Intelligent agricultural shared service system based on AIGC

Abstract

The invention provides an intelligent agriculture sharing service system based on AI GC, and relates to the technical field of intelligent agriculture. The AI GC-based intelligent agricultural shared service system comprises a data acquisition module used for acquiring multi-modal data related to agriculture from a plurality of data sources; the data processing module is used for processing the collected data and carrying out standardization and cleaning; the intelligent analysis module is used for analyzing the processed data based on the AI GC technology and generating agricultural management suggestions; the decision generation module is used for making a decision scheme based on the behavior preference and the analysis result of the intelligent database; and the service pushing module is used for sending service decision-making information based on a decision-making result obtained after intelligent analysis. According to the system, the forefront AI technology is integrated, so that intelligence, precision, high efficiency and resource sharing of agricultural production are realized, and transformation from modern agriculture to intelligent agriculture is promoted; and services such as agricultural information consultation, crop disease and insect pest prediction and precise agricultural decision support can be provided for users.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agriculture, and in particular to an intelligent agriculture sharing service system based on AIGC. Background Art

[0002] With the development of agricultural modernization, a large amount of data is generated in the agricultural production process, including but not limited to soil moisture, temperature, light intensity, crop growth status, etc. However, due to the lack of effective data processing and analysis methods, the value of these data has not been fully utilized. The asymmetry of technology and market information at both ends of the agricultural industry has directly caused the two major "pain points" of new agricultural operators' difficulty in obtaining agricultural technology information and agricultural product market supply and demand information in production and operation activities.

[0003] Traditional agricultural service systems often focus on unilateral data collection and simple data analysis, but lack the ability to provide intelligent services. In addition, there is currently a lack of an agricultural sharing service platform on the market that can integrate multiple data sources and use AIGC technology to provide users with personalized services. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent agricultural sharing service system based on AIGC, which solves the problem that traditional agricultural service systems often focus on unilateral data collection and simple data analysis, but lack the ability to provide intelligent services.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an AIGC-based intelligent agriculture sharing service system, comprising: a data acquisition module, for acquiring agriculture-related multimodal data from multiple data sources;

[0008] Data processing module, used to process the collected data and perform standardization and cleaning;

[0009] Intelligent analysis module, used to analyze processed data based on AIGC technology and generate agricultural management suggestions;

[0010] The decision-making module makes decisions based on behavioral preferences and the results of intelligent database analysis;

[0011] The service push module is used to send service decision information based on the decision results obtained after intelligent analysis;

[0012] User interface module, used to interact with users and provide service information;

[0013] The system adopts a layered architecture design, which is mainly divided into four layers: perception layer, network layer, platform layer, and application layer;

[0014] Perception layer: deploy various sensors and intelligent devices to collect farmland environment data and crop growth status in real time;

[0015] Network layer: Using IoT technology, the perception layer data is transmitted to the cloud platform through wireless or wired networks to ensure the real-time and stability of data transmission;

[0016] Platform layer: Integrates AIGC algorithm and big data analysis engine to process, analyze and predict massive data, providing strong technical support for upper-layer applications;

[0017] Application layer: Provides functional interfaces such as intelligent monitoring, resource sharing, service customization, data analysis, and remote control for users such as farmers, agricultural enterprises, and scientific research institutions.

[0018] Preferably, the system perception layer also includes an intelligent agricultural monitoring module for connecting with various sensors and intelligent devices to achieve real-time monitoring of farmland environmental parameters, crop growth conditions and meteorological changes.

[0019] Preferably, the system application layer is used to provide the construction of a sharing platform, construct an agricultural resource sharing platform to provide a personalized user management interface, and support user registration, login, and information maintenance functions.

[0020] Preferably, the intelligent analysis module includes an intelligent analysis platform and an intelligent analysis algorithm;

[0021] Intelligent analysis platform: build an intelligent analysis platform based on AIGC technology, use machine learning algorithms to analyze data, and generate relevant agricultural management suggestions and early warning information based on the analysis results;

[0022] Intelligent analysis algorithm: Through deep learning technology, the model is trained to predict changes in farmland environment.

[0023] Preferably, the service push module recommends the most suitable planting plans and services based on a personalized recommendation algorithm and based on the user's historical behaviors and preferences.

[0024] Preferably, the data acquisition module further includes:

[0025] Video monitoring submodule, used to capture image information of crop growth;

[0026] The meteorological monitoring submodule is used to collect meteorological data.

[0027] Preferably, the user interface module further comprises:

[0028] Mobile applications, which are used to display information and receive instructions on mobile devices;

[0029] Web port, used to provide services in web browsers.

[0030] Preferably, the system further comprises an early warning module, wherein the early warning module is provided with early warning level classification, and different early warning levels can be set according to user preferences.

[0031] (III) Beneficial effects

[0032] The present invention provides a smart agriculture sharing service, data information sharing and distribution processing based on AIGC. The main advantages are:

[0033] 1. Improve agricultural production efficiency and product quality through intelligent management and services;

[0034] 2. Reduce agricultural production costs and reduce resource waste;

[0035] 3. Reduce losses caused by weather changes or pests and diseases;

[0036] 4. Promote the process of agricultural informatization and modernization;

[0037] 5. Provide scientific basis for agricultural decision-making and enhance the comprehensive competitiveness of agriculture;

[0038] 6. Promote sustainable agricultural development and provide scientific basis and technical support for agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0040] Figure 2 A schematic diagram of a user interaction interface of the present invention;

[0041] Figure 3 It is a schematic diagram of the system structure of the present invention;

[0042] Figure 4 It is a schematic diagram of the data acquisition and processing module of the present invention;

[0043] Figure 5 It is a schematic diagram of the intelligent analysis and decision making module of the present invention;

[0044] Figure 6 is a schematic diagram of a user interface module of the present invention;

[0045] Figure 7 It is a schematic diagram of the process of the present invention.

[0046] Among them, 101 is a data collection module; 102 is an intelligent analysis module; 103 is a service push module; 104 is a data processing module; 105 is a decision generation module; 106 is a user interface module. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] like Figure 1-7 As shown, the embodiment of the present invention provides an intelligent agriculture sharing service system based on AIGC, including: a data acquisition module 101, which is used to obtain agriculture-related multimodal data from multiple data sources; the data acquisition module 101 also includes:

[0049] Video monitoring submodule, used to capture image information of crop growth;

[0050] Meteorological monitoring submodule, used to collect meteorological data;

[0051] First, determine the multiple data sources that need to be integrated, such as soil sensors (monitoring soil moisture, temperature, pH value, etc.), weather stations (collecting meteorological data such as temperature, humidity, precipitation, wind speed, etc.), video surveillance cameras (real-time monitoring of crop growth and pest detection), drone remote sensing (obtaining high-resolution images of crop growth and soil conditions), and biosensors (monitoring plant growth indicators and health status). Use wireless sensor networks and Internet of Things technologies to achieve real-time data collection and transmission.

[0052] Then, appropriate time intervals are selected for data collection, such as flexible adjustment according to different data sources and agricultural behavior requirements. The data is then standardized and cleaned by a data preprocessing unit to ensure that the collected data can be quickly accessed and processed, and stored using a data storage unit including cloud storage or edge computing devices;

[0053] When processing data, the data from different sensors are cleaned, standardized and normalized to ensure data consistency and comparability; when processing video and image data, image processing technology is used for target detection and feature extraction;

[0054] Multimodal data fusion: By integrating data from different types of sensors, such as soil moisture sensors, weather stations, and video surveillance, efficient fusion and processing of multimodal data can be achieved. When fusing sensor data, sensor data fusion algorithms such as Kalman filtering, particle filtering, and deep learning are used to integrate information from multiple data sources. When fusing image data, image recognition and machine learning technologies are used to analyze the relationship between video surveillance data and sensor data, thereby obtaining more comprehensive insights.

[0055] The data processing module 104 is used to process the collected data and perform standardization and cleaning;

[0056] First, all data is unified into a processable format, and the units of different data sources are converted. For example, temperature is unified into degrees Celsius, humidity is unified into percentage, etc. At the same time, the timestamp format is unified to ensure that the time scale of all data is consistent; that is, the data processing library is used to build the data framework and format conversion, and a set of data standardization rules is defined to facilitate automated processing; finally, intelligent recognition algorithms are used for data cleaning, including missing value processing, outlier detection, duplicate value processing, and noise reduction processing and cleaning.

[0057] Intelligent analysis module 102, used to analyze the processed data based on AIGC technology and generate agricultural management suggestions; intelligent analysis module 102 includes an intelligent analysis platform and an intelligent analysis algorithm;

[0058] Intelligent analysis platform: build an intelligent analysis platform based on AIGC technology, use machine learning algorithms to analyze data, and generate relevant agricultural management suggestions and early warning information based on the analysis results;

[0059] Intelligent analysis algorithm: Use deep learning technology to train models to predict changes in farmland environment; select appropriate AIGC models according to different agricultural management tasks and conduct model training, such as prediction models for prediction, classification models for identification and classification, recommendation models for intelligent recommendation using recommendation algorithms, and generation models for suggestion generation;

[0060] Then use the processed data to train the selected model, select the appropriate loss function and optimization algorithm to improve the performance of the model, use cross-validation and other methods to evaluate the generalization ability of the model, and finally use the trained AI model to infer new data and generate agricultural management recommendations;

[0061] The decision generation module 105 makes a decision plan based on the behavior preference and the results of the intelligent database analysis. The decision generation module 105 recommends the most suitable planting plan and service based on the personalized recommendation algorithm and the user's historical behavior and preference;

[0062] That is, intelligently generate planting plans based on the results of intelligent analysis: Based on crop growth models and historical data, the AIGC algorithm automatically generates the optimal planting plan, including sowing time, fertilizer application amount, irrigation plan, etc.;

[0063] At the same time, intelligent analysis of pest and disease prediction and prevention: by analyzing crop images and environmental data, the AIGC model predicts the probability of pest and disease occurrence and formulates prevention and control measures in advance; the final content generation assists decision-making: generates illustrated agricultural production reports, market analysis and forecasts, etc. to assist users in decision-making.

[0064] The service push module 103 is used to issue service decision information based on the decision results obtained after intelligent analysis; according to the specific conditions and historical data of the farm, it provides customers with personalized planting suggestions, irrigation plans, etc. to improve crop yields and quality; at the same time, it can provide users with planting suggestions based on real-time monitoring data information and historical records, including seed selection, irrigation cycle, fertilization plan, etc., and provide agricultural product price trend analysis to help farmers reasonably arrange sales plans.

[0065] User interface module 106, used to interact with users and provide service information;

[0066] Further comprising: a mobile application for displaying information and receiving instructions on a mobile device; a web port for providing services in a web browser;

[0067] It supports remote control of agricultural equipment, such as smart irrigation systems and temperature-controlled greenhouses, through mobile phone apps and computers, to achieve fully automated management of agricultural production. At the same time, it introduces an AI-assisted decision-making mechanism to automatically adjust equipment parameters to adapt to environmental changes and crop growth needs, build agricultural machinery leasing, land transfer and other services, promote the rational flow of agricultural resources, and allow users to view farm conditions in real time, receive early warning information, and interact with the system to obtain instant agricultural advice.

[0068] The system adopts a layered architecture design, which is mainly divided into four layers: perception layer, network layer, platform layer, and application layer;

[0069] Perception layer: deploy various sensors and intelligent devices to collect farmland environmental data and crop growth status in real time; the system perception layer also includes an intelligent agricultural monitoring module, which is used to connect with various sensors and intelligent devices to achieve real-time monitoring of farmland environmental parameters, crop growth status and meteorological changes; the system also includes an early warning module, in which the early warning module is set with early warning level division, and different early warning levels can be set according to user preferences;

[0070] Network layer: Using IoT technology, the perception layer data is transmitted to the cloud platform through wireless or wired networks to ensure the real-time and stability of data transmission;

[0071] Platform layer: Integrates AIGC algorithm and big data analysis engine to process, analyze and predict massive data, providing strong technical support for upper-layer applications;

[0072] Application layer: It provides intelligent monitoring, resource sharing, service customization, data analysis, remote control and other functional interfaces for farmers, agricultural enterprises, scientific research institutions and other users. The system application layer is used to build a sharing platform, build an agricultural resource sharing platform, provide a personalized user management interface, and support user registration, login, and information maintenance functions.

[0073] In one embodiment:

[0074] Case 1: Using intelligent analysis algorithms to predict weather changes in the next week and combining soil moisture monitoring data to provide irrigation suggestions for growers;

[0075] Case 2: Using image recognition technology to identify disease spots on crops, and generating corresponding pesticide spraying plans through AIGC;

[0076] Case 3: Based on market analysis, provide farmers with recommendations on the best harvest time and sales channels.

[0077] Specific implementation example: Assuming that in a farm, the system can provide farmers with accurate planting plans through AIGC technology based on the specific conditions of the farm, such as soil type, climate conditions, etc. For example, the system can analyze recent rainfall and temperature data, predict the best irrigation time for crops in the future, and generate corresponding irrigation suggestions; at the same time, it can detect the spots on the leaves of crops through image recognition technology, automatically identify the disease type, and give corresponding treatment suggestions.

[0078] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The AIGC-based intelligent agriculture sharing service system is characterized by: include: A data acquisition module (101), used for acquiring agricultural-related multimodal data from multiple data sources; A data processing module (104), used to process the collected data and perform standardization and cleaning; An intelligent analysis module (102), used to analyze the processed data based on AIGC technology and generate agricultural management suggestions; A decision generation module (105) makes a decision plan based on the behavior preference and the results of the intelligent database analysis; A service push module (103), used to send service decision information based on the decision result obtained after intelligent analysis; A user interface module (106), used to interact with users and provide service information; The system adopts a layered architecture design, which is mainly divided into four layers: perception layer, network layer, platform layer, and application layer; Perception layer: deploy various sensors and intelligent devices to collect farmland environment data and crop growth status in real time; Network layer: Using IoT technology, the perception layer data is transmitted to the cloud platform through wireless or wired networks to ensure the real-time and stability of data transmission; Platform layer: Integrates AIGC algorithm and big data analysis engine to process, analyze and predict massive data, providing strong technical support for upper-layer applications; Application layer: Provides functional interfaces such as intelligent monitoring, resource sharing, service customization, data analysis, and remote control for users such as farmers, agricultural enterprises, and scientific research institutions.

2. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized by: The system perception layer also includes an intelligent agricultural monitoring module, which is used to connect with various sensors and intelligent devices to realize real-time monitoring of farmland environmental parameters, crop growth conditions and meteorological changes.

3. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized in that: The system application layer is used to provide the construction of a sharing platform, build an agricultural resource sharing platform, provide a personalized user management interface, and support user registration, login, and information maintenance functions.

4. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized by: The intelligent analysis module (102) includes an intelligent analysis platform and an intelligent analysis algorithm; Intelligent analysis platform: build an intelligent analysis platform based on AIGC technology, use machine learning algorithms to analyze data, and generate relevant agricultural management suggestions and early warning information based on the analysis results; Intelligent analysis algorithm: Through deep learning technology, the model is trained to predict changes in farmland environment.

5. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized in that: The decision generation module (105) recommends the most suitable planting plan and service based on a personalized recommendation algorithm and user historical behavior and preferences.

6. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized by: The data acquisition module (101) further comprises: Video monitoring submodule, used to capture image information of crop growth; The meteorological monitoring submodule is used to collect meteorological data.

7. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized by: The user interface module 106 further includes: Mobile applications, which are used to display information and receive instructions on mobile devices; Web port, used to provide services in web browsers.

8. The AIGC-based intelligent agriculture sharing service system according to claim 1 is characterized by: The system also includes an early warning module, wherein the early warning module is provided with early warning level divisions, and different early warning levels can be set according to user preferences.

Images