CN113723760A - Wisdom agricultural thing networking platform - Google Patents
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Abstract
The invention provides an intelligent agricultural Internet of things platform, which comprises an agricultural big data management unit, an agricultural knowledge map unit, an algorithm and model prediction analysis unit and an agricultural machine control unit; agricultural big data management, agricultural algorithm and model prediction analysis, agricultural knowledge map and agricultural machine control are integrated; the agricultural production remote control system has the advantages that data collected in agricultural production can be monitored conveniently and timely, intrinsic information contained in the data is mined, various artificial intelligence algorithms and models are combined, expert system knowledge is combined, agricultural production is guided and planned, and remote control over equipment such as agricultural machinery is achieved by combining the related technology of the Internet of things.
Description
Technical Field
The invention belongs to the field of Internet of things, and particularly relates to an intelligent agricultural Internet of things platform.
Background
In recent years, with the development of science and technology, agriculture has been developed from a simple 'knife tillage and fire planting' era to an intelligent era of modern mechanical seeding, fertilizing, deinsectization and harvesting, and has been developed into a comprehensive industry involving multiple aspects. With scientific progress and further development of technology, the traditional agricultural production mode cannot meet the current economic development requirement, and the combination of the internet plus and agriculture can provide a new idea for solving the problem. By combining a series of emerging internet technologies such as internet of things, cloud computing and artificial intelligence, the problems of large industrial scale, low efficiency and the like in agricultural production are solved.
As a novel agricultural production mode, the development of intelligent agriculture is not separated from the Internet of things. In agricultural production, a lot of data are generated, and the data are acquired by a sensor in an intelligent agricultural system, transmitted and summarized through a network and finally used. The technology of the internet of things is produced for realizing the interconnection of everything, and is very suitable for the requirement. Meanwhile, the popularization of the personal computer also provides convenience for agricultural data management, and the monitoring, analysis and decision of the whole agricultural production process can be realized through one networked computer under the support of the internet technology. This is achieved by the following patent: design and development of an intelligent agricultural Internet of things platform.
Disclosure of Invention
The invention provides an intelligent agricultural Internet of things platform, which integrates agricultural big data management, agricultural algorithm and model prediction analysis, agricultural knowledge map and agricultural machine control; the agricultural production remote control system has the advantages that data collected in agricultural production can be monitored conveniently and timely, intrinsic information contained in the data is mined, various artificial intelligence algorithms and models are combined, expert system knowledge is combined, agricultural production is guided and planned, and remote control over equipment such as agricultural machinery is achieved by combining the related technology of the Internet of things.
The invention is realized by the following scheme:
an intelligent agricultural Internet of things platform:
the platform comprises an agricultural big data management unit, an agricultural knowledge map unit, an algorithm and model prediction analysis unit and an agricultural machine control unit;
the agricultural big data management unit is used for receiving various data from sensors arranged in a farm in real time, sending the data to the server through a network, and displaying the data to a user;
the agricultural knowledge map unit guides agricultural production by using the existing experience;
the algorithm and model prediction analysis unit calculates and infers multi-mode data in the crop growth process, identifies crop diseases and insect pests, predicts growth trend and yield, and makes decisions and schedules on mechanical equipment such as agricultural machinery and the like;
the agricultural machinery control unit remotely controls agricultural machinery or equipment in an online control mode of a webpage end according to the existing data and the data of other units, and can plan the work of the agricultural machinery or equipment.
Further, the agricultural big data management unit monitors various dimensional data generated in agricultural production, including climate data and soil data; the climate data comprises temperature, humidity, carbon dioxide concentration and wind speed; the soil data comprises soil ion concentration, soil temperature and soil humidity;
the current crop growth condition is displayed in real time through data, and the subsequent plant diseases and insect pests or growth vigor are predicted.
Furthermore, the agricultural knowledge map unit constructs a knowledge map through the existing agricultural data and carries out visual display, and the knowledge map provides a data basis for the algorithm and the model prediction analysis unit.
Further, the algorithm and model predictive analysis unit includes a generation confrontation network model, a graph convolution neural network model, a circulation neural network model, a multi-modal machine learning model, a knowledge graph, and a sequence-to-sequence model.
Furthermore, the platform uses Java EE architecture, SpringBoot, Vue.js and MyBatis technical framework, and combines Spring security framework to realize authority and security mechanism, Redis cache realizes performance and concurrent optimization, and the database uses Mysql database.
The invention has the beneficial effects
The invention can conveniently monitor the summarized data in the agricultural production in real time, mine the intrinsic information contained in the data, guide and plan the agricultural production by combining various artificial intelligence algorithms and models and expert system knowledge, and realize the remote control of equipment such as agricultural machinery and the like by combining the related technology of the Internet of things.
Drawings
FIG. 1 is a general architecture of the platform of the present invention;
FIG. 2 is a classification of platform functions of the present invention;
FIG. 3 is an agricultural data management section of the present invention;
FIG. 4 is the leaf area index LAI concept;
FIG. 5 illustrates the AI model and algorithm portion of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In conjunction with the figures 1 to 5,
an intelligent agricultural Internet of things platform:
the platform comprises an agricultural big data management unit, an agricultural knowledge map unit, an algorithm and model prediction analysis unit and an agricultural machine control unit;
the agricultural big data management unit is used for receiving various data from sensors arranged in the farm in real time, and the position of the data in the system is the first place. The data relation establishing system module can receive various data from sensors arranged in a farm in real time, send the data to the server through a network and display the data to a user;
fig. 3 is an illustration of a data management section. A data real-time visualization platform is designed and manufactured for a plurality of data sources, and the data condition of the data sources can be monitored in real time. The method comprises the steps of real-time sensor data, whether the equipment data state is normal or not, real-time images, recent data statistics and data quantity overview.
The agricultural knowledge map unit guides agricultural production by using the existing experience;
the algorithm and model prediction analysis unit calculates and infers multi-mode data in the crop growth process, identifies crop diseases and insect pests, predicts growth trend and yield, and makes decisions and schedules on mechanical equipment such as agricultural machinery and the like;
in the currently mainly realized online calculation model of the Leaf Area Index (LAI), a user can realize online calculation of the leaf area index by uploading pictures or using default demonstration pictures.
The agricultural machinery control unit remotely controls agricultural machinery or equipment in an online control mode of a webpage end according to the existing data and the data of other units, and can plan the work of the agricultural machinery or equipment.
The agricultural big data management unit monitors various dimensional data generated in agricultural production, including climate data and soil data; the climate data comprises temperature, humidity, carbon dioxide concentration, wind speed and the like; the soil data comprises soil ion concentration, soil temperature, soil humidity and the like;
the current crop growth condition is displayed in real time through data, and the subsequent plant diseases and insect pests or growth vigor are predicted.
The agricultural knowledge map is used as an expert system, and can guide agricultural production by utilizing the existing experience. The body construction standard of the agricultural field is established, agricultural data on Baidu encyclopedia and books are extracted and researched, the knowledge graph of the agricultural field is established, the knowledge graph and research results are visually displayed, and a data basis is provided for research such as knowledge reasoning and prediction; and constructing a knowledge graph through the existing agricultural data, and performing visual display, wherein the knowledge graph provides a data basis for an algorithm and a model prediction analysis unit.
The application of the artificial intelligence method in the agricultural field is more and more extensive, the traditional agricultural industry mainly cultivates, weeds, irrigates and reaps by manpower, the mode is influenced by numerous factors such as manpower and material resources, huge manpower consumption and investment can be generated, and the algorithm and model prediction analysis unit comprises a generation confrontation network model, a graph convolution neural network model, a circulation neural network model, a multi-mode machine learning model, a knowledge graph and a sequence-to-sequence model. And deploying the existing algorithm and model, and designing and manufacturing a uniform display page for the existing algorithm and model.
The platform uses Java EE architecture, Spring boot, Vue.js and MyBatis technical framework, combines Spring security framework to realize authority and security mechanism, Redis cache realizes optimization of performance and concurrency, and the database uses Mysql database.
The platform will add more algorithms and modules as it is continuously developed and researched.
Spring and Spring boot frames. The advent of the Spring framework rescued Java EE development from a cumbersome and heavy development model, with the main core technologies of facet-oriented programming (AOP) and control reversal (IoC). The Java Spring framework flexibly manages transactions in an declarative mode, and development efficiency and quality are improved. The Spring framework is not limited to server-side development, and any Java application can benefit from Spring from the point of simplicity, testability and loose coupling. Spring frames are also a super-adhesive platform that, in addition to providing their own function, also provide the ability to adhere to other techniques and frames.
The spring boot is an extension of a spring framework, and development and release of applications are simpler and more convenient by providing various means such as automatic configuration and tomcat embedding.
The current Spring boot framework is the most mainstream framework used by the background part of the development system, so Spring + Spring boot is selected as the background development framework of the system.
The Mybatis framework. This is an excellent persistence tier framework that supports custom SQL, stored procedures, and advanced mapping. MyBatis exempts almost all JDBC code from the task of setting parameters and obtaining a result set. The mapping of database data to programs can be realized through the framework, and the persistence operation of the data can be realized.
Mysql relational database. The database was selected as the means for storing most agricultural data (knowledge maps use the database Neo4j to store data)
Spring security framework. The core functions include authentication, authorization and attack protection. The framework is selected, so that various functions of registration and login, authority control, system safety and the like can be realized.
Js framework. Html, css and java script are the basis of front-end development, and are respectively used for realizing webpage content, webpage style and webpage behavior. Js is one of three main stream front-end frameworks, and integrates Html, css and java script organically, so that the system has the advantages of light weight, data binding, extensible plug-in and the like. The method is used for developing a main frame of a front-end project.
Element UI and Lay UI. Are all well-known css frameworks. The method can be used for beautifying the webpage and improving the user experience.
Regarding the algorithm and model predictive analysis section, there is currently done on-line calculation of the LAI leaf area index. By deploying the deep learning environment at the cloud, deploying the designated model and developing a front-end page and a data transmission interface for interaction with a user, online calculation and demonstration of the deep learning model can be realized, and important guidance information is provided for agricultural production and research.
The intelligent agriculture internet of things platform provided by the invention is introduced in detail, the principle and the implementation mode of the intelligent agriculture internet of things platform are explained, and the explanation of the embodiment is only used for helping to understand the method and the core idea of the intelligent agriculture internet of things platform; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (5)
1. The utility model provides an wisdom agricultural thing networking platform which characterized in that:
the platform comprises an agricultural big data management unit, an agricultural knowledge map unit, an algorithm and model prediction analysis unit and an agricultural machine control unit;
the agricultural big data management unit is used for receiving various data from sensors arranged in a farm in real time, sending the data to the server through a network, and displaying the data to a user;
the agricultural knowledge map unit guides agricultural production by using the existing experience;
the algorithm and model prediction analysis unit calculates and infers multi-mode data in the crop growth process, identifies crop diseases and insect pests, predicts growth trend and yield, and makes decisions and schedules on mechanical equipment such as agricultural machinery and the like;
the agricultural machinery control unit remotely controls agricultural machinery or equipment in an online control mode of a webpage end according to the existing data and the data of other units, and can plan the work of the agricultural machinery or equipment.
2. The platform of claim 1, wherein:
the agricultural big data management unit monitors various dimensional data generated in agricultural production, including climate data and soil data; the climate data comprises temperature, humidity, carbon dioxide concentration and wind speed; the soil data comprises soil ion concentration, soil temperature and soil humidity;
the current crop growth condition is displayed in real time through data, and the subsequent plant diseases and insect pests or growth vigor are predicted.
3. The platform of claim 1, wherein:
the agricultural knowledge map unit constructs a knowledge map through the existing agricultural data and carries out visual display, and the knowledge map provides a data basis for the algorithm and the model prediction analysis unit.
4. The platform of claim 1, wherein:
the algorithm and model predictive analysis unit comprises a generation confrontation network model, a graph convolution neural network model, a circulation neural network model, a multi-mode machine learning model, a knowledge graph and a sequence-to-sequence model.
5. The platform of claim 1, wherein:
the platform uses Java EE architecture, Spring boot, Vue.js and MyBatis technical framework, combines Spring security framework to realize authority and security mechanism, Redis cache realizes optimization of performance and concurrency, and the database uses Mysql database.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114331753A (en) * | 2022-03-04 | 2022-04-12 | 阿里巴巴达摩院(杭州)科技有限公司 | Intelligent farm work method and device and control equipment |
| CN115035512A (en) * | 2022-05-24 | 2022-09-09 | 合肥工业大学 | Crop nutrition state diagnosis method and system based on multi-mode deep learning |
| CN115937689A (en) * | 2022-12-30 | 2023-04-07 | 安徽农业大学 | Agricultural pest intelligent identification and monitoring technology |
| CN116258238A (en) * | 2022-12-07 | 2023-06-13 | 嘉应学院 | Crop management system based on Internet of things |
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| CN112199508A (en) * | 2020-08-10 | 2021-01-08 | 淮阴工学院 | Parameter adaptive agricultural knowledge graph recommendation method based on remote supervision |
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| CN109003198A (en) * | 2018-06-22 | 2018-12-14 | 肖晓飞 | A kind of precision agriculture management platform and method based on big data technology |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114331753A (en) * | 2022-03-04 | 2022-04-12 | 阿里巴巴达摩院(杭州)科技有限公司 | Intelligent farm work method and device and control equipment |
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| CN115937689B (en) * | 2022-12-30 | 2023-08-11 | 安徽农业大学 | Intelligent identification and monitoring technology for agricultural pests |
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