CN111257971A - Meteorological platform with artificial intelligence service ability and learning ability - Google Patents
Meteorological platform with artificial intelligence service ability and learning ability Download PDFInfo
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- CN111257971A CN111257971A CN202010054161.4A CN202010054161A CN111257971A CN 111257971 A CN111257971 A CN 111257971A CN 202010054161 A CN202010054161 A CN 202010054161A CN 111257971 A CN111257971 A CN 111257971A
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Abstract
The invention discloses a meteorological platform with artificial intelligence service capability and learning capability, which comprises a server end, an Internet of things terminal and a mobile display end, wherein the server end is respectively connected with the Internet of things terminal and the mobile display end through a network; the server end comprises an intelligent control unit, a weather shorthand unit, a small weather editing unit, a weather cloud picture unit, a weather public opinion unit and an AI weather anchor unit; the intelligent control unit comprises a voice synthesis module, a voice dictation module, a voice transcription module, a semantic understanding module, a knowledge base, a machine learning module, a natural language processing module and a knowledge map module. According to the invention, new meteorological knowledge and skills are acquired and learned through AI intelligent learning, and the performance of the system is continuously improved, so that faster and better services are provided for users; the invention can automatically generate the AI weather anchor and interact with the real-time voice of the user, thereby replacing the traditional problem of collecting and displaying weather data information in a manual mode.
Description
Technical Field
The invention relates to the technical field of weather, in particular to a weather platform with artificial intelligence service capability and learning capability.
Background
In recent years, artificial intelligence has been developed rapidly, and has become a hot spot for research and pursuit in various countries in the world. Developed countries such as the United states, Germany, Japan and the like take the development of artificial intelligence as a great strategy for improving the national competitiveness and bring relevant plans and policies.
On one hand, the artificial intelligence has wide application range, and is particularly prominent in the fields of medical treatment, finance, security protection, automobiles and the like. The weather application is also an important field of high-performance computing, and the artificial intelligence technology brings rare opportunities to the development of observation, forecast, service and other businesses and also brings great challenges. Therefore, the characteristics of the development of artificial intelligence technology also have great influence on the meteorological service.
On the other hand, the machine learning, natural language processing, computer vision and other important artificial intelligence technologies have profound influences on weather in different fields and influence the life of audiences to different degrees.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a weather platform with artificial intelligence service capability and learning capability, and solves the technical problem that a large amount of manpower and material resources are consumed to collect data information, edit weather news and the like, and a large amount of resources are occupied by the traditional weather service platform.
The purpose of the invention is realized by the following technical scheme:
a meteorological platform with artificial intelligence service capability and learning capability comprises a server side, an Internet of things terminal and a mobile display side, wherein the server side is respectively connected with the Internet of things terminal and the mobile display side through a network;
the server end comprises an intelligent control unit, a weather shorthand unit, a small weather editing unit, a weather cloud picture unit, a weather public opinion unit and an AI weather anchor unit;
the intelligent control unit comprises a voice synthesis module, a voice dictation module, a voice transcription module, a semantic understanding module, a knowledge base, a machine learning module, a natural language processing module and a knowledge map module; the intelligent control unit is used for intelligently processing data acquisition, data processing and data output, and understanding and learning natural language;
the weather shorthand unit is used for quickly analyzing and converting weather data, and the weather shorthand unit is used for forming a structured file through regional data, weather conditions and behavior data for subsequent archiving;
the weather small editing unit is used for automatically generating a weather broadcast manuscript through the acquired weather data information; the small weather editing unit is also used for deep training and learning a weather broadcast manuscript model;
the meteorological cloud picture unit is used for displaying a current satellite cloud picture in real time;
the weather public opinion unit is used for displaying a weather hot event and a specific weather event;
the AI weather anchor unit is used for automatically generating an image picture of the virtual weather anchor, and the AI weather anchor is also used for broadcasting weather forecast, weather hot events and weather cautions in a voice mode.
Further, the weather platform further comprises an AI weather customer service module, wherein the AI weather customer service module is used for automatically generating a virtual customer service image picture, and the AI weather customer service module is also used for voice recognition and voice synthesis and realizing voice interaction with a user.
Further, the voice synthesis module is used for automatically generating sounds similar to the timbre and naturalness of human voice, and is also used for synthesizing multi-timbre and multi-language sounds;
the voice dictation module is used for identifying different languages and different dialects and converting the identified languages and dialects into Chinese for storage;
the voice transcription module is used for realizing voice conversion into characters, and the voice transcription module is also used for punctuation prediction and intelligent sentence break;
the semantic understanding module is used for understanding the intention of the natural language and endowing the machine with understanding cognitive ability;
the knowledge base is used for inputting mass meteorological data and is also used for carrying out comparison analysis on the acquired meteorological data;
the machine learning module is used for learning through a deep neural network according to mass data in the knowledge base so as to acquire new meteorological knowledge or skills and continuously improve the performance of the machine learning module;
the natural language processing module is used for reasonably organizing meteorological elements by using a natural language generation technology and forming a description text by using paragraph planning, sentence planning and sentence optimization processing;
the knowledge graph module is used for visually displaying the relevance among the factors by utilizing the visual graph and revealing the dynamic development rule in the meteorological field.
Further, the AI weather anchor unit further includes a machine translation module and a multilingual speech synthesis module, the machine translation module is used for implementing multilingual translation, and the multilingual speech synthesis module is used for implementing customized speech synthesis broadcast service.
Further, the AI weather anchor unit further comprises a voice interaction module, wherein the voice interaction module is used for voice recognition, voice synthesis and semantic understanding, and generates real-time data or voice broadcast content for the understood semantics.
Further, the early warning system further comprises an early warning module, wherein the early warning module is used for early warning severe weather.
The invention has the beneficial effects that:
the invention can acquire and learn new meteorological knowledge and skills through AI intelligent learning, and continuously improve the performance of the system, thereby providing faster and better service for users; the invention can automatically generate the AI weather anchor and interact with the real-time voice of the user, thereby replacing the traditional problem of collecting and displaying weather data information in a manual mode.
Drawings
FIG. 1 is a schematic diagram of the system connection of the present invention;
FIG. 2 is a block diagram of the present invention;
in the figure, 10-a server terminal, 20-an internet of things terminal, 30-a mobile display terminal, 11-an intelligent control unit, 12-a weather shorthand unit, 13-a weather small compilation unit, 14-a weather cloud picture unit, 15-a weather public opinion unit and 16-an AI weather anchor unit.
Detailed Description
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Example (b):
a meteorological platform with artificial intelligence service capability and learning capability is shown in figure 1 and comprises a server end 10, an Internet of things terminal 20 and a mobile display end 30, wherein the server end 10 is respectively connected with the Internet of things terminal 20 and the mobile display end 30 through a network;
as shown in fig. 2, the server 10 includes an intelligent control unit 11, a weather shorthand unit 12, a small weather compilation unit 13, a weather cloud picture unit 14, a weather public opinion unit 15 and an AI weather anchor unit 16;
the intelligent control unit 11 comprises a voice synthesis module, a voice dictation module, a voice transcription module, a semantic understanding module, a knowledge base, a machine learning module, a natural language processing module and a knowledge map module; the intelligent control unit is used for intelligently processing data acquisition, data processing and data output, and understanding and learning natural language;
the weather stenography unit 12 is used for rapidly analyzing and converting weather data, and the weather stenography unit 12 is used for forming a structured file through regional data, weather conditions and behavior data for subsequent filing;
the weather small editing unit 13 is used for automatically generating a weather broadcast manuscript through the acquired weather data information; the small weather editing unit 13 is also used for deep training and learning a weather broadcast manuscript model;
the meteorological cloud picture unit 14 is used for displaying the current satellite cloud picture in real time;
the weather public opinion unit 15 is used for showing weather hot events and specific weather events;
the AI weather anchor unit 16 is configured to automatically generate an image frame of a virtual weather anchor, and the AI weather anchor unit 16 is further configured to voice-broadcast a weather forecast, a weather hot event, and a weather notice.
Further, the weather platform further comprises an AI weather customer service module, wherein the AI weather customer service module is used for automatically generating a virtual customer service image picture, and the AI weather customer service module is also used for voice recognition and voice synthesis and realizing voice interaction with a user.
Further, the voice synthesis module is used for automatically generating sounds similar to the timbre and naturalness of human voice, and is also used for synthesizing multi-timbre and multi-language sounds;
the voice dictation module is used for identifying different languages and different dialects and converting the identified languages and dialects into Chinese for storage;
the voice transcription module is used for realizing voice conversion into characters, and the voice transcription module is also used for punctuation prediction and intelligent sentence break;
the semantic understanding module is used for understanding the intention of the natural language and endowing the machine with understanding cognitive ability;
the knowledge base is used for inputting mass meteorological data and is also used for carrying out comparison analysis on the acquired meteorological data;
the machine learning module is used for learning through a deep neural network according to mass data in the knowledge base so as to acquire new meteorological knowledge or skills and continuously improve the performance of the machine learning module;
the natural language processing module is used for reasonably organizing meteorological elements by using a natural language generation technology and forming a description text by using paragraph planning, sentence planning and sentence optimization processing;
the knowledge graph module is used for visually displaying the relevance among the factors by utilizing the visual graph and revealing the dynamic development rule in the meteorological field.
In the embodiment, the server 10, the internet of things terminal 20 and the mobile display 30 are adopted, and the application range of the internet of things terminal 20 includes platform capabilities of an entity robot, AI weather anchor in various intelligent large screens, an intelligent sound box, an intelligent home, an intelligent toy and the like; the mobile display terminal supports WeChat small programs and flat panel viewing; the voice inquiry can be realized, and the user can check the information at any time and any place.
The embodiment also comprises a permission management module, wherein the permission management module can configure corresponding permissions for management level cadres and common employees and can also configure security measures for different data.
Example 2:
in this embodiment, on the basis of embodiment 1, the AI weather anchor unit 16 further includes a machine translation module and a multilingual speech synthesis module, where the machine translation module is configured to implement multilingual translation, and the multilingual speech synthesis module is configured to implement a customized speech synthesis broadcast service.
Further, the AI weather anchor unit 16 further includes a voice interaction module, and the voice interaction module is configured to perform voice recognition, voice synthesis, and semantic understanding, and generate real-time data or voice broadcast content for the understood semantic.
Further, the early warning system further comprises an early warning module, wherein the early warning module is used for early warning severe weather.
The embodiment can be used for establishing the knowledge graph of a typical scene (such as agriculture, a transportation junction and the like) by applying deep learning based on meteorological big data, and effectively predicting and analyzing meteorological resource requirements.
The weather compilation module 13 of the invention comprises the steps of automatically generating Chinese weather forecast manuscript and extending the geographic coverage range; refining time granularity, short-time weather forecast and weather early warning; and urban weather, tourism weather, traffic weather, agricultural weather, electric power weather, weather early warning, climate change, disaster prevention and reduction and weather science popularization.
The invention can acquire and learn new meteorological knowledge and skills through AI intelligent learning, and continuously improve the performance of the system, thereby providing faster and better service for users; the invention can automatically generate the AI weather anchor and interact with the real-time voice of the user, thereby replacing the traditional problem of collecting and displaying weather data information in a manual mode.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (6)
1. A meteorological platform with artificial intelligence service capability and learning capability is characterized by comprising a server end, an Internet of things terminal and a mobile display end, wherein the server end is respectively connected with the Internet of things terminal and the mobile display end through a network;
the server end comprises an intelligent control unit, a weather shorthand unit, a small weather editing unit, a weather cloud picture unit, a weather public opinion unit and an AI weather anchor unit;
the intelligent control unit comprises a voice synthesis module, a voice dictation module, a voice transcription module, a semantic understanding module, a knowledge base, a machine learning module, a natural language processing module and a knowledge map module; the intelligent control unit is used for intelligently processing data acquisition, data processing and data output, and understanding and learning natural language;
the weather shorthand unit is used for quickly analyzing and converting weather data, and the weather shorthand unit is used for forming a structured file through regional data, weather conditions and behavior data for subsequent archiving;
the weather small editing unit is used for automatically generating a weather broadcast manuscript through the acquired weather data information; the small weather editing unit is also used for deep training and learning a weather broadcast manuscript model;
the meteorological cloud picture unit is used for displaying a current satellite cloud picture in real time;
the weather public opinion unit is used for displaying a weather hot event and a specific weather event;
the AI weather anchor unit is used for automatically generating an image picture of the virtual weather anchor, and the AI weather anchor is also used for broadcasting weather forecast, weather hot events and weather cautions in a voice mode.
2. The weather platform with artificial intelligence service capability and learning capability of claim 1, further comprising an AI weather service module, wherein the AI weather service module is configured to automatically generate a virtual customer service image, and the AI weather service module is further configured to perform voice recognition, voice synthesis, and perform voice interaction with a user.
3. The weather platform with artificial intelligence service capability and learning capability of claim 1,
the voice synthesis module is used for automatically generating sounds similar to the timbre and the naturalness of human voice, and is also used for synthesizing multi-timbre and multi-language sounds;
the voice dictation module is used for identifying different languages and different dialects and converting the identified languages and dialects into Chinese for storage;
the voice transcription module is used for realizing voice conversion into characters, and the voice transcription module is also used for punctuation prediction and intelligent sentence break;
the semantic understanding module is used for understanding the intention of the natural language and endowing the machine with understanding cognitive ability;
the knowledge base is used for inputting mass meteorological data and is also used for carrying out comparison analysis on the acquired meteorological data;
the machine learning module is used for learning through a deep neural network according to mass data in the knowledge base so as to acquire new meteorological knowledge or skills and continuously improve the performance of the machine learning module;
the natural language processing module is used for reasonably organizing meteorological elements by using a natural language generation technology and forming a description text by using paragraph planning, sentence planning and sentence optimization processing;
the knowledge graph module is used for visually displaying the relevance among the factors by utilizing the visual graph and revealing the dynamic development rule in the meteorological field.
4. The weather platform with artificial intelligence service capability and learning capability of claim 1, wherein the AI weather cast unit further comprises a machine translation module for implementing multi-language translation and a multi-language voice synthesis module for implementing customized voice synthesis broadcasting service.
5. The weather platform with artificial intelligence service capability and learning capability of claim 1, wherein the AI weather anchor unit further comprises a voice interaction module for voice recognition, voice synthesis and semantic understanding, and generating real-time data or voice broadcast content for the understood semantics.
6. The weather platform with artificial intelligence service capability and learning capability of claim 1, further comprising an early warning module for early warning of severe weather.
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Cited By (5)
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| CN112995297A (en) * | 2021-02-06 | 2021-06-18 | 四川省农业科学院农业信息与农村经济研究所 | Sichuan crop breeding result service system based on knowledge map |
| CN113254473A (en) * | 2021-07-05 | 2021-08-13 | 中国气象局公共气象服务中心(国家预警信息发布中心) | Method and device for acquiring weather service knowledge |
| CN113961670A (en) * | 2021-10-27 | 2022-01-21 | 云途万象网络科技河北有限公司 | An intelligent system for automatically replying to weather information by voice |
| CN117934718A (en) * | 2024-01-26 | 2024-04-26 | 南宁市气象局 | A platform and method for constructing multiple meteorological virtual scenes |
| CN119130130A (en) * | 2024-08-22 | 2024-12-13 | 交通运输部水运科学研究所 | Meteorological disaster risk classification and early warning method for inland river ship formations |
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| CN119130130A (en) * | 2024-08-22 | 2024-12-13 | 交通运输部水运科学研究所 | Meteorological disaster risk classification and early warning method for inland river ship formations |
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