CN115166862B

CN115166862B - Intelligent observation meteorological station

Info

Publication number: CN115166862B
Application number: CN202210831459.0A
Authority: CN
Inventors: 刘钧; 金佳宁; 李楠; 刘俊杰; 高旭宾; 张彬彬; 张冬岩; 曲鹏飞
Original assignee: Huayun Sounding Beijing Meteorological Technology Corp
Current assignee: Huayun Sounding Beijing Meteorological Technology Corp
Priority date: 2022-07-14
Filing date: 2022-07-14
Publication date: 2023-03-31
Anticipated expiration: 2042-07-14
Also published as: CN115166862A

Abstract

The utility model provides an intelligence observation meteorological station, including intercommunicating's data processing center and a plurality of meteorological station, through the meteorological data package of waiting to match that uploads of the first meteorological station in the receiving meteorological station, it carries out meteorological data object identification to wait to match the meteorological data package, and return at least one first meteorological data object that arrives to first meteorological station, receive the acknowledgement information that first meteorological station sent next, match corresponding matching meteorological data and send to first meteorological station in the meteorological data resource storehouse of deploying earlier through the second meteorological data object, treat through matching meteorological data and match the meteorological data package and perfect. The matching meteorological data matched with the meteorological data packet to be matched are searched in the resource library to complement or correct the meteorological data packet to be matched, perfect meteorological data are treated from different dimensions, the defect of incomplete meteorological data is overcome, and the perfect process flow is simple and efficient and high in accuracy.

Description

Intelligent observation meteorological station

Technical Field

The application relates to the field of meteorological observation, in particular to an intelligent observation meteorological station.

Background

The meteorological data are observation data about air pressure, temperature, humidity, wind direction, wind speed, precipitation and the like acquired by a meteorological station, and weather can be predicted by analyzing the meteorological data. When the weather station acquires the weather data, sometimes the acquired weather data is incomplete because of equipment failure or extreme conditions, or when a certain weather is verified through single weather data, an accurate result cannot be obtained, so that the weather prediction result of weather analysis can be influenced directly. Therefore, a solution is needed that can be perfected when the meteorological data is incomplete.

Disclosure of Invention

In order to improve the above problem, the embodiments of the present application provide an intelligent observation weather station.

The technical scheme adopted by the embodiment of the application is as follows:

in a first aspect, the embodiments of the present application provide an intelligent observation weather station, including a data processing center and a plurality of weather stations, each weather station being provided with a weather sensor and a communicator, the weather sensor being configured to sense weather data and transmit the sensed weather data to the data processing center through the communicator, the data processing center including a computer device, the computer device including a processor and a memory which are communicated with each other, the processor being configured to retrieve a computer program from the memory and implement the following weather data perfecting procedures by executing the computer program:

receiving at least one meteorological data packet to be matched uploaded by a first meteorological station in the meteorological stations;

carrying out meteorological data object identification on the meteorological data packet to be matched, and returning at least one identified meteorological data object to the first meteorological station;

receiving confirmation information sent by the first meteorological station, wherein the confirmation information comprises a plurality of groups of second meteorological data objects calibrated in the first meteorological station;

matching corresponding matching meteorological data in a meteorological data resource library which is deployed in advance through a second meteorological data object and sending the matching meteorological data to a first meteorological station, wherein the meteorological data resource library comprises at least two groups of first representative meteorological data, each group of first representative meteorological data comprises at least two meteorological data objects, and the matching meteorological data are collected through the second meteorological station;

and perfecting the weather data packet to be matched through the matching weather data.

Further, the meteorological data resource library is obtained by deploying the following steps:

acquiring a plurality of groups of second representative meteorological data;

respectively carrying out meteorological data object identification on the multiple groups of acquired second representative meteorological data to obtain third meteorological data objects contained in each second representative meteorological data;

determining first representative meteorological data containing counting values of a plurality of third meteorological data objects from the second representative meteorological data;

for each first representative meteorological data, performing correlation analysis on each third meteorological data object contained in the first representative meteorological data, and determining a representative meteorological data set contained in the first representative meteorological data, wherein each representative meteorological data set at least comprises two related third meteorological data objects;

extracting, for each representative meteorological data set, first data features of each third meteorological data object therein;

the first representative meteorological data, the representative meteorological data set contained in the first representative meteorological data, the first data characteristic of each third meteorological data object contained in the representative meteorological data set, and the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristic are respectively saved in a meteorological data resource base.

Further, the weather data repository also includes a representative weather data set contained in each of the first representative weather data sets, a first data feature representing each of the third weather data objects contained in the weather data set, and a first mapping relationship between the first representative weather data, the representative weather data set and the first data feature, and matches corresponding matching weather data from the weather data repository through the second weather data object and transmits the matching weather data to the first weather station, including:

determining a first counting value of the second meteorological data object, matching a first meteorological data object containing a counting value of a third meteorological data object as a first counting value in a meteorological data resource library to represent a meteorological data set, and determining third representative meteorological data containing the first meteorological data object to represent the meteorological data set;

respectively extracting second data characteristics of each second meteorological data object;

respectively determining first matching degrees of each second data characteristic and the corresponding first representative data characteristic, and obtaining a second matching degree corresponding to each third representative meteorological data through each first matching degree, wherein the first representative data characteristic is that a first meteorological data object covered by each third representative meteorological data represents the first data characteristic of each third meteorological data object corresponding to each second meteorological data object in a meteorological data set;

and taking each third-generation meter meteorological data as matched meteorological data, and arranging the priority of the matched meteorological data through a second matching degree.

Further, obtaining a second matching degree corresponding to each third-generation meteorological data through each first matching degree comprises:

and performing global processing on each first matching degree aiming at each third-generation meteorologic data to obtain a second matching degree corresponding to the third-generation meteorologic data.

Further, before the first representative weather data, the representative weather data set included in the first representative weather data, the first data feature of each third weather data object included in the representative weather data set, and the first mapping relationship between the first representative weather data, the representative weather data set, and the first data feature are respectively stored in the weather data repository, the method further includes:

for each representative meteorological data set, combining all first data features covered by the representative meteorological data set into a first multi-meteorological data object feature;

respectively storing the first representative meteorological data, the representative meteorological data set contained in the first representative meteorological data, the first data characteristic of each third meteorological data object contained in the representative meteorological data set, and the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristic in a meteorological data resource library, wherein the first mapping relation comprises the following steps:

the first representative meteorological data, the representative meteorological data sets contained in the first representative meteorological data, and the first data characteristics of each third meteorological data object contained in the representative meteorological data set are respectively stored in the meteorological data resource library, the first mapping relation between the first representative meteorological data, the representative meteorological data sets and the first data characteristics, and the second mapping relation between the first representative meteorological data, the representative meteorological data sets and the first multi-meteorological data object characteristics are respectively stored in the meteorological data resource library.

Further, the weather data repository also includes respective first representative weather data, a second mapping relationship representing the weather data set and the characteristics of the first multiple weather data objects, and matches the corresponding matching weather data from the weather data repository through the second weather data object, including:

determining a second count value of the second meteorological data object, matching the second meteorological data object containing the count value of the third meteorological data object as a second count value from the meteorological data resource library to represent a meteorological data set, and determining fourth representative meteorological data containing the second meteorological data object representing the meteorological data set;

respectively extracting third data characteristics of each second meteorological data object, and combining the extracted third data characteristics into a second multi-meteorological data object characteristic;

respectively determining fourth matching degrees of the second multi-meteorological-data object features and the representative multi-meteorological-data object features, and determining the fourth matching degrees as fifth matching degrees corresponding to the fourth representative meteorological data, wherein the representative multi-meteorological-data object features are first multi-meteorological-data object features corresponding to a second meteorological-data object representative meteorological data set contained in the fourth representative meteorological data;

and taking each fourth representative meteorological data as matched meteorological data, and arranging the priority of the matched meteorological data through a fifth matching degree.

Further, determining a fourth degree of matching between the second multi-meteorological data object characteristics and each representative multi-meteorological data object characteristic respectively comprises:

determining each third data characteristic contained in the second multi-meteorological data object characteristics;

determining each second representative data characteristic contained in each fourth representative meteorological data representative multi-meteorological data object characteristic;

respectively determining a sixth matching degree of each third data characteristic and the corresponding second representative data characteristic;

performing global processing on each sixth matching degree aiming at each fourth representative meteorological data to obtain a seventh matching degree;

and determining each seventh matching degree as a fourth matching degree of the second multi-meteorological-data object characteristics and each representative multi-meteorological-data object characteristic.

The intelligent integrated processor comprises a hardware structure and embedded software, the hardware structure comprises the embedded processor, a real-time clock circuit, a program memory, a data memory, a ZigBee module, a communication interface, a serial interface, a USB interface, an SD card interface, a detection circuit, an indicator light and a power supply interface, the embedded software comprises a main control module, a data acquisition processing and monitoring module, a communication module and a software upgrading module, the main control module is used for completing the logic control of the system, the data acquisition processing and monitoring module is used for collecting the meteorological information collected by the intelligent measuring instrument and completing the calculation, comprehensive quality control, data storage and state monitoring of data, the communication module is used for interacting with the intelligent measuring instrument and the peripheral equipment and providing data for the service center station, and the software upgrading module is used for upgrading the embedded software locally or remotely.

Further, when a plurality of intelligent measuring instruments carry out ascending meteorological data transmission through intelligent node controller and intelligent integrated processor, adopt zigBee to carry out radio communication, when zigBee radio communication is unblocked, change over to using RS485 bus communication mode, when zigBee radio communication resumes normally, resume to use zigBee radio communication.

In a second aspect, an embodiment of the present application provides a meteorological data improvement method applied to a data processing center in communication with a plurality of meteorological stations, the method including:

receiving confirmation information sent by the first meteorological station, wherein the confirmation information comprises a plurality of groups of second meteorological data objects calibrated in the first meteorological data objects by the first meteorological station;

acquiring a plurality of groups of second representative meteorological data;

In a third aspect, an embodiment of the present application provides a meteorological data improvement apparatus, including:

the receiving module is used for receiving at least one meteorological data packet to be matched uploaded by a first meteorological station in the meteorological stations;

the identification module is used for identifying meteorological data objects of the meteorological data package to be matched and returning at least one identified first meteorological data object to the first meteorological station;

the receiving module is also used for receiving confirmation information sent by the first meteorological station, wherein the confirmation information comprises a plurality of groups of second meteorological data objects calibrated in the first meteorological data objects by the first meteorological station;

the data matching module is used for matching corresponding matching meteorological data in a meteorological data resource library which is deployed in advance through a second meteorological data object and sending the matching meteorological data to the first meteorological station, wherein the meteorological data resource library comprises at least two groups of first representative meteorological data, each group of the first representative meteorological data comprises at least two meteorological data objects, and the matching meteorological data are collected through the second meteorological station;

and the data perfecting module is used for perfecting the meteorological data packet to be matched through the matching meteorological data.

The intelligent observation meteorological station provided by the embodiment of the application comprises a data processing center and a plurality of meteorological stations which are communicated with each other, wherein the data processing center comprises a processor and a memory, the processor runs a program stored in the memory to complete meteorological data, specifically, at least one meteorological data packet to be matched is uploaded by a first meteorological station in the meteorological station and then carries out meteorological data object identification on the meteorological data packet to be matched, at least one identified first meteorological data object is returned to the first meteorological station, confirmation information sent by the first meteorological station is received, the confirmation information comprises a plurality of groups of second meteorological data objects calibrated in the first meteorological station by the first meteorological station, then matching corresponding matching meteorological data in a meteorological data resource library deployed in advance is carried out through the second meteorological data objects and sent to the first meteorological station, wherein the meteorological data resource library comprises at least two groups of first representative meteorological data, each group of first representative meteorological data comprises at least two meteorological data objects, the matching data are collected through the second meteorological station and are finally matched, and the meteorological data packets are completed through the matching. This application embodiment is through looking for the matching meteorological data that matches the second meteorological station collection of meteorological data package assorted of treating that matches that gathers with first meteorological station in the resource storehouse, in order to treat that the matching meteorological data package is mended or is revised, first meteorological station and second meteorological station are different meteorological stations, but possess the same meteorological data object, consequently, can treat perfect meteorological data from different dimensions and perfect, the incomplete defect of meteorological data has been remedied, and simultaneously, perfect process is that directly realize in ready-made resource storehouse, the flow is simple high-efficient, the degree of accuracy is high.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those of ordinary skill in the art upon examination of the following and the accompanying drawings or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which example numerals represent similar mechanisms throughout the various views of the drawings.

FIG. 1 is a schematic diagram illustrating the components of an intelligent observation weather station provided in an embodiment of the present application.

Fig. 2 is a schematic application architecture diagram of a weather station according to an embodiment of the present application.

Fig. 3 is a schematic hardware structure diagram of an intelligent integrated processor provided in an embodiment of the present application.

Fig. 4 is a schematic diagram of a software component of an intelligent integrated processor provided in an embodiment of the present application.

Fig. 5 is a block diagram of a data processing center according to an embodiment of the present disclosure.

Fig. 6 is a flowchart of a meteorological data improvement method provided in an embodiment of the present application.

Fig. 7 is a schematic diagram of a functional module architecture of a device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

Fig. 1 is a schematic diagram illustrating a configuration of an intelligent observation weather station 10 according to an embodiment of the present disclosure. The smart observational weather station 10 includes a weather station 300 and a data processing center 100. It should be noted that the intelligent observation weather station 10 provided in the embodiment of the present application can be understood as a weather station system, which includes the weather station 300 for collecting weather data and the data processing center 100 for analyzing the weather data. The weather station 300 includes a weather sensor 310 and a communicator 320, the weather sensor 310 being configured to sense weather data and transmit the sensed weather data to the data processing center 100 via the communicator 320. Because the various meteorological sensors 310 arranged in the meteorological station 300 belong to the prior art, for example, a wind speed sensor, a temperature sensor, a humidity sensor, an image sensor, etc. are arranged, which is not described herein again. The communication device 320 may adaptively select among existing communication devices according to a communication distance and a communication environment.

In practical applications, referring to fig. 2, in the intelligent observation weather station 10 provided in the embodiment of the present application, the weather station 300 may include an intelligent measuring instrument, a plurality of intelligent node controllers adapted to wired and wireless modes, an intelligent integrated processor, a solar power supply system, peripheral devices, and supporting software. The intelligent measuring instrument is a data measuring and collecting device of a new generation of intelligent regional automatic meteorological station, and consists of an induction device and a data processing module, wherein the data processing module comprises hardware and application software, the hardware comprises a high-performance processor (CPU), a high-precision A/D conversion circuit, a high-precision clock circuit, a program memory, a data memory, a power supply unit, a communication interface, an induction device interface, a monitoring circuit, an indicator light and the like, the digitization of sensor signals is realized, the data format is uniform, the flexible interchange and maintenance are convenient, and the sensor-level quality control and the state monitoring are convenient to realize.

The intelligent integrated processor is a front-end data processing core device of the intelligent automatic weather station. The intelligent integrated processor consists of hardware and embedded software, wherein the hardware comprises a high-performance embedded processor, a high-precision real-time clock circuit, a large-capacity program memory, a data memory, a ZigBee module, a communication interface, a serial interface, a USB interface, an SD card interface, a detection circuit, an indicator light, a power supply interface and the like. The system has strong data processing capacity and can meet the data processing requirements of various complex weather detection systems. The embedded software is responsible for realizing functions such as data collection, data storage, data uploading, clock synchronization and the like according to observation requirements, for example, a hardware structure shown in fig. 3, wherein a hardware circuit comprises a high-performance processor, a high-precision clock circuit, a memory, an I/O interface (a ZigBee interface, an RS-232 interface, an ethernet interface, an optical fiber interface, a USB interface, an SD card interface), a monitoring circuit, a power supply interface, an indicator light and the like. In terms of software composition, please refer to fig. 4, the embedded software includes four functional modules. The system comprises a main control module, a data acquisition, processing and monitoring module, a communication module and a software upgrading module. The main control module: the logic control of the system is mainly completed, including initialization of the system, call of functions (processes), response of interrupts, management of RTC clocks, and the like. The data acquisition, processing and monitoring module: the intelligent measuring instrument mainly collects meteorological information collected by the intelligent measuring instrument, and completes functions of data calculation, comprehensive quality control, data storage, state monitoring and the like. A communication module: the intelligent measuring instrument mainly interacts with the intelligent measuring instrument and peripheral equipment and provides data for the service central station, and the interaction mode can adopt wired or wireless communication. A software upgrading module: the function of upgrading the embedded software locally or remotely is mainly realized.

The intelligent node controller comprises a data communication function, supports two-way data transmission of two communication modes of ZigBee and RS-485, and supports automatic switching between the two communication modes. The command which can be issued in the RS-485 mode is returned by the RS-485 bus, and the command issued in the ZigBee mode is returned by the ZigBee channel. The command issued by the integrated processor through the ZigBee channel can be received and forwarded to the intelligent measuring instrument. The intelligent measuring instrument can receive and analyze the command sent by the RS-485 bus by the integrated processor, and then performs function response or forwards the command to the intelligent measuring instrument according to the type of the command. In addition, a ZigBee networking function is supported, and in a non-networking state, the intelligent node controller can realize automatic power-on detection, identify the type of the currently connected intelligent measuring instrument, and automatically generate an MAC address according to the type and the ID of the intelligent measuring instrument to perform automatic networking. And manual networking can be performed according to the type and the ID of the connected intelligent measuring instrument. After networking, the intelligent node controller can realize point-to-point data transmission with the intelligent measuring instrument. Due to point-to-point communication, the intelligent node controller and the intelligent measuring instrument are fixed in type and ID. If other types or other intelligent measuring instruments of the same type need to be replaced, the point-to-point transmission can be carried out only by networking again after network quitting and generating a new MAC address. In addition, the system also has the functions of battery state detection and charging control, can realize real-time monitoring of the battery state, report information such as the battery voltage, the charging state, the charging stage and the like, and control the charging current according to the voltage, thereby realizing stable charging. And finally, the system also has a board card detection function, can realize board card state detection, and monitors the voltage of the board card in real time. The ZigBee wireless communication mode is preferentially used when the intelligent meteorological element measuring instruments transmit the uplink meteorological data through the intelligent node controller and the intelligent integrated processor, the RS485 bus communication mode is automatically switched to be used when the ZigBee wireless communication is not smooth, and the ZigBee wireless communication is automatically switched to be used when the ZigBee wireless communication is recovered to be normal. The mechanism uses an RS485 bus communication mode when the intelligent integrated processor writes a downlink control command and a program into the intelligent node controller and each element intelligent measuring instrument.

The intelligent integrated processor program, the intelligent node controller program and the intelligent measuring instrument program in the weather station respectively customize and develop special programs for remote program upgrading functions, can also communicate with the cloud, and carry out matched development of batch site remote program upgrading software modules for cloud center software, so that the weather station system has a set of relatively complete remote program upgrading function system. The functional system can be used for carrying out controlled batch remote program upgrading on all online intelligent automatic meteorological stations at a cloud center, can be used for carrying out program accurate upgrading on sub-equipment, and comprises an intelligent integrated processor, a ZigBee coordinator, intelligent node controllers and meteorological element intelligent measuring instruments.

When the weather station is in software and hardware modularization design, effective operation state information of each intelligent sub-device can be extracted and output, meanwhile, operation logs of the intelligent devices are recorded for troubleshooting, and the intelligent devices are collected by the intelligent integrated processor in a unified mode. The operation state information includes: the system comprises various sub-equipment main board temperatures, main board voltages, main board currents, battery voltages, 4G signal intensity, 4G bit error rates, a whole station self-checking state, a cabinet door state, an SD card residual capacity, various meteorological element observers working states, a ZigBee communication state, an RS485 communication state, a 4G communication state, a Beidou positioning module state, the number of visible satellites of the Beidou positioning module and the like. And one part of the real-time operation states are uploaded to the cloud center in real time along with data uploaded by the intelligent automatic weather station, and other operation states can be remotely inquired in the cloud center for use in troubleshooting. The operation log includes: the intelligent integrated processor receives the running logs of the intelligent sub-devices, the running time of the system started by the intelligent integrated processor last time, the total power of data uploaded by the meteorological element observers, the success rate of data uploaded by the meteorological element observers through ZigBee and RS485 respectively, and the like.

In addition, the intelligent equipment management system also has the function of recording and storing serial numbers of the intelligent equipment and metrological calibration verification information. The intelligent measuring instrument program of each meteorological element records the equipment serial number, and the serial number can reflect the factory year and batch of the equipment and can be used for equipment production batch tracing and maintenance date tracing. Historical metrological calibration verification information of the equipment can be recorded in each meteorological element intelligent measuring instrument program, factory calibration information is recorded by a manufacturer when the equipment leaves a factory, verification information can be recorded by a metrological verification mechanism when metrological verification is carried out regularly after the equipment is used, and the information can be stored in the intelligent measuring instrument for a long time and can be remotely inquired and used by a cloud center. Calibration and certification information includes: calibration or certification time, calibration or certification expiry date, calibration or certification authority number, calibration or certification attendant number, calibration or certification certificate number, calibration or certification parameters (standard indication, measured instrument indication).

For economy of disclosure, other modules in the weather station 300 are not described in detail herein.

The data processing center 100 includes a computer device, which may be an electronic device with a data processing module, such as a server, a personal computer, etc., referring to fig. 2, which is a block schematic diagram of the computer device 100 provided in the embodiment of the present application, the computer device 100 includes a processor 120 and a memory 130, which are in communication with each other, and a communication unit 160 for communicating with the weather station 300.

The processor 120, the memory 130, and the communication unit 160 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via one or more I/O interfaces 150 or signal lines. The weather data perfecting device 110 includes at least one software function module that can be stored in the memory 130 in the form of software or firmware or solidified in the operating system. The processor 120 is used to execute executable modules stored in the memory 130, such as software functional modules and computer programs included in the weather data improvement device 110, to perform weather data analysis and weather prediction provided by the weather station 300.

The processor 120 may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and a Digital Signal Processor (DSP). The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. Further, a general purpose processor may be a microprocessor.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processor 120 executes the program after receiving the execution instruction. The communication unit 160 is used to establish a communication connection between the computer device 100 and the weather station 300, and to transmit and receive data via a network.

It will be appreciated that the configurations shown in fig. 1 and 2 are merely illustrative and that the various devices may include more or fewer components than shown in fig. 1 or 2 or have different configurations than shown in fig. 1 or 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Referring to fig. 3, when the processor 120 executes the software function modules and the computer program included in the weather data improving apparatus 110 to improve the weather data, the following processes 210-250 are included, and the following steps of the processes will be described.

And step 210, receiving at least one meteorological data packet to be matched uploaded by a first meteorological station in the meteorological stations.

In the embodiment, different weather stations are described in a first weather station and a second weather station, and the terms "first" and "second" have no special meaning. The meteorological data package to be matched is a meteorological data package which needs to be completed, meteorological data in the meteorological data package can include conventional meteorological data, the display form of the meteorological data is not limited, for example, the meteorological data include air pressure, temperature, humidity, wind speed and the like, the display form can be displayed through a matrix of time and numerical values, and can also be displayed through a curve corresponding to the time and the numerical values, so that the meteorological data are easy to understand, different types of meteorological data are continuous in time, so that the accuracy of meteorological data analysis is guaranteed, and in special cases, such as equipment damage, sensor shielding, electromagnetic interference, circuit short circuit and the like, data are lost in a certain period of time and even a jumping period of time, the final meteorological data are incomplete, the meteorological data analysis is incomplete, therefore, the meteorological data need to be completed, and the completed content can be completed or corrected. The number of the meteorological data packets to be matched uploaded by the first meteorological station can be one or more, and the meteorological data packets can be processed one by one in the improvement process.

And step 220, performing meteorological data object identification on the meteorological data packet to be matched, and returning at least one identified first meteorological data object to the first meteorological station.

Within a specific range, a plurality of weather stations are arranged at some time, and the weather stations can collect and record weather data of the same weather object, for example, n weather stations are arranged in the A city and the B city, and an image, a moving speed or an electric field intensity in a certain area are recorded at the same time in a certain day. Whether two meteorological data correspond to the same target can be analyzed through comparison of the meteorological data corresponding to the meteorological data objects, and therefore the meteorological data objects can be coordinates, time, meteorological data types, meteorological data descriptions and the like.

And step 230, receiving confirmation information sent by the first weather station.

The validation information includes a plurality of sets of second meteorological data objects calibrated in the first meteorological data objects by the first meteorological station. The action of demarcating here can be that the people is markd, also can be that the weather station is markd by oneself, confirms the weather data object that needs to be perfect through the demarcation, prevents mistake affirmation or extra affirmation to and the specific region of accurate locking, guarantee the unity of weather data.

And 240, matching corresponding matching meteorological data in a meteorological data resource library which is deployed in advance through the second meteorological data object and sending the matching meteorological data to the first meteorological station.

The weather data resource library comprises at least two groups of first representative weather data, each group of first representative weather data comprises at least two weather data objects, and the matching weather data is collected through a second weather station.

In this embodiment, the meteorological data repository is deployed by the following steps:

and 241, acquiring multiple groups of second representative meteorological data.

In the embodiment of the application, before deploying the meteorological data resource library, a plurality of groups of second representative meteorological data need to be acquired and uploaded through the meteorological station.

And 242, respectively performing meteorological data object identification on the multiple sets of acquired second representative meteorological data to obtain third meteorological data objects contained in each second representative meteorological data.

After obtaining the plurality of sets of second representative meteorological data, in order to form a resource library in which each meteorological data package simultaneously has at least two meteorological data objects, the meteorological data object identification needs to be performed on the obtained plurality of second representative meteorological data, so as to obtain a third meteorological data object included in each second representative meteorological data, and at this time, it can be known that each second representative meteorological data includes a plurality of meteorological data objects.

At step 243, the first representative meteorological data including a plurality of third meteorological data objects is determined from the second representative meteorological data.

In order to ensure that each weather data package in the deployed resource library comprises at least two weather data objects, after the third weather data object in each second representative weather data is determined, the first representative weather data with the count value of at least two third weather data objects is obtained from the second representative weather data, that is, the number of each third weather data object in the first representative weather data is at least two.

In step 244, for each of the first representative meteorological data, correlation analysis is performed on each of the third meteorological data objects included in the first representative meteorological data, and a representative meteorological data set included in the first representative meteorological data is determined.

Wherein each representative meteorological data set includes at least two related third meteorological data objects. After the first representative meteorological data is obtained, in order to determine at least two third meteorological data objects which may appear in the same meteorological data package at the same time, for each first representative meteorological data, performing correlation analysis on each third meteorological data object included in the first representative meteorological data to determine a representative meteorological data set included in the first representative meteorological data, wherein the correlation analysis mainly analyzes whether the directionality of the result of each third meteorological data object is uniform, and each representative meteorological data set at least includes two third meteorological data objects which have a correlation relationship, that is, how many representative meteorological data sets, that is, how many sets of at least two third meteorological data objects which may appear in the same meteorological data package at the same time, are included in each first representative meteorological data. As a way of correlation analysis, the distance between the coordinates of two meteorological data objects may be compared, and when the distance is less than a distance threshold, the correlation requirement is satisfied, and the two meteorological data objects are determined as a set of first representative meteorological data sets.

Step 245, extracting the first data characteristic of each third meteorological data object in each representative meteorological data set.

To match the weather data packages from the weather data repository by data feature matching, after the representative weather data sets have been determined, the first data features of all third weather data objects contained in each representative weather data set are identified. For the extraction of the first data feature, adaptive extraction can be performed according to the data type and converted into a general description result, for example, when the data form of the meteorological data object is a curve, the global feature of the curve image is extracted, and when the data form of the meteorological data object is a data list, the time peak value of the data, that is, the maximum value time is extracted.

Step 246, respectively storing the first representative meteorological data, the representative meteorological data set included in the first representative meteorological data, the first data characteristic of each third meteorological data object included in the representative meteorological data set, and the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristic in the meteorological data resource base.

After extracting the first data characteristics of the third meteorological data objects contained in each representative meteorological data set, determining the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristics, and then storing the first representative meteorological data, the representative meteorological data set contained in the first representative meteorological data, the first data characteristics of the third meteorological data objects contained in the representative meteorological data sets, and the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristics in a meteorological data repository, thereby deploying the meteorological data repository.

By way of example, two first representative meteorological data X and Y are provided, wherein the first representative meteorological data X comprises a representative meteorological data set Z, the representative meteorological data set Z comprises a third meteorological data object Z1 and a third meteorological data object Z2, the third meteorological data object Z1 comprises a first data characteristic m, and the third meteorological data object Z2 comprises a first data characteristic n. The second representative weather data Y comprises a representative weather data set T and a representative weather data set G, the representative weather data set T comprises a third weather data object T1 and a third weather data object T2, the third weather data object T1 comprises a first data feature o, the third weather data object T2 comprises a first data feature p, the representative weather data set G comprises a third weather data object G1 and a third weather data object G2, the third weather data object T1 comprises a first data feature q, and the third weather data object G2 comprises a first data feature G.

The above information may be stored in the form of a list in the weather data repository. And storing corresponding mapping relations, wherein the exemplary mapping relations are as follows:

the method comprises the following steps that first representative meteorological data X-representative meteorological data set Z-a first data feature m and a first data feature n;

the second representative meteorological data Y-representative meteorological data set T-the first data characteristic o and the first data characteristic p;

the second representative meteorological data Y-representative meteorological data set G-the first data characteristic q, the first data characteristic G.

In the case that the representative weather data set included in each first representative weather data, the first data feature of each third weather data object included in each representative weather data set, and the first mapping relationship between each first representative weather data, representative weather data set, and the first data feature are stored in the weather data repository deployed in advance, step 240 may include:

step 241', determining a first count value of said second weather data object, determining a first weather data object representative weather data set in said weather data repository matching the count value of said third weather data object to said first count value, and determining a third representative weather data covering said first weather data object representative weather data set.

In order to match the weather data containing the second weather data object from the weather data repository, a first count value of the second weather data object is determined, which first count value characterizes a total number of the second weather data object. Because the weather data resource library stores each first representative weather data, each first representative weather data set contained in each first representative weather data, and the first data characteristics of each third representative weather data contained in each first representative weather data set, it is convenient to find the first target first representative weather data set, in which the count value of the third representative weather data is the first count value, from the weather data resource library, and determine the third representative weather data containing the first weather data object representative weather data set.

Continuing with the example above, if the second weather data object is set as the coordinates and image, the first count value of the second weather data object is 2, the count value of the third representative weather data in the first representative weather data set Z contained in the first representative weather data X in the weather data repository is 2, and the count value of the third representative weather data in the representative weather data sets T and G contained in the first representative weather data Y is 2. Finding the first weather data object representative weather data sets Z, T and G from the weather data resource library, and determining third representative weather data X comprising the first weather data object representative weather data set Z and third representative weather data Y comprising the first weather data object representative weather data set T and the first weather data object representative weather data set G.

Step 242', second data features of the respective second meteorological data objects are extracted.

In order to achieve the data feature matching, to match the weather data from the weather data repository, the second data features of the second weather data objects need to be extracted respectively, and the process of extracting the second data features of each second weather data object is already stated in the foregoing, and is not repeated for saving space.

And step 243', respectively determining first matching degrees of each second data feature and the corresponding first representative data feature, and obtaining a second matching degree corresponding to each third representative meteorological data through each first matching degree.

The first representative data characteristic is that the first meteorological data object covered by each third representative meteorological data represents the first data characteristic of each third meteorological data object corresponding to each second meteorological data object in the meteorological data set.

After the third-generation meteorological data are searched from the meteorological data resource library, the first meteorological data objects contained in each third-generation meteorological data represent the first data characteristics of each third meteorological data object corresponding to each second meteorological data object in the meteorological data set, and each determined first data characteristic is determined as the first representative data characteristic. The third meteorological data objects corresponding to each second meteorological data object can be determined by comparing the similarity, for each second meteorological data object, the similarity between the second characteristic of the second meteorological data object and the first data characteristic of each third meteorological data object is respectively determined to obtain the matching degree, then the third meteorological data object corresponding to the highest similarity is determined as the third meteorological data object corresponding to the second meteorological data object, and finally the first data characteristic contained in the third meteorological data object corresponding to the second meteorological data object is determined as the first representative data characteristic corresponding to the second characteristic of the second meteorological data object. And for each third-generation Meteorological data, carrying out global processing on each first matching degree to obtain a second matching degree corresponding to the third-generation Meteorological data. The global processing method is not limited, for example, merging or fusing, specifically, the average value of the first matching degrees may be used as the second matching degree, and the first matching degrees may also be summed in a weighted manner, and the obtained result may be used as the second matching degree corresponding to the third-generation meteorological data.

And 244', taking each third-generation meteorological data as matched meteorological data, and arranging the priority of the matched meteorological data through the second matching degree.

And after the second matching degree corresponding to each third-generation Meteorological data is obtained, taking each third-generation Meteorological data as the matched meteorological data, and performing priority arrangement on the matched meteorological data based on the second matching degree, wherein the arrangement mode can be from high to low.

In this application, as an embodiment, before step 246, the method may further include: for each representative meteorological data set, all first data features covered by the representative meteorological data set are combined into one first multi-meteorological data object feature.

In order to match the meteorological data from the meteorological data resource library through the simultaneous matching of a plurality of characteristics, for each representative meteorological data set, after the first data characteristics of each third meteorological data object contained in the representative meteorological data set are obtained, the contained first data characteristics are combined into a first plurality of meteorological data object characteristics.

The step of storing in the weather data repository each first representative weather data, the set of representative weather data included in each first representative weather data, the first data characteristic of each third weather data object included in each set of representative weather data, and the first mapping relationship of each first representative weather data, set of representative weather data, and first data characteristic may comprise:

storing each first representative meteorological data, the representative meteorological data sets contained by each first representative meteorological data, the first data characteristics of each third meteorological data object contained by each representative meteorological data set, and the first mapping relation between each first representative meteorological data, the representative meteorological data sets and the first data characteristics, and the second mapping relation between each first representative meteorological data, the representative meteorological data sets and the first multiple meteorological data object characteristics in a meteorological data repository.

For each representative meteorological data set, after combining the included first data features into a first multi-meteorological-data-object feature, storing each first representative meteorological data, the representative meteorological data set included in each first representative meteorological data, the first data features of each third meteorological data object included in each representative meteorological data set, and the first mapping relation between each first representative meteorological data, the representative meteorological data set and the first data features in the meteorological data repository, and deploying the second mapping relation between each first representative meteorological data, the representative meteorological data set and the first multi-meteorological-data-object features, and then storing the second mapping relation in the meteorological data repository.

The following are exemplified:

let the first representative meteorological data X comprise a representative meteorological data set Z comprising a third meteorological data object Z1 and a third meteorological data object Z2, the third meteorological data object Z1 comprising a first data characteristic m, the third meteorological data object Z2 comprising a first data characteristic n.

Let it be further assumed that the second representative meteorological data Y comprises a representative meteorological data set T and a representative meteorological data set G, the representative meteorological data set T comprises a third meteorological data object T1 and a third meteorological data object T2, the third meteorological data object T1 comprises a first data feature p, the third meteorological data object T2 comprises a first data feature q, the representative meteorological data set G comprises a third meteorological data object G1 and a third meteorological data object G2, the third meteorological data object T1 comprises a first data feature d, the third meteorological data object G2 comprises a first data feature e.

The stored mapping relation can refer to:

the first representative meteorological data X-represents meteorological data set Z-a third meteorological data object Z1 and a third meteorological data object Z2;

the second representative meteorological data Y-representative meteorological data set T-a third meteorological data object T1 and a third meteorological data object T2;

the second representative meteorological data Y-representative meteorological data set G-the third meteorological data object G1, the third meteorological data object G2.

When merging, for the representative meteorological data set Z, the included first data features m and n are merged into a first multi-meteorological data object feature m-n. For the representative meteorological data set T, the included respective first data features p and q are combined into a first multi-meteorological data object feature p-q. For the representative meteorological data set G, the contained first data features d and e are combined into a first multi-meteorological data object feature d-e.

Then, the new second mapping relationship is:

the method comprises the following steps that first representative meteorological data X-representative meteorological data set Z-first multi-meteorological data object characteristics m-n;

the second representative meteorological data Y-representative meteorological data set T-first multi-meteorological data object characteristics p-q;

the second representative meteorological data Y-representative meteorological data set G-the first multiple meteorological data object features d-e.

At this time, step 246 includes: the first representative weather data, the representative weather data set included in the first representative weather data, the first data feature of each of the third weather data objects included in the representative weather data set, and the first mapping relationship between the first representative weather data, the representative weather data set and the first data feature, and the second mapping relationship between the first representative weather data, the representative weather data set and the first multi-weather data object feature are respectively saved in the weather data repository.

As a further embodiment, the weather data repository further includes respective first representative weather data, a second mapping of the set of representative weather data to the first plurality of weather data object features. Matching, via the second meteorological data object, the corresponding matching meteorological data from the meteorological data repository may include the steps of:

and step A, determining a second counting value of the second meteorological data object, matching the second meteorological data object representing meteorological data set containing the counting value of the third meteorological data object as the second counting value from the meteorological data resource library, and determining fourth representing meteorological data containing the second meteorological data object representing meteorological data set.

For matching the weather data containing the second weather data object from the weather data resource library, the second count value of the second weather data object needs to be determined, and since the first representative weather data, the matching degree included in each first representative weather data, and the first data characteristic of each third weather data object included in each matching degree are stored in the weather data resource library, the second weather data object representing the weather data set containing the third weather data object whose count value is the second count value can be searched from the weather data resource library, and the fourth representative weather data containing the second weather data object representing the weather data set is determined.

And B, respectively extracting third data characteristics of the second meteorological data objects, and combining the extracted third data characteristics into a second multi-meteorological-data-object characteristic.

To match the meteorological data from the meteorological data repository by multiple features, the third data features need to be extracted from each second meteorological data object one by one, and the extraction process is not repeated. And after the third data features are extracted, the third data features are combined into a second multi-meteorological-data object feature.

And step C, respectively determining fourth matching degrees of the second multi-meteorological-data object characteristics and the representative multi-meteorological-data object characteristics, and determining the fourth matching degrees as fifth matching degrees corresponding to the fourth representative meteorological data.

And each representative multi-meteorological data object characteristic is a first multi-meteorological data object characteristic corresponding to the second meteorological data object representative meteorological data set contained in each fourth representative meteorological data. Determining a fourth degree of match of the second multi-meteorological data object characteristics with the respective representative multi-meteorological data object characteristics may include: determining each third data feature contained in the second multi-meteorological data object features, determining each second representative data feature contained in the representative multi-meteorological data object features of each fourth representative meteorological data, respectively determining a sixth matching degree of each third data feature and the corresponding second representative data feature, performing global processing on each sixth matching degree aiming at each fourth representative meteorological data to obtain a seventh matching degree, and determining each seventh matching degree as the fourth matching degree of the second multi-meteorological data object features and the representative multi-meteorological data object features.

And D, taking the fourth representative meteorological data as matched meteorological data, and arranging the priority of the matched meteorological data through a fifth matching degree.

And step 250, perfecting the weather data packet to be matched through the matching weather data.

Through the above steps 210-240, the matching process of the matching meteorological data is completed, the meteorological data which can be matched with the meteorological data package to be matched is found, and the meteorological data package to be matched is supplemented or corrected through the meteorological data in the matching meteorological data, for example, missing contents in the meteorological data package to be matched are filled through corresponding parts in the matching meteorological data.

In summary, the intelligent observation weather station provided in the embodiment of the present application includes a data processing center and a plurality of weather stations that communicate with each other, the data processing center includes a processor and a memory, the processor runs a program stored in the memory to complete weather data, and specifically, at least one weather data packet to be matched is uploaded from a first weather station in the weather station, and then weather data object identification is performed on the weather data packet to be matched, and at least one identified first weather data object is returned to the first weather station, and then confirmation information sent from the first weather station is received, the confirmation information includes a plurality of sets of second weather data objects calibrated in the first weather data object by the first weather station, and then matching corresponding matching weather data in a weather data resource library deployed in advance is performed through the second weather data object and sent to the first weather station, wherein the weather data resource library includes at least two sets of first-representative weather data, each set of first-representative weather data includes at least two weather data objects, and the matching weather data is collected through the second weather station and finally completed weather data matching package. This application embodiment is through looking for the matching meteorological data that matches the second meteorological station collection of meteorological data package assorted of treating that matches that gathers with first meteorological station in the resource storehouse, in order to treat that the matching meteorological data package is mended or is revised, first meteorological station and second meteorological station are different meteorological stations, but possess the same meteorological data object, consequently, can treat perfect meteorological data from different dimensions and perfect, the incomplete defect of meteorological data has been remedied, and simultaneously, perfect process is that directly realize in ready-made resource storehouse, the flow is simple high-efficient, the degree of accuracy is high.

Referring to fig. 4, the meteorological data improvement apparatus 110 provided in this embodiment of the present application may be configured to perform steps 210 to 240, where the meteorological data improvement apparatus 110 may include a plurality of functional modules, for example, modules implemented by software programs or hardware circuits, and each module performs each step correspondingly. The weather data perfecting apparatus 110 includes a receiving module 111, an identifying module 112, a data matching module 113, and a data perfecting module 114. Wherein, the receiving module 111 is used for executing

steps

210 and 230, the identifying module 112 is used for executing step 220, the data matching module 113 is used for executing step 230, and the data perfecting module 114 is used for executing step 240.

Since the implementation principle is explained in the foregoing description of the step flow, the meteorological data improvement apparatus 110 will not be described in detail herein.

It should be noted that, in this specification, each embodiment is described in a progressive manner, and each embodiment focuses on differences from other embodiments, and portions that are the same as and similar to each other in each embodiment may be referred to.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative and, for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a notebook computer, a server, or an electronic device) to execute all or part of the steps of the methods described in the embodiments of the present application.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An intelligent observation weather station, comprising a data processing center and a plurality of weather stations, each of the weather stations being provided with a weather sensor and a communicator, the weather sensor being configured to sense weather data and transmit the sensed weather data to the data processing center via the communicator, the data processing center comprising a computer device, the computer device comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to complete the following weather data by operating the computer program:

carrying out meteorological data object identification on the meteorological data packet to be matched, and returning at least one identified first meteorological data object to the first meteorological station;

matching corresponding matching meteorological data in a meteorological data resource library which is deployed in advance through the second meteorological data object and sending the matching meteorological data to the first meteorological station, wherein the meteorological data resource library comprises at least two groups of first representative meteorological data, each group of first representative meteorological data comprises at least two meteorological data objects, the matching meteorological data are collected through the second meteorological station, and the second meteorological station is a meteorological station in the plurality of meteorological stations;

perfecting the meteorological data packet to be matched through the matching meteorological data;

the meteorological data resource library is obtained by deployment through the following steps:

acquiring a plurality of groups of second representative meteorological data, wherein the second representative meteorological data are acquired and uploaded through a meteorological station;

determining first representative meteorological data comprising a plurality of third meteorological data objects from the second representative meteorological data;

for each first representative meteorological data, performing correlation analysis on each third meteorological data object contained in the first representative meteorological data, and determining representative meteorological data sets contained in the first representative meteorological data, wherein each representative meteorological data set at least comprises two related third meteorological data objects;

for each representative meteorological data set, extracting first data characteristics of each third meteorological data object;

respectively saving the first representative meteorological data, the representative meteorological data set contained in the first representative meteorological data, the first data characteristic of each third meteorological data object contained in the representative meteorological data set, and the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristic in the meteorological data resource library.

2. The smart weather-observing station of claim 1, wherein the matching of the corresponding matching weather data from the weather data repository to the first weather station via the second weather data object comprises:

determining a first count value of said second meteorological data object, matching in said meteorological data repository a first meteorological data object representative meteorological data set containing a count value of said third meteorological data object as said first count value, determining third representative meteorological data containing said first meteorological data object representative meteorological data set;

respectively extracting second data characteristics of the second meteorological data objects;

respectively determining first matching degrees of each second data feature and a corresponding first representative data feature, and obtaining a second matching degree corresponding to each third-generation representative meteorological data through each first matching degree, wherein the first representative data feature is a first data feature of each third meteorological data object corresponding to each second meteorological data object in a meteorological data set represented by a first meteorological data object covered by each third-generation representative meteorological data;

and taking each third-generation meter meteorological data as matched meteorological data, and arranging the priority of the matched meteorological data through the second matching degree.

3. The intelligent observational weather station of claim 2, wherein said obtaining a second degree of matching for each of said third-generation meteorological data from each of said first degrees of matching comprises:

and aiming at each third-generation meteorologic data, carrying out global processing on each first matching degree to obtain a second matching degree corresponding to the third-generation meteorologic data.

4. The smart weather-observing station of claim 1, further comprising, prior to the storing in the weather-data repository the first representative weather data, the set of representative weather data included in the first representative weather data, the first data characteristic of each third weather-data object included in the set of representative weather data, and the first mapping relationship between the first representative weather data, the set of representative weather data, and the first data characteristic, respectively:

the respectively maintaining in a weather data repository the first representative weather data, the representative weather data set included in the first representative weather data, the first data characteristic of each third weather data object included in the representative weather data set, and the first mapping relationship between the first representative weather data, the representative weather data set, and the first data characteristic, includes:

the first representative weather data, the representative weather data set included in the first representative weather data, the first data feature of each of the third weather data objects included in the representative weather data set, and the first mapping relationship between the first representative weather data, the representative weather data set and the first data feature, and the second mapping relationship between the first representative weather data, the representative weather data set and the first multi-weather data object feature are respectively saved in the weather data repository.

5. The smart observational weather station of claim 4, wherein said matching the corresponding matching weather data from the weather data repository through the second weather data object comprises:

determining a second count value of the second meteorological data object, matching from the meteorological data resource library to a second meteorological data object representative meteorological data set containing a count value of a third meteorological data object as the second count value, and determining fourth representative meteorological data containing the second meteorological data object representative meteorological data set;

respectively extracting third data characteristics of the second meteorological data objects, and combining the extracted third data characteristics into a second multi-meteorological data object characteristic;

respectively determining fourth matching degrees of the second multi-meteorological-data object features and each representative multi-meteorological-data object feature, and obtaining a fifth matching degree corresponding to each fourth representative meteorological data through each fourth matching degree, wherein each representative multi-meteorological-data object feature is a first multi-meteorological-data object feature corresponding to a second meteorological-data object representative meteorological data set contained in each fourth representative meteorological data;

and taking each fourth representative meteorological data as matched meteorological data, and ranking the priorities of the matched meteorological data through the fifth matching degree.

6. The smart weather-station as claimed in claim 5, wherein the determining a fourth degree of match of the second multi-meteorological data object characteristics with the respective representative multi-meteorological data object characteristics, respectively, comprises:

determining respective second representative data features contained in respective ones of the fourth representative weather-data object-representative features, the second representative data features being such that each of the fourth representative weather-data objects contained in the second representative weather-data object represents the first data feature of the respective third weather-data object in the weather-data set corresponding to the respective second weather-data object;

determining each of said seventh degrees of match as a fourth degree of match of said second multi-meteorological data object features with each of said representative multi-meteorological data object features.

7. A weather data improvement method for use in a data processing center in communication with a plurality of weather stations, the method comprising:

respectively saving the first representative meteorological data, the representative meteorological data set contained in the first representative meteorological data, the first data characteristic of each third meteorological data object contained in the representative meteorological data set, and the first mapping relation between the first representative meteorological data, the representative meteorological data set and the first data characteristic.