the insulating material based on the first weighting factor, the second weighting factor, and the third weighting factor to obtain a sixth probability corresponding to the distribution network equipment at each moment;

[0130] The third probability is determined based on the sixth probability corresponding to each moment.

[0131] In one embodiment of the present application, in terms of obtaining the health value of the distribution network device, the processing unit 602 is specifically configured to:

[0132] Obtaining the maximum service life and current service life of the distribution network equipment, and obtaining the number of failures of the distribution network equipment in the second time period;

[0133] The health value is determined according to the maximum service life, the current service life and the number of failures.

[0134] In one embodiment of the present application, in determining the failure probability of the distribution network device based on the first probability, the second probability, and the third probability, the processing unit 602 is specifically configured to:

[0135] A weighted average process is performed on the first probability, the second probability, and the third probability to obtain the failure probability.

[0136] In a specific implementation, the acquisition unit 601 and the processing unit 602 described in the embodiment of the present invention may also execute other implementation methods described in the embodiments of the method for determining the failure probability of other distribution network equipment provided by the embodiment of the present invention, which will not be repeated here.

[0137] See Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, electronic device 700 includes transceiver 701, processor 702 and memory 703. They are connected via bus 704. Memory 703 is used to store computer programs and data, and can transmit data stored in memory 703 to processor 702.

[0138] The processor 702 is configured to read the computer program in the memory 703 and perform the following operations:

[0139] Controlling the transceiver 701 to obtain the health value of the distribution network equipment; obtaining first data corresponding to the distribution network equipment in a rainstorm environment;

[0140] Predicting a first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to represent a probability of failure of the distribution network equipment due to water immersion in a rainstorm environment;

[0141] Controlling the transceiver 701 to obtain second data corresponding to the power distribution network equipment in a rainstorm environment;

[0142] Predicting a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment;

[0143] Controlling the transceiver 701 to obtain third data corresponding to the power distribution network equipment in a rainstorm environment;

[0144] Based on the health value and the third data, predict a third probability corresponding to the distribution network equipment, wherein the third probability is used to characterize the probability of insulation failure of the distribution network equipment in a rainstorm environment;

[0145] A failure probability of the power distribution network equipment is determined based on the first probability, the second probability, and the third probability.

[0146] In one embodiment of the present application, the first probability corresponds to a first time period, the starting time of the first time period being the first time period; the first data includes at least one of the following data: a first submerged height of the distribution network equipment at the first time period, a rainfall rate corresponding to each time period in the first time period, a drainage rate corresponding to each time period in the first time period, a size of a drainage channel, and a terrain slope; in predicting the first probability corresponding to the distribution network equipment based on the first data, the processor 702 is configured to perform the following operations:

[0147] Determining a second submerged height of the distribution network equipment corresponding to each moment in the first time period based on the first submerged height, the rainfall rate corresponding to each moment, the drainage rate corresponding to each moment, the size of the drainage channel, and the terrain slope;

[0148] Determining a fourth probability corresponding to each moment in the first time period based on the second flooding height corresponding to each moment, the height of the cable connector of the distribution network equipment to the ground, and the flood prevention height of the distribution network equipment;

[0149] The first probability is determined based on the fourth probability corresponding to each moment in the first time period.

[0150] In one embodiment of the present application, in determining the second submerged height of the distribution network equipment corresponding to each moment in the first time period based on the first submerged height, the rainfall rate corresponding to each moment, the drainage rate corresponding to each moment, the size of the drainage channel, and the terrain slope, the processor 702 is configured to perform the following operations:

[0151] If the second water immersion height corresponding to each moment is less than or equal to the height of the cable joint to the ground, determining the fourth probability corresponding to each moment to be the first preset value;

[0152] If the second water immersion height corresponding to each moment is greater than the height of the cable joint to the ground and less than the first height, the fourth probability corresponding to each moment is determined based on the second water immersion height corresponding to each moment, the health value of the distribution network equipment, the height of the cable joint to the ground and the flood prevention height, wherein the first height is the sum of the height of the cable joint to the ground and the flood prevention height.

[0153] In one embodiment of the present application, the second probability corresponds to a first time period, and the second data includes at least one of the following data: wind speed, rainfall intensity, and soil moisture corresponding to each moment in the first time period; in predicting the second probability corresponding to the distribution network equipment based on the health value and the second data, the processor 702 is configured to perform the following operations:

[0154] Get the preset maximum wind speed, maximum rainfall intensity, maximum soil moisture, minimum wind speed, minimum rainfall intensity and minimum soil moisture;

[0155] Determining a first value corresponding to the wind speed at each moment based on the maximum wind speed, the minimum wind speed, and the wind speed corresponding to each moment;

[0156] Determining a second value corresponding to the rainfall intensity at each moment based on the maximum rainfall intensity, the minimum rainfall intensity, and the rainfall intensity corresponding to each moment;

[0157] determining a third value corresponding to the soil moisture at each moment based on the maximum soil moisture, the minimum soil moisture, and the soil moisture corresponding to each moment;

[0158] Determine a fifth probability corresponding to the distribution network equipment at each moment based on the first value, the second value, the third value, and the health value of the distribution network equipment corresponding to each moment;

[0159] The second probability is determined based on the fifth probability corresponding to the power distribution network equipment at each moment.

[0160] In one embodiment of the present application, the third probability corresponds to the first time period, and the third data includes at least one of the following data: the ambient humidity corresponding to each moment in the first time period, and the withstand voltage of the insulating material of the distribution network equipment; in terms of predicting the third probability corresponding to the distribution network equipment based on the health value and the third data, the processor 702 is configured to perform the following operations:

[0161] Obtaining a first weighting factor corresponding to the health value of the distribution network equipment, a second weighting factor corresponding to the ambient humidity, and a third weighting factor corresponding to the withstand voltage of the insulation material;

[0162] Processing the health value, the ambient humidity corresponding to each moment, and the withstand voltage of the insulating material based on the first weighting factor, the second weighting factor, and the third weighting factor to obtain a sixth probability corresponding to the distribution network equipment at each moment;

[0163] The third probability is determined based on the sixth probability corresponding to each moment.

[0164] In one embodiment of the present application, in terms of obtaining the health value of the distribution network device, the processor 702 is configured to perform the following operations:

[0165] Obtaining the maximum service life and current service life of the distribution network equipment, and obtaining the number of failures of the distribution network equipment in the second time period;

[0166] The health value is determined according to the maximum service life, the current service life and the number of failures.

[0167] In one embodiment of the present application, in determining the failure probability of the distribution network device based on the first probability, the second probability, and the third probability, the processor 702 is configured to perform the following operations:

[0168] A weighted average process is performed on the first probability, the second probability, and the third probability to obtain the failure probability.

[0169] In a specific implementation, the transceiver 701 and the processor 702 described in the embodiment of the present invention may also execute other implementation methods described in the embodiment of the method for determining the failure probability of distribution network equipment provided by the embodiment of the present invention, which will not be repeated here.

[0170] Specifically, the transceiver 701 can be Figure 6 The acquisition unit 601 of the apparatus 600 for determining the failure probability of the distribution network equipment of the embodiment, the processor 702 may be Figure 6 The processing unit 602 of the failure probability determination device 600 of the distribution network equipment of the embodiment.

[0171] It should be understood that an embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement part or all of the steps of any method for determining the failure probability of distribution network equipment recorded in the above method embodiments.

[0172] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute part or all of the steps of any method for determining the failure probability of distribution network equipment as described in the above method embodiments.

[0173] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0174] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0176] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0177] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0178] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0179] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0180] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for determining the failure probability of distribution network equipment, characterized in that: The method comprises: Obtaining the health value of the distribution network equipment; Acquire first data corresponding to the distribution network equipment in a rainstorm environment; Predicting a first probability corresponding to the distribution network equipment based on the first data, wherein the first probability is used to represent a probability of failure of the distribution network equipment due to water immersion in a rainstorm environment; Acquire second data corresponding to the distribution network equipment in a rainstorm environment; Predicting a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment; Acquire third data corresponding to the distribution network equipment in a rainstorm environment; Based on the health value and the third data, predict a third probability corresponding to the distribution network equipment, wherein the third probability is used to characterize the probability of insulation failure of the distribution network equipment in a rainstorm environment; A failure probability of the power distribution network equipment is determined based on the first probability, the second probability, and the third probability.

2. The method according to claim 1, characterized in that The first probability corresponds to a first time period, where the starting time of the first time period is the first time period; the first data includes at least one of the following data: a first submerged height of the distribution network equipment at the first time period, a rainfall rate corresponding to each time period in the first time period, a drainage rate corresponding to each time period in the first time period, a size of a drainage channel, and a terrain slope; The predicting, based on the first data, a first probability corresponding to the distribution network equipment includes: Determining a second submerged height of the distribution network equipment corresponding to each moment in the first time period based on the first submerged height, the rainfall rate corresponding to each moment, the drainage rate corresponding to each moment, the size of the drainage channel, and the terrain slope; Determining a fourth probability corresponding to each moment in the first time period based on the second flooding height corresponding to each moment, the height of the cable connector of the distribution network equipment to the ground, and the flood prevention height of the distribution network equipment; The first probability is determined based on the fourth probability corresponding to each moment in the first time period.

3. The method according to claim 2, characterized in that The determining, based on the first flooding height, the rainfall rate corresponding to each moment, the drainage rate corresponding to each moment, the size of the drainage channel, and the terrain slope, of the second flooding height of the distribution network equipment corresponding to each moment in the first time period includes: If the second water immersion height corresponding to each moment is less than or equal to the height of the cable joint to the ground, determining the fourth probability corresponding to each moment to be the first preset value; If the second water immersion height corresponding to each moment is greater than the height of the cable joint to the ground and less than the first height, the fourth probability corresponding to each moment is determined based on the second water immersion height corresponding to each moment, the health value of the distribution network equipment, the height of the cable joint to the ground and the flood prevention height, wherein the first height is the sum of the height of the cable joint to the ground and the flood prevention height.

4. The method according to any one of claims 1 to 3, characterized in that The second probability corresponds to the first time period, and the second data includes at least one of the following data: wind speed, rainfall intensity, and soil moisture corresponding to each moment in the first time period; The predicting, based on the health value and the second data, a second probability corresponding to the distribution network equipment includes: Get the preset maximum wind speed, maximum rainfall intensity, maximum soil moisture, minimum wind speed, minimum rainfall intensity and minimum soil moisture; Determining a first value corresponding to the wind speed at each moment based on the maximum wind speed, the minimum wind speed, and the wind speed corresponding to each moment; Determining a second value corresponding to the rainfall intensity at each moment based on the maximum rainfall intensity, the minimum rainfall intensity, and the rainfall intensity corresponding to each moment; determining a third value corresponding to the soil moisture at each moment based on the maximum soil moisture, the minimum soil moisture, and the soil moisture corresponding to each moment; Determine a fifth probability corresponding to the distribution network equipment at each moment based on the first value, the second value, the third value, and the health value of the distribution network equipment corresponding to each moment; The second probability is determined based on the fifth probability corresponding to the power distribution network equipment at each moment.

5. The method according to any one of claims 1 to 4, characterized in that The third probability corresponds to the first time period, and the third data includes at least one of the following data: the ambient humidity corresponding to each moment in the first time period, and the withstand voltage of the insulation material of the distribution network equipment; The predicting, based on the health value and the third data, a third probability corresponding to the distribution network equipment includes: Obtaining a first weighting factor corresponding to the health value of the distribution network equipment, a second weighting factor corresponding to the ambient humidity, and a third weighting factor corresponding to the withstand voltage of the insulation material; Processing the health value, the ambient humidity corresponding to each moment, and the withstand voltage of the insulating material based on the first weighting factor, the second weighting factor, and the third weighting factor to obtain a sixth probability corresponding to the distribution network equipment at each moment; The third probability is determined based on the sixth probability corresponding to each moment.

6. The method according to any one of claims 1 to 5, characterized in that The obtaining of the health value of the distribution network equipment includes: Obtaining the maximum service life and current service life of the distribution network equipment, and obtaining the number of failures of the distribution network equipment in the second time period; The health value is determined according to the maximum service life, the current service life and the number of failures.

7. The method according to any one of claims 1 to 6, characterized in that The determining, based on the first probability, the second probability, and the third probability, of the failure probability of the distribution network equipment includes: A weighted average process is performed on the first probability, the second probability, and the third probability to obtain the failure probability.

8. A device for determining the failure probability of distribution network equipment, characterized in that: The device comprises: an acquisition unit and a processing unit; An acquiring unit, configured to acquire a health value of the distribution network equipment; and acquire first data corresponding to the distribution network equipment in a rainstorm environment; a processing unit, configured to predict, based on the first data, a first probability corresponding to the distribution network equipment, wherein the first probability is used to represent a probability of failure of the distribution network equipment due to water immersion in a rainstorm environment; The acquiring unit is further configured to acquire second data corresponding to the distribution network equipment in a rainstorm environment; The processing unit is further configured to predict a second probability corresponding to the distribution network equipment based on the health value and the second data, wherein the second probability is used to characterize the probability of a line failure of the distribution network equipment in a rainstorm environment; The acquiring unit is further configured to acquire third data corresponding to the distribution network equipment in a rainstorm environment; The processing unit is further configured to predict a third probability corresponding to the distribution network equipment based on the health value and the third data, wherein the third probability is used to represent a probability of insulation failure of the distribution network equipment in a rainstorm environment; The processing unit is further configured to determine the failure probability of the distribution network equipment based on the first probability, the second probability, and the third probability.

9. An electronic device, characterized in that: include: A processor and a memory, the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 7.

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

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