CN111024695A - All-in-one AI intelligent water environment-friendly real-time monitoring system - Google Patents
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Abstract
The invention discloses a world-integrated AI intelligent water environment-friendly real-time monitoring system, which comprises an unmanned aerial vehicle body, a terminal computing platform and water environment detection equipment; the unmanned aerial vehicle body is used for data acquisition and data transmission and comprises a positioning navigation module, a flight control module, an image acquisition module and a wireless communication module; the image acquisition module, the flight control module and the positioning navigation module are in communication connection with the terminal computing platform through the wireless communication module; the water environment detection equipment is used for acquiring data information of the water environment; data acquired by the water environment detection equipment are transmitted to a terminal computing platform in a wireless communication mode; the terminal computing platform is used for acquiring and processing data and comprises a data processing module and a database engine module; the system expands the monitoring range of water area monitoring and realizes real-time and automation of monitoring.
Description
Technical Field
The invention relates to the technical field of water environment pollution monitoring, in particular to a heaven-earth integrated AI intelligent water environment-friendly real-time monitoring system.
Background
Aiming at an application system in the water environmental protection monitoring industry, the prior art mainly has two modes: provided is a system for manual monitoring and water environment quality on-line monitoring.
Manual monitoring: the method mainly comprises the steps that a patrolman uses a smart phone or other mobile terminals to manually patrol the water area, and reports found problems through a carried terminal device after the problems are found, the method uses the traditional terminal-server informatization technology, the manual patrol is seriously depended on, the patrolling speed is low, the cost is high, the water area coverage is limited by manpower, and the coverage rate is only 5%.
Online monitoring: the method has the advantages that the installed fixed monitoring points are used as water environment monitoring terminals, real-time online data communication of the monitoring points is achieved through the network technology, real-time water environment water quality data can be achieved, however, the types of the point detection equipment are multiple, some fixed station rooms need to be monitored, the construction cost of a single monitoring point is high, the equipment cannot move, the water environment condition of the fixed point can only be monitored, the universe water environment cannot be effectively monitored (the coverage rate is extremely low, monitoring equipment is only arranged at a specific point), and the method is not suitable for large-scale water environment monitoring.
Furthermore, in the prior art, some water quality detection devices utilize the unmanned aerial vehicle to sample the water area to be monitored, then the taken-back sample needs to be tested manually, and the pollution condition of the water environment in the water area is judged and monitored.
Therefore, in order to expand the monitoring range of water area monitoring and realize real-time monitoring and automation of monitoring, a technical problem to be solved by those skilled in the art is urgently needed.
Disclosure of Invention
In order to solve the problems that the monitoring range is small, real-time monitoring cannot be achieved, and the monitoring process cannot be automated in water environment pollution monitoring in the prior art, the invention provides a world-wide integrated AI intelligent water environment-friendly real-time monitoring system.
The embodiment of the invention provides a world-integrated AI intelligent water environment-friendly real-time monitoring system, which comprises: the unmanned aerial vehicle comprises an unmanned aerial vehicle body, a terminal computing platform and water environment detection equipment;
the unmanned aerial vehicle body is used for data acquisition and data transmission and comprises a positioning navigation module, a flight control module, an image acquisition module and a wireless communication module;
the image acquisition module, the flight control module and the positioning navigation module are in communication connection with the terminal computing platform through a wireless communication module;
the water environment detection equipment is used for acquiring data information of the water environment and is positioned in a monitored water area;
the data acquired by the water environment detection equipment is transmitted to the terminal computing platform in a wireless communication mode;
the terminal computing platform is used for acquiring and processing data and comprises a data processing module and a data engine module.
According to the invention, the unmanned aerial vehicle is used for collecting images, so that the monitoring equipment can move, the range of a monitored water area is enlarged, the pollution condition of the water environment is predicted by combining the data collected by the water environment detection equipment, and the accuracy of judging the water environment pollution condition is ensured.
In one embodiment, the aquatic environment detecting apparatus includes: water level gauge, rain gauge, flow detection equipment, water quality detection equipment, soil moisture content check out test set.
The water quality detection equipment comprises various detectors for detecting multiple water quality indexes such as water temperature, pH, conductivity, COD (Chemical Oxygen Demand), TOC (Total Organic Carbon), ammonia nitrogen, nitrate nitrogen, phosphate, chlorophyll and the like;
it should be pointed out that, in order to meet the requirements of different water qualities, all or part of the water quality indexes can be selected in a targeted manner for monitoring, and other water quality online detection instruments can also be added on the basis;
the water environment detection equipment can be used for comprehensively monitoring the water environment, so that the accuracy of a monitoring result of the water environment is ensured.
In one embodiment, the positioning navigation module is a combined navigation system composed of a GPS navigation system and an inertial navigation system; the GPS navigation system is used for acquiring the geographic coordinate information of the unmanned aerial vehicle, and the inertial navigation system is used for acquiring the attitude data, the acceleration data and the like of the unmanned aerial vehicle.
In one embodiment, the image acquisition module is used for acquiring image information of the water environment, and the acquisition device is any one of a high-resolution CCD camera, a light optical camera, an infrared scanner and a laser scanner.
In one embodiment, the wireless communication module is a LORA wireless communication module, and data transmission between the unmanned aerial vehicle and the terminal computing platform is achieved through the wireless communication module.
In one embodiment, the flight control module controls the flight of the unmanned aerial vehicle according to the command sent by the terminal computing platform, issues a flight command related to the unmanned aerial vehicle through the terminal computing platform, and the flight controller receives the command to control the flight of the unmanned aerial vehicle, wherein the flight command includes: the flight speed, flight place, flight attitude, etc. of the unmanned aerial vehicle.
In one embodiment, the database engine module is used for storing and calling the water environment data information acquired by the water environment detection equipment and the image data acquired by the image acquisition module.
In one embodiment, the data processing module comprises: an image detection unit and a pollution diffusion prediction unit;
the image detection unit includes: training a neural network by adopting a large number of water environment related pictures through the combination of Adam optimization algorithm and super convergence to obtain an image detection model based on the extraction and fusion of an HIS color model and LBP texture features; the image detection model is used for detecting whether the water environment is polluted.
Adam is a first-order optimization algorithm capable of replacing a traditional random gradient descent process, updating neural network weights based on training data iteration, and the Adam algorithm is different from the traditional random gradient descent. The stochastic gradient descent keeps a single learning rate (i.e., alpha) updating all weights, and the learning rate does not change during the training process. Adam designs independent adaptive learning rates for different parameters through first moment estimation and second moment estimation of random gradients, and the network training speed is improved by 3.5 times by combining Adam optimization algorithm and super convergence.
The pollution diffusion prediction unit; establishing a four-dimensional water pollution diffusion prediction model by using the data acquired by the water environment detection equipment and combining the image detection result of the image detection model and the 3D space data; when the image detection model outputs the polluted information of the monitored area, the four-dimensional water pollution diffusion prediction model establishes the diffusion process of the simulated pollutants according to the polluted information of the monitored area and the data acquired by the water environment detection equipment, and determines the pollution source and the pollution range.
The pollution condition of the water environment is judged through the image detection model, the pollution condition is combined with data acquired by water environment detection equipment, the diffusion process of pollutants can be rapidly simulated within 10 seconds by utilizing the water pollution diffusion prediction model, the pollution source and the pollution range are determined, and the prediction speed is high.
In one embodiment, the terminal computing platform further comprises an automatic alarm module; the automatic alarm module receives the processing result of the data processing module and alarms according to the result of the data processing module; the alarm mode is one or more of sound, light or vibration alarm.
The invention provides a world-wide integrated AI intelligent water environment-friendly real-time monitoring system, which utilizes an artificial intelligence technology to process image data acquired by an unmanned aerial vehicle in real time and combine with water environment monitoring equipment data to judge the water environment pollution condition in real time and submit the judgment result data to an automatic alarm module for pollution early warning treatment.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of a space-ground integrated AI intelligent water environment-friendly real-time monitoring system provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, the invention provides a world-wide integrated AI intelligent water environment-friendly real-time monitoring system, which comprises an unmanned aerial vehicle body, a terminal computing platform and water environment detection equipment; the unmanned aerial vehicle body is used for data acquisition and data transmission and comprises a positioning navigation module, a flight control module, an image acquisition module and a wireless communication module; the image acquisition module, the flight control module and the positioning navigation module are in communication connection with the terminal computing platform through the wireless communication module; the water environment detection equipment is used for acquiring data information of the water environment and is positioned in a monitoring water area; data acquired by the water environment detection equipment are transmitted to a terminal computing platform in a wireless communication mode; the terminal computing platform is used for acquiring and processing data and comprises a data processing module and a data engine module.
In this embodiment, through using unmanned aerial vehicle to gather the image, realized monitoring devices's removal, enlarged the scope in monitoring waters to combine the data that water environment check out test set gathered to predict the pollution situation of water environment, guaranteed the accuracy that water environment pollution situation judged.
In one embodiment, the above-mentioned water environment detecting apparatus includes: the device comprises a water level meter, a rain gauge, flow detection equipment, water quality detection equipment and soil moisture content detection equipment; the water quality detection equipment comprises various detectors for detecting multiple water quality indexes such as water temperature, pH, conductivity, COD (Chemical Oxygen Demand), TOC (Total Organic Carbon), ammonia nitrogen, nitrate nitrogen, phosphate, chlorophyll and the like; it should be pointed out that, in order to meet the requirements of different water qualities, all or part of the water quality indexes can be selected in a targeted manner for monitoring, and other water quality online detection instruments can also be added on the basis;
in the embodiment, the water environment detection equipment can be used for comprehensively monitoring the water environment, so that the accuracy of the monitoring result of the water environment is ensured.
In one embodiment, the positioning navigation module is a combined navigation system composed of a GPS navigation system and an inertial navigation system; coordinate information of the unmanned aerial vehicle can be obtained through the integrated navigation system; the GPS navigation system in the navigation system is used for collecting geographic coordinate information of the unmanned aerial vehicle, the inertial navigation system is used for collecting attitude data, acceleration data and the like of the unmanned aerial vehicle, and the positioning navigation module transmits the coordinate information, the attitude data and the acceleration data of the unmanned aerial vehicle to the terminal computing platform through the wireless communication module.
In one embodiment, the image acquisition module is used for acquiring image information of the water environment, and the acquisition device is any one of a high-resolution CCD camera, a light optical camera, an infrared scanner and a laser scanner.
Among them, a high-resolution CCD digital camera is taken as an example; a CCD camera may also be called a CCD image sensor, and a CCD is a semiconductor device that can convert an optical image into a digital signal. The tiny photosensitive substances implanted on the CCD are called pixels (pixels); the larger the number of pixels contained in a CCD, the higher the resolution of the picture it provides. The CCD acts like a film, but it converts the image pixels into digital signals. The CCD has many capacitors arranged in order to sense light and convert the image into digital signal. Each small capacitor can transfer the charged charges to the adjacent capacitor under the control of an external circuit; furthermore, the camera of the CCD camera is a rotating shaft type camera.
In one embodiment, the wireless communication module is a LORA wireless communication module, wherein LORA is an ultra-long-range wireless transmission scheme based on spread spectrum technology. One great advantage of LORA is: only two AA batteries are required to connect the devices. If used only for transmitting and receiving data, can be used continuously for 10 years. LORA is not only low power but also medium and long distance transmission, up to 20 km in outdoor viewing distance.
In one embodiment, the flight control module controls the unmanned aerial vehicle to fly according to an instruction sent by the terminal computing platform; issue relevant unmanned aerial vehicle flight instruction through terminal computing platform, flight controller receives the flight of instruction control unmanned aerial vehicle, and wherein the flight instruction includes: the flight speed, flight place, flight attitude, etc. of the unmanned aerial vehicle.
In one embodiment, the data engine module is used for storing and calling the water environment data information acquired by the water environment detection equipment and the image data acquired by the image acquisition module.
As shown in fig. 1, in one embodiment, the data processing module includes: an image detection unit and a pollution diffusion prediction unit; the image detection unit comprises a neural network training unit, a color model feature extraction unit and a LBP texture feature extraction unit, wherein the neural network is trained by adopting a large number of water environment related pictures through the combination of an Adam optimization algorithm and super convergence, and an image detection model based on the extraction and fusion of an HIS color model and LBP texture features is obtained; whether the water environment is polluted or not can be detected through the image detection model.
Adam is a first-order optimization algorithm capable of replacing a traditional random gradient descent process, updating neural network weights based on training data iteration, and the Adam algorithm is different from the traditional random gradient descent. The stochastic gradient descent keeps a single learning rate (i.e., alpha) updating all weights, and the learning rate does not change during the training process. Adam designs independent adaptive learning rates for different parameters through first moment estimation and second moment estimation of random gradients, and the network training speed is improved by 3.5 times by combining Adam optimization algorithm and super convergence.
The pollution diffusion prediction unit establishes a four-dimensional water pollution diffusion prediction model by using data acquired by water environment detection equipment and combining an image detection result of an image detection model and 3D space data; when the image detection model outputs the polluted information of the monitoring area, the four-dimensional water pollution diffusion prediction model establishes the diffusion process of the simulated pollutants according to the polluted information of the monitoring area and the data acquired by the water environment detection equipment, and determines the pollution source and the pollution range.
In the embodiment, the pollution condition of the water environment is judged through the image detection model, the pollution condition is combined with data acquired by water environment detection equipment, the diffusion process of pollutants can be rapidly simulated within 10 seconds by utilizing the water pollution diffusion prediction model, the pollution source and the pollution range are determined, and the prediction speed is high.
As shown in fig. 1, the terminal computing platform further comprises an automatic alarm module; the automatic alarm module receives the processing result of the data processing module and gives an alarm according to the result of the data processing module; the alarm mode is one or more of sound, light or vibration alarm; for example, when the data processing result is light pollution, the sound alarm is activated, when the data processing result is moderate pollution, the light and sound modes are jointly activated, and when the data processing result is severe pollution, the 3 alarm modes are jointly activated.
The invention provides a world-wide integrated AI intelligent water environment-friendly real-time monitoring system, which utilizes an artificial intelligence technology to process image data acquired by an unmanned aerial vehicle in real time and combine with water environment monitoring equipment data to judge the water environment pollution condition in real time and submit the judgment result data to an automatic alarm module for pollution early warning treatment.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. The utility model provides an integrative AI intelligence water environmental protection real-time monitoring system in world which characterized in that includes: the unmanned aerial vehicle comprises an unmanned aerial vehicle body, a terminal computing platform and water environment detection equipment;
the unmanned aerial vehicle body is used for data acquisition and data transmission and comprises a positioning navigation module, a flight control module, an image acquisition module and a wireless communication module;
the image acquisition module, the flight control module and the positioning navigation module are in communication connection with the terminal computing platform through a wireless communication module;
the water environment detection equipment is used for acquiring data information of the water environment and is positioned in a monitored water area;
the data acquired by the water environment detection equipment is transmitted to the terminal computing platform in a wireless communication mode;
the terminal computing platform is used for acquiring and processing data and comprises a data processing module and a database engine module.
2. The all-in-one AI intelligent water environment protection real-time monitoring system of claim 1, wherein said water environment detection equipment comprises: water level gauge, rain gauge, flow detection equipment, water quality detection equipment, soil moisture content check out test set.
3. The system of claim 1, wherein the positioning navigation module is a combined navigation system consisting of a GPS navigation system and an inertial navigation system.
4. The system as claimed in claim 1, wherein the image acquisition module is used for acquiring image information of water environment, and the acquisition device is any one of high resolution CCD camera, light optical camera, infrared scanner and laser scanner.
5. The system of claim 1, wherein the wireless communication module is a LORA wireless communication module.
6. The system of claim 1, wherein the flight control module controls the unmanned aerial vehicle to fly according to instructions sent by the terminal computing platform.
7. The integrated AI intelligent water environment protection real-time monitoring system of claim 1 wherein said database engine module is adapted to store and recall water environment data information obtained by said water environment detection device and image data obtained by said image acquisition module.
8. The integrated AI intelligent water environmental protection real-time monitoring system of claim 1 wherein said data processing module comprises: an image detection unit and a pollution diffusion prediction unit;
the image detection unit includes: training a neural network by adopting a large number of water environment related pictures through the combination of Adam optimization algorithm and super convergence to obtain an image detection model based on the extraction and fusion of an HIS color model and LBP texture features; the image detection model is used for detecting whether the water environment is polluted;
the pollution diffusion prediction unit; establishing a four-dimensional water pollution diffusion prediction model by using the data acquired by the water environment detection equipment and combining the image detection result of the image detection model and the 3D space data; when the image detection model outputs the polluted information of the monitored area, the four-dimensional water pollution diffusion prediction model establishes the diffusion process of the simulated pollutants according to the polluted information of the monitored area and the data acquired by the water environment detection equipment, and determines the pollution source and the pollution range.
9. The integrated AI intelligent water environmental protection real-time monitoring system of claim 1 wherein said terminal computing platform further comprises an automatic alarm module; the automatic alarm module receives the processing result of the data processing module and alarms according to the result of the data processing module; the alarm mode is one or more of sound, light or vibration alarm.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111426810A (en) * | 2020-05-11 | 2020-07-17 | 河海大学 | Air-space-ground-integration-oriented water environment monitoring system deployment method |
| CN111967357A (en) * | 2020-08-05 | 2020-11-20 | 茅台学院 | Intelligent sorghum disease identification system and identification method based on machine vision |
| CN112946216A (en) * | 2021-01-29 | 2021-06-11 | 海西中科生态环境监测有限公司 | Intelligent water quality monitoring system |
| CN113109344A (en) * | 2021-05-07 | 2021-07-13 | 南京邮电大学 | Novel real-time efficient water quality monitoring system based on internet of things |
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