Intelligent agricultural production management and early warning service system based on AIGC

Abstract

The invention discloses an intelligent agricultural production management and early warning service system based on AIGC, various sensors and monitoring equipment are deployed, soil humidity, temperature, illumination intensity, crop growth state and other data are collected and transmitted to a cloud server for processing through the Internet of Things technology, an intelligent analysis platform based on the AIGC technology is established, and the early warning service system is applied to agricultural production management and early warning. A machine learning algorithm is utilized to analyze the data, related agricultural management suggestions and early warning information are generated according to an analysis result, when the analysis result shows that potential risks exist, the system can automatically trigger an early warning mechanism, timely notify a farmer through a short message, an E-mail or an application program pushing mode, and provide early warning information for the farmer through a mobile application program or a Web port. A farmer can conveniently access information and services provided by the system, and the system function is continuously optimized according to feedback.

Description

Technical Field

[0001] The present invention relates to the fields of smart agriculture, big data analysis, cloud computing and artificial intelligence technology, and in particular to an intelligent system that can integrate data from multiple sensors and provide agricultural production management and early warning services through artificial intelligence generated content (AIGC) technology. Background Art

[0002] Agricultural production management is a key factor in ensuring the growth and yield of crops. In traditional agricultural production management methods, farm managers mainly rely on their experience and manual observation to make decisions and operations. This reliance limits the further improvement of agricultural production efficiency and quality. However, with the rapid development of Internet of Things technology, intelligent agricultural production management systems are attracting widespread attention in the agricultural field and bringing new opportunities. At present, although some intelligent monitoring and early warning systems have been applied in the agricultural production process, most of these systems rely on traditional data analysis methods, which are not sensitive enough to complex and changeable agricultural environmental factors and lack targeted intelligent suggestions. In addition, existing intelligent agricultural systems can often only process a single type of data and cannot effectively integrate information from multiple sources, resulting in limited prediction and early warning capabilities. Therefore, an intelligent agricultural production management and early warning service system based on AIGC is provided to solve the above problems. Summary of the invention

[0003] In order to solve the problems in the above background technology, the technical solution adopted by the present invention to solve the technical problems is: an intelligent agricultural production management method based on AIGC, which includes the following steps:

[0004] Step 1: First, deploy sensors and monitoring equipment in the planting area to collect data on soil moisture, temperature, light intensity, and crop growth status, and transmit them to the cloud server for processing through the Internet of Things technology;

[0005] Step 2: 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;

[0006] Step 3: When the analysis results show that there is a potential risk in the planting area, the system will automatically trigger the early warning mechanism and notify the farmer in time via SMS, email or application push;

[0007] Step 4: Through mobile applications or web portals, farmers can easily access the information and services provided by the system and continuously optimize system functions based on feedback.

[0008] As a preferred technical solution of the present invention, in step one, the sensor includes a temperature sensor and a soil moisture sensor, the monitoring equipment includes image acquisition and video monitoring, the soil moisture sensor is used to collect soil moisture, the temperature sensor is used to detect the temperature in the on-site environment, the image acquisition is used to monitor the light intensity on site, and the video monitoring is used to monitor the growth status of crops in real time.

[0009] As a preferred technical solution of the present invention, in step two, the intelligent analysis platform is used for AIGC intelligent scene analysis, image analysis, image recognition, video analysis, and video recognition.

[0010] As a preferred technical solution of the present invention, in step three, the early warning information of potential risks includes potential pests and diseases, abnormal climate, and abnormal soil temperature and humidity.

[0011] As a preferred technical solution of the present invention, in step four, after the farmer logs into the application management page, the mobile application can send text messages, receive text messages, receive images, upload images, push videos, and handle exceptions, and the farmer can set up and adjust the system in the application interface.

[0012] As a preferred technical solution of the present invention, in step 2, the machine learning algorithm includes a classification algorithm, a clustering algorithm and an anomaly detection algorithm. The classification algorithm learns classification rules through training data and divides new data samples into different categories. The clustering algorithm divides data samples with similar characteristics into different clusters through training data. The anomaly detection algorithm learns normal behavior patterns through training data and identifies data samples that are significantly different from normal behavior patterns as anomalies.

[0013] As an optimal technical solution of the present invention, the classification algorithm includes naive Bayes, decision tree, support vector machine, random forest, the clustering algorithm includes hierarchical clustering and density clustering, and the anomaly detection algorithm includes statistical anomaly detection, distance anomaly detection, and density anomaly detection.

[0014] The AIGC-based intelligent agricultural production management system includes a data acquisition module, a data processing module, an intelligent analysis module, an early warning module, a user interaction module, and a user feedback module. The data acquisition module is used to collect soil moisture, temperature, light intensity, and crop growth status data, and transmit them to the cloud server for processing through the Internet of Things technology.

[0015] As a preferred technical solution of the present invention, the data processing module is used to process and analyze the collected data, and adjust the temperature, humidity and light accordingly according to the analysis results. The intelligent analysis module is used to build an intelligent analysis platform based on AIGC technology, use machine learning algorithms to analyze the data, and generate relevant agricultural management suggestions and early warning information according to the analysis results.

[0016] As a preferred technical solution of the present invention, when the early warning module is used to detect potential risks in the planting area, the system will automatically trigger the early warning mechanism and promptly notify the farmer via SMS, email or application push. The user interaction module is used for farmers to log in to the application and access the information and services provided by the system. The user feedback module is used for farmers to log in to the application to provide feedback, thereby continuously optimizing system functions.

[0017] The present invention has the following advantages: the present invention can carry out refined management and intelligent early warning of agricultural production, realize refined management and intelligent early warning of agricultural production, improve the yield and quality of crops, reduce production costs, and reduce losses caused by weather changes or pests and diseases. The management system makes it more intuitive and easy to understand, making it convenient for users to understand greenhouse environmental parameters and crop growth conditions in real time, and providing scientific basis and technical support for agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the operating principle of the intelligent agricultural production management system of the preferred embodiment of the present invention;

[0019] Figure 2 It is a schematic diagram of the intelligent agricultural production management method according to the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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. Many specific details are explained in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0021] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] Please refer to Figure 1 and Figure 2 The intelligent agricultural production management method based on AIGC of the present invention comprises the following steps:

[0023] Step 1: First, deploy sensors and monitoring equipment in the planting area to collect data on soil moisture, temperature, light intensity, and crop growth status, and transmit them to the cloud server for processing through the Internet of Things technology;

[0024] Step 2: 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;

[0025] Step 3: When the analysis results show that there is a potential risk in the planting area, the system will automatically trigger the early warning mechanism and notify the farmer in time via SMS, email or application push;

[0026] Step 4: Through mobile applications or web portals, farmers can easily access the information and services provided by the system and continuously optimize system functions based on feedback.

[0027] In step one, the sensors include temperature sensors and soil moisture sensors, and the monitoring equipment includes image acquisition and video monitoring. The soil moisture sensor is used to collect soil moisture, the temperature sensor is used to detect the temperature in the on-site environment, the image acquisition is used to monitor the light intensity on site, and the video monitoring is used to monitor the growth status of crops in real time. In step two, the intelligent analysis platform is used for AIGC intelligent scene analysis, image analysis, image recognition, video analysis, and video recognition. In step three, the warning information of potential risks includes potential pests and diseases, climate anomalies, and soil temperature and humidity anomalies. In step four, the mobile application is used by farmers to log in to the application management page to send text messages, receive text messages, receive images, upload images, push videos, and handle exceptions. Farmers can set and adjust the system in the application interface. The sensors can be set to one or more types according to the detection requirements, and when in use, a series of sensors and monitoring equipment can be deployed. The system can monitor soil moisture, temperature changes, and crop growth in real time. When the system detects a possible drought risk, it will not only issue a warning, but also generate an irrigation recommendation through AIGC based on historical data and current conditions, thereby helping farmers take measures to avoid losses.

[0028] In step two, machine learning algorithms include classification algorithms, clustering algorithms and anomaly detection algorithms. The classification algorithm learns classification rules through training data and divides new data samples into different categories. The clustering algorithm divides data samples with similar characteristics into different clusters through training data. The anomaly detection algorithm learns normal behavior patterns through training data and identifies data samples that are significantly different from normal behavior patterns as anomalies. Classification algorithms include naive Bayes, decision trees, support vector machines, and random forests. Clustering algorithms include hierarchical clustering and density clustering. Anomaly detection algorithms include statistical anomaly detection, distance anomaly detection, and density anomaly detection, thereby enabling intelligent analysis and early warning to automatically identify potential risks such as pests and diseases, climate anomalies, and issue early warnings.

[0029] The intelligent agricultural production management system based on AIGC includes data acquisition module, data processing module, intelligent analysis module, early warning module, user interaction module and user feedback module. The data acquisition module is used to collect soil moisture, temperature, light intensity and crop growth status data, and transmit them to the cloud server for processing through Internet of Things technology.

[0030] The data processing module is used to process and analyze the collected data, and adjust the temperature, humidity, and light according to the analysis results. The intelligent analysis module is used to 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. The early warning module is used to automatically trigger the early warning mechanism when potential risks are found in the planting area, and promptly notify the farmer through SMS, email or application push. The user interaction module is used for farmers to log in to the application and access the information and services provided by the system. The user feedback module is used for farmers to log in to the application to provide feedback, and then continuously optimize the system function. Using AIGC technology, based on the large amount of agricultural production and environmental data collected, intelligent analysis reports and suggestions for specific scenarios are generated. According to the specific conditions and historical data of the farm, farmers are provided with personalized planting suggestions, irrigation plans, etc., to improve crop yield and quality. The user interface of the user interaction module allows farmers to view the farm status in real time, receive early warning information, and interact with the system to obtain instant agricultural advice.

[0031] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0032] Other parts of the present invention not described in detail belong to the prior art and will not be described in detail here.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The intelligent agricultural production management method based on AIGC is characterized by: The following steps are involved: Step 1: First, deploy sensors and monitoring equipment in the planting area to collect data on soil moisture, temperature, light intensity, and crop growth status, and transmit them to the cloud server for processing through the Internet of Things technology; Step 2: 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; Step 3: When the analysis results show that there is a potential risk in the planting area, the system will automatically trigger the early warning mechanism and notify the farmer in time via SMS, email or application push; Step 4: Through mobile applications or web portals, farmers can easily access the information and services provided by the system and continuously optimize system functions based on feedback.

2. The AIGC-based intelligent agricultural production management method according to claim 1, characterized in that: In step one, the sensor includes a temperature sensor and a soil moisture sensor, and the monitoring equipment includes image acquisition and video monitoring. The soil moisture sensor is used to collect soil moisture, the temperature sensor is used to detect the temperature in the on-site environment, the image acquisition is used to monitor the light intensity on site, and the video monitoring is used to monitor the growth status of crops in real time.

3. The AIGC-based intelligent agricultural production management method according to claim 1, characterized in that: In step two, the intelligent analysis platform is used for AIGC intelligent scene analysis, image analysis, image recognition, video analysis, and video recognition.

4. The AIGC-based intelligent agricultural production management method according to claim 1, characterized in that: In step three, early warning information of potential risks includes potential pests and diseases, abnormal climate, and abnormal soil temperature and humidity.

5. The AIGC-based intelligent agricultural production management method according to claim 1, characterized in that: In step four, after the farmer logs into the application management page, the mobile application can send and receive text messages, receive images, upload images, push videos, and handle exceptions. The farmer can also set up and adjust the system in the application interface.

6. The AIGC-based intelligent agricultural production management method according to claim 1, characterized in that: In step two, the machine learning algorithm includes a classification algorithm, a clustering algorithm and anomaly detection algorithm. The classification algorithm learns classification rules through training data and divides new data samples into different categories. The clustering algorithm divides data samples with similar characteristics into different clusters through training data. The anomaly detection algorithm learns normal behavior patterns through training data and identifies data samples that are significantly different from normal behavior patterns as anomalies.

7. The AIGC-based intelligent agricultural production management method according to claim 6, characterized in that: The classification algorithms include naive Bayes, decision tree, support vector machine, and random forest; the clustering algorithms include hierarchical clustering and density clustering; and the anomaly detection algorithms include statistical anomaly detection, distance anomaly detection, and density anomaly detection.

8. The intelligent agricultural production management system based on AIGC is characterized by: It includes a data acquisition module, a data processing module, an intelligent analysis module, an early warning module, a user interaction module, and a user feedback module. The data acquisition module is used to collect soil moisture, temperature, light intensity, and crop growth status data, and transmit them to the cloud server for processing through the Internet of Things technology.

9. The AIGC-based intelligent agricultural production management early warning service system according to claim 8, characterized in that: The data processing module is used to process and analyze the collected data, and adjust the temperature, humidity, and light accordingly according to the analysis results. The intelligent analysis module is used to build an intelligent analysis platform based on AIGC technology, use machine learning algorithms to analyze data, and generate relevant agricultural management recommendations and early warning information based on the analysis results.

10. The AIGC-based intelligent agricultural production management and early warning service system according to claim 8, characterized in that: The early warning module is used to detect potential risks in the planting area. When the system automatically triggers the early warning mechanism, it will promptly notify the farmer via SMS, email or application push. The user interaction module is used for farmers to log in to the application and access the information and services provided by the system. The user feedback module is used for farmers to log in to the application to provide feedback, thereby continuously optimizing system functions.

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