Intelligent irrigation method and device based on Internet of Things

Abstract

The invention relates to the field of intelligent irrigation, in particular to an intelligent irrigation method and device based on the Internet of Things, and the method comprises the steps: constructing a soil standard model, obtaining soil data in real time, inputting the soil data into the soil standard model, and outputting a region division result through the soil standard model; based on a regional division result, judging whether the soil data meets an irrigation condition or not; when the soil data meets the irrigation condition, acquiring meteorological data, water pressure data and historical irrigation data; according to the meteorological data, the water pressure data and the historical irrigation data, irrigation parameters needed by the corresponding area of the soil are predicted; and determining corresponding irrigation behaviors in combination with the soil data and the irrigation parameters of the corresponding areas. Soil data is monitored in real time, so that the system can dynamically adjust an irrigation plan, invalid irrigation after rainfall is avoided, a targeted irrigation strategy is made according to the soil humidity level and topographic features, slope water and soil loss is prevented, and it is ensured that the flat ground is prevented from being too wet to cause root system oxygen deficit.

Description

Technical Field

[0001] The present application relates to the field of intelligent irrigation technology, and in particular to an intelligent irrigation method and device based on the Internet of Things. Background Art

[0002] IoT smart irrigation is a method of using IoT technology to automate agricultural irrigation by integrating sensors, network communications, data processing and execution equipment to form an intelligent irrigation system.

[0003] The existing sprinkler irrigation systems often rely on fixed irrigation plans and are difficult to flexibly adjust according to actual soil moisture and terrain. For example, a sprinkler irrigation system set for timed irrigation may continue to irrigate after rainfall, resulting in water waste and plant root hypoxia. The core of this defect lies in the lack of real-time monitoring and intelligent adjustment mechanisms, which cannot respond to environmental changes in a timely manner. Secondly, since the irrigation requirements on slopes and flat lands are significantly different, if the same irrigation frequency and irrigation amount are used, it is easy to cause soil erosion on slopes, while flat land will cause root hypoxia due to excessive moisture.

[0004] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide an intelligent irrigation method and device based on the Internet of Things.

[0006] In order to achieve the above-mentioned invention object, the present application proposes an intelligent irrigation method based on the Internet of Things, the method comprising:

[0007] Constructing a soil standard model for analyzing soil data and classifying the soil data into moisture levels and terrain;

[0008] Acquire soil data in real time, input the soil data into the soil standard model, and output the result of regional division through the soil standard model;

[0009] Based on the result of the area division, determining whether the soil data meets the irrigation conditions;

[0010] When the soil data meets the irrigation conditions, obtaining meteorological data, water pressure data and historical irrigation data;

[0011] Predicting the irrigation parameters required for the soil corresponding area based on the meteorological data, water pressure data and historical irrigation data;

[0012] The corresponding irrigation behavior is determined by combining soil data and irrigation parameters of the corresponding area.

[0013] The present application also provides an intelligent irrigation device based on the Internet of Things, including:

[0014] A construction module is used to construct a soil standard model, to analyze soil data, and to classify the soil data into moisture levels and terrain;

[0015] An input module, used for acquiring soil data in real time, inputting the soil data into the soil standard model, and outputting the result of regional division through the soil standard model;

[0016] A judgment module, used for judging whether the soil data meets the irrigation conditions based on the result of the area division;

[0017] An acquisition module, used for acquiring meteorological data, water pressure data and historical irrigation data when the soil data meets the irrigation conditions;

[0018] A prediction module, used for predicting the irrigation parameters required for the corresponding area of ​​the soil according to the meteorological data, water pressure data and historical irrigation data;

[0019] The combination module is used to combine soil data and irrigation parameters of the corresponding area to determine the corresponding irrigation behavior.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0022] The intelligent irrigation method and device based on the Internet of Things of the present application embodiment monitors soil data in real time, such as humidity, temperature and pH value, so that the system can dynamically adjust the irrigation plan to avoid ineffective irrigation after rainfall, thereby effectively saving water resources. According to the soil moisture level and terrain characteristics (such as slope and flat land), a targeted irrigation strategy is formulated to prevent soil erosion on the slope and ensure that the flat land avoids excessive moisture and causes root hypoxia. Combined with meteorological data and historical irrigation records, the system can predict the water demand of each region, reasonably arrange the irrigation frequency and irrigation amount, and maximize the utilization efficiency of water resources. Accurate irrigation control ensures that the plant roots obtain suitable water, promotes healthy growth, and improves crop yield and quality. Through the intelligent adjustment mechanism, the system can quickly respond to environmental changes (such as rainfall or temperature changes), adjust irrigation behavior, and further reduce resource waste. Through efficient water management and irrigation strategies, sustainable agricultural practices are promoted, which contributes to the long-term development of ecological environment protection and agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an intelligent irrigation method based on the Internet of Things according to an embodiment of the present application;

[0024] Figure 2 This is a flow chart of an intelligent irrigation method based on the Internet of Things according to an embodiment of the present application;

[0025] Figure 3 This is a schematic block diagram of the structure of an intelligent irrigation device based on the Internet of Things according to an embodiment of the present application;

[0026] Figure 4 A schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] Reference Figure 1 In an embodiment of the present application, a smart irrigation method based on the Internet of Things is provided, the method comprising:

[0030] S1. Constructing a soil standard model for analyzing soil data, and dividing the soil data into moisture levels and terrain;

[0031] S2, acquiring soil data in real time, and inputting the soil data into the soil standard model, and outputting the result of regional division through the soil standard model;

[0032] S3. Based on the result of the area division, determining whether the soil data meets the irrigation conditions;

[0033] S4. Predicting the irrigation parameters required for the corresponding area of ​​the soil based on the meteorological data, water pressure data and historical irrigation data;

[0034] S5. Determine corresponding irrigation behavior by combining soil data and irrigation parameters of the corresponding area.

[0035] As described in the above steps S1-S2, data such as soil moisture, temperature, and pH value can be continuously monitored by sensors. This process ensures that irrigation decisions are based on the latest soil status. When the soil moisture is lower than the set threshold, the system can respond quickly to prevent plants from being affected by water shortage. The system analyzes the soil data obtained in real time to determine whether the basic conditions for irrigation are met. This judgment mechanism can effectively reduce unnecessary irrigation and avoid wasting water resources. For example, after rainfall, the system automatically determines whether the soil moisture meets the standard, thereby avoiding over-irrigation. Soil moisture data is graded and regionalized based on terrain features (such as slope and soil type). This process helps the system understand the irrigation needs of different regions. Slopes require more frequent irrigation, while flat land needs to consider water retention capacity to formulate corresponding strategies; by collecting and analyzing a large amount of soil data, a standard model is established to analyze the characteristics of the soil. The model can include multiple factors such as moisture level, terrain (slope, soil type, etc.). Different types of soils have different requirements and retention capacities for water. Establishing a standard model can help the system understand these differences and provide a scientific basis for subsequent irrigation decisions. It can divide soil areas more accurately, improve the pertinence of irrigation, and avoid waste of resources. Use sensors (such as humidity sensors, temperature sensors, etc.) to monitor soil conditions in real time, and input the data into the soil standard model for processing. Soil conditions change over time, and real-time data can reflect current soil conditions, making irrigation decisions more timely and accurate. The system's response speed is enhanced, so that irrigation measures can be adjusted in time to prevent over- or under-irrigation.

[0036] As described in steps S3-S6 above, by integrating external meteorological information (such as precipitation forecasts), water pressure status, and historical irrigation records, the system is able to more comprehensively assess the water demand of each area. For example, if rainfall is forecasted in the next few days, the system can adjust the irrigation plan accordingly to reduce water waste. Based on meteorological, water pressure, and historical data, the system intelligently predicts the specific irrigation parameters (such as irrigation volume and frequency) required for each area. This predictive capability can improve the accuracy of irrigation. If a region has historically shown specific water requirements under specific meteorological conditions, the system can set the corresponding irrigation strategy in advance. Combining all the data, the system is able to generate personalized irrigation behavior plans. This process ensures that irrigation is both efficient and effective, maximizes water resource utilization, and ensures healthy plant growth. The system can irrigate certain specific areas more frequently during dry periods and reduce water volume during wet periods to ensure sustainable use of resources. The parsed soil data is compared with the preset irrigation conditions to determine whether the current soil state meets the irrigation standards. By setting clear irrigation thresholds, unnecessary water use can be avoided and plants can be ensured to grow under optimal growth conditions. Effectively prevent water waste while promoting healthy crop growth. Relevant meteorological data (such as rainfall, temperature), water pressure data (water supply capacity) and historical irrigation data (past irrigation conditions) are obtained through a network interface or API. Meteorological factors and water supply capacity have a direct impact on irrigation demand. Historical data can help the system understand the past irrigation effects and optimize future irrigation plans. Comprehensive data analysis improves the accuracy of predictions and makes irrigation parameters more reasonable. Using the acquired meteorological data, water pressure data and historical irrigation data, the required irrigation volume for each area is predicted through algorithms (such as regression analysis, machine learning, etc.). Through data-driven methods, the water demand of different areas can be estimated more accurately to avoid blind irrigation. Improve irrigation efficiency, ensure that plants get the required water, and reduce the waste of water resources. According to the previous analysis results, combined with soil data and predicted irrigation parameters, formulate specific irrigation strategies (such as irrigation time, irrigation volume and irrigation method). Specific irrigation strategies can achieve precise irrigation, ensure that the water supply is just right, and promote the healthy growth of crops. Optimizing the use of irrigation resources helps to improve agricultural production efficiency and reduce operating costs.

[0037] As mentioned above, real-time monitoring of soil data, such as moisture, temperature, and pH, enables the system to dynamically adjust irrigation plans to avoid ineffective irrigation after rainfall, thereby effectively saving water resources. Targeted irrigation strategies are formulated based on soil moisture levels and terrain features (such as slopes and flat land) to prevent soil erosion on slopes and ensure that flat land avoids excessive moisture and root hypoxia. Combined with meteorological data and historical irrigation records, the system can predict the water demand of each region, reasonably arrange irrigation frequency and irrigation volume, and maximize the efficiency of water resource utilization. Precise irrigation control ensures that plant roots receive appropriate moisture, promotes healthy growth, and improves crop yield and quality. Through intelligent adjustment mechanisms, the system can quickly respond to environmental changes (such as rainfall or temperature changes), adjust irrigation behavior, and further reduce resource waste. Promoting sustainable agricultural practices through efficient water management and irrigation strategies will contribute to ecological environmental protection and the long-term development of agriculture.

[0038] Reference Figure 2 In one embodiment, the soil data is parsed, the soil data is divided into moisture levels and terrain, and a regional division result is obtained. The method includes:

[0039] S21, analyzing the soil data to obtain soil moisture parameters and soil type;

[0040] S22, obtaining plant species parameters, and setting a humidity division threshold according to the plant species parameters and soil type;

[0041] S23, classifying the soil according to the humidity classification threshold and the soil humidity parameter;

[0042] S24, obtaining topographic data of the soil position, and dividing the soil position into different terrain levels according to preset inclination angles and topographic features;

[0043] S25. Based on the soil moisture division and terrain division, output the result of regional division.

[0044] As described in the above steps, soil moisture parameters (such as water content) and soil types (such as sandy soil and clay) are collected and obtained through sensors or learning model analysis. Understanding soil characteristics helps provide suitable growth conditions for different plants. For example, clay has a strong ability to retain water and is suitable for crops with high water requirements. Humidity division thresholds are set according to the growth requirements of plants (such as water and nutrients) to ensure that different plants grow in a suitable environment. Through targeted settings, irrigation plans can be optimized. For example, drought-tolerant plants require a lower soil moisture threshold, while moisture-loving plants require a higher humidity. Using the humidity division threshold, soil moisture parameters are compared to form different humidity levels (such as dry, moderate, and moist). This division allows the irrigation system to more accurately determine the irrigation needs of each area and improve water use efficiency. Collect terrain information of the soil location, including slope and terrain characteristics, and divide the terrain into levels based on the preset tilt angle. Topography affects water flow and retention. Areas with larger slopes are prone to drainage and require special consideration of irrigation methods. Through terrain division, water resources can be managed more effectively. Based on the results of the division of humidity and terrain, different areas are marked as specific irrigation management zones, such as high-humidity flat land and low-humidity slopes. Specific irrigation strategies are formed so that the irrigation system can be dynamically adjusted according to regional needs, improve overall water resource utilization, and promote sustainable agricultural practices.

[0045] In one embodiment, the method of combining soil data and irrigation parameters of a corresponding area to determine corresponding irrigation behavior includes:

[0046] Construct irrigation analysis models;

[0047] Acquire soil moisture parameters and soil types in each area, and analyze soil drought index according to the soil moisture parameters and soil types;

[0048] Acquire plant species parameters of the area, and analyze plant irrigation demand data according to the plant species parameters;

[0049] The irrigation parameters, plant irrigation demand data and drought index are input into the irrigation analysis model, and the analysis results are output through the irrigation analysis model to obtain the number of irrigations, irrigation frequency, irrigation intensity and irrigation type, wherein the irrigation intensity includes the amount of water required per square meter.

[0050] As mentioned above, the model integrates multiple parameters such as soil moisture, soil type, and plant needs to establish a mathematical model using algorithms (such as regression analysis, machine learning, etc.). The model predicts irrigation needs based on the input parameters. By building a model, precise irrigation management can be achieved. If the model identifies that a certain type of plant grows best under a specific soil moisture, the irrigation plan can allocate water resources more reasonably and reduce waste. Through sensors or soil sampling analysis, soil moisture parameters and soil types (such as sand, clay, loam, etc.) in different regions are obtained. These data help analyze the water retention capacity and drainage of the soil. Understanding soil characteristics is crucial for formulating irrigation plans. Sandy soils drain quickly and require frequent irrigation, while clay soils have strong water retention capacity and may reduce the number of irrigations. Based on soil moisture and soil type, the drought index (such as the degree of soil moisture loss) is calculated, which is usually quantified using a formula or empirical formula. The drought index provides a basis for irrigation decisions. A high drought index indicates that more water is needed, and the irrigation system can increase the water supply accordingly. Collect and classify the plant species grown in the area to obtain parameters such as water and nutrients required for their growth. Different plants have significant differences in their water requirements. For example, cacti are suitable for dry environments and have low irrigation requirements, while rice requires a humid environment and has high irrigation requirements, so it is crucial to identify plant species. According to the growth stage, species and season of the plant, its specific water requirements are quantified. Combined with soil type and moisture, a comprehensive irrigation demand database is established. This analysis makes irrigation planning more targeted and timely. In the dry season, the irrigation demand increases, and the model can dynamically adjust the irrigation amount to meet the needs of plants. The above acquired data and analysis results are integrated as input parameters and entered into the irrigation analysis model for calculation. By integrating multi-dimensional information through the model, more comprehensive irrigation recommendations can be obtained. Taking into account all variables, the model can output the best irrigation strategy to maximize water resource utilization. It is worth mentioning that in the drought index, the degree of drought can be divided into different levels, such as normal, mild drought, moderate drought and severe drought for quantification. The more severe the drought, the higher the drought index.

[0051] The model calculates specific irrigation behavior based on input parameters, including:

[0052] Irrigation frequency: How often you irrigate during a specific period of time.

[0053] Irrigation frequency: The time interval at which irrigation occurs.

[0054] Irrigation intensity: The amount of water required per square meter, usually related to plant needs and soil characteristics.

[0055] Irrigation type: Choose drip irrigation, sprinkler irrigation or other methods, depending on the needs of the soil and plant types. Through precise irrigation strategies, ensure that the water needed for plant growth is supplied in a timely manner. Drip irrigation can reduce water evaporation and is suitable for crops with high water requirements, while sprinkler irrigation is suitable for large areas of fields.

[0056] In one embodiment, the method of predicting the irrigation parameters required for the soil corresponding area based on the meteorological data, water pressure data and historical irrigation data includes:

[0057] Obtaining the meteorological data of each area, and analyzing the temperature, precipitation and wind speed parameters of the corresponding area according to the meteorological data;

[0058] Evaluate precipitation parameters and evaporation parameters based on temperature, precipitation and wind speed parameters of the corresponding area;

[0059] Acquire water pressure data of a corresponding area, and analyze water supply parameters of the area within a preset time according to the water pressure data;

[0060] Acquire historical irrigation data of the corresponding area, and analyze the average irrigation amount of each irrigation in the corresponding area according to the historical irrigation data;

[0061] Constructing an irrigation prediction model, inputting the precipitation parameter, evaporation parameter and water supply parameter into the irrigation prediction model, predicting the irrigation parameters required for the corresponding area through the irrigation prediction model, and outputting the prediction results;

[0062] Based on the output prediction results and the historical average irrigation amount, analyze the floating parameters between the prediction results and the historical average irrigation amount;

[0063] Verifying whether the floating parameter is within a preset parameter range;

[0064] When the floating parameter is within a preset parameter interval, the predicted result is judged to be a credible result.

[0065] As mentioned above, obtaining meteorological data for each area (such as temperature, precipitation, and wind speed) is to understand the climate characteristics of the area. High temperatures and low precipitation usually increase evaporation and irrigation needs. By analyzing these meteorological parameters, water losses can be more accurately assessed. For example, during a dry season, higher temperatures and lower precipitation mean that more irrigation is needed to maintain plant health. Calculating precipitation and evaporation parameters from meteorological data helps understand the input (precipitation) and output (evaporation) of water. From a meteorological perspective, evaporation is dependent on temperature and wind speed. Through this assessment, the actual water requirements of soil and plants under specific climate conditions can be quantified. For example, if precipitation is low and evaporation is high, a higher irrigation schedule will be required. Water pressure data reflects the water supply capacity of the irrigation system, and by analyzing this data, you can understand the amount of water that can be supplied within a preset time. Ensure that the irrigation system can provide enough water during high demand periods. If water pressure is low, it may be necessary to adjust the irrigation schedule or consider other sources of water supply. Obtaining historical irrigation data and analyzing the average irrigation amount per time can provide a baseline for future irrigation plans. Understanding historical trends can help predict future needs. For example, if irrigation volumes have increased regularly in a particular season in the past, the model can be adjusted based on this pattern. Precipitation parameters, evaporation parameters, and water supply parameters are input into the model, and regression analysis or machine learning algorithms are usually used to predict irrigation demand. The model can integrate the effects of multiple variables to give a more accurate forecast of irrigation demand. Machine learning models can automatically identify complex relationships between climate and irrigation demand and optimize irrigation strategies. Compare the forecast results with the historical average irrigation volume to analyze their fluctuations. This helps to identify the stability and accuracy of the forecast. If the fluctuation of the forecast results and historical data is within an acceptable range, the forecast results can be considered reliable. A preset fluctuation parameter range is set to judge the credibility of the forecast results. This verification step ensures the practicality of the model output and avoids unreasonable irrigation recommendations due to extreme weather or data errors. If the forecast shows that a large amount of irrigation is needed, but the historical data does not support it, further analysis or adjustment of the model may be required.

[0066] In one embodiment, after the step of combining the soil data and the irrigation parameters of the corresponding area to determine the corresponding irrigation behavior, the method includes:

[0067] Real-time acquisition of soil moisture parameters after soil irrigation;

[0068] analyzing the moisture variation data of the area according to the soil moisture parameters after the soil is irrigated and the soil moisture parameters when the soil is not irrigated;

[0069] According to the humidity change data, determining whether the irrigation behavior meets the conditions for continued use;

[0070] If the humidity change data is within a preset humidity change interval, it is determined that the irrigation behavior meets the conditions for continued use.

[0071] As mentioned above, the soil moisture data after irrigation is obtained in real time through sensors or monitoring systems. These sensors can be buried in the soil and use resistivity or moisture sensing technology to measure the moisture content in the soil. Timely monitoring of soil moisture can provide accurate information on moisture status. The soil moisture measured after irrigation can help understand whether the moisture has reached the expected level and avoid over-irrigation or water shortage. Compare the soil moisture parameters after irrigation with the soil moisture parameters without irrigation to calculate the moisture change data. The calculation method can simply obtain the increase or decrease in moisture by subtraction, or use statistical methods to evaluate the change trend. Analyzing the moisture change data helps to evaluate the irrigation effect. For example, if the moisture increases by 20% after irrigation, it means that the irrigation is effective. But if the moisture change is not obvious, the irrigation method or water volume may need to be re-evaluated. This can guide subsequent irrigation decisions. The moisture change data is compared with the preset moisture standards to determine whether the current irrigation strategy is still effective. The preset standards can be empirical values ​​based on plant needs, soil type and climatic conditions. This judgment step ensures the effectiveness and timeliness of irrigation behavior. If the moisture change indicates that the soil is still moist, unnecessary re-irrigation can be avoided to save water resources. For example, if the measured humidity change is less than the preset threshold, it may indicate that the plant does not need much water, and the system can reduce the frequency of irrigation. Set a reasonable humidity change interval based on the needs of the soil and plants. For example, a range can be set, and if the humidity increase is between 10% and 30%, the irrigation is considered effective. In this way, the accuracy of irrigation decisions can be effectively controlled. If the humidity change data is within this range, it indicates that the current irrigation strategy can continue to be used, otherwise the strategy may need to be adjusted. For example, if the humidity increases by more than 30%, it may cause the soil to be too wet, causing root respiration to be blocked; if the increase is less than 10%, it may mean insufficient irrigation.

[0072] In one embodiment, after the step of determining whether the irrigation behavior satisfies the condition for continued use, the method further includes:

[0073] If the humidity change data is not within the preset humidity change range, it is determined that the irrigation behavior does not meet the conditions for continued use;

[0074] Analyzing the reason why the irrigation behavior does not meet the requirements for continued use according to the humidity change data;

[0075] If the reason for not meeting the requirement for continued use is insufficient humidity, an adjustment strategy of increasing the irrigation frequency and changing the irrigation type is generated;

[0076] If the reason for not satisfying the continued use is excessive humidity, an adjustment strategy of reducing the irrigation frequency and changing the irrigation type is generated.

[0077] As mentioned above, when it is determined that the humidity change data is not within the preset humidity change range, the system will compare it with the set humidity change standard. For example, if the set humidity change range is 10% to 30%, but the actual humidity change is only 5% or 40%, it means that the current irrigation behavior does not meet the standard. The main effect of this step is to ensure the effectiveness of the irrigation strategy. When the humidity change is not within a reasonable range, it indicates that there is a problem with soil moisture management, which helps to timely discover and correct the irrigation strategy to prevent plant health from being affected. By analyzing the humidity change data, the system can determine the specific reasons for not meeting the conditions. For example, insufficient humidity may indicate insufficient irrigation water or too long irrigation intervals; while excessive humidity may be due to excessive irrigation water or poor drainage. Identifying the reasons for non-satisfaction helps to develop targeted adjustment strategies. After understanding the specific problems, farmers can adjust irrigation behaviors more targetedly, thereby improving water resource utilization and crop health. When the system determines that the humidity is insufficient, it will generate strategies to increase irrigation frequency and change irrigation types. For example, if the sensor shows that the soil moisture is continuously below the set threshold, it may be recommended to increase the number of irrigations per week or switch to a more efficient drip irrigation system to ensure water penetration. The implementation of this strategy can effectively increase the moisture content of the soil to meet the needs of plant growth. For example, increasing the frequency of irrigation can quickly replenish soil moisture, help plants better absorb water and nutrients, and thus promote growth. If the humidity is excessive, the system will generate a strategy to reduce the frequency of irrigation and change the type of irrigation. For example, if the soil moisture continues to exceed the safe range, it may be recommended to reduce the frequency of irrigation. This strategy can prevent excessive soil moisture and avoid the risk of root suffocation and disease. For example, reducing the frequency of irrigation helps give the soil time to drain and maintain appropriate moisture, thereby promoting healthy plant growth.

[0078] In one embodiment, the irrigation types include drip irrigation and sprinkler irrigation, wherein the sprinkler irrigation includes fixed sprinkler irrigation and mobile sprinkler irrigation.

[0079] As mentioned above, drip irrigation is a method of irrigation that delivers water directly to the roots of plants through a pipe system. It gradually drips water into the soil at a low flow rate and low pressure. Drip irrigation systems can be installed on the surface or underground to avoid evaporation and loss of water. The working principle of the drip irrigation system is to ensure that water penetrates directly into the root area of ​​the plant through slow and continuous dripping. For example, the root system of the crop can get exactly the amount of water it needs, avoiding unnecessary water waste. Drip irrigation delivers water to the root system of the plant, avoiding water evaporation caused by spraying water on the surface. This is especially important in arid or hot areas. Drip irrigation technology can reduce the waste of a lot of water resources, especially in areas with water shortages, and its water-saving effect is very obvious. Compared with traditional irrigation methods, drip irrigation is more efficient and significantly reduces water loss from the soil surface. Drip irrigation only provides water to the roots instead of spraying directly on the leaves, thereby reducing leaf diseases or mold growth caused by excessive water. Fixed sprinkler irrigation is an irrigation method that fixes the nozzle in a certain position and sprays water evenly to a certain area through high-pressure water flow. This sprinkler system is suitable for covering larger farmland or lawns, with a wide spraying range. The working principle of fixed sprinkler irrigation is to set fixed sprinkler heads, use water pumps to pressurize, and spray water through the nozzles in a fan-shaped or circular manner. The coverage of each sprinkler head is fixed, so full field coverage can be achieved by arranging multiple sprinklers. By adjusting the pressure of the water flow and the flow rate of the sprinkler head, the fixed sprinkler irrigation system can control the amount of water sprayed and the coverage range. When the water flow pressure increases, the distance and range of spraying will increase accordingly. Combining soil type, plant type and terrain characteristics, a reasonable choice of irrigation method can significantly improve the efficiency of water resource utilization and optimize plant growth: Drip irrigation: suitable for areas with scarce water resources or strong soil drainage, especially in drought, sloping land and other environments. Fixed sprinkler irrigation: suitable for large flat farmland, suitable for lawns and field crops, and can ensure uniform irrigation. Mobile sprinkler irrigation: high flexibility, suitable for a wide and diverse agricultural environment, and can flexibly adjust irrigation according to crop needs.

[0080] In one embodiment, the method further includes: setting a climate sensor at each of the multiple regional locations, receiving temperature and humidity parameters of different regional locations in real time; identifying the direct sunlight area and the shadow area according to the temperature and humidity parameters of different regional locations; obtaining the type parameters of the planting in the corresponding area based on the identification result, and analyzing the irrigation behavior in the direct sunlight area or the shadow area according to the type parameters. From the above analysis, by arranging climate sensors in multiple areas, the temperature and humidity are monitored in real time to provide basic data for irrigation decisions. Identify the lighting conditions of different areas, understand which areas are exposed to direct sunlight and which are in the shadow, so as to provide an important basis for subsequent irrigation behavior. According to the planting type of each area, analyze its water demand under different lighting conditions, so that a more scientific irrigation plan can be formulated. Different irrigation strategies are adopted for different lighting conditions to ensure that plants grow in the best state. It is assumed that in a shared garden in an urban community, residents plant a variety of plants, including vegetables, flowers and shrubs. Because some areas in the garden are exposed to direct sunlight, while other areas are in the shadow all year round, the water demand of plants varies significantly. Install climate sensors in each key area of ​​the garden (direct sunlight and shaded areas) to collect temperature and humidity data in real time. Use data analysis to identify direct sunlight areas and shaded areas. For example, the temperature in direct sunlight areas may be higher and the humidity may be lower. Collect information on the plant species planted in each area and combine their water demand characteristics. For example, some plants require more frequent shallow irrigation in direct sunlight, while others require less water. Develop personalized irrigation plans based on environmental conditions and plant needs in different areas. For example, plants in direct sunlight areas may need daily morning irrigation, while plants in shaded areas can be deeply irrigated every two days. Through an automated irrigation system, the irrigation time and water volume are automatically adjusted based on sensor feedback to ensure that each plant receives the appropriate amount of water.

[0081] The intelligent irrigation method based on the Internet of Things of this application monitors soil data in real time, such as humidity, temperature and pH value, so that the system can dynamically adjust the irrigation plan to avoid ineffective irrigation after rainfall, thereby effectively saving water resources. According to the soil moisture level and terrain characteristics (such as slope and flat land), a targeted irrigation strategy is formulated to prevent soil erosion on the slope and ensure that the flat land avoids excessive moisture and causes root hypoxia. Combined with meteorological data and historical irrigation records, the system can predict the water demand of each region, reasonably arrange the irrigation frequency and irrigation amount, and maximize the utilization efficiency of water resources. Accurate irrigation control ensures that the plant roots obtain appropriate moisture, promotes healthy growth, and improves crop yield and quality. Through the intelligent adjustment mechanism, the system can quickly respond to environmental changes (such as rainfall or temperature changes), adjust irrigation behavior, and further reduce resource waste. Through efficient water management and irrigation strategies, sustainable agricultural practices are promoted, which is conducive to ecological environmental protection and the long-term development of agriculture.

[0082] Reference Figure 3 In an embodiment of the present application, a smart irrigation device based on the Internet of Things is also provided, including:

[0083] Construction module 1 is used to construct a soil standard model for analyzing soil data and dividing the soil data into moisture levels and terrain;

[0084] An input module 2 is used to obtain soil data in real time, input the soil data into the soil standard model, and output the result of regional division through the soil standard model;

[0085] A judgment module 3 is used to judge whether the soil data meets the irrigation conditions based on the result of the area division;

[0086] An acquisition module 4 is used to acquire meteorological data, water pressure data and historical irrigation data when the soil data meets the irrigation conditions;

[0087] Prediction module 5, used to predict the irrigation parameters required for the corresponding area of ​​the soil according to the meteorological data, water pressure data and historical irrigation data;

[0088] The combination module 6 is used to determine the corresponding irrigation behavior by combining the soil data and the irrigation parameters of the corresponding area.

[0089] As described above, it can be understood that the various components of the IoT-based smart irrigation device proposed in this application can realize the functions of any one of the IoT-based smart irrigation methods described above, and the specific structure will not be repeated.

[0090] Reference Figure 4 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent irrigation method based on the Internet of Things is implemented.

[0091] The processor executes the IoT-based smart irrigation method, including: constructing a soil standard model for analyzing soil data, and dividing the soil data into moisture levels and terrain; acquiring soil data in real time, and inputting the soil data into the soil standard model, and outputting the result of regional division through the soil standard model; judging whether the soil data meets the irrigation conditions based on the result of regional division; when the soil data meets the irrigation conditions, acquiring meteorological data, water pressure data and historical irrigation data; predicting the irrigation parameters required for the corresponding area of ​​the soil according to the meteorological data, water pressure data and historical irrigation data; and determining the corresponding irrigation behavior in combination with the soil data and the irrigation parameters of the corresponding area.

[0092] In one feasible embodiment, by analyzing the humidity change data of each area after irrigation, potential problems can be identified, such as excess or insufficient water, which leads to poor plant growth or disease. Combine humidity data with climate data such as temperature and precipitation to analyze the impact of climate change on soil moisture. Establish a growth model for plants at different humidity levels to analyze whether the plants in the current area are adapted to the existing humidity conditions. Evaluate the adaptability of soil types to different plants based on humidity data and recommend more suitable crop selections. Establish an intelligent system based on humidity changes, adjust irrigation strategies based on real-time data, and ensure that plant water demand matches actual supply. Develop a risk warning model for humidity changes to indicate potential drought or waterlogging risks so that timely measures can be taken. Integrate humidity changes, soil properties, and plant growth data to establish a visualization platform to help farmers intuitively understand environmental changes. Regularly evaluate the effectiveness of irrigation and planting strategies based on long-term monitoring data and make necessary adjustments.

[0093] The above-mentioned IoT-based smart irrigation method monitors soil data in real time, such as humidity, temperature, and pH value, so that the system can dynamically adjust the irrigation plan to avoid ineffective irrigation after rainfall, thereby effectively saving water resources. Targeted irrigation strategies are formulated according to soil moisture levels and terrain features (such as slopes and flat land) to prevent soil erosion on slopes and ensure that flat land avoids excessive moisture and root hypoxia. Combined with meteorological data and historical irrigation records, the system can predict the water demand of each region, reasonably arrange irrigation frequency and irrigation volume, and maximize the utilization efficiency of water resources. Precise irrigation control ensures that plant roots obtain appropriate moisture, promotes healthy growth, and improves crop yield and quality. Through intelligent adjustment mechanisms, the system can quickly respond to environmental changes (such as rainfall or temperature changes), adjust irrigation behavior, and further reduce resource waste. Promoting sustainable agricultural practices through efficient water management and irrigation strategies will contribute to ecological environmental protection and the long-term development of agriculture.

[0094] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, an intelligent irrigation method based on the Internet of Things is implemented, comprising the steps of: constructing a soil standard model for parsing soil data, and dividing the soil data into moisture levels and terrain; acquiring soil data in real time, and inputting the soil data into the soil standard model, and outputting a result of regional division through the soil standard model; judging whether the soil data meets the irrigation conditions based on the result of regional division; acquiring meteorological data, water pressure data and historical irrigation data when the soil data meets the irrigation conditions; predicting the irrigation parameters required for the corresponding area of ​​the soil based on the meteorological data, water pressure data and historical irrigation data; and determining the corresponding irrigation behavior in combination with the soil data and the irrigation parameters of the corresponding area.

[0095] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0096] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0097] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An intelligent irrigation method based on the Internet of Things, characterized in that: The method comprises: Constructing a soil standard model for analyzing soil data and classifying the soil data into moisture levels and terrain; Acquire soil data in real time, input the soil data into the soil standard model, and output the result of regional division through the soil standard model; Based on the result of the area division, determining whether the soil data meets the irrigation conditions; When the soil data meets the irrigation conditions, obtaining meteorological data, water pressure data and historical irrigation data; Predicting the irrigation parameters required for the soil corresponding area based on the meteorological data, water pressure data and historical irrigation data; The corresponding irrigation behavior is determined by combining soil data and irrigation parameters of the corresponding area.

2. The intelligent irrigation method based on the Internet of Things according to claim 1, characterized in that: The method of outputting the result of regional division by the soil standard model comprises: Analyze the soil data to obtain soil moisture parameters and soil type; Acquire plant species parameters, and set a humidity division threshold according to the plant species parameters and soil type; Classifying the soil according to the moisture classification threshold and the soil moisture parameter; Obtaining topographic data of the soil location, and dividing the soil location into different terrain levels according to preset inclination angles and topographic features; Based on the division of soil moisture and terrain, the results of regional division are output.

3. The intelligent irrigation method based on the Internet of Things according to claim 2, characterized in that: The method of combining soil data with irrigation parameters of a corresponding area to determine corresponding irrigation behavior comprises: Construct irrigation analysis models; Acquire soil moisture parameters and soil types in each area, and analyze soil drought index according to the soil moisture parameters and soil types; Acquire plant species parameters of the area, and analyze plant irrigation demand data according to the plant species parameters; The irrigation parameters, plant irrigation demand data and drought index are input into the irrigation analysis model, and the analysis results are output through the irrigation analysis model to obtain the number of irrigations, irrigation frequency, irrigation intensity and irrigation type, wherein the irrigation intensity includes the amount of water required per square meter.

4. The intelligent irrigation method based on the Internet of Things according to claim 1, characterized in that: The method of predicting the irrigation parameters required for the soil corresponding area according to the meteorological data, water pressure data and historical irrigation data comprises: Obtaining the meteorological data of each area, and analyzing the temperature, precipitation and wind speed parameters of the corresponding area according to the meteorological data; Evaluate precipitation parameters and evaporation parameters based on temperature, precipitation and wind speed parameters of the corresponding area; Acquire water pressure data of a corresponding area, and analyze water supply parameters of the area within a preset time according to the water pressure data; Acquire historical irrigation data of the corresponding area, and analyze the average irrigation amount of each irrigation in the corresponding area according to the historical irrigation data; Constructing an irrigation prediction model, inputting the precipitation parameter, evaporation parameter and water supply parameter into the irrigation prediction model, predicting the irrigation parameters required for the corresponding area through the irrigation prediction model, and outputting the prediction result; Based on the output prediction results and the historical average irrigation amount, analyze the floating parameters between the prediction results and the historical average irrigation amount; Verifying whether the floating parameter is within a preset parameter range; When the floating parameter is within a preset parameter interval, the predicted result is judged to be a credible result.

5. The intelligent irrigation method based on the Internet of Things according to claim 3, characterized in that: After the step of combining soil data and irrigation parameters of the corresponding area to determine the corresponding irrigation behavior, the method includes: Real-time acquisition of soil moisture parameters after soil irrigation; analyzing the moisture variation data of the area according to the soil moisture parameters after the soil is irrigated and the soil moisture parameters when the soil is not irrigated; According to the humidity change data, determining whether the irrigation behavior meets the conditions for continued use; If the humidity change data is within a preset humidity change interval, it is determined that the irrigation behavior meets the conditions for continued use.

6. The smart irrigation method based on the Internet of Things according to claim 5, characterized in that: After the step of determining whether the irrigation behavior meets the conditions for continued use, the method further includes: If the humidity change data is not within the preset humidity change range, it is determined that the irrigation behavior does not meet the conditions for continued use; Analyzing the reason why the irrigation behavior does not meet the requirements for continued use according to the humidity change data; If the reason for not meeting the requirement for continued use is insufficient humidity, an adjustment strategy of increasing the irrigation frequency and changing the irrigation type is generated; If the reason for not satisfying the continued use is excessive humidity, an adjustment strategy of reducing the irrigation frequency and changing the irrigation type is generated.

7. The smart irrigation method based on the Internet of Things according to claim 6, characterized in that: The irrigation types include drip irrigation and sprinkler irrigation, wherein the sprinkler irrigation includes fixed sprinkler irrigation and mobile sprinkler irrigation.

8. An intelligent irrigation device based on the Internet of Things, characterized in that: include: A construction module is used to construct a soil standard model, to analyze soil data, and to classify the soil data into moisture levels and terrain; An input module, used for acquiring soil data in real time, inputting the soil data into the soil standard model, and outputting the result of regional division through the soil standard model; A judgment module, used for judging whether the soil data meets the irrigation conditions based on the result of the area division; An acquisition module, used for acquiring meteorological data, water pressure data and historical irrigation data when the soil data meets the irrigation conditions; A prediction module, used for predicting the irrigation parameters required for the corresponding area of ​​the soil according to the meteorological data, water pressure data and historical irrigation data; The combination module is used to combine soil data and irrigation parameters of the corresponding area to determine the corresponding irrigation behavior.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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