Method, device, equipment, medium and product for generating weather forecast information

Abstract

The application provides a method, device, equipment, medium and product for generating weather forecast information. The method comprises the following steps: when target weather forecast information is generated based on demand information, a target production line path available for the target weather forecast information is determined in a unified directory according to the demand information, so as to realize obtaining target weather data according to the target production line path and generating the target weather forecast information according to the target weather data. The production line paths corresponding to multiple target meteorological elements in multiple production dates are automatically collected in the unified directory. Each target meteorological element corresponds to multiple production line paths. The multiple production line paths correspond to different time scales and different spatial scales. The target meteorological element is a meteorological element required by the target weather forecast information. Therefore, when multiple demands for weather forecast information for different time scales, different meteorological elements and the like are responded to, the target weather data can be obtained based on the unified directory, and the flexibility of weather forecast information generation is improved.

Description

Technical Field

[0001] This application relates to the field of meteorological technology, and in particular to a method, apparatus, equipment, medium and product for generating meteorological forecast information. Background Technology

[0002] Because different meteorological elements have different physical properties and different impacts on forecast accuracy, meteorological data corresponding to each meteorological element can be produced through different numerical models and processing methods, so that the meteorological data of each meteorological element can be obtained on independent production lines.

[0003] In related technologies, weather forecast information generation systems typically rely on a single numerical model, which makes the system less flexible in generating weather forecast information when dealing with meteorological data generated from multiple source numerical models. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and product for generating meteorological forecast information, in order to solve the problem of poor flexibility in generating meteorological forecast information in related technologies.

[0005] Firstly, this application provides a method for generating weather forecast information, including:

[0006] The requirement information for obtaining target weather forecast information includes the target forecast date, target time scale, and target spatial scale;

[0007] Based on the required information, it is determined whether there is a target production line path in the unified directory that can be used to generate the target weather forecast information. The unified directory includes production line paths corresponding to multiple target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths. The time scale and / or spatial scale of the multiple production line paths are different. The target meteorological element is the meteorological element required for the target weather forecast information.

[0008] If so, then obtain the target meteorological data according to the target production line path, and generate the target meteorological forecast information based on the target meteorological data.

[0009] In this embodiment, when generating target weather forecast information based on demand information, the available target production line paths for the target weather forecast information can be determined from a unified catalog according to the demand information. This enables the acquisition of target meteorological data based on the target production line paths and the generation of target weather forecast information based on the target meteorological data. The unified catalog automatically aggregates production line paths corresponding to various target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths, and these multiple production line paths correspond to different time and spatial scales. Furthermore, the target meteorological element is the meteorological element required for the target weather forecast information. This allows for the acquisition of target meteorological data based on the unified catalog when addressing various demands for weather forecast information at different time scales and with different meteorological elements, thereby improving the flexibility of weather forecast information generation.

[0010] In one possible implementation, based on the demand information, determining whether a target production line path exists in the unified directory that can generate the target weather forecast information includes:

[0011] Based on the target forecast date, determine whether there is a production line path for the first production date among the production line paths of the target meteorological elements under the multiple production dates, where the first production date is the date closest to the target forecast date;

[0012] If so, then based on the target time scale and the target spatial scale, determine whether there exists a target production line path in the production line path for the first production date that corresponds to the target time scale and the target spatial scale.

[0013] In this embodiment, among the production line paths for target meteorological elements under multiple production dates determined based on the target forecast date, it is determined whether there exists a production line path for a first production date, where the first production date is the date closest to the target forecast date. If so, it can be determined whether there exists a production line path corresponding to the target time scale and target spatial scale within the production line path for the first production date, thereby enabling the acquisition of the target meteorological data required for the production of the target meteorological product using the target production line path.

[0014] In one possible implementation, the demand information further includes target meteorological elements and user preferences; the method further includes:

[0015] Acquire meteorological data for multiple meteorological elements under the current production date. The meteorological data for each meteorological element includes meteorological data at multiple time scales and multiple spatial scales.

[0016] Store meteorological data of the various meteorological elements and generate the actual storage path of the meteorological data for each meteorological element;

[0017] The meteorological data of the target meteorological element is determined from the meteorological data of the multiple meteorological elements, and the target meteorological data corresponding to the target spatial scale and the target time scale are determined from the meteorological data of the target meteorological element.

[0018] Based on the actual storage path of the target meteorological data, a soft link is created for the target meteorological data in the unified directory to generate the production line path corresponding to the target meteorological element under the current production date.

[0019] In this embodiment, by storing meteorological data of multiple meteorological elements produced on each production date, and then soft-linking the meteorological data of the target meteorological element to a unified directory, it is convenient to find the target meteorological data directly in the unified directory when generating target forecast information, thereby improving the flexibility of target meteorological forecast information production.

[0020] In one possible implementation, the method further includes:

[0021] According to a preset time interval, monitoring operations are performed on the production line paths corresponding to multiple target meteorological elements under multiple production dates in the unified catalog to obtain monitoring results. The monitoring operations include: determining whether the meteorological data under each production line path is readable and whether the size of the meteorological data under each production line path is accurate.

[0022] If the monitoring results indicate an anomaly in any production line path, the unified directory will be updated.

[0023] In this embodiment, the production line path of the target meteorological elements under the unified directory can be monitored in real time to avoid interruption of the production of target meteorological products due to the unavailability of target meteorological data.

[0024] In one possible implementation, there are multiple target meteorological variables, and the target meteorological data includes candidate meteorological data corresponding to each target meteorological variable. For any target meteorological variable, there are multiple sets of candidate meteorological data for the target meteorological variable, and each set of candidate meteorological data has a different data source. The demand information also includes user preferences, and the user preferences include the source of the target data and / or sensitivity to errors.

[0025] Based on the actual storage path of the target meteorological data, create a soft link for the target meteorological data in the unified directory to generate the production line path corresponding to the target meteorological element under the current production date, including:

[0026] For any target meteorological variable, based on the user preference, a first meteorological data is determined from multiple sets of candidate meteorological data for the target meteorological variable; and based on the actual storage path of the first meteorological data, a soft link is created for the first meteorological data in the unified directory to generate the production line path corresponding to the first meteorological data under the current production date.

[0027] In this embodiment, a set of candidate meteorological data that meets the user's requirements can be selected from multiple sets of candidate meteorological data and a soft link can be created in a unified directory according to the user's preferences, thereby improving the accuracy of meteorological product production.

[0028] In one possible implementation, the user preference includes sensitivity to error, and determining a first meteorological data set from multiple candidate meteorological data sets based on the user preference includes:

[0029] Determine the data source for each group of candidate meteorological data from historical meteorological data within the first preset time period;

[0030] Historical weather forecast information is generated from the historical meteorological data. The historical weather forecast information indicates multiple forecast data. The first preset time period is the time period before the current production date.

[0031] Acquire various real-time data within the first preset time period;

[0032] Based on the various forecast data and the various real-time data, a comparison index is determined, which includes one or more of the following: root mean square error, correlation coefficient, and average deviation.

[0033] Based on the comparison indicators of each set of candidate meteorological data, the priority values ​​of the data sources of multiple sets of candidate meteorological data are obtained;

[0034] Based on the priority values ​​of the data sources of multiple sets of candidate meteorological data, the first meteorological data is determined from the multiple sets of candidate meteorological data.

[0035] In this embodiment, if the user's preference includes sensitivity to error, a set of candidate meteorological data that meets the user's requirements can be found by calculating and comparing indicators, which can improve the accuracy of meteorological product production.

[0036] Secondly, this application provides a weather forecast information generation device, comprising:

[0037] The acquisition module is used to acquire the demand information for target weather forecast information, which includes the target forecast date, target time scale, and target spatial scale.

[0038] The determination module is used to determine, based on the requirement information, whether there is a target production line path in the unified directory that can be used to generate the target weather forecast information. The unified directory includes production line paths corresponding to multiple target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths. The time scale and / or spatial scale of the multiple production line paths are different. The target meteorological element is the meteorological element required for the target weather forecast information.

[0039] The generation module is used to obtain target meteorological data based on the target production line path if a target production line path exists in the unified directory that can be used to generate the target meteorological forecast information, and then generate the target meteorological forecast information based on the target meteorological data.

[0040] In one possible implementation, the module is specifically used for:

[0041] Based on the target forecast date, determine whether there is a production line path for the first production date among the production line paths of the target meteorological elements under the multiple production dates, where the first production date is the date closest to the target forecast date;

[0042] If so, then based on the target time scale and the target spatial scale, determine whether there exists a target production line path in the production line path for the first production date that corresponds to the target time scale and the target spatial scale.

[0043] In one possible implementation, the demand information further includes target meteorological elements and user preferences; the device further includes a processing module, which is used to:

[0044] Acquire meteorological data for multiple meteorological elements under the current production date. The meteorological data for each meteorological element includes meteorological data at multiple time scales and multiple spatial scales.

[0045] Store meteorological data of the various meteorological elements and generate the actual storage path of the meteorological data for each meteorological element;

[0046] The meteorological data of the target meteorological element is determined from the meteorological data of the multiple meteorological elements, and the target meteorological data corresponding to the target spatial scale and the target time scale are determined from the meteorological data of the target meteorological element.

[0047] Based on the actual storage path of the target meteorological data, a soft link is created for the target meteorological data in the unified directory to generate the production line path corresponding to the target meteorological element under the current production date.

[0048] In one possible implementation, the processing module is also used for:

[0049] According to a preset time interval, monitoring operations are performed on the production line paths corresponding to multiple target meteorological elements under multiple production dates in the unified catalog to obtain monitoring results. The monitoring operations include: determining whether the meteorological data under each production line path is readable and whether the size of the meteorological data under each production line path is accurate.

[0050] If the monitoring results indicate an anomaly in any production line path, the unified directory will be updated.

[0051] In one possible implementation, there are multiple target meteorological variables, and the target meteorological data includes candidate meteorological data corresponding to each target meteorological variable. For any target meteorological variable, there are multiple sets of candidate meteorological data for the target meteorological variable, and each set of candidate meteorological data has a different data source. The demand information also includes user preferences, and the user preferences include the source of the target data and / or sensitivity to errors.

[0052] The processing module is specifically used for:

[0053] For any target meteorological variable, based on the user preference, a first meteorological data is determined from multiple sets of candidate meteorological data for the target meteorological variable; and based on the actual storage path of the first meteorological data, a soft link is created for the first meteorological data in the unified directory to generate the production line path corresponding to the first meteorological data under the current production date.

[0054] In one possible implementation, the user preferences include sensitivity to error, and the processing module is specifically used for:

[0055] Determine the data source for each group of candidate meteorological data from historical meteorological data within the first preset time period;

[0056] Historical weather forecast information is generated from the historical meteorological data. The historical weather forecast information indicates multiple forecast data. The first preset time period is the time period before the current production date.

[0057] Acquire various real-time data within the first preset time period;

[0058] Based on the various forecast data and the various real-time data, a comparison index is determined, which includes one or more of the following: root mean square error, correlation coefficient, and average deviation.

[0059] Based on the comparison indicators of each set of candidate meteorological data, the priority values ​​of the data sources of multiple sets of candidate meteorological data are obtained;

[0060] Based on the priority values ​​of the data sources of multiple sets of candidate meteorological data, the first meteorological data is determined from the multiple sets of candidate meteorological data.

[0061] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0062] The memory stores computer-executed instructions;

[0063] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect.

[0064] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a computer, are used to implement the method described in the first aspect.

[0065] The computer-readable storage medium provided in this application embodiment can execute the technical solutions in the above method embodiments, and its beneficial effects are similar, so they will not be described again here.

[0066] Fifthly, this application provides a computer program product, including a computer program, which, when executed by a computer, is used to implement the method described in the first aspect.

[0067] The computer program product provided in this application embodiment can execute the technical solutions in the above method embodiments, and its beneficial effects are similar, so they will not be described again here.

[0068] The meteorological forecast information generation method, apparatus, equipment, medium, and product provided in this application, when generating target meteorological forecast information based on demand information, can determine the available target production line path for the target meteorological forecast information in a unified catalog according to the demand information, thereby realizing the acquisition of target meteorological data based on the target production line path and the generation of target meteorological forecast information based on the target meteorological data. The unified catalog automatically collects production line paths corresponding to various target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths, and these multiple production line paths correspond to different time and spatial scales. Furthermore, the target meteorological element is the meteorological element required for the target meteorological forecast information. This allows for the acquisition of target meteorological data based on the unified catalog when addressing various demands for meteorological forecast information at different time scales and with different meteorological elements, thus improving the flexibility of meteorological forecast information generation. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0070] Figure 1 A flowchart illustrating a method for generating weather forecast information provided in this application embodiment;

[0071] Figure 2 A flowchart illustrating another method for generating weather forecast information provided in this application embodiment;

[0072] Figure 3 A flowchart illustrating another method for generating weather forecast information provided in this application embodiment;

[0073] Figure 4 This is a schematic diagram of the structure of a weather forecast information generation device provided in an embodiment of this application;

[0074] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0075] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0076] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0077] Because different meteorological elements have different physical properties and different impacts on forecast accuracy, meteorological data for each meteorological element can be produced through different numerical models, so that meteorological data for each meteorological element can be obtained on separate production lines.

[0078] To meet diverse needs, the production of weather forecast information requires the integration of meteorological data from multiple numerical models. However, in related technologies, weather forecast information generation systems typically rely on a single numerical model. This makes it difficult for the production system to integrate meteorological data from multiple numerical models when dealing with such data, resulting in poor flexibility in the production of weather forecast information.

[0079] Furthermore, weather forecast information can be time-scaled, medium-term (e.g., 3-10 days), extended-term (e.g., 10-30 days), or long-term (e.g., more than 30 days). The requirements for meteorological data sources and processing methods vary depending on the time-scale. This results in relatively independent production lines for the meteorological data required for each time-scale. Since the production of weather forecasts at different time scales often uses meteorological data generated by different numerical models, current weather forecast generation systems cannot switch between the meteorological data required for different time scales. This necessitates manual configuration when generating weather forecasts across time scales, increasing operational complexity and the time required for forecast generation.

[0080] Furthermore, the rigidity of current weather forecast generation systems is mainly reflected in their complex production processes, resulting in high complexity in data extraction during forecast production. Different meteorological elements and time scales require different data processing methods. Although weather forecast generation systems can process these different meteorological elements and time scales separately, in practical use, the complex data rules and wide distribution of data sources when acquiring meteorological data from different numerical models lead to difficulties in data extraction and rule management, hindering the efficient production of subsequent weather forecasts.

[0081] Furthermore, while weather forecast information can be distributed via platforms like File Transfer Protocol (FTP) after generation, the distribution process lacks effective monitoring. For example, the integrity of the distributed data cannot be monitored in real time, nor can the success of the distribution be determined promptly. In addition, distribution efficiency and timeliness cannot be effectively monitored and recorded, making it difficult to track and analyze the distribution status when problems arise. More importantly, existing generation systems cannot automatically restart distribution when it fails, requiring manual intervention for troubleshooting and resending. This not only reduces distribution efficiency but also easily leads to delays, failing to meet the demands for efficient and accurate data transmission.

[0082] Therefore, this application provides a method for generating meteorological forecast information. When generating target meteorological forecast information based on demand information, the method can determine the target production line path available for the target meteorological forecast information in a unified catalog according to the demand information. This enables the acquisition of target meteorological data based on the target production line path and the generation of target meteorological forecast information based on the target meteorological data. The unified catalog automatically aggregates production line paths corresponding to various target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths, and these multiple production line paths correspond to different time and spatial scales. Furthermore, the target meteorological element is the meteorological element required for the target meteorological forecast information. This allows for the acquisition of target meteorological data based on the unified catalog when addressing various demands for meteorological forecast information at different time scales and with different meteorological elements, thereby improving the flexibility of meteorological forecast information generation.

[0083] In this application embodiment, meteorological forecast information can also be referred to as meteorological products. Meteorological forecast information can be, for example, weather forecast reports, climate simulation reports, and disaster warning reports. Taking weather forecast reports as an example, weather forecast reports can include single-point weather forecast reports or grid weather forecast reports. A single-point weather forecast report refers to a weather forecast report for a specific coordinate point, such as a weather forecast report for 30 degrees north latitude and 110 degrees east longitude. A grid weather forecast report refers to a weather forecast report for a certain range, such as a weather forecast report for the range of 30-40 degrees north latitude and 110-120 degrees east longitude.

[0084] It is understood that the aforementioned weather forecast information may be in the form of data products or non-data products (such as images), and this application does not impose any restrictions on this.

[0085] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0086] Figure 1 This is a flowchart illustrating a method for generating weather forecast information provided in an embodiment of this application. The method can be executed by a weather forecast information generating device, which can be implemented using a computer program; it can also be implemented using a medium storing the relevant computer program, such as a USB flash drive and / or optical disc; or it can be implemented using a physical device integrating or installing the relevant computer program, such as a chip or electronic device. The electronic device can be a server, server cluster, or computer, etc. The following description uses a server as an example. Figure 1 As shown, the method may include the following steps:

[0087] S101. Requirements for obtaining target weather forecast information, including target forecast date, target time scale, and target spatial scale.

[0088] In one possible implementation, users can express key information about the target weather forecast in natural language, such as "I need the weather forecast for a certain area for the next 5 days, with a spatial scale of 0.1 degrees". The server can then identify this key information using a large language model to obtain the required information.

[0089] The target timescale refers to the forecast time range of the target weather forecast information. The target timescale can be short-term (e.g., 1-3 days), medium-term (e.g., 3-10 days), extended-term (e.g., 10-30 days), or long-term (e.g., more than 30 days). Based on the key information "the next 3 days" mentioned above, the target timescale can be determined to be medium-term.

[0090] The target forecast date refers to the forecast date of the target weather forecast information. Based on the key information "the next 3 days" mentioned above, the target forecast date can be determined according to the date of the key information entered by the user.

[0091] The target spatial scale can be understood as grid precision, referring to the division of the forecast area into latitude and longitude grids. Each latitude and longitude grid represents a basic forecast unit. The smaller the grid (i.e., the higher the resolution), the stronger the ability of the target weather forecast information generation system to capture local weather phenomena. Typically, the target spatial scale can be 0.1° or 0.01°, etc. 0.1° means that the span of each grid unit in the latitude and longitude directions is 0.1 degrees, and 0.01° means that the span of each grid unit in the latitude and longitude directions is 0.01 degrees. Since each degree of latitude on Earth corresponds to approximately 111 kilometers (in mid-latitude regions), a 0.1° grid size covers an area of ​​approximately 11.1 km × 11.1 km, and a 0.01° grid size covers an area of ​​approximately 1.11 km × 1.11 km. Users can specify the precision of the meteorological data required to generate target weather forecast information through the target spatial scale (the smaller the target spatial scale, the higher the precision of the meteorological data).

[0092] S102. Based on the demand information, determine whether there is a target production line path in the unified catalog that can generate target weather forecast information.

[0093] The server can determine whether a target production line path for generating target weather forecast information exists in the unified directory based on the demand information. If a target production line path for generating target weather forecast information exists in the unified directory, then S103 is executed.

[0094] The unified catalog includes production line paths corresponding to various target meteorological elements for multiple production dates. Each target meteorological element corresponds to multiple production line paths, and these paths have different time and / or spatial scales. The target meteorological elements are the meteorological elements required for target weather forecast information. In other words, the unified path compiles production line paths corresponding to various target meteorological elements for multiple production dates, facilitating the direct acquisition of relevant meteorological data when generating target weather forecast information.

[0095] It should be noted that, taking any production line path a as an example, this production line path 1 corresponds to time scale 1 and spatial scale 1. The meteorological data indicated by this production line path a is used to generate the meteorological forecast information corresponding to this time scale 1 and spatial scale 1. In other words, when generating the meteorological forecast information corresponding to this time scale 1 and spatial scale 1, it can be generated based on the meteorological data indicated by this production line path a.

[0096] For example, for ease of understanding, the unified catalog can be referenced in Table 1.

[0097] As shown in Table 1, for any target meteorological element, there can be multiple production line paths under the same time and spatial scales. It can be understood that the meteorological data indicated by each production line path can come from different data sources. These data sources can be, for example, numerical models, such as the Weather Research and Forecasting Model (WRF), or other meteorological models, such as meteorological models that generate meteorological data using spherical cubic grids (for example, they can divide the globe into 6 regions, effectively solving the theoretical bottleneck of the convergence of the North and South Poles, and through a more uniform grid distribution, they can maximize the computing power of modern large-scale parallel computing supercomputers).

[0098] It is understandable that the unified directory is updated in real time. For example, the server can monitor the production line paths corresponding to various target meteorological elements under multiple production dates in the unified directory at preset time intervals to determine the availability of each production line path. For instance, when a new production line path is added for the target meteorological element "temperature", that production line path needs to be added to the unified directory; or, for example, when production line path 1 for the target meteorological element "temperature" is unavailable (e.g., the meteorological data corresponding to production line path 1 is corrupted), that production line path 1 needs to be deleted from the unified directory.

[0099] Table 1

[0100]

[0101] S103. Obtain target meteorological data based on the target production line path, and generate target meteorological forecast information based on the target meteorological data.

[0102] Once the target production line path is determined, the server can trigger the process of generating target weather forecast information. That is, the server can obtain target weather data based on the target production line path and generate target weather forecast information based on the target weather data.

[0103] In one possible implementation, after the server generates the target weather forecast information, it can obtain the user's File Transfer Protocol (FTP) account and password information based on the user corresponding to the target weather forecast information, and automatically distribute the target weather forecast information through the FTP server based on the account and password information.

[0104] For example, the server can obtain information such as the FTP account and password corresponding to the target weather forecast information through a distribution configuration file. This distribution configuration file includes the FTP accounts and passwords of multiple users. The server can automatically publish the target weather forecast information to the user corresponding to the target weather forecast information based on their FTP account and password. This system realizes the automatic distribution of target weather forecast information through an FTP server, reducing reliance on manual operation. Compared to the lack of monitoring and low efficiency in the distribution process of existing technologies, this application can monitor the integrity of the distributed target weather forecast information and the success rate of the distribution process in real time, ensuring that the target weather forecast information is delivered to the user terminal in a timely and accurate manner. This improvement enhances overall work efficiency and ensures the accuracy of data transmission, meeting the requirements for efficient and accurate data transmission.

[0105] In this embodiment, when generating target weather forecast information based on demand information, the available target production line paths for the target weather forecast information can be determined from a unified catalog according to the demand information. This enables the acquisition of target meteorological data based on the target production line paths and the generation of target weather forecast information based on the target meteorological data. The unified catalog automatically aggregates production line paths corresponding to various target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths, and these multiple production line paths correspond to different time and spatial scales. Furthermore, the target meteorological element is the meteorological element required for the target weather forecast information. This allows for the acquisition of target meteorological data based on the unified catalog when addressing various demands for weather forecast information at different time scales and with different meteorological elements, thereby improving the flexibility of weather forecast information generation.

[0106] In one possible implementation, when the target weather forecast information is generated, the production line path in the unified directory is also in a state of real-time updating (for example, there may be a situation where the meteorological data corresponding to the production line path is abnormal, and the production line path is in an updating state). The server can determine whether the target production line path required for the target weather forecast information exists in the generation paths collected in the unified directory.

[0107] The following explains how the server determines whether a target production line path exists in the unified directory that can be used to generate target weather forecast information.

[0108] Based on the target forecast date, the server determines whether there is a production line path for the first production date among the production line paths of the target meteorological elements under multiple production dates. The first production date is the date closest to the target forecast date.

[0109] It is understandable that the target forecast date is a date that has not yet occurred, i.e. a future date, while multiple production dates are historical dates. The first production date is the date closest to the target forecast date. Using meteorological data of the production line path on the date closest to the target forecast date can improve the accuracy of the target weather forecast information.

[0110] In one possible implementation, the first production date can be a specific date, for example, if the target forecast date is January 15 to January 20, and the date closest to January 15 among multiple production dates is January 1, then the first production date is January 1.

[0111] In one possible implementation, the first production date can be a time range; for example, in the above example, the first production date could be from January 1st to January 3rd.

[0112] It should be noted that the above examples are based on months and days. The production date can also be specified down to the hour, such as January 1, 22:21, to obtain more detailed meteorological data. This application does not impose any restrictions on this.

[0113] If, among the production line paths for target meteorological elements under multiple production dates, there is no production line path for the first production date, for example, a prompt message can be output to indicate to the user that there is no production line path for the first production date and whether the first production date needs to be changed. If an instruction to change the first production date is received, the first production date can be determined within a preset time range before the target forecast date.

[0114] If there is a production line path for the first production date among the production line paths for target meteorological elements under multiple production dates, the server can determine whether there is a production line path corresponding to the target time scale and the target spatial scale in the production line path for the first production date.

[0115] Taking Table 1 as an example, if the target time scale is short-term, the target spatial scale is scale 1, the first production date is date 1, the target meteorological variables are temperature and humidity, and the production line path for the first production date includes path 1, path 2, path 3, path 4, path 5, path 6, etc., the server can determine the target production line path, including path 1 corresponding to "temperature" and path 4 corresponding to "humidity", from these production line paths based on the target time scale and the target spatial scale.

[0116] In this embodiment, among the production line paths for target meteorological elements under multiple production dates determined based on the target forecast date, it is determined whether there exists a production line path for a first production date, where the first production date is the date closest to the target forecast date. If so, it can be determined whether there exists a production line path corresponding to the target time scale and target spatial scale within the production line path for the first production date, thereby enabling the acquisition of the target meteorological data required for the production of the target meteorological product using the target production line path.

[0117] The following section explains the production line paths corresponding to multiple target meteorological elements under multiple production dates that are aggregated in a unified path.

[0118] Figure 2 This is a flowchart illustrating another method for generating weather forecast information provided in this application embodiment. This method can be executed by a weather forecast information generating device, which can be implemented using a computer program; it can also be implemented using a medium storing the relevant computer program, such as a USB flash drive and / or optical disc; or it can be implemented using a physical device integrating or installing the relevant computer program, such as a chip or electronic device. The electronic device can be a server, server cluster, or computer, etc. The following description uses a server as an example. Figure 2 As shown, the method may include the following steps:

[0119] S201. Obtain meteorological data for multiple meteorological elements under the current production date. The meteorological data for each meteorological element includes meteorological data at multiple time scales and multiple spatial scales.

[0120] For any given time and space scale, the meteorological data at that time and space scale can include multiple data points. For example, in Table 2, the meteorological element "temperature" has meteorological data 1 and meteorological data 2 at the time scale "short-term" and the spatial scale "scale 1". The data sources of meteorological data 1 and meteorological data 2 are different. For example, meteorological data 1 is generated by WRF, while meteorological data 2 is generated by other meteorological models.

[0121] S202. Store meteorological data of multiple meteorological elements and generate the actual storage path of meteorological data for each meteorological element.

[0122] For example, meteorological data for various meteorological elements and their actual storage paths are shown in Table 2.

[0123] Table 2

[0124]

[0125] It is understandable that meteorological data 1 to meteorological data 12 represent multiple sets of meteorological data. Taking temperature as an example, each set of meteorological data includes temperature values ​​of different pressure layers (or altitude layers). The temperature index of each pressure layer (or altitude layer) can include the temperature values ​​of multiple latitude and longitude grids in multiple forecast areas (i.e., each latitude and longitude grid corresponds to a temperature index). The size of each latitude and longitude grid is related to scale 1 (for example, if scale 1 is 0.1°, then the size of the latitude and longitude grid covers an area of ​​approximately 11.1 km × 11.1 km).

[0126] S203. Determine the meteorological data of the target meteorological element from the meteorological data of multiple meteorological elements, and determine the target meteorological data corresponding to the target spatial scale and the target time scale from the meteorological data of the target meteorological element.

[0127] The server can determine the meteorological data of the target meteorological element from multiple meteorological data based on the required information of the target meteorological forecast. For example, if the target meteorological element includes temperature and humidity, the target spatial scale is scale 1, and the target time scale is short-term, then the server can determine the target meteorological data corresponding to "scale 1 and short-term" from the temperature meteorological data, and determine the target meteorological data corresponding to "scale 1 and short-term" from the humidity meteorological data. Taking Table 2 as an example, the target meteorological data includes meteorological data 1, meteorological data 2, and meteorological data 5.

[0128] In other words, if there are multiple target meteorological variables, the target meteorological data includes candidate meteorological data corresponding to each target meteorological variable. Taking the example above, the target meteorological data includes candidate meteorological data corresponding to temperature (i.e., meteorological data 1 and meteorological data 2) and candidate meteorological data corresponding to humidity (i.e., meteorological data 5).

[0129] S204. Based on the actual storage path of the target meteorological data, create a soft link for the target meteorological data in a unified directory to generate the production line path corresponding to the target meteorological element under the current production date.

[0130] The server can create soft links for the target meteorological data in a unified directory based on the actual storage path of the target meteorological data, in order to generate the production line path corresponding to the target meteorological elements for the current production date.

[0131] Since there can be multiple target meteorological variables, the target meteorological data includes candidate meteorological data corresponding to each target meteorological variable, and there can be multiple sets of candidate meteorological data corresponding to each target meteorological variable, with each set of candidate meteorological data coming from a different source.

[0132] It is understandable that after creating a soft link to the target meteorological data in the unified directory, the production line path of the target meteorological data in the unified directory can point to the actual storage path of the target meteorological data. When accessing the production line path, the actual storage path is accessed. Deleting the production line path in the unified directory will not affect the original target meteorological data. However, deleting the original target meteorological data will cause the production line path in the unified directory to become invalid.

[0133] For example, a server can use a command to create a symbolic link to establish a symbolic link.

[0134] exist Figure 2 In a corresponding embodiment, meteorological data of multiple meteorological elements produced on each production date are stored, and then the meteorological data of the target meteorological element is soft-linked to a unified directory. This facilitates direct retrieval of the target meteorological data in this unified directory when generating target forecast information, providing a unified interface for subsequent meteorological forecast information production, reducing the complexity of meteorological forecast information production, and improving the flexibility of meteorological forecast information production. Furthermore, compared to the single data source management method in the prior art, this application significantly improves overall production efficiency.

[0135] In one possible implementation, the server can create symbolic links to all candidate meteorological data in the target meteorological data under a unified directory. In this case, for a target meteorological variable with multiple sets of candidate meteorological data, when generating the target meteorological forecast information, any one set of candidate meteorological data can be selected from the multiple sets of candidate meteorological data to generate the target meteorological forecast information.

[0136] In one possible implementation, the server can monitor the production line path of the target meteorological elements under a unified directory to avoid interruption of the production of the target meteorological product due to the unavailability of the target meteorological data.

[0137] Specifically, the server can perform monitoring operations on the production line paths corresponding to various target meteorological elements under multiple production dates in the unified directory at preset time intervals, and obtain monitoring results. The monitoring operations include: determining whether the meteorological data under each production line path is readable and whether the size of the meteorological data under each production line path is accurate. If the monitoring results indicate that any production line path has an anomaly, the unified directory is updated.

[0138] For example, if any production line path is abnormal, the server can delete the production line path under the unified directory; or re-acquire the meteorological data under the production line path. After acquiring the meteorological data, if it is determined that the meteorological data is readable and the size of the storage space occupied by the meteorological data is accurate, a soft link to the meteorological data can be created under the unified directory.

[0139] In one possible implementation, for a target meteorological variable with multiple sets of candidate meteorological data, the server can determine a set of candidate meteorological data that meets the user's requirements in the following way:

[0140] The demand information corresponding to the target weather forecast information also includes user preferences, which include the target data source and / or sensitivity to errors. For any target weather variable, the first weather data is determined from multiple sets of candidate weather data for the target weather variable based on the user preferences. Based on the actual storage path of the first weather data, a soft link is created for the first weather data in a unified directory to generate the production line path corresponding to the first weather data for the current production date.

[0141] The target data source can be a user-specified meteorological data source, such as WRF. If the user preference includes "sensitive to error," it indicates that the target weather forecast information is a product that is relatively sensitive to error. Furthermore, the user can indicate that the target weather forecast is sensitive to the error of a certain meteorological element, such as temperature error. This allows the server to determine a set of candidate meteorological data that meets the user's requirements based on the user's preferences, enabling the creation of soft links for these candidate meteorological data under a unified path, which can then be directly used when generating the target weather forecast information.

[0142] The following explains how the server determines the first meteorological data from multiple candidate meteorological data sets based on user preferences:

[0143] The server can determine the data source of each set of candidate meteorological data from historical meteorological data within a first preset time period;

[0144] It also acquires historical meteorological data to generate historical weather forecast information, which indicates various forecast data. The first preset time period is the period before the current production date.

[0145] This includes various forecast data, such as forecast values ​​for meteorological elements like temperature and humidity. The first preset time period can be a historical time period, for example, a time period prior to the first production date.

[0146] The server can then obtain multiple real-time data within a first preset time period. It can be understood that multiple forecast data and multiple real-time data correspond to the same meteorological elements. For example, multiple real-time data may include real-time values ​​of meteorological elements such as temperature and humidity.

[0147] Based on multiple forecast and actual data sets, comparison indicators were determined, including one or more of the following: root mean square error (RMSE), correlation coefficient, and average bias. RMSE measures the absolute deviation between forecast and actual data; CORR reflects the linear correlation between them; and bias reflects the overall direction and magnitude of the deviation, i.e., the degree to which historical weather forecasts are systematically overestimated or underestimated.

[0148] In one possible implementation, the server can calculate the root mean square error, correlation coefficient, and average deviation for each type of meteorological data in the forecast and actual data, which can be determined by the following formula:

[0149]

[0150] Where Si is the i-th forecast value, Oi is the i-th actual value, and n is the number of samples.

[0151]

[0152] Where Si is the i-th forecast value, Oi is the i-th actual value, and n is the number of samples.

[0153]

[0154] Where Si is the i-th predicted value, Oi is the i-th actual value, and n is the number of samples. It is the mean of n forecast values. It is the average of n real values.

[0155] In one possible implementation, if the user preference indicates that the target weather forecast is sensitive to errors in a certain meteorological element, such as temperature, then the server can calculate the root mean square error, correlation coefficient, and average deviation only for the temperature in the forecast data and the actual data.

[0156] After calculating one or more of the root mean square error, correlation coefficient, and mean deviation, the server can determine the priority values ​​of the data sources for multiple sets of candidate meteorological data based on the comparison indicators for each set of candidate meteorological data. Then, based on the priority values ​​of the data sources for multiple sets of candidate meteorological data, the first meteorological data is determined from among the multiple sets of candidate meteorological data.

[0157] For example, for any set of candidate meteorological data, if the server calculates the root mean square error, correlation coefficient, and mean deviation, these values ​​can be standardized to a range of 0-1. Then, the sum of these standardized values ​​yields the priority value for that set of candidate meteorological data. Based on the priority values ​​of the data sources of multiple sets of candidate meteorological data, the first meteorological data can be determined from among them; for example, the candidate meteorological data with the highest priority value can be designated as the first meteorological data.

[0158] In one possible implementation, after obtaining the values ​​of root mean square error, correlation coefficient and mean deviation, which are standardized to the 0-1 range, a weighted total score can be calculated based on the weights of each comparison indicator (e.g., RMSE: 40%, CORR: 30%, BIAS: 30%) to obtain the priority value of each group of candidate meteorological data.

[0159] In this embodiment, if the user's preference includes sensitivity to error, a set of candidate meteorological data that meets the user's requirements can be found by calculating and comparing indicators, which can improve the accuracy of meteorological product production.

[0160] In one possible implementation, before calculating the root mean square error, correlation coefficient, and average deviation, the server can preprocess various real-time data within a first preset time period, such as removing outliers, to ensure data comparability.

[0161] In one possible implementation, when the demand information for the target weather forecast changes, such as changes in the target weather variables or user preferences in the demand information that the server can obtain, the server can update the production line path in the unified directory (including adding or deleting) according to the changed demand information, thereby achieving seamless switching without affecting the production of the target weather forecast information.

[0162] Figure 3 A flowchart illustrating another method for generating weather forecast information provided in this application embodiment is shown below. Figure 3As shown, this method can be executed by a weather forecast information generating device, which can be implemented through a computer program; it can also be implemented through a medium storing the relevant computer program, such as a USB flash drive and / or optical disc; or it can be implemented through a physical device that integrates or installs the relevant computer program, such as a chip or electronic device. The electronic device can be a server, server cluster, or terminal device such as a computer. The following explanation uses a server as an example. Figure 3 As shown, the method may include the following steps:

[0163] S301. Requirements for obtaining target weather forecast information, including target forecast date, target time scale, and target spatial scale.

[0164] S302. Obtain meteorological data for multiple meteorological elements under the current production date. The meteorological data for each meteorological element includes meteorological data at multiple time scales and multiple spatial scales.

[0165] S303. Store meteorological data of multiple meteorological elements and generate the actual storage path of meteorological data for each meteorological element.

[0166] S304. Determine the meteorological data of the target meteorological element from the meteorological data of multiple meteorological elements, and determine the target meteorological data corresponding to the target spatial scale and the target time scale from the meteorological data of the target meteorological element.

[0167] S305. For any target meteorological variable, determine the data source of each group of candidate meteorological data from historical meteorological data within the first preset time period.

[0168] S306. Obtain historical meteorological data to generate historical meteorological forecast information. The historical meteorological forecast information indicates various forecast data. The first preset time period is the time period before the current production date.

[0169] S307. Obtain various real-time data within the first preset time period.

[0170] S308. Based on multiple forecast data and multiple actual data, determine the comparison indicators, which include one or more of the following: root mean square error, correlation coefficient, and average deviation.

[0171] S309. Based on the comparison indicators of each group of candidate meteorological data, obtain the priority values ​​of the data sources of multiple groups of candidate meteorological data.

[0172] S310. Based on the priority values ​​of the data sources of multiple sets of candidate meteorological data, determine the first meteorological data among the multiple sets of candidate meteorological data.

[0173] S311. Based on the actual storage path of the first meteorological data, create a soft link for the target meteorological data in a unified directory to generate the production line path corresponding to the target meteorological element under the current production date.

[0174] S312. According to the preset time interval, perform monitoring operations on the production line paths corresponding to multiple target meteorological elements under multiple production dates in the unified catalog, and obtain the monitoring results.

[0175] The monitoring operations include: determining whether the meteorological data under each production line path is readable and whether the size of the meteorological data under each production line path is accurate.

[0176] S313. If the monitoring results indicate that any production line path is abnormal, the unified catalog shall be updated.

[0177] S314. Based on the target forecast date, determine whether there is a production line path for the first production date among the production line paths of the target meteorological elements under multiple production dates. The first production date is the date closest to the target forecast date.

[0178] If so, then execute S315.

[0179] S315. Based on the target time scale and target spatial scale, determine whether there exists a production line path corresponding to the target time scale and target spatial scale in the production line path of the first production date as the target production line path.

[0180] If so, then execute S316.

[0181] S316. Obtain target meteorological data based on the target production line path, and generate target meteorological forecast information based on the target meteorological data.

[0182] The specific implementation method and technical effects in this embodiment are similar to those in the above embodiments, and will not be repeated here.

[0183] Figure 4 This is a schematic diagram of the structure of a weather forecast information generation device provided in an embodiment of this application, such as... Figure 4 As shown, the weather forecast information generation device 40 includes: an acquisition module 401, a determination module 402, and a generation module 403.

[0184] The acquisition module 401 is used to acquire the target weather forecast information, which includes the target forecast date, the target time scale, and the target spatial scale.

[0185] The determination module 402 is used to determine, based on the demand information, whether there is a target production line path in the unified catalog that can be used to generate target weather forecast information. The unified catalog includes production line paths corresponding to multiple target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths. The time scale and / or spatial scale corresponding to the multiple production line paths are different. The target meteorological elements are the meteorological elements required for the target weather forecast information.

[0186] The generation module 403 is used to obtain target meteorological data based on the target production line path if there is a target production line path available for generating target meteorological forecast information in the unified directory, and then generate target meteorological forecast information based on the target meteorological data.

[0187] In one possible implementation, the determining module 402 is specifically used for:

[0188] Based on the target forecast date, determine whether there is a production line path for the target meteorological element under multiple production dates, where the first production date is the date closest to the target forecast date.

[0189] If so, then based on the target time scale and target spatial scale, determine whether there exists a production line path corresponding to the target time scale and target spatial scale in the production line path of the first production date as the target production line path.

[0190] In one possible implementation, the demand information also includes target meteorological elements and user preferences; the device further includes a processing module 404, which is used for:

[0191] Acquire meteorological data for multiple meteorological elements under the current production date. The meteorological data for each meteorological element includes meteorological data at multiple time scales and multiple spatial scales.

[0192] It stores meteorological data for multiple meteorological elements and generates the actual storage path for the meteorological data of each meteorological element.

[0193] The meteorological data of the target meteorological element is determined from the meteorological data of multiple meteorological elements, and the target meteorological data corresponding to the target spatial scale and the target time scale are determined from the meteorological data of the target meteorological element.

[0194] Based on the actual storage path of the target meteorological data, create a soft link for the target meteorological data in a unified directory to generate the production line path corresponding to the target meteorological element for the current production date.

[0195] In one possible implementation, processing module 404 is further configured to:

[0196] According to a preset time interval, monitoring operations are performed on the production line paths corresponding to multiple target meteorological elements under multiple production dates in a unified catalog to obtain monitoring results. The monitoring operations include: determining whether the meteorological data under each production line path is readable and whether the size of the meteorological data under each production line path is accurate.

[0197] If the monitoring results indicate that there is an anomaly in any production line path, the unified catalog will be updated.

[0198] In one possible implementation, there are multiple target meteorological variables, and the target meteorological data includes candidate meteorological data corresponding to each target meteorological variable. For any target meteorological variable, there are multiple sets of candidate meteorological data for the target meteorological variable, and the data sources of each set of candidate meteorological data are different. The requirement information also includes user preferences, which include the source of the target data and / or sensitivity to errors.

[0199] Processing module 404 is specifically used for:

[0200] For any target meteorological variable, the first meteorological data is determined from multiple candidate meteorological data according to user preferences; and a soft link is created for the first meteorological data in a unified directory according to the actual storage path of the first meteorological data, so as to generate the production line path corresponding to the first meteorological data under the current production date.

[0201] In one possible implementation, user preferences include sensitivity to error, and processing module 404 is specifically used for:

[0202] The data source for each group of candidate meteorological data is determined to be historical meteorological data within the first preset time period.

[0203] Historical weather forecast information is generated by acquiring historical meteorological data. The historical weather forecast information indicates various forecast data, and the first preset time period is the period before the current production date.

[0204] Obtain various real-time data within a first preset time period.

[0205] Based on multiple forecast data and multiple real-time data, comparison indicators are determined, including one or more of the following: root mean square error, correlation coefficient, and mean deviation.

[0206] Based on the comparison indicators of each set of candidate meteorological data, the priority values ​​of the data sources of multiple sets of candidate meteorological data are obtained.

[0207] Based on the priority values ​​of the data sources of multiple sets of candidate meteorological data, the first meteorological data is determined from the multiple sets of candidate meteorological data.

[0208] The apparatus in this embodiment can be used to execute the technical solutions of the above method embodiments. The specific implementation methods and technical effects are similar, and will not be described again here.

[0209] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 5 As shown, the electronic device 50 may include at least one processor 501 and a memory 702.

[0210] Memory 502 is used to store programs. Specifically, the program may include program code, which includes computer-executable instructions.

[0211] The memory 502 may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0212] The processor 501 is used to execute computer execution instructions stored in the memory 502 to implement the method described in the foregoing method embodiments. The processor 501 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0213] Optionally, the electronic device 50 may also include a communication interface 503. In specific implementations, if the communication interface 503, memory 502, and processor 501 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0214] Optionally, in a specific implementation, if the communication interface 503, memory 502, and processor 501 are integrated on a single chip, then the communication interface 503, memory 502, and processor 501 can communicate through an internal interface.

[0215] Electronic devices 50 can be servers, server clusters, and terminal devices such as computers.

[0216] The electronic device in this embodiment can be used to execute the technical solutions of the above method embodiments. The specific implementation methods and technical effects are similar, and will not be repeated here.

[0217] This application provides a computer-readable storage medium, which may include various media capable of storing computer-executable instructions, such as USB flash drives, portable hard drives, read-only memory (ROM), RAM, disks, or optical discs. Specifically, the computer-readable storage medium stores computer-executable instructions, which, when executed by a computer, cause the technical solution shown in the above method embodiment to be executed. The specific implementation and technical effects are similar and will not be repeated here.

[0218] This application provides a computer program product, including a computer program. When the computer program is executed by a computer, the technical solution shown in the above method embodiment is executed. The specific implementation method and technical effect are similar, and will not be repeated here.

[0219] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0220] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0221] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0222] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0223] When an integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, graphics processing unit (GPU), field-programmable gate array (FPGA), digital signal processing (DSP) chip, and application-specific integrated circuit (ASIC), etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random-access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0224] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, ROM, RAM, portable hard drives, magnetic disks, or optical disks.

[0225] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0226] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0227] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for generating weather forecast information, characterized in that, include: The requirement information for obtaining target weather forecast information includes the target forecast date, target time scale, and target spatial scale; Based on the target forecast date, determine whether there is a production line path for a first production date among the production line paths of target meteorological elements under multiple production dates in the unified catalog, where the first production date is the date closest to the target forecast date; If so, then based on the target time scale and the target spatial scale, determine whether there exists a production line path corresponding to the target time scale and the target spatial scale as the target production line path in the production line path of the first production date; The unified catalog includes production line paths corresponding to multiple target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths. The time scale and / or spatial scale of the multiple production line paths are different. The target meteorological elements are the meteorological elements required for the target meteorological forecast information. If so, then obtain the target meteorological data according to the target production line path, and generate the target meteorological forecast information based on the target meteorological data.

2. The method according to claim 1, characterized in that, The demand information also includes target meteorological elements and user preferences; the method further includes: Acquire meteorological data for multiple meteorological elements under the current production date. The meteorological data for each meteorological element includes meteorological data at multiple time scales and multiple spatial scales. Store meteorological data of the various meteorological elements and generate the actual storage path of the meteorological data for each meteorological element; The meteorological data of the target meteorological element is determined from the meteorological data of the multiple meteorological elements, and the target meteorological data corresponding to the target spatial scale and the target time scale are determined from the meteorological data of the target meteorological element. Based on the actual storage path of the target meteorological data, a soft link is created for the target meteorological data in the unified directory to generate the production line path corresponding to the target meteorological element under the current production date.

3. The method according to claim 2, characterized in that, The method further includes: According to a preset time interval, monitoring operations are performed on the production line paths corresponding to multiple target meteorological elements under multiple production dates in the unified catalog to obtain monitoring results. The monitoring operations include: determining whether the meteorological data under each production line path is readable and whether the size of the meteorological data under each production line path is accurate. If the monitoring results indicate an anomaly in any production line path, the unified directory will be updated.

4. The method according to claim 2 or 3, characterized in that, The target meteorological elements are multiple, and the target meteorological data includes candidate meteorological data corresponding to each target meteorological element. For any target meteorological element, there are multiple sets of candidate meteorological data for the target meteorological element, and the data sources of each set of candidate meteorological data are different. The demand information also includes user preferences, and the user preferences include the source of the target data and / or sensitivity to errors. Based on the actual storage path of the target meteorological data, create a soft link for the target meteorological data in the unified directory to generate the production line path corresponding to the target meteorological element under the current production date, including: For any target meteorological element, the first meteorological data is determined from multiple sets of candidate meteorological data for the target meteorological element according to the user preference. Based on the actual storage path of the first meteorological data, a soft link is created for the first meteorological data in the unified directory to generate the production line path corresponding to the first meteorological data for the current production date.

5. The method according to claim 4, characterized in that, The user preferences include sensitivity to error. Based on the user preferences, the first meteorological data is determined from multiple sets of candidate meteorological data, including: Determine the data source for each group of candidate meteorological data from historical meteorological data within the first preset time period; Historical weather forecast information generated from the historical meteorological data is obtained. The historical weather forecast information indicates multiple forecast data, and the first preset time period is the time period before the current production date. Acquire various real-time data within the first preset time period; Based on the various forecast data and the various real-time data, a comparison index is determined, which includes one or more of the following: root mean square error, correlation coefficient, and average deviation. Based on the comparison indicators of each set of candidate meteorological data, the priority values ​​of the data sources of multiple sets of candidate meteorological data are obtained; Based on the priority values ​​of the data sources of multiple sets of candidate meteorological data, the first meteorological data is determined from the multiple sets of candidate meteorological data.

6. A device for generating weather forecast information, characterized in that, include: The acquisition module is used to acquire the demand information for target weather forecast information, which includes the target forecast date, target time scale, and target spatial scale. The determination module is used to determine, based on the requirement information, whether there is a target production line path in the unified directory that can be used to generate the target weather forecast information. The unified directory includes production line paths corresponding to multiple target meteorological elements under multiple production dates. Each target meteorological element corresponds to multiple production line paths. The time scale and / or spatial scale of the multiple production line paths are different. The target meteorological element is the meteorological element required for the target weather forecast information. The generation module is used to obtain target meteorological data based on the target production line path if a target production line path exists in the unified directory that can be used to generate the target meteorological forecast information, and to generate the target meteorological forecast information based on the target meteorological data. The determining module is specifically used to determine, based on the target forecast date, whether there is a production line path for a first production date among the production line paths of target meteorological elements under multiple production dates in the unified catalog, where the first production date is the date closest to the target forecast date; If so, then based on the target time scale and the target spatial scale, determine whether there exists a target production line path in the production line path for the first production date that corresponds to the target time scale and the target spatial scale.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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