Mangrove forest pest remote monitoring and intelligent prevention and control system based on Internet of Things

Abstract

The invention discloses a mangrove forest disease and insect pest remote monitoring and intelligent prevention and control system based on the Internet of Things. A plurality of modules such as an Internet of Things sensor network, an unmanned aerial vehicle inspection system, a high-definition monitoring camera network, intelligent prevention and control equipment and a software platform are integrated; the Internet of Things sensor network monitors environmental parameters and pest and disease damage signs in real time, the unmanned aerial vehicle inspection system provides aerial inspection support, and the high-definition monitoring camera network realizes 24-hour uninterrupted monitoring on the ground; the software platform is responsible for data acquisition, transmission, analysis and early warning release, and provides a remote control interface for management personnel to perform accurate prevention and control; the intelligent prevention and control equipment automatically or remotely executes prevention and control tasks according to the monitoring data and the early warning information; the system also has additional functions of energy management, network communication, user authority management and the like, and continuous and stable operation and data security of the system are ensured. The implementation of the system can effectively improve the intelligent level of mangrove forest pest control, and provides powerful support for protecting a mangrove forest ecological system.

Description

Technical Field

[0001] The present invention relates to the fields of Internet of Things technology, big data analysis, artificial intelligence and agricultural pest control, and specifically to a system for remote monitoring and intelligent prevention and control of mangrove pests and diseases. Background Art

[0002] Mangroves are woody plant communities that grow in the intertidal zones of tropical and subtropical coasts. Due to their unique ecological environment and biodiversity, they are known as "sea forests" and "coastal guards."

[0003] However, as an important ecosystem and biodiversity conservation area, the frequent occurrence of diseases and insect pests poses a serious threat to the health and survival of mangroves.

[0004] Traditional monitoring methods rely on manual inspections, which are not only inefficient and have limited coverage, but also make it difficult to detect and effectively control pests and diseases in a timely manner.

[0005] Therefore, it is particularly important to develop a system that can monitor in real time, intelligently identify pests and diseases, and automatically take prevention and control measures. Summary of the invention

[0006] The purpose of the present invention is to provide a remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things. The system can monitor the environmental parameters and signs of pests and diseases in the mangrove area in real time, and realize accurate identification and prevention and control of pests and diseases through drone inspections, high-definition surveillance camera networks and intelligent prevention and control equipment, thereby improving the efficiency and accuracy of mangrove pest and disease prevention and control.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things, the system comprising:

[0009] a) IoT sensor network: deployed in mangrove areas to monitor environmental parameters and signs of pests and diseases in real time, including but not limited to temperature and humidity, soil nutrients, light intensity and specific pest and disease indicators;

[0010] b) UAV inspection system: equipped with high-definition cameras and multi-spectral imaging equipment, it can execute preset flight paths to conduct regular or on-demand aerial inspections of mangroves to capture early signs of pests and diseases and the health of vegetation;

[0011] c) High-definition surveillance camera network: Fixed or mobile cameras are set up in key areas of mangroves to provide 24-hour uninterrupted ground monitoring to enhance the real-time and accuracy of monitoring;

[0012] d) Intelligent control equipment: Automatic or remote control of pest control tasks based on the results of monitoring data analysis, such as automatic spraying devices or biological control releasers;

[0013] e) Software platform: Integrates data collection and transmission modules, big data analysis and AI recognition modules, early warning and decision support systems, and remote control interfaces for data processing, analysis, early warning issuance, and issuance of prevention and control instructions.

[0014] Furthermore, the IoT sensor network further comprises:

[0015] Temperature and humidity sensors: used to monitor the ambient temperature and humidity in mangrove areas to assess the possibility of pests and diseases;

[0016] Soil nutrient sensor: monitors the nutrient content in the soil to determine the growth status of vegetation and potential risks of pests and diseases;

[0017] Pest and disease monitoring sensors: Directly monitor the presence, type and abundance of pests and diseases, such as using traps, baits or bio-indicator plants.

[0018] Furthermore, the drone inspection system also includes:

[0019] Autonomous flight control system: adjusts the flight path based on the preset flight plan and real-time environmental data to ensure inspection efficiency and safety;

[0020] Data processing unit: Integrate preliminary data processing capabilities on the drone to quickly identify abnormal areas and adjust the inspection strategy when necessary.

[0021] Furthermore, the big data analysis and AI recognition modules of the software platform include:

[0022] Data preprocessing module: cleans and integrates raw data from different sensors to prepare for analysis;

[0023] Machine learning algorithms: Apply classification, clustering, and prediction algorithms to conduct in-depth analysis of pest and disease data to identify pest and disease types, occurrence stages, and spread trends;

[0024] Knowledge base and expert system: Integrate professional knowledge in the field of pest and disease control to provide support and verification for AI identification.

[0025] Furthermore, the early warning and decision support system can automatically generate early warning information on pests and diseases, including warning levels, affected areas, and recommended prevention and control measures, based on the output of the big data analysis and AI recognition modules; and provide an interactive interface that allows managers to adjust early warning thresholds and prevention and control strategies based on actual conditions.

[0026] Furthermore, the remote control interface provides:

[0027] Real-time video monitoring and data display capabilities allow managers to intuitively understand the current status of the mangrove area;

[0028] The function of issuing prevention and control instructions allows managers to remotely control intelligent prevention and control equipment for precise prevention and control based on early warning information;

[0029] The prevention and control effectiveness evaluation tool evaluates the effectiveness of prevention and control measures based on follow-up monitoring data, and provides feedback to the early warning and decision support system to optimize future strategies.

[0030] Furthermore, the system also includes:

[0031] Energy management system: optimizes the energy usage of sensors, drones and intelligent prevention and control equipment to ensure the continuous and stable operation of the system;

[0032] Network communication module: supports multiple communication protocols and frequency bands to ensure stable data transmission in different environments;

[0033] User rights management system: Assign different access rights according to roles to ensure system security and data privacy.

[0034] Beneficial effects of the present invention:

[0035] 1. Improve monitoring efficiency: Real-time monitoring and rapid response to mangrove pests and diseases can be achieved through IoT sensor networks and drone inspection systems;

[0036] 2. Improve the accuracy of prevention and control: Use big data analysis and AI recognition technology to conduct in-depth analysis of pest and disease data to achieve accurate identification and prevention and control of pests and diseases;

[0037] 3. Enhance system reliability: through additional functional modules such as high-definition surveillance camera network, energy management system and network communication module, ensure the continuous and stable operation of the system and the secure transmission of data;

[0038] 4. Optimize management decisions: Provide early warning and decision support systems and remote control interfaces to provide managers with scientific and convenient decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a system structure diagram of a remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things of the present invention;

[0040] Figure 2 This is a system flow chart of a remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0042] In the description of the embodiments of the present invention, it should be noted that if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invented product is usually placed when used. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0043] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components must be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0044] In the description of the embodiments of the present invention, "plurality" means at least 2.

[0045] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] Example:

[0047] The technical solution of the present invention includes the following parts:

[0048] 1. IoT Sensor Network:

[0049] Deployed in mangrove areas, it is used to monitor environmental parameters and signs of pests and diseases in real time, including but not limited to temperature and humidity, soil nutrients, light intensity and specific pest and disease indicators. Among them, temperature and humidity sensors are used to monitor ambient temperature and humidity, soil nutrient sensors are used to monitor soil nutrient content, and pest and disease monitoring sensors directly monitor the existence, type and quantity of pests and diseases.

[0050] 2. UAV inspection system: Equipped with high-definition cameras and multi-spectral imaging equipment, it can execute preset flight paths and conduct regular or on-demand aerial inspections of mangroves. The UAV inspection system also includes an autonomous flight control system and a data processing unit. The former adjusts the flight path according to the preset flight plan and real-time environmental data, and the latter integrates preliminary data processing capabilities on the UAV to quickly identify abnormal areas.

[0051] 3. High-definition surveillance camera network: Fixed or mobile cameras are set up in key areas of mangroves to provide 24-hour uninterrupted ground monitoring to enhance the real-time and accuracy of monitoring.

[0052] 4. Intelligent control equipment: Based on the analysis results of monitoring data, automatic or remote control is used to perform pest and disease control tasks, such as automatic spraying devices or biological control releasers.

[0053] 5. Software platform: Integrates data collection and transmission modules, big data analysis and AI recognition modules, early warning and decision support systems, and remote control interfaces. The big data analysis and AI recognition modules include data preprocessing modules, machine learning algorithms, knowledge bases, and expert systems, which are used to conduct in-depth analysis of pest and disease data and identify pest and disease types, occurrence stages, and spread trends. The early warning and decision support system automatically generates pest and disease warning information based on the analysis results, and provides an interactive interface for managers to adjust warning thresholds and prevention and control strategies. The remote control interface provides functions such as real-time video monitoring, data display, issuance of prevention and control instructions, and prevention and control effect evaluation.

[0054] 6. Additional functional modules: The system also includes an energy management system, a network communication module and a user authority management system; the energy management system is used to optimize the energy usage of sensors, drones and intelligent prevention and control equipment, the network communication module supports multiple communication protocols and frequency bands to ensure stable data transmission, and the user authority management system assigns different access rights according to roles to ensure system security and data privacy.

[0055] The workflow of the system is divided into the following steps:

[0056] 1. Real-time monitoring of environmental parameters and signs of pests and diseases

[0057] 1. IoT sensor network work

[0058] Temperature and humidity sensors, soil nutrient sensors, and light intensity sensors are deployed in the mangrove area to monitor environmental parameters in real time;

[0059] Pest and disease monitoring sensors (such as traps, snares, bio-indicator plants, etc.) directly monitor the presence, type and quantity of pests and diseases;

[0060] The data collected by the sensor is transmitted to the data acquisition and transmission module of the software platform via a wireless network.

[0061] 2. Data Preprocessing

[0062] The data preprocessing module of the software platform cleans and integrates the raw data from different sensors in preparation for subsequent analysis;

[0063] 2. Drone inspection and high-definition monitoring

[0064] 1. Drone inspection

[0065] Drones conduct aerial inspections of mangroves following pre-set flight paths or flight paths adjusted based on real-time environmental data;

[0066] High-definition cameras and multispectral imaging equipment capture early signs of pests and diseases and vegetation health;

[0067] The data processing unit on the drone performs preliminary processing on the inspection data and quickly identifies abnormal areas;

[0068] 2. HD surveillance camera network

[0069] Fixed or mobile cameras set up in key areas of mangroves provide 24-hour uninterrupted ground monitoring;

[0070] The monitoring data is transmitted to the software platform in real time to enhance the real-time and accuracy of monitoring;

[0071] 3. Data Analysis and Warning Release

[0072] 1. Big data analysis and AI recognition

[0073] The software platform’s big data analysis and AI recognition modules use machine learning algorithms to conduct in-depth analysis of pest and disease data;

[0074] Identify the types, occurrence stages and spread trends of pests and diseases, and integrate professional knowledge in the field of pest and disease control to provide support and verification for AI identification;

[0075] 2. Early warning and decision support

[0076] Automatically generate pest and disease warning information based on the output of big data analysis and AI recognition, including warning level, affected areas and recommended prevention and control measures;

[0077] Provide an interactive interface that allows managers to adjust warning thresholds and prevention and control strategies based on actual conditions;

[0078] 4. Intelligent prevention and control and remote control

[0079] 1. Execution of intelligent prevention and control equipment

[0080] Based on the monitoring data analysis results and early warning information, intelligent prevention and control equipment (such as automatic spraying devices, biological control releasers, etc.) automatically or remotely controls to perform pest and disease prevention and control tasks.

[0081] 2. Remote control interface operation

[0082] Managers can view video surveillance and data display in real time through the remote control interface to understand the current status of the mangrove area;

[0083] According to the early warning information, remotely control the intelligent prevention and control equipment for precise prevention and control;

[0084] Use prevention and control effectiveness evaluation tools to evaluate the effectiveness of prevention and control measures based on follow-up monitoring data, and provide feedback to the early warning and decision support system to optimize future strategies;

[0085] 5. System optimization and maintenance

[0086] 1. Energy management

[0087] The energy management system optimizes the energy usage of sensors, drones and intelligent control equipment to ensure the continuous and stable operation of the system;

[0088] 2. Network communication

[0089] The network communication module supports multiple communication protocols and frequency bands to ensure stable data transmission in different environments;

[0090] 3. User rights management

[0091] The user rights management system assigns different access rights according to roles to ensure system security and data privacy.

[0092] In summary, the system realizes efficient and intelligent monitoring and prevention of mangrove pests and diseases by integrating Internet of Things technology, big data analysis, artificial intelligence and advanced technologies in the field of agricultural pest control. The close cooperation of various links in the workflow ensures the stability and accuracy of the system.

[0093] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things, characterized in that: The system includes: a) IoT sensor network: deployed in mangrove areas to monitor environmental parameters and signs of pests and diseases in real time, including but not limited to temperature and humidity, soil nutrients, light intensity and specific pest and disease indicators; b) UAV inspection system: equipped with high-definition cameras and multi-spectral imaging equipment, it can execute preset flight paths to conduct regular or on-demand aerial inspections of mangroves to capture early signs of pests and diseases and the health of vegetation; c) High-definition surveillance camera network: Fixed or mobile cameras are set up in key areas of mangroves to provide 24-hour uninterrupted ground monitoring to enhance the real-time and accuracy of monitoring; d) Intelligent control equipment: Automatic or remote control of pest control tasks based on monitoring data analysis results, such as automatic spraying devices or biological control releasers; e) Software platform: Integrates data collection and transmission modules, big data analysis and AI recognition modules, early warning and decision support systems, and remote control interfaces for data processing, analysis, early warning issuance, and issuance of prevention and control instructions.

2. The remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things according to claim 1 is characterized by: The IoT sensor network further comprises: Temperature and humidity sensors: used to monitor the ambient temperature and humidity in mangrove areas to assess the possibility of pests and diseases; Soil nutrient sensor: monitors the nutrient content in the soil to determine the growth status of vegetation and potential risks of pests and diseases; Pest and disease monitoring sensors: Directly monitor the presence, type and abundance of pests and diseases, such as using traps, baits or bio-indicator plants.

3. The remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things according to claim 1 is characterized by: The drone inspection system also includes: Autonomous flight control system: adjusts the flight path based on the preset flight plan and real-time environmental data to ensure inspection efficiency and safety; Data processing unit: Integrate preliminary data processing capabilities on the drone to quickly identify abnormal areas and adjust the inspection strategy when necessary.

4. The remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things according to claim 1 is characterized by: The big data analysis and AI recognition modules of the software platform include: Data preprocessing module: cleans and integrates raw data from different sensors to prepare for analysis; Machine learning algorithms: Apply classification, clustering, and prediction algorithms to conduct in-depth analysis of pest and disease data to identify pest and disease types, occurrence stages, and spread trends; Knowledge base and expert system: Integrate professional knowledge in the field of pest and disease control to provide support and verification for AI identification.

5. The remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things according to claim 1 is characterized by: The early warning and decision support system can automatically generate pest and disease early warning information based on the output of the big data analysis and AI recognition modules, including the early warning level, affected areas and recommended prevention and control measures; and provide an interactive interface that allows managers to adjust early warning thresholds and prevention and control strategies based on actual conditions.

6. The remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things according to claim 1 is characterized by: The remote control interface provides: Real-time video monitoring and data display capabilities allow managers to intuitively understand the current status of the mangrove area; The function of issuing prevention and control instructions allows managers to remotely control intelligent prevention and control equipment for precise prevention and control based on early warning information; The prevention and control effectiveness evaluation tool evaluates the effectiveness of prevention and control measures based on follow-up monitoring data, and provides feedback to the early warning and decision support system to optimize future strategies.

7. A remote monitoring and intelligent prevention and control system for mangrove pests and diseases based on the Internet of Things according to any one of claims 1 to 6, characterized in that: The system also includes: Energy management system: optimizes the energy usage of sensors, drones and intelligent prevention and control equipment to ensure the continuous and stable operation of the system; Network communication module: supports multiple communication protocols and frequency bands to ensure stable data transmission in different environments; User rights management system: Assign different access rights according to roles to ensure system security and data privacy.

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