WO2020258007A1 - Atmospheric pollutant unmanned aerial vehicle tracing system and method based on big data technology - Google Patents

Abstract

The present invention relates to the technical field of big data. Disclosed are an atmospheric pollutant unmanned aerial vehicle tracing system and method based on big data technology. The method comprises: an unmanned aerial vehicle carrying a single-chip microcomputer, and driving a sensor on the single-chip microcomputer to measure basic atmospheric data information; in addition, using the single-chip microcomputer carried on the unmanned aerial vehicle to drive a camera to photograph and collect evidence, and the single-chip microcomputer analyzing picture information in real time, and storing abnormal pictures obtained through corresponding analysis in an in-built FLASH of the single-chip microcomputer; moreover, using an ATK-ESP8266 serial port to realize communication between an atmospheric environment detection platform of the unmanned aerial vehicle and a ground terminal; and uploading data files to a big data HDFS, and using a big data analysis framework MapReduce for analysis to obtain a pollution condition summary of various pollutant indexes of measured regions, and writing an analysis result into the HDFS, so that environmental protection law enforcement officers can conveniently check and use the pollution condition summary, remediation is focused on severely polluted regions according to the actual conditions, helping to improve the atmospheric environment.

Description

Air pollutant UAV traceability system and method based on big data technology Technical field

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.

Background technique

In the real environment, due to the limitations of the stage of economic development, unreasonable economic development methods are often accompanied by damage to the environment. The deteriorating air pollution situation has led to many bad consequences. For example, it harms human health, causes certain damage to the growth of animals and plants, and is harmful to long-term economic development. With the progress of the times, the state attaches great importance to environmental governance, changes in economic development methods, and the advocacy of green economy. At present, the importance of atmospheric environment has risen to the national level. In the process of advancing industrialization and urbanization, more and more attention has been paid to the governance of the atmospheric environment. How to quickly and accurately find the source of pollutants for centralized treatment is particularly important. In real life, 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. In addition, in real life, there are some 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.

Summary of the invention

In order to solve the above problems, 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.

The present invention provides the following technical solutions: 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.

Furthermore, the drone is a quadcopter drone.

Further, the camera is an OV2640 camera.

Further, the single-chip microcomputer is an STM32F407 single-chip microcomputer.

Further, the wireless module is an ATK-ESP8266 serial port.

Further, the temperature and humidity sensor is a DHT11 temperature and humidity sensor.

Further, the hydrogen sulfide sensor is an MQ136 hydrogen sulfide sensor.

Furthermore, a method for air pollutant UAV traceability based on big data technology also includes the following specific steps:

S1. Utilizing the maneuverability of the quadcopter drone, atmospheric environment inspectors can choose to manually remotely control the drone to fly, control the drone's flight trajectory, and go to the source of atmospheric pollutant emissions to be tested for on-site inspection;

S2. 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;

S3. 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;

S4. 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;

S5. 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;

S6. Configure the ATK-ESP8266 serial wireless module on the single chip microcomputer. 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;

S7, through the wireless module, after the PC computer terminal and the ATK-ESP8266 serial wireless module are connected, two-way data communication can be carried out;

S8. 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;

S9. 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;

S10. Upload the structured data file to the big data storage framework HDFS, and use the big data analysis framework MapReduce to perform data analysis to obtain a summary of the air pollutants in each area to be detected, and write the analysis results to the big data storage In the framework of HDFS, it is convenient for environmental protection law enforcement personnel to view, and based on the analysis results, it is effective for key rectification of high pollution areas.

Compared with the prior art, 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. At the same time, the use of the big data storage analysis framework can efficiently analyze air pollutants and trace the source pollutants.

High maneuverability: 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 . Taking advantage of the good maneuverability of multi-rotor drones, the source of air pollutant emissions is often not suitable for human detection due to factors such as geographical environment. Moreover, 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.

High real-time performance: In the real environment, many of the sources of air pollutants are private chemical companies. Under the vigorous governance of the party and the state, companies have actively cooperated with the control of air pollution sources, but there are many black-hearted companies that are interested in Under the influence of China, air pollutants continue to be emitted. If manual testing is used, it is difficult to completely avoid the fact that individual companies that emit air pollution sources resort to fraud and shut down the emission of pollution sources in advance, which has caused great obstacles to the treatment of air pollution sources. 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.

Description of the drawings

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.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

Moreover, the technical solutions between the various embodiments of the present invention can be combined with each other, but they must be based on what can be achieved by a person of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions It does not exist and does not fall within the scope of protection required by the present invention.

Please refer to Figures 1-3. 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. At the same time, 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. Using the STM32G high-performance processor chip, 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. At the same time, 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. 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. At the same time, in units of days, 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. When enough data text files are collected and stored, use the big data analysis framework MapReduce to analyze and analyze the specific detected area 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:

1. Atmospheric basic information collection and detection

The air pollutant sensor acquisition module is driven by the drone equipped with STM32F407 single-chip microcomputer to collect basic air pollutant environmental data.

(1) Collect temperature and humidity

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. In the STM32 microcontroller, 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.

(2) Hydrogen sulfide collection module

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. In a normal environment where there is no toxic hydrogen sulfide gas, use the ADC function of the STM32 microcontroller to measure a voltage value as the basic voltage value. When in a hydrogen sulfide environment, 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. Also in the STM32 single-chip microcomputer, 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.

(3) Camera acquisition module

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. Moreover, the image sensor module has low operating voltage and small size, which is very suitable for application in the development of embedded systems. In the system scheme of this design, 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. If UXGA timing and RGB565 format are used, then the color of a pixel can be composed of two bytes, so that each line has 3200 pixel clocks and 3200 bytes output. Figure 3 shows the frame timing in UXGA mode, clearly showing the way the camera OV2640 data output. Using the frame timing shown in Figure 3 to read the camera data, the image information collected by the camera can be obtained.

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. At the same time, in order to facilitate the later data processing in the JPEG mode, it is necessary to open the frame interrupt and write the DCMI interrupt service function. At the same time, the normal mode of direct acquisition is adopted for the JPEG data output by the camera. Then output the information collected by the camera to the memory through channel 1 of DMA2 data stream 1, and complete the DCMI DMA transmission in this way. Finally, set the image size format collected by the camera, use JPEG mode, turn on the DCMI capture function, and drive the OV2640 camera for image capture.

2. Camera image processing part

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. In fact, 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. Then 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.

3. Big data storage analysis part

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. When 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. When connected, the PC and ATK-ESP8266 communicate with each other to send data. According to the program programmed on the STM32F407 microcontroller, 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. At the same time, 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.

In addition to displaying the received basic atmospheric data on the host computer, 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. Then upload the disk file to the big data storage framework HDFS for storage. 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.

The big data storage analysis process is shown in Figure 2. When the file data stored in HDFS reaches a certain amount of data, the big data analysis framework MapReduce is used for analysis. 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. In the Map Task phase, the Map() function processes the main business logic, and in the Reduce phase, the Reduce() phase processes the main business logic, as shown in Figure 3 below. According to the MapReduce programming specification, it is divided into three stages: Mapper, Reducer, and Driver. In the 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. 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. If the air pollutant exceeds the standard in the corresponding period, the value value is 1, otherwise it is 0. When the data processing of the map() function is completed, OutputCollector.collect() will be called to output the result. Inside this function, it will partition the generated key/value (call Partitioner) and write it into a ring memory buffer. When the ring buffer is full, MapReduce will write the data to the local disk and generate a temporary file. Then enter the Reduce Task stage.

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.

The above are only preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. However, all equivalent modifications or changes made by those of ordinary skill in the art based on the disclosure of the present invention should be included Within the scope of protection described in the claims.

Claims (8)

1. An air pollutant UAV traceability system based on big data technology is characterized by: UAV, single chip microcomputer, atmospheric pollutant detection sensor, atmospheric environment detection platform, temperature and humidity sensor, hydrogen sulfide sensor, 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 computer is equipped with atmospheric pollutant detection sensors, temperature and humidity sensors, hydrogen sulfide sensors, wireless Module; the single-chip computer includes a CPU, DMA, and FLASH storage unit; the single-chip computer is connected to the PC computer terminal through a wireless module, and the PC computer terminal is connected to the big data HDFS.
2. The air pollutant UAV traceability system based on big data technology according to claim 1, wherein the UAV is a quadcopter UAV.
3. The air pollutant drone traceability system based on big data technology according to claim 1, characterized in that: the camera is an OV2640 camera.
4. The air pollutant UAV traceability system based on big data technology according to claim 1, characterized in that: the single-chip microcomputer is an STM32F407 single-chip microcomputer.
5. The air pollutant UAV traceability system based on big data technology according to claim 1, characterized in that: the wireless module is an ATK-ESP8266 serial port.
6. The air pollutant UAV traceability system based on big data technology according to claim 1, wherein the temperature and humidity sensor is a DHT11 temperature and humidity sensor.
7. The air pollutant UAV traceability system based on big data technology according to claim 1, wherein the hydrogen sulfide sensor is an MQ136 hydrogen sulfide sensor.
8. A UAV traceability method for air pollutants based on big data technology is characterized by including the following steps:

   S1. Utilizing the maneuverability of the quadcopter drone, atmospheric environment inspectors can choose to manually remotely control the drone to fly, control the drone's flight trajectory, and go to the source of atmospheric pollutant emissions to be tested for on-site inspection;

   S2. 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;

   S3. 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;

   S4. 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;

   S5. 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;

   S6. Configure the ATK-ESP8266 serial wireless module on the single chip microcomputer. 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;

   S7, through the wireless module, after the PC computer terminal and the ATK-ESP8266 serial wireless module are connected, two-way data communication can be carried out;

   S8. 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;

   S9. 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;

   S10. Upload the structured data file to the big data storage framework HDFS, and use the big data analysis framework MapReduce to perform data analysis to obtain a summary of the air pollutants in each area to be detected, and write the analysis results into the big data HDFS It is convenient for environmental protection law enforcement personnel to check, and based on the analysis results, effectively focus on remediation of high-polluting areas.

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