CN113010849A

CN113010849A - Grassland environment evaluation method based on Internet of things

Info

Publication number: CN113010849A
Application number: CN202110208156.9A
Authority: CN
Inventors: 郭洪飞; 杨贺轩; 何智慧; 王芳; 张锐; 郗风江; 任亚平; 朝宝
Original assignee: Jinan University
Current assignee: Jinan University
Priority date: 2021-02-24
Filing date: 2021-02-24
Publication date: 2021-06-22

Abstract

The invention provides a grassland environment evaluation method based on the Internet of things, and belongs to the technical field of ecological environment monitoring. The invention provides a grassland environment evaluation method based on the Internet of things, wherein various environmental factors in grassland information have different weights for grazing of herdsmen, the characteristics of the factors are analyzed and compared, a multi-sensor data fusion algorithm is adopted, the grassland environment data are analyzed and processed, an evaluation matrix is constructed, and the grassland environment grade is scientifically evaluated.

Description

Grassland environment evaluation method based on Internet of things

Technical Field

The invention relates to the technical field of ecological environment monitoring, in particular to a grassland environment evaluation method based on the Internet of things.

Background

In recent years, the economic construction of inner cover and surrounding areas is promoted in China, and powerful guarantee is provided for improving the living standard of local people. The grassland is a great natural resource in the regions, is the root for people to live in pasturing areas and semi-pasturing areas, and also is the main body for forming the homeland resources, and the grassland resources play an important role in the process of maintaining the development of grazing and animal husbandry, and the reasonable utilization of the grassland resources can maintain the regional ecological balance and provide a powerful material basis for the aspects of production life, culture development and the like of minority. The protection and the construction of the grassland are beneficial to the sustainable development of the grassland animal husbandry and can also effectively guarantee the integrity of the natural ecosystem.

The environment of meadow is directly influencing the healthy growth of livestock, and meadow information receives natural climate's influence on the one hand, and on the other hand also receives the inside factor influence in herdsman's living area, thereby environmental index is various and the influence factor is numerous makes meadow information management get up complicatedly.

From the perspective of a traditional grassland environment monitoring and management mode, the traditional management mode has human participation to a great extent, the monitoring and management efficiency and the scientificity are restricted by the uncertainty of the human participation, and researchers are constantly searching for improving the efficiency and the scientificity of a monitoring and management system.

In recent years, the internet of things has not been developed as an important branch of the internet. By establishing the grassland environment monitoring and managing information system taking the internet of things as a core, on one hand, the grassland resource management efficiency is improved, the management flow is standardized, the scientificity of the grassland environment management is realized, and visual, accurate, rapid and comprehensive grassland environment related information is provided for managers.

After the grassland environment informatization is realized, the environment monitoring is not limited by objective conditions such as space, time, climate and the like, and the grassland environment information provides a more time-saving and labor-saving way for the utilization, construction, protection and other work of grassland resources in future. Through the digitalization of the grassland environment information and the combination of modern technological means such as big data and wireless data transmission, the method can realize smoother information circulation, more convenient information acquisition, more timely monitoring state understanding and more rapid information analysis and processing.

The comprehensive monitoring system constructed based on the wireless sensor network is developed and popularized by China's Huayunxing elephant technology group company in 2018, the system introduces technologies such as multi-element sensing, wireless data transmission and the like, has strong expansion performance and error correction capability, integrates monitoring equipment data such as grassland environment observation, natural weather judgment, climate change and the like into the existing information sharing platform, and enables resource management to be more informationized. In 2019, the Hepu company in China proposes a scheme of a grassland Internet of things monitoring and management system, and the grassland Internet of things monitoring and management system is established to monitor the utilization degree of grassland resources and the occurrence and development of grassland disasters, and meanwhile, the trend of ecological environment change can be reasonably predicted, the balance condition of grassland and livestock is analyzed, and guidance and service are provided for the development and production of grassland.

The prior art has at least the following disadvantages:

1. there is a varying degree of human involvement;

2. the monitoring of the grassland environment index is not rich enough;

3. the mutual influence among all the environmental factors is not considered comprehensively;

4. the grassland environment evaluation is carried out after various environmental indexes are not comprehensively analyzed.

Disclosure of Invention

In order to solve the technical problems in the prior art, the grassland environment evaluation method based on the Internet of things applies the Internet of things technology to the grassland environment evaluation, combines the NB-IoT wireless communication technology and the image processing technology to perform special processing on the test point images, adopts a new method to obtain the NDVI value, and more scientifically realizes the management of the grassland environment monitoring information. Aiming at different influence weights of various environmental factors in the grassland information on grazing of the herdsman, analyzing and comparing the characteristics of the factors, synthesizing the influence weights of various environmental factors to obtain an evaluation probability matrix, analyzing and modeling the grassland environmental data to obtain the evaluation matrix, and scientifically evaluating the grassland environmental grade.

The invention provides a grassland environment evaluation method based on the Internet of things, which comprises the following steps:

a data acquisition step, namely a step of acquiring data,

the method comprises the steps of collecting grassland environment data, wherein the grassland environment data are used for evaluating the grassland environment and comprise monitoring spot grassland images, atmospheric pressure, illumination intensity, air temperature, air humidity, rainfall, wind speed, wind direction, soil pH value and soil humidity;

a data transmission step, in which the data is transmitted,

transmitting the collected grassland environment data to a server through the Internet of things;

a data pre-processing step, namely,

the server preprocesses the acquired data;

changing pixel points with pixel values not in the range of the three-color channel into white with the pixel values of the three-color channel being (0,0, 0);

dividing the number of pixel points of the pixel value in the three-color channel range by the total pixel point number to obtain the vegetation coverage index NDVI value of the monitoring point;

the three color channel ranges are G >90, R <190, B <155, where G is a green channel pixel value, R is a red channel pixel value, and B is a blue channel pixel value;

an evaluation matrix construction step, wherein,

determining an influence factor set U, wherein the influence factor set U comprises atmospheric pressure, illumination intensity, air temperature, air humidity, rainfall, wind speed, wind direction, soil pH value, soil humidity and vegetation coverage index NDVI;

determining a set of evaluation grades V, wherein the evaluation grades comprise very suitable, generally suitable, common and bad;

sequentially combining the probability values of each influence factor in the influence factor set U to each evaluation level in the evaluation level set V into an evaluation probability matrix P;

determining the weight of each factor according to the physical characteristics of each factor, wherein the weight of each factor is marked by adopting a 1-9 proportional scaling method;

obtaining a judgment matrix A through the weight comparison of all factors;

calculating the arithmetic mean value of each row of the judgment matrix A, and forming a column vector by using the mean value of each row

Vector the column

Carrying out normalization processing to obtain a system weight vector

The resulting weight vector

Multiplying the evaluation probability matrix P to obtain an evaluation set vector B;

an evaluation step of evaluating the quality of the sample,

and taking the evaluation grade corresponding to the maximum value as a final evaluation result according to the evaluation set vector B.

Preferably, the data preprocessing step specifically includes:

acquiring the height and width of the monitoring point image through shape statements;

calculating the total number of pixel points of the shot monitoring point image;

traversing each pixel point, when the pixel value of the pixel point is in the three-color channel range, the pixel point meets the requirement, the pixel point value meeting the requirement is accumulated, when the pixel value is not in the three-color channel range, the pixel point does not meet the requirement, and the pixel point is changed into white with the pixel value of the three-color channel being (0,0, 0);

and after traversing, performing division operation on the pixel point values meeting the requirements and the total pixel point number to obtain the NDVI value of the monitoring point, and storing the NDVI value into a database.

Preferably, the probability value of each influencing factor in the influencing factor set U to each evaluation level in the evaluation level set V is calculated by:

according to the physical characteristics of the factors, the probability value of each influence factor in the influence factor set U to each evaluation level in the evaluation level set V is obtained by adopting the following formula;

wherein,

x is the value of the influencing factor;

a, b, c and d are sequentially adjacent evaluation criteria respectively.

Preferably, the internet of things is a narrowband internet of things NB-IoT, and the server is a cloud server.

Preferably, the internet of things performs the following operations on the received grassland environment data:

performing character string decoding on the grassland environment data;

according to the difference of sensors for collecting different data, format conversion is carried out on the grassland environment data according to the response frame format of the sensors;

and storing the data after format conversion into a database.

Preferably:

an illumination sensor is adopted to collect illumination intensity, the working voltage of the illumination intensity is 2.4-5.5V, the error is +/-5% when the illumination precision is 25 ℃, and the illumination intensity range is 0-200 kLux;

collecting air temperature and air humidity by adopting a temperature and humidity sensor, wherein the working voltage of the temperature and humidity sensor is 2.4-5.5V, the temperature range is-40-80 ℃, the temperature resolution is 0.1 ℃, the humidity resolution is 0.1% RH, and a data transmission interface is an IIC bus interface;

an air pressure sensor is adopted to collect atmospheric pressure, the working voltage of the air pressure sensor is 2.4-5.5V, the air pressure measurement range is 10-1200mbar, the resolution is 0.012mbar, and the measurement error is +/-1.5 mbar at the temperature of 25 ℃ and under the standard atmospheric pressure;

collecting rainfall by adopting a rainfall sensor, wherein the rainfall sensor is powered by a 12-24V DC power supply, the measurement range is less than or equal to 30mm/min, the measurement precision is 0.2mm, and the operation temperature is-30-80 ℃;

collecting soil pH value and soil humidity by using a soil sensor, wherein the soil sensor is powered by a 12-24V DC power supply, the humidity measurement precision is +/-2.5% in the range of 0-55%, +/-4.5% in the range of 55-100%, and the measurement range is 0-100%; the pH value measurement range is 3-9pH, and the measurement precision is +/-0.3 pH;

acquiring wind speed and wind direction by adopting a wind speed and wind direction sensor, wherein the wind speed and wind direction sensor adopts a 12-24V power supply for power supply, the wind speed measurement response time is less than 5s, the wind speed measurement range is 0-30m/s, and the wind speed measurement precision is +/-1 m/s; the wind direction measuring range is 0-360 degrees, and the wind direction measuring precision is +/-3 degrees.

Compared with the prior art, the invention has the following beneficial effects:

1. according to the invention, the web client sends out a request, a plurality of sensors perform automatic monitoring, and the result is displayed on the web client by combining NB-IoT wireless communication, cloud service and the web server, so that no human participation is realized in the whole process, the influence of human factors is small, and the monitoring system is more scientific and accurate;

2. the invention comprehensively considers a plurality of grassland environmental factors such as atmospheric pressure, illumination intensity, air temperature, air humidity, rainfall, wind speed, wind direction, soil pH value, soil humidity and the like, has richer monitoring indexes and more scientific and accurate monitoring system;

3. the method and the device consider the mutual influence among all environmental factors, for example, the temperature is reduced when the wind speed is high, and different evaluation probabilities are set aiming at different factors, so that the monitoring data analysis is more accurate, the evaluation result is more accurate, and the reliability is higher.

4. According to the NDVI calculating method, the NDVI values of the monitoring points are obtained by dividing the pixel point record values meeting the requirements and the total pixel point number, and the calculating process of the vegetation coverage index of the small-area is simplified.

5. The invention changes the pixel value of the pixel point which is not in the three-color channel range into (0,0,0), and the accurate NDVI value can be obtained by the method.

Drawings

FIG. 1 is a method flow diagram of one embodiment of the present invention;

FIG. 2 is a system architecture diagram of one embodiment of the present invention as applied to a grassland environment evaluation system;

FIG. 3 is a process flow diagram of a master control module when the meadow environment evaluation system is applied according to an embodiment of the invention;

fig. 4 is an NB-IoT wireless communication module transmission flow diagram of an embodiment of the present invention when applied to a grassland environment evaluation system;

FIG. 5 is an NB-IoT wireless communication module process flow diagram for application of one embodiment of the present invention in a grassland environment evaluation system;

FIG. 6 is a flow diagram of web services processing when applied to a grassland environment evaluation system, according to an embodiment of the present invention;

FIG. 7 is a data pre-processing flow of one embodiment of the invention.

Detailed Description

The embodiments of the present invention will be described in detail below with reference to fig. 1-7, and are applied to a grassland environment monitoring system for performing an operation environment evaluation using the method of the present invention.

The invention provides a grassland environment evaluation method based on the Internet of things, which is applied to a grassland environment monitoring system, the grassland environment monitoring system adopts the method of the invention to evaluate the operating environment, and the system comprises:

the system comprises a detection device, a transmission device, a cloud server and a web client;

the detection device comprises a plurality of sensors and a main control module, wherein the sensors are used for collecting grassland environment data, the grassland environment data are used for evaluating the grassland environment, and the grassland environment data comprise monitoring images of a grassland point, atmospheric pressure, illumination intensity, air temperature, air humidity, rainfall, wind speed, wind direction, soil pH value and soil humidity;

the main control module is used for driving a plurality of sensors to collect data, monitoring instructions of the NB-IoT wireless communication module and transmitting the collected grassland environment data to a cloud server according to the instructions; the peripheral resources of the main control module comprise: 2 IIC bus interfaces, 2 USART interfaces, an ADC1 conversion module, 2 universal timers TIM2 and TIM 3; the two USART interfaces of the main control module are respectively connected with the NB-IoT wireless communication module and the rainfall sensor module through the RS485 conversion module;

the transmission device comprises an NB-IoT wireless communication module, is connected with the detection device and is used for sending instructions to the main control module and receiving the meadow environment data transmitted by the detection device;

the transmission device is also connected with the cloud server and used for receiving the instruction from the cloud server and transmitting the received grassland environment data;

the cloud server comprises a cloud service management interface, a data receiving module, a database, a data processing and analyzing module and a web server; the cloud service management interface is used for managing cloud services, system permissions and providing entries of a remote login system;

the data receiving module receives data transmitted by the NB-IoT wireless communication module;

the data receiving module is connected with the database and stores the received data in the database;

the database is connected with the data processing and analyzing module, the data processing and analyzing module processes the received data to obtain an evaluation result, and the evaluation result is stored in the database;

the data processing and analyzing module is used for preprocessing the received data; during preprocessing, changing pixel points with pixel values not within a three-color channel range into white with the pixel values of (0,0,0) of the three-color channel, and dividing the number of the pixel points with the pixel values within the three-color channel range by the total number of the pixel points to obtain the vegetation coverage index NDVI value of the monitoring point, wherein the three-color channel range is G >90, R <190 and B <155, G is a green channel pixel value, R is a red channel pixel value, and B is a blue channel pixel value;

the web server receives a request from the web client, acquires the requested data from the database and transmits the data back to the web client;

the web client is used for submitting a request and displaying data.

As a preferred embodiment, the data processing and analyzing module performs the following operations:

wherein,

x is the value of the influencing factor;

a, b, c and d are sequentially adjacent evaluation criteria respectively;

obtaining a judgment matrix A through the weight comparison of all factors;

Vector the column

Carrying out normalization processing to obtain a system weight vector

The resulting weight vector

And multiplying the evaluation probability matrix P to obtain an evaluation set vector B.

As a preferred embodiment, the preprocessing the received data by the data processing and analyzing module specifically includes:

As a preferred embodiment, the main control module performs the following operations:

after the system is initialized, carrying out timer initialization and serial port configuration;

the main control module enters a sleep mode;

when a query instruction sent by a cloud server is received, the main control module is awakened, and meanwhile, the query instruction is sent to each sensor;

receiving data sent by each sensor;

checking whether the data is complete, and if the data is complete, sending the data to the NB-IoT wireless communication module.

As a preferred embodiment, the NB-IoT wireless communication module performs the following settings at initialization:

setting a serial port number, a baud rate, a check bit, a data bit and a stop bit;

opening a serial port;

and selecting a transparent transmission mode, and setting a target IP address and an application port number.

As a preferred embodiment, the NB-IoT wireless communication module performs the following operations:

instantiating a Socket object;

carrying out initialization setting;

starting to monitor the connection request;

if the connection is successfully established, sending an inquiry request to the detection device;

receiving data sent by the detection device;

performing character string decoding on the data;

carrying out format conversion on the data according to the response frame formats of different sensors;

and storing the data after format conversion into a database.

As a preferred embodiment, the cloud service management interface may perform the following operations: the method comprises the steps of controlling the opening and closing of cloud service, changing a mirror image, resetting a password, switching an operating system and creating backup.

In a preferred embodiment, the data collected by the sensor is sent to the NB-IoT wireless communication module by the main control module.

As a preferred embodiment, the USART interface is converted into the RS485 interface through the RS485 conversion module.

As a preferred embodiment, an illumination sensor is adopted for measuring the illumination intensity, the operating voltage of the illumination intensity is 2.4-5.5V, the error is +/-5% when the illumination precision is 25 ℃, and the illumination intensity range is 0-200 kLux;

a temperature and humidity sensor is used for measuring air temperature and air humidity, the working voltage of the temperature and humidity sensor is 2.4-5.5V, the temperature range is-40-80 ℃, the temperature resolution is 0.1 ℃, the humidity resolution is 0.1% RH, and a data transmission interface is an IIC bus interface.

An air pressure sensor is adopted for measuring the atmospheric pressure, the working voltage of the air pressure sensor is 2.4-5.5V, the air pressure measuring range is 10-1200mbar, the resolution is 0.012mbar, and the measuring error is +/-1.5 mbar at the temperature of 25 ℃ and under the standard atmospheric pressure.

The rainfall is measured by adopting a rainfall sensor, the rainfall sensor is powered by a 12-24V DC power supply, the measurement range is less than or equal to 30mm/min, the measurement precision is 0.2mm, and the operating temperature is-30-80 ℃;

the soil sensor is used for measuring the soil pH value and the soil humidity, the soil sensor is powered by a 12-24V DC power supply, the humidity measurement precision is +/-2.5% in the range of 0-55%, +/-4.5% in the range of 55-100%, and the measurement range is 0-100%; the pH value measurement range is 3-9pH, and the measurement precision is +/-0.3 pH;

a wind speed and wind direction sensor is adopted for measuring wind speed and wind direction, a 12-24V power supply is adopted for supplying power for the wind speed and wind direction sensor, the wind speed measurement response time is less than 5s, the wind speed measurement range is 0-30m/s, and the wind speed measurement precision is +/-1 m/s; the wind direction measuring range is 0-360 degrees, and the wind direction measuring precision is +/-3 degrees.

Example 1

According to an embodiment of the present invention, the transmission flow of the NB-IoT wireless communication module when the present invention is applied to a grassland environment monitoring system is described as follows:

the communication module starts working after parameters are set, module initialization is firstly carried out, the system starts seeking network access after the module initialization is completed, a TCP request can be established after the target network access is successful, data communication is started after the TCP request is established, the cloud end can send data to the communication module, the communication module communicates with the data acquisition terminal device through a serial port, the data acquisition terminal device can also send data to the communication module through the serial port, and the communication module sends the data to the cloud end server through the established TCP connection.

Example 2

According to an embodiment of the present invention, the following describes in detail the processing flow of the web server when the present invention is applied to a grassland environment monitoring system:

the embodiment of the invention designs and realizes a program part of a web service side based on a PHP development language, a CI development framework and an MVC development mode, wherein the main functions of the program part are to receive data sent by a client side and access a database according to requirements, and the program part is divided into different folders according to different functions of files, wherein the different folders mainly comprise Models, Views, controls, Config, a plurality of CI framework system folders and the like, and the different folders correspond to different functions. The model folder stores Data _ models.php, User _ models.php and other files, and mainly operates Data and Data in the vegetation Data table; the Views folder is used for storing files such as logic, php, zhmc, php and the like and is used for interacting with clients; the controls folder is used for controlling the circulation of the whole request and business processing, wherein the business processing comprises user input, verification, page jump and the like.

When a herdsman or an administrator client sends an access request through a browser address, the herdsman or the administrator client firstly enters an index. php file under a root directory, the index. php file jumps to a User. php file, enters a check _ locking 1 function according to the address, the check _ locking 1 function acquires data acquisition terminal id data and date data sent by a login page of the client, meanwhile, the check _ locking 1 function loads a User _ modules. php file under a Models folder and calls a show _ view function under the User _ modules. php file, the User _ modules inherits a CI _ Model class, the show _ view function accesses information of a database according to the requirement, the database selects and extracts required data according to the received data acquisition terminal id data and date data, the data are transmitted to a Web client program from the database, and the client displays the data.

Php is an index file, and receives an access request; php is used to receive data screening conditions; php is used to declare various implementation methods, including accepting database data, passing to the front-end page.

Example 3

According to an embodiment of the present invention, the following describes the data preprocessing process when the present invention is applied to a grassland environment monitoring system:

the image processing of the invention is a processing method based on an OpenCV visual library, and OpenCV is a code open source computer visual library and can realize a plurality of general algorithms in the aspects of image processing and computer vision. When an image processing program processes image data, an OpenCV (open circuit computer library) library is firstly introduced into the program, an image data path to be processed is defined, and the data path is obtained through cv. Then, entering an image processing main function, firstly obtaining the height and the width of the image, thereby calculating the number of pixel points in the image, and obtaining the height and the width of the current image and the number of channels through shape sentences; defining a global variable for recording the number of pixel points meeting the requirement; and traversing each pixel point, increasing the record value when the pixel value of the pixel point is in the three-color channel range, and changing the pixel point into white when the pixel point does not meet the requirement. And after traversing, performing division operation through the recorded values in the variables and the total pixel quantity to obtain a value smaller than an integer 1, wherein the value is the NDVI value of the monitoring point, and storing the NDVI data into a database.

Example 4

The evaluation matrix construction process of the present invention is described in detail below, according to one embodiment of the present invention.

sequentially combining the probability values of each influence factor in the influence factor set U to each evaluation level in the evaluation level set V into an evaluation probability matrix P; the following formula is used as the probability value calculation function:

wherein,

x is the value of the influencing factor;

a, b, c and d are sequentially adjacent evaluation criteria respectively;

obtaining a judgment matrix A through the weight comparison of all factors;

Vector the column

Carrying out normalization processing to obtain a system weight vector

The resulting weight vector

Example 5

The environmental evaluation using the environmental evaluation system of the present invention will be described in detail below, according to one embodiment of the present invention.

TABLE 1 data set

Environmental index	9 hours data
		Temperature of	22.6℃
Humidity of air	60.9％RH
		Illumination of light	31237Lux
Air pressure	101.9KPa
		Rainfall amount	0.1mm
Wind speed	0.3m/s
		Wind direction	N
NDVI	0.89
		Humidity of soil	38.8％RH
Alkalinity of brewing	6.07

And quantifying the relative importance of each factor index by adopting a 1-9 proportional scaling method to obtain a judgment matrix, wherein the judgment matrix A is shown as the following formula.

Adding each row of the judgment matrix A to obtain the arithmetic mean value of each row, and forming a column vector by the mean value of each row

Will be provided with

Then, the vector obtained by normalization processing is carried out

Column vector

I.e. the system weight vector, the weight vector

The following were used:

and (3) bringing 10 factor sets such as the temperature and the air humidity in the data set into each corresponding probability value calculation function to obtain an evaluation probability matrix R corresponding to the data set, wherein the evaluation probability matrix R is shown as the following formula:

after the evaluation probability matrix R is obtained, multiplying the weight vector in the system by the membership degree matrix R formula (6-1) to obtain an evaluation set vector B through calculation, wherein the evaluation set vector B is shown as the following formula.

B(0，0.015，0.391，0.594)

It can be seen that the grassland environmental rating in the data set is 0% likely to be "bad", 1.5% likely to be "normal", 39.1% likely to be "generally fit", and 59.4% likely to be "very fit". The evaluation level corresponding to the maximum value of "59.4%" in the evaluation set vector B is taken as the final result, and it is determined that the grassland environment level at this time is "very suitable".

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims

1. A grassland environment evaluation method based on the Internet of things is characterized by comprising the following steps:

a data acquisition step, namely a step of acquiring data,

a data transmission step, in which the data is transmitted,

a data pre-processing step, namely,

the server preprocesses the acquired data;

the pretreatment comprises the following steps:

an evaluation matrix construction step, wherein,

obtaining a judgment matrix A through the weight comparison of all factors;

Vector the column

Carrying out normalization processing to obtain a system weight vector

The resulting weight vector

an evaluation step of evaluating the quality of the sample,

2. The meadow environment evaluation method based on the internet of things as claimed in claim 1, wherein the data preprocessing step specifically comprises:

3. The Internet of things-based meadow environment evaluation method as claimed in claim 1, wherein the probability value of each influence factor in the influence factor set U to each evaluation level in the evaluation level set V is calculated by the following method:

wherein,

x is the value of the influencing factor;

a, b, c and d are sequentially adjacent evaluation criteria respectively.

4. The Internet of things-based meadow environment evaluation method of claim 1, wherein the Internet of things is a narrowband Internet of things (NB-IoT), and the server is a cloud server.

5. The Internet of things-based meadow environment evaluation method of claim 1, wherein the Internet of things performs the following operations on the received meadow environment data:

performing character string decoding on the grassland environment data;

and storing the data after format conversion into a database.

6. The grassland environment evaluation method based on the Internet of things of claim 1, wherein an illumination sensor is used for collecting illumination intensity, the operating voltage of the illumination intensity is 2.4-5.5V, the error is +/-5% when the illumination precision is 25 ℃, and the illumination intensity range is 0-200 kLux;