A farmland precision irrigation system and method based on the Internet of Things

Abstract

The invention discloses a farmland precision irrigation system and method based on the Internet of Things, belonging to the technical field of water-saving irrigation systems; the system includes a node multi-source heterogeneous perception interconnection subsystem, a precision irrigation system modeling subsystem, and an intelligent processing and scheduling optimization subsystem; the node multi-source heterogeneous perception interconnection subsystem realizes the accurate perception measurement of irrigation water flow and the convergence of multivariate heterogeneous data; the precision irrigation system modeling subsystem and the intelligent processing and scheduling optimization subsystem are unidirectionally linked, and the intelligent processing and scheduling optimization of data are realized by optimizing data collection and analysis and estimating water resources and modeling utility. At the same time, a precision irrigation method matching the system is designed, and the temporal and spatial variation law of regionalized agricultural net irrigation water demand is quantitatively analyzed, so as to realize the precise control of the farmland irrigation system, effectively save water resources, and also reduce the use cost, with better economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of water-saving irrigation systems, and in particular to a farmland precision irrigation system and method based on the Internet of Things. Background Art

[0002] Water is an important basic resource to ensure the development of national economy and society. However, in recent years, the problem of water resource security has become increasingly serious. In particular, as a large agricultural country, my country is currently in a critical stage of industrialization adjustment, and the contradiction between agricultural and industrial water use is inevitable. However, as the largest freshwater consumer, agricultural irrigation has an irrigation efficiency of less than 40%. The needs of global economic development and food security have put forward a new concept of irrigation - precision irrigation. Precision irrigation is the use of excellent irrigation systems to accurately apply water and nutrients according to the different growth periods and soil conditions of different crops, and precise irrigation at the right time and in the right amount. The measures to achieve precision irrigation are roughly divided into three types. The first is engineering measures, such as field engineering measures and water transfer engineering measures; the second is agronomic measures, such as deep plowing or protective tillage, mulching and moisture conservation, water-adaptive planting, etc.; the third is management measures, such as the implementation of water resources optimization scheduling, improvement of irrigation system, computer system automation control irrigation, etc.

[0003] Precision irrigation technology based on the Internet of Things is an important technology and development direction of modern agriculture. The existing precision irrigation systems on the market are often achieved through simple, programmed fixed-point and timed irrigation, which has a high cost and cannot be adjusted intelligently and effectively according to the actual growth conditions of crops and influencing factors such as the environment, soil or irrigation methods. In order to solve the above problems, the present invention proposes a farmland precision irrigation system and method based on the Internet of Things. Summary of the invention

[0004] The purpose of the present invention is to provide a farmland precision irrigation system and method based on the Internet of Things to solve the problems raised in the background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A farmland precision irrigation system based on the Internet of Things, characterized in that it includes a node multi-source heterogeneous perception interconnection subsystem, a precision irrigation system modeling subsystem, an intelligent processing and scheduling optimization subsystem, and a gateway node design and implementation subsystem; the precision irrigation system modeling subsystem and the intelligent processing and scheduling optimization subsystem are unidirectionally linked, and the precision irrigation system modeling subsystem and the node multi-source heterogeneous perception interconnection subsystem are bidirectionally linked;

[0007] The precision irrigation system modeling subsystem includes a problem modeling module, a feature modeling module and a demand analysis module; the problem modeling module includes a task model unit, a resource model unit, an optimization target unit and a constraint condition unit; the feature modeling module includes a water-deficient area feature model unit, a blind area feature model unit and a crop space model unit; the demand analysis module includes a data collection demand analysis unit, an irrigation demand analysis unit and a maintenance demand analysis unit;

[0008] The intelligent processing and scheduling optimization subsystem includes a data collection route planning unit, a blind area / water-deficient area irrigation task scheduling unit, and a pesticide spraying business scheduling unit;

[0009] The node multi-source heterogeneous perception interconnection subsystem includes a water flow perception sensor node unit, a precision irrigation Internet of Things system and networking unit, and a data multi-source fusion unit.

[0010] A method for precise irrigation of farmland based on the Internet of Things, characterized by comprising the following contents:

[0011] S1. Based on the node multi-source heterogeneous perception interconnection subsystem, the multi-network integrated IoT gateway access and cluster networking technology are used to realize the IoT gateway node network access according to the agricultural IoT access requirements; for heterogeneous data, the heterogeneous data of water resources agricultural irrigation IoT terminals are safely processed;

[0012] S2, based on the feature modeling module, using the water shortage area feature model unit, the blind area feature model unit and the crop space model unit to determine the supplementary method for crop irrigation;

[0013] S3. Based on the demand analysis module, the data collection demand, irrigation demand and maintenance demand information of the precision irrigation model are obtained, and a regional agricultural irrigation scheduling method is designed;

[0014] S4, the joint feature modeling module, demand analysis module and node multi-source heterogeneous perception interconnection subsystem complete the refined data collection work;

[0015] S5. Based on the distribution characteristics of irrigation pipe networks / ditches, farmland and irrigation methods, use the intelligent processing and scheduling optimization subsystem to carry out route planning and task scheduling, study the deployment method of key monitoring points in pipes and canals, build an irrigation pipe fault diagnosis and optimization model, analyze the correlation of irrigation fault points, and provide support for irrigation pipe fault diagnosis decision-making; build an irrigation effect rapid evaluation model to comprehensively evaluate the cost and utility of irrigation scheduling;

[0016] S6, the combined problem modeling module and the intelligent processing and scheduling optimization subsystem complete the optimization of fine irrigation scheduling and processing;

[0017] S7. Based on the contents of S1-S6, a method of precise irrigation of farmland based on the Internet of Things is summarized.

[0018] Preferably, the network access method described in S1 includes but is not limited to ZigBee, LoRa, WiFi, 5G, etc.

[0019] Preferably, the design of the regionalized agricultural irrigation scheduling method described in S3 specifically refers to: studying the dynamic characteristics of water demand for different crops and different growth periods, integrating meteorological factors, soil factors and irrigation method data, constructing a dynamic change model of water demand for crops throughout the growth period, quantitatively analyzing the spatiotemporal variation of regionalized agricultural net irrigation water demand, and then constructing a regional net irrigation water demand model and a regional irrigation forecast model.

[0020] Preferably, the irrigation channel fault diagnosis and optimization model in S5 specifically includes a channel monitoring and fault diagnosis module, a variable irrigation scheduling optimization strategy module and an irrigation effectiveness evaluation module.

[0021] Preferably, the irrigation effect rapid evaluation model in S5 specifically includes a crop water potential lossless estimation module, a crop and planting information extraction module and a large-area crop irrigation evaluation module.

[0022] Compared with the prior art, the present invention provides a farmland precision irrigation system and method based on the Internet of Things, which has the following beneficial effects:

[0023] (1) The present invention supports multi-network integrated IoT gateway access and cluster networking technology. According to the access requirements of the agricultural IoT, the IoT gateway node is implemented to support network access methods such as ZigBee, LoRa, WiFi, and 5G. For heterogeneous data, the heterogeneous data of the water resources agricultural irrigation IoT terminal can be securely processed.

[0024] (2) The present invention constructs a fine irrigation model for crops, including water-deficient area feature modeling, irrigation blind area feature modeling, and crop spatial parameter modeling, etc.; and determines a supplementary method for crop irrigation.

[0025] (3) The present invention studies the dynamic change characteristics of water demand of different crops in different growth periods, integrates data such as meteorological factors, soil factors and irrigation methods, and studies the dynamic change model of water demand of crops throughout their growth period. It quantitatively analyzes the temporal and spatial variation law of regional agricultural net irrigation water demand.

[0026] (4) Based on the distribution characteristics of irrigation pipe networks / ditches, farmland and irrigation methods, the present invention studies the deployment method of key monitoring points in pipes and ditches, models and analyzes the correlation of irrigation fault points, and provides support for irrigation pipe fault diagnosis and decision-making. An irrigation utility evaluation model is constructed to comprehensively evaluate the cost and utility of irrigation scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the overall concept of a farmland precision irrigation system and method based on the Internet of Things proposed by the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0029] Embodiment 1:

[0030] See also Figure 1 The present invention proposes a farmland precision irrigation system based on the Internet of Things, including a node multi-source heterogeneous perception interconnection subsystem, a precision irrigation system modeling subsystem and an intelligent processing and scheduling optimization subsystem; the precision irrigation modeling subsystem and the intelligent processing and scheduling optimization subsystem are unidirectionally linked, and the precision irrigation system modeling subsystem and the node multi-source heterogeneous perception interconnection subsystem are bidirectionally linked.

[0031] The precision irrigation system modeling subsystem includes a problem modeling module, a feature modeling module and a demand analysis module; the problem modeling module includes a task model unit, a resource model unit, an optimization target unit and a constraint condition unit; the feature modeling module includes a water-scarce area feature model unit, a blind area feature model unit and a crop space model unit; the demand analysis module includes a data collection demand analysis unit, an irrigation demand analysis unit and a maintenance demand analysis unit;

[0032] Intelligent processing and scheduling optimization subsystem, including data collection route planning unit, blind area / water-deficient area irrigation task scheduling unit and pesticide spraying business scheduling unit;

[0033] The node multi-source heterogeneous perception interconnection subsystem includes water flow perception sensor node units, precision irrigation Internet of Things system and networking units, and data multi-source fusion units.

[0034] A method for precise irrigation of farmland based on the Internet of Things, including the following contents:

[0035] S1. Node multi-source heterogeneous perception interconnection subsystem, using multi-network integrated IoT gateway access and cluster networking technology, realizes IoT gateway node network access according to the access requirements of agricultural IoT; for heterogeneous data, realizes secure processing of heterogeneous data of water resources agricultural irrigation IoT terminals; network access methods include but are not limited to ZigBee, LoRa, WiFi, 5G, etc.;

[0036] S2, based on the feature modeling module, using the water shortage area feature model unit, the blind area feature model unit and the crop space model unit to determine the supplementary method for crop irrigation;

[0037] S3. Based on the demand analysis module, the data collection demand, irrigation demand and maintenance demand information of the precision irrigation model are obtained, and a regional agricultural irrigation scheduling method is designed;

[0038] Designing regional agricultural irrigation scheduling methods specifically refers to: studying the dynamic characteristics of water demand for different crops and different growth periods, integrating meteorological factors, soil factors and irrigation method data, building a dynamic change model of crop water demand during the entire growth period, quantitatively analyzing the temporal and spatial variation of regional agricultural net irrigation water demand, and then building a regional net irrigation water demand model and a regional irrigation forecast model;

[0039] S4, the joint feature modeling module, demand analysis module and node multi-source heterogeneous perception interconnection subsystem complete the refined data collection work;

[0040] S5. Based on the distribution characteristics of irrigation pipe networks / ditches, farmland and irrigation methods, use the intelligent processing and scheduling optimization subsystem to carry out route planning and task scheduling, study the deployment method of key monitoring points in pipes and canals, build an irrigation pipe fault diagnosis and optimization model, analyze the correlation of irrigation fault points, and provide support for irrigation pipe fault diagnosis decision-making; build an irrigation effect rapid evaluation model to comprehensively evaluate the cost and utility of irrigation scheduling;

[0041] The irrigation canal fault diagnosis and optimization model specifically includes a canal monitoring and fault diagnosis module, a variable irrigation scheduling optimization strategy module, and an irrigation utility evaluation module;

[0042] The irrigation effect rapid evaluation model specifically includes a crop water potential lossless estimation module, a crop and planting information extraction module, and a large-area crop irrigation assessment module;

[0043] S6, the combined problem modeling module and the intelligent processing and scheduling optimization subsystem complete the optimization of fine irrigation scheduling and processing;

[0044] S7. Based on the contents of S1-S6, a method of precise irrigation of farmland based on the Internet of Things is summarized.

[0045] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for precise irrigation of farmland based on the Internet of Things, characterized in that: The method is implemented by a farmland precision irrigation system based on the Internet of Things, the system comprising a node multi-source heterogeneous perception interconnection subsystem, a precision irrigation system modeling subsystem and an intelligent processing and scheduling optimization subsystem; the precision irrigation system modeling subsystem and the intelligent processing and scheduling optimization subsystem are unidirectionally linked, and the precision irrigation system modeling subsystem and the node multi-source heterogeneous perception interconnection subsystem are bidirectionally linked; The precision irrigation system modeling subsystem includes a problem modeling module, a feature modeling module and a demand analysis module; the problem modeling module includes a task model unit, a resource model unit, an optimization target unit and a constraint condition unit; the feature modeling module includes a water-deficient area feature model unit, a blind area feature model unit and a crop space model unit; the demand analysis module includes a data collection demand analysis unit, an irrigation demand analysis unit and a maintenance demand analysis unit; The intelligent processing and scheduling optimization subsystem includes a data collection route planning unit, a blind area / water-deficient area irrigation task scheduling unit, and a pesticide spraying business scheduling unit; The node multi-source heterogeneous perception interconnection subsystem includes a water flow perception sensor node unit, a precision irrigation Internet of Things system and networking unit, and a data multi-source fusion unit; The method includes the following contents: S1, node multi-source heterogeneous perception interconnection subsystem, using multi-network integrated IoT gateway access and cluster networking technology, according to the access requirements of agricultural IoT, realize IoT gateway node network access; for heterogeneous data, realize the secure processing of heterogeneous data of water resources agricultural irrigation IoT terminals; S2, based on the feature modeling module, using the water shortage area feature model unit, the blind area feature model unit and the crop space model unit to determine the supplementary method for crop irrigation; S3. Based on the demand analysis module, the data collection demand, irrigation demand and maintenance demand information of the precision irrigation model are obtained, and a regional agricultural irrigation scheduling method is designed; S4, the joint feature modeling module, demand analysis module and node multi-source heterogeneous perception interconnection subsystem complete the refined data collection work; S5. Based on the distribution characteristics of irrigation pipe networks / ditches, farmland and irrigation methods, use the intelligent processing and scheduling optimization subsystem to carry out route planning and task scheduling, study the deployment method of key monitoring points in pipes and canals, build an irrigation pipe fault diagnosis and optimization model, analyze the correlation of irrigation fault points, and provide support for irrigation pipe fault diagnosis decision-making; build an irrigation effect rapid evaluation model to comprehensively evaluate the cost and utility of irrigation scheduling; S6, the combined problem modeling module and the intelligent processing and scheduling optimization subsystem complete the optimization of fine irrigation scheduling and processing; S7. Based on the contents of S1-S6, a method of precise irrigation of farmland based on the Internet of Things is summarized.

2. The method for precise irrigation of farmland based on the Internet of Things according to claim 1, characterized in that: The network access methods described in S1 include but are not limited to ZigBee, LoRa, WiFi, and 5G.

3. The method for precise irrigation of farmland based on the Internet of Things according to claim 1, characterized in that: The design of the regionalized agricultural irrigation scheduling method described in S3 specifically refers to: studying the dynamic characteristics of water demand for different crops and different growth periods, integrating meteorological factors, soil factors and irrigation method data, constructing a dynamic change model of water demand for crops throughout the growth period, quantitatively analyzing the temporal and spatial variation patterns of regionalized agricultural net irrigation water demand, and then constructing a regional net irrigation water demand model and a regional irrigation forecast model.

4. The method for precise irrigation of farmland based on the Internet of Things according to claim 1, characterized in that: The irrigation canal fault diagnosis and optimization model described in S5 specifically includes a canal monitoring and fault diagnosis module, a variable irrigation scheduling optimization strategy module, and an irrigation utility evaluation module.

5. The method of the farmland precision irrigation system based on the Internet of Things according to claim 1, characterized in that: The irrigation effect rapid evaluation model described in S5 specifically includes a crop water potential lossless estimation module, a crop and planting information extraction module, and a large-area crop irrigation evaluation module.

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