Farmland environment multi-element real-time monitoring system based on Internet of Things

Abstract

The invention discloses a farmland environment multi-element real-time monitoring system based on the Internet of Things, and relates to the field of farmland environment real-time monitoring, and the system comprises a farmland environment monitoring module, a two-dimensional code recognition module and a block chain tracing module. An embedded chip STM32 single chip microcomputer, a sensor, a camera and a narrowband Internet of Things NB-IoT communication technology are introduced into the farmland environment monitoring unit; a two-dimensional code is used as a unique identifier of an oat product, public data in the whole process from raw material collection to production and manufacturing, storage and transportation, quarantine detection and the like are recorded, and the two-dimensional code is printed and bound to a corresponding product package; and the block chain tracing unit is based on a Hyperledger Fabric alliance block chain and comprises a data storage layer, a service layer and an interface layer. The two-dimensional code recognition module and the block chain tracing module are adopted to provide quality data tracking and information supervision services for users, and the farmland environment monitoring module can count the whole growth process of plants to make data more real and transparent.

Description

Technical Field

[0001] The present invention relates to the field of farmland environment real-time monitoring, and in particular to a farmland environment multi-factor real-time monitoring system based on the Internet of Things. Background Art

[0002] Farmland environment factors refer to various environmental factors that affect crop growth and yield. Soil is the basis for crop growth, and its properties directly affect crop growth. Important soil-related factors include soil moisture, soil temperature, soil pH, soil nutrient content (such as nitrogen, phosphorus, potassium, etc.) and soil texture.

[0003] Climate conditions have a significant impact on the growth cycle and yield of crops. Key climate factors include air temperature, air humidity, precipitation, wind speed, wind direction, and solar radiation.

[0004] Light is a necessary condition for plants to carry out photosynthesis and is crucial to the growth and development of crops. Light intensity, duration and quality are all important factors of light.

[0005] Water is one of the most critical resources in agricultural production. In addition to soil moisture, it also includes irrigation water quality, irrigation methods and drainage conditions.

[0006] Biodiversity in farmland ecosystems is also an important environmental factor, including crop pests and diseases, beneficial organisms (such as pollinators), microbial communities, etc.

[0007] Human activities in agricultural production will also have an impact on the farmland environment, such as fertilization, irrigation, farming methods, pesticide use, and the degree of agricultural mechanization.

[0008] In general, through real-time monitoring and management of these environmental factors, we can better understand the operating mechanism of farmland ecosystems, provide data support for precision agriculture, and thus improve agricultural production efficiency and sustainability.

[0009] Real-time monitoring of multiple factors in farmland environment based on the Internet of Things can not only significantly improve the efficiency and quality of agricultural production, but also promote the rational use of resources and the sustainable development of agriculture.

[0010] Therefore, there is an urgent need for a real-time monitoring system of multiple factors of farmland environment based on the Internet of Things. Summary of the invention

[0011] To solve the above problems, this application proposes a real-time monitoring system for multiple factors of farmland environment based on the Internet of Things, including:

[0012] Farmland environment monitoring module: The farmland environment monitoring unit introduces embedded chip STM32 single-chip microcomputer, sensor, camera and narrowband Internet of Things NB-IoT communication technology;

[0013] QR code recognition module: Use QR code as the unique identification of oat products to record the public data of the whole process from raw material collection to production, storage and transportation, quarantine and testing. The QR code will be printed and bound to the corresponding product packaging;

[0014] Blockchain traceability module: The blockchain traceability unit is based on the Hyperledger Fabric alliance blockchain, including a data storage layer, a service layer, and an interface layer.

[0015] Preferably, the sensor includes a barometer, a temperature and humidity sensor, a soil moisture sensor, and a light intensity sensor.

[0016] Preferably, the narrowband Internet of Things NB-IoT communication technology belongs to the narrowband Internet of Things NB-IoT communication technology of the low power wide area network LPWAN, which is used to upload, store and display data information collected by the sensor network on the Internet of Things cloud platform for monitoring the farmland environment.

[0017] Preferably, the QR code recognition module belongs to the industrial Internet identification system. Identification is the basis of the network and is also the "identity card" used to distinguish different items, entities, and Internet of Things objects. The QR code is used as the unique identification of agricultural products to record the data of the entire process of agricultural products from raw material collection to production, warehousing and transportation, and quarantine and testing.

[0018] Preferably, the storage layer includes CouchDB on the chain, MysQL, Redis, and FastDFS off the chain;

[0019] The interface layer includes CLI, API, SDK and events;

[0020] The service layer is the core part of the blockchain traceability module, including management services, member services, blockchain services, chain code services, and security and cryptographic services;

[0021] The management services include identity management, ledger management, transaction management and smart contracts;

[0022] The member services include registration and login and attribute certificates;

[0023] The blockchain services include distributed ledger, sorting service, P2P network protocol and endorsement verification;

[0024] The chaincode service includes a container environment and a file repository.

[0025] Preferably, the sensor arrangement includes:

[0026] Acquire farmland information, analyze the farmland information to obtain farmland horizontal height, farmland area, farmland plant density per unit area and plant height;

[0027] A "meter"-shaped extension line is established with the center of the farmland as the origin. A first barometer is set up at the origin. A unit length comparison table and a height difference threshold are preset according to the sensitivity of the plant to the horizontal height. The unit length is determined according to the unit length comparison table.

[0028] Calculate the height difference of farmland level under adjacent unit lengths to obtain the initial height difference;

[0029] Take the "meter" extension line as the display baseline, define the adjacent unit length as the pyramid base n, define the initial height difference as the second layer n-1 of the pyramid, add the adjacent initial height differences to get the height difference of the n-2 layer, add the adjacent n-2 layer height differences to get the height difference of the n-3 layer...calculate layer by layer to get the pyramid top number, that is, the height difference of the top;

[0030] Compare the height differences of different layers with the height difference threshold, obtain the unit length of the height difference above the height difference threshold and define it as the intermediate point on the extension line, take the midpoint of the current intermediate point and the intermediate point closest to the current intermediate point in the direction of the origin as the placement point, and place a barometer at the placement point.

[0031] Preferably, the first temperature and humidity sensors are evenly placed at the edge of the farmland, the farmland is divided into regions according to the density of the farmland plants per unit area to obtain dense areas and the size of the dense areas is calculated, and the second humidity sensor is established at the center of the dense area;

[0032] An area-humidity sensor comparison table is preset, and the number of humidity sensors is determined according to the area size of the dense area and the fan-shaped is established with the center point of the dense area as the center of the circle;

[0033] Install the first soil moisture sensors at the placement points respectively, configure the number of soil moisture sensors according to the area of ​​the farmland, divide the farmland into grids and evenly place the soil moisture sensors on the grid nodes;

[0034] The farmland is divided into height areas according to the plant heights, and a light intensity sensor is established at the center of the height area.

[0035] Preferably, the farmland environment monitoring module is equipped with an abnormal inspection unit;

[0036] The abnormal inspection unit collects sensor data, and randomly selects two sensors to perform difference calculation on the sensor data at the same time point to obtain a sensor difference value;

[0037] Stability thresholds for different sensors are preset. If the sensor difference value exceeds the stability threshold, an abnormal alarm will be issued.

[0038] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the real-time monitoring system of multiple elements of farmland environment based on the Internet of Things is implemented.

[0039] A storage medium stores computer executable instructions, which, when loaded and executed by a processor, implement the contents of the multi-factor real-time monitoring system for farmland environment based on the Internet of Things.

[0040] In summary, compared with traditional technologies, the real-time monitoring system of multiple factors of farmland environment based on the Internet of Things of the present invention adopts a two-dimensional code recognition module and a blockchain traceability module to provide users with quality data tracking and information supervision services. The farmland environment monitoring module can perform statistics on the overall growth process of the plants to make the data more real and transparent, and also proposes a sensor arrangement method, setting different installation strategies for different sensors to make the collected data more detailed and complete.

[0041] The technical method of the present invention is further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a module diagram of the farmland environment multi-factor real-time monitoring system based on the Internet of Things of the present invention;

[0043] Figure 2 This is a schematic diagram of the blockchain traceability module of the farmland environment multi-factor real-time monitoring system based on the Internet of Things of the present invention;

[0044] Figure 3 This is a monthly change chart of air pressure and temperature in an embodiment of the farmland environment multi-factor real-time monitoring system based on the Internet of Things of the present invention;

[0045] Figure 4 This is a monthly variation diagram of soil moisture and air humidity in an embodiment of a farmland environment multi-factor real-time monitoring system based on the Internet of Things of the present invention;

[0046] Figure 5 This is a monthly change graph of light intensity in an embodiment of a farmland environment multi-factor real-time monitoring system based on the Internet of Things of the present invention;

[0047] Figure 6 This is a graph showing the time-varying light intensity of an embodiment of the real-time monitoring system for multiple factors of farmland environment based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0048] The technical method of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of the components and steps, the numerical expressions and the numerical values ​​described in these embodiments do not limit the scope of the present application.

[0049] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0050] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0051] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0052] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0053] like Figure 1 As shown, the present invention provides a real-time monitoring system for multiple factors of farmland environment based on the Internet of Things, including:

[0054] Farmland environment monitoring module: The farmland environment monitoring unit introduces embedded chip STM32 single-chip microcomputer, sensor, camera and narrowband Internet of Things NB-IoT communication technology to deploy sensor network, monitor farmland environment in real time, and build a software and hardware combination platform;

[0055] Furthermore, the sensors include barometers, temperature and humidity sensors, soil moisture sensors, and light intensity sensors. That is, the perception layer, which mainly relies on sensors such as light, temperature and humidity, air pressure, soil nutrients, and soil moisture to collect data on oat raw material production and farmers' planting, and uses the transmission layer NB-IoT, WiFi, Ethernet and other communication equipment and protocols to automatically upload data to terminal web pages and applets.

[0056] Furthermore, the narrowband Internet of Things NB-IoT communication technology belongs to the narrowband Internet of Things NB-IoT communication technology of the low-power wide area network LPWAN, which is used to upload, store and display data information collected by the sensor network on the Internet of Things cloud platform for monitoring the farmland environment.

[0057] Furthermore, the sensor arrangement includes:

[0058] Acquire farmland information, analyze the farmland information to obtain farmland horizontal height, farmland area, farmland plant density per unit area and plant height;

[0059] A "meter"-shaped extension line is established with the center of the farmland as the origin. A first barometer is set up at the origin. A unit length comparison table and a height difference threshold are preset according to the sensitivity of the plant to the horizontal height. The unit length is determined according to the unit length comparison table.

[0060] Calculate the height difference of farmland level under adjacent unit lengths to obtain the initial height difference;

[0061] Take the "meter" extension line as the display baseline, define the adjacent unit length as the pyramid base n, define the initial height difference as the second layer n-1 of the pyramid, add the adjacent initial height differences to get the height difference of the n-2 layer, add the adjacent n-2 layer height differences to get the height difference of the n-3 layer...calculate layer by layer to get the pyramid top number, that is, the height difference of the top;

[0062] Compare the height differences of different layers with the height difference threshold, obtain the unit length of the height difference above the height difference threshold and define it as the intermediate point on the extension line, take the midpoint of the current intermediate point and the intermediate point closest to the current intermediate point in the direction of the origin as the placement point, and place a barometer at the placement point.

[0063] Furthermore, the first temperature and humidity sensors are evenly placed at the edge of the farmland, the farmland is divided into regions according to the density of the farmland plants per unit area to obtain dense areas and the size of the dense areas is calculated, and the second humidity sensor is established at the center of the dense area;

[0064] An area-humidity sensor comparison table is preset, and the number of humidity sensors is determined according to the area size of the dense area and the fan-shaped is established with the center point of the dense area as the center of the circle;

[0065] Install the first soil moisture sensors at the placement points respectively, configure the number of soil moisture sensors according to the area of ​​the farmland, divide the farmland into grids and evenly place the soil moisture sensors on the grid nodes;

[0066] The farmland is divided into height areas according to the plant heights, and a light intensity sensor is established at the center of the height area.

[0067] Furthermore, the farmland environment monitoring module is equipped with an abnormal inspection unit;

[0068] The abnormal inspection unit collects sensor data, and randomly selects two sensors to perform difference calculation on the sensor data at the same time point to obtain a sensor difference value;

[0069] Stability thresholds for different sensors are preset. If the sensor difference value exceeds the stability threshold, an abnormal alarm will be issued.

[0070] QR code recognition module: Use QR code as the unique identification of oat products to record the public data of the whole process from raw material collection to production, storage and transportation, quarantine and testing. The QR code will be printed and bound to the corresponding product packaging. Users can understand the relevant traceability and anti-counterfeiting information by installing the corresponding reading software;

[0071] Furthermore, the QR code recognition module belongs to the industrial Internet identification system. Identification is the basis of the network and is also the "identity card" used to distinguish different items, entities, and Internet of Things objects. The QR code is used as the unique identification of agricultural products to record the data of the entire process of agricultural products from raw material collection to production, warehousing and transportation, and quarantine and testing.

[0072] like Figure 2 As shown, blockchain traceability module: the blockchain traceability unit is based on the Hyperledger Fabric alliance blockchain, including a data storage layer, a service layer and an interface layer.

[0073] Blockchain technology can provide traceability information of relevant roles to each party in the supply chain, but node users can only view and store product data in a shared system and cannot make modifications at will. Its main advantages include decentralization, traceability, openness and transparency, collective maintainability, non-forgeability, non-fictitiousness and non-tamperability.

[0074] Furthermore, the storage layer includes CouchDB on the chain, MysQL, Redis, and FastDFS off the chain;

[0075] The interface layer includes CLI, API, SDK and events;

[0076] The service layer is the core part of the blockchain traceability module, including management services, member services, blockchain services, chain code services, and security and cryptographic services;

[0077] The management services include identity management, ledger management, transaction management and smart contracts;

[0078] The member services include registration and login and attribute certificates;

[0079] The blockchain services include distributed ledger, sorting service, P2P network protocol and endorsement verification;

[0080] The chaincode service includes a container environment and a file repository.

[0081] Based on the sensor network and QR code identification, a real-time monitoring system for multiple factors of farmland environment based on the Internet of Things and the Hyperledger Fabric alliance blockchain is built to open up the interface between the blockchain network and the Internet of Things monitoring system and improve the overall performance of the system.

[0082] An electronic device, characterized in that it includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the content of the real-time monitoring system of multiple elements of farmland environment based on the Internet of Things is implemented.

[0083] A storage medium stores computer executable instructions, which, when loaded and executed by a processor, implement the contents of the multi-factor real-time monitoring system for farmland environment based on the Internet of Things.

[0084] like Figure 3-6 As shown in the figure, in order to analyze the reliability and practicality of the farmland environment monitoring system, a farmland in Inner Mongolia was selected as an experimental site, and experimental tests were carried out from March to April. From 0 to 23:00 every day, the air pressure (Pre), air temperature (Tem), air humidity (Hum) and light intensity (Light) data were uploaded every 1 hour, and the soil humidity (Soil_Hum) data were uploaded every 3 hours. All the air pressure, temperature, humidity, and soil humidity data collected from March to April were averaged for 24 hours, and the light intensity data at 13:00 was selected to obtain the daily farmland environment information from March to April.

[0085] The light intensity changes at 13:00 every day from March to April. Its physical meaning is the luminous flux irradiating to unit area.

[0086] It can be seen from the hourly data that the peak light intensity generally occurs between 12:00 and 14:00. The sowing period can be adjusted according to the optimal light requirements of oats to improve oat yield and efficiency.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. The real-time monitoring system of multiple factors of farmland environment based on Internet of Things is characterized by: Includes the following modules: Farmland environment monitoring module: The farmland environment monitoring unit introduces embedded chip STM32 single-chip microcomputer, sensor, camera and narrowband Internet of Things NB-IoT communication technology; QR code recognition module: Use QR code as the unique identification of oat products to record the public data of the whole process from raw material collection to production, storage and transportation, quarantine and testing. The QR code will be printed and bound to the corresponding product packaging; Blockchain traceability module: The blockchain traceability unit is based on the Hyperledger Fabric alliance blockchain, including a data storage layer, a service layer, and an interface layer.

2. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 1 is characterized in that: Sensors include barometer, temperature and humidity sensor, soil moisture sensor, and light intensity sensor.

3. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 2 is characterized in that: The narrowband Internet of Things NB-IoT communication technology belongs to the narrowband Internet of Things NB-IoT communication technology of the low-power wide area network LPWAN, and is used to upload, store and display data information collected by the sensor network on the Internet of Things cloud platform for monitoring the farmland environment.

4. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 3 is characterized in that: The QR code recognition module belongs to the industrial Internet identification system. Identification is the basis of the network and is also the "identity card" used to distinguish different items, entities, and Internet of Things objects. The QR code is used as the unique identification of agricultural products to record the data of the entire process of agricultural products from raw material collection to production, warehousing and transportation, and quarantine and testing.

5. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 4 is characterized in that: The storage layer includes CouchDB on the chain, MysQL, Redis, and FastDFS off the chain; The interface layer includes CLI, API, SDK and events; The service layer is the core part of the blockchain traceability module, including management services, member services, blockchain services, chain code services, and security and cryptographic services; The management services include identity management, ledger management, transaction management and smart contracts; The member services include registration and login and attribute certificates; The blockchain services include distributed ledger, sorting service, P2P network protocol and endorsement verification; The chaincode service includes a container environment and a file repository.

6. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 2 is characterized in that: The sensor arrangement includes: Acquire farmland information, analyze the farmland information to obtain farmland horizontal height, farmland area, farmland plant density per unit area and plant height; A "meter" extension line is established with the center of the farmland as the origin. A first barometer is set up at the origin. A unit length comparison table and a height difference threshold are preset according to the sensitivity of the plant to the horizontal height. The unit length is determined according to the unit length comparison table. Calculate the height difference of farmland level under adjacent unit lengths to obtain the initial height difference; Take the "meter" extension line as the display baseline, define the adjacent unit length as the pyramid base n, define the initial height difference as the second layer n-1 of the pyramid, add the adjacent initial height differences to get the height difference of the n-2 layer, add the adjacent n-2 layer height differences to get the height difference of the n-3 layer...calculate layer by layer to get the pyramid top number, that is, the height difference of the top; Compare the height differences of different layers with the height difference threshold, obtain the unit length of the height difference above the height difference threshold and define it as the intermediate point on the extension line, take the midpoint of the current intermediate point and the intermediate point closest to the current intermediate point in the direction of the origin as the placement point, and place a barometer at the placement point.

7. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 6 is characterized in that: Evenly place the first temperature and humidity sensors at the edge of the farmland, divide the farmland into regions according to the density of the farmland plants per unit area to obtain dense areas and calculate the size of the dense areas, and establish the second humidity sensor at the center of the dense areas; An area-humidity sensor comparison table is preset, and the number of humidity sensors is determined according to the area size of the dense area and the fan-shaped is established with the center point of the dense area as the center of the circle; Install the first soil moisture sensors at the placement points respectively, configure the number of soil moisture sensors according to the area of ​​the farmland, divide the farmland into grids and evenly place the soil moisture sensors on the grid nodes; The farmland is divided into height areas according to the plant heights, and a light intensity sensor is established at the center of the height area.

8. The farmland environment multi-factor real-time monitoring system based on the Internet of Things according to claim 7 is characterized in that: The farmland environment monitoring module is equipped with an abnormal inspection unit; The abnormal inspection unit collects sensor data, and randomly selects two sensors to perform difference calculation on the sensor data at the same time point to obtain a sensor difference value; Stability thresholds for different sensors are preset. If the sensor difference value exceeds the stability threshold, an abnormal alarm will be issued.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it implements the content of the real-time monitoring system of multiple elements of farmland environment based on the Internet of Things as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by the processor, implement the contents of the real-time monitoring system for multiple factors of farmland environment based on the Internet of Things as described in any one of claims 1 to 7.

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