An intelligent environmental monitoring system based on the Internet of Things

Abstract

The present invention discloses an environment intelligent monitoring system based on the Internet of Things, which specifically relates to the field of the Internet of Things, including a monitoring area division module, an environment data collection module, an environment data analysis module, a biological data collection module, a biological data processing module, a biological data analysis module, a comprehensive analysis module, and an intelligent early warning module. The present invention collects and analyzes data on a target crop planting area from two aspects of environmental data and biological data, and finally performs a comprehensive analysis on the data to realize intelligent monitoring of the environment. The present invention can realize the integration between different data, provide more comprehensive and accurate data analysis results, improve the accuracy and reliability of environmental monitoring, improve the safety of the target crop planting area through intelligent early warning of the target crop planting area, and can also optimize the allocation of agricultural production resources, promote green agricultural environmental protection, real-time monitoring and precise management.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and more specifically, to an intelligent environmental monitoring system based on Internet of Things. Background Art

[0002] Agriculture is an important part of the primary industry. It uses the growth and development laws of animals and plants to artificially cultivate and obtain products. The agricultural environment is a comprehensive factor that affects the survival and development of agricultural organisms, including natural factors and natural factors that have been artificially modified.

[0003] Traditional agricultural environmental monitoring systems can only provide a single data source, and the data is too general and not comprehensive. In addition, the system usually only provides basic data analysis, such as average, maximum, minimum, etc., which lacks accuracy and reliability. These data require manual processing and analysis, and cannot respond to environmental changes in a timely manner, which limits the practical application of the system. In order to solve the above problems, this paper proposes an environmental intelligent monitoring system based on the Internet of Things. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an environment intelligent monitoring system based on the Internet of Things, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent environmental monitoring system based on the Internet of Things, comprising:

[0006] The monitoring area division module is used to determine the target crop planting area as the target monitoring area, divide the target monitoring area into sub-monitoring areas of equal area for sampling survey, and mark them as 1, 2...n in sequence;

[0007] The environmental data acquisition module is used to collect the light parameters, soil parameters and water quality parameters of each sub-monitoring area, and transmit the collected data to the environmental data analysis module;

[0008] The environmental data analysis module includes a light parameter analysis unit, a soil parameter analysis unit and a water quality parameter analysis unit, which are used to establish a mathematical model, import the data transmitted by the environmental data acquisition module into the mathematical model to calculate the photosynthesis efficiency index, soil characteristic index and water quality characteristic index of each sub-monitoring area, and transmit them to the comprehensive analysis module;

[0009] The biological data collection module is used to collect the fruiting parameters, growth parameters and disease parameters of each sub-monitoring area, and transmit the collected data to the biological data processing module;

[0010] The biological data processing module is used to process the fruiting parameters, growth parameters and disease parameters transmitted by the biological data acquisition module, and transmit the processed data to the biological data analysis module;

[0011] The biological data analysis module is used to establish a biological data analysis model, import the data transmitted by the biological data processing module into the biological data analysis model, calculate the crop health index of each sub-monitoring area, and transmit it to the comprehensive analysis module;

[0012] The comprehensive analysis module is used to establish a comprehensive analysis model, import the data transmitted by the biological data analysis module and the environmental data analysis module into the comprehensive analysis model, calculate the comprehensive optimization index of the target crop planting area, and transmit it to the intelligent early warning module;

[0013] The intelligent early warning module is used to provide intelligent early warning for the environment of the target crop planting area according to the comprehensive optimization index of the target crop planting area transmitted by the comprehensive analysis module and the preset value of the comprehensive optimization index.

[0014] Preferably, the lighting parameters include light intensity, lighting time, greenhouse light transmittance and crop quantity, which are marked as , , as well as , soil parameters include soil quality, soil moisture, soil bulk density, soil pore volume, soil volume, and soil pH, which are marked as , , , , as well as , water quality parameters include water flow rate, water pH, water dissolved oxygen concentration and water turbidity, which are marked as , , as well as , where i=1, 2…n, i represents the i-th sub-monitoring area;

[0015] The fruiting parameters include fruiting number, bird feeding number, artificial pollination number and insect pollination number, which are marked as , , as well as , growth parameters include crop sample quality and growth time, decibels are marked and , the disease parameters include the lesion area, leaf area, number of crop diseases and number of crops, which are marked as , , as well as , where i=1, 2…n, i represents the i-th sub-monitoring area.

[0016] Preferably, the environmental data acquisition module collects light intensity and illumination time by installing a light intensity sensor and a timer in the greenhouse, collects greenhouse transmittance by installing a transmittance sensor on the surface of the greenhouse, collects the number of crops by installing a visual sensor just above the crops, collects soil samples by a soil collector, collects soil quality, soil moisture and soil bulk density of the soil samples by a drying weighing method, collects soil pore volume and soil volume of the soil samples by placing the soil samples in a measuring cup, collects soil pH of the soil samples by pH test paper, collects water flow velocity by installing an ultrasonic flow meter at the water outlet, collects water pH by pH test paper, collects water dissolved oxygen concentration by a dissolved oxygen meter, and collects water turbidity by a turbidity meter;

[0017] The biological data collection module collects the number of fruits, the number of birds feeding, the number of insect pollination, the number of artificial pollination, the area of ​​lesions, the area of ​​leaves, the number of crop diseases and the number of crops by installing a visual sensor just above the crops, and collects the quality of crop samples by weighing one crop in each sub-monitoring area.

[0018] Preferably, the illumination parameter analysis unit is used to establish an illumination parameter analysis model, and the specific mathematical model is: , represents the photosynthesis efficiency index of the ith sub-monitoring area, represents the light intensity of the ith sub-monitoring area, represents the illumination time of the ith sub-monitoring area, represents the greenhouse light transmittance of the ith sub-monitoring area, represents the number of crops in the ith sub-monitoring area, Indicates the number of sub-monitoring areas, represents the area of ​​each sub-monitoring area, Represents other influencing factors of photosynthesis efficiency index.

[0019] Preferably, the soil parameter analysis unit is used to establish a soil parameter analysis model, and the specific mathematical model is: , represents the soil property index of the ith sub-monitoring area, represents the soil quality of the ith sub-monitoring area, represents the soil moisture in the ith sub-monitoring area, represents the soil bulk density of the ith sub-monitoring area, represents the soil pore volume of the ith sub-monitoring area, represents the soil volume of the ith sub-monitoring area, represents the soil pH of the ith sub-monitoring area, Represents other influencing factors of soil property index.

[0020] Preferably, the water quality parameter analysis unit is used to establish a water quality parameter analysis model, and the specific mathematical model is: , represents the water quality characteristic index of the ith sub-monitoring area, represents the water flow velocity in the ith sub-monitoring area, represents the water quality pH value of the ith sub-monitoring area, represents the dissolved oxygen concentration in the water quality of the ith sub-monitoring area, represents the water turbidity of the ith sub-monitoring area, Indicates the number of sub-monitoring areas, Represents other influencing factors of water quality characteristic index.

[0021] Preferably, the biological data analysis model is specifically expressed as: , represents the crop health index of the ith sub-monitoring area, represents the fruiting rate of the ith sub-monitoring area, represents the growth rate of the ith sub-monitoring area, represents the disease index of the ith sub-monitoring area, Indicates other influencing factors of crop health index.

[0022] Preferably, the comprehensive analysis model is specifically expressed as: , represents the comprehensive optimization index of the target crop planting area, represents the crop health index of the ith sub-monitoring area, represents the photosynthesis efficiency index of the ith sub-monitoring area, represents the soil property index of the ith sub-monitoring area, Represents the water quality characteristic index of the i-th sub-monitoring area.

[0023] Preferably, the preset value of the comprehensive optimization index is marked as ,when When , it means that the preset value of the comprehensive optimization index is less than or equal to the comprehensive optimization index of the target crop planting area, indicating that the environment of the target crop planting area is normal, then the monitoring of each sub-monitoring area is maintained. When , it means that the preset value of the comprehensive optimization index is greater than the comprehensive optimization index of the target crop planting area, indicating that the environment of the target crop planting area is abnormal, and an early warning signal is issued.

[0024] Technical effects and advantages of the present invention:

[0025] The present invention collects and analyzes data on the target crop planting area from two aspects of environmental data and biological data, and finally conducts comprehensive analysis of the data to realize intelligent monitoring of the environment. The present invention can realize the integration of different data, provide more comprehensive and accurate data analysis results, improve the accuracy and reliability of environmental monitoring, and more accurately understand the growth needs of crops. Through intelligent early warning of the target crop planting area, the safety of the target crop planting area is improved, and the allocation of agricultural production resources can be optimized, and green agricultural environmental protection, real-time monitoring and precise management can be promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] refer to Figure 1 An environmental intelligent monitoring system based on the Internet of Things is shown, including a monitoring area division module, an environmental data acquisition module, an environmental data analysis module, a biological data acquisition module, a biological data processing module, a biological data analysis module, a comprehensive analysis module and an intelligent early warning module.

[0029] The monitoring area division module is used to determine the target crop planting area as the target monitoring area, divide the target monitoring area into sub-monitoring areas of equal area for sampling survey, and mark them as 1, 2...n in sequence.

[0030] The environmental data acquisition module is used to collect the illumination parameters, soil parameters and water quality parameters of each sub-monitoring area, and transmit the collected data to the environmental data analysis module.

[0031] The lighting parameters include light intensity, lighting time, greenhouse light transmittance and crop quantity, which are marked as , , as well as , soil parameters include soil quality, soil moisture, soil bulk density, soil pore volume, soil volume, and soil pH, which are marked as , , , , as well as , water quality parameters include water flow rate, water pH, water dissolved oxygen concentration and water turbidity, which are marked as , , as well as , where i=1, 2…n, i represents the i-th sub-monitoring area.

[0032] The environmental data acquisition module collects light intensity and lighting time by installing a light intensity sensor and a timer in the greenhouse, collects greenhouse transmittance by installing a transmittance sensor on the surface of the greenhouse, collects the number of crops by installing a visual sensor just above the crops, collects soil samples by a soil collector, collects soil quality, soil moisture and soil bulk density of the soil samples by a drying weighing method, collects soil pore volume and soil volume of the soil samples by placing the soil samples in a measuring cup, collects soil acidity and alkalinity of the soil samples by pH test paper, collects water flow velocity by installing an ultrasonic flow meter at the water outlet, collects water acidity and alkalinity by pH test paper, collects water dissolved oxygen concentration by a dissolved oxygen meter, and collects water turbidity by a turbidity meter.

[0033] The environmental data analysis module includes a light parameter analysis unit, a soil parameter analysis unit and a water quality parameter analysis unit, which are used to establish a mathematical model, import the data transmitted by the environmental data acquisition module into the mathematical model to calculate the photosynthesis efficiency index, soil characteristic index and water quality characteristic index of each sub-monitoring area, and transmit them to the comprehensive analysis module.

[0034] The illumination parameter analysis unit is used to establish an illumination parameter analysis model, and the specific mathematical model is: , represents the photosynthesis efficiency index of the ith sub-monitoring area, represents the light intensity of the ith sub-monitoring area, represents the illumination time of the ith sub-monitoring area, represents the greenhouse light transmittance of the ith sub-monitoring area, represents the number of crops in the ith sub-monitoring area, Indicates the number of sub-monitoring areas, represents the area of ​​each sub-monitoring area, Represents other influencing factors of photosynthesis efficiency index.

[0035] The soil parameter analysis unit is used to establish a soil parameter analysis model, and the specific mathematical model is: , represents the soil property index of the ith sub-monitoring area, represents the soil quality of the ith sub-monitoring area, represents the soil moisture in the ith sub-monitoring area, represents the soil bulk density of the ith sub-monitoring area, represents the soil pore volume of the ith sub-monitoring area, represents the soil volume of the ith sub-monitoring area, represents the soil pH of the ith sub-monitoring area, Represents other influencing factors of soil property index.

[0036] The water quality parameter analysis unit is used to establish a water quality parameter analysis model, and the specific mathematical model is: , represents the water quality characteristic index of the ith sub-monitoring area, represents the water flow velocity in the ith sub-monitoring area, represents the water quality pH value of the ith sub-monitoring area, represents the dissolved oxygen concentration in the water quality of the ith sub-monitoring area, represents the water turbidity of the ith sub-monitoring area, Indicates the number of sub-monitoring areas, Represents other influencing factors of water quality characteristic index.

[0037] The biological data collection module is used to collect fruiting parameters, growth parameters and disease parameters of each sub-monitoring area, and transmit the collected data to the biological data processing module.

[0038] The fruiting parameters include fruiting number, bird feeding number, artificial pollination number and insect pollination number, which are marked as , , as well as , growth parameters include crop sample quality and growth time, decibels are marked and , the disease parameters include the lesion area, leaf area, number of crop diseases and number of crops, which are marked as , , as well as , where i=1, 2…n, i represents the i-th sub-monitoring area.

[0039] The biological data collection module collects the number of fruits, the number of birds feeding, the number of insect pollination, the number of artificial pollination, the area of ​​lesions, the area of ​​leaves, the number of crop diseases and the number of crops by installing a visual sensor just above the crops, and collects the quality of crop samples by weighing one crop in each sub-monitoring area.

[0040] The biological data processing module is used to process the fruiting parameters, growth parameters and disease parameters transmitted by the biological data acquisition module, and transmit the processed data to the biological data analysis module.

[0041] The biological data processing module calculates the fruiting rate of each sub-monitoring area through the fruiting parameter. The specific mathematical model is: , represents the fruiting rate of the ith sub-monitoring area, represents the number of solidification in the ith sub-monitoring area, represents the number of birds feeding in the ith sub-monitoring area, represents the number of artificial pollinations in the ith sub-monitoring area, represents the number of insect pollination in the ith sub-monitoring area. The growth rate of each sub-monitoring area is calculated by the growth parameter. The specific mathematical model is: , represents the growth rate of the ith sub-monitoring area, represents the quality of crop samples in the ith sub-monitoring area, represents the growth time of the ith sub-monitoring area, and the disease index of each sub-monitoring area is calculated through the disease parameters. The specific mathematical model is: , represents the disease index of the ith sub-monitoring area, represents the lesion area of ​​the ith sub-monitoring area, represents the leaf area of ​​the ith sub-monitoring area, represents the number of crop diseases in the ith sub-monitoring area, Represents the number of crops in the ith sub-monitoring area.

[0042] The biological data analysis module is used to establish a biological data analysis model, import the data transmitted by the biological data processing module into the biological data analysis model, calculate the crop health index of each sub-monitoring area, and transmit it to the comprehensive analysis module.

[0043] The biological data analysis model is specifically expressed as: , represents the crop health index of the ith sub-monitoring area, represents the fruiting rate of the ith sub-monitoring area, represents the growth rate of the ith sub-monitoring area, represents the disease index of the ith sub-monitoring area, Indicates other influencing factors of crop health index.

[0044] The biological data analysis model is combined into a crop health index mathematical model by setting the fruit set rate, growth rate and disease index, and then reflects the influence trend of the fruit set rate, growth rate and disease index factors of the crops on a final result in this embodiment, and maximizes the intuitive impact of this mathematical model on this system.

[0045] The comprehensive analysis module is used to establish a comprehensive analysis model, import the data transmitted by the biological data analysis module and the environmental data analysis module into the comprehensive analysis model, calculate the comprehensive optimization index of the target crop planting area, and transmit it to the intelligent early warning module.

[0046] The comprehensive analysis model is specifically expressed as: , represents the comprehensive optimization index of the target crop planting area, represents the crop health index of the ith sub-monitoring area, represents the photosynthesis efficiency index of the ith sub-monitoring area, represents the soil property index of the ith sub-monitoring area, Represents the water quality characteristic index of the i-th sub-monitoring area.

[0047] The intelligent early warning module is used to provide intelligent early warning for the environment of the target crop planting area according to the comprehensive optimization index of the target crop planting area transmitted by the comprehensive analysis module and the preset value of the comprehensive optimization index.

[0048] The preset value of the comprehensive optimization index is marked as ,when When , it means that the preset value of the comprehensive optimization index is less than or equal to the comprehensive optimization index of the target crop planting area, indicating that the environment of the target crop planting area is normal, then the monitoring of each sub-monitoring area is maintained. When , it means that the preset value of the comprehensive optimization index is greater than the comprehensive optimization index of the target crop planting area, indicating that the environment of the target crop planting area is abnormal, and an early warning signal is issued.

[0049] The present invention divides the target crop planting area into sub-monitoring areas and numbers them through a monitoring area division module, collects light parameters, soil parameters and water quality parameters of each sub-monitoring area through an environmental data acquisition module, calculates the photosynthesis efficiency index, soil characteristic index and water quality characteristic index of each sub-monitoring area through an environmental data analysis module, collects fruiting parameters, growth parameters and disease parameters of each sub-monitoring area through a biological data acquisition module, processes the data transmitted by the biological data acquisition module through a biological data processing module, calculates the crop health index of each sub-monitoring area through a biological data analysis module, calculates the comprehensive optimization index of the target crop planting area through a comprehensive analysis module, and performs intelligent early warning on the environment of the target crop planting area through an intelligent early warning module.

[0050] The present invention collects and analyzes data on the target crop planting area from two aspects of environmental data and biological data, and finally conducts comprehensive analysis of the data to realize intelligent monitoring of the environment. The present invention can realize the integration of different data, provide more comprehensive and accurate data analysis results, improve the accuracy and reliability of environmental monitoring, and more accurately understand the growth needs of crops. Through intelligent early warning of the target crop planting area, the safety of the target crop planting area is improved, and the allocation of agricultural production resources can be optimized, and green agricultural environmental protection, real-time monitoring and precise management can be promoted.

[0051] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0052] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent environmental monitoring system based on the Internet of Things, characterized in that: include: The monitoring area division module is used to determine the target crop planting area as the target monitoring area, divide the target monitoring area into sub-monitoring areas of equal area for sampling survey, and mark them as 1, 2...n in sequence; The environmental data acquisition module is used to collect the light parameters, soil parameters and water quality parameters of each sub-monitoring area, and transmit the collected data to the environmental data analysis module; The environmental data analysis module includes a light parameter analysis unit, a soil parameter analysis unit and a water quality parameter analysis unit, which are used to establish a mathematical model, import the data transmitted by the environmental data acquisition module into the mathematical model to calculate the photosynthesis efficiency index, soil characteristic index and water quality characteristic index of each sub-monitoring area, and transmit them to the comprehensive analysis module; the biological data acquisition module is used to collect the fruiting parameters, growth parameters and disease parameters of each sub-monitoring area, and transmit the collected data to the biological data processing module; The biological data processing module is used to process the fruiting parameters, growth parameters and disease parameters transmitted by the biological data acquisition module, and transmit the processed data to the biological data analysis module; The biological data analysis module is used to establish a biological data analysis model, import the data transmitted by the biological data processing module into the biological data analysis model, calculate the crop health index of each sub-monitoring area, and transmit it to the comprehensive analysis module; The comprehensive analysis module is used to establish a comprehensive analysis model, import the data transmitted by the biological data analysis module and the environmental data analysis module into the comprehensive analysis model, calculate the comprehensive optimization index of the target crop planting area, and transmit it to the intelligent early warning module; The intelligent early warning module is used to provide intelligent early warning for the environment of the target crop planting area according to the comprehensive optimization index of the target crop planting area and the preset value of the comprehensive optimization index transmitted by the comprehensive analysis module; The illumination parameter analysis unit is used to establish an illumination parameter analysis model, and the specific mathematical model is: AL i Represents the photosynthesis efficiency index of the ith sub-monitoring area, Ls i Indicates the light intensity of the ith sub-monitoring area, Lt i Indicates the illumination time of the ith sub-monitoring area, Lm i represents the greenhouse light transmittance of the ith sub-monitoring area, Lq i represents the number of crops in the ith sub-monitoring area, n represents the number of sub-monitoring areas, ACR represents the area of ​​each sub-monitoring area, and μ1 represents other influencing factors of the photosynthesis efficiency index; The soil parameter analysis unit is used to establish a soil parameter analysis model, and the specific mathematical model is: AS i represents the soil property index of the ith sub-monitoring area, Sq i represents the soil quality of the ith sub-monitoring area, Sh i represents the soil moisture in the ith sub-monitoring area, Sb i represents the soil bulk density of the ith sub-monitoring area, Sp i represents the soil pore volume of the ith sub-monitoring area, Sv i represents the soil volume of the ith sub-monitoring area, Sph i represents the soil pH of the ith sub-monitoring area, μ2 represents other influencing factors of the soil property index; The water quality parameter analysis unit is used to establish a water quality parameter analysis model, and the specific mathematical model is: AW1 represents the water quality characteristic index of the ith sub-monitoring area, Wv i represents the water flow velocity in the ith sub-monitoring area, Wph i represents the water quality pH value of the ith sub-monitoring area, Wo i represents the dissolved oxygen concentration of water quality in the ith sub-monitoring area, Wu i represents the water turbidity of the ith sub-monitoring area, n represents the number of sub-monitoring areas, and μ3 represents other influencing factors of the water quality characteristic index; The biological data analysis model is specifically expressed as: i =VF i ×VG i ×VD i ×λ,η i represents the crop health index of the ith sub-monitoring area, VF i represents the fruiting rate of the ith sub-monitoring area, VG i represents the growth rate of the ith sub-monitoring area, VD i represents the disease index of the ith sub-monitoring area, and λ represents other influencing factors of the crop health index; The comprehensive analysis model is specifically expressed as: ξ represents the comprehensive optimization index of the target crop planting area, η i represents the crop health index of the ith sub-monitoring area, AL i represents the photosynthesis efficiency index of the ith sub-monitoring area, AS i represents the soil property index of the ith sub-monitoring area, AW i Represents the water quality characteristic index of the i-th sub-monitoring area.

2. The environmental intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The lighting parameters include light intensity, lighting time, greenhouse light transmittance and crop quantity, which are marked as Ls i , Lt i , Lm i and Lq i , soil parameters include soil quality, soil moisture, soil bulk density, soil pore volume, soil volume and soil pH, which are marked as Sq i , Sh i , Sb i 、Sp i 、Sv i and Sph i , water quality parameters include water flow rate, water pH, water dissolved oxygen concentration and water turbidity, which are marked as Wv i , Wph i ,Wo i and Wu i , where i = 1, 2, ... n, i represents the i-th sub-monitoring area; The fruit setting parameters include fruit set number, bird feeding number, artificial pollination number and insect pollination number, which are marked as F i , Fb i , Fp i and Fe i , growth parameters include crop sample quality and growth time, respectively marked as Gr i and Gt i , the disease parameters include the lesion area, leaf area, number of crop diseases and number of crops, which are marked as Dv i 、Db i 、Dp i and Lq i , where i=1, 2,...n, and i represents the i-th sub-monitoring area.

3. The environmental intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The environmental data acquisition module collects light intensity and illumination time by installing a light intensity sensor and a timer in the greenhouse, collects greenhouse transmittance by installing a transmittance sensor on the surface of the greenhouse, collects the number of crops by installing a visual sensor just above the crops, collects soil samples by a soil collector, collects soil quality, soil moisture and soil bulk density of the soil samples by drying and weighing method, collects soil pore volume and soil volume of the soil samples by placing the soil samples in a measuring cup, collects soil pH of the soil samples by pH test paper, collects water flow velocity by installing an ultrasonic flow meter at the water outlet, collects water pH by pH test paper, collects water dissolved oxygen concentration by a dissolved oxygen meter, and collects water turbidity by a turbidity meter; The biological data collection module collects the number of fruits, the number of birds feeding, the number of insect pollination, the number of artificial pollination, the area of ​​lesions, the area of ​​leaves, the number of crop diseases and the number of crops by installing a visual sensor just above the crops, and collects the quality of crop samples by weighing one crop in each sub-monitoring area.

4. The environmental intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The preset value of the comprehensive optimization index is marked as ξ N , when N ≤ξ, it means that the preset value of the comprehensive optimization index is less than or equal to the comprehensive optimization index of the target crop planting area, indicating that the environment of the target crop planting area is normal, and the monitoring of each sub-monitoring area is maintained. N >ξ, it means that the preset value of the comprehensive optimization index is greater than the comprehensive optimization index of the target crop planting area, indicating that the environment in the target crop planting area is abnormal, and an early warning signal is issued.

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