Intelligent water pump remote intelligent irrigation and drainage method and system based on soil gas fusion

Abstract

A kind of intelligence water pump remote intelligent irrigation and drainage method and system based on soil gas fusion, collect farmland soil moisture data, weather data, water level data and water pump flowmeter data, and combine with the region information input by user, farmland area, crop type, root total depth, field water-holding capacity, planting time and harvesting time, automatically establish crop growth cycle model, analyze different growth stages soil moisture threshold, root depth, irrigation wet layer depth, calculate crop evapotranspiration, irrigation water and irrigation frequency;Fusion multi-source environmental data generates irrigation control strategy, and uses Internet of Things to control water pump start-stop;According to historical irrigation data analysis water supply effect, optimize irrigation model, realize water saving and energy saving;According to future weather data, realize unmanned management.It includes perception layer, transmission layer, decision layer and execution layer, realizes farmland environment monitoring, intelligent irrigation and drainage decision, drought and flood warning, water pump remote control integrated management.

Description

Technical Field

[0001] This invention belongs to the field of agricultural intelligent irrigation technology, specifically referring to a remote intelligent irrigation and drainage method and system based on soil moisture fusion using smart water pumps. Background Technology

[0002] With the development of large-scale and intensive agricultural production, farmland irrigation methods are gradually shifting from traditional manual irrigation to automation and intelligence. In the process of traditional farmland irrigation, it is usually necessary to manually inspect the soil moisture and crop growth, and control the start and stop of water pumps and irrigation time based on experience. This method has problems such as high labor input costs, slow irrigation response speed, and insufficient irrigation precision.

[0003] Some existing automatic irrigation systems improve irrigation automation to some extent by using soil moisture sensors to control the operation of irrigation equipment based on soil moisture content thresholds. However, existing irrigation methods based solely on soil moisture detection mainly rely on the current soil condition for control, failing to fully consider weather changes, crop growth stages, and the water variation patterns in different farmland areas, which can easily lead to problems such as delayed or excessive irrigation.

[0004] Meanwhile, existing farmland irrigation systems typically lack the ability to analyze historical irrigation data and cannot adjust irrigation strategies based on data feedback generated during long-term operation, making it difficult for the system to adapt to the actual needs of different regions, different soil types, and different crop planting environments.

[0005] In addition, although some smart irrigation systems currently have remote control functions, they mainly realize the start and stop of water pumps. They lack an intelligent decision-making mechanism that integrates soil moisture data, meteorological data, crop growth models and actual irrigation effects, and cannot achieve precise irrigation control based on multi-source data fusion. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, the present invention provides a remote intelligent irrigation and drainage method and system based on moisture and gas fusion, so as to at least partially solve the above-mentioned technical problems.

[0007] The technical solution adopted in this invention is as follows: This invention proposes a remote intelligent irrigation and drainage method for smart water pumps based on moisture-gas fusion, comprising the following steps: Real-time data on farmland environmental conditions is acquired through sensors installed within the target farmland area. This data includes soil moisture (soil water content in dryland or water level in paddy fields), meteorological data, and water pump flow rate data. Basic planting parameters for the target farmland are input via a user terminal. These parameters include the farmland's location, area, crop type, total root depth, field capacity, planting time, and expected harvest time. Based on these parameters, a corresponding crop growth cycle model is automatically established, and soil moisture content threshold ranges, root depths, and other parameters are defined according to different crop growth stages. The appropriate irrigation wetting layer depth (less than or equal to root depth) is determined. Based on real-time meteorological data, the reference crop evapotranspiration (ET0) is calculated using the Penman formula and converted into actual crop evapotranspiration (ETc) using the crop coefficient (Kc). Then, based on the current soil moisture content and the target control limit, the time interval (irrigation frequency) for the next irrigation is estimated. Real-time soil moisture data, meteorological data, and crop growth stage water requirement patterns are integrated and analyzed to calculate the current soil moisture deficit in the root zone, thereby obtaining the net irrigation quota (unit: mm) for this irrigation and converting it into the actual gross irrigation water demand based on the irrigation water utilization coefficient.

[0008] Based on irrigation water demand, pump operation control parameters are generated and transmitted to intelligent control equipment via wireless communication to control the start, stop, and power adjustment of irrigation pumps. During irrigation, soil moisture changes and actual water supply data are continuously collected, and the irrigation decision model is continuously optimized through rolling learning based on historical irrigation data to achieve adaptive precision irrigation under different farmland environments. Drought or flood warnings are issued based on future weather information. In hot weather, evaporation and transpiration are calculated to increase irrigation water usage, while in rainy weather, irrigation water usage is reduced and drainage is calculated.

[0009] Furthermore, soil moisture data is collected by soil moisture sensors installed in different areas of farmland. These sensors are used to detect water levels in paddy fields or soil moisture content in dry land, and the detected soil moisture data is uploaded to a cloud data platform. The cloud data platform analyzes the water distribution in the irrigation area based on the differences in soil moisture content in different detection areas.

[0010] Furthermore, meteorological data is acquired through meteorological sensors and external meteorological data interfaces. The meteorological data includes ambient temperature, air humidity, rainfall, wind speed and direction, and light intensity. The method calculates the evapotranspiration of crops based on the current meteorological data, which is then used to calculate irrigation water consumption. Based on future meteorological changes, the preset irrigation plan is dynamically adjusted. When rainfall is predicted, the irrigation amount is reduced or flood level analysis is performed to calculate the drainage amount and drain water in a timely manner. During hot and dry weather, the irrigation amount is increased. For paddy fields, water pumps can be remotely controlled to circulate irrigation and drainage to cool the paddy fields.

[0011] Furthermore, the crop growth cycle model determines the current growth stage of the crop based on the input crop type, planting time, and harvest time, and calls the upper and lower limits of soil moisture for the corresponding growth stage. The crop growth cycle model estimates the root length and wetting layer depth of the crop at different growth stages based on the input root depth, planting time, and harvest time. Based on the upper and lower limits of soil moisture for different growth stages, root depth, input field capacity, and farmland area, the model calculates the irrigation water consumption. When the soil moisture content is detected to be lower than the lower limit of soil moisture for the corresponding growth stage, the model calculates the irrigation water consumption and generates an irrigation start command. When the soil moisture content is detected to be higher than the upper limit of soil moisture for the corresponding growth stage, the model generates an irrigation stop command.

[0012] Furthermore, the pump operation control parameters include pump start / stop status, operating frequency, and continuous operating time; the method controls the pump to adopt a low-power long-term operation mode based on the calculated irrigation demand, thereby saving irrigation water consumption and pump energy consumption, and allowing water to slowly enter the irrigation area to reduce channel water transport losses and soil seepage losses during irrigation, thus further saving water.

[0013] Furthermore, the actual water supply is detected by a tubular electromagnetic flowmeter installed at the outlet of the water pump; the method calculates the single irrigation water consumption, cumulative irrigation water consumption, and water consumption per unit area based on the real-time flow data collected by the electromagnetic flowmeter, and compares the calculation results with the theoretical irrigation quota for crops to generate irrigation water-saving analysis data for water-saving efficiency evaluation.

[0014] Furthermore, this invention proposes a remote intelligent irrigation and drainage system based on soil moisture and atmospheric moisture fusion, comprising a perception layer, a transmission layer, a decision-making layer, and an execution layer. The perception layer includes a soil moisture acquisition module, a meteorological acquisition module, a water level detection module, and a flow detection module, used to collect farmland environmental status data. The transmission layer includes a wireless communication module and an IoT gateway, used to transmit the data collected by the perception layer to a cloud platform. The decision-making layer includes a cloud computing analysis module, a crop model module, and an intelligent decision-making module, used to analyze irrigation needs and generate control strategies based on the collected data and user-input planting parameters. The execution layer includes an intelligent control cabinet, a pump control module, and a remote control terminal, used to control the operation of the irrigation pumps according to the control strategies.

[0015] Furthermore, the sensing layer includes an integrated soil moisture and water level sensor, which is installed in the farmland monitoring area to simultaneously collect soil moisture content and field water accumulation status; the flow detection module includes a tubular electromagnetic flow meter installed in the water delivery pipeline to detect the water flow in real time during the irrigation process.

[0016] Furthermore, the decision-making layer includes a user parameter input unit, a crop model unit, a water requirement calculation unit, a model optimization unit, and an early warning unit. The user parameter input unit receives regional information, farmland area, crop type, root depth, soil field water holding capacity, planting time, and harvest time through a mobile terminal program. The crop model unit establishes corresponding soil moisture control ranges and wetting layer depths based on crop type and growth stage. The water requirement calculation unit calculates irrigation demand based on soil moisture data, meteorological data, crop type, growth stage, wetting layer depth, soil moisture control range, and irrigation area. The model optimization unit analyzes water transport and seepage losses in different irrigation areas based on historical irrigation data and updates the water requirement calculation parameters. The early warning unit issues drought or flood warnings based on future meteorological information. For future high-temperature weather, it calculates evapotranspiration and increases irrigation water usage. For future rainfall, it calculates soil moisture changes and predicts drainage volume.

[0017] Furthermore, the execution layer includes an intelligent control cabinet, irrigation pumps, and drainage pumps; both irrigation and drainage pumps are axial flow pumps. The intelligent control cabinet includes a controller, a frequency converter module, an electrical protection module, and a communication module. The intelligent control cabinet remotely starts and stops the irrigation and drainage pumps according to the control commands sent by the decision-making layer, and adjusts the pump operating power according to irrigation needs.

[0018] Compared with the prior art, the present invention has the following advantages: By integrating soil moisture data with meteorological data, this method breaks through the traditional approach of relying solely on soil moisture for irrigation control. It can comprehensively consider current soil conditions, future weather changes, and crop growth stages to achieve more precise irrigation decisions.

[0019] By establishing a crop growth cycle model based on crop type, planting time, and harvest time, and setting corresponding soil moisture control ranges according to different growth stages, the irrigation process can meet the water requirements of different crop growth stages, thereby improving the accuracy of crop water management.

[0020] By connecting the intelligent control cabinet with the remote communication module, the pump can be started and stopped remotely and its operating parameters can be adjusted, reducing manual on-site operation and improving the level of automation in farmland irrigation.

[0021] By adopting a low-power, long-term irrigation method and adjusting the pump operation status according to the calculated actual water demand, the irrigation water flow is made more uniform, effectively reducing channel water transport losses and soil seepage losses caused by high-flow irrigation, and improving water resource utilization.

[0022] By installing electromagnetic flowmeters, the water supply during actual irrigation can be monitored in real time. Combined with analysis of theoretical irrigation needs, statistical data on irrigation water use can be generated, providing data support for water conservation management and irrigation cost accounting.

[0023] By using a historical data rolling learning mechanism, the system analyzes the water variation patterns in different farmland areas, identifies actual irrigation deviations caused by factors such as channel loss and soil seepage, and automatically optimizes subsequent irrigation parameters, enabling the system to have continuous optimization capabilities.

[0024] By adopting a system architecture consisting of a perception layer, a transmission layer, a decision-making layer, and an execution layer, integrated operation of farmland environmental monitoring, data analysis, intelligent decision-making, and water pump control can be achieved, making it suitable for smart irrigation applications in small and medium-sized farmland. Attached Figure Description

[0025] Figure 1 This is a flowchart of the remote intelligent irrigation and drainage method based on moisture fusion proposed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the intelligent water pump remote intelligent irrigation system based on moisture fusion proposed in an embodiment of the present invention.

[0026] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0029] like Figures 1-2As shown, this embodiment provides a remote intelligent irrigation and drainage method and system based on soil moisture fusion, used for intelligent irrigation and drainage management in farmland areas. This system comprehensively collects and analyzes farmland soil moisture information, meteorological environment information, pump operating status information, and crop growth status information through IoT sensing technology, cloud computing technology, and intelligent control technology. It provides drought and flood warnings based on future weather trends and automatically generates irrigation or drainage control strategies based on the analysis results. Through remote control of pump operation, it achieves precise regulation of farmland moisture.

[0030] This embodiment uses 50-100 mu of farmland as the application target. A smart water pump station is set up in the target farmland area. The smart water pump station includes irrigation pumps and drainage pumps. Both irrigation and drainage pumps are 250ZLB-4 type axial flow pumps, which are used for farmland irrigation and drainage, respectively. The axial flow pumps are equipped with Y160M-4 type electric motors with a power of 11kW. The operating flow rate of the axial flow pumps is approximately 540m³ / h, and the design head is 5-6m, which meets the irrigation and drainage needs of small and medium-sized farmland.

[0031] The intelligent water pump station is equipped with a smart control cabinet. Inside the cabinet are a controller, a frequency converter module, an electrical protection module, and a communication module. This module receives control commands from the cloud platform and controls the starting, stopping, and frequency adjustment of the irrigation and drainage pumps accordingly. The outlets of the irrigation and drainage pumps are connected to water delivery pipelines, which are equipped with tubular electromagnetic flow meters to detect the pump output flow, cumulative water delivery, and actual irrigation and drainage volume.

[0032] This embodiment employs a perception layer, a transmission layer, a decision-making layer, and an execution layer to construct a smart water pump remote intelligent irrigation and drainage system. The perception layer collects farmland environmental status data, the transmission layer facilitates data transmission between field equipment and the cloud platform, the decision-making layer analyzes irrigation and drainage needs based on collected data and user input parameters, and the execution layer controls water pump operation based on the decision results.

[0033] The sensing layer includes a soil moisture acquisition module, a meteorological acquisition module, a water level detection module, and a flow rate detection module.

[0034] The soil moisture acquisition module employs an integrated soil moisture and water level sensor. This sensor is installed in different monitoring areas of the farmland to collect data on farmland moisture status based on different planting patterns. When applied to dryland crops, the sensor detects changes in soil moisture content within the root zone; when applied to paddy fields, the sensor detects changes in field water level to obtain information on irrigation and waterlogging conditions.

[0035] The system analyzes the differences in soil moisture content or field water level between different areas based on data collected from multiple monitoring areas, and obtains the overall water distribution status of farmland, providing a data basis for subsequent irrigation and drainage decisions.

[0036] The meteorological data acquisition module includes temperature and humidity detection units, rainfall detection units, wind speed and direction detection units, and sunlight detection units. It also acquires future weather forecast information through an external meteorological data interface. Based on the collected real-time meteorological data and future weather trends, the system determines changes in crop evapotranspiration and farmland moisture.

[0037] The transmission layer includes a wireless communication module and an IoT gateway. The wireless communication module uses 4G / 5G communication and can be combined with LoRaWAN and NB-IoT communication methods to realize data transmission between sensing devices and the cloud platform. Soil moisture data, meteorological data, water level data, and flow data collected by the sensing layer are uploaded to the cloud data platform through the wireless communication module.

[0038] Users input basic planting parameters for the target farmland through a mobile terminal program. These parameters include the farmland's location, area, type of crop, total root depth, field water holding capacity, planting time, and expected harvest time.

[0039] The cloud platform establishes a corresponding crop growth cycle model based on the basic planting parameters input by the user, and determines the current growth stage according to the crop type and planting time. Based on the water requirement patterns of different crops at different growth stages, corresponding upper and lower limits for soil moisture control are set.

[0040] Meanwhile, the system estimates the root length corresponding to different growth stages based on the input root depth, crop growth time, and harvest time, and determines the irrigation wetting layer depth. The wetting layer depth is less than or equal to the crop root depth, so that the irrigation water volume can meet the effective root water absorption needs.

[0041] Furthermore, the decision-making level calculates the reference crop evapotranspiration ET0 based on real-time collected meteorological data, and calculates the actual crop evapotranspiration ETc based on the crop coefficient Kc corresponding to the current crop. The system calculates the root zone soil moisture deficit based on the actual crop evapotranspiration, current soil moisture content, field capacity, and target control moisture content.

[0042] Based on the calculated soil moisture deficit and the depth of the irrigation wetting layer, the system calculates the net irrigation quota required for this irrigation and, in conjunction with the irrigation water utilization coefficient, calculates the actual gross irrigation water demand.

[0043] When the current soil moisture content is detected to be lower than the soil moisture lower limit set for the corresponding growth stage, the system determines that the farmland is in a water shortage state and generates an irrigation start command based on the calculated irrigation water volume.

[0044] When the system detects that the current soil moisture content has reached the upper limit of soil moisture set for the corresponding growth stage, it generates an irrigation stop command.

[0045] Furthermore, the system provides drought and flood warnings based on future weather information.

[0046] When future weather forecasts indicate persistent high temperatures, low humidity, and insufficient rainfall, the system calculates crop evapotranspiration based on the forecast weather data and predicts future soil moisture decline trends. If the predicted soil moisture is insufficient to meet crop needs, the system increases the calculated irrigation water volume and generates irrigation control commands in advance, causing irrigation pumps to start ahead of schedule and improving the farmland's drought resistance.

[0047] When future weather forecasts indicate continuous rainfall, the system conducts a flood risk analysis based on future rainfall, current soil moisture content, and farmland water storage capacity. For dryland farmland, the system calculates the estimated water accumulation based on the remaining water storage space in the soil; for paddy fields, the system predicts the future water level based on changes in field water levels. When the predicted water accumulation exceeds a set threshold, the system calculates the estimated drainage volume and generates drainage control commands to proactively control the operation of drainage pumps.

[0048] In rice-growing areas, under high-temperature conditions, the system can also control the circulation of irrigation and drainage pumps based on water temperature and field environment conditions, thereby reducing field temperature by supplementing low-temperature water sources and improving the stability of the crop growth environment.

[0049] The execution layer controls the operation of the water pump based on the control parameters generated by the decision layer. The water pump operation control parameters include the water pump start / stop status, operating frequency, and continuous operating time.

[0050] During irrigation, the system controls the irrigation pumps to operate in a low-power, long-term mode based on the calculated irrigation demand, so that the water flows slowly into the wet areas of the farmland, reducing water loss in channels and rapid soil seepage during high-flow irrigation, while also reducing pump operating energy consumption.

[0051] The system continuously collects actual water supply data using electromagnetic flowmeters installed in the water supply pipeline, and calculates the water consumption per irrigation cycle, cumulative irrigation water consumption, and water consumption per unit area based on the flow data. The system compares the actual irrigation water consumption with the theoretical irrigation quota for crops to generate irrigation water-saving analysis data for evaluating the effectiveness of irrigation water saving.

[0052] Furthermore, this embodiment includes a model optimization module for continuously learning and optimizing the irrigation decision-making model based on historical irrigation and drainage operation data. The model optimization module collects data on soil moisture content changes, paddy field water level changes, actual water supply, pump operating time, and crop water response during historical irrigation processes. It then analyzes water transport losses, seepage losses, and actual water consumption patterns in different farmland areas using AI data modeling.

[0053] The model optimization module predicts the water loss in the next irrigation cycle based on historical data and adjusts the irrigation calculation parameters so that the calculated irrigation water consumption gradually approaches the actual optimal water consumption to meet the crop growth needs.

[0054] Through the above methods, this embodiment integrates soil moisture data, meteorological forecast data, crop growth models, and water pump control systems to achieve intelligent control of farmland irrigation and drainage processes. Compared to traditional manual irrigation methods, this system can respond to droughts and floods in advance based on future weather changes, automatically adjust irrigation and drainage strategies according to the actual water needs of crops, reduce the need for manual management, improve water resource utilization efficiency, and achieve water-saving, energy-saving, and unmanned farmland management.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A remote intelligent irrigation and drainage method using smart water pumps based on moisture-gas fusion, characterized in that, Includes the following steps: Real-time data on farmland environmental conditions is acquired by sensor data acquisition devices installed in the target farmland area. The environmental conditions data include soil moisture data (soil moisture content in dry land and water level in paddy fields), meteorological data, and water pump flow rate data. The user terminal inputs the basic planting parameters of the target farmland, which include the farmland's location, farmland area, type of crop planted, total root depth, field water holding capacity, crop planting time, and expected harvest time. The corresponding crop growth cycle model is automatically established based on the basic planting parameters, and the corresponding soil moisture content threshold range, root depth and corresponding irrigation wetting layer depth (less than or equal to root depth) are divided according to different growth stages of the crop. Based on real-time meteorological data, the reference crop evapotranspiration (ET0) is calculated using the Penman formula and converted into actual crop evapotranspiration (ETc) using the crop coefficient (Kc). Then, based on the current soil moisture content and the target control limit, the time interval (irrigation frequency) for the next irrigation is estimated. The real-time soil moisture data, meteorological data, and crop water requirement patterns at different growth stages are integrated and analyzed to calculate the current root zone soil moisture deficit, thereby obtaining the net irrigation quota for this irrigation. This quota can be converted into the actual gross irrigation water demand based on the irrigation water utilization coefficient. Pump operation control parameters are generated based on the irrigation water demand and transmitted to the intelligent control equipment via wireless communication to control the start, stop, and power adjustment of the irrigation pumps. During the irrigation process, data on changes in soil moisture and actual water supply after irrigation are continuously collected, and the irrigation decision model is continuously learned and optimized in combination with historical irrigation data to achieve adaptive precision irrigation under different farmland environments. Drought or flood warnings are issued based on future weather information. For hot weather, evaporation and transpiration are calculated to increase irrigation water usage. For rainy weather, irrigation water usage is reduced and drainage is calculated.

2. The method for remote intelligent irrigation and drainage using smart water pumps based on moisture-gas fusion as described in claim 1, characterized in that: The soil moisture data is collected by soil moisture sensors installed in different areas of farmland. The soil moisture sensors are used to detect the water level of paddy fields or the soil moisture content of dry land, and upload the detected soil moisture data to the cloud data platform. The cloud-based data platform analyzes the water distribution within the irrigation area based on the differences in soil moisture content in different detection areas.

3. The method for remote intelligent irrigation and drainage using smart water pumps based on moisture-gas fusion as described in claim 1, characterized in that: The meteorological data is acquired through meteorological sensors and external meteorological data interfaces, and includes ambient temperature, air humidity, rainfall, wind speed and direction, and light intensity. The method calculates crop evaporation and transpiration based on current meteorological data, which is then used to calculate irrigation water consumption. Based on future meteorological changes, the preset irrigation plan is dynamically adjusted. When rainfall is predicted, the irrigation amount is reduced or flood level analysis is performed to calculate drainage volume and drain water in a timely manner. During hot and dry weather, the irrigation amount is increased. For paddy fields, water pumps can be remotely controlled to circulate irrigation and drainage to cool the paddy fields.

4. The method for remote intelligent irrigation and drainage using smart water pumps based on moisture-gas fusion according to claim 1, characterized in that: The crop growth cycle model determines the current growth stage of the crop based on the input crop type, planting time, and harvest time, and calls the upper and lower limits of soil moisture for the corresponding growth stage; the crop growth cycle model estimates the root length and wetting layer depth of the crop at different growth stages based on the input root depth, planting time, and harvest time; and calculates the irrigation water consumption based on the upper and lower limits of soil moisture for different growth stages, root depth, input field capacity, and farmland area. When the soil moisture content is detected to be lower than the lower limit of soil moisture for the corresponding growth stage, the irrigation water consumption is calculated and an irrigation start command is generated. When the soil moisture content is detected to have reached the upper limit of soil moisture for the corresponding growth stage, an irrigation stop command is generated.

5. The method for remote intelligent irrigation and drainage using smart water pumps based on moisture-gas fusion according to claim 1, characterized in that: The pump operation control parameters include pump start / stop status, operating frequency, and continuous operating time; The method controls the water pump to operate in a low-power, long-term mode based on the calculated irrigation demand, thereby saving irrigation water and pump energy. It also allows water to flow slowly into the irrigation area, reducing channel water loss and soil seepage loss during irrigation, and further saving water.

6. The method for remote intelligent irrigation and drainage using smart water pumps based on moisture-gas fusion according to claim 1, characterized in that: The actual water supply is detected by a tubular electromagnetic flow meter installed at the outlet of the water pump. The method calculates the single irrigation water consumption, cumulative irrigation water consumption, and water consumption per unit area based on real-time flow data collected by the electromagnetic flowmeter, and compares the calculation results with the theoretical irrigation quota for crops to generate irrigation water-saving analysis data for water-saving efficiency evaluation.

7. A smart water pump remote intelligent irrigation and drainage system based on moisture and gas fusion, characterized in that, It includes the perception layer, transmission layer, decision-making layer, and execution layer; The sensing layer includes a soil moisture acquisition module, a meteorological acquisition module, a water level detection module, and a flow rate detection module, which are used to collect farmland environmental status data. The transmission layer includes a wireless communication module and an IoT gateway, used to transmit data collected by the sensing layer to the cloud platform; The decision-making layer includes a cloud computing analysis module, a crop model module, and an intelligent decision-making module, which are used to analyze irrigation needs and generate control strategies based on collected data and user-input planting parameters. The execution layer includes an intelligent control cabinet, a water pump control module, and a remote control terminal, which are used to control the operation of irrigation water pumps according to control strategies.

8. A remote intelligent irrigation and drainage system based on moisture and gas fusion of smart water pumps according to claim 7, characterized in that: The sensing layer includes an integrated soil moisture and water level sensor, which is installed in the farmland monitoring area to simultaneously collect soil moisture content and field water accumulation status. The flow detection module includes a tubular electromagnetic flow meter installed in the water supply pipeline for real-time detection of water flow during irrigation.

9. A remote intelligent irrigation and drainage system based on moisture and gas fusion of smart water pumps according to claim 7, characterized in that: The decision-making layer includes a user parameter input unit, a crop model unit, a water requirement calculation unit, a model optimization unit, and an early warning unit; The user parameter input unit receives regional information, farmland area, crop type, root depth, soil field water holding capacity, planting time, and harvest time through a mobile terminal program. The crop model unit establishes the corresponding soil moisture control range and wetting layer depth according to the crop type and growth stage; The water demand calculation unit calculates the irrigation demand based on soil moisture data, meteorological data, crop type, growth stage, wetting layer depth, soil moisture control range, and irrigation area. The model optimization unit analyzes the water conveyance and leakage losses in different irrigation areas based on historical irrigation data and updates the water demand calculation parameters. The early warning unit issues drought or flood warnings based on future meteorological information. For future high-temperature weather, it calculates evaporation and transpiration and increases irrigation water usage. For future rainfall weather, it calculates soil moisture changes and predicts drainage volume.

10. A remote intelligent irrigation and drainage system based on moisture and gas fusion of smart water pumps according to claim 7, characterized in that: The execution layer includes an intelligent control cabinet, an irrigation pump, and a drainage pump; Both the irrigation pump and the drainage pump are axial flow pumps. The intelligent control cabinet includes a controller, a frequency conversion adjustment module, an electrical protection module, and a communication module. The intelligent control cabinet remotely starts and stops the irrigation and drainage pumps according to the control commands sent by the decision-making level, and adjusts the pump operating power according to irrigation needs.

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