Method and device for detecting snow damage degree of distribution network line and processor

Abstract

The invention discloses a method, a device and a processor for detecting the degree of violent snow hazard of a distribution network line, and belongs to the technical field of power grid snow covering prediction, and the method comprises the steps: obtaining the position information of a to-be-detected distribution network line; acquiring environment information around the distribution network line to be detected based on the position information; and according to the position information and the environment information, through a multi-factor analysis method, determining the snowstorm hazard degree of the distribution network line to be detected. According to the application, accurate detection of the snow damage degree of the distribution network line to be detected can be realized, and the distribution network line with relatively high risk can be identified, so that technicians can clean a snow damage prevention channel in advance, prepare emergency materials and a standby power supply, and timely carry out disaster response and emergency repair.

Description

Technical Field

[0001] The present application relates to the technical field of power grid snow cover prediction, and specifically to a method, device and processor for detecting the degree of damage caused by heavy snowfall on distribution network lines. Background Art

[0002] The base temperature in the south is relatively high. Even if snowfall occurs during the low temperature process, the snow particles have a high moisture content and strong adhesion. In addition, the trees and bamboos in the south are dense and the vegetation canopy is large, which easily causes snowfall to adhere and accumulate. When heavy snow occurs, it is particularly easy for trees and bamboos to fall and press the lines, causing the distribution network lines to break and poles to fall, which seriously threatens the safety of the distribution network. In the prior art, manual observation methods are mainly used to detect the degree of damage caused by heavy snow in the distribution network, and it is difficult to obtain accurate detection results. Therefore, the prior art has the problem of low accuracy of detection results. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method, apparatus, processor, computer equipment and machine-readable storage medium for detecting the degree of damage caused by heavy snow in distribution network lines, so as to solve the problem of low accuracy of detection results in the prior art.

[0004] In order to achieve the above-mentioned purpose, the first aspect of the embodiment of the present application provides a method for detecting the degree of damage caused by heavy snow on a distribution network line, the method comprising:

[0005] Obtain the location information of the distribution network line to be tested;

[0006] Acquire environmental information around the distribution network line to be tested based on location information;

[0007] According to the location information and environmental information, the degree of hazard caused by heavy snow on the distribution network line to be tested is determined through multi-factor analysis.

[0008] In an embodiment of the present application, the risk level of heavy snow cover of the distribution network line to be tested is determined through a multi-factor analysis method based on location information and environmental information, including: judging whether there is a vegetation-covered area on the distribution network line to be tested based on the environmental information; when there is no vegetation-covered area on the distribution network line to be tested, judging that the degree of heavy snow cover hazard of the distribution network line to be tested is low risk; when there is a vegetation-covered area on the distribution network line to be tested, determining the degree of heavy snow cover hazard of the distribution network line to be tested based on the location information.

[0009] In an embodiment of the present application, determining the degree of hazard of a heavy snowstorm on a distribution network line to be tested based on location information includes: determining a target area where the distribution network line to be tested is located based on the location information; determining a numerical pattern of the target area; predicting meteorological information of the target area within a preset time through the numerical pattern; and determining the degree of hazard of a heavy snowstorm on the distribution network line to be tested based on the meteorological information.

[0010] In an embodiment of the present application, the meteorological information includes the precipitation phase state of the target area within a preset time and the snowfall amount of the target area within a preset time.

[0011] In an embodiment of the present application, determining the degree of blizzard hazard of the distribution network line to be tested based on meteorological information includes: determining the same change in precipitation based on the precipitation phase in the target area within a preset time; determining the degree of blizzard hazard of the distribution network line to be tested based on the same change in precipitation.

[0012] In an embodiment of the present application, determining the degree of blizzard hazard of the distribution network line to be tested based on meteorological information includes: determining the degree of blizzard hazard of the distribution network line to be tested based on the snowfall in the target area within a preset time; wherein the snowfall in the target area within the preset time is proportional to the degree of blizzard hazard of the distribution network line to be tested.

[0013] A second aspect of an embodiment of the present application provides a processor configured to execute the above-mentioned method for detecting the degree of damage caused by heavy snow on distribution network lines.

[0014] A third aspect of an embodiment of the present application provides a device for detecting the degree of damage caused by heavy snow on a distribution network line, the device comprising:

[0015] A location information acquisition module is used to obtain the location information of the distribution network line to be tested;

[0016] An environmental information acquisition module is used to acquire environmental information around the distribution network line to be tested based on the location information;

[0017] The snowstorm hazard level determination module is used to determine the snowstorm hazard level of the distribution network line to be tested through a multi-factor analysis method based on location information and environmental information.

[0018] A fourth aspect of an embodiment of the present application provides a computer device, including:

[0019] The above-mentioned processor or the above-mentioned device for detecting the degree of damage caused by heavy snow on distribution network lines.

[0020] A fifth aspect of an embodiment of the present application provides a machine-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the above-mentioned method for detecting the degree of damage caused by heavy snow on distribution network lines is implemented.

[0021] The above technical solution first obtains the location information of the distribution network line to be tested, then obtains the environmental information around the distribution network line to be tested based on the location information, and finally determines the degree of blizzard damage to the distribution network line to be tested through a multi-factor analysis method based on the location information and the environmental information. This application can realize accurate detection of the degree of blizzard damage to the distribution network line to be tested.

[0022] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0024] Figure 1 A flow chart of a method for detecting the degree of damage caused by heavy snow on a distribution network line provided in an embodiment of the present application;

[0025] Figure 2 A structural block diagram of a device for detecting the degree of damage caused by heavy snow on distribution network lines provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0027] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0028] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0029] Since the base temperature in the south is relatively high, even if snow falls during low temperature, the snow particles have high moisture content and strong adhesion. In addition, the trees and bamboos in the south are dense and the vegetation canopy is large, which easily causes snow to adhere and accumulate. When heavy snow occurs, it is particularly easy for trees and bamboos to fall and press the lines, causing the distribution network lines to break and poles to fall, which seriously threatens the safety of the distribution network. Therefore, in the operation of the distribution network, a fast distribution network heavy snow hazard analysis method is needed to guide the distribution network snow disaster prevention work.

[0030] Figure 1 A flow chart of a method for detecting the degree of damage caused by heavy snow on a distribution network line provided in an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for detecting the degree of damage caused by heavy snow on a distribution network line. The method is described by taking the application of the method to a processor as an example. The method may include the following steps.

[0031] Step S101: Acquire the location information of the distribution network line to be tested.

[0032] Step S102: Acquire environmental information around the distribution network line to be tested based on the location information.

[0033] Step S103: Determine the degree of snowstorm damage to the distribution network line to be tested by multi-factor analysis based on the location information and environmental information.

[0034] Specifically, the location information of the distribution network line to be tested is the coordinate information of the line. According to the coordinate information of the distribution network line to be tested, the environmental information around the distribution network line to be tested can be obtained. In one example, the environmental information may include land use type information and vegetation type information. Environmental information can be collected by manual on-site observation. Since the frequency of environmental changes along the distribution network line is relatively small, after collecting the initial environmental information, the initial environmental information can be locally updated directly according to the changes in the environment, without collecting the entire line, which is conducive to reducing labor costs.

[0035] It can be understood that since the vegetation along the distribution network line to be tested can accumulate snow, the degree of blizzard damage to the distribution network line to be tested is affected. Therefore, the processor can determine the degree of blizzard damage to the distribution network line to be tested through a multi-factor analysis method based on the location information and environmental information of the distribution network line to be tested. In one example, the factors affecting the degree of blizzard damage to the distribution network line to be tested may include vegetation factors around the network line, phase change factors, and snowfall factors.

[0036] The above technical solution first obtains the location information of the distribution network line to be tested, then obtains the environmental information around the distribution network line to be tested based on the location information, and finally determines the degree of blizzard damage to the distribution network line to be tested through a multi-factor analysis method based on the location information and environmental information. This application can accurately detect the degree of blizzard damage to the distribution network line to be tested, and can identify distribution network lines with higher risks, so that technical personnel can clear the anti-blizzard channel in advance, prepare emergency supplies and backup power supplies, and carry out disaster response and emergency repairs in a timely manner.

[0037] In an embodiment of the present application, based on location information and environmental information, a multi-factor analysis method is used to determine the risk level of heavy snow coverage of the distribution network line to be tested, which may include: judging whether there is a vegetation-covered area on the distribution network line to be tested based on the environmental information; if there is no vegetation-covered area on the distribution network line to be tested, judging that the degree of heavy snow hazard of the distribution network line to be tested is low risk; if there is a vegetation-covered area on the distribution network line to be tested, determining the degree of heavy snow hazard of the distribution network line to be tested based on the location information.

[0038] It can be understood that environmental information may include land use type information and vegetation type information, etc. The processor can determine whether there is a vegetation-covered area on the distribution network line to be tested based on the environmental information. Among them, the vegetation-covered area refers to an area where the height of the vegetation exceeds the height of the distribution network line. Due to different vegetation types, its height may be higher or lower than the height of the distribution network line. In the case where the height of the vegetation exceeds the distribution network line, snow will form on the vegetation in a blizzard environment. When the snow coverage exceeds a certain degree, the snow on the vegetation may fall onto the distribution network line, thereby causing a safety hazard. Therefore, the vegetation coverage condition can be used as one of the factors for judging the degree of blizzard hazard of the distribution network line to be tested.

[0039] Specifically, according to the distribution network line information, land use type information and vegetation type information, if there is no obvious vegetation around the distribution network line, or only crops or low thatch and other vegetation, the risk of blizzard damage to the distribution network is low. If there are large shrubs, trees, bamboo forests, etc. with heights close to or higher than the line around the distribution network line, there may be a risk of blizzard damage to the distribution network. Based on the location information of the distribution network line to be tested, further analysis is performed according to subsequent steps to determine the degree of blizzard damage to the distribution network line to be tested.

[0040] In an embodiment of the present application, determining the degree of hazard of a heavy snowstorm on a distribution network line to be tested based on location information may include: determining a target area where the distribution network line to be tested is located based on the location information; determining a numerical pattern of the target area; predicting meteorological information of the target area within a preset time through the numerical pattern; and determining the degree of hazard of a heavy snowstorm on the distribution network line to be tested based on the meteorological information.

[0041] It can be understood that the area where the distribution network line to be tested is located, that is, the target area, can be determined according to the location information of the distribution network line to be tested. Through the numerical model of the target area, the meteorological information of the target area within the preset time can be predicted. Among them, the preset time refers to a period of time in the future, which can be set manually according to actual conditions, for example, the meteorological information of the target area in the next 7 days is predicted through a numerical model. When predicting the meteorological information of the target area within the preset time through a numerical model, different meteorological conditions also have a great influence on the judgment result of the degree of blizzard damage to the distribution network line to be tested. Therefore, the processor can determine the degree of blizzard damage to the distribution network line to be tested based on the meteorological information.

[0042] In an embodiment of the present application, the meteorological information includes the precipitation phase state of the target area within a preset time and the snowfall amount of the target area within a preset time.

[0043] In an embodiment of the present application, determining the degree of blizzard hazard of the distribution network line to be tested based on meteorological information may include: determining the same change in precipitation based on the precipitation phase in the target area within a preset time; determining the degree of blizzard hazard of the distribution network line to be tested based on the same change in precipitation.

[0044] Specifically, the numerical model predicts the meteorological information of the target area in the next 7 days. According to the changes in the precipitation phase in the numerical model in the next 7 days, the changes in the precipitation phase during the entire rain and snow process can be known; if there is freezing rain before snowfall, or it is a mixed precipitation state of ice and snow, due to the strong adhesion of freezing rain and the heavy weight of freezing rain, on the one hand, the ability of the distribution network to bear heavy snow in the ice-covered state is low, and on the other hand, the trees and bamboos are more likely to bend branches and fall down due to the combined effects of ice and snow, and the harm of heavy snow to the distribution network is significantly greater than that of simple snowfall.

[0045] In an embodiment of the present application, determining the degree of blizzard hazard of the distribution network line to be tested based on meteorological information may include: determining the degree of blizzard hazard of the distribution network line to be tested based on the snowfall in the target area within a preset time; wherein the snowfall in the target area within the preset time is proportional to the degree of blizzard hazard of the distribution network line to be tested.

[0046] Specifically, the numerical model predicts the meteorological information of the target area in the next 7 days. According to the numerical model's snowfall prediction results for the next 7 days, the snowfall in the entire process can be guided. The greater the snowfall, the greater the degree of harm. Generally speaking, when the snowfall reaches the blizzard level, the risk of blizzard in the distribution network is high.

[0047] Thus, through the above scheme, based on multi-factor analysis, distribution network lines with higher risks can be identified so that technicians can clear the channels in advance to prevent blizzards, prepare emergency supplies and backup power supplies, and carry out disaster response and emergency repairs in a timely manner.

[0048] Through the above technical solution, the location information of the distribution network line to be tested is first obtained, and then the environmental information around the distribution network line to be tested is obtained based on the location information. Finally, according to the location information and environmental information, the degree of blizzard damage to the distribution network line to be tested is determined through a multi-factor analysis method. This application can accurately detect the degree of blizzard damage to the distribution network line to be tested, and can identify distribution network lines with higher risks, so that technical personnel can clear the anti-blizzard channel in advance, prepare emergency supplies and backup power supplies, and carry out disaster response and emergency repairs in a timely manner.

[0049] In a specific embodiment of the present application, a method for analyzing the degree of damage caused by heavy snow in a distribution network considering multiple factors is provided. The method may include the following steps:

[0050] Step 1: Collect basic data.

[0051] Collect the distribution network line coordinate information of the forecast area, collect the land use type information of the forecast area, collect the vegetation type data of the forecast area, and collect the precipitation phase and snowfall prediction results of the numerical model in the next 7 days.

[0052] Step 2: Risk analysis considering vegetation factors around distribution lines.

[0053] According to the distribution network line information, land use type information and vegetation type information, if there is no obvious vegetation around the distribution network line, or there are only crops or low thatch and other vegetation, the risk of distribution network blizzard damage is low; if there are large shrubs, trees, bamboo forests, etc. that are close to or higher than the line around the distribution network line, there may be a risk of distribution network blizzard, and further analysis will be carried out according to the subsequent steps.

[0054] Step 3: Risk analysis considering phase change factors of precipitation process.

[0055] According to the changes in precipitation phase in the numerical model in the next 7 days, we can know the changes in precipitation phase during the whole rain and snow process. If freezing rain appears before snowfall, or it is a mixed precipitation state of ice and snow, due to the strong adhesion of freezing rain and the heavy weight of freezing rain, on the one hand, the ability of the distribution network to bear heavy snow under ice-covered state is low, and on the other hand, due to the combined effect of ice and snow, the possibility of branches bending and bamboos falling is greater. The harm of heavy snow to the distribution network is significantly greater than that of simple snowfall.

[0056] Step 4: Risk analysis considering snowfall factor.

[0057] The snowfall forecast results for the next seven days based on the numerical model can guide the snowfall during the entire process. The greater the snowfall, the greater the degree of damage. Generally speaking, when the snowfall reaches the blizzard level, the risk of blizzard in the distribution network is high.

[0058] In this way, according to the multi-factor analysis method of steps one to four, distribution network lines with higher risks can be identified, and anti-blizzard channel clearing, emergency supplies and backup power supplies can be prepared in advance, and disaster response and emergency repairs can be carried out in a timely manner.

[0059] The embodiment of the present application also provides a processor configured to execute the method for detecting the degree of damage caused by heavy snow on distribution network lines in the above-mentioned implementation.

[0060] Figure 2 This is a structural block diagram of a device for detecting the degree of damage caused by heavy snow on a distribution network line provided in an embodiment of the present application. Figure 2 As shown, the embodiment of the present application also provides a device 200 for detecting the degree of damage caused by heavy snow on a distribution network line, and the device 200 includes:

[0061] The location information acquisition module 210 is used to acquire the location information of the distribution network line to be tested.

[0062] The environmental information acquisition module 220 is used to acquire environmental information around the distribution network line to be tested based on the location information.

[0063] The snowstorm hazard level determination module 230 is used to determine the snowstorm hazard level of the distribution network line to be tested through a multi-factor analysis method based on the location information and the environmental information.

[0064] The above-mentioned device 200 for detecting the degree of hazard of heavy snow on distribution network lines first obtains the location information of the distribution network line to be tested, then obtains the environmental information around the distribution network line to be tested based on the location information, and finally determines the degree of hazard of heavy snow on the distribution network line to be tested through a multi-factor analysis method according to the location information and the environmental information. The present application can accurately detect the degree of hazard of heavy snow on the distribution network line to be tested, and can identify distribution network lines with higher risks, so that technical personnel can clear the channels for anti-heavy snow in advance, prepare emergency supplies and backup power supplies, and carry out disaster response and emergency repairs in a timely manner.

[0065] In one embodiment, the snowstorm hazard level determination module 230 is also used to: determine whether there is a vegetation-covered area on the distribution network line to be tested based on environmental information; if there is no vegetation-covered area on the distribution network line to be tested, determine that the snowstorm hazard level of the distribution network line to be tested is low risk; if there is a vegetation-covered area on the distribution network line to be tested, determine the snowstorm hazard level of the distribution network line to be tested based on location information.

[0066] In one embodiment, the blizzard hazard level determination module 230 is also used to: determine the target area where the distribution network line to be tested is located based on the location information; determine the numerical model of the target area; predict the meteorological information of the target area within a preset time through the numerical model; and determine the blizzard hazard level of the distribution network line to be tested based on the meteorological information.

[0067] In one embodiment, the meteorological information includes the precipitation phase state of the target area within a preset time and the snowfall amount of the target area within a preset time.

[0068] In one embodiment, the snowstorm hazard level determination module 230 is further used to: determine the precipitation uniformity change according to the precipitation phase state of the target area within a preset time; and determine the snowstorm hazard level of the distribution network line to be tested according to the precipitation uniformity change.

[0069] In one embodiment, the blizzard hazard level determination module 230 is further used to determine the blizzard hazard level of the distribution network line to be tested according to the snowfall in the target area within a preset time; wherein the snowfall in the target area within the preset time is proportional to the blizzard hazard level of the distribution network line to be tested.

[0070] An embodiment of the present application also provides a computer device, including: the processor in the above embodiment or the device for detecting the degree of damage caused by heavy snow on distribution network lines in the above embodiment.

[0071] An embodiment of the present application also provides a machine-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method for detecting the degree of damage caused by heavy snow on distribution network lines in the above-mentioned embodiment is implemented.

[0072] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0078] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0080] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for detecting the degree of damage caused by heavy snow on distribution network lines, characterized in that: The method comprises: Obtain the location information of the distribution network line to be tested; Acquire environmental information around the distribution network line to be tested based on the location information; The degree of hazard of heavy snow on the distribution network line to be tested is determined by a multi-factor analysis method according to the location information and the environmental information.

2. The method according to claim 1, characterized in that The step of determining the snowstorm coverage risk level of the distribution network line to be tested by a multi-factor analysis method according to the location information and the environmental information includes: Determine whether there is a vegetation-covered area on the distribution network line to be tested according to the environmental information; In the case where there is no vegetation-covered area on the distribution network line to be tested, determining that the snowstorm hazard level of the distribution network line to be tested is low risk; In the case that there is a vegetation-covered area on the distribution network line to be tested, the degree of blizzard damage to the distribution network line to be tested is determined according to the location information.

3. The method according to claim 2, characterized in that Determining the degree of hazard of heavy snow on the distribution network line to be tested according to the location information includes: Determine the target area where the distribution network line to be tested is located based on the location information; determining a numerical mode of the target area; Predicting the weather information of the target area within a preset time by using the numerical model; The degree of hazard of heavy snow on the distribution network line to be tested is determined according to the meteorological information.

4. The method according to claim 3, characterized in that The meteorological information includes the precipitation phase state of the target area within a preset time and the snowfall amount of the target area within a preset time.

5. The method according to claim 4, characterized in that Determining the degree of blizzard damage to the distribution network line to be tested according to the meteorological information includes: Determining the precipitation change according to the precipitation phase state of the target area within the preset time; The degree of hazard of heavy snow on the distribution network line to be tested is determined according to the same change of precipitation.

6. The method according to claim 4, characterized in that Determining the degree of blizzard damage to the distribution network line to be tested according to the meteorological information includes: Determine the degree of hazard of heavy snowfall on the distribution network line to be tested according to the snowfall in the target area within the preset time; The amount of snowfall in the target area within the preset time is proportional to the degree of hazard of heavy snow on the distribution network line to be tested.

7. A processor, characterized in that: The method is configured to execute the method for detecting the degree of damage caused by heavy snow on distribution network lines according to any one of claims 1 to 6.

8. A device for detecting the degree of damage caused by heavy snow on distribution network lines, characterized in that: The device comprises: A location information acquisition module is used to obtain the location information of the distribution network line to be tested; An environmental information acquisition module, used to acquire environmental information around the distribution network line to be tested based on the location information; The blizzard hazard level determination module is used to determine the blizzard hazard level of the distribution network line to be tested through a multi-factor analysis method based on the location information and the environmental information.

9. A computer device, characterized in that: include: The processor according to claim 7 or the device for detecting the degree of damage caused by heavy snow on distribution network lines according to claim 8.

10. A machine-readable storage medium storing a program or an instruction, characterized in that: When the program or the instruction is executed by the processor, the method for detecting the degree of damage caused by heavy snow on distribution network lines according to any one of claims 1 to 6 is implemented.

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