A heavy rainfall early warning method and system in a power grid area

Abstract

The present invention relates to a method and system for early warning of heavy rainfall in a power grid area, comprising: using historical precipitation observation values ​​to construct a spatial load matrix corresponding to a meteorological observation station; determining a forecast time series sequence of an extreme precipitation index based on historical precipitation forecast values ​​and real-time precipitation forecast values; and performing a heavy rainfall early warning based on the spatial load matrix, the forecast time series sequence, and the real-time precipitation forecast values. The present invention achieves rapid and accurate early warning of heavy rainfall, thereby providing a data basis for power grid disaster prevention departments.

Description

Technical Field

[0001] The present invention relates to the field of electric power meteorology, and in particular to a method and system for early warning of heavy rainfall in a power grid area. Background Art

[0002] Against the backdrop of global warming, extreme heavy rainfall events occur frequently, which can easily trigger secondary disasters such as floods, landslides, and mudslides, causing a series of accidents such as damage to power grid facilities, line shutdowns, and power outages in substations, resulting in huge economic and social losses.

[0003] Given the enormous challenges that heavy rainfall poses to the safe and stable operation and maintenance of the power grid, flood prevention work during the flood season every year has become a top priority for the power grid's disaster prevention and mitigation work. The essence of power grid flood prevention is an emergency response to heavy rainfall weather processes. Decision makers formulate and implement countermeasures to reduce the damage caused by heavy rainfall weather based on real-time meteorological monitoring and future forecast results, combined with information such as hydrology, geography, and power grid conditions.

[0004] Previous research in the field of power meteorological disaster prevention has primarily focused on meteorological factors and disasters. This research involves forecasting the temporal and spatial evolution of meteorological factors and proposing strategies to mitigate damage based on the characteristics of meteorological disasters. Due to the complexity and uncertainty of the causative factors of meteorological disasters, the evolution of disasters often leads to numerous derivative, secondary, and coupled events. Disaster management often involves numerous semi-structured and unstructured decision-making issues, making disaster response a complex and arduous process.

[0005] In recent years, scenario analysis methods have been proposed in the field of power meteorological disaster prevention. Through the "scenario-response" analysis method, complex problems are simplified and clarified, and scenario content based on meteorological disaster characteristic information is provided to disaster responders. The degree of harm of disaster events is described. Based on the analysis of past scenarios, response decisions that match the scenarios are determined or new decisions are constructed, so that disaster responders can formulate reasonable response plans within an effective time.

[0006] Disastrous precipitation is characterized by its long duration, wide impact area, and high intensity. In meteorological monitoring, qualitative concepts such as "regional heavy rain" and "persistent heavy rain" are commonly used to identify disastrous rainstorms. However, there are no established rules for quantitative identification. These are typically determined by manually defining screening criteria within a specific region based on parameters such as the number of observed heavy rain days (duration), the area of ​​heavy rain (spatial extent), and the maximum precipitation during the process. In forecasting, concepts such as regional and persistent heavy rain are also qualitatively proposed by forecasters based on future forecast results, with few quantitative identification methods. Furthermore, in monitoring, the extreme nature of precipitation processes is typically assessed by comparing post-event precipitation monitoring data from a single station with historical data. In forecasting, this is currently mostly measured using the Extreme Weather Index (EFI) developed in ensemble forecasting. However, ensemble forecasts typically require the simultaneous execution of dozens of numerical weather forecast models. Developing an extreme weather index based on model returns requires decades of back-calculation of historical weather data, consuming significant high-performance computing resources.

[0007] During operational operations, based on weather forecast results, the main characteristics of precipitation weather processes are extracted and their extremes are analyzed, providing key meteorological disaster characteristic information for scenario response analysis. Response decisions that match the scenarios are then formulated through a "scenario-response" model. Extracting precipitation weather process characteristics is particularly critical, and feature extraction must be both scientific and rational, while also taking into account economic and practical considerations. A technical challenge that urgently needs to be addressed is how to extract characteristic information from precipitation forecasts based on the results of a single deterministic numerical weather forecast business model, or a combination of the results of a few large-scale and medium-scale numerical weather forecast business models, to effectively distinguish between disastrous rainfall such as "regional heavy rain" and "persistent heavy rain," describe the main characteristics of the precipitation process in terms of time, space, and intensity, and reflect the extremes of the precipitation process. Summary of the Invention

[0008] In view of the deficiencies of the existing technology, the purpose of the present invention is to provide a method and system for early warning of heavy rainfall in a power grid area, which can achieve rapid and accurate early warning of heavy rainfall, thereby providing data basis for the power grid disaster prevention department.

[0009] The purpose of the present invention is achieved by adopting the following technical solutions:

[0010] The present invention provides a method for early warning of heavy rainfall in a power grid area, the improvement of which lies in that the method comprises:

[0011] Using historical precipitation observations, the spatial loading matrix corresponding to the meteorological observation station is constructed;

[0012] Based on the historical precipitation forecast values ​​and the real-time precipitation forecast values, the forecast time series of the extreme precipitation index is determined;

[0013] A heavy rainfall warning is issued based on the spatial load matrix, the forecast time series and the real-time precipitation forecast value.

[0014] Preferably, the method of using historical precipitation observations to construct a spatial load matrix corresponding to a meteorological observation station includes:

[0015] Construct historical precipitation observation data matrix based on historical precipitation observation values ;

[0016] Using historical precipitation observation data matrix Determine the observational time series of extreme precipitation indices;

[0017] Determine the spatial load matrix corresponding to the meteorological observation station based on the observation time series of the extreme precipitation index;

[0018] Among them, the historical precipitation observation data matrix is a matrix with p rows and n columns, , For the region Meteorological observation stations during the warning period The previous The standardized moment average value of the precipitation observation value of the historical period is located in the historical precipitation observation data matrix No. Rank List, , , is the total number of meteorological observation stations in the region, The number of preset time periods.

[0019] Preferably, determining the forecast time series of the extreme precipitation index based on the historical precipitation forecast values ​​and the real-time precipitation forecast values ​​includes:

[0020] Based on the historical precipitation forecast values ​​and real-time precipitation forecast values, a precipitation forecast data matrix combining history and real-time is constructed. ;

[0021] Using historical and real-time precipitation forecast data matrix Determine the forecast time series of extreme precipitation indices;

[0022] Among them, the historical and real-time precipitation forecast data matrix is a matrix with p rows and n+1 columns, , For the region Meteorological observation stations during the warning period The previous The standardized mean value of the precipitation forecast value for each period is located in the precipitation forecast data matrix that combines historical and real-time data. No. Rank List, , , is the total number of meteorological observation stations in the region, For the preset number of time periods, when the historical and real-time combined precipitation forecast data matrix The element subscript in When the value is 0, the element corresponds to the real-time precipitation forecast value. When the historical and real-time combined precipitation forecast data matrix The element subscript in The value is , then the element corresponds to the historical precipitation forecast value.

[0023] Preferably, the performing of a heavy rainfall warning according to the spatial load matrix, the forecast time series and the real-time precipitation forecast value includes:

[0024] Determining the range of heavy rainfall in the area during the warning period based on the spatial load matrix and the real-time forecast precipitation during the warning period;

[0025] Calculate the percentile corresponding to the normalized value of the forecast value of the extreme precipitation index during the warning period in the percentile curve drawn by each element in the forecast time series;

[0026] A heavy rainfall warning is issued based on the percentile and the range of heavy rainfall in the area during the warning period.

[0027] The present invention provides a heavy rainfall early warning system in a power grid area, wherein the system comprises:

[0028] A construction module is used to construct the spatial loading matrix corresponding to the meteorological observation station using historical precipitation observations;

[0029] A determination module, used to determine a forecast time series of an extreme precipitation index based on historical precipitation forecast values ​​and real-time precipitation forecast values;

[0030] The early warning module is used to issue a heavy rainfall early warning based on the spatial load matrix, the forecast time series and the real-time precipitation forecast value.

[0031] Compared with the closest prior art, the present invention has the following beneficial effects:

[0032] The technical solution provided by the present invention utilizes historical precipitation observations to construct a spatial load matrix corresponding to a meteorological observation station; determines a forecast time series sequence for an extreme precipitation index based on historical precipitation forecast values ​​and real-time precipitation forecast values; and issues a heavy rainfall warning based on the spatial load matrix, the forecast time series sequence, and the real-time precipitation forecast values. This solution mines and analyzes precipitation data from multiple dimensions, including space, time (historical and real-time), forecasting, and monitoring, thereby maximizing the accuracy of heavy rainfall warnings and providing effective data support for power grid disaster prevention departments.

[0033] The technical solution provided by the present invention is simple to calculate and can ensure the rapidity of heavy rainfall early warning.

[0034] The technical solution provided by the present invention has strong operability and is easy to promote. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a method for early warning of heavy rainfall that affects the safety of power grid facilities in a region;

[0036] Figure 2 is a schematic diagram of a curve drawn based on a forecast time series of an extreme precipitation index in a certain region in an embodiment of the present invention;

[0037] Figure 3 It is a structural diagram of a heavy rainfall early warning system that affects the safety of power grid facilities in the region. DETAILED DESCRIPTION

[0038] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0040] Heavy rainfall is one of the main disasters that affect the safe operation of power grids. When making “scenario-response” analysis and decision-making in power grid flood prevention, it is often necessary to extract key feature information of meteorological events and determine the response strategy that matches the scenario based on the scenario analysis. The key feature information of meteorological events can be considered from two aspects: precipitation feature analysis and forecast precipitation extremes. However, the current feature analysis of heavy rainfall is often based on qualitative descriptions, and quantitative descriptions also require artificial standards to be formulated. In terms of forecast precipitation extremes, it is also based on the collective forecast results that consume computing resources, which makes the extraction of key feature information of meteorological events insufficient in terms of accuracy and computational simplicity. Based on this, the present invention provides a heavy rainfall early warning method within a power grid area, such as Figure 1 Said method comprises:

[0041] Step 101 is used to construct a spatial load matrix corresponding to the meteorological observation station using historical precipitation observation values;

[0042] Step 102 is used to determine a forecast time series sequence of an extreme precipitation index based on historical precipitation forecast values ​​and real-time precipitation forecast values;

[0043] Step 103 is used to issue a heavy rainfall warning based on the spatial load matrix, the forecast time series and the real-time precipitation forecast value.

[0044] In the best embodiment of the present invention, the extreme precipitation index can characterize the precipitation intensity of the precipitation process in the period to which it belongs.

[0045] Specifically, step 101 includes:

[0046] Step 101-1: Construct a historical precipitation observation data matrix based on historical precipitation observations ;

[0047] Step 101-2, using the historical precipitation observation data matrix Determine the observational time series of extreme precipitation indices;

[0048] Step 101-3, determining the spatial load matrix corresponding to the meteorological observation station according to the observation time series of the extreme precipitation index;

[0049] Among them, the historical precipitation observation data matrix is a matrix with p rows and n columns, ,

[0050] For the region Meteorological observation stations during the warning period The previous The standardized moment average value of the precipitation observation value of the historical period is located in the historical precipitation observation data matrix No. Rank List, , , is the total number of meteorological observation stations in the region, The number of preset time periods.

[0051] Furthermore, the step 101-2 includes:

[0052] Step 101-2-1, decomposing the historical precipitation observation data matrix using the empirical orthogonal decomposition algorithm , obtain the historical precipitation observation data matrix The corresponding time function matrix ;

[0053] Step 101-2-2, solve the problem that satisfies the constraints hour The minimum value of the time function matrix Before interception Row element generator matrix ;

[0054] Step 101-2-3, according to the matrix Calculate the observational time series of extreme precipitation indices;

[0055] in, is the time function matrix Middle The cumulative variance contribution rate of the row elements is equal to the matrix The first non-zero eigenvalue in the descending sequence of eigenvalues, Set a threshold for the cumulative variance contribution rate, is greater than 1 and less than A positive integer, is the historical precipitation observation data matrix The transposed matrix of is the total number of meteorological observation stations in the region.

[0056] Furthermore, the step 101-2-1 includes:

[0057] The historical precipitation observation data matrix is ​​determined as follows: The corresponding time function matrix :

[0058]

[0059] Where, is the historical precipitation observation data matrix The corresponding spatial function matrix The transpose of

[0060] Among them, the historical precipitation observation data matrix The corresponding spatial function matrix for OK column matrix, , is a matrix The first non-zero eigenvalue in the descending sequence of The eigenvector corresponding to the eigenvalue elements, .

[0061] Furthermore, the step 101-2-3 includes:

[0062] The observation time series of extreme precipitation index is determined as follows: :

[0063]

[0064] Where, The area is in the warning period The previous The observed value of the extreme precipitation index for each period, , is the number of preset time periods;

[0065] Among them, the following formula is used to determine the area in the warning period The previous Observed values ​​of extreme precipitation index for each period :

[0066]

[0067] Where, is a matrix No. Rank Column element value.

[0068] Furthermore, the step 101-3 includes:

[0069] Step 101-3-1, in the historical precipitation observation data matrix Take the first c columns of elements and generate the matrix ;

[0070] Step 101-3-2, intercepting the first c elements in the extreme precipitation index observation time series sequence to generate an extreme precipitation index observation matrix Q with c rows and 1 column;

[0071] Step 101-3-3, fitting the matrix using a linear regression algorithm and matrix Q, generated after fitting Matrix with 1 row and 1 column ;

[0072] Among them, the matrix Middle The row element is the The space load of each meteorological observation station, The total number of time periods included in the precipitation accumulation time window set for the warning.

[0073] In the preferred embodiment of the present invention, the historical precipitation observation data matrix is ​​used. The spatial load matrix is ​​reconstructed using the first c elements of the observation time series of the extreme precipitation index, which improves the calculation accuracy of the spatial load of the meteorological observation station.

[0074] Specifically, step 102 includes:

[0075] Step 102-1: Construct a historical and real-time precipitation forecast data matrix based on historical precipitation forecast values ​​and real-time precipitation forecast values. ;

[0076] Step 102-2: Using the historical and real-time precipitation forecast matrix Determine the forecast time series of extreme precipitation indices;

[0077] Among them, the historical and real-time precipitation forecast data matrix is a matrix with p rows and n+1 columns, , For the region Meteorological observation stations during the warning period The previous The standardized mean value of the precipitation forecast value for each period is located in the precipitation forecast data matrix that combines historical and real-time data. No. Rank List, , , is the total number of meteorological observation stations in the region, For the preset number of time periods, when the historical and real-time combined precipitation forecast data matrix The element subscript in When the value is 0, the element corresponds to the real-time precipitation forecast value. When the historical and real-time combined precipitation forecast data matrix The element subscript in The value is , then the element corresponds to the historical precipitation forecast value.

[0078] Furthermore, the step 102-2 includes:

[0079] Step 102-2-1, using the empirical orthogonal decomposition algorithm to decompose the historical and real-time combined precipitation forecast data matrix , obtain the historical and real-time combined precipitation forecast data matrix The corresponding time function matrix ;

[0080] Step 102-2-2, solve the problem that satisfies the constraints hour The minimum value of the time function matrix Before interception Row element generator matrix ;

[0081] Step 102-2-3, according to the matrix Calculate the forecast time series of extreme precipitation indices;

[0082] in, is the time function matrix Middle The cumulative variance contribution rate of the row elements is equal to the matrix The first non-zero eigenvalue in the descending sequence of eigenvalues, Set a threshold for the cumulative variance contribution rate, is greater than 1 and less than A positive integer, The precipitation forecast data matrix combining historical and real-time The transposed matrix of is the total number of meteorological observation stations in the region.

[0083] Furthermore, the step 102-2-1 includes:

[0084] The historical and real-time combined precipitation forecast data matrix is ​​determined as follows: The corresponding time function matrix :

[0085]

[0086] Where, The precipitation forecast data matrix combining historical and real-time The corresponding spatial function matrix The transpose of

[0087] Among them, the historical and real-time combined precipitation forecast data matrix The corresponding spatial function matrix for OK column matrix, , is a matrix The first non-zero eigenvalue in the descending sequence of The eigenvector corresponding to the eigenvalue elements, .

[0088] Furthermore, the step 102-2-3 includes:

[0089] The forecast time series of extreme precipitation index is determined as follows: :

[0090]

[0091] Where, The area is in the warning period The previous Forecast value of extreme precipitation index for each period After normalization, the value , is the number of preset time periods;

[0092] Among them, the following formula is used to determine the area in the warning period The previous Forecast value of extreme precipitation index for each period :

[0093]

[0094] Where, is a matrix No. Rank Column element value.

[0095] right The normalized value is recorded as :

[0096]

[0097] in, The area is in the warning period and before The average value of the forecast value of the extreme precipitation index for each period, The area is in the warning period and before The standard deviation of the forecast value of the extreme precipitation index for each period, .

[0098] Specifically, step 103 includes:

[0099] Step 103-1, determining the heavy rainfall range within the area during the warning period based on the spatial load matrix and the real-time forecast precipitation during the warning period;

[0100] Step 103-2, calculating the percentile corresponding to the normalized value corresponding to the forecast value of the extreme precipitation index in the warning period in the percentile curve drawn by each element in the forecast time series;

[0101] Step 103 - 3 , issuing a heavy rainfall warning based on the percentile and the heavy rainfall range in the area to be warned.

[0102] Furthermore, the step 103-1 includes:

[0103] Step 103-1-1, selecting a meteorological observation station from among all meteorological observation stations in the region whose spatial load is greater than the spatial load threshold and whose predicted precipitation in the warning period is greater than the precipitation threshold;

[0104] Step 103-1-2: The forecast range corresponding to the selected meteorological observation station is used as the heavy rainfall range within the area to be warned.

[0105] Furthermore, the step 103-3 includes:

[0106] If the percentile is in the interval , no warning will be given;

[0107] If the percentile is in the interval , a first-level warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced;

[0108] If the percentile is in the interval , a second-level warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced;

[0109] If the percentile is in the interval , a level 3 warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced;

[0110] in, is the first preset warning threshold, is the second preset warning threshold, It is the third preset warning threshold. The third-level warning priority is greater than the second-level warning priority, and the second-level warning priority is greater than the first-level warning priority.

[0111] In the preferred embodiment of the present invention, You can take 50%, You can take 75%, You can take 90%.

[0112] In the best embodiment of the present invention, taking the realization of heavy rainfall warning in a certain area as an example, the forecast rainfall data of the area on the warning day and the 1000 days before the warning day, as well as the measured rainfall data of the area on the warning day and the 1000 days before the warning day are obtained. The above data are processed according to the scheme provided by the present invention to obtain the normalized value of the forecast value of the extreme precipitation index of the area on the warning day and the 1000 days before the warning day. The normalized value of the forecast value of the extreme precipitation index of the area from 1 day before the warning day to 93 days before the warning day is as follows: Figure 2 As shown in the figure, the forecast time series corresponding to the normalized value of the forecast value of the extreme precipitation index on the warning day and the 1000 days before it is obtained, and the percentile curve drawn by each element in the forecast time series is calculated to be the normalized value corresponding to the forecast value of the extreme precipitation index on the warning day. The precipitation accumulation time window set for the percentile warning is set to 10 days. The spatial load of the meteorological observation station in a certain area is set to the rainstorm threshold of 50 mm as the standard, and the spatial load is set to 0.03 as the standard. The heavy rainfall range of a certain area on the warning day is selected. The result can effectively show the "waterlogged area" in the past 10 days and the main affected area of ​​future precipitation, and identify the area with the most significant superposition effect of the two.

[0113] Heavy rainfall warnings are issued based on the above percentiles and heavy rainfall areas.

[0114] The present invention provides a heavy rainfall early warning system in a power grid area, such as Figure 3 As shown, the system includes:

[0115] A construction module is used to construct the spatial loading matrix corresponding to the meteorological observation station using historical precipitation observations;

[0116] A determination module, used to determine a forecast time series of an extreme precipitation index based on historical precipitation forecast values ​​and real-time precipitation forecast values;

[0117] The early warning module is used to issue a heavy rainfall early warning based on the spatial load matrix, the forecast time series and the real-time precipitation forecast value.

[0118] Specifically, the building blocks include:

[0119] The first construction unit is used to construct the historical precipitation observation data matrix based on the historical precipitation observation values. ;

[0120] The first determination unit is used to use the historical precipitation observation data matrix Determine the observational time series of extreme precipitation indices;

[0121] The second determining unit is used to determine the spatial load matrix corresponding to the meteorological observation station according to the observation time series of the extreme precipitation index;

[0122] Among them, the historical precipitation observation data matrix is a matrix with p rows and n columns, , For the region Meteorological observation stations during the warning period The previous The standardized moment average value of the precipitation observation value of the historical period is located in the historical precipitation observation data matrix No. Rank List, , , is the total number of meteorological observation stations in the region, The number of preset time periods.

[0123] Specifically, the first determining unit includes:

[0124] The first decomposition submodule is used to decompose the historical precipitation observation data matrix using the empirical orthogonal decomposition algorithm. , obtain the historical precipitation observation data matrix The corresponding time function matrix ;

[0125] The first solving submodule is used to solve the constraints hour The minimum value of the time function matrix Before interception Row element generator matrix ;

[0126] The first determination submodule is used to determine the matrix Calculate the observational time series of extreme precipitation indices;

[0127] in, is the time function matrix Middle The cumulative variance contribution rate of the row elements is equal to the matrix The first non-zero eigenvalue in the descending sequence of eigenvalues, Set a threshold for the cumulative variance contribution rate, is greater than 1 and less than A positive integer, is the historical precipitation observation data matrix The transposed matrix of is the total number of meteorological observation stations in the region.

[0128] Specifically, the first decomposition submodule is used to:

[0129] The historical precipitation observation data matrix is ​​determined as follows: The corresponding time function matrix :

[0130]

[0131] Where, is the historical precipitation observation data matrix The corresponding spatial function matrix The transpose of

[0132] Among them, the historical precipitation observation data matrix The corresponding spatial function matrix for OK column matrix, , is a matrix The first non-zero eigenvalue in the descending sequence of The eigenvector corresponding to the eigenvalue elements, .

[0133] Specifically, the first determining submodule includes:

[0134] The observation time series of extreme precipitation index is determined as follows: :

[0135]

[0136] Where, The area is in the warning period The previous The observed value of the extreme precipitation index for each period, , is the number of preset time periods;

[0137] Among them, the following formula is used to determine the area in the warning period The previous Observed values ​​of extreme precipitation index for each period :

[0138]

[0139] Where, is a matrix No. Rank Column element value.

[0140] Specifically, the second determining unit includes:

[0141] In the historical precipitation observation data matrix Take the first c columns of elements and generate the matrix ;

[0142] The first c elements in the observation time series of the extreme precipitation index are intercepted to generate the extreme precipitation index observation matrix Q with c rows and 1 column;

[0143] Fit the matrix using a linear regression algorithm and matrix Q, generated after fitting Matrix with 1 row and 1 column ;

[0144] Among them, the matrix Middle The row element is the The space load of each meteorological observation station, The total number of time periods included in the precipitation accumulation time window set for the warning.

[0145] Specifically, the determination module includes:

[0146] The second construction unit is used to construct a historical and real-time precipitation forecast data matrix based on historical precipitation forecast values ​​and real-time precipitation forecast values. ;

[0147] The third determination unit is used to use the precipitation forecast data matrix combined with historical and real-time Determine the forecast time series of extreme precipitation indices;

[0148] Among them, the historical and real-time precipitation forecast data matrix is a matrix with p rows and n+1 columns, , For the region Meteorological observation stations during the warning period The previous The standardized mean value of the precipitation forecast value for each period is located in the precipitation forecast data matrix that combines historical and real-time data. No. Rank List, , , is the total number of meteorological observation stations in the region, For the preset number of time periods, when the historical and real-time combined precipitation forecast data matrix The element subscript in When the value is 0, the element corresponds to the real-time precipitation forecast value. When the historical and real-time combined precipitation forecast data matrix The element subscript in The value is , then the element corresponds to the historical precipitation forecast value.

[0149] Specifically, the third determining unit includes:

[0150] The second decomposition submodule is used to decompose the historical and real-time precipitation forecast data matrix using the empirical orthogonal decomposition algorithm , obtain the historical and real-time combined precipitation forecast data matrix The corresponding time function matrix ;

[0151] The second solving submodule is used to solve the constraints hour The minimum value of the time function matrix Before interception Row element generator matrix ;

[0152] The second calculation submodule is used to calculate the matrix Calculate the forecast time series of extreme precipitation indices;

[0153] in, is the time function matrix Middle The cumulative variance contribution rate of the row elements is equal to the matrix The first non-zero eigenvalue in the descending sequence of eigenvalues, Set a threshold for the cumulative variance contribution rate, is greater than 1 and less than A positive integer, The precipitation forecast data matrix combining historical and real-time The transposed matrix of is the total number of meteorological observation stations in the region.

[0154] Specifically, the second decomposition submodule is used to:

[0155] The historical and real-time combined precipitation forecast data matrix is ​​determined as follows: The corresponding time function matrix :

[0156]

[0157] Where, The precipitation forecast data matrix combining historical and real-time The corresponding spatial function matrix The transpose of

[0158] Among them, the historical and real-time combined precipitation forecast data matrix The corresponding spatial function matrix for OK column matrix, , is a matrix The first non-zero eigenvalue in the descending sequence of The eigenvector corresponding to the eigenvalue elements, .

[0159] Specifically, the second calculation submodule is used to:

[0160] The forecast time series of extreme precipitation index is determined as follows: :

[0161]

[0162] Where, The area is in the warning period The previous Forecast value of extreme precipitation index for each period After normalization, the value , is the number of preset time periods;

[0163] Among them, the following formula is used to determine the area in the warning period The previous Forecast value of extreme precipitation index for each period :

[0164]

[0165] Where, is a matrix No. Rank Column element value.

[0166] Specifically, the early warning module includes:

[0167] A fourth determining unit is configured to determine a heavy rainfall range within the area during the warning period based on the spatial load matrix and the real-time forecast precipitation during the warning period;

[0168] a calculation unit, configured to calculate the percentile corresponding to the normalized value corresponding to the forecast value of the extreme precipitation index in the warning period in the percentile curve drawn by each element in the forecast time series;

[0169] The early warning unit is used to issue a heavy rainfall early warning based on the percentile and the range of heavy rainfall in the area to be warned.

[0170] Specifically, the fourth determining unit includes:

[0171] A selection submodule is used to select a meteorological observation station whose spatial load is greater than a spatial load threshold and whose predicted precipitation in the warning period is greater than a precipitation threshold from all meteorological observation stations in the region;

[0172] The selection submodule is used to use the forecast range corresponding to the selected meteorological observation station as the heavy rainfall range in the area to be warned during the period.

[0173] Specifically, the early warning unit is used to:

[0174] If the percentile is in the interval , no warning will be given;

[0175] If the percentile is in the interval , a first-level warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced;

[0176] If the percentile is in the interval , a second-level warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced;

[0177] If the percentile is in the interval , a level 3 warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced;

[0178] in, is the first preset warning threshold, is the second preset warning threshold, The third preset warning threshold value is a level 3 warning priority that is greater than a level 2 warning priority, and a level 2 warning priority that is greater than a level 1 warning priority. Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0180] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for early warning of heavy rainfall in a power grid area, characterized in that: The method comprises: Using historical precipitation observations, the spatial loading matrix corresponding to the meteorological observation station is constructed; Based on the historical precipitation forecast values ​​and the real-time precipitation forecast values, the forecast time series of the extreme precipitation index is determined; issuing a heavy rainfall warning based on the spatial load matrix, the forecast time series and the real-time precipitation forecast value; The use of historical precipitation observations to construct a spatial load matrix corresponding to a meteorological observation station includes: Construct historical precipitation observation data matrix based on historical precipitation observation values ; Using historical precipitation observation data matrix Determine the observational time series of extreme precipitation indices; Determine the spatial load matrix corresponding to the meteorological observation station based on the observation time series of the extreme precipitation index; Among them, the historical precipitation observation data matrix is a matrix with p rows and n columns, , For the region Meteorological observation stations during the warning period The previous The standardized moment average value of the precipitation observation value of the historical period is located in the historical precipitation observation data matrix No. Rank List, , , is the total number of meteorological observation stations in the region, is the number of preset time periods; The use of historical precipitation observation data matrix The observational time series for determining the extreme precipitation index includes: The empirical orthogonal decomposition algorithm is used to decompose the historical precipitation observation data matrix , obtain the historical precipitation observation data matrix The corresponding time function matrix ; Solve the constraints hour The minimum value of the time function matrix Before interception Row element generator matrix ; According to the matrix Calculate the observational time series of extreme precipitation indices; in, is the time function matrix Middle The cumulative variance contribution rate of the row elements is equal to the matrix The first non-zero eigenvalue in the descending sequence of eigenvalues, Set a threshold for the cumulative variance contribution rate, is greater than 1 and less than A positive integer, is the historical precipitation observation data matrix The transposed matrix of is the total number of meteorological observation stations in the region; The method of decomposing the historical precipitation observation data matrix by using the empirical orthogonal decomposition algorithm , obtain the historical precipitation observation data matrix The corresponding time function matrix ,include: The historical precipitation observation data matrix is ​​determined as follows: The corresponding time function matrix : Where, is the historical precipitation observation data matrix The corresponding spatial function matrix The transpose of Among them, the historical precipitation observation data matrix The corresponding spatial function matrix for OK column matrix, , is a matrix The first non-zero eigenvalue in the descending sequence of The eigenvector corresponding to the eigenvalue elements, ; The matrix Calculate the observation time series of extreme precipitation index, including: The observation time series of extreme precipitation index is determined as follows: : Where, The area is in the warning period The previous The observed value of the extreme precipitation index for each period, , is the number of preset time periods; Among them, the following formula is used to determine the area in the warning period The previous Observed values ​​of extreme precipitation index for each period : Where, is a matrix No. Rank Column element value; The step of determining the spatial load matrix corresponding to the meteorological observation station according to the observation time series of the extreme precipitation index includes: In the historical precipitation observation data matrix Take the first c columns of elements and generate the matrix ; The first c elements in the observation time series of the extreme precipitation index are intercepted to generate the extreme precipitation index observation matrix Q with c rows and 1 column; Fit the matrix using a linear regression algorithm and matrix Q, generated after fitting Matrix with 1 row and 1 column ; Among them, the matrix Middle The row element is the The space load of each meteorological observation station, The total number of time periods included in the precipitation accumulation time window set for the warning; The heavy rainfall warning is performed according to the spatial load matrix, the forecast time series and the real-time precipitation forecast value, including: Determining the range of heavy rainfall in the area during the warning period based on the spatial load matrix and the real-time forecast precipitation during the warning period; Calculate the percentile corresponding to the normalized value of the forecast value of the extreme precipitation index during the warning period in the percentile curve drawn by each element in the forecast time series; A heavy rainfall warning is issued based on the percentile and the range of heavy rainfall in the area during the warning period.

2. The method according to claim 1, wherein Determining the forecast time series of the extreme precipitation index based on the historical precipitation forecast value and the real-time precipitation forecast value includes: Based on the historical precipitation forecast values ​​and real-time precipitation forecast values, a precipitation forecast data matrix combining history and real-time is constructed. ; Using historical and real-time precipitation forecast data matrix Determine the forecast time series of extreme precipitation indices; Among them, the historical and real-time precipitation forecast data matrix is a matrix with p rows and n+1 columns, , For the region Meteorological observation stations during the warning period The previous x The standardized mean value of the precipitation forecast value for each period is located in the precipitation forecast data matrix that combines historical and real-time data. No. Rank List, , , is the total number of meteorological observation stations in the region, For the preset number of time periods, when the historical and real-time combined precipitation forecast data matrix The element subscript in When the value is 0, the element corresponds to the real-time precipitation forecast value. When the historical and real-time combined precipitation forecast data matrix The element subscript in The value is , then the element corresponds to the historical precipitation forecast value.

3. The method according to claim 2, wherein The precipitation forecast data matrix using historical and real-time combination Determine the forecast time series of extreme precipitation indices, including: The empirical orthogonal decomposition algorithm is used to decompose the historical and real-time precipitation forecast data matrix. , obtain the historical and real-time combined precipitation forecast data matrix The corresponding time function matrix ; Solve the constraints hour The minimum value of the time function matrix Before interception Row element generator matrix ; According to the matrix Calculate the forecast time series of extreme precipitation indices; in, is the time function matrix Middle The cumulative variance contribution rate of the row elements is equal to the matrix The first non-zero eigenvalue in the descending sequence of eigenvalues, Set a threshold for the cumulative variance contribution rate, is greater than 1 and less than A positive integer, The precipitation forecast data matrix combining historical and real-time The transposed matrix of is the total number of meteorological observation stations in the region.

4. The method according to claim 3, wherein The empirical orthogonal decomposition algorithm is used to decompose the historical and real-time combined precipitation forecast data matrix. , obtain the historical and real-time combined precipitation forecast data matrix The corresponding time function matrix ,include: The historical and real-time combined precipitation forecast data matrix is ​​determined as follows: The corresponding time function matrix : Where, The precipitation forecast data matrix combining historical and real-time The corresponding spatial function matrix The transpose of Among them, the historical and real-time combined precipitation forecast data matrix The corresponding spatial function matrix for OK column matrix, , is a matrix The first non-zero eigenvalue in the descending sequence of The eigenvector corresponding to the eigenvalue elements, .

5. The method according to claim 3, wherein The matrix Calculate the forecast time series of extreme precipitation indices, including: The forecast time series of extreme precipitation index is determined as follows: : Where, The area is in the warning period The previous Forecast value of extreme precipitation index for each period After normalization, the value , is the number of preset time periods; Among them, the following formula is used to determine the area in the warning period The previous Forecast value of extreme precipitation index for each period : Where, is a matrix No. Rank Column element value.

6. The method according to claim 1, wherein The determining of the heavy rainfall range within the area during the warning period based on the spatial load matrix and the real-time forecast precipitation during the warning period includes: Select a meteorological observation station from among all meteorological observation stations in the region whose spatial load is greater than the spatial load threshold and whose predicted precipitation in the warning period is greater than the precipitation threshold; The forecast range corresponding to the selected meteorological observation station is used as the heavy rainfall range in the area to be warned during the period.

7. The method according to claim 1, wherein The issuing of a heavy rainfall warning based on the percentile and the heavy rainfall range within the area to be warned includes: If the percentile is in the interval , no warning will be given; If the percentile is in the interval , a first-level warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced; If the percentile is in the interval , a second-level warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced; If the percentile is in the interval , a level 3 warning will be issued to the regional power grid disaster monitoring department, and the scope of heavy rainfall in the area during the warning period will be announced; in, is the first preset warning threshold, is the second preset warning threshold, It is the third preset warning threshold. The third-level warning priority is greater than the second-level warning priority, and the second-level warning priority is greater than the first-level warning priority.

8. A system for implementing the method for early warning of heavy rainfall in a power grid area according to claim 1, characterized in that: The system comprises: A construction module is used to construct the spatial loading matrix corresponding to the meteorological observation station using historical precipitation observations; A determination module, used to determine a forecast time series of an extreme precipitation index based on historical precipitation forecast values ​​and real-time precipitation forecast values; The early warning module is used to issue a heavy rainfall early warning based on the spatial load matrix, the forecast time series and the real-time precipitation forecast value.

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