CN115047543B - Power transmission corridor rainfall early warning method and system - Google Patents
Power transmission corridor rainfall early warning method and system Download PDFInfo
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Abstract
The embodiment of the application discloses a power transmission corridor precipitation early warning method and system, which belong to the technical field of weather early warning, wherein the method comprises the following steps: acquiring historical observation data of a target area; determining the precipitation warning threshold distribution of a lattice power transmission corridor corresponding to a target area based on historical observation data of the target area, wherein the precipitation warning threshold distribution of the lattice power transmission corridor comprises precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area; acquiring a precipitation forecast of a target power transmission corridor; and determining the precipitation early warning level of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the corresponding lattice-point power transmission corridor precipitation warning threshold distribution of the target area, and having the advantage of timely and accurately early warning the precipitation of the power transmission corridor.
Description
Technical Field
The invention mainly relates to the technical field of weather early warning, in particular to a method and a system for early warning precipitation of a power transmission corridor.
Background
In recent years, the construction of transmission line engineering in China is rapidly developed. In real life, a strong rainfall disaster can cause catastrophic damage to a power transmission line, huge economic loss is caused by power failure, high recovery and reconstruction cost after disaster is spent, huge influence is brought to life of people, and huge damage is caused to national economy. Although a great amount of manpower and material resources are invested in power disaster prediction, early warning and other aspects in the domestic and foreign power industries and companies, and a technical method and an application system for power disaster prevention and reduction are researched and developed, the use condition and the early warning effect of the power disaster prevention and reduction are still not satisfactory.
Therefore, it is desirable to provide a method and a system for early warning of precipitation in a power transmission corridor, which are used for timely and accurately early warning of precipitation in the power transmission corridor.
Disclosure of Invention
In order to solve the problems that the rainfall early warning effect of a power transmission corridor is poor and the like in the prior art, one of the embodiments of the specification provides a rainfall early warning method for a power transmission corridor, which comprises the following steps: acquiring historical observation data of a target area; determining the precipitation warning threshold distribution of a lattice power transmission corridor corresponding to the target area based on the historical observation data of the target area, wherein the precipitation warning threshold distribution of the lattice power transmission corridor comprises precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area; acquiring a precipitation forecast of a target power transmission corridor; and determining the precipitation early warning level of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area.
In some embodiments, said determining a corresponding grid-tied power transmission corridor precipitation warning threshold distribution for said target region based on historical observation data for said target region comprises: determining an extremum distribution probability based on historical observation data of the target region; determining different levels of precipitation warning thresholds corresponding to a plurality of sub-areas in the target area based on the extreme value distribution probability; and interpolating precipitation warning thresholds of different levels corresponding to the plurality of sub-areas in the target area, and determining precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area.
In some embodiments, the determining the probability of the extremum distribution based on the historical observation data of the target region includes: and determining the extreme value distribution probability based on the historical observation data of the target region according to a probability distribution function and an exponential distribution method.
In some embodiments, the interpolating the precipitation warning thresholds of different levels corresponding to the plurality of sub-areas in the target area to determine the precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area includes: and taking the terrain features as covariates, performing spatial interpolation on the precipitation warning thresholds of different levels corresponding to the plurality of sub-areas in the target area, and determining the precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area.
In some embodiments, the topographical features include at least elevation, slope, and heading.
In some embodiments, said obtaining a precipitation forecast for the target power transmission corridor comprises: acquiring a numerical forecasting product of the target area; and carrying out interpolation processing on the numerical forecasting products of the target area to obtain the precipitation forecast of the target power transmission corridor.
It can be understood that the precipitation of the power transmission corridor in a future period of time cannot be directly obtained due to the problems of resolution and the like, so that the precipitation of the power transmission corridor in the future period of time can be accurately calculated by adopting an interpolation method.
In some embodiments, the interpolating the numerical forecast product of the target area to obtain the precipitation forecast of the target power transmission corridor includes: and taking the topographic features as covariates, and performing spatial interpolation on the numerical prediction products of the target area to obtain the precipitation prediction of the target power transmission corridor.
In some embodiments, the determining the level of early warning of precipitation in the target power transmission corridor based on the precipitation forecast for the target power transmission corridor and the distribution of the target area over the corresponding grid-tied power transmission corridor precipitation warning threshold comprises: determining a microtopography risk factor of the target power transmission corridor; revising the precipitation forecast of the target power transmission corridor based on the micro-terrain risk coefficient to obtain the revised precipitation forecast of the target power transmission corridor; and determining the precipitation early warning level of the target power transmission corridor based on the revised precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area.
In some embodiments, modifying the precipitation forecast for the target power transmission corridor based on the microtopography risk factor to obtain a modified precipitation forecast for the target power transmission corridor comprises: acquiring a sample terrain, a sample microtopography risk coefficient corresponding to the sample terrain, a sample precipitation forecast and an actual precipitation amount corresponding to the sample precipitation forecast; establishing a statistical model based on the sample micro-terrain risk coefficient corresponding to the sample terrain, the sample precipitation forecast and the actual precipitation corresponding to the sample precipitation forecast; revising the precipitation forecast of the target power transmission corridor based on the micro-terrain risk coefficient through the statistical model, and obtaining the revised precipitation forecast of the target power transmission corridor.
One of the embodiments of the present specification provides a power transmission corridor precipitation early warning system, including; a threshold determination module, configured to obtain historical observation data of a target area, and further configured to determine, based on the historical observation data of the target area, precipitation warning threshold distribution of a lattice power transmission corridor corresponding to the target area, where the lattice power transmission corridor precipitation warning threshold distribution includes precipitation warning thresholds of different levels corresponding to power transmission corridors in the target area; the forecast acquisition module is used for acquiring a rainfall forecast of the target power transmission corridor; and the early warning determination module is used for determining the early warning level of the precipitation of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area.
The invention has the beneficial effects that: determining precipitation warning threshold distribution of a grid-point power transmission corridor corresponding to a target area through historical observation data of the target area, and quickly and accurately determining precipitation warning level of the target power transmission corridor based on precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the grid-point power transmission corridor corresponding to the target area; observation data is from meteorological sites, one area corresponds to one value (for example, one county corresponds to one value), so that the historical rainfall of the power transmission corridor cannot be directly obtained, and the historical rainfall of the power transmission corridor can be accurately calculated by adopting an interpolation method, so that rainfall warning thresholds of different levels corresponding to the power transmission corridor in a target area are determined; the terrain features generally have certain influence on precipitation, for example, a windward slope is favorable for precipitation enhancement, a leeward slope is favorable for precipitation attenuation, and the mountaintop, the mountainside, the valley, the choke and the like all have influence on precipitation, so that the terrain features are used as covariates to perform spatial interpolation on precipitation warning thresholds of different levels corresponding to a plurality of sub-areas in the target area, so that the terrain features are considered in determining the precipitation warning thresholds of different levels corresponding to the power transmission corridors in the target area, and the determination is more accurate; the terrain features generally have certain influence on precipitation, the terrain features are used as covariates, spatial interpolation is carried out on numerical prediction products of a target area, and accurate precipitation prediction of a target power transmission corridor can be obtained; and revising the precipitation forecast of the target power transmission corridor based on the micro-terrain risk coefficient through a statistical model, so that the revised precipitation forecast of the target power transmission corridor for determining the precipitation early warning level is more accurate, and the determined precipitation early warning level is more accurate.
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The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
fig. 1 is a schematic view of an application scenario of a power transmission corridor precipitation warning system according to some embodiments of the present application;
FIG. 2 is an exemplary block diagram of a power transmission corridor precipitation warning system according to some embodiments of the present application;
fig. 3 is an exemplary flow chart of a power transmission corridor precipitation warning method according to some embodiments of the present application.
In the figure, 110, a processing device; 120. a network; 130. a user terminal; 140. a storage device.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. It is understood that these exemplary embodiments are given solely to enable those skilled in the relevant art to better understand and implement the present invention, and are not intended to limit the scope of the invention in any way. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to include the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover only the explicitly identified steps or elements as not constituting an exclusive list and that the method or apparatus may comprise further steps or elements.
Although various references are made herein to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on the client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.
Flowcharts are used herein to illustrate the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic view of an application scenario of a power transmission corridor precipitation early warning system according to some embodiments of the present application.
As shown in fig. 1, an application scenario may include a processing device 110, a network 120, a user terminal 130, and a storage device 140.
In some embodiments, processing device 110 may be used to process information and/or data related to power transmission corridor precipitation warning. For example, the processing device 110 may obtain historical observation data for the target area; determining the precipitation warning threshold distribution of a lattice power transmission corridor corresponding to a target area based on historical observation data of the target area, wherein the precipitation warning threshold distribution of the lattice power transmission corridor comprises precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area; acquiring a precipitation forecast of a target power transmission corridor; and determining the precipitation early warning level of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area. Further description of the processing device 110 may be found in other portions of the present application. For example, fig. 3 and its description.
In some embodiments, the processing device 110 may be regional or remote. For example, processing device 110 may access information and/or profiles stored in user terminal 130 and storage device 140 via network 120. In some embodiments, processing device 110 may be directly connected to user terminal 130 and storage device 140 to access information and/or material stored therein. In some embodiments, the processing device 110 may execute on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like.
In some embodiments, the processing device 110 may comprise a processor, which may comprise one or more sub-processors (e.g., a single core processing device or a multi-core processing device). Merely by way of example, a processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, and the like or any combination thereof.
The network 120 may facilitate the exchange of data and/or information in an application scenario. In some embodiments, one or more components in an application scenario (e.g., processing device 110, user terminal 130, and storage device 140) may send data and/or information to other components in the application scenario via network 120. For example, the historical observation data for the target area stored by the storage device 140 may be transmitted to the processing device 110 via the network 120. As another example, the processing device 110 may transmit the precipitation warning level of the target power transmission corridor to the user terminal 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network. For example, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof.
In some embodiments, the user terminal 130 may include one or any combination of a mobile device, a tablet, a laptop, and the like.
In some embodiments, the storage device 140 may be connected to the network 120 to enable communication with one or more components of an application scenario (e.g., the processing device 110, the user terminal 130, etc.). One or more components of the application scenario may access the material or instructions stored in storage device 140 through network 120. In some embodiments, the storage device 140 may be directly connected or in communication with one or more components (e.g., processing device 110, user terminal 130) in an application scenario. In some embodiments, the storage device 140 may be part of the processing device 110.
It should be noted that the foregoing description is provided for illustrative purposes only, and is not intended to limit the scope of the present application. Many variations and modifications will occur to those skilled in the art in light of the teachings herein. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the storage device 140 may be a data storage device comprising a cloud computing platform, such as a public cloud, a private cloud, a community and hybrid cloud, and the like. However, such changes and modifications do not depart from the scope of the present application.
Fig. 2 is an exemplary block diagram of a power transmission corridor precipitation warning system according to some embodiments of the present disclosure.
As shown in fig. 2, a power transmission corridor precipitation warning system may include a threshold determination module, a forecast acquisition module, and a warning determination module.
The threshold determination module may be configured to obtain historical observation data for the target area. The threshold determination module may be further configured to determine, based on historical observation data of the target region, a precipitation warning threshold distribution of a lattice power transmission corridor corresponding to the target region, where the lattice power transmission corridor precipitation warning threshold distribution includes precipitation warning thresholds of different levels corresponding to power transmission corridors in the target region.
The forecast acquisition module may be for acquiring a precipitation forecast for the target power transmission corridor.
The early warning determination module can be used for determining the early warning level of the precipitation of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area.
For more description of the threshold determination module, the forecast acquisition module, and the early warning determination module, reference may be made to fig. 3 and the related description thereof, which are not repeated herein.
Fig. 3 is an exemplary flow chart of a power transmission corridor precipitation warning method according to some embodiments of the present application. As shown in fig. 3, a power transmission corridor precipitation early warning method comprises the following steps. In some embodiments, a power transmission corridor precipitation warning method may be implemented on the processing device 110 or the power transmission corridor precipitation warning system.
Step 310, obtaining historical observation data of the target area. In some embodiments, step 310 may be performed by a threshold determination module.
The historical observation data may be the amount of rainfall for the target area over a continuous long period of time (e.g., 50 years) in the past. It will be appreciated that the target region may comprise a plurality of sub-regions. For example, if the target area is Sichuan, then the sub-areas within the target area may include various cities, such as Chengdu, yaan, etc., and accordingly, the historical observation data of the target area includes the rainfall of the plurality of sub-areas over a continuous long period of time in the past.
In some embodiments, the threshold determination module may obtain historical observation data for the target area from the processing device 110, the user terminal 130, the storage device 140, or an external data source (e.g., a central weather station official website, a chinese weather network, a chinese weather bureau official website, etc.).
And step 320, determining the distribution of the grid-point power transmission corridor precipitation warning threshold values corresponding to the target area based on the historical observation data of the target area. In some embodiments, step 320 may be performed by a threshold determination module.
The grid-point power transmission corridor precipitation warning threshold distribution comprises precipitation warning thresholds of different levels corresponding to the power transmission corridors in the target area. Wherein the different levels of the precipitation warning threshold may correspond to different recurring periods, for example, a first level of the precipitation warning threshold corresponds to a ten year recurring period, a second level of the precipitation warning threshold corresponds to a thirty year recurring period, and a third level of the precipitation warning threshold corresponds to a fifty year recurring period.
In some embodiments, the threshold determination module may determine an extremum distribution probability based on historical observation data of the target region, determine different levels of precipitation warning thresholds corresponding to a plurality of sub-regions in the target region based on the extremum distribution probability, interpolate the different levels of precipitation warning thresholds corresponding to the plurality of sub-regions in the target region, and determine the different levels of precipitation warning thresholds corresponding to the power transmission corridors in the target region.
In some embodiments, the threshold determination module may determine the extremum distribution probability based on historical observation data for the target region according to a probability distribution function and an exponential distribution method. The probability distribution function may include Geng Beier distribution and/or weber distribution, among others.
It can be understood that the observation data is from a meteorological site, and one area corresponds to one value (for example, one county corresponds to one value), so that the historical rainfall of the power transmission corridor cannot be directly obtained, and an interpolation method is required to calculate the historical rainfall of the power transmission corridor.
In some embodiments, the threshold determination module may perform spatial interpolation on different levels of precipitation warning thresholds corresponding to a plurality of sub-regions in the target region by using the terrain features as covariates, and determine different levels of precipitation warning thresholds corresponding to the power transmission corridors in the target region.
In some embodiments, the topographical features may include at least elevation, slope, and heading.
By way of example only, the threshold determination module may perform spatial interpolation on the rainfall alert threshold by using a collaborative Kriging interpolation method based on the statistical relationship between the rainfall and the geographic factors such as Elevation, gradient, and slope, and on the basis of ArcGIS software, in combination with high-resolution DEM (Digital Elevation Model) data (resolution is 30 meters), and participate in interpolation operation with the Elevation, gradient, and slope data as covariates to obtain high-resolution grid-point power transmission corridor alert threshold distribution considering micro-terrain features, where the grid-point power transmission corridor rainfall alert threshold distribution includes rainfall alert thresholds of different levels corresponding to each power transmission corridor in the target area.
And step 330, acquiring a precipitation forecast of the target power transmission corridor. In some embodiments, step 330 may be performed by the forecast acquisition module.
The target power transmission corridor may be a power transmission corridor in the target area for which a precipitation warning level needs to be determined. The precipitation forecast for the target power transmission corridor may be a predicted precipitation for the target power transmission corridor for a future period of time. In some embodiments, the forecast acquisition module may acquire historical observation data for the target area from the processing device 110, the user terminal 130, the storage device 140, or an external data source (e.g., a central weather station official website, a chinese weather network, a chinese weather bureau official website, etc.).
In some embodiments, the forecast acquisition module may acquire a numerical forecast product of the target area, perform interpolation processing on the numerical forecast product of the target area, and acquire a precipitation forecast of the target power transmission corridor.
As can be understood, the precipitation of the power transmission corridor in a future period of time can not be directly obtained generally, so that an interpolation method needs to be adopted to calculate the precipitation of the power transmission corridor in the future period of time.
In some embodiments, the forecast acquisition module may perform spatial interpolation on the numerical forecast product of the target area by using the terrain features as covariates to acquire the precipitation forecast of the target power transmission corridor.
By way of example only, the forecast acquisition module may perform spatial interpolation on a numerical forecast product of the target area by using a coordinated Kriging interpolation method based on the statistical relationship between geographic factors such as Elevation, gradient and slope and rainfall and on the basis of ArcGIS software in combination with high-resolution DEM (Digital Elevation Model) data (the resolution is 30 meters), and obtain the rainfall forecast of the target power transmission corridor by taking the Elevation, gradient and slope data as covariates to participate in the interpolation operation.
In some embodiments, in order to enable data to be more accurate, the early warning determination module may determine a micro-terrain risk coefficient of the target power transmission corridor, revise the precipitation forecast of the target power transmission corridor based on the micro-terrain risk coefficient, acquire the revised precipitation forecast of the target power transmission corridor, and determine a precipitation early warning level of the target power transmission corridor based on the revised precipitation forecast of the target power transmission corridor and the lattice power transmission corridor precipitation warning threshold distribution corresponding to the target area. The microtopography risk coefficient can represent the influence of the microtopography on a precipitation process, for example, a windward slope is favorable for precipitation enhancement, a leeward slope is favorable for precipitation reduction, and a mountain top, a mountain waist, a valley, a choke and the like all have influence on precipitation.
In some embodiments, the microtopography risk factor may be determined based on terrain data of the target power transmission corridor, wherein the terrain data may include a plurality of geographic factors, such as, for example, terrain topography, slope, soil viscosity, soil consistency, and the like. In some embodiments, the early warning determination module may obtain terrain data for the target power transmission corridor based on the EM digital elevation and the geological survey. By way of example only, the early warning determination module may determine the microtopography risk coefficient based on the terrain data of the target power transmission corridor by using an analytic hierarchy process, decompose the complex evaluation process into a plurality of hierarchical structures, and determine the relative importance of each geographic factor through comparative analysis, including establishing the hierarchical structures, constructing a comparison matrix, calculating the weight and the maximum eigenvalue of the geographic factor of each level, and checking consistency.
In some embodiments, the early warning determination module may obtain the sample terrain, a sample micro-terrain risk coefficient corresponding to the sample terrain, the sample precipitation forecast, and an actual precipitation amount corresponding to the sample precipitation forecast, establish a statistical model based on the sample micro-terrain risk coefficient corresponding to the sample terrain, the sample precipitation forecast, and the actual precipitation amount corresponding to the sample precipitation forecast, revise the precipitation forecast of the target power transmission corridor based on the micro-terrain risk coefficient through the statistical model, and obtain the revised precipitation forecast of the target power transmission corridor.
And 340, determining the precipitation early warning level of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area. In some embodiments, step 340 may be performed by the early warning determination module.
The early warning determination module can compare the precipitation forecast of the target power transmission corridor with the precipitation warning thresholds of different levels corresponding to the target power transmission corridor so as to determine the precipitation early warning level of the target power transmission corridor.
For example, for the power transmission corridor a, when the precipitation forecast of the target power transmission corridor is greater than the precipitation threshold corresponding to the ten-year recurrence period and is less than the precipitation threshold corresponding to the thirty-year recurrence period, the early warning determining module may determine that the precipitation early warning level of the target power transmission corridor is blue early warning; when the precipitation forecast of the target power transmission corridor is larger than the precipitation threshold corresponding to the thirty-year recurrence period and smaller than the precipitation threshold corresponding to the fifty-year recurrence period, the early warning determining module can determine that the precipitation early warning level of the target power transmission corridor is orange early warning; when the precipitation forecast of the target power transmission corridor is larger than the precipitation threshold corresponding to the fifty-year recurrence period, the early warning determining module can determine that the precipitation early warning level of the target power transmission corridor is red early warning.
In other embodiments of the present application, there is provided a power transmission corridor precipitation warning apparatus comprising at least one processing device and at least one storage device; the at least one storage device is used for storing computer instructions, and the at least one processing device is used for executing at least part of the computer instructions to realize the power transmission corridor precipitation early warning method.
In still further embodiments of the present application, a computer readable storage medium is provided, the storage medium storing computer instructions which, when executed by a processing device, implement a power transmission corridor precipitation warning method as above.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, though not expressly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, unless explicitly recited in the claims, the order of processing elements and sequences, use of numbers and letters, or use of other designations in this application is not intended to limit the order of the processes and methods in this application. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, and the like, cited in this application is hereby incorporated by reference in its entirety. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the present disclosure.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
1. A power transmission corridor precipitation early warning method is characterized by comprising the following steps:
acquiring historical observation data of a target area;
determining the precipitation warning threshold distribution of a lattice power transmission corridor corresponding to the target area based on the historical observation data of the target area, wherein the precipitation warning threshold distribution of the lattice power transmission corridor comprises precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area;
acquiring a precipitation forecast of a target power transmission corridor;
determining the precipitation early warning level of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area;
the method for determining the grid-point power transmission corridor precipitation warning threshold distribution corresponding to the target area based on the historical observation data of the target area comprises the following steps:
based on the statistical relationship of Elevation, gradient, slope direction and rainfall, on the basis of ArcGIS software, combining high-resolution DEM (Digital Elevation Model) data, performing spatial interpolation on a rainfall alert threshold value by using a collaborative Kriging interpolation method, and taking the Elevation, the gradient and the slope direction as covariates to participate in interpolation operation to obtain high-resolution lattice power transmission corridor rainfall alert threshold value distribution considering micro-terrain features, wherein the lattice power transmission corridor rainfall alert threshold value distribution comprises rainfall alert threshold values of different levels corresponding to all power transmission corridors in a target area;
the precipitation forecast of the target power transmission corridor is obtained, and the method comprises the following steps:
the method comprises the steps of obtaining a sample terrain, a sample micro-terrain risk coefficient corresponding to the sample terrain, a sample precipitation forecast and an actual precipitation amount corresponding to the sample precipitation forecast, building a statistical model based on the sample micro-terrain risk coefficient corresponding to the sample terrain, the sample precipitation forecast and the actual precipitation amount corresponding to the sample precipitation forecast, revising the precipitation forecast of a target power transmission corridor based on the micro-terrain risk coefficient through the statistical model, and obtaining the revised precipitation forecast of the target power transmission corridor, wherein the micro-terrain risk coefficient is determined based on terrain data of the target power transmission corridor by adopting an analytic hierarchy process, the terrain data at least comprises a plurality of geographic factors, such as a terrain landform, a slope direction, soil viscosity and soil consistency, and the analytic hierarchy process comprises the steps of building a hierarchical structure, constructing a comparison matrix, calculating the weight and the maximum characteristic value of the geographic factor of each level and consistency test.
2. The method of claim 1, wherein the determining the grid-tied power transmission corridor precipitation warning threshold distribution corresponding to the target area based on the historical observation data of the target area comprises:
determining an extremum distribution probability based on historical observation data of the target region;
determining different levels of precipitation warning thresholds corresponding to a plurality of sub-areas in the target area based on the extreme value distribution probability;
and interpolating precipitation warning thresholds of different levels corresponding to the plurality of sub-areas in the target area, and determining precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area.
3. The method for warning the precipitation in the power transmission corridor as claimed in claim 2, wherein the determining the probability of extremum distribution based on the historical observation data of the target area comprises:
and determining the extreme value distribution probability based on the historical observation data of the target region according to a probability distribution function and an exponential distribution method.
4. The method for warning of precipitation in a power transmission corridor according to claim 2, wherein the step of interpolating the precipitation warning thresholds of different levels corresponding to the plurality of sub-areas in the target area to determine the precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area comprises:
and taking the terrain features as covariates, performing spatial interpolation on the precipitation warning thresholds of different levels corresponding to the plurality of sub-areas in the target area, and determining the precipitation warning thresholds of different levels corresponding to the power transmission corridor in the target area.
5. The method for warning of precipitation in power transmission corridors as claimed in claim 4, wherein said topographical features include at least elevation, slope and direction of slope.
6. The method for early warning of precipitation in a power transmission corridor as claimed in any one of claims 1 to 5, wherein the step of obtaining a precipitation forecast for the target power transmission corridor comprises:
acquiring a numerical forecasting product of the target area;
and carrying out interpolation processing on the numerical prediction product of the target area to obtain the precipitation prediction of the target power transmission corridor.
7. The method of claim 6, wherein the interpolation of the numerical forecast product of the target area to obtain the precipitation forecast of the target power transmission corridor comprises:
and taking the terrain features as covariates, and carrying out spatial interpolation on the numerical prediction product of the target area to obtain the precipitation forecast of the target power transmission corridor.
8. The method according to any one of claims 1 to 5, wherein the determining the level of the target power transmission corridor for early warning of precipitation based on the precipitation forecast of the target power transmission corridor and the corresponding threshold distribution of the grid power transmission corridor precipitation warnings of the target area comprises:
determining a micro-terrain risk coefficient of the target power transmission corridor;
revising the precipitation forecast of the target power transmission corridor based on the micro-terrain risk coefficient to obtain the revised precipitation forecast of the target power transmission corridor;
and determining the precipitation early warning level of the target power transmission corridor based on the revised precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area.
9. The method of claim 8, wherein the modifying the precipitation forecast of the target power transmission corridor based on the microtopography risk factor to obtain the modified precipitation forecast of the target power transmission corridor comprises:
acquiring a sample terrain, a sample microtopography risk coefficient corresponding to the sample terrain, a sample precipitation forecast and an actual precipitation amount corresponding to the sample precipitation forecast;
establishing a statistical model based on the sample micro-terrain risk coefficient corresponding to the sample terrain, the sample precipitation forecast and the actual precipitation corresponding to the sample precipitation forecast;
revising the precipitation forecast of the target power transmission corridor on the basis of the micro-terrain risk coefficient through the statistical model, and obtaining the revised precipitation forecast of the target power transmission corridor.
10. A transmission corridor precipitation early warning system, comprising:
a threshold determination module, configured to obtain historical observation data of a target area, and further configured to determine, based on the historical observation data of the target area, precipitation warning threshold distribution of a lattice power transmission corridor corresponding to the target area, where the lattice power transmission corridor precipitation warning threshold distribution includes precipitation warning thresholds of different levels corresponding to power transmission corridors in the target area;
the forecast acquisition module is used for acquiring a rainfall forecast of the target power transmission corridor;
the early warning determination module is used for determining the precipitation early warning level of the target power transmission corridor based on the precipitation forecast of the target power transmission corridor and the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area;
wherein, the step of determining the precipitation warning threshold distribution of the lattice power transmission corridor corresponding to the target area based on the historical observation data of the target area comprises the following steps:
based on the statistical relationship of Elevation, gradient, slope direction and rainfall, on the basis of ArcGIS software, combining high-resolution DEM (Digital Elevation Model) data, performing spatial interpolation on a rainfall alert threshold value by using a collaborative Kriging interpolation method, and taking the Elevation, the gradient and the slope direction as covariates to participate in interpolation operation to obtain high-resolution lattice power transmission corridor rainfall alert threshold value distribution considering micro-terrain features, wherein the lattice power transmission corridor rainfall alert threshold value distribution comprises rainfall alert threshold values of different levels corresponding to all power transmission corridors in a target area;
the precipitation forecast of the target power transmission corridor is obtained, and the method comprises the following steps:
the method comprises the steps of obtaining a sample terrain, a sample micro-terrain risk coefficient corresponding to the sample terrain, a sample precipitation forecast and an actual precipitation amount corresponding to the sample precipitation forecast, building a statistical model based on the sample micro-terrain risk coefficient corresponding to the sample terrain, the sample precipitation forecast and the actual precipitation amount corresponding to the sample precipitation forecast, revising the precipitation forecast of a target power transmission corridor based on the micro-terrain risk coefficient through the statistical model, and obtaining the revised precipitation forecast of the target power transmission corridor, wherein the micro-terrain risk coefficient is determined based on terrain data of the target power transmission corridor by adopting an analytic hierarchy process, the terrain data at least comprises a plurality of geographic factors, such as a terrain landform, a slope direction, soil viscosity and soil consistency, and the analytic hierarchy process comprises the steps of building a hierarchical structure, constructing a comparison matrix, calculating the weight and the maximum characteristic value of the geographic factor of each level and consistency test.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021129680A1 (en) * | 2019-12-26 | 2021-07-01 | 清华大学 | Snowmelt flood prediction method and apparatus, electronic device, and storage medium |
| CN113496104A (en) * | 2021-07-16 | 2021-10-12 | 中科技术物理苏州研究院 | Rainfall forecast correction method and system based on deep learning |
| CN114706146A (en) * | 2022-03-23 | 2022-07-05 | 成都信息工程大学 | Method for forecasting growth and hail-reducing stage of hail embryo in complex terrain hail storm process |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101101612B (en) * | 2007-07-19 | 2010-08-04 | 中国水利水电科学研究院 | Method for simulating farmland micro-terrain spatial distribution state |
| CN103646157B (en) * | 2013-08-28 | 2017-05-10 | 国网电力科学研究院 | Method for evaluating transmission line fault caused by rainstorm |
| CN105893743B (en) * | 2016-03-25 | 2018-10-02 | 安徽省气象科学研究所 | A kind of computational methods of the transmission line of electricity standard ice thickness based on weather station |
| EP3343249B1 (en) * | 2017-01-03 | 2019-11-20 | UBIMET GmbH | Method for increasing the spatial resolution of a weather prediction |
| CN109063975A (en) * | 2018-07-11 | 2018-12-21 | 国网黑龙江省电力有限公司电力科学研究院 | A kind of electric power microclimate disaster monitoring and prior-warning device |
| CN110598932B (en) * | 2019-09-11 | 2022-04-19 | 电子科技大学 | Weather feature fusion-based power transmission corridor icing early warning method |
| CN110633851B (en) * | 2019-09-11 | 2022-04-19 | 电子科技大学 | Power transmission corridor icing early warning method based on multi-source data |
| CN110633858A (en) * | 2019-09-18 | 2019-12-31 | 国网湖南省电力有限公司 | Clustering early warning method and system for rainstorm geological disasters of power transmission line |
| CN110991879A (en) * | 2019-12-02 | 2020-04-10 | 贵州电网有限责任公司 | Risk assessment method for icing disasters of different spans of power transmission line in ice period |
-
2022
- 2022-08-16 CN CN202210978122.2A patent/CN115047543B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021129680A1 (en) * | 2019-12-26 | 2021-07-01 | 清华大学 | Snowmelt flood prediction method and apparatus, electronic device, and storage medium |
| CN113496104A (en) * | 2021-07-16 | 2021-10-12 | 中科技术物理苏州研究院 | Rainfall forecast correction method and system based on deep learning |
| CN114706146A (en) * | 2022-03-23 | 2022-07-05 | 成都信息工程大学 | Method for forecasting growth and hail-reducing stage of hail embryo in complex terrain hail storm process |
Non-Patent Citations (1)
| Title |
|---|
| A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain;HuadeGuan 等;《Journal of Hydrology》;20090915;第375卷(第3-4期);第578-588页 * |
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