CN113743025A - Meteorological forecast data correction method, device, electronic equipment and storage medium - Google Patents
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Abstract
The application relates to a method and a device for correcting weather forecast data, electronic equipment and a storage medium, belonging to the technical field of weather forecast, wherein the method comprises the following steps: acquiring weather forecast data, and preprocessing the weather forecast data to obtain first weather forecast data; inputting the first meteorological forecast data into a meteorological forecast data correction model for correcting to obtain second meteorological forecast data; and outputting the second weather forecast data. According to the method and the device, partial unqualified weather forecast data are screened out through preprocessing the weather forecast data in the modes of weather limit value inspection, weather extreme value inspection, internal consistency inspection, time consistency inspection and the like, the correction is carried out in the weather forecast data correction model, and the second weather forecast data with higher accuracy is output, so that a user can conduct agricultural activities according to guidance of the second weather forecast data with higher accuracy, the agricultural loss can be reduced, and the forecasting effect is obvious.
Description
Technical Field
The present application relates to the field of weather forecasting technologies, and in particular, to a method and an apparatus for correcting weather forecast data, an electronic device, and a storage medium.
Background
Different geography and climate characteristics are suitable for the growth of different agricultural products, and a climate resource foundation is created for the adjustment of agricultural industry structures and the construction of agricultural industry zones. According to weather forecast data, a user decides to fertilize, sow, spray pesticides and the like, most of the weather forecast data are obtained through weather forecast at present, but the accuracy of the weather forecast data obtained through the weather forecast is still to be improved, and the user experience degree is not high.
Disclosure of Invention
The accuracy of weather forecast data acquired based on weather forecast needs to be improved, and the application provides a method and a device for correcting the weather forecast data, electronic equipment and a storage medium.
In a first aspect, an embodiment of the present application provides a method for correcting weather forecast data, including:
acquiring weather forecast data, and preprocessing the weather forecast data to obtain first weather forecast data;
inputting the first meteorological forecast data into a meteorological forecast data correction model for correcting to obtain second meteorological forecast data;
and outputting the second weather forecast data.
Further, the training step of the weather forecast data correction model in the weather forecast data correction method is as follows:
acquiring historical weather forecast data;
training historical weather forecast data to obtain weather forecast data and correcting a first model;
acquiring real-time weather forecast data, and adjusting weather forecast data to correct parameters of the first model through the real-time weather forecast data to obtain a weather forecast data correction model;
wherein, the meteorological forecast data correction model at least comprises: a continuous prediction correction model, a time prediction correction model and a period decomposition correction model.
Further, in the method for correcting weather forecast data, the step of preprocessing the weather forecast data to obtain first weather forecast data includes:
and carrying out climatological limit value inspection, climatic extreme value inspection, internal consistency inspection and time consistency inspection on the meteorological forecast data to obtain first meteorological forecast data.
Further, in the method for correcting weather forecast data, the checking of the climate limit value includes: screening out values of the weather forecast data, wherein the values are outside the range of the climatological limit value;
the climate extreme value check comprises the following steps: screening out numerical values of which the occurrence probability is lower than the preset probability within the preset time range from the meteorological forecast data;
the internal consistency check includes: screening out values of the weather forecast data, wherein the values do not accord with the rule at the same time;
the time consistency check comprises: and screening out the numerical values which do not accord with the rule in the weather forecast data within a preset time range.
Further, in the weather forecast data correcting method, before the first weather forecast data is input into the weather forecast data correcting model and corrected to obtain the second weather forecast data, the method further includes:
storing the first weather forecast data in a memory;
wherein, the memory at least comprises: the system comprises a connector, a query buffer, an analyzer, an optimizer and an executor; the connector is used for: authenticating the identity of the user; the query cache is to: caching sentences of first weather forecast data and a result set of the sentences of the first weather forecast data; the analyzer is to: lexical and grammatical analysis of the first weather forecast data; the optimizer is for: determining an optimal solution for statement execution of the first weather forecast data; the actuator is used for: and executing the optimal scheme.
Further, in the weather forecast data correcting method, before training the historical weather forecast data to obtain the weather forecast data correcting first model, the method further includes:
and performing quality control, structuralization and difference processing on the historical meteorological forecast data, and then performing characteristic subjective evaluation, characteristic selection, characteristic extraction and characteristic reconstruction.
Further, in the weather forecast data correcting method, the manner of outputting the second weather forecast data at least includes:
voice broadcast mode, display screen display window mode.
In a second aspect, an embodiment of the present invention further provides a weather forecast data correcting device, including:
the acquisition module and the preprocessing module: the weather forecast data preprocessing module is used for preprocessing the weather forecast data to obtain first weather forecast data;
a correction module: the weather forecast data correction model is used for correcting the first weather forecast data input into the weather forecast data correction model to obtain second weather forecast data;
an output module: for outputting second weather forecast data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is used for executing the weather forecast data correction method as described in any one of the above by calling the program or the instructions stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a program or instructions for causing a computer to execute the weather forecast data correction method according to any one of the above.
The embodiment of the application has the advantages that: according to the method, the weather forecast data is preprocessed and screened by the modes of weather limit value inspection, weather extreme value inspection, internal consistency inspection, time consistency inspection and the like to obtain the first weather forecast data, then the first weather forecast data is input into the weather forecast data correction model to be corrected to obtain the second weather forecast data with higher accuracy than the weather forecast data, and the second weather forecast data with higher accuracy is output, so that a user can obtain the second weather forecast data with higher accuracy, and can perform corresponding activities according to the second weather forecast data with higher accuracy, such as traveling, fertilizing, sowing, pesticide spraying and the like.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a first schematic diagram illustrating a method for correcting weather forecast data according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a weather forecast data correction method according to an embodiment of the present application;
fig. 3 is a third schematic diagram of a method for correcting weather forecast data according to an embodiment of the present application;
fig. 4 is a schematic diagram of a weather forecast data correcting device according to an embodiment of the present application;
fig. 5 is a schematic block diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the embodiments disclosed below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a first schematic diagram of a method for correcting weather forecast data according to an embodiment of the present application.
In a first aspect, with reference to fig. 1, an embodiment of the present application provides a method for correcting weather forecast data, including three steps S101 to S103:
s101: acquiring weather forecast data, and preprocessing the weather forecast data to obtain first weather forecast data.
Specifically, in this embodiment of the application, the weather forecast data may be local weather forecast data, and the content of the local weather forecast data may include: the system comprises a wind power generator, a wind speed generator, a grass surface temperature generator, a ground temperature generator, a minute rainfall, an hour rainfall, an air pressure climate limit highest value, an air pressure climate limit lowest value, an air temperature climate limit highest value, an air temperature climate limit lowest value, a low temperature climate limit highest value, a low temperature climate limit lowest value, a dew point temperature climate limit highest value, a dew point temperature climate limit lowest value and the like. According to the method and the device, the weather forecast data are preprocessed, partial unqualified weather forecast data are screened out, and the first weather forecast data are obtained.
S102: and inputting the first meteorological forecast data into a meteorological forecast data correction model for correcting to obtain second meteorological forecast data.
Specifically, in the embodiment of the present application, after the first weather forecast data is obtained by screening out part of the unqualified data, the weather forecast data is input into the pre-trained weather forecast data correction model to correct the first weather forecast data to obtain the second weather forecast data, it should be understood that the second weather forecast data is obtained by correcting the first weather forecast data through the pre-trained weather forecast data correction model, and the accuracy of the finally obtained weather forecast data is further improved.
S103: and outputting the second weather forecast data.
Specifically, in the embodiment of the present application, outputting the second weather forecast data may be by performing voice broadcast or display on the weather forecast data through a mobile terminal such as a mobile phone or a tablet, or by performing voice broadcast or display on the weather forecast data through a television, a large screen, or the like.
Fig. 2 is a schematic diagram of a weather forecast data correcting method according to an embodiment of the present application.
Further, with reference to fig. 2, the training step of the weather forecast data correction model in the weather forecast data correction method includes three steps S201 to S203:
s201: and acquiring historical weather forecast data.
Specifically, in the embodiment of the present application, the historical weather forecast data may be historical weather forecast data of the same local last year in the same time period, may also be historical weather forecast data of the latest local 15 days, may also be historical weather forecast data of the latest local 30 days, may be historical weather forecast data of the same local last 3 years in the same time period, may also be historical weather forecast data of the same local last 5 years in the same time period, and the historical weather forecast data is flexibly selected according to actual application.
S202: training historical weather forecast data to obtain weather forecast data and correcting a first model.
Specifically, in the embodiment of the application, the historical meteorological data is flexibly selected according to actual application, and the meteorological forecast data correction first model is obtained by training the historical meteorological forecast data through a random forest algorithm, a deep neural network, a convolutional neural network, a long-short term memory cyclic neural network and the like.
S203: and acquiring real-time weather forecast data, and adjusting the weather forecast data to correct the parameters of the first model through the real-time weather forecast data to obtain a weather forecast data correction model.
Specifically, in the embodiment of the present application, the obtaining of the real-time weather forecast data may be by obtaining the real-time weather forecast data through a local weather observation station, a local surrounding station observation, wind profile radar data, multiple radar data, and the like, and it should be understood that the optimal weather forecast data correction model may be obtained by adjusting parameters of the training result weather forecast data correction first model through the real-time weather forecast data.
Wherein, the meteorological forecast data correction model at least comprises: a continuous prediction correction model, a time prediction correction model and a period decomposition correction model.
Specifically, in the embodiment of the present application, the weather forecast data correcting model at least includes the above three types, and the different models of the continuous prediction correcting model, the time prediction correcting model, and the periodic decomposition correcting model satisfy different weather forecast data requirements and satisfy different and different requirements of users, for example, the continuous prediction correcting model can continuously correct the weather forecast data.
Fig. 3 is a third schematic diagram of a method for correcting weather forecast data according to an embodiment of the present application.
Further, in the method for correcting weather forecast data, the step of preprocessing the weather forecast data to obtain first weather forecast data includes:
the weather forecast data is subjected to a climatological limit check 301, a climatic extreme check 302, an internal consistency check 303 and a time consistency check 304 to obtain first weather forecast data.
Further, in the method for correcting weather forecast data, the climate limit checking 301 includes: screening out values of the weather forecast data, wherein the values are outside the range of the climatological limit value; the climate extreme check 302 includes: screening out numerical values of which the occurrence probability is lower than the preset probability within the preset time range from the meteorological forecast data; the internal consistency check 303 includes: screening out values of the weather forecast data, wherein the values do not accord with the rule at the same time; the temporal consistency check 304 includes: and screening out the numerical values which do not accord with the rule in the weather forecast data within a preset time range.
Specifically, in the embodiment of the present application, the first weather forecast data is obtained by performing preprocessing and screening on the weather forecast data in the manners of performing climate limit value inspection 301, climate extreme value inspection 302, internal consistency inspection 303, time consistency inspection 304, and the like on the weather forecast data to remove part of unqualified weather forecast data, so as to provide data guarantee for obtaining more accurate second weather forecast data.
Further, in the weather forecast data correcting method, before the first weather forecast data is input into the weather forecast data correcting model and corrected to obtain the second weather forecast data, the method further includes:
storing the first weather forecast data in a memory;
wherein, the memory at least comprises: the system comprises a connector, a query buffer, an analyzer, an optimizer and an executor; the connector is used for: authenticating the identity of the user; the query cache is to: caching sentences of first weather forecast data and a result set of the sentences of the first weather forecast data; the analyzer is to: lexical analysis and the effect of the sentences of the lexical analysis first meteorological forecast data; the optimizer is for: determining an optimal solution for statement execution of the first weather forecast data; the actuator is used for: and executing the optimal scheme.
Specifically, the connector in the embodiment of the present application is used for a user to log in a memory to perform identity authentication of the user, including operations of checking an account password and permissions, and if the user account password passes, the connector queries all permissions of the user in a permission table, and then permission logic judgment in the connector depends on the permission data read at this time, that is, as long as the connection is not disconnected in the following, even if an administrator modifies the permissions of the user, the user logging in the memory is not affected.
The query buffer is used for caching the executed statement of the SELECT first weather forecast data and a result set of the statement of the first weather forecast data. After the connector is connected, when a statement for inquiring first weather forecast data is executed, firstly inquiring cache, firstly verifying whether SQL is executed or not by MySQL, and caching the SQL in a memory in a Key-Value form, wherein Key is an inquiry prediction and Value is a result set; if the cache key is hit, the cache key is directly returned to the client, if the cache key is not hit, subsequent operations are executed, and after the cache key is completed, the result is cached, so that the call is facilitated.
If MySQL does not hit the cache, the MySQL enters the analyzer, the analyzer is mainly used for analyzing the SQL statement, and the analyzer also comprises the following steps: firstly, lexical analysis, wherein an SQL statement consists of a plurality of character strings, keywords such as SELECT are extracted, a query table is proposed, field names are proposed, query conditions are proposed, and then the second step is carried out; and step two, analyzing syntax, namely mainly judging whether the input SQL is correct or not and whether the input SQL conforms to the syntax of MySQL.
The optimizer functions to determine the best solution to execute, such as how to select an index when multiple indexes are used, how to select an association order when multiple table lookup is used, and the like.
After the execution scheme is selected, the MySQL executor prepares to start executing the optimal scheme, firstly, whether the user has the right or not is checked before execution, if the user has no right, error information is returned, and if the user has the right, an interface of an engine is called, and the result of interface execution is returned.
Further, in the weather forecast data correcting method, before training the historical weather forecast data to obtain the weather forecast data correcting first model, the method further includes:
and performing quality control, structuralization and difference processing on the historical meteorological forecast data, and then performing characteristic subjective evaluation, characteristic selection, characteristic extraction and characteristic reconstruction.
Specifically, in the embodiment of the application, before the historical weather forecast data is trained to obtain weather forecast data and the first model is corrected, the historical weather forecast data is subjected to quality control, structuralization, difference processing, feature subjective evaluation, feature selection, feature extraction, feature reconstruction and the like, so that the accuracy of training the weather forecast data and the first model is improved, and the accuracy of obtaining the second weather forecast data is further improved.
Further, in the weather forecast data correcting method, the manner of outputting the second weather forecast data at least includes:
voice broadcast mode, display screen display window mode.
For example, outputting the second weather forecast data may alert the user by an alarm, such as a "ticker beep" to alert the user of a heavy storm and please take precautions for the next few hours. Or, the user is reminded of high temperature in the next hours by voice broadcast, for example, the user is informed of 'the temperature exceeds 38 ℃ in the next 5 hours, and the user is reminded of heatstroke prevention' by voice. Alternatively, the weather forecast data may be reminded to the user by displaying a reminder window or an icon on a video. For example, a window pops up on a display screen of a mobile phone, and the window is added with red marks and the like to remind a user that weather forecast data needs to pay attention to information such as forest fires, rainstorms and the like.
Fig. 4 is a schematic diagram of a weather forecast data correcting device according to an embodiment of the present application.
In a second aspect, an embodiment of the present invention further provides a weather forecast data correcting device, including:
the acquisition module 401 and the preprocessing module 402: the weather forecast data preprocessing module is used for acquiring weather forecast data and preprocessing the weather forecast data to obtain first weather forecast data.
Specifically, in this embodiment of the application, the obtaining module 401 obtains weather forecast data, where the weather forecast data may be local weather forecast data, and the content of the local weather forecast data may include: the system comprises a wind power generator, a wind speed generator, a grass surface temperature generator, a ground temperature generator, a minute rainfall, an hour rainfall, an air pressure climate limit highest value, an air pressure climate limit lowest value, an air temperature climate limit highest value, an air temperature climate limit lowest value, a low temperature climate limit highest value, a low temperature climate limit lowest value, a dew point temperature climate limit highest value, a dew point temperature climate limit lowest value and the like.
According to the method and the device, the preprocessing module 402 is used for preprocessing the weather forecast data to screen out partial unqualified weather forecast data to obtain the first weather forecast data, and the accuracy of the weather forecast data is improved by understanding that the weather forecast data obtained by screening out partial unqualified data.
The correction module 403: and the weather forecast data correction module is used for inputting the first weather forecast data into the weather forecast data correction model to correct the first weather forecast data to obtain second weather forecast data.
Specifically, in the embodiment of the present application, after the first weather forecast data is obtained by screening out part of the unqualified data, the weather forecast data is input into the pre-trained weather forecast data correction model, and the correction module 403 corrects the first weather forecast data to obtain the second weather forecast data.
The output module 404: for outputting second weather forecast data.
Specifically, in this embodiment of the application, the outputting module 404 may output the second weather forecast data by performing voice broadcast or display on the weather forecast data through a mobile terminal such as a mobile phone or a tablet, or by performing voice broadcast or display on the weather forecast data through a television, a large screen, or the like.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is used for executing the weather forecast data correction method as described in any one of the above by calling the program or the instructions stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium storing a program or instructions for causing a computer to execute the weather forecast data correction method according to any one of the above.
Fig. 5 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
As shown in fig. 5, the electronic apparatus includes: at least one processor 501, at least one memory 502, and at least one communication interface 503. The various components in the electronic device are coupled together by a bus system 504. A communication interface 503 for information transmission with an external device. It is understood that the bus system 504 is used to enable communications among the components. The bus system 504 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, the various buses are labeled as bus system 504 in fig. 5.
It will be appreciated that the memory 502 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
In some embodiments, memory 502 stores elements, executable units or data structures, or a subset thereof, or an expanded set thereof as follows: an operating system and an application program.
The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application services. A program for implementing any one of the weather forecast data correction methods provided in the embodiments of the present application may be included in the application program.
In the embodiment of the present application, the processor 501 is configured to execute the steps of the embodiments of the weather forecast data correction method provided by the embodiments of the present application by calling a program or an instruction stored in the memory 502, which may be specifically a program or an instruction stored in an application program.
Acquiring weather forecast data, and preprocessing the weather forecast data to obtain first weather forecast data;
inputting the first meteorological forecast data into a meteorological forecast data correction model for correcting to obtain second meteorological forecast data;
and outputting the second weather forecast data.
Any one of the weather forecast data correction methods provided in the embodiments of the present application may be applied to the processor 501, or implemented by the processor 501. The processor 501 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 501. The Processor 501 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of any one of the weather forecast data correction methods provided by the embodiments of the present application may be directly implemented as the execution of a hardware decoding processor, or implemented by the combination of hardware and software units in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 502, and the processor 501 reads the information in the memory 502, and completes the steps of a weather forecast data correction method in combination with the hardware thereof.
Those skilled in the art will appreciate that although some embodiments described herein include some features included in other embodiments instead of others, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments.
Those skilled in the art will appreciate that the description of each embodiment has a respective emphasis, and reference may be made to the related description of other embodiments for those parts of an embodiment that are not described in detail.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for correcting weather forecast data, comprising:
acquiring weather forecast data, and preprocessing the weather forecast data to obtain first weather forecast data;
inputting the first meteorological forecast data into a meteorological forecast data correction model for correction to obtain second meteorological forecast data;
and outputting the second weather forecast data.
2. The weather forecast data correction method according to claim 1, wherein the training step of the weather forecast data correction model is as follows:
acquiring historical weather forecast data;
training the historical weather forecast data to obtain weather forecast data and correcting a first model;
acquiring real-time weather forecast data, and adjusting weather forecast data to correct parameters of a first model through the real-time weather forecast data to obtain a weather forecast data correction model;
wherein the weather forecast data correction model at least comprises: a continuous prediction correction model, a time prediction correction model and a period decomposition correction model.
3. The weather forecast data correcting method of claim 1, wherein the preprocessing the weather forecast data to obtain first weather forecast data comprises:
and carrying out climate limit value inspection, climate extreme value inspection, internal consistency inspection and time consistency inspection on the weather forecast data to obtain the first weather forecast data.
4. The weather forecast data correcting method according to claim 3,
the climatological threshold check comprises: screening out values of the weather forecast data, wherein the values are outside the range of the climatological limit value;
the climate extreme value check comprises: screening out numerical values of which the occurrence probability is lower than the preset probability within a preset time range from the numerical values in the weather forecast data;
the internal consistency check comprises: screening out values of the weather forecast data, wherein the values do not accord with rules at the same time;
the temporal consistency check comprises: and screening out the numerical values which do not accord with the rule in the weather forecast data within a preset time range.
5. The weather forecast data correcting method according to claim 1, wherein before inputting said first weather forecast data into a weather forecast data correcting model for correcting to obtain second weather forecast data, said method further comprises:
storing the first weather forecast data in a memory;
wherein the memory includes at least: the system comprises a connector, a query buffer, an analyzer, an optimizer and an executor;
the connector is used for: authenticating the identity of the user;
the query cache is configured to: caching sentences of first weather forecast data and a result set of the sentences of the first weather forecast data;
the analyzer is to: lexical and grammatical analysis of the effects of the statements of the first weather forecast data;
the optimizer is to: determining an optimal solution for statement execution of the first weather forecast data;
the actuator is used for: and executing the optimal scheme.
6. The weather forecast data correcting method of claim 2, wherein before training the historical weather forecast data to obtain the weather forecast data correcting first model, the method further comprises:
and performing quality control, structuralization and difference processing on the historical meteorological forecast data, and then performing feature subjective evaluation, feature selection, feature extraction and feature reconstruction.
7. The weather forecast data correcting method according to claim 1, wherein said outputting said second weather forecast data at least includes:
voice broadcast mode, display screen display window mode.
8. A weather forecast data correcting device, comprising:
the acquisition module and the preprocessing module: the weather forecast data preprocessing module is used for preprocessing the weather forecast data to obtain first weather forecast data;
a correction module: the weather forecast data correction model is used for correcting the first weather forecast data input into the weather forecast data correction model to obtain second weather forecast data;
an output module: for outputting the second weather forecast data.
9. An electronic device, comprising: a processor and a memory;
the processor is used for executing a weather forecast data correction method according to any one of claims 1 to 7 by calling a program or instructions stored in the memory.
10. A computer-readable storage medium storing a program or instructions for causing a computer to execute the weather forecast data correction method according to any one of claims 1 to 7.
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CN115220131A (en) * | 2022-06-23 | 2022-10-21 | 阿里巴巴(中国)有限公司 | Meteorological data quality inspection method and system |
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