Intelligent grassland disaster monitoring system and method thereof

Abstract

The invention relates to an intelligent grassland disaster monitoring system and method. The system comprises a monitoring terminal, a data center server and a cloud platform. A target grassland area is divided into a plurality of nodes, each node is provided with at least one monitoring terminal, the monitoring terminal interacts with the data center server through a wireless communication module to realize deep mining and prediction of grassland disaster data, and the cloud platform interacts with the data center server to realize deep mining and prediction of the grassland disaster data. And the cloud platform displays the grassland information and the alarm information through a management interface, and is used for sending the alarm information to a corresponding client. Through the monitoring terminal, the data center server and the cloud platform, comprehensive, systematic and continuous data acquisition and real-time monitoring and early warning of grassland disasters are realized, the shortages and deficiencies of grassland disaster monitoring means are made up, and an intelligent grassland disaster ground monitoring system is built.

Description

Technical Field

[0001] The present invention relates to the field of grassland disaster monitoring, and in particular to an intelligent grassland disaster monitoring system and method thereof. Background Art

[0002] Grassland is the world's largest terrestrial ecosystem, with important functions such as conserving water resources, maintaining water and soil, preventing wind and sand, fixing carbon and releasing oxygen, regulating climate, purifying the air, and maintaining biodiversity. It can provide diverse and high-value ecological products.

[0003] my country's grassland area is about 4 billion mu, accounting for 28% of the country's total land area and 94% of the country's total grassland area. my country's grassland is not only the source of many rivers and water conservation areas, but also the main battlefield for curbing soil erosion and land desertification.

[0004] Grassland disasters include natural disasters (droughts, fires, snowstorms) and biological disasters (rodent pests, insect pests, etc.). The existing aerospace monitoring methods for grassland disasters are mainly based on "areas", and the ground monitoring methods are single, primitive, and insufficient. There is a lack of intelligent, effective, continuous and systematic grassland disaster data collection, monitoring and early warning methods based on "points" on the ground. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an intelligent grassland disaster monitoring system, which solves the problems of the existing technology's single, primitive and insufficient ground monitoring means, and the lack of a ground "point"-based intelligent, effective and continuous system for grassland disaster data collection, monitoring and early warning.

[0006] An intelligent grassland disaster monitoring system, comprising:

[0007] Monitoring terminal, the target grassland area is divided into multiple nodes, each node is provided with at least one monitoring terminal, and the monitoring terminal is configured to perform real-time collection of grassland field data of the corresponding node, obtain grassland field data of the target node, normalize the grassland field data, and obtain the most basic indicator value;

[0008] The monitoring terminal includes a displacement sensor, a temperature sensor, a humidity sensor, a smoke sensor, a sand flux sensor, a rain and snow sensor, an external infrared camera, a wireless communication module and a terminal processor. The terminal processor interacts with the position sensor, the temperature sensor, the humidity sensor, the smoke sensor, the sand flux sensor, the rain and snow sensor, the external infrared camera and the wireless communication module to store and process the grassland field data. The monitoring terminal interacts with the data center server through the wireless communication module.

[0009] A data center server, wherein the data center server is configured to monitor grassland disaster conditions and issue early warning signals to achieve in-depth mining and prediction of grassland disaster data, and the data center server includes an information processing module, an intelligent decision-making module, and an information correction module;

[0010] An information processing module, wherein the information processing module is configured to compare grassland on-site data with a preset disaster threshold value, determine whether grassland disasters will occur in the monitoring area, determine a comprehensive risk level of grassland disasters, and send the judgment result to the intelligent decision-making module;

[0011] An intelligent decision-making module, wherein the intelligent decision-making module is configured to receive data for determining the comprehensive risk level of grassland disasters, analyze the data through decision analysis, and then formulate a response plan to deal with grassland disasters, and optimize the decision plan by intelligent optimization, and quickly make an early warning response plan;

[0012] An information correction module is configured to determine the authenticity of the grassland disaster information value and to provide feedback and correction for the collected erroneous information.

[0013] A cloud platform interacts with the data center server, and the cloud platform is configured to interact with the outside world through a management interface, conduct early warning analysis of snow disasters, droughts, fires, rodent infestations, and insect pests based on grassland field data collected by monitoring terminals, display the grassland information and warning information through the management interface, and send warning information to corresponding clients.

[0014] Furthermore, the information processing module calculates the comprehensive natural disaster risk level index of the target area according to the indicator values ​​and weights of indicators at all levels, and then determines the comprehensive natural disaster risk level of the target area based on the comprehensive natural disaster risk level index of the target area.

[0015] Furthermore, the wireless communication module includes a 5G communication unit, a Beidou short message communication unit and a Beidou positioning unit.

[0016] The Beidou positioning unit adopts the Beidou positioning chip L76K, and the Beidou positioning chip has a built-in amplifier, filter, active antenna detection circuit and antenna protection circuit;

[0017] The active antenna detection circuit is used to detect the working state of the active antenna and output a corresponding state detection signal to the antenna protection circuit, so that the antenna protection circuit works according to the state detection signal.

[0018] Furthermore, the cloud platform is used to perform grassland fire early warning analysis based on the grassland monitoring information, including:

[0019] Acquire temperature sensor, infrared sensor, infrared video image and smoke sensor data at the same location and in the same time period;

[0020] The data collected from the temperature sensor, infrared sensor and humidity sensor are integrated to determine whether the received data satisfies the conditions that the infrared thermal radiation information is greater than a set threshold and the infrared radiation value continues to increase, and the received temperature sensor information is greater than a set threshold and the temperature data continues to increase, and the received humidity information is less than a set threshold and the value continues to decrease, and the smoke sensor data is greater than a set threshold, and an early warning is issued to identify whether there is a smoke image in the video image. If so, an early warning is generated to determine a fire.

[0021] Furthermore, the cloud platform is used to perform grassland snow disaster early warning analysis based on the grassland monitoring information, including:

[0022] Obtain rain and snow sensor, temperature sensor, humidity sensor and infrared video image data at the same location and in the same time period;

[0023] The data collected from the rain and snow sensor, the temperature sensor and the humidity sensor are integrated, and if the received temperature sensor information is greater than a set threshold value and the temperature data continues to decrease, and if the received humidity information is less than a set threshold value and the value continues to decrease, and the rain and snow sensor data is greater than the set threshold value, an early warning is issued, and a snow disaster is determined;

[0024] Rain and snow data include the maximum snowfall in 1 hour, the maximum snowfall in 3 hours, the maximum snowfall in 6 hours, the maximum snowfall in 12 hours, the maximum accumulated snowfall during the process, the maximum daily snowfall, the accumulated snowfall during the blizzard process, the duration of the blizzard, the percentage of the blizzard intensity level and the total number of blizzards.

[0025] Furthermore, the cloud platform is used to perform grassland drought early warning analysis based on the grassland monitoring information, including:

[0026] Acquire temperature sensor, infrared sensor, infrared video image and smoke sensor data at the same location and in the same time period;

[0027] The data collected from the temperature sensor, infrared sensor and humidity sensor are integrated to determine that the received data satisfies the conditions that the infrared thermal radiation information is less than a set threshold, the received temperature sensor information is greater than a set threshold, and the temperature data continues to rise, and the received humidity information is less than a set threshold, and the value continues to decrease, and the smoke sensor data is less than a set threshold, and an early warning is issued to identify whether there is a smoke image in the video image. If not, an early warning is generated to determine a drought.

[0028] Furthermore, the cloud platform is used to perform grassland rodent damage early warning analysis based on the grassland monitoring information, including:

[0029] Obtain sand flux sensor, infrared video imagery, and rodent data from the same location and time period;

[0030] The sand flux sensor, infrared video image and rodent data are integrated to determine whether the received data satisfies the sand flux sensor information greater than a set threshold, and whether there is exposed grass in the video image, and whether the received rodent quantity information is greater than a set threshold, and an early warning is issued. If an early warning is generated, it is determined to be a rodent infestation.

[0031] Furthermore, the cloud platform is used to perform grassland pest early warning analysis based on the grassland monitoring information, including:

[0032] Obtain sand flux sensor, infrared video imagery, and locust data from the same location and time period;

[0033] The sand flux sensor, infrared video image and locust data are integrated to determine whether the received data satisfies the sand flux sensor information greater than a set threshold, and whether there is exposed grass in the video image, and whether the received locust quantity information is greater than a set threshold, and an early warning is issued. If an early warning is generated, then insect pests are determined.

[0034] An intelligent grassland disaster monitoring method comprises the following steps:

[0035] Step 1: Determine the monitoring area and monitoring time;

[0036] Step 2: Arrange monitoring devices in the monitoring area;

[0037] Step 3: During the monitoring period, the monitoring terminal is used to continuously monitor and collect environmental data and location information of temperature, humidity, rainfall, snowfall, wind speed, and dust in the monitored area 24 hours a day, 7 days a week, to form a complete and continuous data chain, and transmit it to the data center server in real time;

[0038] Step 4: The data center server aggregates, processes and analyzes the environmental data, location information and image data transmitted in step 3 to obtain analysis result data, and forms corresponding chart information for automatic storage;

[0039] Step 5: The data center server compares the analysis result data with the warning value of grassland natural disasters;

[0040] Step 6: The data center server aggregates, processes, analyzes and stores the image data transmitted by the monitoring terminals in the monitoring area, and compares and analyzes the image data with the analysis result data in step 4;

[0041] Step 7: The data center server further analyzes and compares the comparative data obtained in step 5 and step 6, automatically stores them, and determines whether to issue or cancel the warning;

[0042] Step 8: The cloud platform uses the Geographic Information System (GIS) to display the monitoring data in the form of maps and charts, making it convenient for regulators and emergency departments to view and analyze.

[0043] Beneficial effects of the present invention:

[0044] 1. The present invention carries sensors, Beidou positioning modules, and external infrared cameras on the monitoring device, which can collect data on grassland disasters 24 hours a day, monitor and warn at the same time as monitoring rodents.

[0045] 2. The present invention simultaneously realizes real-time, continuous data collection, monitoring, early warning and precise positioning of grassland natural disasters and biological disasters.

[0046] 3. The present invention realizes multiple uses of the device, reduces construction cost and maintenance cost, and reduces use cost.

[0047] 4. The present invention uses the monitoring terminal as a platform, high-precision sensors, external infrared cameras, and Beidou communication as a data transmission link. Through the background big data system, it can realize comprehensive, systematic, continuous, and intelligent data collection and real-time monitoring and early warning of grassland disasters, and accurately locate the occurrence areas and locations of grassland disasters. It can complete data interaction and result verification with other monitoring systems, make up for the shortcomings and deficiencies of traditional grassland disaster monitoring methods, and build an intelligent grassland disaster ground monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system architecture block diagram of the present invention;

[0049] Figure 2 Schematic diagram of the high-precision sensor structure of the present invention

[0050] Figure 3It is a schematic diagram of the structure of a data center server of the present invention;

[0051] Figure 4 It is a schematic diagram of the overall structure of the monitoring terminal of the present invention;

[0052] Figure 5 It is a schematic diagram of the cross-sectional structure of the monitoring terminal of the present invention.

[0053] In the figure: 1. Shell; 2. Control compartment; 3. Channel; 301. Left outer channel; 302. Inner channel; 303. Right outer channel; 304. Automatic door; 305. First infrared sensor; 306. Second infrared sensor; 307. Pressure sensor; 308. Pressure plate; 309. Support body; 310. Bait box; 311. Radar rangefinder; 312. Infrared camera; 313. DC drive motor; 314. Long hole; 315. Long groove; 316. L-shaped barb. DETAILED DESCRIPTION

[0054] The specific implementation of the present invention is described below in conjunction with the accompanying drawings and embodiments:

[0055] The present invention is further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited thereto.

[0056] Example:

[0057] Please see attached Figure 1 -Attached Figure 5 The embodiment of the present invention provides an intelligent grassland disaster monitoring system, including:

[0058] Monitoring terminal, the target grassland area is divided into multiple nodes, each node is provided with at least one monitoring terminal, and the monitoring terminal is configured to perform real-time collection of grassland field data of the corresponding node, obtain grassland field data of the target node, normalize the grassland field data, and obtain the most basic indicator value;

[0059] The monitoring terminal includes a displacement sensor, a temperature sensor, a humidity sensor, a smoke sensor, a sand flux sensor, a rain and snow sensor, an external infrared camera, a wireless communication module and a terminal processor. The terminal processor interacts with the position sensor, the temperature sensor, the humidity sensor, the smoke sensor, the sand flux sensor, the rain and snow sensor, the external infrared camera and the wireless communication module to store and process the grassland field data. The monitoring terminal interacts with the data center server through the wireless communication module.

[0060] The movement sensor is used to collect the location information of the area where the monitoring terminal is located;

[0061] The temperature sensor is used to collect temperature information of the area where the monitoring terminal is located;

[0062] The humidity sensor is used to collect humidity information of the area where the monitoring terminal is located;

[0063] The smoke sensor is used to detect whether there is smoke in the area where the monitoring terminal is located;

[0064] The wind speed sensor is used to collect wind speed information in the area where the monitoring terminal is located;

[0065] The sand flux sensor is used to collect sand and dust information in the area where the monitoring terminal is located;

[0066] The rain and snow sensor is used to collect meteorological information of the area where the monitoring terminal is located;

[0067] The external infrared camera is used to collect grassland image information in the area where the monitoring terminal is located.

[0068] The monitoring terminal includes a shell 1, wherein the upper part of the shell 1 is provided with a control chamber 2, and the lower part is provided with a channel 3, wherein the channel 3 includes a left outer channel 301, an inner channel 302 and a right outer channel 303, wherein the left outer channel 301 and the right outer channel 303 are respectively arranged on both sides of the inner channel 302, and automatic doors 304 are respectively arranged at the junctions of the inner channel 302, the left outer channel 301 and the right outer channel 303, the left outer channel 301 is provided with a first infrared sensor 305, the right outer channel 303 is provided with a second infrared sensor 306, the inner channel 302 is provided with a pressure sensor 307, and the pressure sensor 307 is arranged in the control chamber 2, and a pressure plate 308 is provided on the top of the pressure sensor 307, and a support body 309 is vertically connected to the two sides of the pressure plate 308, and the support body 30 9 is vertically arranged in the inner channel 302, a gap is provided between the support body 309 and the bottom wall of the inner channel 302, a movable bait box 310 is provided on the support body 309, a radar rangefinder 311 and an infrared camera 312 are provided on the top of the inner channel 310, the radar rangefinder 311, the infrared camera 312, the pressure sensor 307, the first infrared sensor 305 and the second infrared sensor 306 are respectively electrically connected to the terminal processor, the automatic door 304 is rotatably connected to the DC drive motor 313, the shell 1 is a rectangular parallelepiped structure with two ends open, a plurality of long holes 314 are vertically provided on both sides of the channel 3, a plurality of long slots 315 are provided on the bottom wall of the channel 3, and a plurality of L-shaped barbs 316 are evenly provided on the lower edge of the automatic door 304. After confirming the capture of the first mouse, after the pressure sensor is continuously triggered for 3 minutes (this time value can be set in the background), the mouse door is automatically or manually remotely controlled to open, and 1 mouse is counted, and so on, and 24 hours is a calculation unit.

[0069] A data center server, wherein the data center server is configured to monitor grassland disaster conditions and issue early warning signals to achieve in-depth mining and prediction of grassland disaster data, and the data center server includes an information processing module, an intelligent decision-making module, and an information correction module;

[0070] An information processing module, wherein the information processing module is configured to compare grassland on-site data with a preset disaster threshold value, determine whether grassland disasters will occur in the monitoring area, determine a comprehensive risk level of grassland disasters, and send the judgment result to the intelligent decision-making module;

[0071] An intelligent decision-making module, wherein the intelligent decision-making module is configured to receive data for determining the comprehensive risk level of grassland disasters, analyze the data through decision analysis, and then formulate a response plan to deal with grassland disasters, and optimize the decision plan by intelligent optimization, and quickly make an early warning response plan;

[0072] An information correction module is configured to determine the authenticity of the grassland disaster information value and to provide feedback and correction for the collected erroneous information.

[0073] A cloud platform interacts with the data center server, and the cloud platform is configured to interact with the outside world through a management interface, conduct early warning analysis of snow disasters, droughts, fires, rodent infestations, and insect pests based on grassland field data collected by monitoring terminals, display the grassland information and warning information through the management interface, and send warning information to corresponding clients.

[0074] Specifically, the information processing module calculates the comprehensive natural disaster risk level index of the target area according to the indicator values ​​and weights of indicators at all levels, and then determines the comprehensive natural disaster risk level of the target area based on the comprehensive natural disaster risk level index of the target area.

[0075] Specifically, the wireless communication module includes a 5G communication unit, a Beidou short message communication unit and a Beidou positioning unit.

[0076] The Beidou positioning unit adopts the Beidou positioning chip L76K, and the Beidou positioning chip has a built-in amplifier, filter, active antenna detection circuit and antenna protection circuit;

[0077] The active antenna detection circuit is used to detect the working state of the active antenna and output a corresponding state detection signal to the antenna protection circuit, so that the antenna protection circuit works according to the state detection signal.

[0078] It is implemented by using Beidou positioning chip L76K, which is a GNSS module that supports multiple satellite systems (GPS, BeiDou, GLONASS, QZSS), can perform multi-system joint positioning and single-system independent positioning, supports AGNSS function, and can provide users with fast, accurate, and high-performance positioning experience. It should be noted that Beidou positioning chip L76K has built-in low-noise amplifiers and filters. The low-noise amplifier can achieve high sensitivity, high-precision positioning, and fast capture and tracking of signals, and can well guarantee positioning performance even under weak signal conditions. The built-in surface acoustic wave filter can greatly enhance the anti-interference ability of Beidou module. In addition, the Beidou positioning chip integrates an active antenna detection circuit to detect the working state of the active antenna and output the corresponding state detection signal to the antenna protection circuit. For example, the active antenna detection circuit monitors whether the active antenna is in a short circuit or open circuit fault, and outputs the result to the antenna protection circuit so that the antenna protection circuit can process it in time. It is implemented by using Beidou module, which makes the location information more accurate and improves the reliability of the disaster warning monitoring system of the present invention.

[0079] Specifically, the cloud platform is used to perform grassland fire early warning analysis based on the grassland monitoring information, including:

[0080] Acquire temperature sensor, infrared sensor, infrared video image and smoke sensor data at the same location and in the same time period;

[0081] The data collected from the temperature sensor, infrared sensor and humidity sensor are integrated to determine whether the received data satisfies the conditions that the infrared thermal radiation information is greater than a set threshold and the infrared radiation value continues to increase, and the received temperature sensor information is greater than a set threshold and the temperature data continues to increase, and the received humidity information is less than a set threshold and the value continues to decrease, and the smoke sensor data is greater than a set threshold, and an early warning is issued to identify whether there is a smoke image in the video image. If so, an early warning is generated to determine a fire.

[0082] Specifically, the cloud platform is used to perform grassland snow disaster early warning analysis based on the grassland monitoring information, including:

[0083] Obtain rain and snow sensor, temperature sensor, humidity sensor and infrared video image data at the same location and in the same time period;

[0084] The data collected from the rain and snow sensor, the temperature sensor and the humidity sensor are integrated, and if the received temperature sensor information is greater than a set threshold value and the temperature data continues to decrease, and if the received humidity information is less than a set threshold value and the value continues to decrease, and the rain and snow sensor data is greater than the set threshold value, an early warning is issued, and a snow disaster is determined;

[0085] Rain and snow data include the maximum snowfall in 1 hour, the maximum snowfall in 3 hours, the maximum snowfall in 6 hours, the maximum snowfall in 12 hours, the maximum accumulated snowfall during the process, the maximum daily snowfall, the accumulated snowfall during the blizzard process, the duration of the blizzard, the percentage of the blizzard intensity level and the total number of blizzards.

[0086] Specifically, the cloud platform is used to perform grassland drought early warning analysis based on the grassland monitoring information, including:

[0087] Acquire temperature sensor, infrared sensor, infrared video image and smoke sensor data at the same location and in the same time period;

[0088] The data collected from the temperature sensor, infrared sensor and humidity sensor are integrated to determine that the received data satisfies the conditions that the infrared thermal radiation information is less than a set threshold, the received temperature sensor information is greater than a set threshold, and the temperature data continues to rise, and the received humidity information is less than a set threshold, and the value continues to decrease, and the smoke sensor data is less than a set threshold, and an early warning is issued to identify whether there is a smoke image in the video image. If not, an early warning is generated to determine a drought.

[0089] Specifically, the cloud platform is used to perform grassland rodent damage early warning analysis based on the grassland monitoring information, including:

[0090] Obtain sand flux sensor, infrared video imagery, and rodent data from the same location and time period;

[0091] The sand flux sensor, infrared video image and rodent data are integrated to determine whether the received data satisfies the sand flux sensor information greater than a set threshold, and whether there is exposed grass in the video image, and whether the received rodent quantity information is greater than a set threshold, and an early warning is issued. If an early warning is generated, it is determined to be a rodent infestation.

[0092] Specifically, the cloud platform is used to perform grassland pest early warning analysis based on the grassland monitoring information, including:

[0093] Obtain sand flux sensor, infrared video imagery, and locust data from the same location and time period;

[0094] The sand flux sensor, infrared video image and locust data are integrated to determine whether the received data satisfies the sand flux sensor information greater than a set threshold, and whether there is exposed grass in the video image, and whether the received locust quantity information is greater than a set threshold, and an early warning is issued. If an early warning is generated, then insect pests are determined.

[0095] An intelligent grassland disaster monitoring method comprises the following steps:

[0096] Step 1: Determine the monitoring area and monitoring time;

[0097] Step 2: Arrange monitoring devices in the monitoring area;

[0098] Step 3: During the monitoring period, the monitoring terminal is used to continuously monitor and collect environmental data and location information of temperature, humidity, rainfall, snowfall, wind speed, and dust in the monitored area 24 hours a day, 7 days a week, to form a complete and continuous data chain, and transmit it to the data center server in real time;

[0099] Step 4: The data center server aggregates, processes and analyzes the environmental data, location information and image data transmitted in step 3 to obtain analysis result data, and forms corresponding chart information for automatic storage;

[0100] Step 5: The data center server compares the analysis result data with the warning value of grassland natural disasters;

[0101] Step 6: The data center server aggregates, processes, analyzes and stores the image data transmitted by the monitoring terminals in the monitoring area, and compares and analyzes the image data with the analysis result data in step 4;

[0102] Step 7: The data center server further analyzes and compares the comparative data obtained in step 5 and step 6, automatically stores them, and determines whether to issue or cancel the warning;

[0103] Step 8: The cloud platform uses the Geographic Information System (GIS) to display the monitoring data in the form of maps and charts, making it convenient for regulators and emergency departments to view and analyze.

[0104] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that these embodiments can be modified and improved without departing from the principle and spirit of the present invention whose scope is defined by the claims and their equivalents, and these modifications and improvements should also be within the protection scope of the present invention.

Claims

1. An intelligent grassland disaster monitoring system, characterized in that: include: Monitoring terminal, the target grassland area is divided into multiple nodes, each node is provided with at least one monitoring terminal, and the monitoring terminal is configured to perform real-time collection of grassland field data of the corresponding node, obtain grassland field data of the target node, normalize the grassland field data, and obtain the most basic indicator value; The monitoring terminal includes a displacement sensor, a temperature sensor, a humidity sensor, a smoke sensor, a sand flux sensor, a rain and snow sensor, an external infrared camera, a wireless communication module and a terminal processor. The terminal processor interacts with the position sensor, the temperature sensor, the humidity sensor, the smoke sensor, the sand flux sensor, the rain and snow sensor, the external infrared camera and the wireless communication module to store and process the grassland field data. The monitoring terminal interacts with the data center server through the wireless communication module. A data center server, wherein the data center server is configured to monitor grassland disaster conditions and issue early warning signals to achieve in-depth mining and prediction of grassland disaster data, and the data center server includes an information processing module, an intelligent decision-making module, and an information correction module; An information processing module, wherein the information processing module is configured to compare grassland on-site data with a preset disaster threshold value, determine whether grassland disasters will occur in the monitoring area, determine a comprehensive risk level of grassland disasters, and send the judgment result to the intelligent decision-making module; An intelligent decision-making module, wherein the intelligent decision-making module is configured to receive data for determining the comprehensive risk level of grassland disasters, analyze the data through decision analysis, and then formulate a response plan to deal with grassland disasters, and optimize the decision plan by intelligent optimization, and quickly make an early warning response plan; The information correction module is configured to determine the authenticity of the grassland disaster information value and to provide feedback and correction for the collected erroneous information. A cloud platform interacts with the data center server, and the cloud platform is configured to interact with the outside world through a management interface, conduct early warning analysis of snow disasters, droughts, fires, rodent infestations, and insect pests based on grassland field data collected by monitoring terminals, display the grassland information and warning information through the management interface, and send warning information to corresponding clients.

2. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The information processing module calculates the comprehensive natural disaster risk level index of the target area according to the indicator values ​​and weights of indicators at all levels, and then determines the comprehensive natural disaster risk level of the target area based on the comprehensive natural disaster risk level index of the target area.

3. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The wireless communication module includes a 5G communication unit, a Beidou short message communication unit and a Beidou positioning unit. The Beidou positioning unit adopts the Beidou positioning chip L76K, and the Beidou positioning chip has a built-in amplifier, filter, active antenna detection circuit and antenna protection circuit; The active antenna detection circuit is used to detect the working state of the active antenna and output a corresponding state detection signal to the antenna protection circuit, so that the antenna protection circuit works according to the state detection signal.

4. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The cloud platform is used to perform grassland fire early warning analysis based on the grassland monitoring information, including: Acquire temperature sensor, infrared sensor, infrared video image and smoke sensor data at the same location and in the same time period; The data collected from the temperature sensor, infrared sensor and humidity sensor are integrated to determine whether the received data satisfies the conditions that the infrared thermal radiation information is greater than a set threshold and the infrared radiation value continues to increase, and the received temperature sensor information is greater than a set threshold and the temperature data continues to increase, and the received humidity information is less than a set threshold and the value continues to decrease, and the smoke sensor data is greater than a set threshold, and an early warning is issued to identify whether there is a smoke image in the video image. If so, an early warning is generated to determine a fire.

5. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The cloud platform is used to perform grassland snow disaster early warning analysis based on the grassland monitoring information, including: Obtain rain and snow sensor, temperature sensor, humidity sensor and infrared video image data at the same location and in the same time period; The data collected from the rain and snow sensor, the temperature sensor and the humidity sensor are integrated, and if the received temperature sensor information is greater than a set threshold value and the temperature data continues to decrease, and if the received humidity information is less than a set threshold value and the value continues to decrease, and the rain and snow sensor data is greater than the set threshold value, an early warning is issued, and a snow disaster is determined; Rain and snow data include the maximum snowfall in 1 hour, the maximum snowfall in 3 hours, the maximum snowfall in 6 hours, the maximum snowfall in 12 hours, the maximum accumulated snowfall during the process, the maximum daily snowfall, the accumulated snowfall during the blizzard process, the duration of the blizzard, the percentage of the blizzard intensity level and the total number of blizzards.

6. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The cloud platform is used to perform grassland drought early warning analysis based on the grassland monitoring information, including: Acquire temperature sensor, infrared sensor, infrared video image and smoke sensor data at the same location and in the same time period; The data collected from the temperature sensor, infrared sensor and humidity sensor are integrated to determine that the received data satisfies the conditions that the infrared thermal radiation information is less than a set threshold, the received temperature sensor information is greater than a set threshold, and the temperature data continues to rise, and the received humidity information is less than a set threshold, and the value continues to decrease, and the smoke sensor data is less than a set threshold, and an early warning is issued to identify whether there is a smoke image in the video image. If not, an early warning is generated to determine a drought.

7. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The cloud platform is used to perform grassland rodent damage early warning analysis based on the grassland monitoring information, including: Obtain sand flux sensor, infrared video imagery, and rodent data from the same location and time period; The sand flux sensor, infrared video image and rodent data are integrated to determine whether the received data satisfies the sand flux sensor information greater than a set threshold, and whether there is exposed grass in the video image, and whether the received rodent quantity information is greater than a set threshold, and an early warning is issued. If an early warning is generated, it is determined to be a rodent infestation.

8. The intelligent grassland disaster monitoring system according to claim 1 is characterized in that: The cloud platform is used to perform grassland pest early warning analysis based on the grassland monitoring information, including: Obtain sand flux sensor, infrared video imagery, and locust data from the same location and time period; The sand flux sensor, infrared video image and locust data are integrated to determine whether the received data satisfies the sand flux sensor information greater than a set threshold, and whether there is exposed grass in the video image, and whether the received locust quantity information is greater than a set threshold, and an early warning is issued. If an early warning is generated, then insect pests are determined.

9. An intelligent grassland disaster monitoring method, characterized in that: The steps include: Step 1: Determine the monitoring area and monitoring time; Step 2: Arrange monitoring devices in the monitoring area; Step 3: During the monitoring period, the monitoring terminal is used to continuously monitor and collect environmental data and location information of temperature, humidity, rainfall, snowfall, wind speed, and dust in the monitored area 24 hours a day, 7 days a week, to form a complete and continuous data chain, and transmit it to the data center server in real time; Step 4: The data center server aggregates, processes and analyzes the environmental data, location information and image data transmitted in step 3 to obtain analysis result data, and forms corresponding chart information for automatic storage; Step 5: The data center server compares the analysis result data with the warning value of grassland natural disasters; Step 6: The data center server aggregates, processes, analyzes and stores the image data transmitted by the monitoring terminals in the monitoring area, and compares and analyzes the image data with the analysis result data in step 4; Step 7: The data center server further analyzes and compares the comparative data obtained in step 5 and step 6, automatically stores them, and determines whether to issue or cancel the warning; Step 8: The cloud platform uses the Geographic Information System (GIS) to display the monitoring data in the form of maps and charts, making it convenient for regulators and emergency departments to view and analyze.

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