CN220829241U - Intelligent monitoring composite simulation system for space and ground of ecological geographic elements - Google Patents
Intelligent monitoring composite simulation system for space and ground of ecological geographic elements Download PDFInfo
- Publication number
- CN220829241U CN220829241U CN202322621554.0U CN202322621554U CN220829241U CN 220829241 U CN220829241 U CN 220829241U CN 202322621554 U CN202322621554 U CN 202322621554U CN 220829241 U CN220829241 U CN 220829241U
- Authority
- CN
- China
- Prior art keywords
- aerial vehicle
- unmanned aerial
- module
- monitoring system
- ground monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 294
- 239000002131 composite material Substances 0.000 title claims abstract description 18
- 238000004088 simulation Methods 0.000 title claims abstract description 18
- 230000005540 biological transmission Effects 0.000 claims abstract description 40
- 238000013500 data storage Methods 0.000 claims abstract description 30
- 238000007781 pre-processing Methods 0.000 claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 12
- 230000004907 flux Effects 0.000 claims description 46
- 238000013135 deep learning Methods 0.000 claims description 20
- 238000010248 power generation Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 10
- 238000001556 precipitation Methods 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 3
- 239000007789 gas Substances 0.000 description 32
- 238000011160 research Methods 0.000 description 13
- 238000004422 calculation algorithm Methods 0.000 description 8
- 238000013075 data extraction Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 230000010354 integration Effects 0.000 description 7
- 238000000034 method Methods 0.000 description 7
- 230000002265 prevention Effects 0.000 description 7
- 241000209094 Oryza Species 0.000 description 6
- 235000007164 Oryza sativa Nutrition 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 6
- 235000009566 rice Nutrition 0.000 description 6
- 239000002680 soil gas Substances 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 3
- 230000004888 barrier function Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 239000002689 soil Substances 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004720 fertilization Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000004321 preservation Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001932 seasonal effect Effects 0.000 description 2
- 239000010902 straw Substances 0.000 description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- DMBHHRLKUKUOEG-UHFFFAOYSA-N diphenylamine Chemical compound C=1C=CC=CC=1NC1=CC=CC=C1 DMBHHRLKUKUOEG-UHFFFAOYSA-N 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
Landscapes
- Selective Calling Equipment (AREA)
Abstract
The utility model discloses an intelligent air-ground monitoring composite simulation system for ecological geographic elements, which comprises an unmanned aerial vehicle and a ground monitoring system, wherein the unmanned aerial vehicle comprises an unmanned aerial vehicle body, an unmanned aerial vehicle wireless transmission module, an unmanned aerial vehicle data acquisition module and an unmanned aerial vehicle data storage and preprocessing module, the unmanned aerial vehicle data acquisition module is electrically connected with the unmanned aerial vehicle data storage and preprocessing module, and the unmanned aerial vehicle data storage and preprocessing module is electrically connected with the unmanned aerial vehicle wireless transmission module; the ground monitoring system comprises a ground monitoring system box body, a ground monitoring system power module, a ground monitoring system control module, a ground monitoring system data acquisition module, a ground monitoring system data storage module and a ground monitoring system wireless transmission module, wherein the ground monitoring system wireless transmission module is in wireless communication connection with the unmanned aerial vehicle wireless transmission module. The composite simulation system provided by the utility model realizes omnibearing, efficient monitoring and intelligent identification of ecological geographic elements by combining the unmanned aerial vehicle with the ground monitoring system.
Description
Technical Field
The utility model belongs to the technical field of ecological geographic element monitoring, and particularly relates to an intelligent air and ground monitoring composite simulation system for an ecological geographic element.
Background
The ecological geographic elements comprise vegetation coverage, water distribution, land utilization, gas emission of an ecological system, meteorological conditions and the like, and have important significance for ecological environment research and protection. With the continuous expansion of human activities and the influence of climate change, the ecological environment faces serious challenges, and the demand for real-time monitoring and accurate analysis of ecological geographic elements is increasing.
Currently, the monitoring of ecological geographic elements mainly depends on traditional remote sensing technology and ground investigation. Traditional remote sensing technology obtains image data of geographic elements through satellites or planes, and then analyzes and interprets the image data by using an image processing method. Although the remote sensing technology can realize the monitoring of a large-scale area, the requirement of high-precision monitoring of ecological geographic elements is difficult to meet due to the limited resolution and frequency of the remote sensing image. Moreover, the remote sensing technology cannot effectively monitor in certain time periods due to the influence of factors such as weather, cloud layers and the like. In addition, although the ground survey can provide detailed information, it is limited by factors such as manpower, time and cost, and continuous and omnibearing monitoring cannot be realized.
In recent years, the rapid development of unmanned aerial vehicle technology brings new opportunities for ecological geographic element monitoring. The unmanned aerial vehicle has the advantage of flexibility and high resolution, can monitor at lower height to improve the monitoring accuracy. However, the conventional unmanned aerial vehicle still faces the problems of short endurance time, limited monitoring range and the like, and cannot meet the monitoring requirements of a large range and a complex region. The ground monitoring system can provide ground observation and high-precision data, but has limited coverage, and cannot monitor a wide area. The traditional ecological geographic element monitoring method has technical difficulties in realizing seamless air-ground monitoring, intelligent analysis of complex regions and the like. Thus, there is a need for a more efficient, more accurate intelligent monitoring technique to overcome these problems.
The traditional monitoring method is limited by factors such as manpower, time, cost and the like, and continuous monitoring of a large range and complex regions cannot be realized. Therefore, it is important to develop a technology capable of efficiently and accurately performing intelligent air-ground monitoring of ecological geographic elements.
Disclosure of utility model
In order to solve the technical problems, the utility model provides an intelligent air and ground monitoring composite simulation system for ecological geographic elements, which utilizes the combination of an unmanned plane and a ground monitoring system to realize omnibearing, efficient monitoring, intelligent identification and real-time dynamic sensing of the ecological geographic elements and the changes thereof.
In order to achieve the above purpose, the present utility model adopts the following technical scheme:
the system comprises an unmanned aerial vehicle with an ecological geographic element monitoring function and a ground monitoring system, wherein the unmanned aerial vehicle comprises an unmanned aerial vehicle body, an unmanned aerial vehicle wireless transmission module, an unmanned aerial vehicle data acquisition module and an unmanned aerial vehicle data storage and preprocessing module, the unmanned aerial vehicle data acquisition module is electrically connected with the unmanned aerial vehicle data storage and preprocessing module, the unmanned aerial vehicle data storage and preprocessing module is electrically connected with the unmanned aerial vehicle wireless transmission module, and the unmanned aerial vehicle wireless transmission module is in wireless communication connection with an intelligent equipment terminal; the ground monitoring system comprises a ground monitoring system box body, a ground monitoring system power module, a ground monitoring system control module, a ground monitoring system data acquisition module, a ground monitoring system data storage module and a ground monitoring system wireless transmission module, wherein the ground monitoring system power module is electrically connected with the ground monitoring system control module, the ground monitoring system control module is electrically connected with the ground monitoring system data acquisition module, the ground monitoring system data acquisition module is electrically connected with the ground monitoring system data storage module, the ground monitoring system data storage module is electrically connected with the ground monitoring system wireless transmission module, and the ground monitoring system wireless transmission module is in wireless communication connection with the unmanned aerial vehicle wireless transmission module and is simultaneously in wireless communication connection with the intelligent equipment terminal.
Further, unmanned aerial vehicle still includes unmanned aerial vehicle AI degree of depth learning module to and the extreme weather early warning recognition module of unmanned aerial vehicle that is connected with unmanned aerial vehicle AI degree of depth learning module electricity, the extreme weather early warning recognition module of unmanned aerial vehicle is fixed in unmanned aerial vehicle top and both sides face department, and the extreme weather early warning recognition module of unmanned aerial vehicle includes a plurality of miniature measuring sensor and response subassembly, and is a plurality of miniature measuring sensor is one or more in temperature sensor, wind speed sensor, the strong precipitation sensor.
Further, unmanned aerial vehicle still includes the unmanned aerial vehicle barrier that is connected with unmanned aerial vehicle AI degree of depth learning module electricity and prejudges prevention and control module, unmanned aerial vehicle barrier prejudges prevention and control module is fixed in unmanned aerial vehicle's both sides face department, and unmanned aerial vehicle barrier prejudges prevention and control module includes miniature camera and processing center.
The unmanned aerial vehicle AI deep learning module is electrically connected with the unmanned aerial vehicle extreme weather early warning and identifying module and the unmanned aerial vehicle obstacle pre-judging, preventing and controlling module, the unmanned aerial vehicle extreme weather early warning and identifying module algorithm is automatically optimized in the monitoring process to accurately identify extreme weather, and meanwhile, the unmanned aerial vehicle obstacle pre-judging, preventing and controlling module algorithm is optimized to accurately pre-judge obstacles, the calculation force of the data preprocessing module is improved, and the flight route is optimized.
Further, unmanned aerial vehicle still includes unmanned aerial vehicle self preservation protects module that falls, unmanned aerial vehicle falls self preservation protects module and includes a plurality of undercarriage and a plurality of gasbag, and is a plurality of the undercarriage symmetry is located on the lower terminal surface of unmanned aerial vehicle body, every the gasbag is fixed in the bottom of an undercarriage, the top of undercarriage and the lower terminal surface fixed connection of unmanned aerial vehicle body.
Further, unmanned aerial vehicle still includes unmanned aerial vehicle navigation module, unmanned aerial vehicle navigation module is one or two of GPS module and big dipper module, and unmanned aerial vehicle navigation module is connected with unmanned aerial vehicle wireless transmission module and unmanned aerial vehicle AI degree of depth learning module electricity respectively.
Further, the unmanned aerial vehicle data storage and preprocessing module comprises a high-capacity micro storage center, an image splicing and inversion center and a data extraction and integration center, wherein the high-capacity micro storage center has the characteristics of small volume and large data storage capacity and is used for storing unmanned aerial vehicle monitoring data; the image stitching and inverting center is used for stitching and inverting the images shot by the unmanned aerial vehicle and storing the processed images into the high-capacity miniature storage center; the data extraction and integration center is used for extracting and integrating the monitoring data of the unmanned aerial vehicle, for example, extracting elements of the images processed by the image splicing and inversion center, storing the images in the high-capacity micro storage center, and transmitting the images to the intelligent equipment terminal in real time through the unmanned aerial vehicle wireless transmission module so as to facilitate the next analysis; the unmanned aerial vehicle data acquisition module is fixed at the bottom of the unmanned aerial vehicle body and comprises a camera module and a sensor module, wherein the camera module is one or more of a multispectral camera and a high-definition camera, and the sensor module is one or more of a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor and an NH 3 sensor and is used for monitoring air flux on the ground surface; the intelligent equipment terminal is a mobile phone or a computer; be equipped with unmanned aerial vehicle high-efficient solar power system on the unmanned aerial vehicle body.
Further, the ground monitoring system data acquisition module comprises a plurality of measuring sensors, the measuring sensors comprise a ground monitoring system weather monitoring sensor and a ground monitoring system gas flux monitoring sensor, the ground monitoring system weather monitoring sensor and the ground monitoring system gas flux monitoring sensor are electrically connected with the ground monitoring system data acquisition module, the ground monitoring system weather monitoring sensor comprises one or more of a temperature sensor, a sunlight intensity sensor and a tipping bucket type rainfall sensor, and the ground monitoring system weather monitoring sensor is fixed on the outer side wall of the system box body; the ground monitoring system gas flux monitoring sensor comprises one or more of a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor and an NH 3 sensor, a sensor annular sliding rod is arranged on the outer peripheral side of the ground monitoring system box body, the sensor annular sliding rod is electrically connected with a ground monitoring system control module, the ground monitoring system gas flux monitoring sensor is arranged on the lower end face of the sensor annular sliding rod and is in sliding connection with the sensor annular sliding rod, and a proper position is conveniently selected for gas flux monitoring.
Further, the ground monitoring system further comprises a ground monitoring system AI deep learning module, wherein the ground monitoring system AI deep learning module is contained in the ground monitoring system control module, and the overall operation of the ground monitoring system is optimized and upgraded in real time.
Further, a ground monitoring system supporting base capable of automatically adjusting the height is arranged at the bottom of the ground monitoring system box body; the ground monitoring system power module comprises a power generation device and a battery module, the power generation device is electrically connected with the battery module, and the battery module is electrically connected with the ground monitoring system control module.
According to the intelligent air and ground monitoring composite simulation system for the ecological geographic elements, disclosed by the utility model, the unmanned aerial vehicle with the ecological geographic element monitoring function and the ground monitoring system are combined to realize omnibearing, efficient monitoring and intelligent identification of the ecological geographic elements. In the unmanned aerial vehicle with the ecological geographic element monitoring function, a camera module (comprising a multispectral camera, a common high-definition camera and the like) in an unmanned aerial vehicle data acquisition module, an unmanned aerial vehicle data preprocessing module and an unmanned aerial vehicle AI deep learning module are used for monitoring and inverting a normalized vegetation index (NDVI), a Leaf Area Index (LAI), water quality parameters (various water quality parameters such as water color, temperature, turbidity and the like), soil organic carbon content, soil texture and the like of a research area, agricultural activities (agricultural mechanization degree, fertilization frequency, straw returning, cultivation mode and the like), land utilization type, vegetation (forest, grassland, wetland, farmland and the like) type, ecosystem type (farmland ecosystem, forest ecosystem, lake ecosystem and the like), seasonal change of vegetation, fluctuation of water area and the like are real-time dynamic perception is realized; monitoring gas flux of CH 4 and the like above the ground surface through a sensor module (including a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor, an NH 3 sensor and the like) in the unmanned aerial vehicle data acquisition module; in the flight monitoring process, the extreme weather (including a temperature sensor, a wind speed sensor and a strong rainfall sensor) and obstacles (such as tall trees, wires and telegraph poles, flying birds, buildings and the like) are identified and sensed for multiple times through a plurality of measuring sensors (including a temperature sensor, a wind speed sensor and a strong rainfall sensor) and camera modules in the early stage, an unmanned aerial vehicle extreme weather early warning identification module and an unmanned aerial vehicle obstacle pre-judging prevention control module are trained, an unmanned aerial vehicle AI deep learning module is used for self-optimizing an unmanned aerial vehicle extreme weather early warning identification module algorithm and an unmanned aerial vehicle obstacle pre-judging prevention control module algorithm, so that the extreme weather and obstacles are identified and pre-judged more accurately, the calculation power of a data preprocessing module is improved, the flight route is optimized and the like, when the extreme weather such as storm and the like is met, the unmanned aerial vehicle can be sensed and fly to an unmanned aerial vehicle parking cabin near a research area through a self-flight control system, and the extreme weather is sensed, and the flying cabin continues to monitor the research area; the unmanned aerial vehicle data preprocessing module can be used for performing rapid image splicing and inversion, data extraction and integration and the like, and then the preliminarily processed images, data and the like are transmitted to the intelligent equipment terminal in real time through the unmanned aerial vehicle wireless transmission module; the unmanned aerial vehicle can continuously fly for a long time as far as possible through the unmanned aerial vehicle high-efficiency solar power generation device and the high-density high-capacity battery; the unmanned aerial vehicle falls the self-protection module (can but not only be limited to can pop-up gasbag) and falls suddenly and carries out self-protection, reduces unmanned aerial vehicle damage probability as far as possible.
In the ground monitoring system with the ecological geographic element monitoring function, power generation and power supply are mainly performed through a high-efficiency solar panel, and on the other hand, power supply can be accessed through a cable to serve as a power source in continuous overcast and rainy weather; monitoring ground weather conditions through a ground monitoring system weather monitoring sensor (comprising a temperature sensor, a sunlight intensity sensor, a tipping bucket rainfall sensor and the like); monitoring surface gas emission flux by a surface monitoring system gas flux monitoring sensor (including CH 4 sensor, CO 2 sensor, N 2 O sensor, NO sensor, NH 3 sensor, etc.); the optimization and upgrading of the whole operation of the system can be realized through an AI deep learning module in a ground monitoring system control module; the gas flux monitoring sensor of the ground monitoring system can move along the annular sliding rod of the sensor, so that a proper position is conveniently selected for gas flux monitoring.
The unmanned aerial vehicle and the ground monitoring system are connected through wireless communication of the wireless transmission module, so that the unmanned aerial vehicle can conveniently start the ground monitoring system in a remote control mode, and meanwhile, whether the ground monitoring system operates normally or not is monitored. All parameters of the unmanned aerial vehicle and the ground monitoring system in the composite simulation system can be remotely input through an intelligent equipment terminal (such as an intelligent mobile phone APP, an applet or a computer webpage and the like), and a plurality of operation parameters can be packaged and set; the monitoring data and the images can be continuously received at the intelligent equipment terminal, and the monitoring data and the inversion data are presented in a chart form in real time; the automatic restarting and continuous operation after accidental power failure can be realized; the system can be conveniently moved by adopting a pluggable interface design; the master-slave mechanism can be adopted, but is not limited to, so that the later-stage increasing and decreasing units are convenient. The intelligent air and ground monitoring composite simulation system for the ecological geographic elements is not limited by time and space, can monitor the ecological geographic elements of a research area for a long time, and can measure different indexes by replacing different sensors so as to achieve the aim of research.
The utility model utilizes the unmanned aerial vehicle to monitor the middle and low altitudes, acquires high-resolution image data, monitors key places in real time through a ground monitoring system, and performs fusion analysis on data acquired by the unmanned aerial vehicle and ground monitoring data; based on the mode of combining the unmanned aerial vehicle and the ground monitoring system, the comprehensive monitoring and intelligent recognition of the ecological geographic elements can be realized, and more reliable data support is provided for environmental protection and research; by introducing artificial intelligence and machine learning technology, intelligent identification and classification of ecological geographic elements are realized, so that the monitoring precision and efficiency are improved.
Drawings
Fig. 1 is a schematic structural diagram of an unmanned aerial vehicle with an ecological geographic element monitoring function according to the present utility model;
fig. 2 is a schematic structural diagram of the ground monitoring system according to the present utility model.
The intelligent unmanned aerial vehicle comprises a 1-unmanned aerial vehicle body, a 2-unmanned aerial vehicle wireless transmission module, a 3-unmanned aerial vehicle navigation module, a 4-unmanned aerial vehicle data acquisition module, a 5-unmanned aerial vehicle data storage and preprocessing module, a 6-unmanned aerial vehicle AI deep learning module, a 7-unmanned aerial vehicle extreme weather early warning recognition module, an 8-unmanned aerial vehicle obstacle pre-judging, preventing and controlling module, a 9-unmanned aerial vehicle falling self-protection module, a 10-unmanned aerial vehicle efficient solar power generation device, an 11-ground monitoring system box, a 12-ground monitoring system power module, a 13-ground monitoring system control module, a 14-ground monitoring system AI deep learning module, a 15-ground monitoring system wireless transmission module, a 16-ground monitoring system data storage module, a 17-ground monitoring system gas flux monitoring sensor, an 18-sensor annular transmission rod, a 19-temperature sensor, a 20-tipping bucket type rainfall sensor, a 21-sunlight intensity sensor and a 22-ground monitoring system support base.
Description of the embodiments
The present utility model will be further described in detail with reference to the following examples for the purpose of making the objects, technical solutions and advantages of the present utility model more clear, and it should be understood that the specific embodiments described herein are only for explaining the present utility model and are not intended to limit the present utility model.
The application principle of the present utility model will be described in further detail with reference to the accompanying drawings and specific examples (comprehensive monitoring of the rice field soil gas discharge flux and the ground air gas flux).
As shown in fig. 1 and fig. 2, the composite simulation system for intelligent monitoring of the air and the ground of the ecological geographic elements comprises an unmanned aerial vehicle with an ecological geographic element monitoring function and a ground monitoring system, wherein the unmanned aerial vehicle comprises an unmanned aerial vehicle body 1, an unmanned aerial vehicle wireless transmission module 2, an unmanned aerial vehicle data acquisition module 4, an unmanned aerial vehicle data storage and preprocessing module 5, an unmanned aerial vehicle advanced learning module 6 and a plurality of unmanned aerial vehicle extreme weather early warning identification modules 7 electrically connected with the unmanned aerial vehicle advanced learning module 6, a plurality of unmanned aerial vehicle obstacle prejudging and controlling modules 8 electrically connected with the unmanned aerial vehicle advanced learning module 6, a plurality of unmanned aerial vehicle falling self-protection modules 9 electrically connected with the unmanned aerial vehicle advanced learning module 6, and an unmanned aerial vehicle navigation module 3 electrically connected with the unmanned aerial vehicle wireless transmission module 2 and the unmanned aerial vehicle advanced learning module 6, wherein an unmanned aerial vehicle efficient solar power generation device 10 is arranged on the unmanned aerial vehicle body 1, and the unmanned aerial vehicle data acquisition module 4 is electrically connected with the unmanned aerial vehicle data storage and preprocessing module 5, and the unmanned aerial vehicle data storage and preprocessing module 5 is electrically connected with the unmanned aerial vehicle wireless transmission module 2, and the unmanned aerial vehicle wireless transmission module 2 is electrically connected with the intelligent terminal communication equipment; the unmanned aerial vehicle extreme weather early warning and identifying modules 7 are fixed at the top and two side surfaces of the unmanned aerial vehicle body 1, the unmanned aerial vehicle extreme weather early warning and identifying modules 7 comprise a plurality of micro measuring sensors and response components, and the micro measuring sensors are one or more of a temperature sensor, a wind speed sensor and a strong precipitation sensor; the unmanned aerial vehicle obstacle pre-judging, preventing and controlling modules 8 are fixed at two side surfaces of the unmanned aerial vehicle body, and each unmanned aerial vehicle obstacle pre-judging, preventing and controlling module 8 comprises a miniature camera and a processing center; the unmanned aerial vehicle falling self-protection module 9 comprises a plurality of landing gears and a plurality of air bags, wherein the landing gears are symmetrically arranged on the lower end face of the unmanned aerial vehicle body 1, each air bag is fixed at the bottom end of one landing gear, and the top end of the landing gear is fixedly connected with the lower end face of the unmanned aerial vehicle body 1; the unmanned aerial vehicle AI deep learning module 6 is electrically connected with the unmanned aerial vehicle extreme weather early warning and identifying module 7 and the unmanned aerial vehicle obstacle pre-judging and controlling module 8, the algorithm of the unmanned aerial vehicle extreme weather early warning and identifying module 7 is optimized by a monitoring process to accurately identify extreme weather, and meanwhile, the algorithm of the unmanned aerial vehicle obstacle pre-judging and controlling module 8 is optimized to accurately pre-judge obstacles, the calculation power of the data preprocessing module is improved, and the flight route is optimized; the unmanned aerial vehicle navigation module 3 is one or two of a GPS module and a Beidou module, and the unmanned aerial vehicle navigation module 3 is respectively and electrically connected with the unmanned aerial vehicle wireless transmission module 2 and the unmanned aerial vehicle AI deep learning module 6; the unmanned aerial vehicle data storage and preprocessing module 5 comprises a high-capacity micro storage center, an image splicing and inversion center and a data extraction and integration center, wherein the high-capacity micro storage center has the characteristics of small volume and large data storage capacity and is used for storing unmanned aerial vehicle monitoring data; the image stitching and inverting center is used for stitching and inverting the images shot by the unmanned aerial vehicle and storing the processed images into the high-capacity miniature storage center; the data extraction and integration center is used for extracting and integrating the unmanned aerial vehicle monitoring data, for example, the element extraction is carried out on the images processed by the image splicing and inversion center, the images are stored in the high-capacity micro storage center, and the images are transmitted to the intelligent equipment terminal in real time through the unmanned aerial vehicle wireless transmission module, so that the next analysis is facilitated. The unmanned aerial vehicle data acquisition module 4 is fixed at the bottom of the unmanned aerial vehicle body 1, the unmanned aerial vehicle data acquisition module 4 comprises a camera module and a sensor module, the camera module is one or more of a multispectral camera and a high-definition camera, and the sensor module is one or more of a CH 4 sensor, a CO 2 sensor, a N 2 O sensor, a NO sensor and a NH 3 sensor and is used for monitoring air flux on the ground surface; the intelligent equipment terminal is a mobile phone or a computer.
The ground monitoring system comprises a ground monitoring system box 11, a ground monitoring system power module 12, a ground monitoring system control module 13, a ground monitoring system data acquisition module, a ground monitoring system data storage module 16, a ground monitoring system wireless transmission module 15 and a ground monitoring system AI deep learning module 14, wherein the ground monitoring system power module 12 is electrically connected with the ground monitoring system control module 13, the ground monitoring system control module 13 is electrically connected with the ground monitoring system data acquisition module, the ground monitoring system data acquisition module is electrically connected with the ground monitoring system data storage module 16, the ground monitoring system data storage module 16 is electrically connected with the ground monitoring system wireless transmission module 15, and the ground monitoring system wireless transmission module 15 is in wireless communication connection with the unmanned aerial vehicle wireless transmission module 2 and is simultaneously in wireless communication connection with an intelligent equipment terminal.
The ground monitoring system data acquisition module comprises a plurality of measuring sensors, the measuring sensors comprise a ground monitoring system weather monitoring sensor and a ground monitoring system gas flux monitoring sensor 17, the ground monitoring system weather monitoring sensor and the ground monitoring system gas flux monitoring sensor 17 are electrically connected with the ground monitoring system data acquisition module, the ground monitoring system weather monitoring sensor comprises one or more of a temperature sensor 19, a sunlight intensity sensor 21 and a tipping bucket type rainfall sensor 20, and the ground monitoring system weather monitoring sensor is fixed on the outer side wall of the ground monitoring system box 11; the ground monitoring system gas flux monitoring sensor 17 is one or more of a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor and an NH 3 sensor, a sensor annular sliding rod 18 is arranged on the peripheral side of the ground monitoring system box 11, the ground monitoring system gas flux monitoring sensor 17 is electrically connected with the ground monitoring system control module 13 and is arranged on the lower end face of the sensor annular sliding rod 18, and the ground monitoring system gas flux monitoring sensor 17 can move along the sensor annular sliding rod 18 so as to conveniently select a proper position for gas flux monitoring; the ground monitoring system AI deep learning module 14 is contained in the ground monitoring system control module 13 and is used for optimizing and upgrading the whole operation of the system in real time; a ground monitoring system supporting base 22 capable of automatically adjusting the height is arranged at the bottom of the ground monitoring system box 11; the ground monitoring system power module 12 comprises a power generation device and a battery module, the power generation device is electrically connected with the battery module, and the battery module is electrically connected with the ground monitoring system control module 13.
The unmanned aerial vehicle and the ground monitoring system are connected with the ground monitoring system wireless transmission module 15 through the unmanned aerial vehicle wireless transmission module 2 in a wireless communication mode, so that the unmanned aerial vehicle can conveniently start the ground monitoring system in a remote control mode, and meanwhile whether the ground monitoring system operates normally or not is monitored.
In the comprehensive monitoring of the soil gas discharge flux of the paddy field and the air flux on the ground, a proper paddy field monitoring research area is selected first, and a certain number of representative monitoring points are arranged in the paddy field.
(1) Rice field gas emission flux monitoring based on ground monitoring system
A surface monitoring system is installed at each monitoring point. In the ground monitoring system, a ground monitoring system supporting base 22 capable of automatically adjusting the height is arranged at the bottom of the ground monitoring system box 11; the ground monitoring system power module 12 comprises a power generation device and a battery module; the ground monitoring system data acquisition module comprises a plurality of measuring sensors, the measuring sensors are divided into a ground monitoring system weather monitoring sensor and a ground monitoring system gas flux monitoring sensor 17, and the ground monitoring system weather monitoring sensor comprises a temperature sensor 19, a sunlight intensity sensor 21, a tipping bucket type rainfall sensor 20 and the like. The surface monitoring system gas flux monitoring sensor 17 includes a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, a NO sensor, an NH 3 sensor, and the like. In the ground monitoring system with the ecological geographic element monitoring function, power generation and power supply are mainly performed through a high-efficiency solar panel, and on the other hand, power supply can be accessed through a cable to serve as a power source in continuous overcast and rainy weather; monitoring paddy field weather conditions by a ground monitoring system weather monitoring sensor (comprising a temperature sensor 19, a sunlight intensity sensor 21, a tipping bucket rainfall sensor 20 and the like); monitoring paddy field gas discharge flux by a ground monitoring system gas flux monitoring sensor 17 (including CH 4 sensor, CO 2 sensor, N 2 O sensor, NO sensor, NH 3 sensor, etc.); the optimization and upgrading of the overall operation of the system can be realized through the ground monitoring system AI deep learning module 14 in the ground monitoring system control module 13; the gas flux monitoring sensor 17 of the ground monitoring system can move along the annular sliding rod 18 of the sensor, so that a proper position is conveniently selected for gas flux monitoring. The ground monitoring system can realize long-term continuous monitoring and acquire high-precision gas concentration and flux data.
(2) Based on unmanned aerial vehicle medium-low altitude aerial photography, monitoring of air flux on paddy field, paddy field agricultural activities and the like
The unmanned aerial vehicle with the ecological geographic element monitoring function is provided with an unmanned aerial vehicle data acquisition module 4, wherein a camera module in the unmanned aerial vehicle data acquisition module 4 comprises a multispectral camera and a high-definition camera, and a sensor module comprises a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor and an NH 3 sensor which are positioned at the bottom of the unmanned aerial vehicle; the unmanned aerial vehicle extreme weather early warning and identifying module 7 comprises a temperature sensor, a wind speed sensor and a strong precipitation sensor, and is positioned at the top and the side face of the unmanned aerial vehicle; the unmanned aerial vehicle data storage and preprocessing module 5 comprises a high-capacity micro storage center, an image splicing and inversion center and a data extraction and integration center, and a processor and a flash memory in the unmanned aerial vehicle data storage and preprocessing module 5 are highly mixed to form a module unit; the unmanned aerial vehicle navigation module 3 comprises a GPS module and a Beidou module. In the unmanned aerial vehicle with the ecological geographic element monitoring function, a camera module (including a multispectral camera, a thermal infrared camera, a common high-definition camera and the like) in an unmanned aerial vehicle data acquisition module 4, an unmanned aerial vehicle data preprocessing module 5 and an unmanned aerial vehicle AI deep learning module 6 are used for monitoring and inverting the NDVI, LAI, the organic carbon content of soil and the like of a paddy field research area, and realizing the real-time dynamic perception of the agricultural activities (fertilization frequency, irrigation mode and frequency, straw returning, agricultural mechanization degree, cultivation mode and the like) of the paddy field research area, the seasonal change of rice and the like; monitoring the discharge flux of gas such as CH 4 and the like above the paddy field through a sensor module (including a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor, an NH 3 sensor and the like) in the unmanned aerial vehicle data acquisition module 4; in the flight monitoring process, the extreme weather (including a temperature sensor, a wind speed sensor, a strong rainfall sensor) and obstacles (such as tall trees, wires and telegraph poles, flying birds, buildings and the like) are identified and sensed for multiple times through a plurality of measuring sensors (including a temperature sensor, a wind speed sensor and a strong rainfall sensor) and camera modules in the early stage, an unmanned aerial vehicle extreme weather early warning identification module 7 and an unmanned aerial vehicle obstacle pre-judging prevention and control module 8 are trained, an unmanned aerial vehicle extreme weather early warning identification module 7 algorithm and an unmanned aerial vehicle obstacle pre-judging prevention and control module 8 algorithm are automatically optimized through an unmanned aerial vehicle AI deep learning module 6, so that the extreme weather and obstacles are more accurately identified and pre-judged, the calculation force of an unmanned aerial vehicle data preprocessing module 5 is improved, the flight route is optimized, and the like, when the extreme weather such as the storm is met, the unmanned aerial vehicle can automatically sense and fly into an unmanned aerial vehicle parking cabin near a research area through a self flight control system, and the extreme weather is sensed, and the unmanned aerial vehicle is continuously monitored in the research area; the unmanned aerial vehicle data preprocessing module 5 can be used for performing rapid image splicing and inversion, data extraction and integration and the like, and then the primarily processed images, data and the like are transmitted to the intelligent equipment terminal in real time through the unmanned aerial vehicle wireless transmission module 2; the unmanned aerial vehicle can continuously fly for a long time as far as possible through the unmanned aerial vehicle high-efficiency solar power generation device 10 and the high-density high-capacity battery; the unmanned aerial vehicle falls the self-protection module 9 (can but not limited to can pop out the gasbag) and carries out self-protection to suddenly falling, reduces unmanned aerial vehicle damage probability as far as possible. The unmanned aerial vehicle has the flexibility and high-altitude flight capacity, and can cover a large-scale paddy field area to acquire data of multiple periods.
(3) Data fusion and analysis
And the ground monitoring system and the unmanned aerial vehicle are fused to obtain the comprehensive monitoring result of the rice field soil gas emission flux and the ground air body flux. By analyzing and comparing the data, the dynamic process and the spatial distribution characteristics of the gas exchange in the paddy field ecological system can be known.
And finally, visually displaying the monitoring result, and explaining and analyzing the monitoring result. According to the monitoring result, the gas emission and flux change in the paddy field ecological system can be deeply researched, and scientific basis is provided for farmland management and ecological environment protection.
The unmanned aerial vehicle and the ground monitoring system are comprehensively used, the problem that the traditional monitoring method is limited can be solved, the omnibearing high-resolution monitoring of the rice field soil gas emission flux and the ground air flux is realized, and important data support is provided for research and management of a rice field ecological system. The comprehensive monitoring method has the advantages that multi-scale and multi-angle data can be obtained, and more comprehensive paddy field ecosystem information is provided. Through unmanned aerial vehicle's taking photo by plane, can realize the quick remote sensing monitoring to whole paddy field region, help know the overall state of paddy field. The ground monitoring system can provide finer gas flux and concentration data, and helps to reveal the relation between the soil gas emission and the large gas flux, so that the carbon and nitrogen circulation and other processes of the paddy field ecological system are studied deeply.
The foregoing is merely illustrative and explanatory of the structures of this patent, and various modifications and additions may be made to the particular embodiments described, by those skilled in the art, without departing from the structures of the patent or exceeding the scope of the claims, as defined by the claims.
Claims (8)
1. The intelligent air and ground monitoring composite simulation system for the ecological geographic elements is characterized by comprising an unmanned aerial vehicle with an ecological geographic element monitoring function and a ground monitoring system, wherein the unmanned aerial vehicle comprises an unmanned aerial vehicle body, an unmanned aerial vehicle wireless transmission module, an unmanned aerial vehicle data acquisition module and an unmanned aerial vehicle data storage and preprocessing module, the unmanned aerial vehicle data acquisition module is electrically connected with the unmanned aerial vehicle data storage and preprocessing module, the unmanned aerial vehicle data storage and preprocessing module is electrically connected with the unmanned aerial vehicle wireless transmission module, and the unmanned aerial vehicle wireless transmission module is in wireless communication connection with an intelligent equipment terminal; the ground monitoring system comprises a ground monitoring system box body, a ground monitoring system power module, a ground monitoring system control module, a ground monitoring system data acquisition module, a ground monitoring system data storage module and a ground monitoring system wireless transmission module, wherein the ground monitoring system power module is electrically connected with the ground monitoring system control module, the ground monitoring system control module is electrically connected with the ground monitoring system data acquisition module, the ground monitoring system data acquisition module is electrically connected with the ground monitoring system data storage module, the ground monitoring system data storage module is electrically connected with the ground monitoring system wireless transmission module, and the ground monitoring system wireless transmission module is in wireless communication connection with the unmanned aerial vehicle wireless transmission module and is simultaneously in wireless communication connection with the intelligent equipment terminal.
2. The intelligent air and ground monitoring composite simulation system of an ecological geographic element according to claim 1, wherein the unmanned aerial vehicle further comprises an unmanned aerial vehicle AI deep learning module and a plurality of unmanned aerial vehicle extreme weather early warning and identifying modules electrically connected with the unmanned aerial vehicle AI deep learning module, the unmanned aerial vehicle extreme weather early warning and identifying modules are fixed at the top and two side surfaces of the unmanned aerial vehicle, the unmanned aerial vehicle extreme weather early warning and identifying modules comprise a plurality of micro measuring sensors and response components, and the micro measuring sensors are one or more of a temperature sensor, a wind speed sensor and a strong precipitation sensor.
3. The intelligent air and ground monitoring composite simulation system of an ecological geographic element according to claim 2, wherein the unmanned aerial vehicle further comprises a plurality of unmanned aerial vehicle obstacle pre-judging and controlling modules electrically connected with the unmanned aerial vehicle AI deep learning module, the plurality of unmanned aerial vehicle obstacle pre-judging and controlling modules are fixed at two side surfaces of the unmanned aerial vehicle, and the unmanned aerial vehicle obstacle pre-judging and controlling modules comprise miniature cameras and a processing center.
4. The intelligent monitoring composite simulation system for the air and ground of the ecological geographic elements according to claim 2, wherein the unmanned aerial vehicle further comprises an unmanned aerial vehicle falling self-protection module electrically connected with the unmanned aerial vehicle AI deep learning module, the unmanned aerial vehicle falling self-protection module comprises a plurality of landing gears and a plurality of air bags, the landing gears are symmetrically arranged on the lower end face of the unmanned aerial vehicle body, each air bag is fixed at the bottom end of one landing gear, and the top end of the landing gear is fixedly connected with the lower end face of the unmanned aerial vehicle body.
5. The intelligent air and ground monitoring composite simulation system of the ecological geographic elements according to claim 2, wherein the unmanned aerial vehicle further comprises an unmanned aerial vehicle navigation module, the unmanned aerial vehicle navigation module is one or two of a GPS module and a Beidou module, and the unmanned aerial vehicle navigation module is electrically connected with the unmanned aerial vehicle wireless transmission module and the unmanned aerial vehicle AI deep learning module respectively.
6. The intelligent air and ground monitoring composite simulation system for the ecological geographic elements according to claim 1, wherein the unmanned aerial vehicle data acquisition module is fixed at the bottom of the unmanned aerial vehicle body and comprises a camera module and a sensor module, wherein the camera module is one or more of a multispectral camera and a high-definition camera, and the sensor module is one or more of a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor and an NH 3 sensor and is used for monitoring the air flux on the ground surface; the intelligent equipment terminal is a mobile phone or a computer; be equipped with unmanned aerial vehicle high-efficient solar power system on the unmanned aerial vehicle body.
7. The intelligent air and ground monitoring composite simulation system for the ecological geographic elements according to claim 1, wherein the ground monitoring system data acquisition module comprises a plurality of measuring sensors, the plurality of measuring sensors comprise a ground monitoring system weather monitoring sensor and a ground monitoring system gas flux monitoring sensor, the ground monitoring system weather monitoring sensor and the ground monitoring system gas flux monitoring sensor are electrically connected with the ground monitoring system data acquisition module, the ground monitoring system weather monitoring sensor comprises one or more of a temperature sensor, a sunlight intensity sensor and a tipping bucket type rainfall sensor, and the ground monitoring system weather monitoring sensor is fixed on the outer side wall of a ground monitoring system box body; the ground monitoring system gas flux monitoring sensor is one or more of a CH 4 sensor, a CO 2 sensor, an N 2 O sensor, an NO sensor and an NH 3 sensor, a sensor annular sliding rod is arranged on the outer periphery side of the ground monitoring system box body, the sensor annular sliding rod is electrically connected with the ground monitoring system control module, the ground monitoring system gas flux monitoring sensor is arranged on the lower end face of the sensor annular sliding rod and is in sliding connection with the sensor annular sliding rod, and a proper position is conveniently selected for gas flux monitoring.
8. The intelligent monitoring composite simulation system for the space and the ground of the ecological geographic elements according to claim 1, wherein a ground monitoring system supporting base capable of automatically adjusting the height is arranged at the bottom of a box body of the ground monitoring system; the ground monitoring system power module comprises a power generation device and a battery module, the power generation device is electrically connected with the battery module, and the battery module is electrically connected with the ground monitoring system control module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202322621554.0U CN220829241U (en) | 2023-09-26 | 2023-09-26 | Intelligent monitoring composite simulation system for space and ground of ecological geographic elements |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202322621554.0U CN220829241U (en) | 2023-09-26 | 2023-09-26 | Intelligent monitoring composite simulation system for space and ground of ecological geographic elements |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN220829241U true CN220829241U (en) | 2024-04-23 |
Family
ID=90725802
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202322621554.0U Active CN220829241U (en) | 2023-09-26 | 2023-09-26 | Intelligent monitoring composite simulation system for space and ground of ecological geographic elements |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN220829241U (en) |
-
2023
- 2023-09-26 CN CN202322621554.0U patent/CN220829241U/en active Active
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112422783B (en) | Unmanned aerial vehicle intelligent patrol system based on parking apron cluster | |
| KR102300799B1 (en) | Crop state judgment apparatus and crop cultivation information service platform including the same | |
| CN112990262B (en) | Integrated solution system for monitoring and intelligent decision of grassland ecological data | |
| CN202434011U (en) | Automatic monitoring and alarming system for forest fire | |
| CN106875636A (en) | Blue algae monitoring method for early warning and system based on unmanned plane | |
| WO2016123201A1 (en) | Systems, devices, and methods for robotic remote sensing for precision agriculture | |
| CN106197380A (en) | Aquatic vegetation monitoring method based on unmanned plane and system | |
| CN112702565A (en) | System and method for acquiring field plant phenotype information | |
| CN110647170A (en) | Navigation mark inspection device and method based on unmanned aerial vehicle | |
| CN211477203U (en) | Refined monitoring equipment system based on high-resolution remote sensing image | |
| CN111504380A (en) | Geographic environment information monitoring system based on wind-solar hybrid power generation | |
| CN210835732U (en) | Beacon inspection device based on unmanned aerial vehicle | |
| CN206113932U (en) | Aquatic vegetation monitoring system based on unmanned aerial vehicle | |
| CN109631860A (en) | Reservoir house refuse monitoring method and system based on unmanned plane | |
| CN113064221B (en) | Unmanned aerial vehicle meteorological observation system | |
| CN113093309B (en) | Unmanned aerial vehicle and observation system for weather | |
| CN212861863U (en) | Plant community statistics monitoring system based on unmanned aerial vehicle | |
| CN206975496U (en) | A kind of agricultural greenhouse long-distance management system | |
| CN113340275A (en) | Method for positioning remote sensing mapping image of natural resource | |
| CN220829241U (en) | Intelligent monitoring composite simulation system for space and ground of ecological geographic elements | |
| Velasquez et al. | Implementation of a low cost aerial vehicle for crop analysis in emerging countries | |
| CN115797807A (en) | Ocean garbage monitoring method, system and medium based on data of unmanned aerial vehicle-mounted spectrometer | |
| Lopatin et al. | Assessing and mapping energy biomass distribution using a UAV in Finland | |
| CN214374338U (en) | Crop growth monitoring system based on unmanned aerial vehicle remote sensing | |
| Stefanakis et al. | Creation of a remote sensing unmanned aerial system (UAS) for precision agriculture and related mapping applications |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| GR01 | Patent grant | ||
| GR01 | Patent grant |