# Multipoint Ecological Environment Monitoring System with Internet of Things

Authors: Chaoying Chen
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Science and technology is a double-edged sword; while it brings convenience to people, it also threatens the environment of human. The pollution of the ecological environment has reached a point that cannot be ignored. Traditional methods and measures to protect or monitor the environment can no longer meet the increasing environmental pollution. Therefore, we should use the latest scientific and technological achievements to improve our environment. Through high-tech means to monitor the changes in the ecological environment pollution, we always remind people to protect environment. With continuous innovation of sensing, communication, and embedded technology, the monitoring system also needs to be updated accordingly, which has great research significance. With Internet of Things technology, this work has researched and designed a multipoint ecological environment monitoring system. It can realize intelligent monitoring for ecological environment, achieving the goal of integrating the measurement parameters of the ecological environment monitoring, the system modularization, the real-time data transmission, and the integration of monitoring services.


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## Body

## 1. Introduction

With the rapid development of high technology and the continuous progress of society, people are also facing increasingly severe environmental threats while enjoying the convenience brought by new technologies. Air pollution, water pollution, noise pollution, and so on are always endangering people’s health, especially the emergence of haze weather in recent years, which has brought threats to people’s health and caused inconvenience to the normal order of life. At the same time, people’s awareness of environmental protection is gradually improving, and the requirements for home environment and travel environment are also getting higher and higher. With the implementation of the new national air quality inspection PM2.5 standard, it has become the habit of most people to obtain the air pollution index in a timely, accurate, and effective manner and to arrange their travel plans reasonably [1–5].With the rapid development of electronic information technology and semiconductor industry, people are no longer satisfied with the cross-regional, time and space communication between people. It gradually extends to the cross-regional, time and space communication between people and actual objects and objects and objects. As an extension of the Internet and the founder of a new generation of technological revolution, the development of the Internet of Things has gradually penetrated into all walks of life. On the basis of fully integrating information technology, various required sensor nodes are connected in a reasonable way so as to truly realize the interconnection of everything. By changing the traditional control and connection methods, the development direction of the entire industry and even the entire society will be changed. Through the intelligent and networked connection and management of the Internet of Things, better development and progress of human society can be achieved [6–10].The Internet of Things is a product of the rapid development of the Internet, and the core concept of the Internet of Things has received widespread attention from all walks of life as soon as it was put forward. Sensing, communication, and computer technology are the three core technologies that have supported the development of the Internet of Things. Just like this, the Internet of Things exhibits the characteristics of intelligent perception, secure connection, and massive analysis. With the rapid development and application of the Internet of Things technology, a large amount of data and information are created in the application. Corresponding to the traditional computing service method, it has become increasingly unable to meet the requirements of big data. The emergence of multiple cloud services and cloud platforms provides new solutions to this problem. We can choose the right platform to achieve more convenient and efficient management according to our needs. Eco-environmental Internet of Things refers to a collective term that combines the various tasks of ecological environment monitoring, governance, and protection with Internet of Things technology. Eco-environmental Internet of Things is supported by three technologies, sensing, communication, and computer, and realizes the purpose of improving the ecological environment, scientifically managing the environment, reducing environmental protection costs, and enhancing environmental protection capabilities. Environmental protection and the Internet of Things technology are isomorphic, and both involve a large number of things-to-things connection and communication between things and people. Its research content is extensive, involving many aspects, such as the perception of environmental information, the transmission of environmental data, and the intelligent processing and decision-making of environmental big data. The core of the Ecological Environment Internet of Things is to use Internet of Things technology to improve the degree of intelligence in environmental protection, and its essential purpose is to improve the ecological environment [11–15]. 1D-CNN is not more effective as 1D-BN-CNN is. Both are one dimensional, but 1D-BN-CNN have some more features that makes it superior to 1D-CNN. 1D-BN-CNN works more efficiently and give accurate results with lower chance of error.Based on the above development background, this article proposes to use the Internet of Things platform and communication technology to achieve the purpose of real-time monitoring of the regional environment through the connection of various sensor nodes. The Internet of Things technology is applied to the problem of multipoint ecological environment protection, and the convenience and efficiency of the new technology is used to realize real-time monitoring of the multipoint ecological environment. Applying the integration technology of the Internet of Things and cloud platforms, building a comprehensive environmental monitoring framework, and strengthening the digital management of environmental information will bring about a major change in the management model of environmental protection. This is of great significance to the improvement of environmental protection awareness and energy saving and emission reduction.The contributions of this work are as follows: (1) Build a multipoint ecological environment monitoring system with Yeelink IoT platform. Use the main control board and various sensor modules to build a hardware system and write corresponding software programs to collect various ecological environment parameters. In this way, a suitable sensor network is constructed to ensure that various ecological environment parameters can be monitored accurately and timely. (2) Construct a BN-based one-dimensional convolutional neural network (1D-BN-CNN) to evaluate the quality of the ecological environment. It evaluates current ecological environment quality through the real-time environmental data collected by the monitoring site and displays it to users through the Internet of Things platform so that users can effectively evaluate the current ecological environment quality.The sequential framework of paper is as follows:Section1 introduces the paper and explain the proposed work of paper clearly. Section 2 consists of related work of paper. Section 3 explains the system architecture design with different layers and modules. Section 4 explains the ecological environment quality evaluation with CNN. Section 5 elaborates the experiments and discussed the results of experiments completely. Section 6 concludes the paper comprehensively.


## 2. Related Work

The Internet of Things [16] aims at the interconnection of all things, through the use of sensors, positioning systems, radio frequency modules, and other information perception equipment, in accordance with the standard agreement, to unify the physical process and the digital process and realize the intelligent perception, identification, and control of the physical process. The concept of the Internet of Things first appeared in 1998. The Massachusetts Institute of Technology proposed the EPC system based on RFID technology. This system is the prototype of the Internet of Things. It was not until 2005 that literature [17] defined a standardized and unified Internet of Things. It expounds in detail the basic characteristics of the Internet of Things, related technologies, social benefits, and challenges. The research on the Internet of Things has been formalized so far, and many regions at home and abroad have carried out in-depth research on the Internet of Things and regarded the Internet of Things technology as an important indicator in the country’s core scientific and technological fields. Literature [18] officially listed the Internet of Things technology as one of the technologies that have a significant impact on national interests. Literature [19] officially released the Internet of Things Action Plan and proposed a number of basic Internet of Things technologies including privacy protection and chip technology. Literature [20] proposes that the social goal should be to build the world’s most advanced Internet of Things infrastructure.Environmental Internet of Things [21] is the deep integration of Internet of Things technology and environmental protection industry. The environmental Internet of Things has a wide range of applications in environmental monitoring systems, including pollution source location, vehicle emission monitoring, and environmental quality assessment. The development of the Internet of Things has profound significance and impact on environmental protection. China’s environmental monitoring work started in 1975. Although it started a little later, thanks to research on the Internet of Things, China’s environmental monitoring system has taken shape and has become the research focus of many domestic universities [22, 23]. The Green Orbs system, which was jointly participated by Hong Kong University of Science and Technology, Zhejiang Agriculture and Forestry University, Tsinghua University, American IIT, and Tongji University, came into being. The system is used to monitor the forest ecological environment and provide data support for biological research and fire warning. Ocean University of China and Hong Kong University of Science and Technology have jointly developed the OceanSense marine ecological monitoring system, which achieves full coverage of the sea area through multiple nodes, and uses communication modules to send data to the monitoring center. In November 2007, the system was officially launched and has been working continuously for up to 18 months. Its monitoring data has been open-source for use by eco-environmental personnel [24]. Chinese people are increasingly aware of the severity of environmental pollution, and environmental protection work has been put on the development agenda. Over the years, China has also made gratifying progress. According to statistics from relevant departments, as of 2018, my country has 6,729 environmental monitoring stations, 5,645 environmental monitoring institutions, and more than 700,000 environmental monitoring technicians. More than 70 cities across the country are equipped with automatic air monitoring systems and have environmental monitoring satellite communication systems, and an environmental monitoring system has been initially established. The Fifth Plenary Session of the 18th Central Committee further implemented environmental monitoring and management measures. Environmental monitoring will usher in major changes and a new situation. The domestic mainstream environmental monitoring system [25] still faces the following main challenges: (1) The existing monitoring technology cannot meet the increasing diversified requirements of environmental monitoring. (2) A unified standard has not yet been formed in each monitoring system, with large regional differences and poor data versatility. (3) The processing of environmental data by the monitoring system is still in its preliminary stage and lacks an effective processing model.The developed countries in Europe and the United States have studied environmental monitoring systems earlier. They were the first to get rid of the limitation of manual monitoring, and the general environmental parameter standards were formulated, and environmental monitoring was carried out by the environmental monitoring system. With the standardization and unification of the agreement, the environmental monitoring system has become more diversified and standardized [26]. At the beginning of the 20th century, the rapid development of the Internet of Things technology enabled some key problems in the environmental monitoring system to be solved. In 2003, when the European Union, the United States, and a series of countries and regions formulated relevant standards for the Internet of Things, they put forward the concept of the Environmental Internet of Things and carried out in-depth research. In 2002, the University of California monitored the ecological environment parameters on Daya Island through a wireless sensor network, and connected hundreds of sensor nodes through self-organizing multihop routing. The data is forwarded by the base station and then transmitted by satellite communication to the monitoring platform in California, providing environmental researchers with valuable ecological data. In 2004, Japan also successively applied intelligent environmental monitoring platforms and possessed the ability of preliminary data analysis [27]. By this period, environmental monitoring has basically entered the automatic monitoring stage with embedded technology, communication technology, and computing technology as the core.


## 3. System Architecture Design

The Internet of Things refers to the connection of ubiquitous collection nodes and related communication modules in a specific way. By using communication technologies such as local networks or the Internet, sensors, controllers, machines, people, and things are connected in new ways. In order to form a network that connects people and things and things and things and realizes informatization, remote management and control, and intelligence, it is a deep extension of basic communication networks and the Internet and new applications. Use sensing technology and smart devices to collect physical data and convert this physical data into electrical signals. And transmit these collected data through the corresponding communication method, and finally carry out a series of application processing, information analysis and in-depth mining through the terminal equipment so as to truly realize the mutual transmission of data among all things and the sharing of information between each other. In general, it can be summarized as follows: perception access layer, network transmission layer, and practical application layer.


### 3.1. Architecture of IoT

The Internet of Things is based on the integration of existing communication networks, the Internet, and some specific professional network segments and meets the requirements of communication by adding some new functional applications. The Internet of Things should have the following characteristics. Scalability requires that the communication performance of the entire system is not affected by changes in the network communication scale. Transparency means that specific applications are not only dependent on specific connections of the underlying physical characteristics of the network. Consistency refers to the communication methods and characteristics that can span different networks to achieve interoperability and interoperability. Scalability means that the actual application performance will not decrease due to the failure of the entities and devices corresponding to the Internet of Things function. The Internet of Things is gradually developed and improved on the basis of sensor application technology, wireless sensor network technology, database operation technology, and the fast-developing Internet. It is mainly responsible for the comprehensive perception of relevant physical information, reliable data transmission, and comprehensive analysis and application processing.The Internet of Things is composed of three parts: the sensory access part, which includes the use of bar codes or other codes, short-range wireless communication, specific sensor measurement, and other equipment to realize the measurement and identification of physical quantities. The network transmission part is to realize the transmission of data and information through the existing Internet and basic communication networks. The actual application part is to adopt the newly emerged cloud computing platform and big data analysis technology to realize the automatic control and intelligent management of specific information and equipment. Up to now, the entire industry has recognized the following three levels of IoT system architecture. The bottom layer is the perception access layer used to perceive data and information, the second layer is the network communication layer for data information transmission, and the top layer is the actual application layer of related content, as shown in Figure1.Figure 1
IoT system architecture.In this IoT architecture, the interrelationship between the above three layers can be understood in the following way. The perception access layer is equivalent to the perception of external information organs on the structure of our human body. The network transmission layer is equivalent to the neural network center and brain in the human body structure. The actual application layer is equivalent to different positions of people in the entire society.Perception Layer. It recognizes objects and data and collects related information. The perceptual access layer includes sensor tags and corresponding data collectors and specific communication equipment of wireless sensor networks. It is mainly responsible for the collection and processing of information and data and the identification of related objects. By sensing the basic information and data of objects in the access layer, it can enter the transmission range of the Internet of Things.Network Layer. It is mainly responsible for the transmission of related information and the processing of specific data communication methods. The network transmission layer mainly includes the integration of the basic communication network and the Internet, network setting management, and information processing. The perception access layer sends the object’s data to the network layer, and the network layer is responsible for efficiently and reliably transporting it to remote users, extending the scope of the perception of the world.Application Layer. It is the specific application of the Internet of Things, which is combined with different industry needs to achieve different functions, thereby achieving extensive and accurate intelligent management and applications. The actual application layer is the in-depth integration of the Internet of Things technology with professional technologies in other industries, combined with specific industry needs, so as to realize the needs of intelligence.In the Internet of Things system, the information between various levels is not transmitted and conveyed in a single direction. There are also interactive operations and intelligent controls between them, and the information and data content transmitted is diverse.


#### 3.1.1. Perception Layer

The multipoint ecological environment monitoring system can collect environmental parameters, such as wind speed, wind direction, temperature, humidity, noise, air pressure, light intensity, PM2.5 concentration, and CO2 concentration. The required sensor modules mainly include noise sensor, wind speed and direction sensor, temperature sensor, humidity sensor, air pressure sensor, and PM2.5 sensor. These sensor modules are all connected with the one-chip computer through the I/O port; the connection diagram is as shown in Figure 2. Here focus is on the noise sensor, wind speed and direction sensor, temperature sensor, air pressure sensor, humidity sensor, and PM2.5 sensor module.Noise Sensor Module. The acoustic sensor contains a microphone that is very sensitive to sound, which can sensitively perceive changes in external sound and capture and output the value of noise. The noise sensor used in the system is JHM-NS02. Wind speed and direction sensor module: The principle of the wind direction sensor is to feel the rotation of the wind direction arrow to determine and output the wind direction information of the environment, and it can continuously measure and output the values of wind speed and air volume. Normally, the wind speed sensor and the wind direction sensor will be integrated into the same device. The information transmission methods of the wind speed and direction sensor include I2C, RS485, and analog transmission. Based on the principle of reducing the use of I/O ports, this system selects the wind speed and direction sensor with analog transmission; the specific model is RS-FXJT05. The equipment is small in size, easy to assemble, and of a long service life. The materials used have the advantages of good impact resistance, corrosion resistance and strong exposure resistance, which are very suitable for outdoor wind direction and speed information monitoring. The sensor has four wires, which are respectively connected to the positive and negative poles of the power supply and the analog signal collector. Then, it is converted into a digital signal through A/D conversion and operational amplifier and transmitted to the processor for processing.Temperature Sensor Module. There are many types of temperature sensors, and various types of temperature sensor technologies on the market are mature and can meet the requirements of the system. DS18B20 is a digital temperature sensor that directly outputs digital quantities. It has the advantages of strong anti-interference ability, high output accuracy, small size, convenient wiring, and suitable for many occasions. This text chooses DS18B20 as the temperature sensor of the system. When using this module to transmit the acquired temperature data, only one I/O port can be occupied. The power supply of the temperature sensor module can be realized by parasitic power supply, parasitic power strong pull-up power supply, or external power supply. This article uses external power supply.Air Pressure Sensor Module. The air pressure sensor senses changes in ambient air pressure through a component that can be elastically deformed. The MS5611 air pressure sensor selected in this article uses MES technology to achieve high-precision air pressure monitoring. It has a small size, low power consumption, high precision, and a resolution of up to 10 cm. It is widely used in smart phones, smart watches, GPS, and computers. When the sensor is data output, either analog voltage output or digital signal output can be selected. There is a high linear pressure sensor module and a 24-bit ADC inside the MS5611. This analog-to-digital converter can not only output a high-precision 24-bit air pressure value, but also a 24-bit air temperature value. The air pressure module can give high-precision temperature data without adding additional sensors. In this way, it can be combined with the data of the temperature module to obtain a more accurate temperature value.Humidity Sensor Module. Humidity sensors mainly include humidity-sensitive resistors and humidity-sensitive capacitors. These two types of sensors only have different principles for measuring humidity. One is to measure changes in resistance and the other is to measure changes in capacitance. Their stability is relatively poor, and when using them, they must be connected to an external circuit to adjust the signal so that the output voltage can reach an appropriate value before they can be used. Taking into account the actual temperature and humidity values in life, as well as the simplicity and efficiency of the entire data acquisition circuit. There is also an integrated digital humidity sensor DHT11. It is a humidity sensor with a calibrated digital signal output. It applies dedicated digital module acquisition technology and humidity sensing technology to ensure that the product has extremely high reliability and excellent long-term stability. The product has the advantages of fast response, strong anti-interference ability, and high-cost performance. It is a single-wire serial interface, small in size, and low in power consumption, which is convenient for system integration. Therefore, combining the advantages and disadvantages of the above components, the integrated digital humidity sensor DHT11 is selected as the environmental humidity data acquisition sensor of the system.PM2.5 Sensor Module. There are three different types of such sensors. The ordinary type is cheap, but the measurement accuracy is not high, and the stability is relatively poor. The output is level output. Under normal conditions, the output is high level. When pollutant particles enter, the output is low level. When calculating the PM2.5 concentration, it should be based on the low level within 30 seconds. In order to get the concentration of PM2.5, it can be substituted into a complicated calculation formula. Because the sensor itself has a processor, that is to say, the output of the sensor is the result of the measurement, there is no need to process the data in the program. Moreover, the output mode of the measurement results is serial port output, which is easy to use. The laser type has high measurement accuracy, digital output, and stable output, but the price is 7-8 times that of the ordinary type and 3-4 times that of the built-in processor type. In summary, considering the accuracy of the measurement results and the cost of the system, the PM2.5 sensor with its own processing type is selected in this system, and the selected model is GP2Y1050AU0F produced by Sharp.Figure 2
Schematic diagram of sensor module.


#### 3.1.2. Network Layer

The Internet of Things, although the perception layer is the foundation, without a huge network system, the data information sensed by the perception layer cannot be managed and integrated. In the Internet of Things, the network layer is required to be able to transmit the data perceived by the perception layer without barriers, high reliability, and high security. What it solves is the transmission problem of the data obtained by the perception layer within a certain range. Currently, GPRS, 2G/3G/4G, Wi-Fi, and so on are mature and widely used. With the advancement of technology, short-range wireless communication has also developed in recent years. Several common short-range wireless communication methods are EnOcean, ZigBee, Z-wave, Bluetooth, and infrared. As shown in Table1, there is a comparison of several common wireless communication technologies.Table 1
Common wireless communication technology.
ItemZigBeeEnOceanZ-waveBluetoothStandardIEEE802.15.4ISO/IEC14543PrivateIEEE802.15Frequency2.4 G868/315 MHz908.42/868.422.4 GSpeed20–250 kbps125 kbps40 kbps1 m/sDistance10–100 m30–300 m30–100 m10–100 mConsumption6–24 monthsNone required6–20 months6–24 monthsCostLowHighLowLowTable1 talks about the common wireless communication technology with best characteristics. Through the comparison of the advantages and disadvantages of several communication methods, taking into account the system cost and the difficulty of development, the system chooses the short-distance, low-complexity, low-power, low-speed, and low-cost two-way wireless communication technology ZigBee as the choice of network layers. ZigBee is a wireless connection that can work on three frequency bands of 2.4 GHz, 868 MHz, and 915 MHz, with transmission rates of up to 250 kbit/s, 20 kbit/s, and 40 kbit/s, respectively. Its transmission distance is within the range of 10–75 m, but it can continue to increase. It is mainly used for data transmission between various electronic devices with short distances, low power consumption, and low transmission rates, as well as typical applications with periodic data, intermittent data, and low response time data transmission.


#### 3.1.3. Application Layer

The ultimate goal of the Internet of Things is to make better use of the information that is sensed and transmitted. Application is the driving force and purpose of the development of the Internet of Things. The main function of the application layer is to analyze and process the transmitted data, then make correct control, decision making, and realize intelligent management, applications, and services. The application layer relies on public system platforms, such as data processing, middleware, cloud services, network management, and WEB services. The application layer selected by this system is the Yeelink platform, and its service characteristics are shown in Figure3.Figure 3
Service feature map of Yeelink.


### 3.2. Overall System Architecture

The whole system perceives real environmental variables through various sensors in the perception layer and converts the environmental variables into electrical signal data. Collect these data through the ZigBee network. The network layer uses ZigBee’s own networking characteristics to connect all collection nodes together. Finally, the collected data is transmitted to the main processor through the ZigBee network, and the main processor is responsible for the sorting of the data, and the sorted data. It is sent to the Yeelink IoT platform through the network interface. Through the settings on the platform, these nodes can be located and bound to social networks. After the entire system is powered on, you can view real-time campus environmental parameter changes anytime and anywhere through the computer browser, smartphone APP applications, and public social network accounts. The overall system block diagram is as shown in Figure4.Figure 4
System overall architecture diagram.


## 3.1. Architecture of IoT

The Internet of Things is based on the integration of existing communication networks, the Internet, and some specific professional network segments and meets the requirements of communication by adding some new functional applications. The Internet of Things should have the following characteristics. Scalability requires that the communication performance of the entire system is not affected by changes in the network communication scale. Transparency means that specific applications are not only dependent on specific connections of the underlying physical characteristics of the network. Consistency refers to the communication methods and characteristics that can span different networks to achieve interoperability and interoperability. Scalability means that the actual application performance will not decrease due to the failure of the entities and devices corresponding to the Internet of Things function. The Internet of Things is gradually developed and improved on the basis of sensor application technology, wireless sensor network technology, database operation technology, and the fast-developing Internet. It is mainly responsible for the comprehensive perception of relevant physical information, reliable data transmission, and comprehensive analysis and application processing.The Internet of Things is composed of three parts: the sensory access part, which includes the use of bar codes or other codes, short-range wireless communication, specific sensor measurement, and other equipment to realize the measurement and identification of physical quantities. The network transmission part is to realize the transmission of data and information through the existing Internet and basic communication networks. The actual application part is to adopt the newly emerged cloud computing platform and big data analysis technology to realize the automatic control and intelligent management of specific information and equipment. Up to now, the entire industry has recognized the following three levels of IoT system architecture. The bottom layer is the perception access layer used to perceive data and information, the second layer is the network communication layer for data information transmission, and the top layer is the actual application layer of related content, as shown in Figure1.Figure 1
IoT system architecture.In this IoT architecture, the interrelationship between the above three layers can be understood in the following way. The perception access layer is equivalent to the perception of external information organs on the structure of our human body. The network transmission layer is equivalent to the neural network center and brain in the human body structure. The actual application layer is equivalent to different positions of people in the entire society.Perception Layer. It recognizes objects and data and collects related information. The perceptual access layer includes sensor tags and corresponding data collectors and specific communication equipment of wireless sensor networks. It is mainly responsible for the collection and processing of information and data and the identification of related objects. By sensing the basic information and data of objects in the access layer, it can enter the transmission range of the Internet of Things.Network Layer. It is mainly responsible for the transmission of related information and the processing of specific data communication methods. The network transmission layer mainly includes the integration of the basic communication network and the Internet, network setting management, and information processing. The perception access layer sends the object’s data to the network layer, and the network layer is responsible for efficiently and reliably transporting it to remote users, extending the scope of the perception of the world.Application Layer. It is the specific application of the Internet of Things, which is combined with different industry needs to achieve different functions, thereby achieving extensive and accurate intelligent management and applications. The actual application layer is the in-depth integration of the Internet of Things technology with professional technologies in other industries, combined with specific industry needs, so as to realize the needs of intelligence.In the Internet of Things system, the information between various levels is not transmitted and conveyed in a single direction. There are also interactive operations and intelligent controls between them, and the information and data content transmitted is diverse.


### 3.1.1. Perception Layer

The multipoint ecological environment monitoring system can collect environmental parameters, such as wind speed, wind direction, temperature, humidity, noise, air pressure, light intensity, PM2.5 concentration, and CO2 concentration. The required sensor modules mainly include noise sensor, wind speed and direction sensor, temperature sensor, humidity sensor, air pressure sensor, and PM2.5 sensor. These sensor modules are all connected with the one-chip computer through the I/O port; the connection diagram is as shown in Figure 2. Here focus is on the noise sensor, wind speed and direction sensor, temperature sensor, air pressure sensor, humidity sensor, and PM2.5 sensor module.Noise Sensor Module. The acoustic sensor contains a microphone that is very sensitive to sound, which can sensitively perceive changes in external sound and capture and output the value of noise. The noise sensor used in the system is JHM-NS02. Wind speed and direction sensor module: The principle of the wind direction sensor is to feel the rotation of the wind direction arrow to determine and output the wind direction information of the environment, and it can continuously measure and output the values of wind speed and air volume. Normally, the wind speed sensor and the wind direction sensor will be integrated into the same device. The information transmission methods of the wind speed and direction sensor include I2C, RS485, and analog transmission. Based on the principle of reducing the use of I/O ports, this system selects the wind speed and direction sensor with analog transmission; the specific model is RS-FXJT05. The equipment is small in size, easy to assemble, and of a long service life. The materials used have the advantages of good impact resistance, corrosion resistance and strong exposure resistance, which are very suitable for outdoor wind direction and speed information monitoring. The sensor has four wires, which are respectively connected to the positive and negative poles of the power supply and the analog signal collector. Then, it is converted into a digital signal through A/D conversion and operational amplifier and transmitted to the processor for processing.Temperature Sensor Module. There are many types of temperature sensors, and various types of temperature sensor technologies on the market are mature and can meet the requirements of the system. DS18B20 is a digital temperature sensor that directly outputs digital quantities. It has the advantages of strong anti-interference ability, high output accuracy, small size, convenient wiring, and suitable for many occasions. This text chooses DS18B20 as the temperature sensor of the system. When using this module to transmit the acquired temperature data, only one I/O port can be occupied. The power supply of the temperature sensor module can be realized by parasitic power supply, parasitic power strong pull-up power supply, or external power supply. This article uses external power supply.Air Pressure Sensor Module. The air pressure sensor senses changes in ambient air pressure through a component that can be elastically deformed. The MS5611 air pressure sensor selected in this article uses MES technology to achieve high-precision air pressure monitoring. It has a small size, low power consumption, high precision, and a resolution of up to 10 cm. It is widely used in smart phones, smart watches, GPS, and computers. When the sensor is data output, either analog voltage output or digital signal output can be selected. There is a high linear pressure sensor module and a 24-bit ADC inside the MS5611. This analog-to-digital converter can not only output a high-precision 24-bit air pressure value, but also a 24-bit air temperature value. The air pressure module can give high-precision temperature data without adding additional sensors. In this way, it can be combined with the data of the temperature module to obtain a more accurate temperature value.Humidity Sensor Module. Humidity sensors mainly include humidity-sensitive resistors and humidity-sensitive capacitors. These two types of sensors only have different principles for measuring humidity. One is to measure changes in resistance and the other is to measure changes in capacitance. Their stability is relatively poor, and when using them, they must be connected to an external circuit to adjust the signal so that the output voltage can reach an appropriate value before they can be used. Taking into account the actual temperature and humidity values in life, as well as the simplicity and efficiency of the entire data acquisition circuit. There is also an integrated digital humidity sensor DHT11. It is a humidity sensor with a calibrated digital signal output. It applies dedicated digital module acquisition technology and humidity sensing technology to ensure that the product has extremely high reliability and excellent long-term stability. The product has the advantages of fast response, strong anti-interference ability, and high-cost performance. It is a single-wire serial interface, small in size, and low in power consumption, which is convenient for system integration. Therefore, combining the advantages and disadvantages of the above components, the integrated digital humidity sensor DHT11 is selected as the environmental humidity data acquisition sensor of the system.PM2.5 Sensor Module. There are three different types of such sensors. The ordinary type is cheap, but the measurement accuracy is not high, and the stability is relatively poor. The output is level output. Under normal conditions, the output is high level. When pollutant particles enter, the output is low level. When calculating the PM2.5 concentration, it should be based on the low level within 30 seconds. In order to get the concentration of PM2.5, it can be substituted into a complicated calculation formula. Because the sensor itself has a processor, that is to say, the output of the sensor is the result of the measurement, there is no need to process the data in the program. Moreover, the output mode of the measurement results is serial port output, which is easy to use. The laser type has high measurement accuracy, digital output, and stable output, but the price is 7-8 times that of the ordinary type and 3-4 times that of the built-in processor type. In summary, considering the accuracy of the measurement results and the cost of the system, the PM2.5 sensor with its own processing type is selected in this system, and the selected model is GP2Y1050AU0F produced by Sharp.Figure 2
Schematic diagram of sensor module.


### 3.1.2. Network Layer

The Internet of Things, although the perception layer is the foundation, without a huge network system, the data information sensed by the perception layer cannot be managed and integrated. In the Internet of Things, the network layer is required to be able to transmit the data perceived by the perception layer without barriers, high reliability, and high security. What it solves is the transmission problem of the data obtained by the perception layer within a certain range. Currently, GPRS, 2G/3G/4G, Wi-Fi, and so on are mature and widely used. With the advancement of technology, short-range wireless communication has also developed in recent years. Several common short-range wireless communication methods are EnOcean, ZigBee, Z-wave, Bluetooth, and infrared. As shown in Table1, there is a comparison of several common wireless communication technologies.Table 1
Common wireless communication technology.
ItemZigBeeEnOceanZ-waveBluetoothStandardIEEE802.15.4ISO/IEC14543PrivateIEEE802.15Frequency2.4 G868/315 MHz908.42/868.422.4 GSpeed20–250 kbps125 kbps40 kbps1 m/sDistance10–100 m30–300 m30–100 m10–100 mConsumption6–24 monthsNone required6–20 months6–24 monthsCostLowHighLowLowTable1 talks about the common wireless communication technology with best characteristics. Through the comparison of the advantages and disadvantages of several communication methods, taking into account the system cost and the difficulty of development, the system chooses the short-distance, low-complexity, low-power, low-speed, and low-cost two-way wireless communication technology ZigBee as the choice of network layers. ZigBee is a wireless connection that can work on three frequency bands of 2.4 GHz, 868 MHz, and 915 MHz, with transmission rates of up to 250 kbit/s, 20 kbit/s, and 40 kbit/s, respectively. Its transmission distance is within the range of 10–75 m, but it can continue to increase. It is mainly used for data transmission between various electronic devices with short distances, low power consumption, and low transmission rates, as well as typical applications with periodic data, intermittent data, and low response time data transmission.


### 3.1.3. Application Layer

The ultimate goal of the Internet of Things is to make better use of the information that is sensed and transmitted. Application is the driving force and purpose of the development of the Internet of Things. The main function of the application layer is to analyze and process the transmitted data, then make correct control, decision making, and realize intelligent management, applications, and services. The application layer relies on public system platforms, such as data processing, middleware, cloud services, network management, and WEB services. The application layer selected by this system is the Yeelink platform, and its service characteristics are shown in Figure3.Figure 3
Service feature map of Yeelink.


## 3.1.1. Perception Layer

The multipoint ecological environment monitoring system can collect environmental parameters, such as wind speed, wind direction, temperature, humidity, noise, air pressure, light intensity, PM2.5 concentration, and CO2 concentration. The required sensor modules mainly include noise sensor, wind speed and direction sensor, temperature sensor, humidity sensor, air pressure sensor, and PM2.5 sensor. These sensor modules are all connected with the one-chip computer through the I/O port; the connection diagram is as shown in Figure 2. Here focus is on the noise sensor, wind speed and direction sensor, temperature sensor, air pressure sensor, humidity sensor, and PM2.5 sensor module.Noise Sensor Module. The acoustic sensor contains a microphone that is very sensitive to sound, which can sensitively perceive changes in external sound and capture and output the value of noise. The noise sensor used in the system is JHM-NS02. Wind speed and direction sensor module: The principle of the wind direction sensor is to feel the rotation of the wind direction arrow to determine and output the wind direction information of the environment, and it can continuously measure and output the values of wind speed and air volume. Normally, the wind speed sensor and the wind direction sensor will be integrated into the same device. The information transmission methods of the wind speed and direction sensor include I2C, RS485, and analog transmission. Based on the principle of reducing the use of I/O ports, this system selects the wind speed and direction sensor with analog transmission; the specific model is RS-FXJT05. The equipment is small in size, easy to assemble, and of a long service life. The materials used have the advantages of good impact resistance, corrosion resistance and strong exposure resistance, which are very suitable for outdoor wind direction and speed information monitoring. The sensor has four wires, which are respectively connected to the positive and negative poles of the power supply and the analog signal collector. Then, it is converted into a digital signal through A/D conversion and operational amplifier and transmitted to the processor for processing.Temperature Sensor Module. There are many types of temperature sensors, and various types of temperature sensor technologies on the market are mature and can meet the requirements of the system. DS18B20 is a digital temperature sensor that directly outputs digital quantities. It has the advantages of strong anti-interference ability, high output accuracy, small size, convenient wiring, and suitable for many occasions. This text chooses DS18B20 as the temperature sensor of the system. When using this module to transmit the acquired temperature data, only one I/O port can be occupied. The power supply of the temperature sensor module can be realized by parasitic power supply, parasitic power strong pull-up power supply, or external power supply. This article uses external power supply.Air Pressure Sensor Module. The air pressure sensor senses changes in ambient air pressure through a component that can be elastically deformed. The MS5611 air pressure sensor selected in this article uses MES technology to achieve high-precision air pressure monitoring. It has a small size, low power consumption, high precision, and a resolution of up to 10 cm. It is widely used in smart phones, smart watches, GPS, and computers. When the sensor is data output, either analog voltage output or digital signal output can be selected. There is a high linear pressure sensor module and a 24-bit ADC inside the MS5611. This analog-to-digital converter can not only output a high-precision 24-bit air pressure value, but also a 24-bit air temperature value. The air pressure module can give high-precision temperature data without adding additional sensors. In this way, it can be combined with the data of the temperature module to obtain a more accurate temperature value.Humidity Sensor Module. Humidity sensors mainly include humidity-sensitive resistors and humidity-sensitive capacitors. These two types of sensors only have different principles for measuring humidity. One is to measure changes in resistance and the other is to measure changes in capacitance. Their stability is relatively poor, and when using them, they must be connected to an external circuit to adjust the signal so that the output voltage can reach an appropriate value before they can be used. Taking into account the actual temperature and humidity values in life, as well as the simplicity and efficiency of the entire data acquisition circuit. There is also an integrated digital humidity sensor DHT11. It is a humidity sensor with a calibrated digital signal output. It applies dedicated digital module acquisition technology and humidity sensing technology to ensure that the product has extremely high reliability and excellent long-term stability. The product has the advantages of fast response, strong anti-interference ability, and high-cost performance. It is a single-wire serial interface, small in size, and low in power consumption, which is convenient for system integration. Therefore, combining the advantages and disadvantages of the above components, the integrated digital humidity sensor DHT11 is selected as the environmental humidity data acquisition sensor of the system.PM2.5 Sensor Module. There are three different types of such sensors. The ordinary type is cheap, but the measurement accuracy is not high, and the stability is relatively poor. The output is level output. Under normal conditions, the output is high level. When pollutant particles enter, the output is low level. When calculating the PM2.5 concentration, it should be based on the low level within 30 seconds. In order to get the concentration of PM2.5, it can be substituted into a complicated calculation formula. Because the sensor itself has a processor, that is to say, the output of the sensor is the result of the measurement, there is no need to process the data in the program. Moreover, the output mode of the measurement results is serial port output, which is easy to use. The laser type has high measurement accuracy, digital output, and stable output, but the price is 7-8 times that of the ordinary type and 3-4 times that of the built-in processor type. In summary, considering the accuracy of the measurement results and the cost of the system, the PM2.5 sensor with its own processing type is selected in this system, and the selected model is GP2Y1050AU0F produced by Sharp.Figure 2
Schematic diagram of sensor module.


## 3.1.2. Network Layer

The Internet of Things, although the perception layer is the foundation, without a huge network system, the data information sensed by the perception layer cannot be managed and integrated. In the Internet of Things, the network layer is required to be able to transmit the data perceived by the perception layer without barriers, high reliability, and high security. What it solves is the transmission problem of the data obtained by the perception layer within a certain range. Currently, GPRS, 2G/3G/4G, Wi-Fi, and so on are mature and widely used. With the advancement of technology, short-range wireless communication has also developed in recent years. Several common short-range wireless communication methods are EnOcean, ZigBee, Z-wave, Bluetooth, and infrared. As shown in Table1, there is a comparison of several common wireless communication technologies.Table 1
Common wireless communication technology.
ItemZigBeeEnOceanZ-waveBluetoothStandardIEEE802.15.4ISO/IEC14543PrivateIEEE802.15Frequency2.4 G868/315 MHz908.42/868.422.4 GSpeed20–250 kbps125 kbps40 kbps1 m/sDistance10–100 m30–300 m30–100 m10–100 mConsumption6–24 monthsNone required6–20 months6–24 monthsCostLowHighLowLowTable1 talks about the common wireless communication technology with best characteristics. Through the comparison of the advantages and disadvantages of several communication methods, taking into account the system cost and the difficulty of development, the system chooses the short-distance, low-complexity, low-power, low-speed, and low-cost two-way wireless communication technology ZigBee as the choice of network layers. ZigBee is a wireless connection that can work on three frequency bands of 2.4 GHz, 868 MHz, and 915 MHz, with transmission rates of up to 250 kbit/s, 20 kbit/s, and 40 kbit/s, respectively. Its transmission distance is within the range of 10–75 m, but it can continue to increase. It is mainly used for data transmission between various electronic devices with short distances, low power consumption, and low transmission rates, as well as typical applications with periodic data, intermittent data, and low response time data transmission.


## 3.1.3. Application Layer

The ultimate goal of the Internet of Things is to make better use of the information that is sensed and transmitted. Application is the driving force and purpose of the development of the Internet of Things. The main function of the application layer is to analyze and process the transmitted data, then make correct control, decision making, and realize intelligent management, applications, and services. The application layer relies on public system platforms, such as data processing, middleware, cloud services, network management, and WEB services. The application layer selected by this system is the Yeelink platform, and its service characteristics are shown in Figure3.Figure 3
Service feature map of Yeelink.


## 3.2. Overall System Architecture

The whole system perceives real environmental variables through various sensors in the perception layer and converts the environmental variables into electrical signal data. Collect these data through the ZigBee network. The network layer uses ZigBee’s own networking characteristics to connect all collection nodes together. Finally, the collected data is transmitted to the main processor through the ZigBee network, and the main processor is responsible for the sorting of the data, and the sorted data. It is sent to the Yeelink IoT platform through the network interface. Through the settings on the platform, these nodes can be located and bound to social networks. After the entire system is powered on, you can view real-time campus environmental parameter changes anytime and anywhere through the computer browser, smartphone APP applications, and public social network accounts. The overall system block diagram is as shown in Figure4.Figure 4
System overall architecture diagram.


## 4. Ecological Environment Quality Evaluation with CNN

This work designs a BN-based one-dimensional convolutional neural network for quality assessment of the ecological environment. The results of the quality evaluation are transmitted to the user terminal through the Yeelink IoT platform, and the user can make corresponding decisions based on the results of the quality evaluation of the ecological environment.Traditional ecological environment assessment algorithms are mostly calculated using signal processing-based methods in terms of feature learning. Each feature extracted manually has its corresponding physical meaning, and the features are also independent of each other. In the process of algorithmic model learning and classification, the relationship between features is not reflected. Feature extraction also requires a lot of experiments, and the combination of features has a direct impact on the final classification accuracy. Convolutional neural networks can use the operation of convolution to automatically learn the internal relationships of data, extract effective features, and use fully connected networks to synthesize the features. Two-dimensional convolutional neural networks are widely used in image processing, such as image recognition, image annotation, and video classification. For ecological environment data, since the collected environment data is a one-dimensional sequence signal, the two-dimensional convolution operation is not suitable for this. Therefore, for the ecological environment quality assessment problem, convolution and pooling adopt one-dimensional convolution and one-dimensional pooling, respectively. By inputting the collected original environmental signals, the effective features are automatically extracted so that the classification results can be obtained and the quality of the ecological environment can be evaluated.


### 4.1. Eco-Environmental Quality Evaluation Module with 1D-CNN

Although the ability of neural network to learn features can be improved by deepening the number of network layers, the ecological environment data is dense. Moreover, the more network layers, the higher the feature dimension, so the parameters that the network needs to learn are very large, which greatly increases the complexity of the algorithm. The local connection and weight sharing in the convolutional neural network can greatly reduce the parameters that the network needs to train and can also prevent the occurrence of overfitting risks and improve the generalization ability of the model.Since the ecological environment data is one-dimensional, a one-dimensional convolutional neural network ecological environment quality assessment model is proposed. First, the collected original ecological environment signals are input, and the convolutional layer is used to extract the features. Then through the downsampling operation of the pooling layer, significant features are extracted, which also reduces the parameters that need to be trained and prevents overfitting. Then, the neurons of the fully connected network are connected in pairs to synthesize the features extracted by the convolutional layer and the pooling layer, and finally the predicted result is output through the sigmoid classifier.Use the sensor to collect the signal, directly convert the original data into a one-dimensional array,n is the data length, and enter the one-dimensional array into the network, denoted as(1)X=x1,x2,…,xn.Randomly generate a convolution kernelw with a specified length and bias b , where k is the size of the convolution kernel. Perform sliding convolution on this convolution kernel on a one-dimensional time series signal, and obtain the final excitation value through the Tanh activation function. The formula is(2)XL=Tanh∑i=1kXiL−1∗wiL+bL.After the operation of the convolutional layer, this article uses the maximum pooling operation for downsampling and then uses theTanh activation function to obtain the final pooling result. In this way, the original ecological environment data is alternately used to extract features using convolutional pooling operations to obtain more advanced features of the data. After the last layer of the convolutional layer, global average pooling is used to form a feature point matrix, and finally these feature points are combined into the final feature vector.In this work, the evaluation results of ecological environment quality are set into four levels: excellent, good, moderate, and poor. Therefore, the evaluation of ecological environment quality can be regarded as a four-category problem. The output layer uses the sigmoid classifier, and the result directly outputs the evaluation level of the ecological environment quality. The structural parameters of 1D-CNN are shown in Table2.Table 2
Structural parameters of 1D-CNN.
LayerKernel sizeKernel quantityConv116 × 132Pool18 × 132Conv216 × 164Pool28 × 164Conv316 × 1128Pool38 × 11FC41Sigmoid41


### 4.2. Eco-Environmental Quality Evaluation Module with 1D-BN-CNN

1D-CNN obtains the weight parameters that make the model performance the best by training on ecological environment data. However, when the model needs to detect signals with large noisy signals, due to the large difference in the distribution of the samples used in the training set and the samples of the test machine, the classification performance of the 1D-CNN model will decrease, and the accuracy of the classification results will decrease. This chapter will introduce the BN (Batch Normalization) algorithm to improve the adaptive ability of 1D-CNN and propose a 1D-BN-CNN model. The process of neural network training and learning is actually the distribution of learning data. When the data distribution of the training set and the test set are quite different, the model’s fitting ability will be greatly reduced. BN, like the convolutional layer and pooling layer in the convolutional neural network, also belongs to a layer in the network. The BN algorithm can reconstruct and transform the characteristic data learned in the upper layer to transform it into the same distribution as the test set samples. This solves the problem that the generalization ability of the model decreases when the distribution of the ecological environment data training set sample data and the test set sample data are quite different. The parameters of the BN layer are not fixed but can be learned.By introducing learnable parametersα and β , moving and scaling the standardized values, the network has an adaptive ability, and the BN layer is trained together with other layer parameters in the network. First, find the mean value of training samples in a batch size:(3)μ=1s∑i=1sxi,where s is the number of training samples in batch size and xi is a single sample. Then, calculate the variance of the training samples in a batch size:(4)σ2=1s∑i=1sxi−μ2.Then, find the average difference of the training set sample data;ε is a small positive number that is not 0. This is to avoid the situation where the denominator is 0:(5)xi′=xi−μσ2+ε.Finally, the learnable parametersα and β are trained along with the original network model so that the data feature domain of the training set sample is transformed to the data feature domain of the test sample so that the feature domains of the two are consistent. Thereby, it can classify the new target field so that the network has the ability of self-adaptation:(6)yi=αxi′+β.The structure of 1D-BN-CNN is illustrated in Figure5.Figure 5
The structure of 1D-BN-CNN.


## 4.1. Eco-Environmental Quality Evaluation Module with 1D-CNN

Although the ability of neural network to learn features can be improved by deepening the number of network layers, the ecological environment data is dense. Moreover, the more network layers, the higher the feature dimension, so the parameters that the network needs to learn are very large, which greatly increases the complexity of the algorithm. The local connection and weight sharing in the convolutional neural network can greatly reduce the parameters that the network needs to train and can also prevent the occurrence of overfitting risks and improve the generalization ability of the model.Since the ecological environment data is one-dimensional, a one-dimensional convolutional neural network ecological environment quality assessment model is proposed. First, the collected original ecological environment signals are input, and the convolutional layer is used to extract the features. Then through the downsampling operation of the pooling layer, significant features are extracted, which also reduces the parameters that need to be trained and prevents overfitting. Then, the neurons of the fully connected network are connected in pairs to synthesize the features extracted by the convolutional layer and the pooling layer, and finally the predicted result is output through the sigmoid classifier.Use the sensor to collect the signal, directly convert the original data into a one-dimensional array,n is the data length, and enter the one-dimensional array into the network, denoted as(1)X=x1,x2,…,xn.Randomly generate a convolution kernelw with a specified length and bias b , where k is the size of the convolution kernel. Perform sliding convolution on this convolution kernel on a one-dimensional time series signal, and obtain the final excitation value through the Tanh activation function. The formula is(2)XL=Tanh∑i=1kXiL−1∗wiL+bL.After the operation of the convolutional layer, this article uses the maximum pooling operation for downsampling and then uses theTanh activation function to obtain the final pooling result. In this way, the original ecological environment data is alternately used to extract features using convolutional pooling operations to obtain more advanced features of the data. After the last layer of the convolutional layer, global average pooling is used to form a feature point matrix, and finally these feature points are combined into the final feature vector.In this work, the evaluation results of ecological environment quality are set into four levels: excellent, good, moderate, and poor. Therefore, the evaluation of ecological environment quality can be regarded as a four-category problem. The output layer uses the sigmoid classifier, and the result directly outputs the evaluation level of the ecological environment quality. The structural parameters of 1D-CNN are shown in Table2.Table 2
Structural parameters of 1D-CNN.
LayerKernel sizeKernel quantityConv116 × 132Pool18 × 132Conv216 × 164Pool28 × 164Conv316 × 1128Pool38 × 11FC41Sigmoid41


## 4.2. Eco-Environmental Quality Evaluation Module with 1D-BN-CNN

1D-CNN obtains the weight parameters that make the model performance the best by training on ecological environment data. However, when the model needs to detect signals with large noisy signals, due to the large difference in the distribution of the samples used in the training set and the samples of the test machine, the classification performance of the 1D-CNN model will decrease, and the accuracy of the classification results will decrease. This chapter will introduce the BN (Batch Normalization) algorithm to improve the adaptive ability of 1D-CNN and propose a 1D-BN-CNN model. The process of neural network training and learning is actually the distribution of learning data. When the data distribution of the training set and the test set are quite different, the model’s fitting ability will be greatly reduced. BN, like the convolutional layer and pooling layer in the convolutional neural network, also belongs to a layer in the network. The BN algorithm can reconstruct and transform the characteristic data learned in the upper layer to transform it into the same distribution as the test set samples. This solves the problem that the generalization ability of the model decreases when the distribution of the ecological environment data training set sample data and the test set sample data are quite different. The parameters of the BN layer are not fixed but can be learned.By introducing learnable parametersα and β , moving and scaling the standardized values, the network has an adaptive ability, and the BN layer is trained together with other layer parameters in the network. First, find the mean value of training samples in a batch size:(3)μ=1s∑i=1sxi,where s is the number of training samples in batch size and xi is a single sample. Then, calculate the variance of the training samples in a batch size:(4)σ2=1s∑i=1sxi−μ2.Then, find the average difference of the training set sample data;ε is a small positive number that is not 0. This is to avoid the situation where the denominator is 0:(5)xi′=xi−μσ2+ε.Finally, the learnable parametersα and β are trained along with the original network model so that the data feature domain of the training set sample is transformed to the data feature domain of the test sample so that the feature domains of the two are consistent. Thereby, it can classify the new target field so that the network has the ability of self-adaptation:(6)yi=αxi′+β.The structure of 1D-BN-CNN is illustrated in Figure5.Figure 5
The structure of 1D-BN-CNN.


## 5. Experiment and Discussion

This work has designed a system for multipoint ecological environment monitoring. To verify the effectiveness of this system, check the information collected by each sensor on the terminal device. Taking the time span of a certain day as an example, the time interval is set to 1 hour. The wind speed and wind direction information collected by the terminal device are illustrated in Figure6. The temperature and humidity information is illustrated in Figure 7. The air pressure and PM2.5 information is illustrated in Figure 8.Figure 6
Wind speed and direction curve.Figure 7
Temperature and humidity curve.Figure 8
Air pressure and PM2.5 concentration curve.As time changes, the indicators obtained by various sensors are also changing. Comparing the results obtained by the system with the actual situation, it is found that the changes are basically the same. It can be proved that the multipoint ecological environment monitoring system designed in this work can effectively monitor the ecological environment.In addition to constructing a multipoint ecological environment monitoring system, this paper also designs an ecological environment quality assessment algorithm with 1D-BN-CNN. This section will conduct experiments on the evaluation network designed in this work to verify validity and correctness for this model. First, this article selects a part of the ecological environment data to train the network, and the remaining data is used to test the network. The training error and test accuracy are shown in Figure9.Figure 9
Training loss and test accuracy.As the training progresses, the training loss gradually decreases and the test accuracy rate gradually increases. When the epoch is 60, the loss no longer decreases, and the accuracy rate no longer increases, indicating that the network has reached a state of convergence at this time. It can be explained that the network designed in this work is theoretically feasible and the network can converge.In this work, the proposed network is used to predict the quality of the ecological environment on a certain day, and the prediction interval is also set to 1 hour. The experimental result is illustrated in Table3.Table 3
Ecological environmental quality evaluation information.
TimeLevel01:00Excellent02:00Excellent03:00Excellent04:00Excellent05:00Excellent06:00Good07:00Good08:00Good09:00Moderate10:00Moderate11:00Moderate12:00Poor13:00Poor14:00Poor15:00Moderate16:00Moderate17:00Moderate18:00Good19:00Good20:00Good21:00Good22:00Good23:00Good24:00ExcellentThe changes in quality of ecological environment are basically consistent with the changes in the indicators of the sensors, which proves the validity and reliability of the ecological environment quality assessment method designed in this work.


## 6. Conclusion

While science and technology bring convenience to people, they are also continuously destroying the ecological environment. Therefore, this work uses high-tech means to monitor the changes in ecological environment pollution and at the same time reminds citizens that they should always pay attention to protecting the environment. This work designs a multipoint ecological environment monitoring system with Internet of Things. This system can realize real-time intelligent monitoring for ecological environment and achieve the goals of ecological environment monitoring and measurement parameter integration, system modularization, real-time data transmission, and integration of monitoring services. A multi-point ecological environment monitoring system is built with Yeelink IoT platform. Use the main control board and various sensors to build a hardware system, write corresponding software programs, and collect various ecological environment parameters. Construct a 1D-BN-CNN to evaluate quality for ecological environment. It evaluates current ecological environment quality through environmental data collected in real time by monitoring sites and through the Internet of Things platform to display to the terminal.



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*Source: 1481931-2022-05-11.xml*