Internet of Things platform for real-time acquisition and analysis of agricultural production data

Abstract

The invention relates to the technical field of agricultural production, and provides an Internet of Things platform for real-time acquisition and analysis of agricultural production data, which comprises a sensor module, a data transmission module, a cloud server, a data analysis module, a terminal application module, an early warning module, a remote control module, a block chain module and a model optimization module. The Internet of Things platform and the Internet of Things method bring revolutionary changes to agricultural production. The system not only improves the efficiency and quality of agricultural production, reduces the cost and risk, but also promotes the modernization and intelligentization development of agriculture. Through deep integration of scientific and technological means and agricultural production, new vitality and competitiveness are injected into the agricultural field, sustainable development of agriculture is achieved, grain safety and balance of the ecological environment are guaranteed, and positive contributions are made to social stability and development. Meanwhile, beneficial reference is provided for development of other related fields, and the method has wide application prospects and social value.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural production, and particularly to an Internet of Things platform for real-time collection and analysis of agricultural production data. Background Art

[0002] With the continuous advancement of agricultural modernization, higher requirements have been put forward for data collection, analysis, and management in the process of agricultural production. Traditional methods of collecting agricultural production data often have problems such as inaccurate, untimely, and incomplete data, making it difficult to meet the needs of modern agricultural development.

[0003] In terms of agricultural environment monitoring, there is a lack of efficient and accurate multi-parameter synchronous monitoring means, resulting in inaccurate grasp of key factors such as soil and climate. Data transmission also often faces difficulties such as instability and low efficiency, affecting the timely utilization of data.

[0004] Agricultural production decisions also mostly rely on experience, lacking scientific basis and accurate guidance. Moreover, in the face of complex and changing agricultural production situations, existing methods and technologies are difficult to quickly and accurately give effective response strategies, easily leading to problems such as waste of resources, low production efficiency, and failure to timely and effectively solve problems such as pests and diseases.

[0005] At the same time, the security and immutability of data have not been given sufficient attention, and there is a risk of data leakage and tampering, which poses a threat to the stable and sustainable development of agricultural production.

[0006] In addition, the management of agricultural production equipment is relatively extensive, with insufficient intelligence, and it is impossible to achieve remote precise control and efficient collaborative operation.

[0007] Just under such a background, there is an urgent need for a comprehensive Internet of Things platform and method for real-time collection and analysis of agricultural production data to solve the many problems and challenges faced in current agricultural production and promote the development of agriculture towards a more intelligent, efficient, precise, and sustainable direction. Summary of the Invention

[0008] To overcome the defects of the prior art, the purpose of the present invention is to provide an Internet of Things platform for real-time collection and analysis of agricultural production data.

[0009] To achieve the above object, the technical solution of the present invention is realized as follows: An Internet of Things platform for real-time collection and analysis of agricultural production data, characterized by including:

[0010] A sensor module for real-time collection of various data in the agricultural production environment, including but not limited to soil humidity, temperature, pH value, light intensity, air humidity, and carbon dioxide concentration; the sensor module adopts multi-sensor fusion technology, which can automatically calibrate and compensate for errors between different sensors, improving the accuracy of data collection;

[0011] A data transmission module for wirelessly transmitting the data collected by the sensor module to the cloud server; the data transmission module adopts an adaptive transmission protocol, dynamically adjusting the data transmission frequency and priority according to the network condition and data importance to ensure the real-time and integrity of the data;

[0012] A cloud server for receiving, storing, and processing the data transmitted by the data transmission module, and providing data query and analysis services; the cloud server adopts a distributed storage architecture to improve the reliability and scalability of data storage, and at the same time uses cloud computing technology to quickly process and analyze the data;

[0013] A data analysis module running on the cloud server, using data analysis algorithms to analyze agricultural production data to generate agricultural production decision-making suggestions; the data analysis module introduces a deep learning model, which can deeply analyze and predict complex agricultural production data, providing more accurate decision-making support for agricultural production;

[0014] A terminal application module for users to access the cloud server, view agricultural production data and analysis results, and receive agricultural production decision-making suggestions; the terminal application module has an intelligent push function, and according to the user's needs and preferences, timely pushes relevant agricultural production information to the user;

[0015] An early warning module connected to the data analysis module, when the analyzed agricultural production data exceeds the preset threshold, automatically sends out early warning information to remind the user to take measures in time;

[0016] A remote control module, through which users can remotely control agricultural production equipment via the terminal application module to achieve intelligent agricultural production management;

[0017] A blockchain module for encrypting and storing agricultural production data to ensure the security and immutability of the data;

[0018] A model optimization module continuously optimizes the deep learning model according to new agricultural production data to improve the accuracy and adaptability of the model.

[0019] Preferably, the sensors in the sensor module adopt a low-power design, are powered by batteries, and are connected to the data transmission module through wireless communication.

[0020] Preferably, the data transmission module adopts wireless communication technologies such as NB-IoT, LoRa, or Zigbee.

[0021] Preferably, the cloud server has data backup and recovery functions to ensure the security and reliability of data.

[0022] Preferably, the method for real-time collection and analysis of agricultural production data includes the following steps:

[0023] Collect various data in the agricultural production environment in real time through the sensor module, and use multi-sensor fusion technology to automatically calibrate and compensate the errors between different sensors;

[0024] Use the adaptive transmission protocol of the data transmission module to dynamically adjust the transmission frequency and priority of the collected data according to the network condition and data importance, and wirelessly transmit it to the cloud server;

[0025] The cloud server adopts a distributed storage architecture to receive and store data, and uses cloud computing technology to analyze and process the data through the deep learning model of the data analysis module, and generate agricultural production decision-making suggestions, including irrigation suggestions, fertilization suggestions, pest control suggestions, greenhouse environment control suggestions, etc.;

[0026] The user accesses the cloud server through the intelligent push function of the terminal application module, views the agricultural production data and analysis results, and receives relevant agricultural production decision-making suggestions.

[0027] Preferably, the data analysis and processing include data cleaning, data analysis, and data visualization, and the data analysis uses a deep learning model for in-depth analysis and prediction.

[0028] Preferably, the warning methods of the warning module include SMS warning, in-application push warning, and voice warning.

[0029] Preferably, the remote control module supports the control of various agricultural production equipment, including irrigation equipment, fertilization equipment, ventilation equipment, and sunshade equipment.

[0030] Preferably, the blockchain module uses a hash algorithm to encrypt agricultural production data.

[0031] Preferably, the model optimization module uses automated machine learning technology to achieve the automated optimization of the deep learning model.

[0032] The beneficial effects of the present invention are reflected in:

[0033] The Internet of Things platform and method of the present invention have brought revolutionary changes to agricultural production. It not only improves the efficiency and quality of agricultural production, reduces costs and risks, but also promotes the modernization and intelligent development of agriculture. Through the deep integration of technological means and agricultural production, it injects new vitality and competitiveness into the agricultural field, helps to achieve the sustainable development of agriculture, ensures food security and the balance of the ecological environment, and makes positive contributions to the stability and development of society. At the same time, it also provides useful references for the development of other related fields, and has broad application prospects and social value. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In the drawings:

[0035] Figure 1 is a schematic diagram of the system connection structure of the present invention;

[0036] Figure 2 is a flowchart of the method for real-time data collection and analysis of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The present invention will be further described in detail below in conjunction with the drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, rather than all of the embodiments. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. Based on the embodiments of the invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the invention.

[0038] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the invention, the directional indications are only used to explain the relative position relationship and movement conditions between components in a certain specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0039] In addition, "a plurality" means two or more. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the invention.

[0040] Please refer to the attached specification Figure 1 - Figure 2 , the present invention provides an Internet of Things platform for real-time collection and analysis of agricultural production data, including:

[0041] A sensor module for collecting various data in the agricultural production environment in real time, including but not limited to soil humidity, temperature, pH value, light intensity, air humidity, and carbon dioxide concentration. The sensor module adopts multi-sensor fusion technology, which can automatically calibrate and compensate for the errors between different sensors, improve the accuracy of data collection, greatly enhance the accuracy and reliability of agricultural production data, provide a solid foundation for subsequent analysis and decision-making, and avoid misjudgment and wrong decisions caused by data errors.

[0042] A data transmission module for wirelessly transmitting the data collected by the sensor module to the cloud server. The data transmission module adopts an adaptive transmission protocol, dynamically adjusts the data transmission frequency and priority according to the network condition and data importance, ensures the efficiency, real-time performance, and stability of data transmission, can timely transmit key data to the cloud server, and does not delay the critical timing of agricultural production.

[0043] A cloud server for receiving, storing, and processing the data transmitted by the data transmission module, and providing data query and analysis services. The cloud server adopts a distributed storage architecture to improve the reliability and scalability of data storage. At the same time, it uses cloud computing technology to quickly process and analyze the data, comprehensively enhancing the scientificity and rationality of agricultural production.

[0044] A data analysis module running on the cloud server, which uses data analysis algorithms to analyze agricultural production data to generate agricultural production decision-making suggestions. The data analysis module introduces a deep learning model, which can deeply analyze and predict complex agricultural production data, providing more accurate decision-making support for agricultural production.

[0045] A terminal application module for users to access the cloud server, view agricultural production data and analysis results, and receive agricultural production decision-making suggestions. The terminal application module has an intelligent push function, which timely pushes relevant agricultural production information to users according to the needs and preferences of users, improving the user experience and participation.

[0046] An early warning module connected to the data analysis module. When the analyzed agricultural production data exceeds the preset threshold, it automatically sends out early warning information to remind users to take timely measures to reduce agricultural production risks and losses.

[0047] A remote control module. Users can remotely control agricultural production equipment through the terminal application module to achieve intelligent agricultural production management, improving production efficiency and resource utilization efficiency.

[0048] A blockchain module for encrypting and storing agricultural production data, ensuring the security and immutability of data, and maintaining the authority and credibility of data.

[0049] The model optimization module continuously optimizes the deep learning model according to new agricultural production data, improving the accuracy and adaptability of the model so that it can better adapt to different agricultural production scenarios and changes.

[0050] The sensors in the sensor module are designed with low power consumption, powered by batteries, and connected to the data transmission module through wireless communication, enabling them to adapt to the outdoor environment.

[0051] The data transmission module uses wireless communication technologies such as NB-IoT, LoRa, or Zigbee to increase the diversity of communication.

[0052] The cloud server has data backup and recovery functions to ensure the security and reliability of the data.

[0053] A method for real-time collection and analysis of agricultural production data includes the following steps:

[0054] Collect various data in the agricultural production environment in real time through the sensor module, and use multi-sensor fusion technology to automatically calibrate and compensate for errors between different sensors;

[0055] Using the adaptive transmission protocol of the data transmission module, the collected data is dynamically adjusted in terms of transmission frequency and priority according to the network conditions and data importance, and wirelessly transmitted to the cloud server;

[0056] The cloud server adopts a distributed storage architecture to receive and store data, and uses cloud computing technology to analyze and process the data through the deep learning model of the data analysis module, generating agricultural production decision-making suggestions, including irrigation suggestions, fertilization suggestions, pest and disease control suggestions, and greenhouse environment regulation suggestions, etc.;

[0057] Users access the cloud server through the intelligent push function of the terminal application module, view the agricultural production data and analysis results, and receive relevant agricultural production decision-making suggestions.

[0058] Data analysis and processing include data cleaning, data analysis, and data visualization, where data analysis uses a deep learning model for in-depth analysis and prediction.

[0059] The warning methods of the warning module include SMS warning, in-app push warning, and voice warning, which can quickly remind users of the specific situation.

[0060] The remote control module supports the control of various agricultural production equipment, including irrigation equipment, fertilization equipment, ventilation equipment, and sunshade equipment, to achieve remote control and be safer.

[0061] The blockchain module encrypts agricultural production data using the hash algorithm, which can maximize the security of the data.

[0062] The model optimization module uses automated machine learning technology to achieve the automated optimization of deep learning models, and can continuously optimize its own system to adapt to different agricultural planting environments.

[0063] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0064] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed invention.

[0065] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An Internet of Things platform for real-time collection and analysis of agricultural production data, characterized in that, Including: A sensor module for real-time collection of various data in the agricultural production environment, including but not limited to soil humidity, temperature, pH value, light intensity, air humidity, and carbon dioxide concentration; the sensor module adopts multi-sensor fusion technology, which can automatically calibrate and compensate for errors between different sensors, improving the accuracy of data collection; A data transmission module for wirelessly transmitting the data collected by the sensor module to a cloud server; the data transmission module adopts an adaptive transmission protocol to dynamically adjust the frequency and priority of data transmission according to network conditions and data importance, ensuring the real-time and integrity of data; A cloud server for receiving, storing, and processing the data transmitted by the data transmission module, and providing data query and analysis services; The cloud server adopts a distributed storage architecture to improve the reliability and scalability of data storage, and at the same time uses cloud computing technology to quickly process and analyze data; A data analysis module running on the cloud server, which uses data analysis algorithms to analyze agricultural production data to generate agricultural production decision-making suggestions; the data analysis module introduces a deep learning model, which can deeply analyze and predict complex agricultural production data, providing more accurate decision-making support for agricultural production; A terminal application module for users to access the cloud server, view agricultural production data and analysis results, and receive agricultural production decision-making suggestions; The terminal application module has an intelligent push function, and according to the needs and preferences of users, it will push relevant agricultural production information to users in a timely manner; An early warning module, connected to the data analysis module, when the analyzed agricultural production data exceeds the preset threshold, it will automatically send out early warning information to remind users to take measures in a timely manner; A remote control module, through which users can remotely control agricultural production equipment to achieve intelligent agricultural production management; A blockchain module for encrypting and storing agricultural production data to ensure the security and immutability of data; A model optimization module for continuously optimizing the deep learning model according to new agricultural production data to improve the accuracy and adaptability of the model.

2. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that, The sensors in the sensor module adopt a low-power design, are powered by batteries, and are connected to the data transmission module through wireless communication.

3. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that, The data transmission module adopts wireless communication technologies such as NB-IoT, LoRa, or Zigbee.

4. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that, The cloud server has a data backup and recovery function to ensure the security and reliability of data.

5. A method for real-time collection and analysis of agricultural production data, applied to the Internet of Things platform according to any one of claims 1-4, characterized in that, Including the following steps: Real-time collection of various data in the agricultural production environment through the sensor module, and automatically calibrating and compensating for errors between different sensors by using multi-sensor fusion technology; Using the adaptive transmission protocol of the data transmission module, dynamically adjusting the frequency and priority of transmission according to network conditions and data importance for the collected data, and wirelessly transmitting it to the cloud server; The cloud server adopts a distributed storage architecture to receive and store data, and uses cloud computing technology to analyze and process the data through the deep learning model of the data analysis module, generating agricultural production decision-making suggestions, including irrigation suggestions, fertilization suggestions, pest control suggestions, greenhouse environment regulation suggestions, etc.; Users access the cloud server through the intelligent push function of the terminal application module, view agricultural production data and analysis results, and receive relevant agricultural production decision-making suggestions.

6. The method for real-time collection and analysis of agricultural production data according to claim 5, wherein The data analysis and processing include data cleaning, data analysis, and data visualization, where data analysis uses a deep learning model for in-depth analysis and prediction.

7. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that, The warning methods of the warning module include SMS warning, in-application push warning, and voice warning.

8. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that, The remote control module supports the control of various agricultural production devices, including irrigation devices, fertilization devices, ventilation devices, and sunshade devices.

9. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that The blockchain module encrypts agricultural production data using a hash algorithm.

10. The Internet of Things platform for real-time collection and analysis of agricultural production data according to claim 1, characterized in that, The model optimization module uses automated machine learning technology to achieve the automated optimization of the deep learning model.

Images