Intelligent irrigation area water resource management system based on GIS, cloud computing and Internet of Things technology

Abstract

The invention, which relates to the technical field of water resource management, discloses an intelligent irrigation area water resource management system based on GIS, cloud computing and Internet of Things technologies, comprising an Internet of Things sensor layer, a data transmission network layer, a cloud computing platform layer, a GIS analysis layer and a decision support layer. The method specifically comprises the steps that an Internet of Things sensor layer collects data and transmits the data to a cloud computing platform layer through a data transmission network layer, the cloud computing platform layer provides the processed data for a GIS analysis layer for analysis, and finally an analysis result of the GIS analysis layer provides a decision basis for a decision support layer; the intelligent irrigation district water resource management system based on the GIS, cloud computing and Internet of Things technology can monitor, analyze and manage the water resource condition of the irrigation district in real time, thereby improving the utilization efficiency of the water resource and realizing intelligent management.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource management, and in particular to an intelligent irrigation area water resource management system based on GIS, cloud computing and Internet of Things technologies. Background Art

[0002] Water resources are an important resource that is indispensable to human survival and development. As irrigation areas are concentrated areas of agricultural water use, the rational management of water resources is of vital importance to ensuring agricultural production, saving water resources and achieving sustainable development.

[0003] Traditional irrigation water resource management methods face many limitations. In the early days of irrigation management, it mainly relied on manual experience and simple measurement tools. For example, irrigation decisions in irrigation areas are often based on farmers' traditional experience, with fixed time intervals and roughly estimated water volumes. This approach does not take into account the actual soil moisture content in the irrigation area, the different water requirements of crops at different growth stages, and the dynamic changes in meteorological factors.

[0004] From the perspective of data collection, the frequency of manual collection of water level, flow and other data in the past was extremely low, and measurements might be taken only once every few days or even weeks. As a result, it is difficult to obtain real-time information on water resource changes, resulting in a lack of accurate data support when allocating water resources. For example, in the event of sudden rainfall or drought, the irrigation district management department cannot quickly make reasonable water resource allocation decisions due to the inability to obtain relevant data in a timely manner, which may result in a waste of water resources or a shortage of water for crops.

[0005] Furthermore, the traditional management model lacks effective spatial analysis capabilities. Irrigation areas usually cover a large geographical area, and geographical factors such as soil texture, terrain slope, and vegetation coverage in different regions have a significant impact on the infiltration, runoff, and evaporation of water resources. However, the traditional management method cannot comprehensively and meticulously analyze the relationship between these spatial factors and water resources, making it difficult to achieve precise water resource management based on the characteristics of different regions.

[0006] With the development of information technology, GIS technology has made it possible to solve the spatial analysis problems in irrigation area water resources management. GIS can integrate various geospatial data of irrigation areas, such as terrain elevation data, soil type distribution, water system network, etc., and intuitively display the distribution, flow and utilization of water resources in different geographical areas through spatial analysis functions. The emergence of cloud computing technology provides powerful computing resources and storage capabilities for the storage, processing and analysis of massive water resources data. It can quickly process large amounts of data from different data sources and provide flexible computing services according to demand. Internet of Things technology realizes real-time and continuous monitoring and data collection of water resource-related parameters by deploying various sensors in irrigation areas, such as water level sensors, flow sensors, soil moisture sensors, etc.

[0007] Although GIS, cloud computing and Internet of Things technologies have their own applications in the field of water resources management, the research and practice of organically integrating these three technologies to build a comprehensive and efficient intelligent irrigation area water resources management system is relatively insufficient. This invention aims to fill this gap by integrating GIS, cloud computing and Internet of Things technologies to build an intelligent irrigation area water resources management system to improve the efficiency, accuracy and sustainability of irrigation area water resources management. Summary of the invention

[0008] The purpose of the present invention is to provide an intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technologies to solve the problem in the prior art that it is difficult to monitor in real time the actual moisture content of the soil inside the irrigation area, the different water requirements of crops at different growth stages, and the dynamic changes of meteorological factors.

[0009] To achieve the above purpose, the present invention provides an intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology, including an Internet of Things sensor layer, a data transmission network layer, a cloud computing platform layer, a GIS analysis layer and a decision support layer; specifically: the Internet of Things sensor layer collects data, transmits it to the cloud computing platform layer through the data transmission network layer, the cloud computing platform layer provides the processed data to the GIS analysis layer for analysis, and finally the analysis result of the GIS analysis layer provides a decision basis for the decision support layer;

[0010] The IoT sensor layer is the bottom layer of the system and is responsible for data collection; IoT sensors include water level sensors, flow sensors, soil moisture sensors, and meteorological sensors;

[0011] The data transmission network layer is responsible for transmitting the data collected by the IoT sensor layer to the cloud computing platform layer; the data transmission network layer uses ZigBee, LoRa or 4G / 5G wireless communication technology to build a transmission network to ensure that data can be transmitted to the cloud securely and stably;

[0012] After receiving the data transmitted from the data transmission network layer, the cloud computing platform layer stores and processes the data; in the cloud computing platform layer, the data will be stored in the database in the cloud and initially processed and analyzed based on the association rule mining algorithm, such as data cleaning and format conversion.

[0013] The GIS analysis layer uses the data processed by the cloud computing platform layer and combines geographic information system technology to analyze the spatial distribution of water resources, the topographic data volume of irrigation areas, the dynamic change volume of water resources, the water quality change trend volume, and the vegetation coverage volume.

[0014] The decision support layer is the highest layer of the system. It provides decision support for the water resource management of the irrigation area according to the analysis results provided by the GIS analysis layer; for example, formulating a reasonable irrigation plan, a water resource allocation plan, etc. according to the analysis results.

[0015] Preferably, the water level sensor is installed in the water bodies of reservoirs, ponds, and channels in the irrigation area to measure the water level height; the flow sensor is installed at key nodes of the channel to monitor the flow rate of the water flow; the soil moisture sensors are distributed at different positions in the field to collect the soil moisture information; and the meteorological sensors are responsible for collecting the meteorological data of the irrigation area, including temperature, humidity, rainfall, and evaporation.

[0016] Preferably, the sliding average filtering algorithm is used to preprocess the data collected by each sensor in the Internet of Things sensor layer, as follows:

[0017] Let the original data sequence collected by the sensor be x(n), n = 1, 2,..., N, and the sliding window size be M, M < N. Then the calculation expression of the output y(n) after sliding average filtering is as follows:

[0018]

[0019] In the formula, x(n) represents the i-th original data sequence.

[0020] Preferably, the Hamming code check algorithm is used to detect errors in the data transmitted in the data transmission network layer, as follows:

[0021] Step 1: Determine the number of check bits m according to the number of information bits n, satisfying 2 m -m - 1 ≥ n;

[0022] Step 2: According to the coding rule of the Hamming code, combine the information bits and the check bits into the codeword C = c k-1 c k-2 …c1c0, k = n + m;

[0023] Step 3: At the receiving end, the check bit is recalculated based on the received coded word and compared with the received check bit. If they are inconsistent, it means that an error occurred during data transmission.

[0024] Preferably, the terrain data of the irrigation area is analyzed based on a slope calculation algorithm based on a regular grid, and the specific expression is as follows:

[0025]

[0026] Where s(i,j) represents the slope of a grid point (i,j), z(i,j) represents the elevation of the grid point in the i-th row and j-th column, and Δx and Δy are the horizontal and vertical grid spacings.

[0027] Preferably, a water resource allocation plan is formulated in the decision support layer according to a linear programming algorithm, and the specific expression is as follows:

[0028]

[0029] st

[0030]

[0031] In the formula, m represents the water demand area, n represents the water source, and d i represents the water demand of each water demand area i, s j represents the water supply of each water source j, c ij represents the water delivery cost from water source j to water demand area i, x ij is a decision variable, which represents the amount of water allocated from water source j to water demand area i. i=1,2,…,m, indicating that the water demand of each water demand area is met, j = 1, 2, ..., n, representing the water supply limit of each water source; x ij Obtained by linear programming based on the simplex method.

[0032] Therefore, the present invention adopts the above-mentioned intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology, which has the following beneficial effects:

[0033] (1) Through real-time monitoring by IoT sensors and spatial analysis by GIS, the water resource conditions and crop water requirements in different areas of the irrigation area can be accurately understood, thereby achieving accurate irrigation decisions and avoiding waste of water resources;

[0034] (2) The cloud computing platform can quickly process a large amount of water resource data. The decision support layer can adjust the water resource management strategy in a timely manner based on the real-time data analysis results to cope with changes in the internal and external environment of the irrigation area, such as meteorological changes and changes in crop growth conditions.

[0035] (3) Based on the system's comprehensive monitoring, analysis and reasonable allocation of irrigation area water resources, it can better protect water resources, improve water resource utilization efficiency and promote the sustainable use of irrigation area water resources while meeting agricultural production needs;

[0036] (4) It reduces the workload of manual data collection and monitoring, while improving the accuracy of decision-making and reducing costs such as water resource waste and agricultural production losses caused by wrong decisions.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is an overall block diagram of the intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technologies of the present invention. DETAILED DESCRIPTION

[0039] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0040] See also Figure 1 , the intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology includes the Internet of Things sensor layer, data transmission network layer, cloud computing platform layer, GIS analysis layer and decision support layer; specifically: the Internet of Things sensor layer collects data and transmits it to the cloud computing platform layer through the data transmission network layer. The cloud computing platform layer provides the processed data to the GIS analysis layer for analysis. Finally, the analysis results of the GIS analysis layer provide decision-making basis for the decision support layer;

[0041] The IoT sensor layer is the bottom layer of the system and is responsible for data collection. IoT sensors include water level sensors, flow sensors, soil moisture sensors, and meteorological sensors. Water level sensors are installed in water bodies such as reservoirs, ponds, and channels in irrigation areas to measure water levels. Flow sensors are installed at key nodes in channels to monitor the flow of water. Soil moisture sensors are distributed at different locations in the field to collect soil moisture information. Meteorological sensors are responsible for collecting meteorological data in irrigation areas, including temperature, humidity, rainfall, and evaporation. The sliding average filtering algorithm is used to preprocess the data collected by each sensor in the IoT sensor layer, as follows:

[0042] Let the original data sequence collected by the sensor be x(n), where n = 1, 2, …, N, and the sliding window size be M, with M < N. Then the calculation expression for the output y(n) after sliding average filtering is as follows:

[0043]

[0044] In the formula, x(n) represents the i-th original data sequence.

[0045] The data transmission network layer is responsible for transmitting the data collected by the IoT sensor layer to the cloud computing platform layer; the data transmission network layer uses ZigBee, LoRa, or 4G / 5G wireless communication technology to build a transmission network to ensure that the data can be transmitted to the cloud safely and stably; the Hamming code check algorithm is used to detect errors in the data transmitted in the data transmission network layer, as follows:

[0046] Step 1: Determine the number of check bits m according to the number of information bits n, satisfying 2 m -m - 1 ≥ n;

[0047] Step 2: According to the encoding rule of the Hamming code, combine the information bits and check bits into a codeword C = c k-1 c k-2 …c1c0, where k = n + m;

[0048] Step 3: At the receiving end, recalculate the check bits based on the received codeword and compare them with the received check bits. If they are inconsistent, it means that an error has occurred during the data transmission process.

[0049] After receiving the data transmitted from the data transmission network layer, the cloud computing platform layer stores and processes the data; in the cloud computing platform layer, the data will be stored in the database in the cloud and undergoes preliminary processing and analysis based on the association rule mining algorithm, such as data cleaning and format conversion;

[0050] The GIS analysis layer uses the data processed by the cloud computing platform layer and combines geographic information system technology to analyze the spatial distribution of water resources, the topographic data volume of the irrigation area, the dynamic change volume of water resources, the water quality change trend volume, and the vegetation coverage volume; among them, the slope calculation algorithm based on the regular grid is used to analyze the topographic data of the irrigation area, and the specific expression is as follows:

[0051]

[0052] In the formula, s(i, j) represents the slope of a grid point (i, j), z(i, j) represents the elevation value of the grid point in the i-th row and j-th column, and Δx and Δy are the horizontal and vertical grid spacings.

[0053] The decision support layer is the highest layer of the system. It provides decision support for water resource management in irrigation areas based on the analysis results provided by the GIS analysis layer. For example, it formulates reasonable irrigation plans and water resource allocation plans based on the analysis results. The water resource allocation plan is formulated according to the linear programming algorithm in the decision support layer. The specific expression is as follows:

[0054]

[0055] st

[0056]

[0057] In the formula, m represents the water demand area, n represents the water source, and d i represents the water demand of each water-demanding area i (determined according to the crop planting area and growth stage of each water-demanding area), s j represents the water supply of each water source j (determined according to the water storage conditions of the water source), c ij represents the water delivery cost from water source j to water demand area i (determined by factors such as the length of the water delivery channel and terrain), x ij is a decision variable, which represents the amount of water allocated from water source j to water demand area i. i=1,2,…,m, indicating that the water demand of each water demand area is met, j = 1, 2, ..., n, representing the water supply limit of each water source; x ij Obtained by linear programming based on the simplex method.

[0058] Therefore, the present invention adopts the above-mentioned intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology. First, the Internet of Things sensor layer collects data, and then transmits the collected data to the cloud computing platform layer through the data transmission network layer. The cloud computing platform layer provides the processed data to the GIS analysis layer for analysis. Finally, the analysis results of the GIS analysis layer provide a decision-making basis for the decision support layer; through the mutual coordination and cooperation between various levels, the constructed system can monitor, analyze and manage the water resources status of the irrigation area in real time, thereby improving the utilization efficiency of water resources and realizing intelligent management.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution 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 solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. Intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology, characterized by: It includes the Internet of Things sensor layer, data transmission network layer, cloud computing platform layer, GIS analysis layer, and decision support layer; specifically: The Internet of Things sensor layer collects data and transmits it to the cloud computing platform layer through the data transmission network layer. The cloud computing platform layer provides the processed data to the GIS analysis layer for analysis. Finally, the analysis results of the GIS analysis layer provide a decision-making basis for the decision support layer; The Internet of Things sensor layer is the bottom layer of the system and is responsible for data collection; the Internet of Things sensors include water level sensors, flow sensors, soil moisture sensors, and meteorological sensors; The data transmission network layer is responsible for transmitting the data collected by the Internet of Things sensor layer to the cloud computing platform layer; The data transmission network layer uses ZigBee, LoRa, or 4G / 5G wireless communication technology to build a transmission network; After receiving the data transmitted by the data transmission network layer, the cloud computing platform layer stores and processes the data; In the cloud computing platform layer, the data will be stored in a database in the cloud and preliminarily processed and analyzed based on the association rule mining algorithm; The GIS analysis layer uses the data processed by the cloud computing platform layer and combines geographic information system technology to analyze the spatial distribution of water resources, topographic data of the irrigation area, dynamic changes in water resources, trends in water quality changes, and vegetation coverage; The decision support layer is the top layer of the system. It provides decision support for the water resource management of the irrigation area based on the analysis results provided by the GIS analysis layer.

2. The intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology according to claim 1 is characterized by: The water level sensor is installed in the water bodies of reservoirs, ponds, and channels in the irrigation area to measure the water level height; the flow sensor is installed at key nodes of the channel to monitor the flow rate of the water flow; the soil moisture sensors are distributed at different positions in the field to collect soil moisture information; the meteorological sensor is responsible for collecting meteorological data of the irrigation area, including temperature, humidity, rainfall, and evaporation.

3. The intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology according to claim 2 is characterized by: The sliding average filtering algorithm is used to preprocess the data collected by each sensor in the Internet of Things sensor layer, as follows: Let the original data sequence collected by the sensor be x(n), n = 1, 2, …, N, and the sliding window size be M, M < N. Then the calculation expression of the output y(n) after sliding average filtering is as follows: In the formula, x(n) represents the i-th original data sequence.

4. The intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology according to claim 3 is characterized by: The Hamming code check algorithm is used to detect errors in the data transmitted in the data transmission network layer, as follows: Step 1: Determine the number of check bits m according to the number of information bits n, satisfying 2 m -m-1≥n; Step 2: According to the coding rules of Hamming code, the information bits and check bits are combined into a code word C=c k-1 c k-2 …c1c0, k=n+m; Step 3: At the receiving end, recalculate the check bits based on the received codeword and compare them with the received check bits. If they are inconsistent, it means that an error occurred during data transmission.

5. The intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology according to claim 4 is characterized by: The slope calculation algorithm based on regular grid is used to analyze the topographic data of the irrigation area, and the specific expression is as follows: In the formula, s(i, j) represents the slope of a grid point (i, j), z(i, j) represents the elevation value of the grid point in the i-th row and j-th column, and Δz and Δy are the horizontal and vertical grid spacings.

6. The intelligent irrigation area water resources management system based on GIS, cloud computing and Internet of Things technology according to claim 5 is characterized by: In the decision support layer, a water resource allocation plan is formulated according to the linear programming algorithm, and the specific expression is as follows: s.t. In the formula, m represents the water demand area, n represents the water source, and d i represents the water demand of each water demand area i, s j represents the water supply of each water source j, c ij represents the water delivery cost from water source j to water demand area i, x ij is a decision variable, which represents the amount of water allocated from water source j to water demand area i. Indicates that the water demand of each water demand area is met. represents the water supply limit of each water source; x ij Obtained by linear programming based on the simplex method.

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