Method, device and equipment for acquiring grid meteorological prediction data and medium

Abstract

The invention provides a method, a device, equipment and a medium for acquiring grid meteorological prediction data, and the method comprises the steps: acquiring grid real meteorological data of a specified meteorological grid at a past time point, and a plurality of past grid meteorological prediction data provided by different meteorological data sources, the grid real meteorological data comprises real values of a plurality of meteorological elements, and the past grid meteorological prediction data comprises predicted values of a plurality of meteorological elements; calculating the deviation degree between the true values of the meteorological elements and the predicted values; determining a final data source of each meteorological element in the specified meteorological grid according to the deviation degree of each meteorological element; and generating grid meteorological prediction data of the specified meteorological grid in the prediction time period based on the final data source of each meteorological element in the specified meteorological grid.

Description

Technical Field

[0001] This application relates to the field of weather forecasting technology, and in particular to a method, apparatus, device, and medium for acquiring gridded meteorological forecast data. Background Technology

[0002] Currently, in the field of weather forecasting, with continuous technological advancements, gridded weather forecast data from various sources are available. These data sources are diverse, encompassing weather forecasts generated by different meteorological institutions, research teams, and commercial companies using their respective technological methods. Each data source has its unique generation logic and characteristics, exhibiting different advantages and disadvantages in terms of forecast accuracy for meteorological elements.

[0003] For example, some meteorological models from certain data sources take into account the impact of small and medium-sized reservoirs and lakes on air humidity, which can reflect microclimate changes in detail and more accurately reflect meteorological changes in the region. However, they may have limitations in analyzing mountainous terrain.

[0004] Because these single data sources cannot comprehensively and accurately cover the meteorological characteristics of all regions, relying solely on one data source for weather forecasting often fails to meet diverse needs in practical applications. Especially in fields requiring high meteorological accuracy, such as aerospace, agriculture, and energy dispatch, the limitations of a single data source can lead to decision-making errors, causing severe economic losses and even endangering lives. Therefore, how to effectively integrate multi-source gridded weather forecasting data, fully leverage the advantages of each data source, and achieve optimal selection for weather forecasting to improve overall forecast accuracy and surpass the accuracy of using single-source data has become a critical issue urgently needing to be addressed in the field of weather forecasting. Summary of the Invention

[0005] To address the aforementioned problems, this application provides a method, apparatus, device, and medium for acquiring gridded meteorological forecast data.

[0006] Firstly, a method for obtaining grid-based meteorological forecast data is provided. This method includes: obtaining real meteorological data of a specified meteorological grid at a past time point, and multiple past grid-based meteorological forecast data provided by different meteorological data sources. The real meteorological data includes real values ​​of multiple meteorological elements, and the past grid-based meteorological forecast data includes predicted values ​​of multiple meteorological elements. The method also includes: calculating the deviation between the real values ​​of multiple meteorological elements and the multiple predicted values; determining the final data source for each meteorological element in the specified meteorological grid based on the deviation of each meteorological element; and generating grid-based meteorological forecast data for the specified meteorological grid during the forecast period based on the final data source for each meteorological element in the specified meteorological grid.

[0007] Secondly, an apparatus for acquiring gridded meteorological forecast data is provided. The apparatus includes: a data acquisition module configured to acquire real meteorological data of a specified meteorological grid at past time points, and multiple past gridded meteorological forecast data provided by different meteorological data sources, wherein the real meteorological data includes real values ​​of multiple meteorological elements, and the past gridded meteorological forecast data includes predicted values ​​of multiple meteorological elements; a deviation calculation module configured to calculate the deviation between the real values ​​of multiple meteorological elements and the multiple predicted values; a data source determination module configured to determine the final data source of each meteorological element in the specified meteorological grid based on the deviation of each meteorological element; and a data generation module configured to generate gridded meteorological forecast data for the specified meteorological grid during the forecast period based on the final data source of each meteorological element in the specified meteorological grid.

[0008] Thirdly, an electronic device is provided, including a processor, a memory, and a program stored in the memory and capable of running on the processor, wherein when the program is executed by the processor, it implements the steps of any of the methods for acquiring gridded weather forecast data provided in the embodiments of this application.

[0009] A fourth aspect provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of any of the methods for acquiring gridded meteorological forecast data provided in the embodiments of this application.

[0010] In summary, the method, apparatus, equipment, and medium for acquiring gridded meteorological forecast data provided in this application have the following beneficial effects: By acquiring real meteorological data of a specified meteorological grid at past time points and multiple past gridded meteorological forecast data from different meteorological data sources, and calculating the deviation between the real values ​​and multiple predicted values ​​of various meteorological elements, the final data source for each meteorological element in the specified meteorological grid is determined based on the deviation of each meteorological element. This allows for the precise selection of the most accurate meteorological data source for each meteorological element from numerous sources, effectively avoiding the limitations that may exist with a single data source. This significantly improves the accuracy of gridded weather forecast data across various meteorological element dimensions, enabling the forecast results to more realistically and stably reflect actual weather conditions. It also greatly reduces the risk of forecast errors due to data bias or mistakes, providing solid and reliable data support for meteorological forecasting.

[0011] In addition, this method has wide applicability, and can accurately select the most suitable meteorological data source for grid weather forecasting in different regions based on local meteorological characteristics and data conditions, generate high-quality forecast data, and meet diverse weather forecasting needs. Attached Figure Description

[0012] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This diagram illustrates a flowchart of a method for acquiring gridded weather forecast data according to an embodiment of this application. Figure 2 This diagram illustrates a device for acquiring gridded weather forecast data according to an embodiment of this application. Figure 3 This diagram illustrates the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0014] To make the above and other features and advantages of this application clearer, the application is further described below with reference to the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explanation to those skilled in the art, and are exemplary only, not restrictive.

[0015] In the following description, numerous specific details are set forth to provide a thorough understanding of this application. However, it will be apparent to those skilled in the art that the specific details are not required to practice this application. In other instances, well-known steps or operations have not been described in detail to avoid obscuring this application.

[0016] One embodiment of this application provides a method for obtaining grid-based meteorological forecast data. Figure 1 This illustration shows a flowchart of a method for acquiring gridded weather forecast data according to an embodiment of this application. Figure 1 As shown, the method for obtaining gridded weather forecast data includes the following steps.

[0017] S11: Obtain the actual meteorological data of the specified meteorological grid at past time points, as well as the meteorological forecast data of multiple past grids provided by different meteorological data sources.

[0018] In one embodiment of this application, the designated meteorological grid can be one or more meteorological grids in a grid meteorological system. Furthermore, the designated meteorological grid can refer to all meteorological grids covering a designated area, which can be a river region, road area, mountainous area, etc.

[0019] In one embodiment of this application, the past time point refers to a point in time within a specific past time period (such as the past month or the past day). Grid-based real meteorological data refers to the real meteorological data of a specified meteorological grid at a past time point. Grid-based real meteorological data includes the real values ​​of various meteorological elements. In one embodiment of this application, the grid-based real meteorological data can be provided by a national-level meteorological platform or a high-precision ground observation station.

[0020] One embodiment of this application relates to past grid weather forecast data, which refers to the predicted weather data of a weather grid at a past point in time. A set of past grid weather forecast data is provided by a single weather data source. Different weather data sources may include different commercial weather companies and weather research institutions. Among these, commercial weather companies may include, but are not limited to, China's MoWeather and XinZhiWeather. Past grid weather forecast data includes predicted values ​​for various meteorological elements.

[0021] It should be noted that the source of the real meteorological data for the grid is different from the source of the previously preset meteorological data for the grid.

[0022] In one embodiment of this application, various meteorological elements may include, but are not limited to, temperature, precipitation, relative humidity, wind speed, air pressure, visibility, cloud cover, and total solar irradiance.

[0023] S12 calculates the deviation between the actual values ​​of various meteorological elements and multiple predicted values.

[0024] One embodiment of this application relates to a deviation measure used to quantify the difference between the true value of each meteorological element and the predicted value provided by different meteorological data sources.

[0025] In one embodiment of this application, the measurement index of deviation may include, but is not limited to, commonly used error calculation indexes such as absolute error, root mean square error, and correlation coefficient.

[0026] In one embodiment of this application, the deviation can include a single deviation and a fused deviation. The single deviation measures the difference between the actual value of a meteorological element and a single predicted value. The fused deviation measures the difference between the actual value of a meteorological element and a weighted fused value of at least two predicted values.

[0027] S13, determine the final data source for each meteorological element in the specified meteorological grid based on the deviation of each meteorological element.

[0028] The ultimate source of data involved in one embodiment of this application may refer to at least one meteorological data source among different meteorological data sources.

[0029] In one embodiment of this application, the magnitudes of multiple deviations under the same meteorological element are compared, and at least one meteorological data source is assigned as the final data source for this meteorological element based on the comparison results.

[0030] S14, based on the final data source of each meteorological element in the specified meteorological grid, generates grid meteorological forecast data for the specified meteorological grid during the forecast period.

[0031] In one embodiment of this application, the predicted values ​​of the meteorological elements corresponding to the specified meteorological grid in the prediction time period are obtained from the final source of the data for each meteorological element, thereby obtaining the grid meteorological prediction data of the specified meteorological grid in the prediction time period.

[0032] For example, the final data source for humidity is weather company A, and the final data source for precipitation is weather company B. The grid weather forecast data for the specified weather grid during the forecast period includes the humidity data for the specified weather grid provided by weather company A and the precipitation data for the specified weather grid during the forecast period provided by weather company B.

[0033] In one embodiment of this application, S12, calculating the deviation between the actual values ​​of multiple meteorological elements and multiple predicted values ​​may include: for each meteorological element, calculating a single deviation between the actual value and each predicted value.

[0034] In one embodiment of this application, for each meteorological element, a single deviation is calculated between the actual value of the meteorological element and the predicted value of the meteorological element provided by each meteorological data source.

[0035] For example, for a given grid at a past time point, weather company A provides a forecast value of X1, weather company B provides a forecast value of X2, and the actual value is X. For weather element F, calculate the single deviation between X and X1, and calculate the single deviation between X and X2.

[0036] One embodiment of this application relates to a single deviation degree, which may include a single-index deviation degree or a multi-index deviation degree. A single-index deviation degree refers to the deviation degree calculated based on a single index, such as the deviation degree calculated based on absolute error.

[0037] The multi-indicator deviation is the weighted value of multiple indicators, that is, the sum of the products of each indicator and its corresponding weight.

[0038] For example, the deviation of multiple indicators is the sum of the product of absolute error and corresponding weight, plus the product of root mean square error and corresponding weight, and the product of correlation coefficient and corresponding weight.

[0039] It should be noted that the indicators involved in the embodiments of this application refer to the aforementioned measurement indicators.

[0040] In one embodiment of this application, when the single deviation is an absolute error value, for each meteorological element, the absolute error value between the actual value and the predicted value provided by each meteorological data source is calculated.

[0041] In another embodiment of this application, when the single deviation is the root mean square error value, the root mean square error value between the actual value and the predicted value provided by each meteorological data source is calculated for each meteorological element.

[0042] In another embodiment of this application, when the single deviation is the correlation coefficient value, the correlation coefficient value between the actual value and the predicted value provided by each meteorological data source is calculated for each meteorological element.

[0043] In some of the above embodiments, the single deviation between the true value and multiple predicted values ​​can be quickly calculated using the single index deviation, thereby improving the efficiency of deviation calculation.

[0044] In another embodiment of this application, when the single deviation is a first weighted value of the absolute error value and the root mean square error value, a first weighted value between the actual value and the predicted value provided by each meteorological data source is calculated for each meteorological element.

[0045] In another embodiment of this application, when the single deviation is a second weighted value of the absolute error value and the correlation coefficient value, a second weighted value between the actual value and the predicted value provided by each meteorological data source is calculated for each meteorological element.

[0046] In another embodiment of this application, when the single deviation is the third weighted value of the root mean square error value and the correlation coefficient value, a third weighted value is calculated between the actual value and the predicted value provided by each meteorological data source for each meteorological element.

[0047] In another embodiment of this application, when the single deviation is the fourth weighted value of the absolute error value, the root mean square error value and the correlation coefficient value, a fourth weighted value is calculated between the actual value and the predicted value provided by each meteorological data source for each meteorological element.

[0048] In some of the above embodiments, when a single deviation is a weighted value of multiple indicators, the deviation between the true value and multiple predicted values ​​can be measured more accurately.

[0049] In some embodiments of this application, S13, determining the final data source of each meteorological element in a specified meteorological grid based on the deviation degree of each meteorological element includes: for each meteorological element, filtering the minimum single deviation degree from multiple single deviation degrees; when the number of minimum single deviation degrees is 1, determining the meteorological data source corresponding to the minimum single deviation degree as the final data source of the meteorological element.

[0050] Specifically, under the same meteorological element, the magnitude of the single deviation corresponding to each meteorological data source is compared, the minimum single deviation is selected, the number of minimum single deviations is determined, and when the number of minimum single deviations is 1, the meteorological data source corresponding to the minimum single deviation is directly determined as the final data source of the meteorological element.

[0051] It should be noted that when comparing individual deviations, it is necessary to compare individual deviations of the same type, such as comparing the absolute error values ​​corresponding to different meteorological data sources.

[0052] In some of the above embodiments, by calculating a single deviation, the prediction accuracy of each meteorological data source for the same meteorological element can be intuitively quantified, providing an objective basis for subsequent data source selection. Furthermore, the calculation of a single deviation can reveal the advantages of each company on specific elements, avoiding a "one-size-fits-all" approach to data selection and improving the targeting of element-level predictions.

[0053] Furthermore, by minimizing the deviation, the system can automatically filter the most accurate data source from multiple sources without manual intervention, thus improving data processing efficiency.

[0054] In one embodiment of this application, since multiple meteorological data sources have the same single deviation, directly selecting the final data source according to the minimum principle would lead to an unstable final data source. Therefore, after selecting the smallest single deviation from multiple single deviations, the method further includes: When the number of minimum single deviations is greater than 1, obtain the grid real meteorological data of the specified meteorological grid at another time point before the past time point, as well as the meteorological forecast data of multiple past grids provided by different meteorological data sources, repeat step S12, and determine whether the number of minimum single deviations is 1, etc.

[0055] In the above embodiments, the predictive capabilities of meteorological data sources are further differentiated by introducing historical meteorological data from another point in the past, reducing subjective intervention and thus solving the problem of unstable data sources through a secondary screening mechanism.

[0056] In some embodiments of this application, S12, calculating the deviation between the actual values ​​of multiple meteorological elements and multiple predicted values, further includes: for each meteorological element, fusing multiple predicted values ​​to obtain at least one fused predicted value; and calculating the fusion deviation between the actual value and each fused predicted value.

[0057] One embodiment of this application involves fusing multiple predicted values ​​by weighting and fusing every two or more predicted values ​​of the same meteorological element.

[0058] For example, for meteorological element F, meteorological company A's predicted value is X3 and the corresponding fusion weight is W1, meteorological company B's predicted value is X4 and the corresponding fusion weight is W2, and meteorological company C's predicted value is X5 and the corresponding fusion weight is W3. The fusion predicted value of meteorological element F is X3W1+X4W2, X3W1+X5W3, X4W2+X5W3, and X3W1+X4W2+X5W3.

[0059] It should be noted that the fusion weights corresponding to the predicted values ​​can be assigned based on the most recent historical single deviation. For example, if the historical single deviation of weather company A is less than that of weather company B, then the fusion weight of weather company A is greater than that of weather company B. In this way, by dynamically assigning weights based on historical deviations, the interference of low-quality predicted values ​​is reduced, thereby improving the fusion quality.

[0060] Furthermore, similar to single-indicator bias, fusion bias can also include single-index bias or multi-index bias. The calculation method for fusion bias is similar to that for single-indicator bias, and will not be repeated here.

[0061] In some of the above embodiments, different meteorological data sources may use different prediction models. By weighting and fusing multiple prediction values, the advantages of multiple models can be integrated, thereby achieving the goal of integrating multi-source information and optimizing prediction quality.

[0062] In some embodiments of this application, when the deviation includes single deviation and fused deviation, S13, based on the deviation of each meteorological element, the final data source of each meteorological element in the specified meteorological grid is determined, including: for each meteorological element, filtering the minimum deviation from multiple single deviations and multiple fused deviations; when the number of minimum deviations is 1, determining at least one meteorological data source corresponding to the minimum deviation as the final data source of the meteorological element.

[0063] Specifically, under the same meteorological element, the magnitudes of the individual deviation and fusion deviation corresponding to each meteorological data source are compared, the smallest deviation among all deviations is selected, the number of the smallest deviations is determined, and when the number of the smallest deviations is 1, at least one meteorological data source corresponding to the smallest deviation is directly determined as the final data source of the meteorological element.

[0064] Furthermore, when the minimum deviation is the fusion deviation, the final data source for this meteorological element is all meteorological data sources corresponding to the fusion deviation. Moreover, the predicted value of the meteorological element for the specified meteorological grid during the prediction period can be obtained according to the calculation formula of the fusion prediction value.

[0065] For example, for meteorological element F, if the fusion deviation between meteorological company A and meteorological company B is the smallest, then meteorological company A and meteorological company B are determined as the final data sources for meteorological element F, and the value of meteorological element F for the forecast period is the predicted value of meteorological element A * fusion weight W1 + the predicted value of meteorological element B * fusion weight W2.

[0066] In one embodiment of this application, after selecting the minimum deviation from multiple single deviations and multiple fused deviations for each meteorological element, the method further includes: if the number of minimum deviations is greater than 1, obtaining the grid real meteorological data of a specified meteorological grid at another time point before the past time point, and multiple past grid meteorological forecast data provided by different meteorological data sources, and repeatedly performing steps such as calculating the deviation between the real values ​​of multiple meteorological elements and multiple forecast values.

[0067] Specifically, if the number of minimum deviations is greater than 1, acquire grid-based real meteorological data and multiple grid-based meteorological forecast data for another time point Tt, t before time point T. Calculate the deviation between the actual values ​​and multiple forecast values ​​of various meteorological elements corresponding to time point Tt. Filter the minimum deviation for each weather element and determine if the number of minimum deviations is 1. If it is still not 1, acquire grid-based real meteorological data and multiple grid-based meteorological forecast data for time point T-2t again, and repeat the above process until the number of minimum deviations is 1.

[0068] It should be noted that a minimum deviation greater than 1 means there are at least two minimum deviations. For example, a minimum deviation of 0.4 means there are at least two meteorological data sources with a single deviation of 0.4.

[0069] In some embodiments of this application, before calculating the deviation between the actual values ​​of multiple meteorological elements and multiple predicted values ​​in S12, the method further includes: determining whether multiple past grid predicted meteorological data are complete; if it is determined that multiple past grid predicted meteorological data are incomplete, obtaining the grid actual meteorological data of a specified meteorological grid at another time point before the past time point, and multiple past grid meteorological predicted data provided by different meteorological data sources, until it is determined that multiple grid predicted meteorological data are complete.

[0070] In one embodiment of this application, the incompleteness of multiple past grid-based meteorological forecast data refers to the fact that at least one grid-based meteorological data is missing at least one meteorological element data.

[0071] In one embodiment of this application, it is detected whether the meteorological elements contained in each past grid meteorological forecast data are consistent with the meteorological elements contained in the grid actual meteorological data. When at least one past grid meteorological forecast data is inconsistent with the meteorological elements contained in the grid actual meteorological data, it is determined that multiple past grid meteorological forecast data are incomplete. Then, the grid actual meteorological data and past grid meteorological forecast data at another time point Tt, which is a preset time t before the past time point T, are obtained. The integrity verification step is repeated until multiple past grid meteorological forecast data are complete.

[0072] In this way, by verifying the integrity of grid meteorological forecast data, errors in deviation calculation due to missing grid meteorological forecast data can be avoided, and the missing data can be filled by backtracking historical data to ensure the integrity of subsequent deviation analysis.

[0073] Another aspect of this application provides an apparatus for acquiring gridded meteorological forecast data. Figure 2 This illustration shows a schematic diagram of an apparatus for acquiring gridded weather forecast data according to an embodiment of this application, as shown below. Figure 2 As shown, the device 20 includes the following modules.

[0074] The data acquisition module 21 is configured to acquire real meteorological data of a specified meteorological grid at a past time point, as well as multiple past grid meteorological forecast data provided by different meteorological data sources. The real meteorological data of the grid includes the real values ​​of multiple meteorological elements, and the past grid meteorological forecast data includes the predicted values ​​of multiple meteorological elements.

[0075] The deviation calculation module 22 is configured to calculate the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements.

[0076] The data source determination module 23 is configured to determine the final data source of each meteorological element in the specified meteorological grid based on the deviation of each meteorological element.

[0077] The data generation module 24 is configured to generate grid meteorological forecast data for the specified meteorological grid during the forecast period based on the final data source of each meteorological element in the specified meteorological grid.

[0078] In some of the above embodiments, by acquiring real meteorological data of a specified meteorological grid at past time points and multiple past grid meteorological forecast data provided by different meteorological data sources, and calculating the deviation between the real values ​​and multiple forecast values ​​of various meteorological elements, the final data source for each meteorological element in the specified meteorological grid is determined based on the deviation of each meteorological element. In this way, for each meteorological element, the most accurate meteorological data source for prediction can be accurately selected from numerous meteorological data sources, effectively avoiding the limitations that may exist in a single data source. This significantly improves the accuracy of grid weather forecast data in various meteorological element dimensions, enabling the forecast results to more realistically and stably reflect the actual weather conditions, greatly reducing the risk of forecast errors caused by data deviations or mistakes, and providing solid and reliable data support for meteorological forecasting.

[0079] In addition, the device has wide applicability, and can accurately select the most suitable meteorological data source for grid weather forecasting in different regions based on local meteorological characteristics and data conditions, generating high-quality forecast data to meet diverse weather forecasting needs.

[0080] In one embodiment of this application, the deviation calculation module 22 is configured to calculate a single deviation between the actual value and each predicted value for each meteorological element.

[0081] In one embodiment of this application, the deviation calculation module 22 is configured to fuse the multiple predicted values ​​for each meteorological element to obtain at least one fused predicted value; and to calculate the fusion deviation between the actual value and each of the fused predicted values.

[0082] In one embodiment of this application, the data source determination module 23 is configured to filter the minimum single deviation from a plurality of single deviations for each meteorological element; when the number of the minimum single deviations is 1, the meteorological data source corresponding to the minimum single deviation is determined as the final data source of the meteorological element.

[0083] In one embodiment of this application, the data source determination module 23 is configured to, for each meteorological element, filter the minimum deviation from a plurality of single deviations and a plurality of fused deviations; when the number of minimum deviations is 1, determine at least one meteorological data source corresponding to the minimum deviation as the final data source of the meteorological element.

[0084] In one embodiment of this application, the device 20 further includes: a data acquisition module 21, configured to acquire, when the number of minimum deviations is greater than 1, the grid real meteorological data of the designated meteorological grid at another time point before the past time point, and multiple past grid meteorological forecast data provided by different meteorological data sources.

[0085] The repeat execution module is used to repeatedly execute steps such as calculating the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements.

[0086] In one embodiment of this application, the device 20 further includes: an integrity judgment module configured to determine whether the plurality of past grid predicted meteorological data are complete; and a data acquisition module 21 configured to, when it is determined that the plurality of past grid predicted meteorological data are incomplete, acquire the grid real meteorological data of the specified meteorological grid at another time point before the past time point, as well as the plurality of past grid meteorological predicted data provided by different meteorological data sources, until it is determined that the plurality of past grid predicted meteorological data are complete.

[0087] It should be understood that the specific features, operations, and details described above with respect to the methods of this application can also be similarly applied to the apparatus and system of this application, or vice versa. Furthermore, each step of the methods of this application described above can be performed by a corresponding component or unit of the apparatus or system of this application.

[0088] It should be understood that the various modules / units of the device of this application can be implemented wholly or partially through software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of the electronic device in hardware or firmware form or independent of the processor, or it can be stored in the memory of the electronic device in software form for the processor to call to execute the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0089] In another aspect, this application provides an electronic device. Figure 3 This diagram illustrates the structure of an electronic device according to an embodiment of this application, such as... Figure 3 As shown, the electronic device 30 includes a processor 31, a memory 32, and a program stored in the memory and capable of running on the processor. When the program is executed by the processor, it implements the steps of the method for obtaining gridded weather forecast data provided in any of the above embodiments.

[0090] In one embodiment, the electronic device 30 may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the electronic device 30 can be used to provide necessary computing, processing, and / or control capabilities. The memory of the electronic device 30 may include non-volatile storage media and internal memory. The non-volatile storage media may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface and communication interface of the electronic device 30 can be used to connect and communicate with external devices via a network.

[0091] This application also provides a computer-readable storage medium storing instructions, wherein the instructions, when executed by a processor, implement the steps of the method for acquiring gridded weather forecast data provided in any of the above embodiments. Those skilled in the art will understand that the method steps of this application can be performed by a computer program instructing related hardware, such as electronic devices or processors, and the computer program can be stored in a non-transitory computer-readable storage medium. When the computer program is executed, the steps of this application are performed. Depending on the context, any reference to memory, storage, or other media herein may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0092] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for acquiring gridded meteorological forecast data, characterized in that, The method includes: Obtain real meteorological data of a specified meteorological grid at a past time point, as well as multiple past grid meteorological forecast data provided by different meteorological data sources. The real meteorological data of the grid includes the real values ​​of various meteorological elements, and the past grid meteorological forecast data includes the predicted values ​​of various meteorological elements. Calculate the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements; Based on the deviation of each meteorological element, determine the final data source for each meteorological element in the specified meteorological grid; Based on the final data source of each meteorological element in the specified meteorological grid, grid meteorological forecast data for the specified meteorological grid during the forecast period is generated.

2. The method according to claim 1, characterized in that, The calculation of the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements includes: For each meteorological element, calculate the single deviation between the actual value and each predicted value.

3. The method according to claim 2, characterized in that, The calculation of the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements also includes: For each meteorological element, the multiple predicted values ​​are fused to obtain at least one fused predicted value; Calculate the fusion deviation between the true value and each of the fusion prediction values.

4. The method according to claim 2, characterized in that, The process of determining the final data source for each meteorological element in a specified meteorological grid based on the deviation of each meteorological element includes: For each of the meteorological elements, the smallest single deviation is selected from the plurality of single deviations; When the number of minimum single deviations is 1, the meteorological data source corresponding to the minimum single deviation is determined as the final data source of the meteorological element.

5. The method according to claim 3, characterized in that, The process of determining the final data source for each meteorological element in a specified meteorological grid based on the deviation of each meteorological element includes: For each of the meteorological elements, the minimum deviation is selected from multiple individual deviations and multiple fused deviations; When the number of minimum deviations is 1, at least one meteorological data source corresponding to the minimum deviation is determined as the final data source of the meteorological element.

6. The method according to claim 5, characterized in that, After selecting the minimum deviation from multiple individual deviations and multiple fused deviations for each of the meteorological elements, the method further includes: If the number of minimum deviations is greater than 1, obtain the grid's real meteorological data at another time point before the specified meteorological grid, as well as multiple past grid meteorological forecast data provided by different meteorological data sources, and repeatedly perform steps such as calculating the deviation between the real values ​​and multiple forecast values ​​of the multiple meteorological elements.

7. The method according to claim 1, characterized in that, Before calculating the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements, the method further includes: Determine whether the multiple past grid-based predicted meteorological data are complete; If it is determined that the multiple past grid predicted meteorological data are incomplete, the actual meteorological data of the grid at another time point before the specified meteorological time point is obtained, as well as multiple past grid meteorological predicted data provided by different meteorological data sources, until it is determined that the multiple past grid predicted meteorological data are complete.

8. A device for acquiring gridded meteorological forecast data, characterized in that, The device includes: The data acquisition module is configured to acquire real meteorological data of a specified meteorological grid at a past time point, as well as multiple past grid meteorological forecast data provided by different meteorological data sources. The real meteorological data of the grid includes the real values ​​of various meteorological elements, and the past grid meteorological forecast data includes the predicted values ​​of various meteorological elements. The deviation calculation module is configured to calculate the deviation between the actual values ​​and multiple predicted values ​​of the various meteorological elements. The data source determination module is configured to determine the final data source of each meteorological element in the specified meteorological grid based on the deviation of each meteorological element; The data generation module is configured to generate grid meteorological forecast data for the specified meteorological grid during the forecast period, based on the final data source of each meteorological element in the specified meteorological grid.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, it implements the steps of the method for acquiring gridded weather forecast data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, which, when executed by a processor, implement the steps of the method for acquiring gridded weather forecast data as described in any one of claims 1-7.

Images