Intelligent irrigation control system and method for potatoes in high altitude area

Abstract

The invention relates to an intelligent irrigation control system and method for potatoes in a high-altitude area. The intelligent irrigation control system comprises: a water-retaining agent type identification module, which scans a pre-embedded label to obtain water-retaining agent type data; the permeability parameter calculation module is used for dynamically calculating soil permeability parameters based on the characteristics of the water-retaining agent; the oxygen stress risk assessment module fuses the oxygen content, the soil texture and the permeability to generate a risk index; the irrigation oxygen injection instruction generation module dynamically adjusts a soil moisture content threshold value and an oxygen injection strategy according to the risk level; and the control parameter feedback optimization module updates the penetration model in a closed-loop manner through pore structure analysis. According to the method, a charcoal type-permeability characteristic-oxygen stress cooperative regulation mechanism is established in a breakthrough mode, intelligent linkage of irrigation and oxygen injection is achieved, and the water and oxygen utilization efficiency and tuber quality of high-altitude potato planting are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of crop irrigation technology, specifically to an intelligent irrigation control system and method for potatoes in high-altitude areas. Background Technology

[0002] Potato cultivation at high altitudes faces unique environmental challenges, with water management and the coordinated regulation of rhizosphere oxygen supply being crucial for ensuring tuber quality. In recent years, biochar-based water-retaining agents have been widely used in dryland agriculture due to their excellent water-holding capacity. By pre-embedding labeled water-retaining agent granules in the soil, they achieve the dual goals of slow water release and soil improvement. However, when used at altitudes exceeding 2500 meters, while their strong adsorption properties can reduce irrigation frequency, they significantly alter soil pore structure, leading to a sharp drop in oxygen content in the rhizosphere microenvironment after irrigation. Especially when the diurnal temperature range exceeds 15°C, the soil temperature gradient further exacerbates the conflict between water transport and oxygen diffusion, resulting in a significant increase in the incidence of hypoxic malformations during tuber enlargement.

[0003] While existing technologies have attempted to trigger irrigation based on soil moisture thresholds, three fundamental drawbacks remain: First, static irrigation strategies cannot respond to the dynamic impact of biochar on soil permeability, and the oxygen retention effect caused by differences in water-retaining agent types is completely ignored. Second, irrigation control and oxygenation operations are disconnected; when soil moisture content reaches the threshold, the system mechanically performs irrigation without establishing a synergistic mechanism between oxygenation intensity and water infiltration rate. Third, fixed model parameters lead to insufficient adaptability in high-altitude environments; traditional permeability models do not consider the time-varying characteristics of pore structure under the influence of biochar, and the post-irrigation hypoxia problem cannot be fundamentally addressed through closed-loop feedback. These technological limitations make it difficult to achieve both water use efficiency and tuber quality when existing systems are applied in areas above 3000 meters in altitude. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a smart irrigation control system and method for potatoes in high-altitude areas that can synergistically optimize water supply and rhizosphere oxygen supply, dynamically adapt to biochar characteristics, and adapt to high-altitude environmental fluctuations.

[0005] The objective of this invention is achieved through the following solution:

[0006] In a first aspect, the present invention provides an intelligent irrigation control system for potatoes in high-altitude areas, the system being configured with the following modules:

[0007] The water-retaining agent type identification module is used to identify water-retaining agent labels in the soil, scan the pre-embedded labels to obtain the water-retaining agent type code, and generate water-retaining agent type data;

[0008] The permeability parameter calculation module is used to process the permeability calculation based on the water-retaining agent type data and the real-time measured soil porosity, calculate the soil water infiltration rate based on the preset permeability model, and generate permeability parameters.

[0009] The oxygen stress risk assessment module is used to perform oxygen stress risk assessment on real-time monitored oxygen content, pre-stored soil texture parameters and permeability parameters, calculate the degree of rhizosphere oxygen stress risk, and generate an oxygen stress risk index.

[0010] The irrigation oxygenation instruction generation module is used to process the oxygen stress risk index for irrigation oxygenation strategy, adjust the soil moisture trigger threshold and calculate the maximum irrigation duration, adjust the irrigation period and oxygenation intensity based on the preset oxygen stress level threshold and the real-time monitored soil moisture content, generate irrigation oxygenation instructions and send them to the execution equipment.

[0011] The control parameter feedback optimization module is used to optimize the oxygen content monitoring data after irrigation, calculate the tillage depth by combining the water-retaining agent type data and the oxygen stress risk index, and update the calibration parameters of the permeability model by retesting soil porosity.

[0012] In one embodiment, the present invention provides a permeability parameter calculation module for a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0013] The soil structure parameter generation unit is used to process the real-time measured soil porosity, analyze the pore distribution characteristics in combination with soil texture type, and generate soil structure parameters that characterize soil permeability.

[0014] The initial infiltration rate calculation unit is used to process water-retaining agent type data and soil structure parameters based on a preset infiltration rate model, calculate the basic value of water infiltration under the action of biochar, and generate the initial infiltration rate.

[0015] The permeability parameter optimization unit is used to optimize the initial permeability rate, adjust the basic permeability value according to the biochar retention characteristics, and generate permeability parameters. The permeability parameters are used to indicate the actual migration capacity of water in the rhizosphere soil.

[0016] In one embodiment, the present invention provides an oxygen stress risk assessment module for a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0017] The oxygen concentration correction unit is used to process the real-time monitored oxygen content, combine it with the current atmospheric pressure value to correct the oxygen solubility, and generate a corrected oxygen concentration.

[0018] The oxygen transport capacity assessment unit is used to process pre-stored soil texture parameters and permeability parameters, construct a soil oxygen transport capacity assessment matrix, and generate oxygen transport capacity assessment values.

[0019] The oxygen stress risk calculation unit is used to perform risk calculation processing on the corrected oxygen concentration and oxygen transport capacity assessment values, calculate the degree of rhizosphere oxygen stress risk, generate the oxygen stress risk index, and the corrected oxygen concentration is used to indicate the actual oxygen availability level after eliminating the influence of altitude.

[0020] In one embodiment, the present invention provides an irrigation oxygenation command generation module for a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0021] The dynamic soil moisture threshold generation unit is used to perform dynamic threshold processing on the oxygen stress risk index. Based on the preset oxygen stress level threshold, the soil moisture trigger threshold is adjusted to generate a dynamic soil moisture threshold.

[0022] The irrigation duration optimization unit is used to optimize the irrigation duration based on the oxygen stress risk index and dynamic soil moisture threshold, calculate the maximum irrigation duration under biochar action, and generate the osmotic constraint irrigation duration.

[0023] The multi-dimensional instruction generation unit is used to perform collaborative decision-making on the irrigation duration under seepage constraint, the real-time soil moisture content monitored, and the oxygen stress level, to establish a three-dimensional matching relationship between irrigation period and oxygen injection intensity, and to generate multi-dimensional irrigation and oxygen injection instructions that include time period parameters, intensity parameters, and duration parameters.

[0024] In one embodiment, the present invention provides a control parameter feedback optimization module for a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0025] The oxygen recovery assessment unit is used to assess oxygen recovery from post-irrigation oxygen content monitoring data, calculate the oxygen recovery rate deviation value by combining water-retaining agent type data, and generate the biochar inhibition effect coefficient.

[0026] The tillage depth decision unit is used to perform deep decision calculations on the oxygen stress risk index and biochar blocking effect coefficient, construct the tillage depth optimization function, and generate theoretical tillage depth parameters.

[0027] The pore correlation analysis unit is used to perform pore structure correlation analysis on the remeasured soil porosity data and theoretical tillage depth parameters, establish a quantitative relationship between tillage depth and pore improvement, and generate pore optimization correction factors.

[0028] The model parameter iteration unit is used to iterate the model parameters of the pore optimization correction factor, update the dynamic calibration parameters of the permeability model, and generate optimized permeability model parameters.

[0029] Secondly, this invention provides a smart irrigation control method for potatoes in high-altitude areas, applicable to any of the above-mentioned smart irrigation control systems for potatoes in high-altitude areas, comprising the following steps:

[0030] S601: Identify and process water-retaining agent labels in soil, scan pre-embedded labels to obtain water-retaining agent type codes, and generate water-retaining agent type data;

[0031] S602: Perform permeability calculation processing on water-retaining agent type data and real-time measured soil porosity, calculate soil water infiltration rate based on preset permeability model, and generate permeability parameters;

[0032] S603: Perform oxygen stress risk assessment on real-time monitored oxygen content, soil texture coefficient and permeability parameters, calculate the degree of rhizosphere oxygen stress risk and generate oxygen stress risk index;

[0033] S604: The oxygen stress risk index is processed by irrigation and oxygenation strategy, the soil moisture trigger threshold is adjusted and the maximum irrigation duration is calculated. Based on the preset oxygen stress level threshold and the real-time monitored soil moisture content, the irrigation period and oxygenation intensity are adjusted, and irrigation and oxygenation instructions are generated and sent to the execution equipment.

[0034] S605: Feedback optimization processing is performed on the oxygen content monitoring data after irrigation, and the cultivation depth is calculated by combining the water-retaining agent type data and oxygen stress risk index. The calibration parameters of the permeability model are updated by retesting soil porosity.

[0035] Thirdly, this application provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned intelligent irrigation control method for potatoes in high-altitude areas.

[0036] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned intelligent irrigation control method for potatoes in high-altitude areas.

[0037] In summary, the intelligent irrigation control system for potatoes in high-altitude areas provided in this application utilizes a water-retaining agent type identification module for precise analysis of pre-embedded tags, enabling personalized responses to different biochar materials and laying a data foundation for subsequent dynamic modeling. The permeability parameter calculation module integrates water-retaining agent type and real-time porosity data to construct a dynamic permeability model under biochar action, overcoming the shortcomings of traditional static models that ignore the water-retaining agent retention effect. The oxygen stress risk assessment module, based on altitude-corrected oxygen monitoring data and in conjunction with soil texture and permeability parameters, enables quantitative early warning of rhizosphere oxygen supply-demand imbalance, significantly improving the accuracy of hypoxia risk identification. The irrigation oxygen injection command generation module innovatively establishes a three-dimensional collaborative mechanism, dynamically adjusting soil moisture thresholds, maximum irrigation duration, and oxygen injection intensity combination strategies according to risk levels to achieve precise matching of water infiltration and oxygen supply. The control parameter feedback optimization module continuously iterates permeability model parameters through a closed-loop correlation between pore structure changes and cultivation depth decisions, adapting to the time-varying pore characteristics caused by biochar. The entire system, through a technical chain of "identification-modeling-evaluation-decision-optimization", can achieve dynamic coupling of biochar type characteristics, soil infiltration behavior and rhizosphere oxygen environment, ultimately achieving the triple technical effects of efficient water use, significant reduction in tuber deformity rate and adaptive water-oxygen synergistic control in high-altitude potato cultivation.

[0038] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0039] Figure 1 A schematic diagram of a smart irrigation control system for potatoes in high-altitude areas provided in this application embodiment;

[0040] Figure 2 This is a flowchart illustrating a smart irrigation control method for potatoes in high-altitude areas, provided as another embodiment of this application. Detailed Implementation

[0041] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0043] Please see Figure 1 This embodiment illustrates a smart irrigation control system for potatoes in high-altitude areas, provided by an embodiment of this application. This embodiment uses the system's application to a terminal as an example for illustration. It is understood that this system can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. Figure 1 As shown, the intelligent irrigation control system 100 for potatoes in high-altitude areas provided by the present invention includes a water-retaining agent type identification module 110, a permeability parameter calculation module 120, an oxygen stress risk assessment module 130, an irrigation oxygen injection command generation module 140, and a control parameter feedback optimization module 150.

[0044] For example, the water-retaining agent type identification module 110 is used to identify water-retaining agent identifiers in the soil, scan the pre-embedded labels to obtain water-retaining agent type codes, and generate water-retaining agent type data.

[0045] Specifically, in this embodiment, workers deploy devices for identifying water-retaining agent labels at preset intervals within the soil tillage layer. The parts of these devices that contact the soil are made of materials resistant to soil corrosion, adapting to the physicochemical environment of soils at high altitudes. Pre-embedded tags are embedded within the water-retaining agent granules during the production process. The tag surfaces are treated to resist erosion by soil microorganisms and the effects of chemicals. The water-retaining agent type code stored in the pre-embedded tags includes characteristic parameters such as biochar content, particle size distribution, and degree of cross-linking. These parameters are converted into information readable by the identification devices through specific coding rules.

[0046] The water-retaining agent type identification module 110 scans the pre-embedded tags using distributed sensor nodes. These sensor nodes are evenly distributed throughout the planting area, covering the entire area where the water-retaining agent is buried. The deployment depth of the sensor nodes is consistent with the burial depth of the water-retaining agent particles, ensuring that the scanning signal can effectively penetrate the soil medium to reach the tag location. The scanning operation is automatically triggered by the system based on the environmental conditions of the planting area. The triggering mechanism is related to the changing trends of soil moisture and temperature to ensure that identification is performed when the soil condition is stable, reducing the impact of environmental interference on the identification results.

[0047] During the scanning process, the water-retaining agent type identification module 110 emits a specific frequency signal into the soil. This signal is reflected upon encountering the pre-embedded tag and received by the device. The device performs preliminary processing on the received signal, removing interference signals caused by soil particles, moisture, and other factors, and extracting the valid signal containing the tag information. The valid signal is then transmitted to the edge computing gateway via a wireless transmission link, employing an encryption protocol during transmission to prevent data loss or tampering.

[0048] After receiving the signal, the edge computing gateway calls a preset decoding algorithm to parse the signal. Based on the tag's encoding rules, the decoding algorithm converts the binary information contained in the signal into corresponding water-retaining agent characteristic parameters, forming water-retaining agent type data. Once generated, the system associates this data with information such as the collection time and location, storing it in a local database. The database uses a distributed architecture, supporting simultaneous access from multiple nodes, allowing for rapid data retrieval in subsequent steps, providing foundational information for permeability calculations, oxygen stress risk assessments, and other processes. Simultaneously, the system periodically verifies the stored water-retaining agent type data to ensure its integrity and accuracy; when data anomalies are detected, a re-identification process is automatically triggered.

[0049] For example, the permeability parameter calculation module 120 is used to perform permeability calculation processing on water-retaining agent type data and real-time measured soil porosity, calculate soil moisture infiltration rate based on preset permeability model, and generate permeability parameters.

[0050] Specifically, the permeability parameter calculation module 120 calculates soil permeability parameters through a porosity monitoring device and a model calculation component. The porosity monitoring device performs stratified monitoring in the potato root distribution area to obtain soil volume porosity and pore distribution characteristics. The monitoring device can employ a multi-sensor combination to ensure comprehensive capture of soil pore conditions at different depths. The model calculation component incorporates a modified permeability model, which incorporates influencing factors based on water-retaining agent type data.

[0051] During the calculation process, the model is based on porosity monitoring data and dynamically adjusts influencing factors by incorporating parameters such as the degree of crosslinking of water-retaining agents. It then calculates the soil water infiltration rate using a preset formula. The system updates the model parameters by periodically calling real-time porosity data, generating soil water infiltration rate and permeability coefficient decay curves. These curves reflect the changing trend of soil permeability over time, providing a quantitative basis for assessing water transport patterns and oxygen diffusion conditions, enabling the system to dynamically monitor soil water infiltration characteristics.

[0052] For example, the oxygen stress risk assessment module 130 is used to perform oxygen stress risk assessment processing on the real-time monitored oxygen content, pre-stored soil texture parameters and permeability parameters, calculate the degree of rhizosphere oxygen stress risk, and generate an oxygen stress risk index.

[0053] Specifically, the oxygen stress risk assessment module 130 assesses oxygen stress risk through a multi-parameter sensor array and a risk algorithm component. The sensor array is deployed with monitoring points at preset intervals along the potato planting rows, and probes are deployed at the corresponding depth of the tuber cambium layer to collect rhizosphere oxygen partial pressure data in real time. Preferably, soil texture parameters, including the mass percentages of sand, silt, and clay particles in the soil, are pre-stored in the system database. These parameters are obtained through soil sampling and analysis before planting and are periodically calibrated during system operation. The risk algorithm component constructs an assessment model based on the oxygen diffusion equation, using real-time oxygen content data, soil texture parameters, and permeability parameters as input variables. This model determines the degree of rhizosphere oxygen stress risk by calculating an oxygen deficit index. The oxygen deficit index calculation comprehensively considers the ratio of measured oxygen content to the critical oxygen content threshold, as well as the ratio of actual permeability to the baseline permeability. Based on the calculation results, the system classifies risk levels and generates a quantified oxygen stress risk index, providing a direct basis for the formulation of irrigation and oxygenation strategies.

[0054] For example, the irrigation oxygenation instruction generation module 140 is used to process the oxygen stress risk index with an irrigation oxygenation strategy, adjust the soil moisture trigger threshold and calculate the maximum irrigation duration, adjust the irrigation period and oxygenation intensity based on the preset oxygen stress level threshold and the real-time monitored soil moisture content, generate an irrigation oxygenation instruction and send it to the execution device.

[0055] Specifically, when the irrigation oxygenation command generation module 140 processes the oxygen stress risk index using an irrigation oxygenation strategy, it adjusts relevant parameters through a dynamic threshold control mechanism. The soil moisture trigger threshold is adjusted according to the type of water-retaining agent; the trigger threshold for high water-holding agents is lower than that for ordinary soils. The maximum irrigation duration is calculated based on permeability parameters, taking into account field capacity, current moisture content, soil bulk density, irrigated area, irrigation flow rate, and permeability correction coefficient.

[0056] Irrigation timing is adjusted based on real-time monitoring of soil moisture content and diurnal temperature range. When the diurnal temperature range is large, irrigation is carried out during periods of higher daytime temperatures, utilizing temperature changes to promote water infiltration and oxygen diffusion. Oxygen injection intensity is adjusted in a stepped manner, according to different levels of the oxygen stress risk index; pulsed oxygen injection is used in high-risk situations. Based on these adjustments, the system generates irrigation oxygen injection commands, including irrigation start time, duration, flow parameters, oxygen injection intensity, and frequency, which are sent via bus to the irrigation solenoid valves and oxygen injection pump unit.

[0057] For example, the control parameter feedback optimization module 150 is used to perform feedback optimization processing on the oxygen content monitoring data after irrigation, calculate the tillage depth by combining the water-retaining agent type data and the oxygen stress risk index, and update the calibration parameters of the permeability model by retesting the soil porosity.

[0058] Specifically, the control parameter feedback optimization module 150 optimizes control parameters through a post-irrigation monitoring component and a model calibration component. The post-irrigation monitoring component activates at a preset time after irrigation, re-measuring rhizosphere oxygen content using an oxygen sensor array to obtain an oxygen content recovery curve. This curve reflects the improvement effect of irrigation and oxygenation on the rhizosphere oxygen environment, providing feedback data for parameter optimization. The model calibration component, based on the re-measured data and water-retaining agent type data, calculates the optimal tillage depth using a multiple regression algorithm. For different types of water-retaining agents, the algorithm employs differentiated calculation logic; granular and powdered water-retaining agents are adjusted for depth based on the oxygen deficit index and permeability ratio, respectively. Simultaneously, the control parameter feedback optimization module 150 re-measures soil porosity at fixed irrigation cycles, updating the saturated hydraulic conductivity and pore connectivity parameters in the permeability model using multi-source data fusion technology. This allows the model to continuously adapt to changes in soil conditions, forming a closed-loop optimization mechanism.

[0059] In summary, the intelligent irrigation control system for potatoes in high-altitude areas provided in this application utilizes a water-retaining agent type identification module for precise analysis of pre-embedded tags, enabling personalized responses to different biochar materials and laying a data foundation for subsequent dynamic modeling. The permeability parameter calculation module integrates water-retaining agent type and real-time porosity data to construct a dynamic permeability model under biochar action, overcoming the shortcomings of traditional static models that ignore the water-retaining agent retention effect. The oxygen stress risk assessment module, based on altitude-corrected oxygen monitoring data and in conjunction with soil texture and permeability parameters, enables quantitative early warning of rhizosphere oxygen supply-demand imbalance, significantly improving the accuracy of hypoxia risk identification. The irrigation oxygen injection command generation module innovatively establishes a three-dimensional collaborative mechanism, dynamically adjusting soil moisture thresholds, maximum irrigation duration, and oxygen injection intensity combination strategies according to risk levels to achieve precise matching of water infiltration and oxygen supply. The control parameter feedback optimization module continuously iterates permeability model parameters through a closed-loop correlation between pore structure changes and cultivation depth decisions, adapting to the time-varying pore characteristics caused by biochar. The entire system, through a technical chain of "identification-modeling-evaluation-decision-optimization", can achieve dynamic coupling of biochar type characteristics, soil infiltration behavior and rhizosphere oxygen environment, ultimately achieving the triple technical effects of efficient water use, significant reduction in tuber deformity rate and adaptive water-oxygen synergistic control in high-altitude potato cultivation.

[0060] In one embodiment, the present invention provides a permeability parameter calculation module 120 for a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0061] The soil structure parameter generation unit is used to process the real-time measured soil porosity, analyze the pore distribution characteristics in combination with soil texture type, and generate soil structure parameters that characterize soil permeability.

[0062] Specifically, the soil structure parameter generation unit acquires real-time soil porosity data through a soil porosity measurement device. The measurement process covers different sampling points within the planting area to ensure that the data reflects the overall soil porosity. The acquired porosity data is transmitted to the data processing unit for correlation analysis with pre-stored soil texture type information. The soil texture type information is determined based on soil particle composition, including categories such as sandy, loamy, and clayey. The system uses corresponding analytical models to analyze the pore distribution characteristics according to the differences in texture type.

[0063] During the analysis, the system classifies pores into different levels according to their pore size, calculates the proportion of each level of pores in the total pores, and analyzes the connectivity between pores to generate soil structure parameters that characterize soil permeability. These parameters include pore connectivity, pore size distribution index, and pore shape factor, which are used to quantify the soil's ability to conduct water.

[0064] The initial infiltration rate calculation unit is used to process water-retaining agent type data and soil structure parameters based on a preset infiltration rate model, calculate the basic value of water infiltration under the action of biochar, and generate the initial infiltration rate.

[0065] Specifically, the initial infiltration rate calculation unit acquires water-retaining agent type data and soil structure parameters. The water-retaining agent type data includes characteristic information such as biochar content, crosslinking degree, and particle size distribution. Preferably, this unit loads a preset permeability model, which is constructed based on soil hydrodynamic principles and covers the correlation between pore structure and water migration. The model uses the water-retaining agent type data as a correction variable and adjusts the conduction parameters in the model by introducing a biochar effect coefficient. This coefficient is dynamically determined based on the crosslinking degree of the water-retaining agent and the biochar content. Combining the pore connectivity and average pore size in the soil structure parameters, the model calculates the baseline value of water infiltration under the action of biochar, generating the initial infiltration rate. The initial infiltration rate is used to reflect the baseline level of water migration without considering the dynamic retention effect of biochar.

[0066] The permeability parameter optimization unit is used to optimize the initial permeability rate, adjust the basic permeability value according to the biochar retention characteristics, and generate permeability parameters. The permeability parameters are used to indicate the actual migration capacity of water in the rhizosphere soil.

[0067] Specifically, the permeability parameter optimization unit uses the initial permeability rate as the basic data and calls upon a biochar retention characteristic database, which stores the adsorption and retention patterns of different types of water-retaining agents in the soil environment. The unit analyzes the changing trend of biochar's water-holding capacity over time and, combined with the current soil moisture content, determines the retention effect coefficient. By incorporating this coefficient into the adjustment formula for the initial permeability rate, the permeability parameter optimization unit corrects the basic permeability value. The correction process considers the blocking effect of water-retaining agent particles on pores and the dynamic changes in water adsorption capacity. The permeability parameter generated after correction comprehensively reflects the water migration capacity under the combined effect of biochar retention and soil structural characteristics, and is directly used to indicate the actual water conduction efficiency in the rhizosphere soil.

[0068] In one embodiment, the present invention provides an oxygen stress risk assessment module 130 of a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0069] The oxygen concentration correction unit processes the real-time monitored oxygen content, combines it with the current atmospheric pressure value to correct the oxygen solubility, and generates a corrected oxygen concentration.

[0070] Specifically, when processing the real-time monitored oxygen content, the oxygen concentration correction unit simultaneously collects the current atmospheric pressure value through barometric pressure sensors deployed in the monitoring area. Oxygen content monitoring is performed by fiber optic oxygen sensors distributed in the rhizosphere zone. The sensors are evenly arranged according to the potato planting row and plant spacing, with the probes embedded in the soil of the tuber growth layer. The output signal is converted into raw oxygen content data in volume percentage by a signal conditioning circuit. The barometric pressure sensor and the oxygen sensor use the same data acquisition cycle to ensure a one-to-one correspondence between the two sets of data in the time dimension.

[0071] During oxygen solubility correction, the system invokes a correction algorithm based on Henry's Law. This algorithm uses raw oxygen content data, current atmospheric pressure, and synchronously acquired soil temperature data as input variables. Soil temperature data is provided by a temperature sensor deployed at the same location as the oxygen sensor, used to obtain temperature correction coefficients. These coefficients are retrieved from a pre-stored database established using experimental results of oxygen solubility under different temperature conditions, containing the correspondence between temperature and correction coefficients. The corrected oxygen concentration is calculated using the following formula:

[0072]

[0073] Among them, C corr To correct for oxygen concentration, C raw For raw data on oxygen content, P current P represents the current atmospheric pressure. std k represents the standard atmospheric pressure. TThis is the temperature correction factor. The corrected oxygen concentration obtained through this processing eliminates the interference of air pressure changes at high altitudes on oxygen content measurement results, directly reflecting the actual available level of oxygen in the rhizosphere soil.

[0074] The oxygen transport capacity assessment unit is used to process pre-stored soil texture parameters and permeability parameters, construct a soil oxygen transport capacity assessment matrix, and generate oxygen transport capacity assessment values.

[0075] Specifically, when processing pre-stored soil texture and permeability parameters, the oxygen transport capacity assessment unit first retrieves the soil texture parameters of the target plot from the local database. These parameters include clay content, sand content, silt content, and organic matter content, obtained through multi-point soil sampling and laboratory analysis before planting, and are stored in association with the plot coordinate information. The permeability parameter is a quantitative indicator generated in the early stage, reflecting the ability of water to migrate in the soil.

[0076] For example, the soil oxygen transport capacity assessment matrix is ​​constructed by the system through multi-dimensional parameter weighted operations. The row vectors of the matrix consist of eigenvalues ​​corresponding to soil texture parameters, such as clay content corresponding to pore blockage eigenvalues, sand content corresponding to pore connectivity eigenvalues, and organic matter content corresponding to oxygen adsorption eigenvalues. The column vectors are transport efficiency eigenvalues ​​derived from permeability parameters, obtained by normalizing the permeability parameters. The system determines the weights of each parameter using the analytic hierarchy process (AHP). In this process, the influence of soil texture parameters on pore structure and the correlation between permeability parameters and oxygen diffusion rate are first used as judgment criteria. Then, a judgment matrix is ​​constructed through pairwise comparisons, and the weight values ​​of each parameter are obtained after consistency checks. The matrix operation uses a weighted summation method, as shown in the following formula:

[0077]

[0078] Where A is the oxygen transport capacity assessment value, and x i w is the eigenvalue of the i-th parameter. i Let be the weight of the i-th parameter, and n be the total number of parameters involved in the calculation. The result of multiplying each feature value by its corresponding weight and summing the results is the oxygen transport capacity assessment value, which is positively correlated with the soil's oxygen transport capacity.

[0079] The oxygen stress risk calculation unit is used to perform risk calculation processing on the corrected oxygen concentration and oxygen transport capacity assessment values, calculate the degree of rhizosphere oxygen stress risk, generate the oxygen stress risk index, and the corrected oxygen concentration is used to indicate the actual oxygen availability level after eliminating the influence of altitude.

[0080] Specifically, when the oxygen stress risk calculation unit performs risk calculations on the corrected oxygen concentration and oxygen transport capacity assessment values, a two-parameter coupled risk model can be used. In the model, the corrected oxygen concentration is used to characterize the current oxygen supply status of the rhizosphere soil, and the oxygen transport capacity assessment value is used to characterize the soil's potential for oxygen transport and replenishment.

[0081] Preferably, the risk calculation is divided into two stages: In the first stage, the system compares the corrected oxygen concentration with a preset oxygen concentration threshold range. This threshold range is defined based on the oxygen requirements of different potato growth stages, covering the oxygen requirement intervals for key growth stages such as tuber formation and tuber enlargement. An oxygen concentration deviation is generated through comparison; when the corrected oxygen concentration is below the lower limit of the threshold range, the deviation is negative, indicating insufficient oxygen supply. In the second stage, the system compares the oxygen transport capacity assessment value with a preset benchmark value. The benchmark value is determined based on the oxygen transport capacity of similar soils under standard conditions, generating a transport capacity coefficient. When the assessment value is lower than the benchmark value, the coefficient is less than 1, indicating that the transport capacity has not reached the standard level.

[0082] Preferably, the system substitutes the oxygen concentration deviation and the transport capacity coefficient into the risk index calculation formula:

[0083] R=(\vertD\vert×a+(1-K)×b)×100

[0084] Where R is the oxygen stress risk index, D is the oxygen concentration deviation, K is the transport capacity coefficient, and a and b are preset proportional coefficients with a+b=1. The calculated quantitative value from 0 to 100 is the oxygen stress risk index; the larger the value, the higher the degree of rhizosphere oxygen stress risk. The generated oxygen stress risk index serves as the core basis for subsequent adjustments to irrigation oxygenation strategies. The corrected oxygen concentration eliminates the influence of altitude, ensuring the reliability of the risk assessment.

[0085] In one embodiment, the present invention provides an irrigation oxygenation command generation module 140 of a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0086] The dynamic soil moisture threshold generation unit is used to perform dynamic threshold processing on the oxygen stress risk index. Based on the preset oxygen stress level threshold, the soil moisture trigger threshold is adjusted to generate a dynamic soil moisture threshold.

[0087] Specifically, the dynamic soil moisture threshold generation unit first retrieves a preset oxygen stress level threshold. This threshold is divided into several intervals according to the oxygen stress risk index, with each interval corresponding to a specific risk level. After receiving the oxygen stress risk index, the system compares it with the level threshold to determine the current risk level. The adjustment of the soil moisture trigger threshold is based on the association rule between risk level and water-retaining agent type. The basic trigger threshold is obtained from the soil moisture database, which stores soil moisture thresholds for different soil textures under standard conditions. The adjustment process is implemented through a formula:

[0088] T dynamic =T base ×(1-c×L)

[0089] Among them, T dynamic T is the dynamic soil moisture threshold. base The system uses the following formula to calculate the dynamic soil moisture threshold, where c is the adjustment coefficient for the water-retaining agent type and L is the risk level coefficient. This threshold is updated in real time as the oxygen stress risk changes, ensuring that the irrigation triggering conditions are compatible with the rhizosphere oxygen environment.

[0090] The irrigation duration optimization unit is used to optimize the irrigation duration based on the oxygen stress risk index and dynamic soil moisture threshold, calculate the maximum irrigation duration under biochar action, and generate the osmotic constraint irrigation duration.

[0091] Specifically, the irrigation duration optimization unit simultaneously acquires the oxygen stress risk index and dynamic soil moisture threshold. The calculation of the maximum irrigation duration needs to combine the water-holding characteristics of the biochar water-retaining agent with the soil permeability. The core formula is:

[0092]

[0093] Among them, t max For the maximum irrigation duration, T dynamic For dynamic soil moisture threshold, θ current ρ represents real-time soil moisture content, h represents the depth of irrigation influence, K represents the permeability parameter generated in the previous stage, and f(R) is the risk index correction function, which decreases as the oxygen stress risk index increases. The system compares the calculation results with the preset upper limit of the safe duration and takes the smaller value as the permeability-constrained irrigation duration. This duration ensures that water is replenished to the dynamic soil moisture threshold, while limiting the irrigation duration during high-risk periods through the risk correction function, thus avoiding excessive irrigation that could exacerbate oxygen stress.

[0094] The multi-dimensional instruction generation unit is used to perform collaborative decision-making on the irrigation duration under seepage constraint, the real-time soil moisture content monitored, and the oxygen stress level, to establish a three-dimensional matching relationship between irrigation period and oxygen injection intensity, and to generate multi-dimensional irrigation and oxygen injection instructions that include time period parameters, intensity parameters, and duration parameters.

[0095] Specifically, when the multi-dimensional instruction generation unit is working, it integrates data on irrigation duration under infiltration constraints, real-time soil moisture content, and oxygen stress level. The establishment of the three-dimensional matching relationship is based on a preset decision rule base. The rule base contains the correspondence between different soil moisture content ranges, oxygen stress levels, irrigation time periods, and oxygen injection intensity. Among them, the irrigation time period is adjusted with reference to real-time diurnal temperature difference data. When the temperature difference exceeds the preset range, the rule base prioritizes matching the daytime period, using temperature conditions to promote water infiltration and oxygen diffusion. The oxygen injection intensity is set according to the oxygen stress level. As the level increases, the intensity increases accordingly, and the oxygen injection intensity and irrigation flow rate are linked in a proportional manner.

[0096] For example, the multi-dimensional instruction generation unit verifies the matching rationality through matrix operations. The row vector of the matrix represents the rate of change of soil moisture content, and the column vector represents the correspondence coefficient between oxygen stress level and oxygen injection intensity. The matching is confirmed to be valid when the calculation result meets a preset threshold. The finally generated multi-dimensional irrigation oxygen injection instruction includes the irrigation start time, duration, working power of the oxygen injection equipment, and operating interval. The instruction is sent to the execution equipment controller via wired transmission.

[0097] In one embodiment, the present invention provides a control parameter feedback optimization module 150 for a smart irrigation control system for potatoes in high-altitude areas, which is configured with the following units:

[0098] The oxygen recovery assessment unit is used to assess the oxygen recovery of post-irrigation oxygen content monitoring data, calculate the oxygen recovery rate deviation value by combining the water-retaining agent type data, and generate the biochar inhibition effect coefficient.

[0099] Specifically, during the operation of the oxygen recovery assessment unit, oxygen content monitoring data is collected at multiple time points after irrigation. The monitoring data is provided by a rhizosphere fiber optic oxygen sensor, with sampling time points including 1 hour, 3 hours, 6 hours, and 12 hours after irrigation, forming a time-series data of oxygen content changes. The system first calculates the actual oxygen recovery rate, obtained by the ratio of the oxygen concentration difference between adjacent time points to the time interval, using the following formula:

[0100]

[0101] Among them, v actual C represents the actual oxygen recovery rate. corr,t2 Ccorr,t2 represent the corrected oxygen concentrations at two time points, while t1 and t2 are the corresponding time values. Simultaneously, the system retrieves the theoretical oxygen recovery rate v from the database based on the water-retaining agent type data. theoretical This value was determined based on oxygen recovery experimental data of similar water-retaining agents under standard soil conditions. The oxygen recovery rate deviation is calculated using d = v actual -v theoretical The formula for calculating the biochar retardation effect coefficient is as follows:

[0102]

[0103] Here, β is the biochar inhibition effect coefficient; a larger value indicates a stronger inhibition effect of biochar on oxygen recovery. This coefficient comprehensively reflects the deviation between the type of water-retaining agent and the actual oxygen recovery capacity, providing a basis for subsequent decisions on the depth of inter-cultivation.

[0104] The tillage depth decision unit is used to perform in-depth decision calculations on the oxygen stress risk index and biochar blocking effect coefficient, construct the tillage depth optimization function, and generate theoretical tillage depth parameters.

[0105] Specifically, the tillage depth decision unit simultaneously acquires the oxygen stress risk index R and the biochar blocking effect coefficient β. The construction of the tillage depth optimization function aims to improve soil aeration and porosity, and the core formula is:

[0106] H = H0 × (1 + α × R + γ × β)

[0107] Wherein, H represents the theoretical cultivation depth parameter, H0 is the basic cultivation depth (preset according to soil texture type, such as a smaller value for sandy soil and a larger value for clay soil), α is the oxygen stress risk correction coefficient, and γ is the inhibition effect correction coefficient. The system determines the values ​​of α and γ by searching a cultivation effect database, which stores the correlation data between porosity improvement rate and risk index and inhibition coefficient at different cultivation depths. When the oxygen stress risk index increases or the inhibition effect coefficient increases, the theoretical cultivation depth increases accordingly to enhance soil aeration. The calculation results need to be compared with the preset upper and lower limits of depth to ensure that the cultivation operation is carried out within the safe range of the potato root system. The final generated theoretical cultivation depth parameter is used to guide the operation of cultivation equipment.

[0108] The pore correlation analysis unit is used to perform pore structure correlation analysis on the remeasured soil porosity data and theoretical tillage depth parameters, establish a quantitative relationship between tillage depth and pore improvement, and generate pore optimization correction factors.

[0109] Specifically, during the operation of the porosity correlation analysis unit, the system receives remeasured soil porosity data and theoretical tillage depth parameters. The soil porosity remeasurement uses the same measuring instrument as the previous measurement, conducted 24 hours after tillage, acquiring porosity data for the 0-30cm soil layer in 5cm strata, forming a comparison dataset with the porosity data before tillage. The quantitative relationship is established through regression analysis. The system uses the theoretical tillage depth H as the independent variable and the change in porosity Δφ (the difference between porosity after and before tillage) in each soil layer as the dependent variable, constructing a linear regression model:

[0110] Δφ=k×H+b

[0111] Where k is the porosity improvement coefficient (reflecting the influence of tillage depth on porosity), and b is the intercept term (characterizing the change in basic porosity). The formula for generating the porosity optimization correction factor δ is:

[0112]

[0113] Where, Δφ actual Δφ represents the actual change in porosity. theoretical This represents the theoretical change in porosity calculated based on a regression model. δ reflects the deviation between the actual porosity improvement and the theoretical value, and is used for subsequent model parameter adjustments.

[0114] The model parameter iteration unit is used to iterate the model parameters of the pore optimization correction factor, update the dynamic calibration parameters of the permeability model, and generate optimized permeability model parameters.

[0115] Specifically, during the model parameter iteration unit's execution, the system calls upon the original calibration parameters of the permeability model, which include the water-retaining agent type correction factor and porosity correction coefficient. The iterative processing updates the dynamic calibration parameters in the permeability model using the porosity optimization correction factor δ as the core input, with the following formula:

[0116] K new =K old ×δ

[0117] Among them, K new To optimize the parameters of the penetration model, K old The system calibrates the parameters of the previous permeability model. It optimizes the parameters through multiple iterations. After each iteration, the new parameters are substituted into the permeability calculation process and compared with measured water infiltration data. If the deviation exceeds a preset range, the iteration process is repeated until the deviation meets the accuracy requirements. The updated and optimized permeability model parameters more accurately reflect the impact of changes in soil pore structure after tillage on water infiltration, improving the reliability of subsequent irrigation strategy formulation.

[0118] Preferably, such as Figure 2 As shown, this invention provides a smart irrigation control method for potatoes in high-altitude areas, applicable to any of the above-mentioned smart irrigation control systems for potatoes in high-altitude areas, comprising the following steps:

[0119] S601: Identify and process water-retaining agent labels in soil, scan pre-embedded labels to obtain water-retaining agent type codes, and generate water-retaining agent type data;

[0120] S602: Perform permeability calculation processing on water-retaining agent type data and real-time measured soil porosity, calculate soil water infiltration rate based on preset permeability model, and generate permeability parameters;

[0121] S603: Perform oxygen stress risk assessment on real-time monitored oxygen content, soil texture coefficient and permeability parameters, calculate the degree of rhizosphere oxygen stress risk and generate oxygen stress risk index;

[0122] S604: The oxygen stress risk index is processed by irrigation and oxygenation strategy, the soil moisture trigger threshold is adjusted and the maximum irrigation duration is calculated. Based on the preset oxygen stress level threshold and the real-time monitored soil moisture content, the irrigation period and oxygenation intensity are adjusted, and irrigation and oxygenation instructions are generated and sent to the execution equipment.

[0123] S605: Feedback optimization processing is performed on the oxygen content monitoring data after irrigation, and the cultivation depth is calculated by combining the water-retaining agent type data and oxygen stress risk index. The calibration parameters of the permeability model are updated by retesting soil porosity.

[0124] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0125] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described intelligent irrigation control method for potatoes in high-altitude areas.

[0126] In one embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described intelligent irrigation control method for potatoes in high-altitude areas.

[0127] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0128] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0129] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart irrigation control system for potatoes in high-altitude areas, characterized in that, include: The water-retaining agent type identification module is used to identify water-retaining agent labels in the soil, scan the pre-embedded labels to obtain the water-retaining agent type code, and generate water-retaining agent type data; The permeability parameter calculation module is used to perform permeability calculation processing on the water-retaining agent type data and the real-time measured soil porosity, calculate the soil water infiltration rate based on the preset permeability model, and generate permeability parameters. The oxygen stress risk assessment module is used to perform oxygen stress risk assessment processing on the real-time monitored oxygen content, pre-stored soil texture parameters and the permeability parameters, calculate the degree of rhizosphere oxygen stress risk, and generate an oxygen stress risk index. The irrigation oxygenation instruction generation module is used to process the oxygen stress risk index with irrigation oxygenation strategy, adjust the soil moisture trigger threshold and calculate the maximum irrigation duration, adjust the irrigation period and oxygenation intensity based on the preset oxygen stress level threshold and the real-time monitored soil moisture content, generate irrigation oxygenation instructions and send them to the execution device. The control parameter feedback optimization module is used to perform feedback optimization processing on the oxygen content monitoring data after irrigation, calculate the tillage depth by combining the water-retaining agent type data and the oxygen stress risk index, and update the calibration parameters of the permeability model by retesting soil porosity.

2. The system according to claim 1, characterized in that, The permeability parameter calculation module is configured with the following units: The soil structure parameter generation unit is used to process the real-time measured soil porosity, analyze the pore distribution characteristics in combination with soil texture type, and generate soil structure parameters that characterize soil permeability. The initial infiltration rate calculation unit is used to process the water-retaining agent type data and the soil structure parameters based on the preset infiltration rate model, calculate the basic value of water infiltration under the action of biochar, and generate the initial infiltration rate. The permeability parameter optimization unit is used to optimize the initial permeability rate, adjust the basic permeability value according to the biochar retention characteristics, and generate permeability parameters, which are used to indicate the actual migration capacity of water in the rhizosphere soil.

3. The system according to claim 1, characterized in that, The oxygen stress risk assessment module is configured with the following units: The oxygen concentration correction unit is used to process the real-time monitored oxygen content, perform oxygen solubility correction in conjunction with the current atmospheric pressure value, and generate a corrected oxygen concentration. The oxygen transport capacity assessment unit is used to process the pre-stored soil texture parameters and the permeability parameters, construct the soil oxygen transport capacity assessment matrix, and generate oxygen transport capacity assessment values. The oxygen stress risk calculation unit is used to perform risk calculation processing on the corrected oxygen concentration and the oxygen transport capacity assessment value, calculate the degree of rhizosphere oxygen stress risk, and generate an oxygen stress risk index. The corrected oxygen concentration is used to indicate the actual oxygen availability level after eliminating the influence of altitude.

4. The system according to claim 1, characterized in that, The irrigation oxygen injection command generation module is configured with the following units: The dynamic soil moisture threshold generation unit is used to perform dynamic threshold processing on the oxygen stress risk index, adjust the soil moisture trigger threshold based on the preset oxygen stress level threshold, and generate a dynamic soil moisture threshold. The irrigation duration optimization unit is used to optimize the irrigation duration based on the oxygen stress risk index and the dynamic soil moisture threshold, calculate the maximum irrigation duration under biochar action, and generate the osmotic constraint irrigation duration. The multi-dimensional instruction generation unit is used to perform collaborative decision-making processing on the seepage-constrained irrigation duration, real-time monitored soil moisture content, and oxygen stress level, establish a three-dimensional matching relationship between irrigation period and oxygen injection intensity, and generate multi-dimensional irrigation and oxygen injection instructions containing period parameters, intensity parameters, and duration parameters.

5. The system according to claim 1, characterized in that, The control parameter feedback optimization module is configured with the following units: The oxygen recovery assessment unit is used to perform oxygen recovery assessment on the oxygen content monitoring data after irrigation, calculate the oxygen recovery rate deviation value in combination with the water-retaining agent type data, and generate the biochar inhibition effect coefficient. The tillage depth decision unit is used to perform deep decision calculations on the oxygen stress risk index and biochar blocking effect coefficient, construct a tillage depth optimization function, and generate theoretical tillage depth parameters. The pore correlation analysis unit is used to perform pore structure correlation analysis on the remeasured soil porosity data and the theoretical tillage depth parameters, establish a quantitative relationship between tillage depth and pore improvement, and generate pore optimization correction factors. The model parameter iteration unit is used to perform model parameter iteration processing on the pore optimization correction factor, update the dynamic calibration parameters of the permeability model, and generate optimized permeability model parameters.

6. A method for intelligent irrigation control of potatoes in high-altitude areas, applied to the intelligent irrigation control system for potatoes in high-altitude areas as described in any one of claims 1-5, characterized in that, Includes the following steps: S601: Identify and process water-retaining agent labels in soil, scan pre-embedded labels to obtain water-retaining agent type codes, and generate water-retaining agent type data; S602: Perform permeability calculation processing on the water-retaining agent type data and the real-time measured soil porosity, calculate the soil water infiltration rate based on the preset permeability model, and generate permeability parameters; S603: Perform oxygen stress risk assessment on the real-time monitored oxygen content, soil texture coefficient and the permeability parameter, calculate the degree of rhizosphere oxygen stress risk, and generate an oxygen stress risk index; S604: The oxygen stress risk index is processed by irrigation and oxygenation strategy, the soil moisture trigger threshold is adjusted and the maximum irrigation duration is calculated, the irrigation period and oxygenation intensity are adjusted based on the preset oxygen stress level threshold and the real-time monitored soil moisture content, and an irrigation and oxygenation command is generated and sent to the execution device. S605: Feedback optimization processing is performed on the oxygen content monitoring data after irrigation, and the tillage depth is calculated by combining the water-retaining agent type data and the oxygen stress risk index. The calibration parameters of the permeability model are updated by retesting soil porosity.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in claim 6.

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