Power grid icing zone position prediction method, device, equipment, medium and program product

Abstract

The invention relates to a power grid icing zone position prediction method and device, equipment, a medium and a program product, and the method comprises the steps: carrying out the calculation based on historical north pole sea ice data and historical meteorological reanalysis data, and obtaining a historical east road cold air activity intensity index and a historical water vapor transmission intensity index; performing phase state identification inversion on the historical meteorological reanalysis data to obtain historical icing data, and constructing an icing climate tendency index model through a preset algorithm; on the basis of the predicted north pole sea ice data and the predicted meteorological reanalysis data, a predicted east road cold air activity intensity index and a predicted water vapor conveying intensity index are obtained through operation; performing prediction based on the predicted east road cold air activity intensity index and the predicted water vapor transport intensity index through an icing climate tendency index model to obtain a predicted icing climate tendency index; based on comparative analysis of the predicted icing climate tendency index and the historical icing climate tendency index, the icing zone position movement trend information is obtained, and the accuracy of predicting the position of the icing zone is improved.

Description

Technical Field

[0001] This disclosure relates to the field of power system disaster prevention and mitigation technology, and in particular to a method, device, equipment, medium and program product for predicting the location of power grid icing zones. Background Technology

[0002] This section is intended to provide background or context for embodiments of this disclosure. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] Power line icing is a major disaster affecting the safe operation of power grids. Accurately predicting the distribution and changing trends of icing zones is crucial for the planning, design, and operation and maintenance of power facilities.

[0004] Accelerated warming in the Arctic has triggered profound changes in the atmospheric circulation in the mid-to-high latitudes, altering the frequency and intensity of the convergence of cold and warm air masses in northern regions. This has led to changes in the pattern of rain, snow, and freezing weather, resulting in long-term trend changes in the location and intensity of the ice cover zone.

[0005] However, methods based on historical statistics in related technologies cannot effectively capture the new characteristics brought about by nonlinear changes in the climate system, and the accuracy of predicting the location of the ice cover zone is low. Summary of the Invention

[0006] In order to at least partially solve one of the technical problems in the related technologies, this disclosure provides a method, apparatus, equipment, medium and program product for predicting the location of power grid icing zones.

[0007] To achieve the above objectives, a first aspect of the exemplary embodiments of this disclosure provides a method for predicting the location of power grid icing zones, comprising: Historical Arctic sea ice data and historical meteorological reanalysis data are determined. Based on the historical Arctic sea ice data and the historical meteorological reanalysis data, the historical intensity index of cold air activity along the eastern route is calculated. Based on the historical meteorological reanalysis data, the historical water vapor transport intensity index is calculated. Historical icing data is obtained by performing phase identification and inversion on the historical meteorological reanalysis data. Based on the historical icing data, the historical cold air activity intensity index from the east, and the historical water vapor transport intensity index, an icing climate tendency index model is constructed using a preset algorithm. The predicted Arctic sea ice data and the predicted meteorological reanalysis data are determined. Based on the predicted Arctic sea ice data and the predicted meteorological reanalysis data, the predicted intensity index of cold air activity in the east is calculated. Based on the predicted meteorological reanalysis data, the predicted water vapor transport intensity index is calculated. The predicted icing climate tendency index is obtained by using the icing climate tendency index model based on the predicted intensity index of cold air activity in the eastern route and the predicted intensity index of water vapor transport. The historical icing climate tendency index is determined, and the predicted icing climate tendency index is compared and analyzed with the historical icing climate tendency index to obtain information on the movement trend of the icing belt.

[0008] In some exemplary embodiments, the calculation of the historical eastward cold air activity intensity index based on the historical Arctic sea ice data and the historical meteorological reanalysis data includes: Identify historical sea ice concentration anomalies in the Barents Sea-Kara Sea region from the historical Arctic sea ice data, historical geopotential height anomalies in the Ural Mountains-Siberia region from the historical meteorological reanalysis data, and historical meridional geopotential height gradients of the East Asian Trough. The historical sea ice concentration anomalies, the historical geopotential height anomalies, and the historical meridional geopotential height gradient are standardized. Based on the standardized historical sea ice concentration anomalies, historical geopotential height anomalies, and historical meridional geopotential height gradients, the weighting coefficients are determined through multiple linear regression, and the historical eastward cold air activity intensity index is calculated.

[0009] In some exemplary embodiments, the step of calculating the historical water vapor transport intensity index based on the historical meteorological reanalysis data includes: Determine the historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific in the historical meteorological reanalysis data; The historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific are standardized. The historical water vapor transport intensity index is calculated based on the standardized historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific.

[0010] In some exemplary embodiments, the step of constructing an icing climate tendency index model based on the historical icing data, the historical eastward cold air activity intensity index, and the historical water vapor transport intensity index using a preset algorithm includes: The historical cold air activity intensity index and the historical water vapor transport intensity index are used as independent variables, and the historical icing data are used as dependent variables. The coefficients of the icing climate tendency index model are obtained by fitting the model using the preset algorithm. The icing climate tendency index model includes linear terms and cross terms.

[0011] In some exemplary embodiments, the step of obtaining the predicted icing climate tendency index by using the icing climate tendency index model based on the predicted intensity index of cold air activity from the east and the predicted intensity index of water vapor transport includes: The predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport are input into the icing climate tendency index model; The icing climate tendency index model is used to make predictions based on the predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport. Obtain the predicted icing climate tendency index output by the icing climate tendency index model.

[0012] In some exemplary embodiments, the step of comparing and analyzing the predicted icing climate tendency index with the historical icing climate tendency index to obtain information on the movement trend of the icing belt includes: Compare the spatial distribution of the predicted icing climate tendency index with the historical icing climate tendency index; By identifying regions where the predicted icing climate tendency index has increased compared to the historical icing climate tendency index, information on the movement trend of the icing belt is obtained.

[0013] Based on the same inventive concept, a second aspect of the exemplary embodiments of this disclosure provides a power grid icing zone location prediction device, comprising: The historical circulation index determination module is configured to determine historical Arctic sea ice data and historical meteorological reanalysis data, calculate the historical eastward cold air activity intensity index based on the historical Arctic sea ice data and the historical meteorological reanalysis data, and calculate the historical water vapor transport intensity index based on the historical meteorological reanalysis data. The icing climate tendency index model construction module is configured to perform phase identification and inversion on the historical meteorological reanalysis data to obtain historical icing data, and to construct the icing climate tendency index model based on the historical icing data, the historical cold air activity intensity index from the east and the historical water vapor transport intensity index through a preset algorithm. The predicted circulation index determination module is configured to determine the predicted Arctic sea ice data and the predicted meteorological reanalysis data, calculate the predicted intensity index of the cold air activity in the east based on the predicted Arctic sea ice data and the predicted meteorological reanalysis data, and calculate the predicted water vapor transport intensity index based on the predicted meteorological reanalysis data. The icing climate tendency index prediction module is configured to predict the icing climate tendency index by using the icing climate tendency index model based on the predicted intensity index of cold air activity in the east and the predicted intensity index of water vapor transport. The ice cover zone movement trend analysis module is configured to determine the historical ice cover climate tendency index, and to obtain the ice cover zone position movement trend information by comparing and analyzing the predicted ice cover climate tendency index with the historical ice cover climate tendency index.

[0014] Based on the same inventive concept, a third aspect of the exemplary embodiments of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0015] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of this disclosure provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0016] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0017] The technical solution provided in this disclosure has the following advantages compared with the prior art: This disclosure starts with changes in the fundamental circulation system that drive icing weather, couples key factors from Arctic sea ice data and historical meteorological reanalysis data, more comprehensively reflects the complexity affecting icing weather, improves the accuracy of predicting the location of the power grid icing zone, and obtains information on the movement trend of the icing zone, providing a basis for long-term planning. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0019] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0021] Figure 1 A schematic diagram of an application scenario for the power grid icing zone location prediction method provided as an exemplary embodiment of this disclosure; Figure 2 A flowchart illustrating a method for predicting the location of icing zones in a power grid, provided as an exemplary embodiment of this disclosure; Figure 3A schematic diagram of a power grid icing zone location prediction device provided as an exemplary embodiment of the present disclosure; Figure 4 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present disclosure. Detailed Implementation

[0022] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0023] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0024] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0025] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0026] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0027] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0028] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0029] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0030] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0031] As described in the background section, power line icing is a major disaster affecting the safe operation of power grids. Accurately predicting the distribution and changing trends of icing zones is crucial for the planning, design, and operation and maintenance of power facilities.

[0032] Accelerated warming in the Arctic has triggered profound changes in the atmospheric circulation in the mid-to-high latitudes, altering the frequency and intensity of the convergence of cold and warm air masses in northern regions. This has led to changes in the pattern of rain, snow, and freezing weather, resulting in long-term trend changes in the location and intensity of the ice cover zone.

[0033] However, methods based on historical statistics in related technologies cannot effectively capture the new characteristics brought about by nonlinear changes in the climate system, and the accuracy of predicting the location of the ice cover zone is low.

[0034] The inventors of this disclosure have discovered that the reason for the "low accuracy in predicting the location of the ice-covered zone" in the aforementioned related technologies is: The historical statistical methods used in related technologies cannot effectively capture the new characteristics brought about by nonlinear changes in the climate system. Short-term numerical models are unable to reflect the long-term effects of slowly changing forcing factors such as sea ice and subtropical high pressure, and fail to make forward-looking predictions based on the fundamental atmospheric circulation mechanism that drives icing phenomena.

[0035] To address the aforementioned issues, this disclosure provides a scheme for predicting the location of power grid icing zones, specifically including: Historical Arctic sea ice data and historical meteorological reanalysis data are determined. Based on these data, a historical eastern cold air activity intensity index and a historical water vapor transport intensity index are calculated. Phase identification and inversion are performed on the historical meteorological reanalysis data to obtain historical icing data. Based on this data, along with the historical eastern cold air activity intensity index and the historical water vapor transport intensity index, an icing climate trend index model is constructed using a preset algorithm. Forecast Arctic sea ice data and forecast meteorological reanalysis data are determined. Based on these data, a forecast eastern cold air activity intensity index and a forecast water vapor transport intensity index are calculated. The icing climate trend index model is used to predict the icing climate trend index based on the forecast eastern cold air activity intensity index and the forecast water vapor transport intensity index. Finally, a historical icing climate trend index is determined. By comparing and analyzing the predicted icing climate trend index with the historical icing climate trend index, information on the movement trend of the icing belt is obtained.

[0036] Based on physical mechanisms and taking into account the synergistic effects of key circulation factors in the Arctic-subtropical region, a long-term prediction of the location of the ice-covered belt was made. Specifically, starting from the fundamental changes in the circulation system that drive ice-covered weather, key factors in Arctic sea ice data and historical meteorological reanalysis data were coupled to more comprehensively reflect the complexity affecting ice-covered weather, improve the accuracy of predicting the location of the power grid ice-covered belt, and obtain information on the movement trend of the ice-covered belt, providing a basis for long-term planning.

[0037] After introducing the basic principles of this disclosure, various non-limiting embodiments of this disclosure will be described in detail below.

[0038] refer to Figure 1 This is a schematic diagram of an application scenario of the power grid icing zone location prediction method provided in the exemplary embodiments of this disclosure.

[0039] This application scenario includes a data acquisition device 110, a server 120, and a terminal device 130. The data acquisition device 110, server 120, and terminal device 130 can all be connected via wired or wireless communication networks to achieve data interaction.

[0040] Data acquisition device 110 can be a device in a power system that has the function of acquiring Arctic sea ice data and meteorological reanalysis data. Data acquisition device 110 can collect and process Arctic sea ice data and meteorological reanalysis data from the natural environment, or it can acquire Arctic sea ice data and meteorological reanalysis data from existing databases, crawl Arctic sea ice data and meteorological reanalysis data from the Internet, or acquire Arctic sea ice data and meteorological reanalysis data uploaded by users when using client applications.

[0041] Server 120 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0042] Terminal device 130 may be an electronic device located close to the user side, possessing data transmission and multimedia input / output functions, including but not limited to desktop computers, mobile phones, portable computers, tablet computers, media players, smart wearable devices, personal digital assistants (PDAs), or other electronic devices capable of performing the aforementioned functions. This electronic device may include a processor and a display screen with touch input functionality. The display screen is used to present a graphical user interface (GUI), which can display an application interface. The processor is used to process application data, generate the GUI, and control the display of the GUI on the screen.

[0043] In some exemplary embodiments, the method for predicting the location of power grid icing zones can run on server 120.

[0044] When the power grid icing zone location prediction method is running on server 120, server 120 is used to provide power grid icing zone location prediction services to users of terminal device 130. Terminal device 130 has a client installed that communicates with server 120.

[0045] Data acquisition device 110 acquires Arctic sea ice data and meteorological reanalysis data, including historical Arctic sea ice data, historical meteorological reanalysis data, predicted Arctic sea ice data, and predicted meteorological reanalysis data.

[0046] Server 120 acquires historical Arctic sea ice data, historical meteorological reanalysis data, predicted Arctic sea ice data, and predicted meteorological reanalysis data from data acquisition equipment 110 in the target power system.

[0047] Server 120 determines historical Arctic sea ice data and historical meteorological reanalysis data. Based on the historical Arctic sea ice data and historical meteorological reanalysis data, it calculates the historical intensity index of cold air activity along the eastern route and the historical moisture transport intensity index. It then performs phase identification and inversion on the historical meteorological reanalysis data to obtain historical icing data. Based on the historical icing data, the historical intensity index of cold air activity along the eastern route, and the historical moisture transport intensity index, it constructs an icing climate trend index model using a preset algorithm. Next, it determines predicted Arctic sea ice data and predicted meteorological reanalysis data. Based on the predicted Arctic sea ice data and predicted meteorological reanalysis data, it calculates the predicted intensity index of cold air activity along the eastern route and the predicted moisture transport intensity index. Using the icing climate trend index model, it predicts the predicted icing climate trend index based on the predicted intensity index of cold air activity along the eastern route and the predicted moisture transport intensity index. Finally, it determines the historical icing climate trend index and compares and analyzes the predicted icing climate trend index with the historical icing climate trend index to obtain information on the movement trend of the icing belt.

[0048] Server 120 sends the power grid icing zone location prediction results (such as the predicted icing climate tendency index and ice zone location movement trend information) to the client. The client then displays the power grid icing zone location prediction results to the user to help the user take countermeasures based on the power grid icing zone location prediction results and ensure the stability of the power system.

[0049] The following is combined Figure 1 The above application scenarios are used to describe the power grid icing zone location prediction method according to exemplary embodiments of this disclosure. It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this disclosure, and the embodiments of this disclosure are not limited in any way. Rather, the embodiments of this disclosure can be applied to any applicable scenario.

[0050] refer to Figure 2 This is a flowchart illustrating a method for predicting the location of icing zones in a power grid, provided in an exemplary embodiment of this disclosure.

[0051] This method can be executed by a power grid icing zone location prediction device, which can be implemented in software and / or hardware and is generally integrated into electronic equipment.

[0052] like Figure 2 As shown, the method for predicting the location of power grid icing zones includes the following steps: Step S210: Determine historical Arctic sea ice data and historical meteorological reanalysis data. Based on the historical Arctic sea ice data and the historical meteorological reanalysis data, calculate the historical eastward cold air activity intensity index. Based on the historical meteorological reanalysis data, calculate the historical water vapor transport intensity index.

[0053] In some exemplary embodiments, determining historical Arctic sea ice data and historical meteorological reanalysis data includes: Acquire Arctic sea ice data and meteorological reanalysis data for a preset historical time period to serve as historical Arctic sea ice data and historical meteorological reanalysis data.

[0054] In some exemplary embodiments, the historical meteorological reanalysis data includes: ERA5 meteorological reanalysis data.

[0055] As an example, historical data from the past 30-50 years is collected, including Arctic sea ice data and ERA5 meteorological reanalysis data.

[0056] In some exemplary embodiments, the calculation of the historical eastward cold air activity intensity index based on the historical Arctic sea ice data and the historical meteorological reanalysis data includes: Identify historical sea ice concentration anomalies in the Barents Sea-Kara Sea region from the historical Arctic sea ice data, historical geopotential height anomalies in the Ural Mountains-Siberia region from the historical meteorological reanalysis data, and historical meridional geopotential height gradients of the East Asian Trough. The historical sea ice concentration anomalies, the historical geopotential height anomalies, and the historical meridional geopotential height gradient are standardized. Based on the standardized historical sea ice concentration anomalies, historical geopotential height anomalies, and historical meridional geopotential height gradients, the weighting coefficients are determined through multiple linear regression, and the historical eastward cold air activity intensity index is calculated.

[0057] In practical implementation, the definition and quantification of the key circulation factor – the intensity index of cold air activity along the eastern route (I) ECA ): This index quantifies the intensity of cold air masses affecting my country via an eastward path, resulting from the development of meridional circulation caused by the reduction of Arctic sea ice. A higher value indicates a greater likelihood and intensity of increased cold air activity predicted from the east.

[0058]

[0059] First item This represents a standardized quantification of the reduction in sea ice in the Barents Sea-Kara Sea.

[0060] Molecular-ΔSICBSO This represents the negative anomaly of sea ice concentration (SIC) in the key Barents-Kara Sea region (70°-80°N, 30°-90°E) relative to the climatological mean. Taking its negative value indicates a decrease in sea ice concentration (ΔSIC). BSO When the value is negative, this term becomes positive, thus aligning with the positive contribution direction of the strengthening cold air.

[0061] Denominator σ ΔSIC ΔSIC represents a historical period BSO Standard deviation of annual series.

[0062] This study directly quantifies the starting point of the causal chain of Arctic sea ice reduction, which leads to the release of local sensible and latent heat, providing a thermodynamic basis for the formation of the upstream high-pressure ridge.

[0063] Second item This represents the standardized quantification of the enhancement of the Ural-Siberian high-pressure ridge.

[0064] The molecule represents the positive anomaly of the winter average 500 hPa geopotential height relative to the climatological mean in the Ural Mountains-Siberia region (60°-70°N, 60°-90°E).

[0065] The denominator represents the standard deviation.

[0066] This quantifies the intermediate link of high-pressure ridge development. The reduction of Arctic sea ice, through the Rossby wave response, often results in positive geopotential height anomalies (i.e., the strengthening or northward extension of the high-pressure ridge) in the Ural Mountains-Siberia region, which is a key circulation configuration guiding the southward movement of polar cold air.

[0067] Third item This represents the standardized quantization of the meridional dimension of the East Asian trough.

[0068] The molecule represents the 500 hPa meridional geopotential height gradient along the East Asian coast. Specifically, it can be defined as: Z 500hPa, 60°N, 140°E - Z 500hPa, 40°N, 120°E Representative locations upstream and downstream of the East Asian Trough were selected here.

[0069] The denominator represents the standard deviation.

[0070] This quantifies the result of enhanced meridional circulation. When the meridional gradient increases (especially when the height value is relatively higher at high latitudes and relatively lower at low latitudes), it indicates that the meridional degree of the westerly winds increases, the high-pressure ridge is more likely to extend northward, and guide cold air southward along a more easterly path, forming eastward-bound cold air.

[0071] Using historical data (e.g., the past 30-50 years), with the standardized three parameters as independent variables and the observed intensity of cold air activity along the eastern route of Northeast / North China in winter (e.g., negative anomaly of average winter surface temperature) as the dependent variable, a multiple linear regression was performed. The coefficients obtained from the regression are the optimal weights w1, w2, w3.

[0072] These weights objectively reflect the relative contributions of Arctic sea ice, high-pressure ridges, and meridional circulation to the eventual strengthening of cold air activity along the eastward path. Each weight should be positive, as they all point in the direction of cold air intensification.

[0073] In some exemplary embodiments, the step of calculating the historical water vapor transport intensity index based on the historical meteorological reanalysis data includes: Determine the historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific in the historical meteorological reanalysis data; The historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific are standardized. The historical water vapor transport intensity index is calculated based on the standardized historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific.

[0074] In practical implementation, the key circulation factor is the water vapor transport intensity index (I). Moisture Definition and quantification of ) This index combines the contributions of the Northwest Pacific storm axis (which provides low-level moisture and upward momentum) and the subtropical high (which guides warm and humid airflow) to the transport of moisture to northern my country.

[0075]

[0076] First item This represents the storm axis intensity index in the Northwest Pacific. It is typically calculated by taking the average of the variance of the 500 hPa geopotential height after band-pass filtering (e.g., 2-8 days) over the key region (e.g., 30°-50°N, 140°E-180°) and dividing it by the standard deviation. This standardization process resolves the dimension issue, making it a dimensionless reference value.

[0077] A strengthened storm axis means more frequent and stronger extratropical cyclone activity, which facilitates the transport of water vapor from the ocean to the continent. γ is a weighting coefficient, usually a positive value.

[0078] The second term (WPSHI) represents the Western Pacific Subtropical High Intensity Index. It is usually standardized by dividing by the standard deviation of the National Climate Center standard, which is the average geopotential height of the area enclosed by the 5880 gpm isobar on the 500 hPa isobaric surface.

[0079] A strengthening subtropical high facilitates the northward transport of warm, moist air from low latitudes by southerly or southwesterly airflows to its west. δ is a weighting coefficient, typically a positive value.

[0080] The index The higher the value, the more abundant the warm and humid air is transported to northern my country.

[0081] Through the above exemplary embodiments, this disclosure couples three key factors—Arctic sea ice, the Pacific storm axis, and the subtropical high—to more comprehensively reflect the complexity of the impacts of global warming and improve the accuracy of predicting the location of the power grid icing zone.

[0082] Step S220: Perform phase identification and inversion on the historical meteorological reanalysis data to obtain historical icing data. Based on the historical icing data, the historical cold air activity intensity index from the east, and the historical water vapor transport intensity index, construct an icing climate tendency index model through a preset algorithm.

[0083] In some exemplary embodiments, the step of performing phase identification and inversion on the historical meteorological reanalysis data to obtain historical icing data includes: Icing data, such as icing thickness and number of days with icing, were obtained by inverting the phase recognition tool in ERA5 meteorological reanalysis data.

[0084] In some exemplary embodiments, the step of constructing an icing climate tendency index model based on the historical icing data, the historical eastward cold air activity intensity index, and the historical water vapor transport intensity index using a preset algorithm includes: The historical cold air activity intensity index and the historical water vapor transport intensity index are used as independent variables, and the historical icing data are used as dependent variables. The coefficients of the icing climate tendency index model are obtained by fitting the model using the preset algorithm. The icing climate tendency index model includes linear terms and cross terms.

[0085] In practice, the Ice Cover Climate Prone Index (ICI) is established: The icing climate tendency index is a parameter that comprehensively characterizes the likelihood of icing weather in a certain area during winter.

[0086] ICI = f (I ECA , I Moisture ) = a IECA + b I Moisture + c (I ECA I Moisture ) ICI is the target estimate, which is positively correlated with local icing thickness, number of days with icing, etc.

[0087] a, b, and c are regression coefficients, determined through training with historical data. a and b should be positive values, representing the respective contributions of cold air and water vapor.

[0088] (I ECA I Moisture The term ) represents the nonlinear enhancement effect of the convergence of cold and warm air masses. Even if either the cold air or the water vapor is strong, it is difficult to form strong icing if the other is weak. Only when both are strong simultaneously (i.e., the product term is large) is it most conducive to producing severe rain, snow, and freezing weather. c is usually a positive value.

[0089] In practice, historical data fitting and model validation are performed as follows: Collect historical data from the past 30-50 years, including: Arctic sea ice data and ERA5 meteorological reanalysis data (used to calculate I). ECA and I Moisture The ice thickness and number of days with ice were obtained by inverting the phase identification tool in the ERA5 meteorological reanalysis data. All data were interpolated to achieve a uniform grid resolution.

[0090] Using grid points as units, the historical key circulation factor index (I) is... ECA , I Moisture Using α as the independent variable and the corresponding historical icing data as the dependent variable, the coefficients a, b, c of the ICI formula are determined by using multiple linear regression or other machine learning algorithms (such as random forest or support vector machine).

[0091] The established ICI model was validated using independent samples (such as data from the next 10 years). The calculated ICI index was correlated with the icing data, and a significance test was performed to ensure that it has good simulation ability and stability.

[0092] Step S230: Determine the predicted Arctic sea ice data and the predicted meteorological reanalysis data. Based on the predicted Arctic sea ice data and the predicted meteorological reanalysis data, calculate the predicted intensity index of cold air activity in the east. Based on the predicted meteorological reanalysis data, calculate the predicted moisture transport intensity index.

[0093] In some exemplary embodiments, determining the predicted Arctic sea ice data and the predicted weather reanalysis data includes: Obtain simulation data from global climate models such as the Coupled Model Intercomparison Project (CMIP6) under different greenhouse gas emission scenarios.

[0094] In practice, the ΔSIC for the forecast period (e.g., 2040-2060) is calculated from the data output by these models. BSO Potential height field, etc.

[0095] Substitute the predicted data into the formula (similar to the formula for historical periods) to calculate the predicted period. and .

[0096] Step S240: Based on the predicted intensity index of cold air activity in the east and the predicted intensity index of water vapor transport, the icing climate tendency index model is used to predict the predicted icing climate tendency index.

[0097] In some exemplary embodiments, the step of obtaining the predicted icing climate tendency index by using the icing climate tendency index model based on the predicted intensity index of cold air activity from the east and the predicted intensity index of water vapor transport includes: The predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport are input into the icing climate tendency index model; The icing climate tendency index model is used to make predictions based on the predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport. Obtain the predicted icing climate tendency index output by the icing climate tendency index model.

[0098] In practical implementation, the long-term forecast under the predicted scenario is as follows: Substituting the predicted circulation factor index into the calibrated ICI model, the predicted icing climate tendency index (ICI) is calculated. future .

[0099] Through the above exemplary embodiments, this disclosure directly addresses the fundamental changes in the circulation system that drive icing weather, and the prediction results are scientifically reliable.

[0100] Step S250: Determine the historical icing climate tendency index. Based on the comparison and analysis between the predicted icing climate tendency index and the historical icing climate tendency index, obtain the information on the movement trend of the icing belt.

[0101] In some exemplary embodiments, the step of comparing and analyzing the predicted icing climate tendency index with the historical icing climate tendency index to obtain information on the movement trend of the icing belt includes: Compare the spatial distribution of the predicted icing climate tendency index with the historical icing climate tendency index; By identifying regions where the predicted icing climate tendency index has increased compared to the historical icing climate tendency index, information on the movement trend of the icing belt is obtained.

[0102] In practice, ICI will be used. future ICI compared to historical baseline periods (e.g., 1980-2010) historical Comparison. Through analysis of ICI... future The spatial distribution changes, especially the northern boundary of its significant growth area, can be used to determine the long-term movement trend of the ice-covered belt, that is, the scope and intensity of "northward expansion".

[0103] Through the above exemplary embodiments, the climate model-based prediction scenarios disclosed herein can achieve trend forecasting on a multi-decade scale, providing a basis for ultra-long-term planning.

[0104] To further illustrate the effectiveness of the power grid icing zone location prediction scheme provided in this disclosure, the following example uses the estimated changes in the icing zone in Northeast China around 2050 as an illustration: (1) Data preparation Historical data (1980-2020): ERA5 reanalysis data and Arctic sea ice data were collected for calculating circulation indices, icing data, and model training.

[0105] Collect forecast data (2040-2060): ensemble average data of the five best-performing models in CMIP6 (such as CanESM5, MPI-ESM1-2-LR, MIROC6, UKESM1-0-LL, and NorESM2-LM) under the SSP2-4.5 scenario.

[0106] (2) Model building and validation Using ERA5 reanalysis data and Arctic sea ice data from 1980 to 2010, the average Ig for each winter (December–February) was calculated. ECA Index and I Moisture index.

[0107] Using grid points as units, the historical key circulation factor index (I) is... ECA , I Moisture Using α as the independent variable and the corresponding historical icing data as the dependent variable, the coefficients a, b, c of the ICI model are determined by using multiple linear regression or other machine learning algorithms (such as random forest or support vector machine).

[0108] The results of the validation using data from 2011 to 2020 show that the correlation coefficients between the model-calculated ICI and the number of days with icing and the thickness of icing are all above 0.65, passing the significance test of 0.01.

[0109] (3) Prediction of changes in icing location: Calculate the winter average for 2040-2060 from the CMIP6 model output. and The results show that, compared to 1980-2010, Significantly increased due to the continued reduction of Arctic sea ice. The trend is also increasing due to the strengthening of the subtropical high and the changes in the storm axis.

[0110] Substituting the predicted circulation index into the established ICI model, we obtain the ICI around 2050. future Spatial distribution.

[0111] Results Analysis: Compared with historical periods, ICI future ICI values ​​have generally increased in Northeast China, with the center of high values ​​shifting significantly northward. In areas such as central and eastern Jilin and eastern Heilongjiang, where icing was previously relatively mild, ICI values ​​have increased by more than 50%, indicating a significant increase in the climate risk of future rain, snow, and freezing weather in these regions, and the icing belt is showing a clear "northward expansion" characteristic.

[0112] This disclosure provides a scheme with a clear physical mechanism, strong operability, and the ability to make long-term predictions of the location changes of the ice-covered zone over the next few decades. It considers that ice formation requires two basic conditions—suitable low temperatures and sufficient water vapor. Global warming influences three key circulation systems—Arctic sea ice, the Northwest Pacific storm axis, and the Western Pacific subtropical high—which respectively regulate cold air activity and water vapor transport affecting my country, ultimately determining the northern boundary and intensity of the ice-covered zone. This disclosure achieves long-term prediction by quantifying the changes of these three factors and establishing their relationship with the ice-covered climate tendency index. Its beneficial effects include: This disclosure couples three key factors—Arctic sea ice, the Pacific storm axis, and the subtropical high—to more comprehensively reflect the complexity of the impacts of global warming. This disclosure starts directly from the fundamental changes in the circulation system that drive icing weather, and the prediction results are scientific and reliable; This disclosure presents climate model-based forecast scenarios that enable trend predictions on a multi-decade scale, providing a basis for ultra-long-term planning. The data required for this disclosure are all conventional climate data or model outputs, with clearly defined calculation formulas that are easy to apply in business operations.

[0113] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0114] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] To achieve the above embodiments, this disclosure also proposes a device for predicting the location of power grid icing zones.

[0116] Figure 3 This is a schematic diagram of a power grid icing zone location prediction device provided in an embodiment of the present disclosure. The device can be implemented by software and / or hardware and is generally integrated into an electronic device.

[0117] like Figure 3 As shown, the power grid icing zone location prediction device includes the following modules: The historical circulation index determination module 310 is configured to determine historical Arctic sea ice data and historical meteorological reanalysis data, calculate the historical eastward cold air activity intensity index based on the historical Arctic sea ice data and the historical meteorological reanalysis data, and calculate the historical water vapor transport intensity index based on the historical meteorological reanalysis data. The icing climate tendency index model construction module 320 is configured to perform phase identification and inversion on the historical meteorological reanalysis data to obtain historical icing data, and to construct an icing climate tendency index model based on the historical icing data, the historical cold air activity intensity index from the east and the historical water vapor transport intensity index through a preset algorithm. The predicted circulation index determination module 330 is configured to determine the predicted Arctic sea ice data and the predicted meteorological reanalysis data, calculate the predicted intensity index of the cold air activity in the east based on the predicted Arctic sea ice data and the predicted meteorological reanalysis data, and calculate the predicted water vapor transport intensity index based on the predicted meteorological reanalysis data. The icing climate tendency index prediction module 340 is configured to predict the icing climate tendency index by using the icing climate tendency index model based on the predicted eastern cold air activity intensity index and the predicted water vapor transport intensity index. The ice cover zone movement trend analysis module 350 is configured to determine the historical ice cover climate tendency index, and to obtain the ice cover zone position movement trend information by comparing and analyzing the predicted ice cover climate tendency index with the historical ice cover climate tendency index.

[0118] In some exemplary embodiments, the historical circulation index determination module 310 is specifically configured as follows: Identify historical sea ice concentration anomalies in the Barents Sea-Kara Sea region from the historical Arctic sea ice data, historical geopotential height anomalies in the Ural Mountains-Siberia region from the historical meteorological reanalysis data, and historical meridional geopotential height gradients of the East Asian Trough. The historical sea ice concentration anomalies, the historical geopotential height anomalies, and the historical meridional geopotential height gradient are standardized. Based on the standardized historical sea ice concentration anomalies, historical geopotential height anomalies, and historical meridional geopotential height gradients, the weighting coefficients are determined through multiple linear regression, and the historical eastward cold air activity intensity index is calculated.

[0119] In some exemplary embodiments, the historical circulation index determination module 310 is specifically configured as follows: Determine the historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific in the historical meteorological reanalysis data; The historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific are standardized. The historical water vapor transport intensity index is calculated based on the standardized historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific.

[0120] In some exemplary embodiments, the icing climate tendency index model construction module 320 is specifically configured as follows: The historical cold air activity intensity index and the historical water vapor transport intensity index are used as independent variables, and the historical icing data are used as dependent variables. The coefficients of the icing climate tendency index model are obtained by fitting the model using the preset algorithm. The icing climate tendency index model includes linear terms and cross terms.

[0121] In some exemplary embodiments, the icing climate tendency index prediction module 340 is specifically configured as follows: The predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport are input into the icing climate tendency index model; The icing climate tendency index model is used to make predictions based on the predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport. Obtain the predicted icing climate tendency index output by the icing climate tendency index model.

[0122] In some exemplary embodiments, the icing zone movement trend analysis module 350 is specifically configured as follows: Compare the spatial distribution of the predicted icing climate tendency index with the historical icing climate tendency index; By identifying regions where the predicted icing climate tendency index has increased compared to the historical icing climate tendency index, information on the movement trend of the icing belt is obtained.

[0123] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0124] The power grid icing zone location prediction device provided in this disclosure can execute the power grid icing zone location prediction method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method execution.

[0125] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the power grid icing zone location prediction method in the above embodiments.

[0126] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0127] The following is a detailed reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this disclosure. The electronic device in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0128] like Figure 4As shown, the electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a memory 1008 into a random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for the operation of the electronic device. The processor 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0129] Typically, the following devices can be connected to the I / O interface 1005: input devices 1006 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1007 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; memory devices 1008 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0130] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1009, or installed from memory 1008, or installed from ROM 1002. When the computer program is executed by processor 1001, it performs the functions defined in the power grid icing zone location prediction method of embodiments of this disclosure.

[0131] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0132] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0133] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0134] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the aforementioned power grid icing zone location prediction method.

[0135] Electronic devices can be programmed with computer program code in one or more programming languages ​​or combinations thereof to perform the operations of this disclosure. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0137] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0138] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0139] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0140] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0141] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0142] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for predicting the location of icing zones in power grids, characterized in that, include: Historical Arctic sea ice data and historical meteorological reanalysis data are determined. Based on the historical Arctic sea ice data and the historical meteorological reanalysis data, the historical intensity index of cold air activity along the eastern route is calculated. Based on the historical meteorological reanalysis data, the historical water vapor transport intensity index is calculated. Historical icing data is obtained by performing phase identification and inversion on the historical meteorological reanalysis data. Based on the historical icing data, the historical cold air activity intensity index from the east, and the historical water vapor transport intensity index, an icing climate tendency index model is constructed using a preset algorithm. The predicted Arctic sea ice data and the predicted meteorological reanalysis data are determined. Based on the predicted Arctic sea ice data and the predicted meteorological reanalysis data, the predicted intensity index of cold air activity in the east is calculated. Based on the predicted meteorological reanalysis data, the predicted water vapor transport intensity index is calculated. The predicted icing climate tendency index is obtained by using the icing climate tendency index model based on the predicted intensity index of cold air activity in the eastern route and the predicted intensity index of water vapor transport. The historical icing climate tendency index is determined, and the predicted icing climate tendency index is compared and analyzed with the historical icing climate tendency index to obtain information on the movement trend of the icing belt.

2. The method according to claim 1, characterized in that, The historical eastward cold air activity intensity index, calculated based on the historical Arctic sea ice data and the historical meteorological reanalysis data, includes: Identify historical sea ice concentration anomalies in the Barents Sea-Kara Sea region from the historical Arctic sea ice data, historical geopotential height anomalies in the Ural Mountains-Siberia region from the historical meteorological reanalysis data, and historical meridional geopotential height gradients of the East Asian Trough. The historical sea ice concentration anomalies, the historical geopotential height anomalies, and the historical meridional geopotential height gradient are standardized. Based on the standardized historical sea ice concentration anomalies, historical geopotential height anomalies, and historical meridional geopotential height gradients, the weighting coefficients are determined through multiple linear regression, and the historical eastward cold air activity intensity index is calculated.

3. The method according to claim 1, characterized in that, The calculation of the historical water vapor transport intensity index based on the historical meteorological reanalysis data includes: Determine the historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific in the historical meteorological reanalysis data; The historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific are standardized. The historical water vapor transport intensity index is calculated based on the standardized historical storm axis intensity index of the Northwest Pacific and the historical subtropical high pressure intensity index of the West Pacific.

4. The method according to claim 1, characterized in that, The icing climate tendency index model is constructed based on the historical icing data, the historical cold air activity intensity index from the east, and the historical water vapor transport intensity index, using a preset algorithm. This model includes: The historical cold air activity intensity index and the historical water vapor transport intensity index are used as independent variables, and the historical icing data are used as dependent variables. The coefficients of the icing climate tendency index model are obtained by fitting the model using the preset algorithm. The icing climate tendency index model includes linear terms and cross terms.

5. The method according to claim 1, characterized in that, The process of predicting the icing climate tendency index using the icing climate tendency index model based on the predicted intensity index of cold air activity from the east and the predicted intensity index of water vapor transport, includes: The predicted intensity index of cold air activity along the eastern route and the predicted intensity index of water vapor transport are input into the icing climate tendency index model. The icing climate tendency index model is used to make predictions based on the predicted intensity index of cold air activity in the eastern region and the predicted intensity index of water vapor transport. Obtain the predicted icing climate tendency index output by the icing climate tendency index model.

6. The method according to claim 1, characterized in that, The process of comparing and analyzing the predicted icing climate tendency index with the historical icing climate tendency index to obtain information on the movement trend of the icing zone includes: Compare the spatial distribution of the predicted icing climate tendency index with the historical icing climate tendency index; By identifying regions where the predicted icing climate tendency index has increased compared to the historical icing climate tendency index, information on the movement trend of the icing belt is obtained.

7. A device for predicting the location of icing zones in power grids, characterized in that, include: The historical circulation index determination module is configured to determine historical Arctic sea ice data and historical meteorological reanalysis data, calculate the historical eastward cold air activity intensity index based on the historical Arctic sea ice data and the historical meteorological reanalysis data, and calculate the historical water vapor transport intensity index based on the historical meteorological reanalysis data. The icing climate tendency index model construction module is configured to perform phase identification and inversion on the historical meteorological reanalysis data to obtain historical icing data, and to construct the icing climate tendency index model based on the historical icing data, the historical cold air activity intensity index from the east and the historical water vapor transport intensity index through a preset algorithm. The predicted circulation index determination module is configured to determine the predicted Arctic sea ice data and the predicted meteorological reanalysis data, calculate the predicted intensity index of the cold air activity in the east based on the predicted Arctic sea ice data and the predicted meteorological reanalysis data, and calculate the predicted water vapor transport intensity index based on the predicted meteorological reanalysis data. The icing climate tendency index prediction module is configured to predict the icing climate tendency index by using the icing climate tendency index model based on the predicted intensity index of cold air activity in the east and the predicted intensity index of water vapor transport. The ice cover zone movement trend analysis module is configured to determine the historical ice cover climate tendency index, and to obtain the ice cover zone position movement trend information by comparing and analyzing the predicted ice cover climate tendency index with the historical ice cover climate tendency index.

8. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program / instruction thereon, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program / instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.

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