Coordinated drainage and regulation device for low-permeability black soil areas

Abstract

This invention discloses a synergistic drainage and regulation device for low-permeability black soil areas, belonging to the field of farmland drainage and waterlogging control technology. Addressing the problems of delayed drainage response and susceptibility to seasonal waterlogging and topsoil wetting caused by the heavy, poorly permeable soil in low-permeability black soil areas, this invention includes: an underground drainage unit; a shallow drainage promotion unit; a vertical infiltration guiding unit; a drainage collection unit consisting of a manhole containing a sedimentation chamber and a removable filter basket; an electrically controlled regulating valve and a regulation execution unit; an information sensing unit consisting of soil moisture, water level, water quality, and flow sensors arranged near rodent trails, inside the manhole, and on the main water collection pipe; and a control unit connected to the above units, capable of dynamically regulating drainage and recharge based on waterlogging status and water quality information. This invention is mainly used to improve the drainage efficiency and controllability of farmland in low-permeability black soil areas, achieving synergistic management of waterlogging reduction and water reuse.

Description

Technical Field

[0001] This invention relates to the field of farmland drainage and waterlogging control technology. More specifically, this invention relates to a synergistic drainage and regulation device for low-permeability black soil areas. Background Technology

[0002] In areas with low-permeability black soil, such as the low-lying farmland of the Sanjiang Plain in Northeast my country, the heavy, clayey texture, low porosity, and small permeability coefficient are the main intrinsic factors restricting field drainage efficiency and inducing seasonal waterlogging disasters. After concentrated rainfall or spring snowmelt, water in these soils is difficult to infiltrate, easily leading to waterlogging or oversaturation in the topsoil and surface, resulting in poor soil aeration, affecting crop root growth, and even causing yield reduction. To solve this problem, various drainage technologies have been tried in agricultural production.

[0003] Open ditch drainage is a traditional and widely used method that directly removes surface water by excavating surface ditches, and its construction is relatively simple. However, in low-permeability black soil areas, relying solely on open ditch drainage has significant limitations. Due to the slow soil infiltration rate, excess water above the topsoil and plow pan cannot be quickly drained into the open ditches, resulting in a severely delayed drainage response and failing to effectively solve the problem of waterlogging in the topsoil. In addition, open ditches occupy a large amount of farmland, affecting mechanical operations, and their slopes are prone to collapse, requiring substantial maintenance work.

[0004] Buried drainage technology, by burying permeable pipes at a certain depth underground, can effectively lower the groundwater level and promote soil moisture infiltration. However, conventional buried drainage systems also face challenges in low-permeability soils. Water flows slowly towards the buried pipes in dense soil, causing a delay in drainage initiation and limiting their effectiveness during periods of urgent flood control. Gaps around the buried pipes or the surrounding filter material are easily clogged by fine soil particles, potentially leading to long-term performance degradation. Furthermore, inspecting and cleaning buried pipes is extremely difficult and costly.

[0005] To accelerate the removal of topsoil water, rat tunnel drainage is used as a shallow drainage measure. This involves creating unlined circular channels at the bottom or top of the tillage layer using specialized plows, which quickly collect and guide topsoil water horizontally. However, when used alone, rat tunnels have a shallow drainage depth and limited effectiveness in lowering deeper soil water levels. Furthermore, if the collected water has no outlet, it cannot be effectively drained from the field. Combining rat tunnels with underground pipe systems can theoretically create a complementary effect, but practical engineering challenges exist in achieving this. Simply placing rat tunnels directly above or above underground pipes often results in soil subsidence and fine particle blockage, making it difficult to establish a stable and efficient vertical hydraulic connection. The efficiency of water transfer from the rat tunnels to the underground pipes is low, and the synergistic effect of both systems is not fully realized.

[0006] Furthermore, existing drainage systems generally focus on the function of "drainage," lacking refined control capabilities. Drainage outlets often use fixed openings or simple manual controls, failing to dynamically adjust based on real-time field water conditions (such as soil moisture content and groundwater levels) and agronomical needs (such as moisture retention after drainage or consideration of water reuse). This can lead to two adverse situations: first, insufficient drainage when rapid drainage is needed, prolonging waterlogging; second, excessive drainage during dry periods, losing soil moisture that could be used by crops and potentially discharging nutrients lost in the water, resulting in resource waste and potential non-point source pollution risks. Achieving automation and intelligent control of drainage requires reliable field information sensing methods and effective linkage between execution agencies. However, long-term stable operation in complex field environments and the formulation of multi-objective collaborative control strategies are both challenges encountered in practical applications.

[0007] In summary, in low-permeability black soil areas, existing drainage technologies, whether applied individually or in combination, suffer from slow response, incomplete effects, and difficult maintenance. They also often lack a stable and efficient internal connectivity structure when simply combined. Furthermore, they generally exhibit weak system regulation capabilities and fail to simultaneously address the multi-objective requirements of flood drainage and efficient water resource utilization. The root causes of these problems lie in the constraints of soil physical properties, the technical bottlenecks in hydraulic connections between different drainage components, and the lack of intelligent control methods that match the dynamics of water, salt, and fertilizer in the field. Summary of the Invention

[0008] One object of the present invention is to solve at least the above-mentioned problems and to provide at least the advantages that will be described later.

[0009] Another objective of this invention is to provide a synergistic drainage and regulation device for low-permeability black soil areas, which aims to solve the problems in the prior art in low-permeability black soil areas, such as slow drainage response, unreliable hydraulic connection between rodent tunnels and underground pipes, and lack of effective regulation of the drainage process, which makes it difficult to balance drainage and moisture retention.

[0010] To achieve these objectives and other advantages of the present invention, a synergistic drainage and regulation device for low-permeability black soil areas is provided, comprising: The underground drainage unit includes at least one main water collection pipe buried below the plow layer of the field and multiple branch pipes connected to it. The branch pipes are laid along the direction of crop planting rows and are perforated corrugated pipes covered with a permeable filler layer and a filter geotextile. The shallow drainage unit includes at least one unlined rat tunnel shaped in the tillage layer by a rat tunnel shaping plow, wherein the layout direction of the rat tunnel forms a non-zero angle with the extension direction of the branch pipe. The vertical infiltration unit is a wedge-shaped infiltration body that is set directly below the rat tunnel and is directly connected to the permeable filler layer of the branch pipe in the vertical direction; the wedge-shaped infiltration body is formed by filling with highly permeable material, with its top extending to the bottom of the rat tunnel and its bottom embedded in the permeable filler layer. The drainage collection unit includes an inspection well located at the junction of the branch pipe and the main collection pipe. The inspection well is equipped with a sedimentation chamber, and a removable filter basket is located above the sedimentation chamber. The outlet of the main collection pipe is connected to an external drainage ditch through the inspection well. An inspection port is provided on one side of the inspection well. The control and execution unit includes an electrically controlled regulating valve located at the outlet end of the water collection main pipe, and a reuse electrically controlled valve located on the bypass pipe connecting the inspection well and the field irrigation network. The information sensing unit includes a soil moisture sensor deployed near the depth of the rat trail, a water level sensor and a multi-parameter water quality sensor installed in the inspection well, and a flow sensor installed on the water collection main pipe. The control unit, which is signal-connected to the information sensing unit and the control execution unit, is configured to execute the following coordinated control process: based on the data from the soil moisture sensor and the water level sensor, the waterlogging status is determined; in the waterlogging status, the water level drop rate is used as the control target, and the opening of the electronic control valve is dynamically adjusted through a proportional-integral algorithm; in the non-waterlogging status, based on the detection data from the multi-parameter water quality sensor and the judgment result of the soil nutrient status, the reuse electronic control valve is controlled to perform quantitative recharge.

[0011] Preferably, the process of determining the waterlogging status in the aforementioned coordinated drainage and regulation device for low-permeability black soil areas includes the following steps: S11. Acquire real-time monitoring data from at least two soil moisture sensors located at different positions along the same rodent trail, and calculate the instantaneous soil moisture saturation value S corresponding to each sensor. i (t); S12. For each sensor, calculate the average soil moisture saturation within a time window of time T1 preceding the current time t, and calculate the linear rate of change K of soil moisture saturation within the time window based on the average soil moisture saturation. i ; S13. Calculate the maximum instantaneous value S of soil moisture saturation from all sensors. max (t) and minimum value S min The difference ΔS(t) is the arithmetic mean of the real-time difference ΔS(t) over a time window of time T2 from the current time t. avg ; S14. Obtain the instantaneous water level height H(t) measured by the water level sensor in the inspection well, and calculate its rate of rise V within the first predetermined time period. H ; S15. When the following conditions are met simultaneously, the state is determined to be flooded: Condition a, S max (t) The second predetermined time is greater than the first preset threshold; condition b, the average rate of change K of soil moisture saturation of any sensor. i Greater than zero; condition c, average difference ΔS avg Less than or equal to a preset spatial difference threshold; condition d, water level rise rate V H Greater than zero. Preferably, the coordinated drainage and regulation device for low-permeability black soil areas, upon determining that a waterlogging state has been entered, performs coordinated drainage regulation, specifically including: S31. The measured rate of groundwater level decline at the current moment is used as the process variable, and the target rate of groundwater level decline calculated based on the crop's waterlogging tolerance time requirement is used as the set value. S32. Based on the real-time deviation between the process variable and the set value and its integral, calculate and output the adjustment amount of the opening of the electronic control valve according to the preset proportional coefficient and integral time constant. S33. Monitor the rate of groundwater level decline in real time. Once the rate stabilizes within the target range and remains stable for a third predetermined time, reduce the opening of the electronically controlled regulating valve until the groundwater level recovers to the preset safe burial depth.

[0012] Preferably, the control unit of the coordinated drainage and regulation device for low-permeability black soil areas is further configured to perform forecasting and pre-discharge regulation, specifically including: Obtain quantitative precipitation forecast data for the next 24-72 hours; Based on the monitoring data from current soil moisture sensors and water level sensors, combined with soil saturated water content parameters, the real-time water retention capacity of the field is calculated. When the forecasted cumulative rainfall exceeds the preset ratio of the real-time storage capacity, the opening of the electronically controlled regulating valve is increased to the preset pre-discharge opening at the fourth predetermined time before the forecasted rainfall begins.

[0013] Preferably, the control unit of the synergistic drainage and regulation device for low-permeability black soil areas is further configured to perform nutrient reuse regulation, specifically including: In non-flood conditions, when the flow sensor detects stable drainage, the nitrate nitrogen concentration value measured by the water quality multi-parameter sensor is obtained; Based on the real-time inversion of soil nutrient status, the amount of supplemental nitrogen required to raise the solution nitrogen concentration in the topsoil to the target range is calculated. Based on the amount of supplemented nitrogen and the concentration of nitrate nitrogen in the drainage, the required amount of reinjection water is calculated, and the opening time or degree of the reuse electric control valve is controlled accordingly.

[0014] Preferably, in the aforementioned coordinated drainage and regulation device for low-permeability black soil areas, the control unit executes the instantaneous soil moisture saturation value S when determining the waterlogging state. i The calculation method for S(t) is as follows: i (t) = (θ) i (t) -θ r ) / (θ s -θ r ) Where, θ i (t) represents the volumetric water content monitored in real time by the i-th soil moisture sensor, θ s θ represents the soil saturation water content. r The residual soil moisture content and the soil saturation moisture content θ were both obtained from soil physical property experiments in the black soil region. s Dynamically adjusted according to soil depth and bulk density.

[0015] Preferably, the linear change rate K of soil moisture saturation in the synergistic drainage and regulation device for low-permeability black soil areas is... i The calculation method is as follows: using the soil moisture saturation sequence within the time window T1, the least squares method is used to fit the straight line S=kt+b, where k is the K corresponding to the sensor. i The control unit uses K corresponding to each sensor. i The relative magnitude and spatial distribution of the values, combined with the deployment location of each sensor in the field, identify whether the waterlogging development trend is uniformly distributed in a planar manner or develops in a strip along a specific direction. When it is determined to be a strip development, the dominant diffusion direction of the strip water accumulation is further determined.

[0016] Preferably, in the synergistic drainage and regulation device for low-permeability black soil areas, the proportional coefficient K in the proportional-integral algorithm is... p and integration time constant T i The setting method is: K p =[α×(H max -H min ) / Q max ;T i =β(A×μ) / K, where H max H min These represent the highest and lowest permissible water levels in the inspection well, Q. max The maximum design flow rate of the water collection trunk line is given by α, where A is the field area, μ is the soil water yield, K is the soil saturated hydraulic conductivity, and α and β are dimensionless adjustment coefficients calibrated based on drainage tests in the black soil region.

[0017] Preferably, the calculation model for the real-time storage capacity C(t) of the synergistic drainage and regulation device in the low-permeability black soil area is as follows: Where A is the field area, D is the root layer depth, and θ s (z) represents the saturated volumetric water content of the soil at depth z, and θ(z,t) is the real-time vertical distribution function of water content obtained by interpolation based on soil moisture sensor data; when the predicted cumulative rainfall P f Satisfy P f Pre-leakage is triggered when >γ×C(t), where γ is an empirical coefficient with a value range of 0.6-0.8.

[0018] Preferably, the synergistic drainage and regulation device for the low-permeability black soil area replenishes nitrogen (N). sup The calculation method is as follows: N sup =(N target -N current )×ρ b ×D plow ×A×10 -4 ; Where, N target The target topsoil nitrogen content is expressed in mg / kg, N. current ρ represents the current topsoil nitrogen content obtained from soil nutrient sensors or historical fertilization data. b This refers to the bulk density of the topsoil, expressed in g / cm³. 3 D plow A represents the depth of the cultivated layer in cm, and A represents the area of ​​the field in m². 2 Required recharge water volume V irr Calculate V according to the following formula: irr =N sup / (C drain ×η); Among them, C drain η represents the nitrate nitrogen concentration in the wastewater, expressed in mg / L, and η is the irrigation water utilization coefficient.

[0019] The present invention has at least the following beneficial effects: 1. The collaborative drainage and regulation device for low-permeability black soil areas protected by this invention achieves systematic optimization and intelligent regulation of farmland drainage processes by constructing an integrated system comprising an underground drainage unit, a shallow drainage promotion unit, a vertical infiltration unit, a drainage collection unit, a regulation execution unit, an information sensing unit, and a control unit. This device effectively overcomes the problem of delayed drainage response caused by poor soil permeability in low-permeability black soil areas. Stable and efficient hydraulic connectivity is achieved through a wedge-shaped infiltration body between rodent tunnels and underground pipes, significantly improving the drainage efficiency of topsoil water. Simultaneously, with the collaborative control of multiple types of sensors and adjustable valves, the system can quickly respond and dynamically adjust the drainage intensity during waterlogging. During non-waterlogging periods, it can quantitatively reuse nutrients based on water quality information, thus simultaneously mitigating flooding and disasters while maintaining soil moisture and nutrient resource cycling, improving the overall operational efficiency and sustainability of the drainage system.

[0020] 2. This invention further defines the method for judging waterlogging conditions. By integrating multi-dimensional information such as instantaneous values ​​of soil moisture saturation at multiple locations, trends within a time window, spatial differences, and the rate of groundwater level rise, a more comprehensive and reliable waterlogging identification logic is constructed. This method can not only accurately capture the initial signal of waterlogging, but also distinguish between localized water accumulation and overall waterlogging by analyzing the spatial distribution differences and trends of moisture saturation, thereby avoiding misjudgments caused by abnormal data at a single point or temporary moisture fluctuations. This multi-indicator collaborative judgment method significantly improves the timeliness and accuracy of the system's response to waterlogging events, providing a reliable basis for subsequent precise control.

[0021] 3. This invention specifies a concrete drainage control process under waterlogging conditions. By combining process control targeting the rate of water level decline with a proportional-integral algorithm, closed-loop dynamic adjustment of drainage intensity is achieved. This method can automatically calculate and track the ideal water level decline trajectory based on actual field conditions, making the drainage process more stable and controllable, avoiding the problems of insufficient or excessive drainage that may be caused by traditional fixed-opening drainage. By gradually reducing the valve opening in the later stages of drainage, the system can effectively relieve waterlogging while maximizing the retention of effective soil moisture, achieving an organic unity between drainage and moisture conservation objectives and improving water resource utilization efficiency.

[0022] 4. This invention introduces a pre-release control mechanism based on quantitative precipitation forecasting. By calculating the field's water storage capacity in real time and comparing it with the forecast rainfall, it can proactively lower the field water level before heavy rainfall occurs, creating storage space. This predictive control strategy transforms traditional passive drainage into proactive prevention and control, effectively improving the system's ability to respond to sudden heavy rainfall events and mitigating the risk and severity of waterlogging. The pre-release operation, carried out in a timely manner before rainfall, also allows the drainage system more sufficient response time, helping to avoid waterlogging losses caused by momentary insufficient drainage capacity during peak rainfall periods.

[0023] 5. This invention specifies a method for regulating nutrient reuse under non-waterlogged conditions. By monitoring the nitrate nitrogen concentration in drainage and combining it with soil nutrient status assessment, it can intelligently make decisions and execute quantitative recharge. This technology treats nutrients in drainage as a recyclable resource. When soil nutrients are insufficient, controlled recharge restores some nutrients to the root zone, reducing fertilizer application and non-point source pollution risks while improving the overall utilization efficiency of water and fertilizer resources. This function represents an upgrade of drainage systems from a single "discharge" function to a coordinated "discharge-reuse" management system, contributing to the green and sustainable development of agricultural production.

[0024] 6. This invention clarifies the specific calculation formula for the instantaneous value of soil moisture saturation. By introducing constitutive parameters such as soil saturated water content and residual water content, the calculated saturation more accurately reflects the degree of pore water filling and its relationship with soil physical properties. In particular, the dynamic correction of soil saturated water content with depth and bulk density takes into account the spatial variability of soil characteristics in black soil regions, making the basis for judging waterlogging conditions more scientific and reliable, reducing judgment errors caused by inaccurate parameter values, and improving the robustness and adaptability of the entire perception and decision-making system.

[0025] 7. This invention further refines the calculation method for soil moisture saturation change rate and the function of waterlogging situation identification. By using the least squares method to fit the linear trend within a time window, data fluctuations can be effectively smoothed, revealing more clearly the true direction and rate of moisture change. Based on the spatial distribution characteristics of the change rates from multiple sensors, it identifies whether waterlogging develops uniformly in a planar manner or spreads in a banded pattern, and can determine the dominant diffusion direction, providing important information for the diagnosis of field water movement. This function helps managers understand the spatial development pattern of waterlogging, providing decision support for taking targeted agronomic or engineering measures, and improving the system's spatial perception and diagnostic capabilities.

[0026] 8. This invention provides a method for setting key parameters (proportional coefficient Kp and integral time constant Ti) in the proportional-integral algorithm. This method directly correlates the controller parameters with specific field soil hydraulic properties (such as specific yield and saturated hydraulic conductivity) and system design parameters (such as field area, pipeline flow rate, and water level fluctuation). This physically-based parameter tuning method allows the controller's regulation characteristics to better match the actual field drainage dynamics, improving the stability and regulation quality of the control system. By introducing regulation coefficients based on experimental calibration, the differences between the simplified model and the actual system are also taken into account, making the algorithm more practical and reliable in engineering applications.

[0027] 9. This invention provides a specific calculation model for real-time water retention capacity. This model comprehensively considers the difference between real-time soil moisture content and saturated moisture content at different depths through an integral form, enabling a more accurate quantification of how much additional infiltration water a field can still hold at a given moment. Compared to simple empirical estimation, this model fully considers the heterogeneity and dynamic changes in the vertical distribution of soil moisture, resulting in more accurate calculations of water retention capacity. Based on this, a pre-discharge triggering mechanism can more scientifically assess the relationship between rainfall risk and the risk of field overflow due to water accumulation, making pre-discharge decisions more rational and avoiding unnecessary pre-drainage or insufficient pre-discharge.

[0028] 10. This invention clarifies the calculation formulas for nitrogen replenishment and recharge water volume in nutrient reuse regulation. This calculation framework systematically links the target soil nitrogen content, current nitrogen status, topsoil physical parameters (bulk density, depth), field area, and nutrient concentration in drainage, achieving a quantitative conversion from agronomic requirements to engineering execution quantities. In this way, recharge decisions are no longer empirical but based on a clear nutrient balance principle, ensuring the accuracy of recharge water volume. This effectively replenishes soil nitrogen while avoiding secondary waterlogging or nutrient waste that may result from excessive recharge, making the nutrient reuse process more scientific and controllable.

[0029] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the combined drainage device for low-permeability black soil areas, which integrates underground pipes and rat trail ditches, as described in one of the technical solutions of this invention. Figure 2 This is a schematic diagram of the structure of the branch pipe and the rat tunnel drainage promotion structure in another technical solution of the present invention; Figure 3 This is a schematic diagram of the structure of the water collection well in another technical solution of the present invention.

[0031] Explanation of reference numerals in the attached drawings: 1-Branch pipe; 2-Water collection main pipe; 3-Inspection well; 31-Sedimentation chamber; 32-Filter basket; 33-Inspection port; 4-Rat tunnel; 5-Wedge-shaped seepage guide; 61-Permeable filler layer; 62-Reverse filter geotextile layer; 7-External drainage ditch. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments, so that those skilled in the art can implement it based on the description.

[0033] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0034] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified.

[0035] In the description of this invention, the terms "lateral", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used 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.

[0036] like Figure 1-3 As shown, the present invention provides a synergistic drainage and regulation device for low-permeability black soil areas, comprising: The underground drainage unit includes at least one main water collection pipe 2 buried below the plow layer of the field and multiple branch pipes 1 connected to it. The branch pipes 1 are laid along the direction of crop planting rows and are perforated corrugated pipes covered with a permeable filler layer 61 and a reverse filter geotextile 62. The shallow drainage unit includes at least one unlined rat tunnel 4 formed in the tillage layer by a rat tunnel forming plow, wherein the layout direction of the rat tunnel is at a non-zero angle to the extension direction of the branch pipe 1. The vertical infiltration unit is a wedge-shaped infiltration body 5 located directly below the rat tunnel and directly connected to the permeable filler layer 61 of the branch pipe 1 in the vertical direction; the wedge-shaped infiltration body 5 is formed by filling with highly permeable material, its top extends to the bottom of the rat tunnel, and its bottom is embedded in the permeable filler layer 61. The drainage collection unit includes a manhole 3 located at the intersection of the branch pipe 1 and the main water collection pipe 2. The manhole 3 has a sedimentation chamber 31, and a removable filter basket 32 ​​is provided above the sedimentation chamber 31. The outlet of the main water collection pipe 2 is connected to the external drainage ditch 7 through the manhole 3. A maintenance port 33 is provided on one side of the manhole. The control and execution unit includes an electrically controlled regulating valve located at the outlet of the water collection main pipe 2, and a reuse electrically controlled valve located on the bypass pipe connecting the inspection well and the field irrigation network. The information sensing unit includes a soil moisture sensor deployed near the depth of the rat trail, a water level sensor and a multi-parameter water quality sensor installed in the inspection well, and a flow sensor installed on the water collection main pipe. The control unit, which is signal-connected to the information sensing unit and the control execution unit, is configured to execute the following coordinated control process: based on the data from the soil moisture sensor and the water level sensor, the waterlogging status is determined; in the waterlogging status, the water level drop rate is used as the control target, and the opening of the electronic control valve is dynamically adjusted through a proportional-integral algorithm; in the non-waterlogging status, based on the detection data from the multi-parameter water quality sensor and the judgment result of the soil nutrient status, the reuse electronic control valve is controlled to perform quantitative recharge.

[0037] In the above technical solution, the branch pipe 1 of the underground drainage unit can be a PVC perforated corrugated pipe with an outer diameter of 110 mm, with perforations evenly distributed along the pipe wall. The branch pipe 1 is buried below the plow layer of the field. The permeable filler layer 61 wrapped around the outside of the branch pipe 1 can be gravel, and short-fiber needle-punched geotextile can be wrapped around the outside of the gravel layer as a reverse filter geotextile layer 62. The water collection main pipe 2 can be a PVC solid-wall pipe, connected to the branch pipe 1 through a tee fitting. The unlined rat tunnels of the shallow drainage unit 4 can be shaped by a traction rat tunnel plow. The wedge-shaped infiltration body 5 of the vertical infiltration unit can be set directly below each rat tunnel. The infiltration body 5 can be filled with gravel, with its top in direct contact with the bottom of the rat tunnel, and its bottom embedded downward into the permeable filler layer 61 outside the branch pipe 1, forming a stable vertical water-guiding channel.

[0038] The inspection well 3 of the drainage collection unit can be a precast concrete well cylinder, located at the junction of the branch pipe 1 and the main water collection pipe 2. A removable 304 stainless steel filter basket 32 ​​can be placed above the sedimentation chamber 31. An inspection port 33 is provided at the top of the inspection well 3, covered with an inspection well cover. The electrically controlled regulating valve of the control and execution unit can be an electric flange gate valve, installed at the end of the main water collection pipe 2. The reuse electrically controlled valve can be an electric flange ball valve, installed on the bypass pipe leading from the lower part of the inspection well 3 to the head of the field irrigation network. The soil moisture sensor of the information sensing unit can be a model based on the frequency domain reflection principle, arranged at intervals along the same rat trail, directly buried in the soil below and to the side of the rat trail. The water level sensor can be an submersible hydrostatic level gauge, fixed to the inner wall of the inspection well 3 by a bracket, with its probe submerged in water. The water quality multi-parameter sensor can be an online monitoring probe with an integrated nitrate nitrogen ion selective electrode, installed in the water flow above the filter basket 32 ​​inside the inspection well 3. The flow sensor can be a pipe-type ultrasonic flow meter, installed on the straight pipe section upstream of the electrically controlled regulating valve of the water collection main 2. The control unit can be a modular programmable logic controller, installed in a waterproof control box in the field. All sensor signal cables are protected by conduits before being connected to the input module of the control box, while the drive cables of the electrically controlled regulating valve and the reuse electrically controlled valve are connected to the output module of the control box. The system power supply can be a 300-watt peak power solar panel and a matching 100 Ah battery.

[0039] After being collected by rat tunnels, field water rapidly infiltrates through the wedge-shaped permeable guide 5 directly beneath them to the permeable filler layer 61 surrounding the branch pipe 1, then enters the branch pipe 1, and finally flows into the main collection pipe 2 and inspection well 3. The control unit collects data from all sensors at preset intervals. When determining waterlogging conditions, the soil moisture saturation threshold is set to 0.90. Waterlogging is determined when the instantaneous saturation values ​​measured by the two sensors furthest apart on a rat tunnel both exceed 0.90 for 30 consecutive minutes, and their linear change rate within a 60-minute time window is greater than 0.01 per hour, while the average difference between their saturations is less than 0.05, and the water level in inspection well 3 rises at a rate greater than 0.5 meters per day within 10 minutes. In waterlogging conditions, the control unit activates proportional-integral regulation with a target water level drop rate of 12 centimeters per day, calculated based on crop waterlogging tolerance requirements. The proportional coefficient K... p and integration time constant T i K can be initially set through on-site drainage tests. p It is 0.8, T iThe time is 120 minutes. The control unit calculates and outputs an electrical control valve opening adjustment command every minute based on the deviation between the measured water level drop rate and the target value. The gravel and crushed stone materials used to fill the wedge-shaped permeable body 5 and the permeable filler layer 61 can be purchased from local building materials markets. Key parameters such as soil saturated moisture content and residual moisture content can be obtained by collecting undisturbed soil samples at different depths in the project area and measuring them using the laboratory ring immersion method and pressure membrane method. The operational effect of this device can be verified in typical low-lying black soil farmland. A test plot of approximately 1 hectare was selected, and the changes in soil moisture saturation, groundwater level, and drainage flow rate before and after the system's activation were continuously monitored during the rainy season.

[0040] This technical solution creatively incorporates a permeable body 5, filled with gravel, with a wedge-shaped cross-section, vertically connecting the rat tunnel to the outer filter media of the underground pipe. This structurally ensures a stable, efficient, and less clogged channel for shallow water to flow into the deep drainage pipe. Simultaneously, the solution deeply integrates structural improvements with intelligent control, constructing a complete control system encompassing multi-dimensional information perception, multi-objective decision-making algorithms, and automatic actuators. This system can not only automatically identify waterlogging and dynamically adjust drainage intensity, but also decide on recharge based on the nutrient content of the drainage during non-waterlogging periods, achieving a functional leap from passive drainage to active control, and from simple drainage to water and fertilizer synergistic management. This integrated design, organically combining reliable structural connectivity with intelligent process control, effectively solves the common problems of delayed drainage response and insufficient control capacity in low-permeability areas, achieving synergistic effects of accelerating drainage response speed, improving drainage controllability, and promoting resource recycling.

[0041] In another technical solution, the process of determining the waterlogging status in the collaborative drainage and regulation device for low-permeability black soil areas includes the following steps: S11. Acquire real-time monitoring data from at least two soil moisture sensors located at different positions along the same rodent trail, and calculate the instantaneous soil moisture saturation value S corresponding to each sensor. i (t); S12. For each sensor, calculate the average soil moisture saturation within a time window of time T1 preceding the current time t, and calculate the linear rate of change K of soil moisture saturation within the time window based on the average soil moisture saturation. i ; S13. Calculate the maximum instantaneous value S of soil moisture saturation from all sensors. max (t) and minimum value S min The difference ΔS(t) is the arithmetic mean of the real-time difference ΔS(t) over a time window of time T2 from the current time t. avg ; S14. Obtain the instantaneous water level height H(t) measured by the water level sensor in the inspection well, and calculate its rate of rise V within the first predetermined time period. H ; S15. When the following conditions are met simultaneously, the state is determined to be flooded: Condition a, S max (t) The second predetermined time is greater than the first preset threshold; condition b, the average rate of change K of soil moisture saturation of any sensor. i Greater than zero; condition c, average difference ΔS avg Less than or equal to a preset spatial difference threshold; condition d, water level rise rate V H Greater than zero. Step S11 is specifically as follows: The control unit, through its data acquisition module, periodically reads raw electrical signals from soil moisture sensors located at multiple specific locations along the same rat trail, including upstream, midstream, and downstream sections, and converts them into digital values ​​representing soil volumetric water content. Subsequently, the system immediately retrieves pre-stored parameters in the database, representing soil saturation and residual water content obtained through laboratory measurements for this low-permeability black soil field. For each sensor, the control unit applies a calculation formula to subtract the real-time monitored volumetric water content from the residual water content, and then divides this difference by the difference between the saturated and residual water content, thereby obtaining the instantaneous soil moisture saturation value for the point represented by that sensor. This calculation process normalizes the raw water content data to a relative saturation value between 0 and 1, eliminating the influence of slight differences in soil bulk density at different locations, allowing for direct comparison and comprehensive analysis of monitoring data from different locations.

[0042] Step S12 specifically involves the control unit activating its time-series analysis engine for the instantaneous soil moisture saturation value sequence of each sensor calculated in step S11. The system sets a fixed-length review time window for each sensor, for example, the past six hours. The control unit extracts all historical instantaneous saturation values ​​of the sensor within this time window, forming a data sequence. Next, the system uses the least squares method to perform linear regression analysis on this data sequence, fitting an optimal trend line. The slope of this fitted line is calculated and recorded as the linear change rate of soil moisture saturation for that sensor within the current analysis period. This rate of change is a key trend indicator; a positive value clearly indicates that the soil moisture at that location has been continuously increasing over a period of time, a negative value indicates decline, and a value of zero indicates stability.

[0043] Step S13 specifically involves the control unit scanning and comparing the instantaneous soil moisture saturation values ​​from all sensors at the current moment during each calculation cycle. It quickly identifies the maximum and minimum values ​​and calculates the real-time difference between them. This difference reflects the saturation variations at different locations in the field at the same time. Then, the control unit incorporates this instantaneous difference along with the differences obtained from previous calculation cycles into a longer time window for smoothing. For example, it calculates the arithmetic mean of all instantaneous differences over the past two hours to obtain the average difference. This average effectively smooths out any random fluctuations or instantaneous sensor errors that may exist in a single measurement, stably characterizing the overall uniformity of the spatial distribution of soil moisture saturation within the field. A consistently low average difference indicates that moisture accumulates uniformly in the field, rather than only in localized areas.

[0044] Step S14 specifically involves the following: A water level sensor inside the inspection well, connected to the control unit, continuously provides instantaneous groundwater level data. The control unit records the water level change curve over time and calculates the net rise in water level height within a recent preset first predetermined time period, such as one hour. By dividing this rise by time, the system obtains the current water level rise rate. This rate is a physical quantity that directly reflects the system's water inflow intensity; a positive value indicates that there is a net water flow into the inspection well, meaning the groundwater level is being raised.

[0045] Step S15 specifically involves the following: After completing the real-time calculation of all the above data, the control unit executes a strict four-condition combined logical AND operation. First, it checks condition a: whether the instantaneous value of the highest soil moisture saturation among all sensors has continuously exceeded a first preset threshold for a complete second predetermined time period, ensuring that the oversaturation state is persistent and severe. Second, it checks condition b: whether at least one of the linear change rates calculated by all sensors is greater than zero, confirming the existence of a continuous source of moisture increase in the field. Then, it checks condition c: whether the calculated average difference is less than or equal to a preset spatial difference threshold, excluding localized waterlogging and determining that the waterlogging is a systemic, area-based problem. Finally, it checks condition d: whether the current water level rise rate is greater than zero, providing crucial evidence from a groundwater dynamics perspective that water is migrating and accumulating in the lower layers. Only when all four conditions are simultaneously met within the same judgment period will the control unit's final logical output jump from "non-waterlogged state" to "waterlogged state," thereby accurately and reliably triggering the subsequent coordinated drainage control process.

[0046] The waterlogging condition judgment method in this technical solution involves specific parameter settings and data processing procedures. In the information sensing unit, three soil moisture sensors are deployed at different locations along the same rat trail, located at the beginning, middle, and end of the trail, with a linear spacing of 25 meters between each sensor. These sensors can be frequency domain reflective sensors that measure volumetric water content at a depth of 0.4 meters. They are connected to the control unit via an RS-485 bus, and the data acquisition frequency can be set to once per minute. The water level sensor in the inspection well can be a submersible static pressure transmitter, whose data is also uploaded to the control unit at a frequency of once per minute. The control unit can be an industrial controller with floating-point arithmetic capabilities, and its internal program is configured to execute a continuous waterlogging judgment algorithm.

[0047] The specific parameter settings in the judgment process are shown in the following example: The duration T1 of time window one can be set to 60 minutes, meaning that the control unit calls the historical soil moisture saturation data sequence within 60 minutes before the current time t during calculation. The duration T2 of time window two can be set to 30 minutes. The first preset threshold, which is the critical value used to judge soil oversaturation, can be set to 0.90. The spatial difference threshold can be set to 0.05 to measure the spatial uniformity of field moisture saturation. The first predetermined time is the calculation of the water level rise rate V. H The time interval used can be set to 10 minutes. The second predetermined time, i.e., the time required by condition a for S... max The duration exceeding the threshold can be set to 30 minutes. During the judgment, the control unit calculates the instantaneous soil moisture saturation value S of each sensor in real time. i The required soil saturated moisture content θs and residual moisture content θr for calculation can be determined based on laboratory measurements of soil samples from the 0-50 cm depth topsoil layer of the experimental field, with values ​​of 0.48 cubic centimeters per cubic centimeter and 0.08 cubic centimeters per cubic centimeter, respectively. The linear rate of change K is then calculated. i At that time, the control unit performs linear fitting on 60 saturation data points within the T1 time window using the least squares method, and the resulting slope k is K. i Value. Calculate the average difference ΔS. avg At that time, the control unit first calculates ΔS per minute, and then takes the arithmetic mean of the 30 ΔS values ​​within the T2 window.

[0048] In the above technical solution, during rainfall or irrigation events, the control unit continuously runs a judgment process, first reading the real-time volumetric water content θ from three soil moisture sensors. i And substitute into the formula to calculate S i The program then retrieves data from the past 60 minutes, fits a trend line to each sensor, and obtains its respective K. i Value. Simultaneously, the program finds the three S values ​​at the current time.i The maximum value S max and minimum value S min Calculate the difference ΔS, and then calculate the average ΔS over the past 30 minutes. avg The program also reads water level data and calculates the average rate of rise V of the water level over the past 10 minutes. H Finally, the program performs a logical AND operation on the four conditions: only if S... max The K value is greater than 0.90 for all data points over a continuous 30-minute period; and at least one sensor has a K value greater than 0.90. i The value is greater than zero (indicating that moisture is still increasing); and ΔS avg Less than or equal to 0.05 (indicating that the increase in moisture is relatively uniform in space); and V H Only when the water level is greater than zero (indicating that the groundwater level is rising) will the system ultimately determine that the field has entered a waterlogged state and trigger subsequent drainage control commands.

[0049] The aforementioned technical solution creatively integrates three key types of information: the temporal trend of soil moisture saturation, the spatial uniformity of its distribution, and the dynamics of groundwater levels. It captures the continuous increase in moisture by calculating the linear rate of change of data from multiple sensors, distinguishes between localized waterlogging and widespread flooding by analyzing spatial differences in saturation, and makes a comprehensive judgment based on the direct evidence of rising water levels. This multi-dimensional, multi-criteria collaborative judgment logic significantly improves the accuracy, timeliness, and reliability of the system's identification of flooding events, effectively avoiding the problems of missed and false positives inherent in traditional single-indicator methods.

[0050] In another technical solution, the coordinated drainage and regulation device for low-permeability black soil areas, upon determining that a waterlogging state has been entered, performs coordinated drainage regulation, specifically including: S31. The measured rate of groundwater level decline at the current moment is used as the process variable, and the target rate of groundwater level decline calculated based on the crop's waterlogging tolerance time requirement is used as the set value. S32. Based on the real-time deviation between the process variable and the set value and its integral, calculate and output the adjustment amount of the opening of the electronic control valve according to the preset proportional coefficient and integral time constant. S33. Monitor the rate of groundwater level decline in real time. Once the rate stabilizes within the target range and remains stable for a third predetermined time, reduce the opening of the electronically controlled regulating valve until the groundwater level recovers to the preset safe burial depth.

[0051] The collaborative drainage control process of the above technical solution involves setting specific control targets and implementing closed-loop regulation algorithms. Upon determining that a waterlogging state has been entered, the control unit immediately uses the groundwater level decline rate as the core control target. This target water level decline rate can be determined by back-calculating based on the waterlogging tolerance requirements of major crops at different growth stages. For example, for soybean seedlings, a common crop in the Sanjiang Plain, the waterlogging tolerance depth is 0.3 meters below the surface, and the tolerance duration is typically no more than 3 days. If the current groundwater level has risen to 0.1 meters below the surface, it needs to be lowered to below 0.3 meters within 3 days. The calculated average target decline rate is approximately 6.7 centimeters per day. The control unit can preset this value as the initial setpoint. The process variable is the measured groundwater level decline rate at the current moment, obtained by calculating the change in the water level sensor in the inspection well within the most recent sampling period (e.g., 5 minutes). The proportional coefficient K in the proportional-integral algorithm... p and integration time constant T i The initial value can be set with reference to the system's hydraulic characteristics. Proportional coefficient K p It can be initially set to 0.5, which physically represents the approximate change in the rate of water level drop caused by a 1% change in valve opening; the integral time constant T i The initial time can be set to 180 minutes to eliminate steady-state errors. The third predetermined time, which is the duration required for the water level to stabilize within the target range, can be set to 120 minutes. The safe burial depth can be set according to the depth of the crop root system; for example, it can be set to 0.5 meters for most dryland crops.

[0052] The specific working process of this control procedure is as follows: Once the flooding condition is determined, the control unit immediately enters the closed-loop control mode. First, it reads the instantaneous value H(t) of the water level sensor and calculates the measured rate of water level drop V over the past 5 minutes. actual As a process variable, the control unit will use V. actual With the preset target rate V target (e.g., 6.7 cm per day) are compared, and the real-time deviation e(t) = V is calculated. target -V actual Subsequently, the algorithm calculates the output adjustment amount according to the formula: Valve opening adjustment amount Δu(t) = K p *[e(t)+(1 / Ti)*∫e(t)dt]. The control unit converts this adjustment into an opening command for the electronically controlled regulating valve and sends it. For example, if the current valve opening is 30% and the calculated Δu(t) is +5%, then the new opening command is 35%. The system repeats the above sampling, calculation, and output process in a 1-minute cycle to achieve dynamic adjustment. During drainage, the control unit continuously monitors the rate of water level drop. When the measured rate V... actual For 120 consecutive minutes, the target rate V was consistently maintained. targetWhen the water level is within ±10% of its maximum (i.e., stable between 6.0 and 7.4 cm per day) and has dropped significantly from its peak, the control unit initiates a reduction in the water level opening. The drainage intensity is gradually reduced in small increments (e.g., 0.5% per minute), and this process continues until the water level sensor reading indicates that the groundwater level has dropped below the preset safe depth of 0.5 meters. At this point, the control process ends, and the system may switch to routine monitoring or moisture retention mode.

[0053] The aforementioned technical solution creatively introduces a closed-loop feedback control system with "water level decline rate" as the direct control target. It transforms crop waterlogging tolerance requirements into a traceable process control setpoint and uses a proportional-integral algorithm to continuously adjust the valve opening in real time based on the deviation between the measured rate and the target rate. This control strategy achieves smooth, precise, and adaptive adjustment of drainage intensity, ensuring rapid response and sufficient drainage capacity in the early stages of waterlogging; automatically maintaining a stable and appropriate decline rate after the water level begins to drop, avoiding drastic fluctuations in soil moisture; and gradually reducing drainage intensity in the later stages of waterlogging relief, maximizing the retention of available soil water. This intelligent control method based on dynamic process targets fundamentally overcomes the problems of coarse "on-off" or fixed-mode drainage processes, delayed water situation response, and prominent irrigation-drainage conflicts, achieving precise coordination between drainage efficiency and soil moisture conservation targets over time.

[0054] In another technical solution, the control unit of the coordinated drainage and regulation device for low-permeability black soil areas is further configured to perform forecasting and pre-discharge regulation, specifically including: Obtain quantitative precipitation forecast data for the next 24-72 hours; Based on the monitoring data from current soil moisture sensors and water level sensors, combined with soil saturated water content parameters, the real-time water retention capacity of the field is calculated. When the forecasted cumulative rainfall exceeds the preset ratio of the real-time storage capacity, the opening of the electronically controlled regulating valve is increased to the preset pre-discharge opening at the fourth predetermined time before the forecasted rainfall begins.

[0055] In the aforementioned technical solution, the forecasting and pre-release control function involves the specific implementation of external data acquisition, internal model calculation, and the linkage of execution mechanisms. Quantitative precipitation forecast data can be acquired by automatically accessing the standardized application programming interface provided by the China Meteorological Administration or local meteorological departments at a frequency of once per hour via the 4G or 5G wireless communication module built into the control unit. The acquired data is a gridded quantitative precipitation forecast for the next 72 hours, with a time interval of 6 hours, and an accuracy of up to 0.1 mm. The control unit's program is configured to extract the cumulative rainfall P within the time window of the next 24 to 72 hours. fAs a basis for judgment. The field area A involved in the calculation model of real-time storage capacity C can be entered into the system based on the actual measured value of the specific field. For example, the area of ​​a typical field can be 10,000 square meters. The root layer depth D can be set according to the main crop. For example, the root layer depth of corn is usually taken as 0.6 meters. Soil saturated volumetric water content θ s The vertical distribution function of (z) can be obtained by sampling at different depths (e.g., 0.2, 0.4, 0.6 meters) within the field and fitting the data after laboratory testing. The real-time vertical distribution function of moisture content θ(z, t) is constructed using linear interpolation based on monitoring data from soil moisture sensors deployed at multiple depths (e.g., one each at 0.2 meters, 0.4 meters, and 0.6 meters). The empirical coefficient γ ranges from 0.6 to 0.8, and its specific value can be determined by inversion from historical flooding event data; for example, it can be set to 0.7 in one implementation case. The fourth predetermined time, i.e., the lead time for performing pre-discharge operations before the predicted rainfall begins, can be set according to the system's drainage response time, typically 6 hours. The preset pre-discharge opening degree can be set according to the system's maximum drainage capacity; for example, the opening degree of the electronically controlled regulating valve can be increased to 70% of its maximum opening.

[0056] The specific working process of this forecast and pre-release control function is as follows: The control unit periodically obtains the latest grid forecast data from the meteorological server and locates the grid where the project field is located. The program calculates the cumulative forecast rainfall P for the next 24 to 72 hours. f Simultaneously, the program reads real-time data from all soil moisture and water level sensors, and uses the interpolated θ(z,t) function and a preset θ... s The (z) function calculates the real-time storage capacity C(t) of the current field using an integral formula. After the calculation is complete, the program will output P... f Compare with γ×C(t), where γ takes the value of 0.7. If the triggering condition P is met. f If the value is greater than 0.7 × C(t), the system determines that pre-discharge is necessary. Six hours before the forecast rainfall start time, the control unit sends a command to the electronically controlled regulating valve to adjust its opening from its current state (which may be closed or slightly open) to a pre-discharge opening of 70%. After the pre-discharge operation begins, groundwater in the field is discharged in advance through the drainage system, allowing the groundwater level to drop and thus freeing up soil water storage capacity for the upcoming rainfall. The system will maintain this pre-discharge opening until the forecast rainfall begins, or adjust it according to real-time monitoring of water level changes. If the actual weather conditions change during the pre-discharge period, and the updated forecast data no longer meets the triggering conditions, the control unit can automatically stop the pre-discharge and return the valve opening to its normal state.

[0057] The parameter setting and verification methods for this regulation function are as follows: The key empirical coefficient γ can be calibrated by analyzing historical meteorological data and field water level response records. Specifically, several rainfall events in the past few years that caused significant waterlogging in the fields are selected, and the actual water retention capacity C of the field at that time is deduced based on the soil moisture content and water level data before the rainfall. actual And the critical rainfall P that actually causes urban flooding critical Through statistical analysis of multiple sets of data, the ratio P was determined. critical / C actual The statistical distribution of γ is used, and its mean or a certain quantile (such as the 75th percentile) is taken as the recommended value for γ. The operational effect of this function can be tested in a typical field. A test plot with complete sensors is selected, and different γ values ​​(such as 0.6, 0.7, 0.8) are preset before the rainy season. When a heavy rainfall forecast is received, observe and record whether the system triggers pre-discharge, the amount of pre-discharge drainage, and the actual water accumulation or waterlogging in the field after rainfall. By comparing the pre-discharge effect under different γ values ​​with the final degree of waterlogging, the setting of this parameter can be evaluated and optimized, and the actual contribution of the forecast and pre-discharge strategy to reducing the risk of waterlogging can be verified.

[0058] The aforementioned technical solution creatively integrates meteorological forecast information into the active control logic of the drainage system. By calculating the remaining water storage capacity of the field soil in real time and comparing it with future rainfall forecasts, it can decide and execute a "pre-discharge" operation to lower the field water level before the actual rainfall event occurs. This method transforms the operation mode of the drainage system from "post-event response" to "pre-event prevention." Its innovation lies in the fact that the system is no longer just a drainage channel, but becomes a water regulator with predictive and buffering capabilities. It can proactively utilize the time window before rainfall to free up soil storage capacity in advance, thereby effectively improving the field's buffering capacity to cope with sudden heavy rainfall, reducing the pressure on the drainage system during peak periods, and reducing the risk and severity of waterlogging from the source. This prediction-based proactive prevention and control strategy represents a qualitative leap in addressing the problem of delayed drainage in low-permeability areas compared to passive drainage that relies solely on real-time sensing.

[0059] In another technical solution, the control unit of the synergistic drainage and regulation device for low-permeability black soil areas is further configured to perform nutrient reuse regulation, specifically including: In non-flood conditions, when the flow sensor detects stable drainage, the nitrate nitrogen concentration value measured by the water quality multi-parameter sensor is obtained; Based on the real-time inversion of soil nutrient status, the amount of supplemental nitrogen required to raise the solution nitrogen concentration in the topsoil to the target range is calculated. Based on the amount of supplemented nitrogen and the concentration of nitrate nitrogen in the drainage, the required amount of reinjection water is calculated, and the opening time or degree of the reuse electric control valve is controlled accordingly.

[0060] In the aforementioned technical solution, the nutrient reuse regulation function involves the specific implementation of water quality monitoring, nutrient demand calculation, and precise irrigation control. Regarding the determination of a non-waterlogged state, the control unit primarily relies on data from soil moisture and water level sensors. When the soil moisture saturation at all monitoring points remains below 0.70 for 24 consecutive hours, and the water level in the inspection well is below the safe burial depth of 0.5 meters, the system determines that it is in a non-waterlogged state. At this time, the control unit begins monitoring the flow sensor. When it detects that the drainage flow rate in the main collection pipe is consistently between 5% and 10% of the system's maximum design flow rate for two consecutive hours, it considers that there is stable drainage available for use. The multi-parameter water quality sensor can be an online nitrate nitrogen analyzer, which measures using ultraviolet spectroscopy with a range of 0 to 50 mg / L. It is installed in a location with stable water flow within the inspection well, and the data update interval is 15 minutes. Real-time inversion of soil nutrient status can be based on two data sources: first, a soil nitrate nitrogen rapid measurement sensor deployed at a depth of 0.2 meters in the topsoil layer to directly acquire current nitrogen content data; second, an estimation based on a nutrient balance model, combined with recent fertilization history records (input by the administrator through a human-machine interface). If both data are available, the control unit prioritizes the real-time sensor data.

[0061] The specific parameters involved in calculating the required nitrogen supplementation are set as follows: Target topsoil nitrogen content (N). target The nutrient requirement can be set according to the crop's nutrient requirements; for example, 120 mg / kg can be set during the jointing stage of corn. The current nitrogen content (N) in the topsoil is... current If sensor data is used, its 15-minute moving average can be read directly; if historical data is used for inversion, calculations are performed based on the last fertilization amount, the number of days since fertilization, and the set nutrient decay coefficient. Topsoil bulk density ρ b The value can be set at 1.2 grams per cubic centimeter based on the typical value of black soil in this region. Topsoil depth D plow The standard setting is 0.3 meters. Field area A should be entered as the actual area, for example, 1 hectare is 10,000 square meters. The calculated supplemental nitrogen amount N... sup The unit is kilograms. The concentration of nitrate nitrogen in wastewater, C. drain The average value of the water quality sensor readings over the most recent stable drainage period is taken. The irrigation water utilization coefficient η is determined based on the recharge method; if drip irrigation is used for recharge, the value of η can be set to 0.90.

[0062] In this technical solution, the specific working process of the nutrient reuse regulation function is as follows: After the system confirms that it is not in a waterlogging period and there is stable drainage, the control unit activates the nutrient reuse monitoring thread. First, it reads the real-time nitrate nitrogen concentration measured by the water quality sensor and determines whether it is higher than the set activation threshold, such as 5 mg / L. If it is higher than the threshold, the system obtains the current soil nitrogen status data N. currentAnd calculate the amount of nitrogen to be supplemented, N. sup Assume N target It is 120 mg per kilogram, N current The value is 80 mg / kg. Substituting this into the formula, we can calculate N. sup The concentration was 14.4 kg. Subsequently, the system read the current nitrate nitrogen concentration C in the wastewater. drain The average value, for example, is measured to be 18 mg / L. Using η = 0.90, the required recharge volume V is calculated. irr Approximately 889 cubic meters. The control unit, based on the design flow rate of the reinjection pipeline system, directs the water volume V... irr The system calculates the required opening time for the rechargeable solenoid valve. For example, if the bypass pipe is designed to have a flow rate of 50 cubic meters per hour, the valve will need to be open for approximately 17.8 hours. The control unit then sends an opening command to the rechargeable solenoid valve and starts a timer. Throughout the recharge process, the system continuously monitors the drainage flow and water quality. If a sharp decrease in drainage flow or a sudden change in water concentration occurs, the recharge can be paused and reassessed. Once the recharge volume reaches the target value, the valve automatically closes.

[0063] The methods and sources for setting key parameters are as follows: Target soil nitrogen content N target The fertilization rate is mainly determined based on recommended fertilization indices for different crop growth stages, combined with the local soil fertility level. Data can be obtained from fertilization guidelines published by local agricultural technology extension departments. Soil bulk density ρ b and the depth of the cultivated layer D plow This can be obtained through field sampling and measurement. The value of the irrigation water use coefficient η depends on the design standards and equipment selection of the recharge project. The activation threshold of the nitrate nitrogen concentration in the wastewater needs to balance the nutrient recovery value and operating costs. This can be determined by analyzing historical wastewater quality data and crop nutrient response experiments to avoid the economic and agronomical significance of recharge being minimal when the concentration is too low. The actual effect of this regulation function can be verified in experimental fields. Two plots with similar conditions are selected, one operating the nutrient reuse function and the other serving as a control (drainage is directly discharged). During one maize growing season, the soil nitrogen dynamics, crop growth, and final yield of the two plots are compared, and the total amount of nitrogen recovered by the reuse system is measured to evaluate its resource recycling efficiency.

[0064] The innovation of the above-mentioned technical solution lies in expanding the function of farmland drainage systems from a simple "drainage" to a collaborative management system of "drainage-monitoring-reuse." By integrating water quality sensing elements into the system and designing a quantitative recharge decision algorithm based on real-time soil nutrient requirements and drainage nutrient content, the drainage system can intelligently identify nutrient-rich drainage and precisely recharge it as "nutrient solution" to the root zone when needed by the soil. This technological transformation gives drainage ditches the attributes of "resource recycling pipelines" in a sense. It effectively utilizes previously discarded water and fertilizer resources, reduces the additional input of chemical fertilizers and the environmental risks caused by nutrient runoff, and achieves multiple goals of water conservation, fertilizer retention, and pollution reduction. This system design concept and implementation method, which intelligently couples drainage with irrigation and nutrient management, breaks away from the traditional mindset of single-function drainage engineering. It provides a novel and practical technical approach to solving the problems of efficient water and fertilizer utilization and non-point source pollution control in green agricultural production, constituting a substantial breakthrough over existing technologies, and thus possesses innovation.

[0065] In another technical solution, the coordinated drainage and regulation device for low-permeability black soil areas, wherein the control unit executes the instantaneous value S of soil moisture saturation when judging the waterlogging state. i The calculation method for S(t) is as follows: i (t) = (θ) i (t) -θ r ) / (θ s -θ r ) Where, θ i (t) represents the volumetric water content monitored in real time by the i-th soil moisture sensor, θ s θ represents the soil saturation water content. r The residual soil moisture content and the soil saturation moisture content θ were both obtained from soil physical property experiments in the black soil region. s Dynamically adjusted according to soil depth and bulk density.

[0066] In the above technical solution, the method for calculating soil moisture saturation involves sensor data processing and the application of soil constitutive parameters. A soil moisture sensor based on the frequency domain reflectance principle can be selected, whose direct output is the volumetric water content θ. i The voltage signal is converted into a value in cubic centimeters per cubic centimeter by the analog-to-digital converter and calibration formula built into the control unit. The data acquisition frequency can be set to once per minute. The instantaneous value S of soil moisture saturation is calculated. i The two core soil hydraulic parameters required are soil saturated water content θ. s With soil residual moisture content θ rThe soil moisture content (θ) needs to be obtained through experimental measurements of the black soil characteristics in the project area. The specific measurement method is as follows: within the field where the equipment is deployed, five representative points are selected using a checkerboard sampling method. At each point, undisturbed soil samples are collected at three depths: 0-0.2 meters, 0.2-0.4 meters, and 0.4-0.6 meters. s The determination was performed using the ring sampler immersion saturation method. Specifically, a 100 cubic centimeter ring sampler was used to collect undisturbed soil samples. Filter paper and permeable stones were placed at the bottom of the sample before it was immersed in water to fully saturate the capillary action. The samples were then dried in a 105°C oven to constant weight. The θ of the soil sample at that depth was then calculated. s Value. Soil residual moisture content θ r The moisture content can be determined by using a pressure membrane apparatus, where a saturated soil sample is placed on a clay plate with a pressure of 1.5 MPa and then equilibrated before measuring its moisture content, or by referring to empirical values ​​from research literature on similar types of black soil.

[0067] Soil saturated water content θ s Dynamic adjustments based on soil depth and bulk density are crucial to the implementation of this method. A parameter table is pre-set within the control unit, storing θ values ​​corresponding to different depth ranges. s With θ r The baseline value, and the bulk density correction factor. For example, for the 0 to 0.3 meter topsoil layer, θ s The baseline value can be set to 0.48, θ r The baseline value is set at 0.08; for subsoil layers of 0.3 to 0.6 meters, θ s The baseline value can be set to 0.45, θ r The baseline value is set to 0.10. When the system accesses soil moisture sensor data, it simultaneously reads the sensor's burial depth. During calculation, the corresponding θ is first selected based on the sensor depth. s and θ r The benchmark value is then used. The program will then retrieve the measured soil bulk density data for that location (which can be obtained through sampling and input into the system). If the measured bulk density differs from the reference bulk density used when the benchmark value was obtained, an empirical correction formula will be applied to θ. s Fine-tuning is performed. For example, a simplified correction relation is: θ s _adj=θ s _ref*(ρ b _ref / ρ b _measured), where θ s _ref represents the baseline saturated water content, ρ b _ref represents the reference bulk density (e.g., 1.2 g / cm³), and ρb_measured represents the measured bulk density. After depth selection and bulk density correction, the value ultimately determined is used to calculate the real-time saturation S at the sensor location. i θ s With θ r value.

[0068] The specific workflow of the above technical solution in the control unit is as follows: In each sampling cycle, the control unit reads the raw voltage signal of the i-th soil moisture sensor and converts it into volumetric water content θ through its unique calibration curve. i (t). Simultaneously, based on the sensor's unique identifier, the program retrieves its pre-stored burial depth information and corresponding soil bulk density data. The program then selects θ from the internal parameter table for the corresponding depth range. s and θ r The baseline value was used, and the bulk density data was used to determine θ. s Perform real-time corrections to obtain the corrected θ. s _adj. Finally, the program will use θ i (t), θ r and θ s Substitute _adj into formula S i (t)=(θ i (t) -θ r ) / (θ s _adj -θ r ), calculate the instantaneous value of soil moisture saturation at that moment. This S i The (t) value is a dimensionless number between 0 and 1, which intuitively reflects the proportion of soil pores filled with water and eliminates the influence of differences in soil texture and density at different locations. This makes the sensor data from different locations comparable and provides a standardized and normalized key input for accurate subsequent judgment of waterlogging status.

[0069] The aforementioned technical solution explicitly introduces two constitutive parameters: soil saturated water content and residual water content, transforming the original water content reading into a more physically meaningful saturation index. More importantly, it specifically emphasizes the soil saturated water content θ. s Dynamic corrections are needed based on depth and bulk density. This requirement enables the system to more precisely characterize the spatial differences in soil hydraulic properties in the field, ensuring that the calculated soil moisture saturation S... i (t) can more realistically and consistently reflect the actual saturation level of soil pore water at different locations. This data preprocessing method, based on soil physical properties and considering spatial variability, provides a more scientific, reliable, and unified basic data layer for subsequent intelligent waterlogging identification, significantly improving the robustness and accuracy of the entire perception and decision-making system in the face of complex field environments.

[0070] In another technical solution, the synergistic drainage and regulation device for low-permeability black soil areas has a linear change rate K of soil moisture saturation. i The calculation method is as follows: using the soil moisture saturation sequence within the time window T1, the least squares method is used to fit the straight line S=kt+b, where k is the K corresponding to the sensor.i The control unit uses K corresponding to each sensor. i The relative magnitude and spatial distribution of the values, combined with the deployment location of each sensor in the field, identify whether the waterlogging development trend is uniformly distributed in a planar manner or develops in a strip along a specific direction. When it is determined to be a strip development, the dominant diffusion direction of the strip water accumulation is further determined.

[0071] In the above technical solution, the calculation of soil moisture saturation change rate and the function of waterlogging situation identification involve time series analysis algorithms and spatial data analysis. Linear change rate K i The calculation is based on soil moisture saturation sequence data within a time window T1, the duration of which can be set to 90 minutes. The control unit maintains a data queue of length 90 for each soil moisture sensor to store the instantaneous soil moisture saturation values ​​S arranged in chronological order. i When it is necessary to calculate K of a certain sensor at the current time t... i When the value is reached, the control unit retrieves 90 saturation data points from the sensor queue within the most recent 90 minutes. The algorithm uses the least squares method to perform linear fitting on this set of data points, with the fitting model being S = k*t + b, where t is the time sequence and S is the saturation. By minimizing the sum of the squares of the perpendicular distances from each data point to the fitted line, the slope k and intercept b are solved. The obtained slope k is the linear change rate K of soil moisture saturation corresponding to this sensor. i Its unit is usually the change per minute or hour, and a positive value indicates that the saturation is increasing.

[0072] The implementation of waterlogging situation identification relies on a sensor array deployed at multiple locations within the field. A specific deployment method could be as follows: In a rectangular field with an area of ​​1 hectare, sensors are placed along three parallel rodent trails. Three soil moisture sensors are evenly spaced along each trail, with an adjacent sensor spacing of 25 meters and a lateral spacing of 15 meters between the three trails. This creates a monitoring network of nine sensors within the field. After rainfall or irrigation, the control unit calculates the K0 value for each sensor. i The process of identifying the waterlogging situation is as follows: First, the control unit calculates the K values ​​of all nine sensors. i The mean and standard deviation of the values. If all K values ​​are... i The differences between values ​​are small, for example, the standard deviation is less than 15% of the mean, and each K i If the values ​​exhibit an irregular spatial distribution, the system determines that the waterlogging development trend is a uniform, areal pattern, indicating that moisture is increasing across the field at similar rates. If the K values ​​of some adjacent sensors... iIf the value is significantly higher than other sensors (e.g., more than 50% higher), and these high-value sensors roughly form a line or band in space, the system determines that the waterlogging development trend is a banded development along a specific direction. After determining it to be a banded development, the system further analyzes the high K values. i The direction of the sensor's connection, combined with field micro-topographic data (such as pre-inputted slope and elevation information), determines the dominant diffusion direction of strip-shaped water accumulation. For example, if high K... i If the direction of the line connecting the sensors is consistent with the natural drainage path of the field or the direction of a known low-lying area, the system determines that the water is accumulating and spreading along that direction.

[0073] The setting methods and verification methods for relevant parameters in the above technical solution are as follows: The length of the time window T1 needs to balance the timeliness of trend identification and the ability to resist interference. Too long a window will lead to response lag, while too short a window will be easily affected by data fluctuations. It can be determined by analyzing data from the soil moisture rise phase in historical waterlogging events and observing the time required for the trend to stabilize; generally, it can be set to 60 to 120 minutes. The threshold for judging the zonal development trend (e.g., K) is also important. i Values ​​exceeding 50% can also be calibrated using historical data. The practical effectiveness of this situational awareness function can be verified through field trials: in a controlled irrigation test, non-uniform water accumulation conditions are artificially created, and the system's response to each sensor K is recorded. i The calculation results of the value and the judgment results of the development trend are compared with the water accumulation range and diffusion direction of manual field survey to verify the accuracy of its identification.

[0074] The above technical solution not only calculates the changing trend of water saturation at each monitoring point, but also creatively proposes a method for spatial collaborative analysis of the changing trends at multiple monitoring points, thereby achieving automatic identification of the "situation" of waterlogging development. This is achieved by analyzing the rate of change K at each point. i By analyzing the relative magnitude and spatial distribution of water levels, the system can distinguish between two development patterns—area-like waterlogging and strip-like water accumulation—which have different implications for production management and engineering maintenance, and can infer the direction of dominant diffusion. This function provides crucial diagnostic information that traditional monitoring methods cannot offer, enabling managers not only to understand "where there is excessive water" but also to predict "how the water will develop," thus providing direct decision support for implementing differentiated and more targeted drainage or agronomic measures (such as focusing on dredging a specific drainage path). This leap from static distribution description to dynamic process pattern recognition enhances the depth and intelligence of farmland water monitoring, constituting a substantial improvement over existing technologies.

[0075] In another technical solution, the collaborative drainage and regulation device for low-permeability black soil areas uses a proportional-integral algorithm with a proportionality coefficient K. p and integration time constant T iThe setting method is: K p =[α×(H max -H min ) / Q max ;T i =β(A×μ) / K, where H max H min These represent the highest and lowest permissible water levels in the inspection well, Q. max The maximum design flow rate of the water collection trunk line is given by α, where A is the field area, μ is the soil water yield, K is the soil saturated hydraulic conductivity, and α and β are dimensionless adjustment coefficients calibrated based on drainage tests in the black soil region.

[0076] In the above technical solution, the key parameter setting method of the proportional-integral algorithm involves the quantitative correlation between control system parameters and field physical and design parameters. Among these, the maximum allowable water level H in the inspection well... max With the lowest water level H min The settings can be determined based on the crop's waterlogging tolerance depth and the depth of the root activity layer. For example, to prevent waterlogging damage to the topsoil, H... max It can be set to 0.2 meters below the ground surface; to maintain the groundwater level for normal crop growth, H min It can be set to 0.8 meters below the ground surface. Maximum design flow rate Q of the water collection main. max This can be calculated based on design standards. For example, for a field with an area of ​​1 hectare, according to the standard of 10-year return period, one-day rainstorm, and drainage to the flood-resistant depth within one day, Q... max The system can be designed to handle 50 cubic meters per hour. The field area A should be entered into the system based on actual measurements, such as 10,000 square meters. Soil water yield μ and saturated hydraulic conductivity K are key soil hydraulic parameters and need to be determined through field or laboratory experiments. Water yield μ can be obtained by conducting a sudden drop in water level in a test field, measuring the ratio of water discharged per unit area of ​​soil to the drop in water level. For typical low-permeability black soil, the μ value may be around 0.03. Saturated hydraulic conductivity K can be obtained by measuring undisturbed soil samples using a constant head or variable head permeameter indoors; its value may be 0.5 meters per day. The calibration of dimensionless adjustment coefficients α and β needs to be performed through field drainage tests.

[0077] The specific working process of the parameter setting method in the above technical solution is as follows: After the system is installed and before it is officially put into automated operation, a controller parameter calibration test needs to be conducted. The test is carried out during a representative period of stable weather, when there is no waterlogging in the field. The operator manually adjusts the electronically controlled regulating valve to a medium opening, such as 40%, through the host computer software, and then records the process of the water level in the inspection well starting to drop from a certain initial value. By analyzing the water level drop curve, the response characteristics of the system can be preliminarily estimated. Based on these test data, combined with the known H... max H min Qmax Parameters such as A, μ, and K can be calibrated using optimization algorithms, with α and β being the most suitable. For example, initially setting α to 0.8 and β to 1.2, and substituting them into the formula to calculate K... p and T i The initial values ​​of α and β are then used. Subsequently, in simulated or actual mild waterlogging events, this set of parameters is applied for closed-loop control to observe whether the water level drop is smooth, rapid, and without overshoot. Based on the actual control effect, the values ​​of α and β can be fine-tuned, for example, α can be adjusted to 0.9 and β to 1.1, and the results can be verified again until satisfactory control performance is obtained. The calibrated values ​​of α and β, along with other fixed parameters, are permanently written into the control unit's program as inherent parameters of the proportional-integral algorithm for the drainage control system of this specific field.

[0078] The verification of the above technical solution setting method and the sources of relevant parameters are as follows: For the determination of soil water yield μ and saturated hydraulic conductivity K, multiple undisturbed soil samples need to be collected from various points in the project area. For the determination of water yield μ, it is recommended to use a dual-ring percolator on-site to obtain more representative values. The calibration process of parameters α and β is essentially a process of system identification and controller tuning. In addition to the aforementioned experimental methods, an empirical method can also be used for initial setting, followed by self-tuning and fine-tuning based on the control effect during actual operation. The technical effect achieved by this method is that it directly links the controller's mathematical parameters with specific, measurable soil physical properties and engineering system design parameters, enabling the controller's dynamic adjustment characteristics to automatically adapt to the drainage dynamics characteristics of a specific field. This avoids the blindness of traditionally relying entirely on trial and error based on engineering experience, improving the rationality of the initial setting of the control system, its adjustment stability, and its portability across different fields.

[0079] The above technical solution proposes a set of deterministic formulas that link the mathematical parameters of the controller with the physical and design parameters of the field system. This method clearly reveals K... p It should be consistent with the system water level control range (H) max -H min ) and maximum drainage capacity (Q max Related to T i The parameters should be related to the field's water storage capacity (A×μ) and the ease of soil drainage (K). By introducing adjustment coefficients α and β calibrated based on field tests, the differences between theoretical formulas and complex actual environments are also taken into account. This setting method transforms the determination of control parameters from a purely empirical "art" to a "technology" guided by physical models, greatly improving the efficiency, scientific nature, and adaptability of parameter tuning across different fields. It provides key technical support for the standardization and engineering promotion of intelligent control of drainage systems in low-permeability black soil areas, constituting a substantial improvement over existing controller parameter tuning methods.

[0080] In another technical solution, the calculation model for the real-time storage capacity C(t) of the synergistic drainage and regulation device in the low-permeability black soil area is as follows: ; Where A is the field area, D is the root layer depth, and θ s (z) represents the saturated volumetric water content of the soil at depth z, and θ(z,t) is the real-time vertical distribution function of water content obtained by interpolation based on soil moisture sensor data; when the predicted cumulative rainfall P f Satisfy P f Pre-leakage is triggered when >γ×C(t), where γ is an empirical coefficient with a value range of 0.6-0.8.

[0081] In the above technical solution, the real-time water retention capacity calculation model involves data acquisition, interpolation, and integration of the vertical distribution of soil moisture in the field. The field area A is a known fixed parameter; for example, a standard field is 100 meters by 100 meters, and area A is 10,000 square meters. The root layer depth D is determined based on the main crop; for maize, it can be set to 0.6 meters. The key function in the model is the soil saturated volumetric water content θ. s Distribution function θ with depth z s (z) needs to be established through stratified sampling and laboratory measurements. The specific method is as follows: Three representative points are selected within the field, and undisturbed soil samples are collected at 0.1-meter intervals from a depth of 0 to 0.6 meters at each point, totaling 18 samples. In the laboratory, the saturated volumetric water content of each soil sample's depth layer is determined using the ring immersion saturation method. The measurement results are arranged according to depth sequence, and a cubic spline interpolation method is used for fitting to generate a description of θ. s The function θ that varies continuously with depth z s (z) is a basic parameter of the model that is pre-stored in the control unit.

[0082] The construction of the real-time vertical distribution function θ(z,t) of soil moisture content relies on a multi-layer soil moisture sensor network deployed in the field. A feasible deployment scheme is as follows: a vertical profile monitoring point is set at the center of the field, and four sensors are buried at this point at depths of 0.15 m, 0.30 m, 0.45 m, and 0.60 m, respectively. Each sensor measures the volumetric moisture content once per minute and uploads the data to the control unit. At any given time t, the control unit reads the real-time moisture content data θ(0.15,t), θ(0.30,t), θ(0.45,t), and θ(0.60,t) at these four depths. Subsequently, the control unit uses a linear interpolation algorithm, with these four data points as nodes, to construct a continuous function θ(z,t) from the surface (z=0) to the bottom of the root zone (z=D=0.6 m), describing the moisture content distribution of the entire profile at the current time. The empirical coefficient γ ranges from 0.6 to 0.8, and its actual value can be determined through historical data analysis, for example, by taking 0.7.

[0083] The specific operation and calculation process of the model in the above technical solution are as follows: The control unit starts the real-time storage capacity calculation thread each time it needs to assess the possibility of pre-leakage (e.g., automatically every 6 hours). The thread first calls the pre-stored θs(z) function. Then, it reads the real-time data from all multi-layer sensors to construct the current θ(z,t) function. Then, the program performs numerical calculations on the integral expression. In actual programming, this integral can be approximated by discretizing the depth range from 0 to D into several small segments (e.g., every 0.01 meters): C(t)≈A*Σ[θ s (z j )-θ(z j , t)]*Δz, where z j Let Δz be the center depth of each discrete segment and Δz be the segment length. The calculated C(t) is in cubic meters, representing the additional infiltration water that the root zone soil can hold under current conditions. The control unit then compares this C(t) value with the obtained future forecast cumulative rainfall P. f Compare them. When condition P is satisfied... f When >γ*C(t), for example, the predicted rainfall P f If the real-time storage capacity exceeds 0.7 times, the pre-release control logic will be triggered.

[0084] The key parameters in the model of the above technical solution are set and verified as follows: The determination of the vertical function θs(z) of soil saturated moisture content is fundamental and must be completed and entered into the system before project implementation. The interpolation accuracy of the real-time moisture content vertical function θ(z,t) can be improved by increasing the number of sensor layers or using a more accurate interpolation algorithm (such as polynomial fitting). The calibration of the empirical coefficient γ requires collecting historical rainfall event data, including the soil moisture content profile before rainfall, total rainfall, and whether surface runoff or severe waterlogging occurred. By back-calculating the critical C value that just does not produce runoff or waterlogging in multiple events and comparing it with the actual rainfall, a reasonable range for γ can be statistically determined, thus determining a robust value. The effectiveness of the model can be verified through simulation: inputting different initial moisture content profiles and forecast rainfall of different intensities, observing whether the C(t) value calculated by the model and the pre-release triggering decision are consistent with the detailed simulation results based on the hydraulic model.

[0085] The aforementioned technical solution proposes a dynamic retention capacity calculation method based on real-time vertical soil moisture distribution monitoring data, using an integral form. This method quantifies the remaining "space" of the entire soil mass from the surface to the root zone before it reaches full saturation. It fully recognizes and utilizes the uneven distribution of soil moisture across the soil profile, enabling a more accurate reflection of the field's real, ever-changing rainwater storage potential. The pre-discharge triggering mechanism built upon this core transforms the decision of "how much to drain before a rainstorm" from a rough, empirical estimate into a scientific decision based on precise physical quantity calculations. This significantly improves the accuracy and reliability of forecasting and pre-discharge regulation, preventing insufficient or excessive drainage. This advancement from static, single-point estimation to dynamic, profile-based precise calculation represents a significant improvement in farmland water situation forecasting and regulation technology.

[0086] In another technical solution, the synergistic drainage and regulation device for low-permeability black soil areas replenishes nitrogen (N). sup The calculation method for (kg) is: N sup =(N target -N current )×ρ b ×D plow ×A×10 −4 ; Where, N target The target topsoil nitrogen content is expressed in mg / kg, N. current ρ represents the current topsoil nitrogen content obtained from soil nutrient sensors or historical fertilization data. b This refers to the bulk density of the topsoil, expressed in g / cm³. 3 D plow A represents the depth of the cultivated layer in cm, and A represents the area of ​​the field in m². 2 Required recharge water volume Virr (m) 3 Calculate V according to the following formula: irr =N sup / (C drain ×η); Among them, C drain The concentration of nitrate nitrogen in the wastewater is expressed in mg / L, and η is the irrigation water utilization coefficient. The unit conversion in the above formula is (1 mg / L = 1 g / m³). 3 ).

[0087] In the above technical solution, the calculation method for nitrogen supplementation and recharge water volume in nutrient reuse regulation involves the comprehensive application of agronomic parameters, soil physical parameters, and water quality monitoring data. The target topsoil nitrogen content (N) is... target The setting of nutrient requirements should be based on the specific crop's nutrient needs at different growth stages. For example, for maize grown in the black soil region of Northeast China, the suitable target range for alkaline available nitrogen content in the topsoil during the topsoil application stage from jointing to the tasseling stage can be 120 to 150 mg / kg. In practice, agronomists can select a specific value, such as 130 mg / kg, as the nitrogen content based on local soil testing and fertilization recommendations. target Enter the data into the control system. Current topsoil nitrogen content (N). current The data can be obtained in two ways: First, by using a soil nitrate nitrogen rapid measurement sensor buried at a depth of 0.2 meters in the topsoil layer. This sensor uses ion-selective field-effect transistor technology to monitor the nitrate nitrogen concentration in the soil solution in real time and convert it into soil nitrogen content through a calibration model. Second, by combining the nitrogen application record of the last basal fertilizer or topdressing, the number of days since the fertilization, and the set nitrogen apparent residue rate model for inversion estimation. The system prioritizes the use of real-time sensor data.

[0088] In the above technical solution, the specific parameter values ​​required for the calculation are as follows: topsoil bulk density ρ b Unspoiled soil samples can be collected from multiple points within the field at a depth of 0 to 0.3 meters. The average value is then determined using the ring sampler method. A typical black soil value is 1.2 grams per cubic centimeter. (Tillage depth D) plow The standard tillage depth is usually set to 0.3 meters. The field area A should be entered based on actual measurements, such as 1 hectare (10,000 square meters). The 10 in the formula... -4 The unit conversion factor is used. The concentration of nitrate nitrogen in the wastewater, C. drain The water quality is monitored in real time by an online multi-parameter water quality sensor installed in the inspection well. The data can be taken as the 15-minute moving average of the monitored values ​​during the most recent stable drainage period, for example, if the measured value is 20 mg / L. The irrigation water utilization coefficient η is determined according to the type of irrigation system used for recharge. If the recharge water is applied through an existing drip irrigation system in the field, the η value can be set to 0.90; if it is applied through a simple furrow irrigation method, the η value may be 0.70.

[0089] In the above technical solution, the specific workflow of the calculation method in the system is as follows: When the system meets the conditions of non-waterlogging and stable drainage, the control unit starts the nutrient recovery assessment thread. First, it acquires the current topsoil nitrogen content data, assuming that N is measured by a sensor. current The value is 90 milligrams per kilogram. The system calls the preset N. target Value (130 mg per kilogram). N target N current and ρ b =1.2 grams per cubic centimeter, D plow Substituting parameters such as =30 cm, A = 10000 square meters, etc., into the formula N sup =(130-90)×1.2×30×10000×10 -4 The amount of pure nitrogen N that needs to be added is calculated. sup The concentration was 1440 grams, or 1.44 kilograms. Subsequently, the system read the current nitrate nitrogen concentration C in the wastewater as monitored by the water quality sensor. drain Let's assume it's 20 mg per liter. Combining η = 0.90, substituting into the formula V... irr =1.44×10 6 The required recharge volume V is calculated using the formula / (20×0.90). irr Approximately 80,000 liters, or 80 cubic meters. The control unit, based on the design flow rate of the reinjection bypass pipeline (e.g., 20 cubic meters per hour), converts the reinjection water volume into a command that the valve needs to be opened for 4 hours, and controls the reuse electric control valve to execute the command.

[0090] In the above technical solution, the source and setting method of key parameters in the calculation framework are as follows: Target nitrogen content N target Adjustments should be made based on crop nutrient management guidelines published by local agricultural research institutions, and in conjunction with basic soil fertility. Soil bulk density ρ b and the depth of the cultivated layer D plow These are fundamental soil physical properties and must be obtained through field sampling and measurement. Drainage nitrate nitrogen concentration C drain The accuracy of monitoring depends on the regular calibration and maintenance of water quality sensors. The irrigation water use coefficient η is an engineering efficiency parameter, and its value should be determined by the design standards and operational management level of the irrigation system. The scientific validity and feasibility of this method can be verified through field comparative trials: the nutrient recycling function is implemented in experimental fields, the total amount of nitrogen recovered through recharge throughout the growing season is accurately measured, and soil nitrogen dynamics and crop nitrogen uptake are monitored simultaneously. By comparing with control fields that use conventional fertilization without wastewater recycling, the nutrient recycling rate and its impact on crop yield are evaluated.

[0091] The aforementioned technical solution provides a complete quantitative calculation framework that can be embedded in an automatic control system. For the first time, it links three key variables—"nitrogen deficit in the topsoil," "nitrogen concentration in drainage," and "requiring water volume for recharge"—through explicit mathematical formulas. This framework enables the system to automatically calculate, based on real-time monitoring data, the amount of nutrient-rich drainage water needed for recharge to replenish soil nitrogen to the target level, achieving closed-loop intelligent control from "perception-decision-execution." This signifies that the function of the drainage system has expanded from simple environmental protection (flood control) to resource recycling (nutrient recovery), transforming traditionally one-way nutrient loss into precisely reusable resources. This method provides core algorithmic support for realizing a "closed-loop" agricultural drainage resource system, not only reducing fertilizer input and non-point source pollution risks but also improving the overall utilization efficiency of water and fertilizer resources. It represents a substantial enrichment and innovation of existing farmland water and fertilizer management technologies.

[0092] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0093] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A synergistic drainage and regulation device for low-permeability black soil areas, characterized in that, include: The underground drainage unit includes at least one main water collection pipe buried below the plow layer of the field and multiple branch pipes connected to it. The branch pipes are laid along the direction of crop planting rows and are perforated corrugated pipes covered with a permeable filler layer and a filter geotextile. The shallow drainage unit includes at least one unlined rat tunnel shaped in the tillage layer by a rat tunnel shaping plow, wherein the layout direction of the rat tunnel forms a non-zero angle with the extension direction of the branch pipe. The vertical infiltration unit is a wedge-shaped infiltration body that is set directly below the rat tunnel and is directly connected to the permeable filler layer of the branch pipe in the vertical direction; the wedge-shaped infiltration body is formed by filling with highly permeable material, with its top extending to the bottom of the rat tunnel and its bottom embedded in the permeable filler layer. The drainage collection unit includes an inspection well located at the junction of the branch pipe and the main water collection pipe. The inspection well is equipped with a sedimentation chamber, and a removable filter basket is located above the sedimentation chamber. The outlet of the main water collection pipe is connected to an external drainage ditch through the inspection well. The control and execution unit includes an electrically controlled regulating valve located at the outlet end of the water collection main pipe, and a reuse electrically controlled valve located on the bypass pipe connecting the inspection well and the field irrigation network. The information sensing unit includes a soil moisture sensor deployed near the depth of the rat trail, a water level sensor and a multi-parameter water quality sensor installed in the inspection well, and a flow sensor installed on the water collection main pipe. The control unit, which is signal-connected to the information sensing unit and the control execution unit, is configured to execute the following coordinated control process: based on the data from the soil moisture sensor and the water level sensor, the waterlogging status is determined; in the waterlogging status, the water level drop rate is used as the control target, and the opening of the electronic control valve is dynamically adjusted through a proportional-integral algorithm; in the non-waterlogging status, based on the detection data from the multi-parameter water quality sensor and the judgment result of the soil nutrient status, the reuse electronic control valve is controlled to perform quantitative recharge.

2. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 1, characterized in that, The process of determining the state of waterlogging includes the following steps: S11. Acquire real-time monitoring data from at least two soil moisture sensors located at different positions along the same rodent trail, and calculate the instantaneous soil moisture saturation value S corresponding to each sensor. i (t); S12, for each sensor, calculate its average soil moisture saturation degree within a time window one of T1 duration traced back from the current time t, and calculate the linear change rate K of the soil moisture saturation degree within the time window one based on the average soil moisture saturation degree i ; S13, the maximum value S of the instantaneous values of the soil moisture saturation of all sensors max (t) and the minimum value S min (t) and the difference value AS(t) of the minimum value S avg ; the arithmetic mean value AS of the real-time difference values AS(t) within a time window two of T2 duration tracing back from the current time t S14. Obtain the instantaneous water level height H(t) measured by the water level sensor in the inspection well, and calculate its rate of rise V within the first predetermined time period. H ; S15. When the following conditions are met simultaneously, the state is determined to be flooded: Condition a, S max (t) The second predetermined time is greater than the first preset threshold; condition b, the average rate of change K of soil moisture saturation of any sensor. i Greater than zero; condition c, average difference ΔS avg Less than or equal to a preset spatial difference threshold; condition d, water level rise rate V H Greater than zero.

3. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 2, characterized in that, Once a waterlogging condition is determined, coordinated drainage control is implemented, specifically including: S31. The measured rate of groundwater level decline at the current moment is used as the process variable, and the target rate of groundwater level decline calculated based on the crop's waterlogging tolerance time requirement is used as the set value. S32. Based on the real-time deviation between the process variable and the set value and its integral, calculate and output the adjustment amount of the opening of the electronic control valve according to the preset proportional coefficient and integral time constant. S33. Monitor the rate of groundwater level decline in real time. Once the rate stabilizes within the target range and remains stable for a third predetermined time, reduce the opening of the electronically controlled regulating valve until the groundwater level recovers to the preset safe burial depth.

4. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 1, characterized in that, The control unit is further configured to perform forecast and pre-leakage control, specifically including: Obtain quantitative precipitation forecast data for the next 24-72 hours; Based on the monitoring data from current soil moisture sensors and water level sensors, combined with soil saturated water content parameters, the real-time water retention capacity of the field is calculated. When the forecasted cumulative rainfall exceeds the preset ratio of the real-time storage capacity, the opening of the electronically controlled regulating valve is increased to the preset pre-discharge opening at the fourth predetermined time before the forecasted rainfall begins.

5. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 1, characterized in that, The control unit is further configured to perform nutrient reuse regulation, specifically including: In non-flood conditions, when the flow sensor detects stable drainage, the nitrate nitrogen concentration value measured by the water quality multi-parameter sensor is obtained; Based on the real-time inversion of soil nutrient status, the amount of supplemental nitrogen required to raise the solution nitrogen concentration in the topsoil to the target range is calculated. Based on the amount of supplemented nitrogen and the concentration of nitrate nitrogen in the drainage, the required amount of reinjection water is calculated, and the opening time or degree of the reuse electric control valve is controlled accordingly.

6. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 1, characterized in that, The control unit executes the instantaneous soil moisture saturation value S when determining the waterlogging state. i The calculation method for S(t) is as follows: i (t) = (θ) i (t) -θ r ) / (θ s -θ r ) Where, θ i (t) represents the volumetric water content monitored in real time by the i-th soil moisture sensor, θ s θ represents the soil saturation water content. r The residual soil moisture content and the soil saturation moisture content θ were both obtained from soil physical property experiments in the black soil region. s Dynamically adjusted according to soil depth and bulk density.

7. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 2, characterized in that, The linear rate of change of soil moisture saturation K i The calculation method is as follows: using the soil moisture saturation sequence within the time window T1, the least squares method is used to fit the straight line S=kt+b, where k is the K corresponding to the sensor. i The control unit uses K corresponding to each sensor. i The relative magnitude and spatial distribution of the values, combined with the deployment location of each sensor in the field, identify whether the waterlogging development trend is uniformly distributed in a planar manner or develops in a strip along a specific direction. When it is determined to be a strip development, the dominant diffusion direction of the strip water accumulation is further determined.

8. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 3, characterized in that, In the proportional-integral algorithm, the proportionality coefficient K p and integration time constant T i The setting method is: K p =[α×(H max -H min ) / Q max ;T i =β(A×μ) / K, where H max H min These represent the highest and lowest permissible water levels in the inspection well, Q. max The maximum design flow rate of the water collection trunk line is given by α, where A is the field area, μ is the soil water yield, K is the soil saturated hydraulic conductivity, and α and β are dimensionless adjustment coefficients calibrated based on drainage tests in the black soil region.

9. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 4, characterized in that, The calculation model for the real-time storage capacity C(t) is as follows: Where A is the field area, D is the root layer depth, and θ s (z) represents the saturated volumetric water content of the soil at depth z, and θ(z,t) is the real-time vertical distribution function of water content obtained by interpolation based on soil moisture sensor data; when the predicted cumulative rainfall P f Satisfy P f Pre-leakage is triggered when >γ×C(t), where γ is an empirical coefficient with a value range of 0.6-0.

8.

10. The synergistic drainage and regulation device for low-permeability black soil areas as described in claim 5, characterized in that, Nitrogen supplementation N sup The calculation method is as follows: N sup =(N target -N current )×ρ b ×D plow ×A×10 -4 ; Where, N target The target topsoil nitrogen content is expressed in mg / kg, N. current ρ represents the current topsoil nitrogen content obtained from soil nutrient sensors or historical fertilization data. b This refers to the bulk density of the topsoil, expressed in g / cm³. 3 D plow A represents the depth of the cultivated layer in cm, and A represents the area of ​​the field in m². 2 Required recharge water volume V irr Calculate V according to the following formula: irr =N sup / (C drain ×η); Among them, C drain η represents the nitrate nitrogen concentration in the wastewater, expressed in mg / L, and η is the irrigation water utilization coefficient.

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