Meteorological data processing method and device, readable storage medium

Abstract

The present disclosure provides a meteorological data processing method and device, and a readable storage medium. The meteorological data processing method comprises: acquiring data of a plurality of meteorological data sources, combining the data of the plurality of meteorological data sources for integrated processing to obtain meteorological service data, monitoring data quality of at least one target meteorological data source in the plurality of meteorological data sources, and adjusting a weight of data of the target meteorological data source in the integrated processing according to the data quality. The method can not only increase the stability of the provision of the meteorological service data, but also increase the accuracy of the meteorological service data.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to a meteorological data processing method and apparatus, and a readable storage medium. BACKGROUND

[0002] Meteorological conditions have different influences on the take-off, navigation, landing and other various flight activities of an aircraft. The purpose of aviation meteorological service is to ensure the safety and efficiency of aviation transportation and aviation operation scheduling. Adverse weather has adverse effects on flight safety and benefits, which can be avoided or reduced by human efforts. Therefore, aviation meteorological service should continuously improve the aviation meteorological service quality control and management system, improve the meteorological detection capability, forecasting and early warning capability, product development capability, meteorological information transmission capability and meteorological technology innovation capability, meet the growing demand of aviation transportation safety and aviation operation scheduling for meteorological service, and adapt to the overall development of aviation industry.

[0003] With the rapid development of science and technology, the sources of meteorological data are increasing, the types of data are constantly enriched, and the amount of data is growing exponentially. How to quickly process, fuse, interpret and apply these data, and provide more stable and accurate meteorological services for users in the big data and Internet environment, poses a huge challenge to us. SUMMARY

[0004] An aspect of the present disclosure provides a meteorological data processing method, comprising: acquiring data of a plurality of meteorological data sources, combining the data of the plurality of meteorological data sources for integrated processing to obtain meteorological service data, monitoring data quality of at least one target meteorological data source in the plurality of meteorological data sources, and adjusting a weight of data of the at least one target meteorological data source in the integrated processing according to the data quality.

[0005] For example, the plurality of meteorological data sources include at least one basic meteorological data source and a plurality of supplementary meteorological data sources.

[0006] For example, the at least one basic meteorological data source includes grid live data; and the plurality of supplementary meteorological data sources include at least two of meteorological report data, satellite data, radar data and nearby weather station data.

[0007] For example, the monitoring includes dynamic monitoring at a predetermined frequency, and dynamically adjusting the weight of the data of the target meteorological data source in the integrated processing according to the data quality at the predetermined frequency.

[0008] For example, the monitoring includes being based on one or more of the following methods: meteorological principles, probabilistic statistical models, machine learning methods, business experience and logical rules.

[0009] For example, the data quality includes stability of data update, abnormal interruption, data abnormality, and accuracy of data.

[0010] For example, according to the data quality, adjusting the weight of data of the target meteorological data source in the integrated processing includes: when it is judged that the target meteorological data source has a stability of data update lower than a first predetermined threshold, or an abnormal interruption occurs, or a frequency of data abnormality is higher than a second predetermined threshold, or an accuracy of data is lower than a third predetermined threshold, the weight of data of the target meteorological data source in the integrated processing is reduced, or the target meteorological data source is removed from the integrated processing.

[0011] For example, according to the data quality, adjusting the weight of data of the target meteorological data source in the integrated processing includes: when it is judged that the target meteorological data source has a stability of data update higher than or equal to a first predetermined threshold, and an abnormal interruption occurs, or a frequency of data abnormality is lower than a second predetermined threshold, or an accuracy of data is higher than a third predetermined threshold, data of the target meteorological data source is added to the integrated processing, or the weight of data of the target meteorological data source in the integrated processing is increased.

[0012] For example, combining data of the plurality of meteorological data sources for integrated processing to obtain service data includes: the integrated processing is based on one or more of meteorological principles, probabilistic statistical models, machine learning methods, business experience, and logical rules.

[0013] For example, combining data of the plurality of meteorological data sources for integrated processing to obtain service data includes: in the integrated processing, data of the plurality of meteorological data sources is weighted and averaged.

[0014] For example, the weight of the basic meteorological data source is higher than the weight of the supplementary meteorological data source.

[0015] Another aspect of the present disclosure also provides a meteorological data processing apparatus, comprising: a data acquisition unit configured to acquire data of a plurality of meteorological data sources; a data integrated processing unit configured to combine data of the plurality of meteorological data sources for integrated processing to obtain service data; a monitoring unit configured to monitor data quality of at least one target meteorological data source among the meteorological data sources, and according to the data quality, adjust the weight of data of the at least one target meteorological data source in the integrated processing.

[0016] Still another aspect of the present disclosure also provides a meteorological data processing apparatus, comprising: at least one processor; and a computer device readable storage medium, wherein the computer device readable storage medium comprises computer executable instructions, when the instructions are executed by the processor, for implementing the foregoing meteorological data processing method.

[0017] Another aspect of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium includes computer-executable instructions, and when the instructions are executed by a processor, they are used to implement the aforementioned meteorological data processing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings of the embodiments of the present disclosure. Obviously, the drawings described below only relate to some embodiments of the present disclosure and are not intended to limit the present disclosure.

[0019] Figure 1A A flow chart of a method for processing meteorological data according to an embodiment of the present disclosure is shown;

[0020] Figure 1B for Figure 1A The specific flow chart of step 104 in the method shown;

[0021] Figure 2 A schematic diagram of a meteorological data processing method according to an embodiment of the present disclosure is shown;

[0022] Figure 3 A schematic block diagram of a meteorological data processing device according to an embodiment of the present disclosure is shown;

[0023] Figure 4 A schematic block diagram of a meteorological data processing device according to another embodiment of the present disclosure is shown;

[0024] Figure 5 A schematic diagram illustrating the architecture of an exemplary electronic device according to an embodiment of the present disclosure;

[0025] Figure 6 A schematic diagram of a storage medium according to an embodiment of the present disclosure is shown;

[0026] Figure 7 A diagram showing an exemplary application scenario for executing the meteorological data processing method provided by any embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0028] The terms used herein to describe embodiments of the present application are not intended to limit and / or restrict the scope of the present application. For example, unless otherwise defined, technical terms or scientific terms used in the present disclosure should be understood as having the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains.

[0029] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals or characters can refer to components or elements performing substantially the same functions.

[0030] At least one embodiment of the present disclosure provides a meteorological data processing method. The meteorological data processing method comprises: acquiring data of a plurality of meteorological data sources; combining the data of the plurality of meteorological data sources for integrated processing to obtain meteorological service data; monitoring data quality of at least one target meteorological data source in the plurality of meteorological data sources; and adjusting the weight of the data of the target meteorological data source in the integrated processing according to the data quality. The method not only can increase the stability of the provision of meteorological service data, but also can increase the accuracy of meteorological service data.

[0031] Figure 1A A flowchart of a meteorological data processing method according to an exemplary embodiment of the present disclosure is shown, which comprises steps 101-104.

[0032] In step 101, data of a plurality of meteorological data sources is acquired.

[0033] For example, the plurality of meteorological data sources comprises at least one basic meteorological data source and a plurality of supplementary meteorological data sources. The basic meteorological data source here can be any form of meteorological data source, for example, a meteorological data source that can take into account stability and accuracy can be selected as a basic meteorological data source. For example, the data provided by the basic meteorological data source can provide basic meteorological services.

[0034] For example, the data of the at least one basic meteorological data source can include grid live data. The grid live data is a data product from the China Meteorological Administration, with a spatial resolution of 5-10KM, which can be acquired through the network and stored in the form of a file. The grid live data is based on a grid, including data related to geographical information and time. For example, in the live data, there are a variety of elements, such as temperature, air pressure, precipitation, wind direction, wind speed, etc. Each spatial position corresponds to the corresponding various elements at different times, such as 16:00 on May 28, 2020, East longitude 116.25 degrees, North latitude 23.26 degrees, 850 hPa, temperature 19℃, relative humidity 50%, precipitation 1.5mm / h, etc. Through the grid data, the customer can be provided with live information of air pressure, temperature, wind speed, precipitation and solar radiation, etc. meteorological elements within each time resolution and each spatial resolution.

[0035] In order to guarantee the accuracy and stability of the basic meteorological data, the data from multiple other meteorological data sources are also acquired as supplementary meteorological data, because the data from a single data source can be distorted during transmission and reception or can be subject to faults such as device disconnection.

[0036] For example, the multiple supplementary meteorological data sources can include meteorological report data, satellite data, radar data, and nearby weather station data.

[0037] For example, the meteorological report data includes airport meteorological report (METAR) data, special weather report (SPECI), terminal airport weather forecast (TAF), and the like.

[0038] The METAR data is a routine live report of an airport itself, containing various meteorological elements and weather phenomena, and is usually provided at fixed time intervals. For example, the following METAR data:

[0039] ZSPD 170800Z 32007MPS 7000SCT040 FEW070 05 / M04 Q1028 NOSIG In the above message data, the various data items in turn relate to an airport, a time, a wind direction and speed, a visibility, a cloud layer, a temperature, a correction, and a forecast. It can be known from the translation of the above message data that the Shanghai Pudong International Airport, at coordinated universal time 17:08 on the 17th, the ground wind direction is 320, the wind speed is 7 meters per second, the visibility is 7000 meters, there are three-fourth clouds at 4000 feet, there is one-two clouds at 7000 feet, the temperature is 5 degrees Celsius, the dew point is -4 degrees Celsius, the corrected sea pressure is 1028, and the weather has not changed significantly in the past two hours. It should be pointed out that the METAR data can not include all 12 items of information involved in the data format, for example, runway visibility, past weather, wind shear, and the like can be omitted. Of course, the same information can also be displayed in many pieces, for example, the cloud layer information, so that the cloud layer conditions of different height layers are recorded and sent out.

[0040] In addition to the above routine METAR data, a special weather report (SPECI) can also be provided when one or more meteorological elements reach a specified standard between two routine weather reports.

[0041] In addition, the weather message related to the airport can also be a terminal aerodrome forecast (TAF), and the TAF report is a report established for a 5-mile radius around an airport, and the TAF report is usually provided for a larger airport. Each TAF report is valid for only 24 hours, and is updated 4 times a day at 00:00Z, 06:00Z, 12:00Z, and 18:00Z. The TAF report uses the same descriptor symbols and abbreviations as the METAR report.

[0042] Radar is one of the important means for the meteorological industry to monitor real-time weather, which can feedback cloud amount, cloud height and moving speed, and can also measure wind speed and direction at different altitudes, which plays an important role in determining the location and characteristics of weather systems. Therefore, the radar data usually includes the distribution and changes of clouds and rain in the atmosphere, precipitation intensity, cloud height and thickness, wind direction and speed in different atmospheric layers, and other meteorological elements.

[0043] Meteorological satellites have wide observation range, high observation frequency, fast observation time, and high observation data quality, and are not limited by natural conditions and regional conditions. Various meteorological remote sensors carried by satellites receive and measure visible light, infrared and microwave radiation of the earth and its atmosphere, and electromagnetic waves reflected by satellite navigation systems, and transmit signals to ground stations. The ground station restores the electrical signals transmitted by the satellite, draws pictures of various cloud layers, wind speed and direction, ground and sea surface, and further processes and calculates to obtain various meteorological data. Satellite meteorological data can be used to calculate the inversion of cloud amount and cloud shape, and to a certain extent, the inversion of precipitation.

[0044] In addition, meteorological stations are also set up near some airports. For example, an "information radius" is set with the airport as the center, and the data of the "nearby meteorological stations" within the information radius can also be used as airport data.

[0045] Of course, the above are only a few examples of sources of supplementary meteorological data, and data from more meteorological data sources can also be used. In the following description, the data of the grid live data as the basic meteorological data source is taken as an example for description, but the embodiments of the present disclosure are not limited thereto, for example, satellite meteorological data can be used as the data of the basic meteorological data source.

[0046] In step 102, the data of the plurality of meteorological data sources obtained is combined for integrated processing to obtain meteorological service data.

[0047] For example, data of the basic meteorological data source and data of the plurality of supplementary meteorological data sources are integrated to obtain meteorological service data. Then, the obtained meteorological service data can be stored in a database, and can be distributed according to corresponding data interfaces to provide services such as query, analysis interface, and data display through a visualization interface for users, and embodiments of the present disclosure do not limit this.

[0048] According to embodiments of the present disclosure, "integration processing" refers to processing of combining several data together, for example, to obtain more complete and accurate meteorological service data. For example, the integration processing can be based on one or more of the following: meteorological principles, probabilistic statistical models, machine learning methods, business experience, logical rules. For example, the probability distribution of different meteorological data sources can be obtained according to the probabilistic statistical model, and data deviating too much can be removed; each meteorological element can be classified and regressed according to the machine learning method; the weights of different meteorological data sources can be adjusted according to business experience; meteorological data sources can be selected according to logical rules, etc. In embodiments of the present disclosure, integration processing based on meteorological principles, probabilistic statistical models, machine learning methods, business experience and logical rules can be implemented according to conventional methods.

[0049] For example, in some embodiments, "integration processing" includes integration processing of multiple data of different meteorological data sources, for example, combining different meteorological elements in different meteorological data sources, and merging the same meteorological elements in different meteorological data sources, for example, weighted average, to obtain new meteorological elements. Thus, the meteorological service data obtained after integration processing includes all meteorological elements of one or more meteorological data sources used.

[0050] For example, meteorological data obtained from different meteorological data sources contains the same or different meteorological elements, so that the combination of meteorological data obtained from different meteorological data sources can improve the accuracy of the subsequent obtained meteorological service data. For example, grid live data can provide weather, temperature, wind power, wind direction, relative humidity, visibility and precipitation, etc., radar data and satellite data can provide precipitation, METAR data can provide airport name, issue time, wind power information, visibility, runway visual range, weather, cloud layer, air temperature, dew point, corrected sea pressure, etc., meteorological station data can provide temperature, wind direction and speed, precipitation, air pressure, etc. And in the case of combining meteorological data obtained from different meteorological data sources, it is rare that these different meteorological data sources are offline at the same time, so the overall stability of the meteorological data can be improved to a greater extent.

[0051] For example, the weighted average of the data from the basic meteorological data source and the supplementary meteorological data source can be regarded as the weighted average of each meteorological element in the data of the basic meteorological data source and the data of the supplementary meteorological data source. For example, the grid live data, the radar data, the satellite data and the weather station data all have the meteorological element of precipitation, but there are certain differences between each other, so the data of the grid live data, the radar data, the satellite data and the weather station data related to the meteorological element of precipitation can be weighted and averaged. According to meteorological principles, the correlation between elements often needs to be considered. For example, the temperature and the atmospheric pressure change after being integrated, and correspondingly, the relative humidity also changes. Therefore, for some meteorological elements that are not integrated, adaptive correction can be made according to the integrated results of other meteorological elements.

[0052] For example, the data from different meteorological data sources have different time resolutions and spatial resolutions, so a unified standard needs to be established for data alignment of data with different spatial and time resolutions. For example, the radar data can be grid data with a spatial resolution of 1 KM (kilometer) and a time resolution of 6 minutes, the satellite data can be grid data with a spatial resolution of 5 KM and a time resolution of 1 hour, and the weather station data is data within the radius of the station information, and the time resolution can be 1-5 minutes. Therefore, the resolutions of the data of the multiple supplementary meteorological data sources are aligned with the time resolution and the spatial resolution of the grid live data by means of extraction and interpolation, that is, if the time resolution or the spatial resolution of the supplementary meteorological data source is higher than the spatial or time resolution of the grid live data, the spatial or time resolution of the grid live data is adapted by means of extraction, and vice versa, the resolution is increased by means of interpolation. By establishing a unified standard for data alignment of data with different spatial and time resolutions, the accuracy of the processed meteorological service data can be further improved.

[0053] Because distortion or equipment failure may occur during the transmission and reception of data from each data source, the authenticity of the data of some meteorological data sources may be reduced or even unavailable. Therefore, when performing integration processing such as weighted average, the weights of the data of different meteorological data sources can be dynamically adjusted, so as to reflect the authenticity of the data of the meteorological data sources and improve the accuracy of the processed meteorological service data.

[0054] In step 103, the data quality of at least one target meteorological data source in the multiple meteorological data sources is monitored.

[0055] For example, according to one embodiment of the present disclosure, the data quality of at least one target meteorological data source in the plurality of meteorological data sources can be monitored at predetermined time intervals (e.g., every 6 minutes, 10 minutes, or 30 minutes, etc.). Here, the target meteorological data source refers to an object currently selected for detection in the plurality of meteorological data sources, which is selected, for example, in sequence, randomly, etc.

[0056] The method of monitoring data quality can also include monitoring based on meteorological principles, probabilistic statistical models, machine learning methods, business experience, and logical rules, etc. For example, the data quality can include data update stability, data accuracy, abnormal interruption, data abnormality, etc. Here, the data accuracy is based on the fluctuation and dispersion of data within a predetermined time interval and the deviation from the true situation, and if the fluctuation and dispersion of data and the deviation from the true situation increase, the data quality decreases; the data update stability is based on the frequency of data updates within a predetermined time interval, and if the frequency of data updates decreases, the data quality decreases; the abnormal interruption of data is based on the frequency of data transmission interruption, and if the frequency of data transmission interruption increases, the data quality decreases; and the data abnormality is based on the frequency of data abnormality, such as data overflow out of a reasonable range or data fixed at a certain value for a long time, and if the frequency of data abnormality increases, the data quality decreases. Of course, the indicators of data quality are not limited to the examples listed, and other indicators of data quality can be selected and monitored according to actual conditions.

[0057] In step 104, the weight of the data of at least one target meteorological data source in the plurality of meteorological data sources in the integrated processing is adjusted according to the monitored data quality.

[0058] For example, when the data quality is dynamically monitored at a predetermined frequency, the weight can be adjusted once every predetermined frequency accordingly.

[0059] It should be noted that, Figure 1A The functions marked in the flowchart blocks in Figure 1A When the method shown in FIG. 1 is initially executed, the data quality of at least one target meteorological data source in the plurality of meteorological data sources can be monitored first, the weight of the data of at least one target meteorological data source in the plurality of meteorological data sources in the integrated processing is adjusted according to the monitored data quality, and the data of the plurality of meteorological data sources is integrated according to the adjusted weight.

[0060] Figure 1B For Figure 1AA flow chart of one example of step 104 in the illustrated method. In this example, in step 104_1, a judgment is made on the data quality of a certain meteorological data source, and when it is judged that the data update stability of the meteorological data source is lower than a first predetermined threshold, or the frequency of abnormal interruption or data anomaly is higher than a second predetermined threshold, or the data accuracy is lower than a third predetermined threshold, step 104_2a is executed to reduce the weight of the data of the target meteorological data source in the integration processing, or remove the data of the target meteorological data source from the integration processing, otherwise, when it is judged in step 104_1 that the data update stability of a certain meteorological data source is higher than the first predetermined threshold, and the frequency of abnormal interruption or data anomaly is lower than the second predetermined threshold, and the data accuracy is higher than the third predetermined threshold, step 104_2b is executed to add the data of the target meteorological data source to the integration processing, or increase the weight of the data of the target meteorological data source in the integration processing.

[0061] The first predetermined threshold, the second predetermined threshold and the third predetermined threshold described above can be set according to experience and needs as the basis for calculation and evaluation.

[0062] Here, the data quality of each meteorological data source is positively correlated (for example, proportional) to the weight of the data of the meteorological data source in the integration processing, that is, the better the quality of the data, the higher the weight. For example, when only one data source is available, the weight of the data source at this time is 1.

[0063] It should be understood that adjusting the weight of the data of the target meteorological data source in the integration processing according to the quality of the data of each meteorological data source can adopt other judgment criteria, and is not limited to the judgment criteria listed above. For example, the meteorological data source being adjusted can be a basic meteorological data source or a supplementary meteorological data source. In some embodiments, for example, in the initial state of a certain processing period, the weight of the basic meteorological data source can be set to be higher than that of other supplementary meteorological data sources, and then over time, after one or more adjustments, the weight of the basic meteorological data source can become lower than that of one or more supplementary meteorological data sources.

[0064] Figure 2 A schematic diagram of a meteorological data processing method according to an embodiment of the present disclosure is shown. In one example, as shown in FIG. 1, the meteorological data processing method includes the following steps: Figure 2As shown, the grid live data is used as the basic data of the live data, and can be used as the live data source alone when there is no other data source; the multiple supplementary data are respectively weather report data, satellite data, radar data and nearby weather station data. Similarly, as described above, different weather data sources can be used as the basic data source and the supplementary data source. The multiple weather data sources form a multi-data source set, and the data of the multi-data source set is processed by the above algorithm to obtain the integrated processed weather data. The integrated processed weather data is more complete, stable and accurate than the basic data source and the supplementary data source before processing.

[0065] The integrated processed weather data can be processed according to a certain weather algorithm to generate weather service data. For example, the weather algorithm can be an aviation-related weather algorithm, such as calculating the possibility of aircraft turbulence, aircraft icing, thunderstorm generation, strong convection generation, etc. After the calculation of the weather algorithm, the original weather data is converted into aviation-related weather service data, which can be stored, visualized images are generated, and then provided to users through a data service interface.

[0066] For example, when the integrated processed weather data is processed by the weather algorithm to generate weather service data for real-time weather forecasting, more accurate and stable live data can be obtained. In the multi-data source set, the data quality of each weather data source needs to be dynamically monitored and evaluated. If the data quality of a certain weather data source is poor (data update is unstable, abnormal interruption, data anomaly or data inaccuracy, etc.), the weight of the data source in the integrated processing is reduced, or the calculation of a certain data source is abandoned (the weight is zero). On the contrary, the weather data source is added to the integrated processing, or the weight of the weather data source is increased. Then, the integrated processing is performed according to the monitored and evaluated weight to obtain the final synthesis result.

[0067] Thus, in the above embodiment, by integrating the data of multiple weather data sources in a weighted average manner and dynamically adjusting the weight of each weather data source in real time, more stable and robust weather live data can be provided, which more objectively reflects the real atmospheric conditions and enhances the accuracy.

[0068] Another embodiment of the present disclosure provides a weather data processing device. Figure 3 A schematic block diagram of the weather data processing device 300 is shown. As shown in the figure, the weather data processing device 300 can include a data acquisition unit 301, a data integration processing unit 302 and a monitoring unit 303. Figure 3 As shown, the weather data processing device 300 can include a data acquisition unit 301, a data integration processing unit 302 and a monitoring unit 303.

[0069] According to at least one embodiment of the present disclosure, the data acquisition unit 301 is configured to acquire data from multiple meteorological data sources; the data integration processing unit 302 is configured to combine the data from the multiple meteorological data sources for integrated processing to obtain service data; the monitoring unit 303 is configured to monitor the data quality of at least one target meteorological data source among the meteorological data sources, and adjust the weight of the data of the at least one target meteorological data source in the integrated processing based on the data quality.

[0070] For example, in at least one example, the monitoring unit 303 is further configured to dynamically monitor at a predetermined frequency, and dynamically adjust the weight of the data of the target meteorological data source in the integration process at a predetermined frequency according to the data quality.

[0071] The data acquisition unit 301 , the data integration processing unit 302 and the monitoring unit 303 may be implemented by software, hardware, firmware or any combination thereof.

[0072] Another embodiment of the present disclosure further provides a meteorological data processing device, Figure 4 A schematic diagram of a meteorological data processing device according to an embodiment of the present disclosure is disclosed. Figure 4 As shown, the meteorological data processing device 400 may include one or more processors 401 and one or more memories 402. The memories 402 store computer-executable instructions that, when executed by the one or more processors 401, may perform the meteorological data processing method described above. The one or more memories 402 and the one or more processors 401 may be interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0073] For example, one or more memories 402 and one or more processors 401 can be set in a single machine, a server, or a cloud to execute Figure 1A One or more steps in the described method for processing meteorological data.

[0074] For example, the one or more processors 401 may be a central processing unit (CPU), a digital signal processor (DSP), or other processing units with data processing capabilities and / or program execution capabilities, such as a field programmable gate array (FPGA). For example, the central processing unit (CPU) may be an X86 or ARM architecture. The one or more processors 401 may be general-purpose processors or special-purpose processors, and may control other components in the electronic device 400 to perform desired functions.

[0075] For example, the one or more memories 402 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, a flash memory, etc.

[0076] One or more computer program modules 402_1 may be stored on a computer-readable storage medium. Computer program modules 402_1 include executable code. One or more processors 401 may execute one or more computer program modules 402_1 to implement various functions of electronic device 400. The computer-readable storage medium may also store various applications and data, as well as data used and / or generated by the applications. The specific functions and technical effects of electronic device 400 can be found in the description of the meteorological data processing method above and will not be elaborated upon here.

[0077] The method or apparatus according to at least one embodiment of the present disclosure may also be Figure 5 The architecture of the computer device 500 shown in FIG. Figure 5 As shown, the computing device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output 506, a hard disk 507, etc. The storage device in the computing device 500, such as the ROM 503 or the hard disk 507, may store various data or files used for processing and / or communication of the meteorological data processing method provided by the present disclosure, as well as program instructions executed by the CPU. Of course, Figure 5 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 5 One or more components of a computing device are shown.

[0078] Yet another embodiment of the present disclosure provides a non-transitory readable storage medium. Figure 6 is a schematic block diagram of a non-transitory readable storage medium 600 provided by at least one embodiment of the present disclosure. Figure 6 As shown, the non-transitory readable storage medium 600 includes computer program instructions 601 stored thereon. When the computer program instructions 601 are executed by a processor, one or more steps in the meteorological data processing method provided by at least one embodiment of the present disclosure are performed.

[0079] For example, the storage medium can be any combination of one or more computer-readable storage media, such as one computer-readable storage medium containing computer-readable program code for obtaining data of a plurality of meteorological data sources, another computer-readable storage medium containing computer-readable program code for combining data of the plurality of meteorological data sources for integrated processing to obtain meteorological service data, and still another computer-readable storage medium containing computer-readable program code for monitoring data quality of at least one target meteorological data source of the plurality of meteorological data sources and adjusting a weight of data of the target meteorological data source in the integrated processing according to the data quality. Of course, each of the above program codes can also be stored in the same computer-readable medium, and the embodiments of the present disclosure do not limit this. For example, when the program code is read by a computer, the computer can execute the program code stored in the computer storage medium, such as the meteorological data processing method provided by any embodiment of the present disclosure.

[0080] For example, the storage medium can include a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a flash memory, or any combination of the above storage media, and can also be other applicable storage media.

[0081] Figure 7 An exemplary application scenario diagram for performing the meteorological data processing method provided by any embodiment of the present disclosure is shown. As shown in the diagram, the system 700 can include a user terminal 701, an application server 702, a network 703, and one or more data servers 704. Figure 7

[0082] For example, it can be understood that the user terminal 701 can be any other type of electronic device capable of performing reception, processing, and display of data, which can include but is not limited to a desktop computer, a notebook computer, a tablet computer, a smart home device, a wearable device, a vehicle-mounted electronic device, a medical electronic device, etc.

[0083] For example, the application server 702 is a single server or a server group, and each server in the server group is connected through a wired network or a wireless network. The server group can be centralized or distributed. It can be local or remote. For example, the application server 701 can be a general-purpose server or a special-purpose server, and can be a virtual server or a cloud server, etc., configured to implement the meteorological data processing method of at least one embodiment of the present disclosure.

[0084] ​For example, the network 703 can be a single network, or a combination of at least two different networks. For example, the network 703 can include, but is not limited to, one or a combination of local area networks, wide area networks, public networks, private networks, the Internet, mobile communication networks, etc.

[0085] For example, the one or more data servers 704 can be servers for obtaining and storing meteorological data sources, and different meteorological data sources are usually stored in different data servers.

[0086] For example, in one example, the user terminal 701 requests a meteorological service from the application server 702 through the network 703. The application server 702 can request meteorological data from the one or more data servers 704 via the network 703 or other techniques (e.g., Bluetooth communication, infrared communication, etc.). The data server 704 sends the requested meteorological data to the application server 702 in response to the request. Next, the application server 702 combines the obtained meteorological data, performs integration processing to obtain meteorological service data, monitors the data quality of the obtained meteorological data, and adjusts the weight of the corresponding meteorological data in the integration processing or adds / removes the corresponding meteorological data based on the data quality. The application server 702 can further process the obtained meteorological service data, generate meteorological service data according to a corresponding algorithm, and generate pictures according to the meteorological service data, and can send the corresponding meteorological service data (or pictures) to the user terminal 701 according to the meteorological service request of the user terminal.

[0087] Those skilled in the art can understand that the disclosed content of the present disclosure can have various modifications and improvements.

[0088] The various apparatuses or units described above can be implemented by hardware, or by software, firmware, or a combination of some or all of the three.

[0089] Those skilled in the art can understand that all or part of the steps in the above method can be directed by a program.

[0090] The program can be stored in a computer readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, etc. Alternatively, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiments can be implemented in the form of hardware or in the form of a software functional module. The present disclosure is not limited to any specific form of combination of hardware and software.

[0091] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0092] The foregoing is a summary of the present disclosure, and is not to be considered as limiting its scope. While several exemplary embodiments of the present disclosure have been described, it should be apparent that many modifications are possible in the exemplary embodiments without departing from the inventive teachings and advantages of the present disclosure. Accordingly, all such modifications are intended to be included within the scope of the present disclosure as defined in the following claims. In the claims, means-plus-function clauses are used where function-words have been omitted for clarity. It will be understood that such function words have been omitted from such clauses in recent patent legislation for the purpose of providing greater flexibility in claim interpretation. Accordingly, it is not intended that the use of such clauses limit the claims in any way.

Claims

1. A meteorological data processing method, comprising: Get data from multiple meteorological data sources, Combining the data of the plurality of meteorological data sources and performing weight-based integration processing to obtain meteorological service data, monitoring data quality of at least one target meteorological data source among the plurality of meteorological data sources, and According to the data quality, the weight of the data of the target meteorological data source in the integration process is adjusted; The data quality includes the stability of data updates, abnormal interruptions, data anomalies and data accuracy. The combining of the data from the plurality of meteorological data sources for weight-based integration processing includes: merging data of the same meteorological element from different meteorological data sources, and combining data of different meteorological elements from different meteorological data sources based on the weights corresponding to the respective meteorological data sources; Among them, according to the data quality, the weight of the data of the target meteorological data source in the integrated processing is adjusted, including: when it is judged that the target meteorological data source has a data update stability lower than a first predetermined threshold, or the frequency of abnormal interruptions or data anomalies is higher than a second predetermined threshold, or the data accuracy is lower than a third predetermined threshold, the weight of the data of the target meteorological data source in the integrated processing is reduced, or it is removed from the integrated processing.

2. The method according to claim 1, wherein The plurality of meteorological data sources include at least one basic meteorological data source and a plurality of supplementary meteorological data sources.

3. The method according to claim 2, wherein: The at least one basic meteorological data source includes gridded real-time data; and the plurality of supplementary meteorological data sources include at least two of weather report data, satellite data, radar data, and nearby weather station data.

4. The method according to any one of claims 1 to 3, wherein: The monitoring includes: dynamically monitoring at a predetermined frequency, and dynamically adjusting the weight of the data of the target meteorological data source in the integration process at the predetermined frequency according to the data quality.

5. The method according to any one of claims 1 to 3, wherein: The monitoring includes monitoring based on one or more of the following: meteorological principles, probability statistical models, machine learning methods, business experience and logical rules.

6. The method according to any one of claims 1 to 3, wherein: Adjusting the weight of the data from the target meteorological data source in the integration process according to the data quality further includes: When it is determined that the target meteorological data source has a data update stability that is higher than or equal to a first predetermined threshold, and the frequency of abnormal interruptions or data anomalies is lower than a second predetermined threshold, and the data accuracy is higher than a third predetermined threshold, the data of the target meteorological data source is added to the integrated processing, or the weight of the data of the target meteorological data source in the integrated processing is increased.

7. The method according to any one of claims 1 to 3, wherein: Combining the data from the plurality of meteorological data sources for integrated processing to obtain service data includes: The integration process is performed based on one or more of the following: meteorological principles, probabilistic statistical models, machine learning methods, business experience, and logical rules.

8. The method according to claim 7, wherein: Combining the data from the plurality of meteorological data sources for integration processing to obtain service data includes: performing weighted averaging on the data from the plurality of meteorological data sources during the integration processing.

9. The method according to claim 2 or 3, wherein: The weight of the basic meteorological data source is higher than the weight of the supplementary meteorological data source.

10. A meteorological data processing device comprising: A data acquisition unit, used to acquire data from multiple meteorological data sources, a data integration processing unit, configured to combine the data of the plurality of meteorological data sources and perform weight-based integration processing to obtain service data; a monitoring unit configured to monitor the data quality of at least one target meteorological data source among the meteorological data sources, and adjust the weight of the data of the target meteorological data source in the integration process according to the data quality; The data quality includes the stability of data updates, abnormal interruptions, data anomalies and data accuracy. The data integration processing unit is further configured to: merge data of the same meteorological element in different meteorological data sources based on weights corresponding to the plurality of meteorological data sources, and combine data of different meteorological elements in different meteorological data sources; In which, the monitoring unit is further configured to: when it is judged that the target meteorological data source has a data update stability lower than a first predetermined threshold, or the frequency of abnormal interruptions or data anomalies is higher than a second predetermined threshold, or the data accuracy is lower than a third predetermined threshold, reduce the weight of the data of the target meteorological data source in the integrated processing, or remove it from the integrated processing.

11. A meteorological data processing device comprising: at least one processor; A computer-readable storage medium, wherein the computer-readable storage medium comprises computer-executable instructions, and when the instructions are executed by the processor, is used to implement the method according to any one of the preceding claims 1 to 9.

12. A computer-readable storage medium, wherein: The computer device readable storage medium includes computer executable instructions, and when the instructions are executed by a processor, is used to implement the method of any one of the preceding claims 1-9.

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