WO2020258007A1 - Système et procédé de traçage par un véhicule aérien sans pilote de polluant atmosphérique sur la base d'une technologie de mégadonnées - Google Patents

Système et procédé de traçage par un véhicule aérien sans pilote de polluant atmosphérique sur la base d'une technologie de mégadonnées Download PDF

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WO2020258007A1
WO2020258007A1 PCT/CN2019/092685 CN2019092685W WO2020258007A1 WO 2020258007 A1 WO2020258007 A1 WO 2020258007A1 CN 2019092685 W CN2019092685 W CN 2019092685W WO 2020258007 A1 WO2020258007 A1 WO 2020258007A1
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data
big data
chip microcomputer
uav
atmospheric
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PCT/CN2019/092685
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English (en)
Chinese (zh)
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徐鹤
裴苏玉
王汝传
李鹏
朱枫
沙超
徐哲
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南京邮电大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/18—File system types
    • G06F16/182—Distributed file systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/51—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/76—Television signal recording
    • H04N5/765—Interface circuits between an apparatus for recording and another apparatus
    • H04N5/77—Interface circuits between an apparatus for recording and another apparatus between a recording apparatus and a television camera
    • H04N5/772—Interface circuits between an apparatus for recording and another apparatus between a recording apparatus and a television camera the recording apparatus and the television camera being placed in the same enclosure

Definitions

  • the invention belongs to the field of big data technology, and in particular relates to an air pollutant UAV traceability system and method based on big data technology.
  • air pollution emission sources are often not suitable for manual detection due to environmental factors such as geography, and solutions that deploy sensors at the source of the pollution sources often have the disadvantages of high cost, poor flexibility, and limited deployment control range, and cannot be dealt with in time. Unexpected situation.
  • black-hearted companies If manual pollutant detection is adopted, these black-hearted companies will often falsify during manual monitoring, stop the discharge of pollution sources, and have an adverse impact on the government's environmental governance. Utilizing the air pollutant UAV traceability system can effectively reduce labor costs, improve work efficiency, and contribute to atmospheric environmental governance.
  • the present invention provides a system and method capable of real-time, maneuvering, monitoring, analysis, and tracking of air pollution sources, using the maneuverability of drones to mount sensors to collect basic data of air pollutants, and using big data to store and analyze Frame analysis to trace the source of air pollution.
  • an air pollutant drone traceability system based on big data technology, including drones, single-chip computers, air pollutant detection sensors, atmospheric environment detection platforms, temperature and humidity sensors, hydrogen sulfide sensors, Camera, wireless module, PC computer terminal, big data storage framework HDFS, big data analysis framework MapReduce, the drone is equipped with a camera and a single-chip computer, and the single-chip is equipped with an atmospheric pollutant detection sensor, a temperature and humidity sensor, and hydrogen sulfide Sensors, wireless modules; the single-chip microcomputer includes CPU, DMA, and FLASH storage units; the single-chip microcomputer is connected to the PC computer terminal through the wireless module, and the PC computer terminal is connected to the big data HDFS.
  • the drone is a quadcopter drone.
  • the camera is an OV2640 camera.
  • the single-chip microcomputer is an STM32F407 single-chip microcomputer.
  • the wireless module is an ATK-ESP8266 serial port.
  • the temperature and humidity sensor is a DHT11 temperature and humidity sensor.
  • the hydrogen sulfide sensor is an MQ136 hydrogen sulfide sensor.
  • a method for air pollutant UAV traceability based on big data technology also includes the following specific steps:
  • the UAV is equipped with a single-chip microcomputer, which is equipped with atmospheric basic data detection sensors, and the single-chip microcomputer initializes the sensor configuration;
  • the single-chip microcomputer is equipped with a temperature and humidity sensor to measure the temperature and humidity data near the source of the pollutants;
  • the hydrogen sulfide sensor mounted on the single-chip microcomputer measures the concentration of hydrogen sulfide toxic and harmful gas near the source of the pollutant
  • the single-chip microcomputer drives the camera through the DCMI interface, the camera starts image collection, the data collected by the camera is transferred to the memory of the single-chip microcomputer through DMA, and the CPU uses the image processing program to save the abnormal image information if it detects abnormal image information Stored in the FLASH storage unit built in the microcontroller for subsequent processing;
  • This module is a high-performance wireless transmission module with a built-in TCP/IP protocol stack. If the required serial port configuration has been performed, the module can transmit from the serial port The data is converted into WIFI data, and then real-time information data transmission is carried out with the terminal, while ensuring the stability of the transmission;
  • the PC computer After the PC computer receives the data transmitted from the drone atmospheric environment detection platform, it will display the data on the host computer.
  • the PC computer and the ATK-ESP8266 serial wireless module adopt the customer service server mode, using TCP/IP Protocol, stable data transmission;
  • the data received from the PC is not only displayed on the host computer on the terminal, but also structured and written into a text file;
  • the beneficial effects of the present invention are: the design of the present invention is novel, and the feasibility is high.
  • the present invention proposes an air pollutant drone traceability system based on big data technology, which utilizes the mobile drone
  • the performance and the high-performance single-chip microprocessor onboard can effectively complete the collection of atmospheric pollutants, reduce labor costs and improve work efficiency.
  • the use of the big data storage analysis framework can efficiently analyze air pollutants and trace the source pollutants.
  • the unmanned aerial vehicle used in the present invention is a quadrotor, which has simple mechanics and good flight stability. The way it balances its own weight is to use aerodynamics, which can fly autonomously and fly remotely under human control .
  • the source of air pollutant emissions is often not suitable for human detection due to factors such as geographical environment.
  • some atmospheric pollutants such as hydrogen sulfide gas are toxic and harmful gases. If manual detection is used, it will cause certain harm to the health of the inspectors. Utilizing the vertical take-off and landing and free hovering functions of the four-axis UAV can break through the restrictions of the geographical environment and detect various sources of pollutants, improving efficiency, ensuring the safety of environmental monitoring personnel, and reducing safety risks.
  • the invention uses a four-axis unmanned aerial vehicle to suddenly detect the air pollutant emissions of the enterprise at any time without notifying the enterprise, and record it in time as evidence, and the detection cost is low.
  • High reliability Based on the massive data analysis of big data, it can carry out accurate statistical analysis on the data files of long-term inspection records, and make detailed statistics on the pollution situation of each area tested, and the reliability of the analysis results is high.
  • Figure 1 is a schematic diagram of the structure of the atmospheric environment monitoring platform in the present invention.
  • Figure 2 is a schematic diagram of the big data storage analysis process in the present invention.
  • Fig. 3 is a schematic diagram of the main logic processing flow of the Map() and Reduce() functions of the big data storage analysis part in the embodiment of the present invention.
  • the air pollutant traceability system for drones based on big data technology proposed in the embodiment of the present invention can be divided into two parts, including the design of the drone atmospheric environment detection platform and the large Data storage and analysis part.
  • the design part of the UAV atmospheric environment detection platform is shown in Figure 1.
  • the UAV is equipped with STM32F407 MCU, and uses STM32F407 MCU to drive basic atmospheric detection and acquisition sensor modules, including DHT11 temperature and humidity sensor module, MQ136 hydrogen sulfide toxic gas acquisition module and other air Contaminant sensor module.
  • STM32F407 single-chip microcomputer drives OV2640 image sensor.
  • STM32F407 uses the Cortex-M4 processing core introduced by ARM. Its main frequency can reach 168MHz and has a very high processing speed.
  • the image information collected by the OV2640 sensor can be processed and analyzed in real time.
  • the microprocessor chip has abundant peripheral resources, including 19KB SRAM, two 32-bit timers, and three 12-bit ADC peripherals.
  • the microprocessor chip has a built-in 1024KB FLASH, which has a large enough storage space. It is precisely because of STM32's abundant peripheral resources and sufficient storage space that when the single-chip microcomputer detects abnormal atmospheric environmental pollutants, it can be written into the built-in FLASH of the STM32F407 single-chip microcomputer in time, and the abnormal image information after image processing is stored in the single-chip microcomputer.
  • the built-in FLASH is precisely because of STM32's abundant peripheral resources and sufficient storage space that when the single-chip microcomputer detects abnormal atmospheric environmental pollutants, it can be written into the built-in FLASH of the STM32F407 single-chip microcomputer in time, and the abnormal image information after image processing is stored in the single-chip microcomputer.
  • the built-in FLASH is precisely because of STM32's abundant peripheral resources and sufficient storage space that when the single-chip microcomputer
  • the UAV atmospheric environment detection platform uses the ATK-ESP8266 high-performance serial-wireless module, and the onboard ai-thinker's ESP8266 module communicates with the STM32F407 through the serial port 3. After the UAV atmospheric detection platform has detected the atmospheric pollutant data, it communicates with the PC upper computer terminal through the ESP8266 serial wireless module.
  • the terminal receives and displays the upper computer written by itself to receive the data transmitted by the drone through the ESP8266 serial port-wireless module, and displays the actual data measured by the drone's atmospheric environment detection platform in the upper computer in real time.
  • the capture screen of the OV2640 camera is displayed on the machine.
  • the basic atmospheric data collected by the drone is written into a text file.
  • the data file adopts a structured storage method to specifically record the specific values of various atmospheric indicators in the air pollutant collection area of the drone, and Store the structured data text files on the disk and upload them to the big data storage framework HDFS.
  • Each air pollutant index has the phenomenon of exceeding the standard and the severity of exceeding the standard. Take the analysis of the amount of data collected within a certain period of time as an example, specifically analyze how many days a certain air pollutant indicator in the area has exceeded the standard during the detection period In order to judge the severity of exceeding the standard, the pollution situation of each detected area is finally determined, the source of pollution is traced, and environmental management personnel are notified to focus on rectification.
  • a specific operation method of air pollutant UAV traceability system based on big data technology includes the following parts:
  • the air pollutant sensor acquisition module is driven by the drone equipped with STM32F407 single-chip microcomputer to collect basic air pollutant environmental data.
  • STM32F407 microcontroller drives DHT11 sensor module, which is a digital sensor integrating temperature and humidity.
  • the microcontroller uses a single bus to communicate with the DHT11 module. After the DHT11 module is driven by a single bus, the DHT11 will transmit the temperature and humidity data collected internally to the microcontroller at a time. The microcontroller only needs to transmit the DHT11 through the single bus. Data analysis can get temperature and humidity data.
  • the timer interrupt is turned on to collect the temperature and humidity module, which ensures that the temperature and humidity data can be collected in real time.
  • the MQ136 module is used to collect hydrogen sulfide toxic gas, and the module is powered by 5V voltage. After the sensor module is powered and warmed up, its AOUT pin will automatically output the analog signal, and the ADC peripheral function of the STM32 microcontroller is used to read the AOUT pin The output analog signal quantity is converted into a digital signal quantity.
  • the actual hydrogen sulfide gas is measured for every 0.1V increase in the voltage. Increase the concentration of 200ppm to measure the concentration of hydrogen sulfide gas in the measured environment.
  • the timer is turned on to collect the hydrogen sulfide gas, and the timer interrupt time is 200m, which ensures that the hydrogen sulfide gas can be collected in real time.
  • the camera adopts the OV2640 image sensor produced by OV Company.
  • the OV2640 image sensor has a wealth of functional modules, including analog amplification, analog signal processing for gain control, 10-bit A/D conversion, full-column photosensitive function, and a powerful digital signal processor, including lens compensation and color space conversion , Black and white point compensation, etc.
  • the image sensor module has low operating voltage and small size, which is very suitable for application in the development of embedded systems.
  • the SCCB timing sequence is adopted to drive the 0V2640 camera sensor to realize the initialization of the module and other tasks. Set the transmission rate of the camera sensor, image format and other parameters through SCCB timing.
  • the camera module also comes with an 8-bit microprocessor, which comes with 512 bytes of SRAM and 4KB of ROM, which can fine-tune the image quality.
  • the image data output of the camera OV2640 is under the common control of pixel clock (PLCK), frame synchronization signal (VSYNC) and line synchronization signal (HREF/HSYNC). Shown in Figure 2 is the line output timing of the camera OV2640, when the line synchronization signal HREF is high, output image data. After HREF goes high, one PCLK clock cycle will complete an 8/10-bit data output. In the present invention, an 8-bit interface is used, so a PCLK clock will output one byte. If RGB/YUV is used In the output format, one tp is equal to two Tpclk. If the Raw format is used, one tp is equal to one Tpclk.
  • the steps for STM32 microcontroller to drive OV2640 through DCMI interface are as follows: First, configure the control pins of the camera OV2640, configure the camera's built-in registers, and configure the camera's working mode. After configuring the OV2640, configure the IO port related to the OV2640 module in the DCMI interface on the microcontroller, and enable the related clock, set the related IO mode to the multiplexing function, and select the DCMI multiplexing. Then carry on the relevant configuration of the DCMI interface on the STM32, where the VSPOL and data width parameters are all set through the DCMI_CR register.
  • the image processing part is to process one frame of data collected by the camera.
  • the size of one frame of image data transmitted by DMA is 4800 bytes.
  • the image processing is to process the bytes output by the camera in real time. Save these 4800 (60*80) bytes in a two-dimensional array, which is equivalent to dividing this frame of image into 60 rows, each with 80 bytes. In this way, the processing of one frame of image can be converted into processing the 60 lines of data separately.
  • set a counter to count the number of specified pixels in the frame of image information. When the number of specified pixels reaches a certain threshold, the frame of image data is regarded as abnormal image data, and the frame of image data is saved in The built-in FLASH of the STM32 microcontroller is convenient for later viewing.
  • the PC terminal is connected to the ESP8266 high-performance serial port-wireless module mounted on the drone via WIFI, and the received image data and atmospheric basic data are displayed on the upper computer in real time, and the upper computer is written by QT.
  • the computer is connected to the ATK-ESP8266 module, set the end computer protocol type to TCP Client, and set the corresponding address and port number of the server.
  • the PC and ATK-ESP8266 communicate with each other to send data.
  • the KEY0 button on the microcontroller development board controls the transmission of data from the ATKESP8266 module to the PC, and it can be displayed in real time on the host computer.
  • the upper computer written in QT also has the function of sending data to the ATK-ESP8266 module, and controls the opening and closing of individual functions of the STM32 microcontroller.
  • the basic atmospheric information collected by the drone is written into a file in a structured storage method.
  • Each row of data files records the data collected by the drone during the period.
  • the specific values of temperature, humidity, hydrogen sulfide and other detected gases in the area are written into the disk file.
  • the HDFS storage framework is highly fault-tolerant and can automatically save multiple copies. When a copy is lost, it can be automatically restored to process large amounts of data and ensure data uniqueness.
  • MapReduce is a programming framework for distributed computing programs. MapReduce is easy to program and has good scalability. MapReduce is also highly fault-tolerant. For example, if one of the machines goes down, the tasks on it can be transferred to another node.
  • the MapReduce phase can be divided into Map Task phase and Reduce Task phase.
  • Map() function processes the main business logic
  • Reduce() phase processes the main business logic, as shown in Figure 3 below.
  • MapReduce programming specification it is divided into three stages: Mapper, Reducer, and Driver.
  • Mapper programming stage first convert the text content of the structured stored data file into String, because each line of the data file is the value of various basic atmospheric data such as data collection and recording time, temperature and humidity, hydrogen sulfide, and the data file Separate the data with spaces.
  • map() In the map() function, divide a line of content into words according to the spaces, and save the basic atmospheric data in the String array fields[N], fields[0] are the values of the temperature and humidity indicators , Fields[1] is the value of hydrogen sulfide gas, that is, fields[N] is the value of the N-1 item of atmospheric pollutant data, and then the value of fields[n] is compared with the standard value of the atmospheric index, if If the value exceeds the mark, it will be recorded as exceeding, and the indicator will be output as ⁇ air pollutant, 1>, otherwise the indicator will be output as ⁇ air pollutant, 0>, and the data file will be formed after the map() function is processed A series of new key/value values, that is, a certain air pollutant is the key.
  • Reduce Task phase In the Reduce Task phase, first remotely copy the data generated in the Map Task phase from each Map Task, and for a piece of data, if its size exceeds a certain threshold, it will be written to the disk, otherwise it will be directly placed in the memory, Reduce Task The files in the memory and disk will be merged while copying data remotely. Then the reduce() function written by myself processes the key/value data from Map Task.
  • the data input to the reduce() function is a set of numbers aggregated by key. As long as the value of the same atmospheric data indicator is entered, In the reduce() function, add all the value values in the reduce() function to get the time that the air pollutant exceeds the standard within a certain period of time. Finally, the reduce() function will write the obtained results of various air pollutants exceeding the limit in a certain period of time into the big data storage framework HDFS, and store the analysis results for easy recording and viewing.

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Abstract

La présente invention se rapporte au domaine technique des mégadonnées. L'invention concerne un système et un procédé de traçage par un véhicule aérien sans pilote de polluant atmosphérique basés sur une technologie de mégadonnées. Le procédé comprend les étapes suivantes : un véhicule aérien sans pilote porte un microordinateur monopuce, et commande un capteur sur le micro-ordinateur monopuce pour mesurer des informations de données atmosphériques de base; en outre, il utilise microordinateur monopuce porté sur le véhicule aérien sans pilote pour commander une caméra pour photographier et collecter des preuves, et le microordinateur monopuce analyse des informations d'image en temps réel, et stocke des images anormales obtenues par une analyse correspondante dans une mémoire FLASH intégrée du microordinateur monopuce; de plus, il utilise un port série ATK-ESP8266 pour réaliser une communication entre une plateforme de détection d'environnement atmosphérique du véhicule aérien sans pilote et un terminal au sol; et télécharge des fichiers de données sur un HDFS de mégadonnées, et utilise un cadre d'analyse de mégadonnées MapReduce pour l'analyse afin d'obtenir un résumé de conditions de pollution de divers indices de polluants de régions mesurées, et écrire un résultat d'analyse dans le HDFS, de telle sorte que les agents d'application des lois de protection environnementale peuvent vérifier et utiliser de façon pratique le résumé des conditions de pollution, que la réhabilitation est concentrée sur des régions fortement polluées selon les conditions réelles, ce qui aide à améliorer l'environnement atmosphérique.
PCT/CN2019/092685 2019-06-24 2019-06-25 Système et procédé de traçage par un véhicule aérien sans pilote de polluant atmosphérique sur la base d'une technologie de mégadonnées WO2020258007A1 (fr)

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CN113777223A (zh) * 2021-08-12 2021-12-10 北京金水永利科技有限公司 一种大气污染物溯源方法及系统
CN113777223B (zh) * 2021-08-12 2024-04-30 北京金水永利科技有限公司 一种大气污染物溯源方法及系统
CN114822711A (zh) * 2022-04-19 2022-07-29 南开大学 一种基于观测数据的多种大气污染物-温室气体协同溯源方法
CN114878750A (zh) * 2022-05-13 2022-08-09 苏州清泉环保科技有限公司 一种集大气污染监测及溯源为一体的智能化控制系统和方法
CN115016542A (zh) * 2022-07-28 2022-09-06 重庆理工大学 基于高速总线架构的无人机控制系统
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CN116359218A (zh) * 2023-06-02 2023-06-30 北京建工环境修复股份有限公司 一种工业聚集区大气污染移动监测系统
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CN117405563B (zh) * 2023-12-14 2024-03-19 河北师范大学 一种燃料燃烧温室效应污染物监测方法及装置
CN117876185A (zh) * 2024-01-11 2024-04-12 生态环境部土壤与农业农村生态环境监管技术中心 涉水企业特征污染物排放的溯源核算方法和装置

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