Agricultural big data accurate decision support platform based on Internet of Things

Abstract

The invention relates to the technical field of agriculture, and provides an agricultural big data accurate decision support platform based on the Internet of Things, which comprises a data acquisition module, a data storage and management module, a data analysis module, an accurate decision model construction module and a decision visualization module. The agricultural big data accurate decision support platform based on the Internet of Things brings various benefits to agricultural production, is beneficial to improving the benefits and quality of agricultural production, promotes agriculture to develop towards intelligentization, digitization and modernization, and has important application value and social significance.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to an agricultural big data precision decision support platform based on the Internet of Things. Background Art

[0002] With population growth and increasing demand for agricultural products, agricultural production faces increasing challenges. Traditional agricultural production methods, which rely primarily on experience and manual labor, suffer from low production efficiency, severe resource waste, and unstable agricultural product quality. To improve the efficiency and quality of agricultural production and achieve sustainable agricultural development, the introduction of new technologies and methods is urgently needed.

[0003] In recent years, the rapid development of technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) has provided new opportunities for agricultural modernization. IoT enables real-time monitoring of the agricultural production environment and crop growth, providing a foundation for the collection of agricultural big data. Big data can store, manage, and analyze massive amounts of agricultural data, uncovering valuable information and patterns. Artificial intelligence technologies, such as machine learning and deep learning algorithms, can conduct in-depth analysis of agricultural data, providing precise decision support for agricultural production.

[0004] However, current agricultural informatization efforts still face several challenges. For one thing, agricultural data collection is insufficiently comprehensive and accurate, failing to meet the demands of agricultural production decision-making. Furthermore, data storage and management present security risks, while data analysis and application remain in-depth and ineffective. Consequently, the scientific nature and accuracy of agricultural production decisions remain to be improved. Furthermore, existing agricultural decision support systems often lack intuitive presentation methods, making it difficult for users to understand and apply decision results, hindering their effectiveness. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide an agricultural big data accurate decision support platform based on the Internet of Things.

[0006] To achieve the above-mentioned purpose, the technical solution of the present invention is implemented as follows: an agricultural big data precision decision support platform based on the Internet of Things, comprising:

[0007] The data acquisition module is used to collect multi-dimensional agricultural data in real time through IoT devices, including temperature, humidity, light, wind speed, crop height, leaf area, fruit size, soil moisture, fertility, pH, and equipment operation data;

[0008] The data storage and management module uses a distributed data storage architecture combined with blockchain technology to ensure data security, integrity, and immutability, while also classifying, cleaning, and integrating data;

[0009] The data analysis module uses data mining, machine learning, and deep learning algorithms to conduct in-depth analysis of agricultural big data to extract potential information and patterns, and has the ability to automatically identify and correct errors and outliers in the data;

[0010] The module for building an accurate decision-making model builds an accurate decision-making model based on the analysis results to provide decision support for agricultural production, using a hybrid model architecture that integrates fuzzy logic and neural networks;

[0011] The decision visualization module displays decision information to users using three-dimensional virtual reality (VR) and augmented reality (AR) technologies.

[0012] Preferably, the data acquisition module includes various types of sensors and monitoring equipment, has adaptability and low power consumption, and can automatically adjust the acquisition frequency and accuracy according to environmental conditions and data requirements.

[0013] Preferably, the blockchain technology in the data storage and management module adopts the form of a consortium chain, and only authorized agricultural-related institutions and enterprises can participate in data storage and management to ensure data privacy and controllability.

[0014] Preferably, the machine learning algorithms in the data analysis module include decision trees, random forests, support vector machines, etc., and the deep learning algorithm uses a combination of convolutional neural networks and recurrent neural networks to improve data analysis accuracy and generalization ability.

[0015] Preferably, fuzzy logic in the precise decision-making model building module is used to process fuzzy and uncertain information in agricultural production, and neural networks are used to learn and simulate the dynamics of complex agricultural production systems, which are combined through an adaptive fusion mechanism to achieve more precise decision support.

[0016] Preferably, the VR and AR technologies in the decision visualization module can be customized according to user needs and scenarios. Users can immersively experience agricultural production scenes through VR devices or directly view the overlay effects of decision information in actual farmland through AR devices.

[0017] Preferably, the method for making agricultural production decisions based on the agricultural big data precision decision support platform of the Internet of Things includes the following steps:

[0018] Comprehensively collect multi-dimensional agricultural data through the data collection module;

[0019] Utilize distributed storage architecture and blockchain technology to securely process data in the data storage and management module;

[0020] The data analysis module uses a variety of advanced algorithms to analyze data and detect and process abnormal data;

[0021] The precise decision-making model building module builds a hybrid model integrating fuzzy logic and neural network to generate precise decision-making solutions;

[0022] The decision visualization module uses VR and AR technology to display decision results for users to intuitively understand and apply;

[0023] Continuously optimize and improve the platform based on actual decision-making results and user feedback.

[0024] Preferably, when performing data analysis, feature engineering technology is used to preprocess the original data to extract key features to improve data analysis efficiency and accuracy.

[0025] Preferably, after generating an accurate decision-making plan, the decision-making plan is pre-evaluated through simulation technology to predict its possible effects to provide a reference for practical application.

[0026] Preferably, when optimizing and improving the platform, an automated model update and parameter adjustment mechanism is used to update the precise decision-making model in real time based on new data and feedback information to ensure that the platform's decision support always remains accurate and effective.

[0027] The beneficial effects of the present invention are embodied in:

[0028] The IoT-based agricultural big data precision decision support platform of the present invention brings many benefits to agricultural production, helps to improve the efficiency and quality of agricultural production, and promotes the development of agriculture towards intelligence, digitization and modernization, which has important application value and social significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In the attached figure:

[0030] Figure 1 Schematic diagram of the system structure of the present invention;

[0031] Figure 2 The present invention is a flowchart of the method for agricultural production decision-making. DETAILED DESCRIPTION

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described are only some embodiments of the invention, not all embodiments. In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the invention.

[0033] Please refer to the instruction manual Figure 1-Figure 2 , an agricultural big data precision decision support platform based on the Internet of Things, including:

[0034] The data acquisition module is used to collect multi-dimensional agricultural data in real time through IoT devices, including temperature, humidity, light, wind speed, crop height, leaf area, fruit size, soil moisture, fertility, pH, and equipment operation data. This ensures accurate monitoring of the agricultural environment and crop growth status, providing scientific decision-making support for agricultural production.

[0035] The data storage and management module uses a distributed data storage architecture combined with blockchain technology to ensure data security, integrity, and immutability, while also classifying, cleaning, and integrating data;

[0036] The data analysis module uses data mining, machine learning, and deep learning algorithms to conduct in-depth analysis of agricultural big data to extract potential information and patterns. It has the ability to automatically identify and correct errors and outliers in the data, improving the accuracy and usability of analysis results.

[0037] The module for building precise decision-making models builds precise decision-making models based on analysis results to provide decision support for agricultural production. It adopts a hybrid model architecture that integrates fuzzy logic and neural networks to help agricultural producers optimize resource allocation, increase output and benefits, and thus improve the quality of agricultural products.

[0038] The decision visualization module displays decision information to users using three-dimensional virtual reality (VR) and augmented reality (AR) technologies, helping users better understand and apply decision information, thereby improving decision effectiveness and user experience.

[0039] The data acquisition module includes various types of sensors and monitoring equipment, has adaptability and low power consumption, and can automatically adjust the acquisition frequency and accuracy according to environmental conditions and data requirements to ensure the real-time and accuracy of data.

[0040] The blockchain technology in the data storage and management module adopts the form of a consortium chain. Only authorized agricultural-related institutions and enterprises can participate in data storage and management to ensure data privacy and controllability.

[0041] The machine learning algorithms in the data analysis module include decision trees, random forests, support vector machines, etc. The deep learning algorithm uses a combination of convolutional neural networks and recurrent neural networks to improve data analysis accuracy and generalization capabilities.

[0042] In the precise decision-making model construction module, fuzzy logic is used to process fuzzy and uncertain information in agricultural production, and neural networks are used to learn and simulate the dynamics of complex agricultural production systems. Through the adaptive fusion mechanism, they are combined to achieve more precise decision support and help farmers or agricultural enterprises make more scientific decisions.

[0043] VR and AR technologies in the decision visualization module can be customized according to user needs and scenarios. Users can immersively experience agricultural production scenes through VR devices or directly view the overlay effects of decision information in actual farmland through AR devices, effectively improving the practical applicability and interactivity of decision support and facilitating users to understand and apply decision results.

[0044] The method for making agricultural production decisions based on the agricultural big data precision decision support platform based on the Internet of Things includes the following steps:

[0045] Comprehensively collect multi-dimensional agricultural data through the data collection module;

[0046] Utilize distributed storage architecture and blockchain technology to securely process data in the data storage and management module;

[0047] The data analysis module uses a variety of advanced algorithms to analyze data and detect and process abnormal data;

[0048] The precise decision-making model building module builds a hybrid model integrating fuzzy logic and neural network to generate precise decision-making solutions;

[0049] The decision visualization module uses VR and AR technology to display decision results for users to intuitively understand and apply;

[0050] Continuously optimize and improve the platform based on actual decision-making results and user feedback.

[0051] When performing data analysis, feature engineering technology is used to preprocess the raw data and extract key features to improve data analysis efficiency and accuracy.

[0052] After generating an accurate decision-making plan, simulation technology is used to pre-evaluate the decision-making plan and predict its possible effects to provide a reference for practical application.

[0053] When optimizing and improving the platform, an automated model update and parameter adjustment mechanism is used to update the precise decision-making model in real time based on new data and feedback information to ensure that the platform's decision support always remains accurate and effective.

[0054] The above are only 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 principles of the present invention should be included in the scope of protection of the present invention.

[0055] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0056] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. The agricultural big data precision decision support platform based on the Internet of Things is characterized by: include: The data acquisition module is used to collect multi-dimensional agricultural data in real time through IoT devices, including temperature, humidity, light, wind speed, crop height, leaf area, fruit size, soil moisture, fertility, pH, and equipment operation data; The data storage and management module uses a distributed data storage architecture combined with blockchain technology to ensure data security, integrity, and immutability, while also classifying, cleaning, and integrating data; The data analysis module uses data mining, machine learning, and deep learning algorithms to conduct in-depth analysis of agricultural big data to extract potential information and patterns, and has the ability to automatically identify and correct errors and outliers in the data; The module for building an accurate decision-making model builds an accurate decision-making model based on the analysis results to provide decision support for agricultural production, using a hybrid model architecture that integrates fuzzy logic and neural networks; The decision visualization module displays decision information to users using three-dimensional virtual reality (VR) and augmented reality (AR) technologies.

2. The agricultural big data precision decision support platform based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module includes various types of sensors and monitoring equipment, has adaptability and low power consumption, and can automatically adjust the acquisition frequency and accuracy according to environmental conditions and data requirements.

3. The agricultural big data precision decision support platform based on the Internet of Things according to claim 1 is characterized in that: The blockchain technology in the data storage and management module adopts the form of a consortium chain, and only authorized agricultural-related institutions and enterprises can participate in data storage and management to ensure data privacy and controllability.

4. The agricultural big data precision decision support platform based on the Internet of Things according to claim 1 is characterized in that: The machine learning algorithms in the data analysis module include decision trees, random forests, support vector machines, etc. The deep learning algorithm uses a combination of convolutional neural networks and recurrent neural networks to improve data analysis accuracy and generalization ability.

5. The agricultural big data precision decision support platform based on the Internet of Things according to claim 1 is characterized in that: In the precise decision-making model building module, fuzzy logic is used to process fuzzy and uncertain information in agricultural production, and neural networks are used to learn and simulate the dynamics of complex agricultural production systems, which are combined through an adaptive fusion mechanism to achieve more precise decision support.

6. The agricultural big data precision decision support platform based on the Internet of Things according to claim 1 is characterized in that: The VR and AR technologies in the decision visualization module can be customized according to user needs and scenarios. Users can immersively experience agricultural production scenes through VR devices or directly view the superposition effect of decision information in actual farmland through AR devices.

7. A method for making agricultural production decisions using the agricultural big data precision decision support platform based on the Internet of Things according to any one of claims 1 to 6, characterized in that: The following steps are involved: Comprehensively collect multi-dimensional agricultural data through the data collection module; Utilize distributed storage architecture and blockchain technology to securely process data in the data storage and management module; The data analysis module uses a variety of advanced algorithms to analyze data and detect and process abnormal data; The precise decision-making model building module builds a hybrid model integrating fuzzy logic and neural network to generate precise decision-making solutions; The decision visualization module uses VR and AR technology to display decision results for users to intuitively understand and apply; Continuously optimize and improve the platform based on actual decision-making results and user feedback.

8. The method for making agricultural production decisions based on the agricultural big data precision decision support platform based on the Internet of Things according to claim 7 is characterized in that: When performing data analysis, feature engineering technology is used to preprocess the raw data and extract key features to improve data analysis efficiency and accuracy.

9. The method for making agricultural production decisions based on the agricultural big data precision decision support platform based on the Internet of Things according to claim 7 is characterized in that: After generating an accurate decision-making plan, simulation technology is used to pre-evaluate the decision-making plan and predict its possible effects to provide a reference for practical application.

10. The method for making agricultural production decisions based on the agricultural big data precision decision support platform based on the Internet of Things according to claim 7, characterized in that: When optimizing and improving the platform, an automated model update and parameter adjustment mechanism is used to update the precise decision-making model in real time based on new data and feedback information to ensure that the platform's decision support always remains accurate and effective.

Images