Intelligent management system for standardized production of corn seeds

Abstract

The invention discloses an intelligent management system for standardized production of corn seeds, which comprises a data acquisition module, a data transmission module, a data analysis system, a decision support system, a control module and an early warning system, data of a corn production environment, crop growth and an equipment state are collected in real time and transmitted to a cloud database for storage; the data analysis system is used for analyzing and processing real-time data of the cloud database; the decision support system is used for carrying out decision support on the analyzed and processed data through an artificial intelligence algorithm and providing detailed data of seeding time, fertilizing amount and irrigation; and the control module is used for converting the data provided by the decision support system to adjust production parameters. The invention relates to the technical field of agricultural production management, and the system can achieve the intelligent management of the production process, improves the production efficiency, optimizes the resource allocation, and guarantees the production quality of corn seeds through the technologies of the Internet of Things, big data analysis, artificial intelligence and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural production management, and in particular relates to an intelligent management system for standardized production of corn seeds. Background Art

[0002] With the development of modern agriculture, traditional corn seed production management faces numerous challenges, including low automation levels in the production process, inaccurate resource allocation, unrealistic environmental monitoring, and unscientific decision-making. These issues result in low corn seed production efficiency, difficulty ensuring quality, and suboptimal resource utilization. Existing seed production management systems rely heavily on manual operations, making it difficult to monitor and adjust the production process in real time, hindering precise and refined management. Furthermore, they lack effective early warning mechanisms and are susceptible to changes in the external environment.

[0003] Therefore, there is an urgent need for an intelligent corn seed production management system that can monitor the production environment and crop growth status in real time, provide scientific decision-making support, accurately control the production links, and achieve efficient utilization of resources and automated management of the production process. Summary of the Invention

[0004] In response to the above situation, in order to make up for the above-mentioned existing defects, the present invention provides an intelligent management system for standardized production of corn seeds. This system can realize intelligent management of the production process through technologies such as the Internet of Things, big data analysis, and artificial intelligence, improve production efficiency, optimize resource allocation, and ensure the production quality of corn seeds.

[0005] The intelligent management system for standardized corn seed production proposed in the present invention includes a data acquisition module, a data transmission module, a data analysis system, a decision support system, a control module and an early warning system. The data acquisition module is used to collect real-time data on the corn production environment, crop growth, and equipment status through various monitoring sensors and transmit the data to a cloud database for storage; the data analysis system is used to analyze and process real-time data in the cloud database; the decision support system is used to provide decision support for the analyzed and processed data through an artificial intelligence algorithm, and provide detailed data on sowing time, fertilizer application amount and irrigation; the control module is used to convert the data provided by the decision support system into production parameters to facilitate precise control of seed sowing, irrigation, and fertilization, reduce manual intervention, and improve production efficiency; the early warning system uses big data and artificial intelligence to monitor environmental data and crop growth status, predict the probability of occurrence of pests and diseases, and provide corresponding prevention and control measures.

[0006] Furthermore, the data acquisition module includes climate sensors, soil sensors, crop growth sensors and mechanical equipment status sensors. The data acquisition module is connected to the automatic seeding machine, fertilizer applicator and intelligent agricultural equipment through various sensors to realize automatic synchronous collection of real-time data. The collected data is transmitted to the cloud database in real time via the wireless network to ensure the real-time and stability of data transmission.

[0007] Furthermore, the data transmission module uses AES encryption technology to protect data to prevent data leakage and tampering. The network of the data transmission module adopts the NB-IoT transmission network with strong adaptability and low power consumption to ensure smooth data transmission.

[0008] Furthermore, the data analysis system integrates big data analysis and machine learning technologies to conduct in-depth analysis of historical data and real-time data in the cloud database. By means of image recognition of crop growth conditions and time series data analysis, it can obtain key changes in the crop growth process and identify potential influencing factors of climate and soil changes.

[0009] Furthermore, the decision support system, based on AI deep learning algorithms, comprehensively analyzes multiple data on climate, soil, and crop status to generate customized production plans. It provides real-time optimization suggestions through real-time feedback on crop growth status and external environmental changes. At the same time, it sets different decision models according to user needs, and makes production recommendations on sowing time, fertilizer application amount, and irrigation amount, thereby improving the level of refinement of agricultural production.

[0010] Furthermore, the control module can coordinate with the production equipment according to the recommendations of the decision support system to adjust the operating parameters of the irrigation system, fertilization equipment and sowing equipment to ensure that the crops have the best growth conditions and improve production efficiency.

[0011] Furthermore, the early warning system monitors ambient temperature, humidity, precipitation and crop growth data, and combines big data analysis and artificial intelligence technology to predict the probability of pest and disease occurrence. The early warning system can also issue and push warnings based on historical data and climate trends so that staff can take preventive measures.

[0012] The beneficial effects achieved by the present invention using the above structure are as follows:

[0013] 1. This system collects real-time data from climate, soil, crop growth, and equipment status sensors to accurately monitor the corn production environment, crop growth status, and equipment operation, ensuring timely understanding of production status and providing support for decision-making.

[0014] 2. After data collection, the information is encrypted through AES and transmitted over the low-power NB-IoT network, which can ensure the security, stability and real-time performance of data transmission, effectively preventing data leakage and tampering.

[0015] 3. Through big data analysis and machine learning technology, this system can conduct in-depth analysis of historical and real-time data, help identify key information on crop growth trends and environmental changes, and predict potential risk factors, such as the impact of climate change on crops, providing a scientific basis for decision-making. Through artificial intelligence and deep learning algorithms, it can generate customized production plans based on multiple data analyses such as climate, soil, and crops, and optimize sowing time, fertilizer application, and irrigation amounts in real time, thereby improving the efficiency and quality of agricultural production.

[0016] 4. Through the control module, the production equipment can be automatically adjusted according to the suggestions of the decision support system, and the production parameters of the planter, fertilizer applicator and irrigation system can be precisely controlled to ensure that crops grow under optimal growth conditions, reduce manual intervention and improve production efficiency.

[0017] 5. The early warning system monitors environmental data and crop growth status, combines big data and artificial intelligence technology, and can predict the probability of pest and disease occurrence in advance, and issue early warnings in time to help operators take preventive measures and avoid losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0019] Figure 1 This is a schematic diagram of the process of the intelligent management system for standardized production of corn seeds of the present invention. Figure 1 ;

[0020] Figure 2 This is a schematic diagram of the process of the intelligent management system for standardized production of corn seeds of the present invention. Figure 2 . DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the accompanying drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0023] like Figure 1 and Figure 2 As shown, the technical solution adopted by the present invention is as follows: The intelligent management system for standardized production of corn seeds proposed by the present invention includes a data acquisition module, a data transmission module, a data analysis system, a decision support system, a control module and an early warning system:

[0024] The data acquisition module includes climate sensors, soil sensors, crop growth sensors and mechanical equipment status sensors. The data acquisition module is connected to automatic seeders, fertilizer spreaders and smart agricultural equipment through various sensors to realize automatic synchronous collection of real-time data, real-time data collection of corn production environment, crop growth and equipment status, and the collected data is transmitted to the cloud database for storage via wireless network in real time to ensure the real-time and stability of data transmission; the data transmission module uses AES encryption technology for data protection to prevent data leakage and tampering. The network of the data transmission module adopts the NB-IoT transmission network with strong adaptability and low power consumption to ensure smooth data transmission; the data analysis system integrates big data analysis and machine learning technology to conduct in-depth analysis of historical data and real-time data in the cloud database, and obtains key information in the crop growth process through image recognition and time series data analysis of crop growth. Changes, identify potential influencing factors of climate and soil changes; the decision support system, based on AI and deep learning algorithms, comprehensively analyzes multiple data on climate, soil, and crop status, generates customized production plans, and makes real-time optimization suggestions through real-time feedback on crop growth status and external environmental changes. At the same time, different decision models are set according to user needs to make production suggestions on sowing time, fertilizer application amount, and irrigation amount, thereby improving the level of refinement of agricultural production; the control module can, based on the suggestions of the decision support system, cooperate with the production equipment to adjust the operating parameters of the irrigation system, fertilization equipment, and sowing equipment to ensure that crops have optimal growth conditions and improve production efficiency; the early warning system monitors ambient temperature, humidity, precipitation, and crop growth data, combines big data analysis and artificial intelligence technology, and predicts the probability of occurrence of pests and diseases. The early warning system can also issue and push warnings based on historical data and climate trends so that staff can take preventive measures.

[0025] During specific use, by installing climate sensors, soil sensors, crop growth sensors and mechanical equipment status sensors, environmental data, crop growth data and equipment status data are collected in the corn seed production area, and the collected real-time data is transmitted to the cloud or local server for data storage and backup. The data analysis system uses big data analysis technology and machine learning algorithms to process the data and identify crop growth trends, environmental changes and other information. The decision support system generates production optimization suggestions based on the data analysis results, and automatically adjusts production parameters through the control module to control temperature and humidity, irrigation accuracy, and fertilizer application in the corn seed production area to ensure that crops have optimal growth conditions and improve production efficiency. The early warning system monitors the production process in real time, analyzes possible problems and sends early warning information to operators in a timely manner to help them respond quickly, and adjusts the optimization plan according to real-time data and production progress to ensure the stability of the production process and improve seed quality.

[0026] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, material, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, material, or apparatus.

[0027] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent management system for standardized corn seed production, characterized by: It includes a data acquisition module, a data transmission module, a data analysis system, a decision support system, a control module and an early warning system. The data acquisition module is used to collect data on the corn production environment, crop growth and equipment status in real time through various monitoring sensors and transmit the data to the cloud database for storage; the data analysis system is used to analyze and process the real-time data of the cloud database; the decision support system is used to support decisions on the analyzed and processed data through artificial intelligence algorithms, and provide detailed data on sowing time, fertilizer application amount and irrigation; the control module is used to convert the data provided by the decision support system into production parameters to facilitate accurate control of seed sowing, irrigation and fertilization, reduce manual intervention and improve production efficiency; the early warning system uses big data and artificial intelligence to monitor environmental data and crop growth status, predict the probability of occurrence of pests and diseases, and provide corresponding prevention and control measures.

2. The intelligent management system for standardized production of corn seeds according to claim 1, characterized in that: The data acquisition module includes climate sensors, soil sensors, crop growth sensors and mechanical equipment status sensors. The data acquisition module is connected to automated seeders, fertilizer spreaders and smart agricultural equipment through various sensors to achieve automatic and synchronous collection of real-time data. The collected data is transmitted to the cloud database in real time via a wireless network, ensuring the real-time and stability of data transmission.

3. The intelligent management system for standardized corn seed production according to claim 1, characterized in that: The data transmission module adopts AES encryption technology for data protection to prevent data leakage and tampering. The network of the data transmission module adopts the NB-IoT transmission network with strong adaptability and low power consumption to ensure smooth data transmission.

4. The intelligent management system for standardized corn seed production according to claim 1, characterized in that: The data analysis system integrates big data analysis and machine learning technologies to conduct in-depth analysis of historical data and real-time data in cloud databases. By identifying crop growth images and analyzing time series data, it can obtain key changes in the crop growth process and identify potential influencing factors of climate and soil changes.

5. The intelligent management system for standardized production of corn seeds according to claim 1, characterized in that: The decision support system, based on AI deep learning algorithms, comprehensively analyzes multiple data on climate, soil, and crop status to generate customized production plans. It provides real-time optimization suggestions through real-time feedback on crop growth status and external environmental changes. At the same time, it sets different decision models based on user needs, and makes production recommendations on sowing time, fertilizer application amount, and irrigation amount, thereby improving the level of refinement of agricultural production.

6. The intelligent management system for standardized corn seed production according to claim 1, characterized in that: The control module can coordinate with production equipment according to the recommendations of the decision support system to adjust the operating parameters of the irrigation system, fertilization equipment and sowing equipment to ensure that crops have optimal growth conditions and improve production efficiency.

7. The intelligent management system for standardized corn seed production according to claim 1, characterized in that: The early warning system monitors ambient temperature, humidity, precipitation, and crop growth data, and combines big data analysis and artificial intelligence technology to predict the probability of pest and disease occurrence. The early warning system can also issue and push warnings based on historical data and climate trends so that staff can take preventive measures.

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