Method for determining drainage key growth stage of farmland crops in plain area

Abstract

The invention particularly relates to a method for determining a drainage key growth stage of farmland crops in a plain area, which relates to the technical field of farmland water management, and comprises the following steps: for each sampling moment, carrying out series superposition on canopy transpiration resistance and soil-root system interface resistance obtained by calculating soil water potential time sequence data; according to the method, micrometeorology and root zone soil water potential time sequence data are integrated, canopy transpiration resistance is calculated based on the energy balance principle, soil-root system interface resistance is connected in series and superposed to obtain the total transmission resistance of an SPAC system, then resistance abnormal events are analyzed and recognized through continuous wavelet transform multiple time scales, and quantitative scoring is achieved in combination with growth stage division. Through standardized processing, scientific threshold setting and a comprehensive scoring mechanism, the waterlogging risk difference of each growth stage can be accurately captured, the key drainage time period and the risk level are determined, accurate data support is provided for farmland drainage regulation and control, and water resource waste caused by blind drainage and crop output reduction caused by untimely drainage are avoided.

Description

Technical Field

[0001] This invention relates to the field of farmland water management technology, and in particular to a method for determining the critical growth stages of crops in plain areas. Background Technology

[0002] In plains farmland production management, waterlogging is one of the most significant water-related disasters affecting crop yield. The essence of waterlogging is the obstruction of water transport channels in the soil-crop-atmosphere continuum (SPAC system), leading to prolonged excessive moisture in the root zone, resulting in root hypoxia and a significant decrease in the crop's water absorption capacity.

[0003] In existing technologies, soil moisture status and atmospheric evaporation demand are usually monitored independently, and drainage and irrigation timing are determined by their respective thresholds. The key judgments for the growth stage rely on the moisture status assessment of a single link.

[0004] However, this method cannot reflect the resistance changes of the entire transmission link in the SPAC system. Especially under meteorological conditions of high humidity and low evaporation, soil moisture content monitoring values ​​may be within the normal range, but crops may already be under water stress due to damaged root function. In this case, the method based on monitoring a single link is difficult to identify the abnormal increase in systemic transmission resistance, thus leading to inaccurate judgment of the critical period for drainage and delaying the drainage time. Summary of the Invention

[0005] The purpose of this invention is to provide a method for determining the critical growth stage of crops in plain farmland by addressing the aforementioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for determining the critical growth stages of crops in farmland in plain areas, including: Acquire micro-meteorological time-series datasets and root zone soil water potential time-series data for the target farmland during the monitoring period. The micro-meteorological time-series datasets include canopy temperature data, air temperature data, relative humidity data, net radiation data, and wind speed data. Based on the aforementioned micrometeorological time series dataset, the canopy evapotranspiration resistance is calculated at each sampling time using the energy balance principle, generating a time series curve of canopy evapotranspiration resistance. At each sampling time, the canopy evaporation resistance is superimposed in series with the soil-root interface resistance calculated from the soil water potential time series data to calculate the total transmission resistance of the SPAC system and generate the time series data of the total transmission resistance of the SPAC system. Continuous wavelet transform is performed on the total transmission resistance time series data of the SPAC system to conduct multi-timescale analysis, identify events of abnormal resistance increase, and generate a set of records of abnormal resistance events. Obtain the growth stage division information of the target crop, overlay the set of resistance abnormal event records with the time interval of the growth stage for analysis, and statistically analyze the occurrence frequency, average duration and average exceedance of resistance abnormal events in each growth stage. Calculate the comprehensive waterlogging risk score for each growth stage, and determine the key growth stage for drainage and its corresponding risk level based on the comprehensive waterlogging risk score.

[0007] Preferably, the acquisition of the root zone soil water potential time series data includes: when the data directly collected by the sensor is soil volumetric water content, the soil volumetric water content is converted into the corresponding soil water potential value using a pre-calibrated soil moisture characteristic curve. The soil moisture characteristic curve is the correspondence between water content and water potential obtained in advance through indoor experiments based on the soil texture of the target farmland.

[0008] Preferably, the calculation of the canopy transpiration resistance includes the following sub-steps: based on the difference between the canopy temperature and the air temperature and aerodynamic resistance, the sensible heat flux is calculated by multiplying the product of air density and air specific heat capacity at constant pressure by the difference between the canopy temperature and the air temperature, and then dividing by the aerodynamic resistance; based on the energy balance equation, the latent heat flux is calculated by subtracting the soil heat flux and the sensible heat flux from the net radiation; based on the latent heat flux, the difference between the saturated vapor pressure and the actual vapor pressure corresponding to the canopy temperature, the canopy transpiration resistance is calculated by multiplying the product of air density and air specific heat capacity at constant pressure by the difference between the saturated vapor pressure and the actual vapor pressure, dividing by the product of the ecliptic constant and the latent heat flux, and then subtracting the aerodynamic resistance; wherein, the saturated vapor pressure corresponding to the canopy temperature is calculated using the Tetens formula, and the actual vapor pressure is calculated by multiplying the saturated vapor pressure corresponding to the air temperature by the relative humidity.

[0009] Preferably, the aerodynamic drag is estimated based on the wind speed and crop canopy height using a logarithmic wind speed profile formula. Specifically, the difference between the wind speed measurement height and the zero-plane displacement height is divided by the momentum roughness length, and the natural logarithm is taken. The square of this natural logarithm is then divided by the product of the square of the von Kármán constant and the wind speed to obtain the aerodynamic drag. The zero-plane displacement height and momentum roughness length are determined based on the crop canopy height according to empirical relationships.

[0010] Preferably, the soil-root interface resistance is calculated as follows: the absolute value of the soil water potential is divided by the product of the root activity correction coefficient and the reference transpiration rate to obtain the soil-root interface resistance; wherein, the root activity correction coefficient is greater than zero and less than or equal to one, and the root activity correction coefficient is preset according to the root growth characteristics of the target crop at different growth stages; the reference transpiration rate is the reference crop evapotranspiration calculated using the FAO Penman-Monteith formula based on the same period's meteorological data under no water stress conditions.

[0011] Preferably, before calculating the total transport resistance of the SPAC system, the time-series data of the canopy transpiration resistance and the soil-root interface resistance are standardized using the Z-score standardization method, and the standardized canopy transpiration resistance and the standardized soil-root interface resistance are superimposed in series to obtain the standardized total transport resistance of the SPAC system.

[0012] Preferably, the specific process of the continuous wavelet transform includes: selecting the Morlet wavelet as the mother wavelet function, performing a continuous wavelet transform on the total transmission resistance time series data of the SPAC system, and calculating the wavelet coefficient matrix at each time scale; based on the wavelet coefficient matrix, calculating the wavelet power spectrum at each time scale, wherein the wavelet power spectrum is the squared modulus of the wavelet coefficients; setting a power spectrum threshold at each time scale, and extracting the time-frequency domain regions where the wavelet power spectrum value exceeds the corresponding power spectrum threshold as time-frequency domain feature patterns of abnormal resistance increase; merging the abnormal time intervals extracted at each time scale, taking the union of the abnormal time intervals that overlap on the time axis, generating the resistance abnormal event record set, and recording the start time, end time, and duration of each resistance abnormal increase event.

[0013] Preferably, the power spectrum threshold is determined based on the statistical characteristics of the time-series data of the total transmission resistance of the SPAC system within the monitoring period, specifically the arithmetic mean of the time-series data of the total transmission resistance of the SPAC system plus the product of the threshold coefficient and the standard deviation; the threshold coefficient is used to control the sensitivity of anomaly detection, and the threshold coefficient is determined according to the sensitivity of the target crop to waterlogging.

[0014] Preferably, the calculation of the comprehensive water damage risk score includes: The occurrence frequency, average duration, and average threshold exceedance of each fertility stage were normalized across all fertility stages using a range-based mean normalization method. The normalized occurrence frequency, average duration, and average threshold exceedance were then multiplied to obtain the comprehensive waterlogging risk score for each fertility stage. The average threshold exceedance was calculated as the arithmetic mean of the time integral of the total transmission resistance of the SPAC system exceeding the resistance anomaly threshold during each resistance anomaly event within the corresponding fertility stage, divided by the event duration. The comprehensive waterlogging risk scores for all fertility stages were sorted from highest to lowest. Fertility stages with comprehensive waterlogging risk scores exceeding a preset risk threshold were identified as critical fertility stages for drainage. These critical fertility stages were then categorized into high-risk, medium-risk, and low-risk levels based on the range of their comprehensive waterlogging risk scores.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention integrates time-series data of micrometeorology and root zone soil water potential, calculates canopy transpiration resistance based on the energy balance principle, and obtains the total transport resistance of the SPAC system by superimposing soil-root interface resistance in series. Then, it identifies anomalous resistance events through continuous wavelet transform multi-timescale analysis and achieves quantitative scoring by combining growth stage segmentation. Through standardized processing, scientifically set thresholds, and a comprehensive scoring mechanism, it can accurately capture the differences in waterlogging risk at each growth stage, identify key drainage periods and risk levels, provide precise data support for farmland drainage regulation, and avoid water waste caused by indiscriminate drainage and crop yield reduction caused by untimely drainage.

[0016] 2. This invention clarifies the calculation standards for key parameters such as soil water potential transformation, canopy transpiration resistance, and aerodynamic resistance. It covers a complete process including sensor data processing, indoor experimental calibration, and application of empirical formulas, with clear steps and adjustable parameters. Parameters such as root vigor correction coefficients and threshold coefficients can be flexibly adjusted to suit the root growth characteristics and waterlogging sensitivity of different crops, making it suitable for various main crops grown in plains areas, such as wheat and corn, without requiring complex modifications to specialized equipment. Furthermore, its quantitative risk scoring method facilitates farmers and agricultural technicians in quickly grasping drainage priorities, optimizing drainage plans, reducing waterlogging control costs, and promoting a shift from "passive response" to "proactive and precise control" of farmland drainage in plains areas, thus contributing to agricultural water conservation, efficiency improvement, and sustainable development. Attached Figure Description

[0017] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0019] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0020] Example 1

[0021] Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0022] In this embodiment, it includes: Step 1: Obtain the micro-meteorological time-series dataset and soil water potential time-series data of the target farmland.

[0023] Obtain canopy temperature data of the target farmland during the monitoring period. Air temperature data Relative humidity data Net radiation data and wind speed data The above data were organized according to time series to generate a micro-meteorological time-series dataset. Simultaneously, root zone soil water potential data were acquired. Time series data of soil water potential are generated.

[0024] Furthermore, the aforementioned canopy temperature data This data, obtained through non-contact temperature measurement of crop canopy surfaces using infrared temperature sensors, includes air temperature data. Relative humidity data Net radiation data and wind speed data This data was collected by the corresponding sensors at the weather station. All the above data were sampled at the same frequency to ensure alignment of the time-series data on the time axis.

[0025] Furthermore, the aforementioned soil water potential data This refers to the soil water potential value in the root zone directly acquired using a tensiometer or water potential sensor. When the data directly acquired by the sensor is the soil volumetric water content... At that time, using the pre-calibrated soil moisture characteristic curve The soil volumetric moisture content is converted into the corresponding soil water potential value. The soil moisture characteristic curve is the correspondence between moisture content and water potential obtained in advance through indoor experiments or empirical formulas based on the soil texture of the target farmland.

[0026] Furthermore, the soil moisture characteristic curve here The soil moisture characteristic curve is a well-known soil physical property description relationship in the industry. The specific form of the soil moisture characteristic curve adopts classic soil moisture characteristic curve models such as the van Genuchten model or the Brooks-Corey model. It is obtained in advance through indoor pressure plate test based on the soil texture of the target farmland and is not a custom calculation function of this invention.

[0027] Step 2: Calculate the time-series curve of canopy transpiration resistance based on the energy balance equation.

[0028] Based on a micrometeorological time-series dataset, canopy evapotranspiration resistance was calculated at each sampling time using the energy balance principle. The canopy evapotranspiration resistance values ​​at all sampling times are arranged in chronological order to generate a canopy evapotranspiration resistance time series curve.

[0029] Canopy transpiration resistance The calculation process is as follows: First, based on canopy temperature With air temperature The difference and aerodynamic drag Calculate sensible heat flux : ; in, air density, The specific heat capacity of air at constant pressure. For aerodynamic drag.

[0030] Furthermore, the aforementioned air density According to air temperature The specific heat capacity of air at constant pressure was obtained through calculations using the ideal gas law. The standard value is taken as 1.013 kJ / (kg·K).

[0031] Then, based on the energy balance equation, using net radiation With sensible heat flux Calculate latent heat flux : ; in, For soil heat flux, The latent heat of vaporization of water, This represents the transpiration rate.

[0032] Furthermore, the latent heat of vaporization of water mentioned above... According to air temperature It is confirmed that the value is approximately 2.45 MJ / kg within the normal temperature range.

[0033] Finally, based on latent heat flux saturated vapor pressure corresponding to canopy temperature With actual water vapor pressure The difference is used to calculate the canopy transpiration resistance. : ; in, This is the constant of the wet and dry meter. Canopy temperature The corresponding saturated vapor pressure, To be based on air temperature and relative humidity The actual water vapor pressure obtained through calculation.

[0034] Furthermore, the aforementioned saturated water vapor pressure Based on canopy temperature The actual water vapor pressure was obtained through the Tetens formula. Through saturated water vapor pressure and relative humidity The product is obtained by calculation, that is Wet and dry meter constants The standard value is taken as 0.067 kPa / °C.

[0035] Furthermore, the aforementioned aerodynamic drag According to wind speed The aerodynamic drag is calculated using the formula for estimating crop canopy height using the logarithmic wind speed profile. ; in, To measure the height for wind speed, Zero planar displacement height The momentum roughness length, This is the von Kármán constant (with a value of 0.41). Zero-plane displacement height. and momentum roughness length Based on crop canopy height Determined based on empirical relationships, usually taking , ,in This refers to the height of the crop canopy.

[0036] Furthermore, the aforementioned soil heat flux Net radiation A fixed proportional approximation, taken during the day. Take at night Or it can be obtained by direct measurement using a soil heat flux plate.

[0037] Step 3: Calculate the timing data of the total transmission resistance of the SPAC system.

[0038] For each sampling time, the canopy evapotranspiration resistance was measured. Resistance at the soil-root interface Perform series superposition calculations to calculate the total transmission resistance of the SPAC system. The total transmission resistance values ​​of the SPAC system at all sampling times are arranged in chronological order to generate time-series data of the total transmission resistance of the SPAC system. The formula for calculating the total transmission resistance of the SPAC system is: ; Among them, soil-root interface resistance Soil water potential Root vitality correction coefficient Compared with reference transpiration rate The following formula can be used to estimate: ; in, This represents the absolute value of soil water potential. This is the root vitality correction coefficient, with a value range of [value range missing]. Root activity correction coefficient during the vigorous vegetative growth period of crops A value close to 1 indicates the root vitality correction coefficient during the later stages of growth or when root function declines. The value is reduced, and the root vitality correction coefficient is adjusted accordingly. The specific values ​​are preset based on the root growth characteristics of the target crop at different growth stages; The reference transpiration rate is the reference crop evapotranspiration calculated using the FAO Penman-Monteith formula based on concurrent meteorological data under conditions of no water stress, or the potential transpiration rate of the target crop measured under conditions of sufficient water supply.

[0039] Furthermore, the aforementioned soil-root interface resistance The calculation principle is based on the physical mechanism of soil water potential driving root water absorption. The larger the absolute value of soil water potential, the stronger the soil water suction and the more difficult it is for roots to absorb water. Therefore, the soil-root interface resistance is directly proportional to the absolute value of soil water potential; root activity correction coefficient. Reflecting the health of the root system's water absorption function, stronger root vitality results in lower water absorption resistance under the same soil water potential; reference is transpiration rate. This is used to convert water potential differences into drag dimensions, referencing transpiration rate as the denominator to give the soil-root interface drag value a time / length dimension, in contrast to canopy transpiration drag. The dimensions are kept consistent.

[0040] Furthermore, in calculating the total transmission resistance of the SPAC system... Previously, regarding canopy transpiration resistance Resistance at the soil-root interface Data preprocessing was performed to eliminate the influence of dimensional differences on the calculations. Specifically, the Z-score normalization method was used to evaluate canopy transpiration resistance. and soil-root interface resistance The time-series data were standardized to obtain the standardized canopy evapotranspiration resistance. and standardized soil-root interface resistance Then, calculate the total transmission resistance of the standardized SPAC system: ; in, This represents the standardized total transmission resistance of the SPAC system. Subsequent calculations of the total transmission resistance of the SPAC system will be based on this standardized total transmission resistance. conduct.

[0041] Step 4: Identify abnormal increases in resistance in the total transmission resistance timing data of the SPAC system.

[0042] For the time-series data of total transmission resistance of the SPAC system, continuous wavelet transform is used to perform multi-time-scale analysis on the time-series data of total transmission resistance of the SPAC system, identify events of abnormal increase in resistance, and generate a set of records of abnormal resistance events. The specific steps include the following: Step 401: Select the mother wavelet function, perform continuous wavelet transform on the total transmission resistance time series data of the SPAC system, and calculate the wavelet coefficient matrix at each time scale. ,in For scale parameters, These are the translation parameters.

[0043] Furthermore, the aforementioned mother wavelet function is the Morlet wavelet, which possesses both good time localization and frequency localization properties, making it suitable for identifying local abrupt changes and periodic features in time-series data. Continuous wavelet transform is performed by applying the mother wavelet function at different scales. and different times and locations The wavelet coefficient matrix is ​​obtained by convolving the wavelet with the signal. The amplitude of wavelet coefficients reflects the energy distribution of the signal at the corresponding time location and time scale.

[0044] Step 402: Based on the wavelet coefficient matrix Calculate the wavelet power spectrum at each time scale. Identify the time-frequency domain regions where energy is concentrated in the wavelet power spectrum.

[0045] Furthermore, wavelet power spectrum The power spectrum, calculated as the square of the wavelet coefficients, reflects the energy intensity of the signal in the corresponding time-scale domain. Regions of concentrated energy in the wavelet power spectrum correspond to time periods and timescale combinations in the original signal where significant changes or anomalous fluctuations occur.

[0046] Step 403: Set power spectrum thresholds at each time scale, extract the time-frequency domain regions where the wavelet power spectrum value exceeds the corresponding power spectrum threshold as time-frequency domain feature patterns of abnormally increased resistance, and record the time scale and time interval corresponding to each time-frequency domain feature pattern.

[0047] Furthermore, the aforementioned power spectrum threshold is determined based on the statistical characteristics of the time-series data of the total transmission resistance of the SPAC system within the monitoring period, specifically the mean of the time-series data of the total transmission resistance of the SPAC system. with standard deviation Linear combination: ; in, The resistance anomaly threshold, To obtain the arithmetic mean of the total transmission resistance time series data of the SPAC system within the monitoring period, To monitor the standard deviation of the total transmission resistance time series data of the SPAC system within the monitoring period, The threshold coefficient is used to control the sensitivity of anomaly detection. The smaller the value, the more sensitive the anomaly detection; threshold coefficient The larger the value, the stricter the anomaly detection; threshold coefficient The value is determined based on the sensitivity of the target crop to waterlogging. For waterlogging-sensitive crops, the threshold coefficient is... The values ​​are relatively small, with a typical range of values ​​being [missing information]. .

[0048] Step 404: Merge the abnormal time intervals extracted from each time scale, take the union of the abnormal time intervals that overlap on the time axis, and generate a set of resistance anomaly event records after multi-scale analysis, recording the start time, end time and duration of each resistance anomaly increase event.

[0049] Furthermore, wavelet transform analysis can separate intraday short-term drag fluctuations from the continuous upward trend of drag across long-term time scales, avoiding misjudging instantaneous drag increases caused by short-term meteorological disturbances as waterlogging risk signals. At the same time, it can identify slow-accumulating drag anomalies that are difficult to detect by a single threshold method.

[0050] Step 5: Based on the overlay analysis of resistance anomalies and reproductive stages, generate the critical reproductive stages for drainage and their risk levels.

[0051] The growth stage information of the target crop was obtained, and the monitoring period was divided into several growth stage time intervals. The set of records of abnormal resistance events was overlaid with the growth stage time intervals for analysis, and the frequency of abnormal resistance events within each growth stage was statistically analyzed. Average duration and average overthreshold ,in Number the reproductive stage.

[0052] Furthermore, the frequency of the above occurrence For the first Total number of adverse events of resistance during each reproductive stage, average duration For the first The arithmetic mean of the duration of each adverse event during each reproductive stage.

[0053] Furthermore, the average overthreshold level For the first During each reproductive stage, the total transmission resistance of the SPAC system exceeded the resistance anomaly threshold in each resistance anomaly event. The average amplitude and the formula for calculating the average overthreshold amount are: ; in, For the first The total number of adverse events of resistance during each reproductive stage For the first The sequence number of the abnormal resistance events during each reproductive stage, and the summation sign. Indicates the first Within each reproductive stage, from the first to the second All resistance anomalies are summed up. For the first The duration of the secondary resistance anomaly event and The first The start and end times of the secondary resistance anomaly event The total transmission resistance of the standardized SPAC system, It is a time variable.

[0054] Furthermore, the aforementioned average overthreshold quantity In the calculation, the integral term Indicates the first The cumulative amount of total transmission resistance of the SPAC system exceeding the resistance anomaly threshold during the second resistance anomaly event, divided by the duration of the resistance anomaly event. Get the first The average over-threshold amplitude of the first resistance anomaly event, for the second All of the reproductive stages The arithmetic mean of the average over-threshold amplitude of the nth resistance anomaly event is obtained. The average threshold exceedance during each reproductive stage. In practical calculations, for discretely sampled time-series data, the above integration operation is numerically calculated using the trapezoidal integration method or the rectangular integration method.

[0055] Furthermore, the aforementioned average overthreshold quantity The calculation reflects the cumulative effect over time, with the integral term... For the resistance anomaly event from the start time until the end time The resistance values ​​exceeding the resistance anomaly threshold over the entire time period are integrated over time. The physical meaning of the integrated term is the cumulative intensity of the resistance anomaly over time, reflecting the cumulative stress load borne by the crop during that time period. This is then divided by the duration. The average overthreshold intensity per unit time was then obtained. The average overthreshold intensity of multiple resistance abnormal events during the reproductive stage was averaged again to obtain the average overthreshold amount during the reproductive stage. The average overthreshold amount comprehensively reflects the persistence and intensity characteristics of resistance abnormalities in the time dimension.

[0056] Furthermore, before calculating the comprehensive risk score for waterlogging at each reproductive stage, the frequency of occurrence was analyzed. Average duration and average overthreshold Data preprocessing is performed. Since the occurrence frequency is a dimensionless quantity (number of occurrences), the average duration is measured in time, and the average threshold exceedance is a standardized dimensionless resistance value, directly multiplying these three values ​​results in a dimensional inconsistency. Therefore, a mean normalization method based on the range is used to preprocess the occurrence frequency separately. Average duration and average overthreshold Normalization was performed across all reproductive stages to obtain the normalized occurrence frequency. Normalized average duration and normalized average overthreshold The normalized parameters all take values ​​within the range of 1. .

[0057] Calculate the comprehensive waterlogging risk score for each reproductive stage. : ; in, For the first Comprehensive risk score for waterlogging at each reproductive stage. For the normalized first The frequency of adverse events of resistance during each reproductive stage For the normalized first The average duration of adverse events of resistance during each reproductive stage For the normalized first The average overthreshold of abnormal resistance events during each reproductive stage.

[0058] Furthermore, the above-mentioned comprehensive risk score for waterlogging... The comprehensive waterlogging risk score is obtained by multiplying three normalized dimensionless parameters. It comprehensively reflects the risk characteristics of abnormal resistance events in the corresponding reproductive stage in three dimensions: frequency of occurrence, duration, and intensity of exceeding the threshold. The higher the comprehensive waterlogging risk score, the greater the risk of waterlogging during the reproductive stage.

[0059] Comprehensive score of waterlogging risk at all stages of the reproductive cycle Sort the data in descending order and extract those with a comprehensive waterlogging risk score exceeding a preset risk threshold. The reproductive stage, with drainage being the critical reproductive stage, is determined by the comprehensive waterlogging risk assessment. The range of values ​​is used to classify the critical reproductive stages of drainage into different risk levels: when When marked as high risk level, When marked as medium risk level, The time marker is marked as low risk level, among which The high-risk threshold The low-risk threshold To meet the minimum risk threshold, all three conditions must be met. The above thresholds are determined based on the target crop waterlogging sensitivity test data or regional historical drainage management experience, and the risk level corresponding to each key growth stage of drainage is output.

[0060] Furthermore, the above-mentioned information on the division of growth stages is based on the varietal characteristics of the target crop and the local agricultural calendar, which predetermine the start and end dates of each growth stage, including but not limited to the sowing period, seedling stage, tillering stage, jointing stage, booting stage, heading and flowering stage, grain filling stage, and maturity stage.

[0061] Technical effects of this embodiment

[0062] This implementation method combines canopy transpiration resistance and soil-root interface resistance in series to construct a quantitative calculation method for the total transport resistance of the SPAC system. Since canopy transpiration resistance comprehensively reflects the degree of obstruction to water loss at the canopy end, and soil-root interface resistance reflects the degree of obstruction to water transport from the soil end to the roots, their series superposition can characterize the total resistance status of the complete water transport link from soil to atmosphere in the SPAC system. Therefore, compared to methods that monitor soil moisture or atmospheric evaporation demand separately, this implementation method can detect the abnormal increase in systemic transport resistance under high humidity and low evaporation conditions, where soil moisture appears sufficient but root water absorption function is impaired. This overcomes the limitation of single-link monitoring in failing to identify transport channel blockages.

[0063] Furthermore, by performing multi-timescale decomposition of the total transport resistance time-series data of the SPAC system based on continuous wavelet transform, resistance fluctuation components at different time scales can be separated, thereby identifying two types of resistance anomaly patterns: slow accumulation and sudden occurrence. The comprehensive waterlogging risk score integrates information from three dimensions: the frequency of occurrence of resistance anomaly events, the duration, and the average threshold exceedance. The frequency dimension reflects the recurrence of resistance anomaly events, the duration dimension reflects the time span during which crop roots are continuously under stress, and the average threshold exceedance dimension reflects the intensity of the resistance anomaly. The product of these three dimensions can distinguish different risk levels corresponding to occasional short-lived anomalies, slight persistent anomalies, and high-intensity persistent anomalies. Therefore, this implementation method can identify the critical growth stages of drainage and their corresponding risk levels from the perspective of the integrity of water transport in the SPAC system.

[0064] Application Examples of Critical Period Detection Methods for Farmland Drainage

[0065] Application scenarios

[0066] In winter wheat production in a plain agricultural area, plot A01, with clay soil and an area of ​​approximately 50 mu (about 3.3 hectares), was planted with variety M21. During the spring of 20XX, the area experienced continuous rain and a rising groundwater level, posing a significant risk of waterlogging. To scientifically formulate a drainage plan, a complete SPAC (Specialized Space Control) system monitoring device was deployed on this plot: an infrared temperature sensor (model IRT-02) was installed at the center of the plot to monitor canopy temperature; a small weather station (model WS-300) was installed to collect data on air temperature, relative humidity, net radiation, and wind speed; and five tensiometers (model T5-15) were deployed at a depth of 20 cm in the crop root zone to monitor soil water potential. The monitoring period was from March 15, 20XX to June 10, 20XX, covering the entire growth stage of winter wheat from the greening stage to maturity, with data sampling every 30 minutes.

[0067] Step 1 Implementation: Acquire micrometeorological time-series datasets and soil water potential time-series data

[0068] During a 72-hour continuous monitoring period from April 8th to April 10th, 20XX, the system collected 144 sets of complete micrometeorological data and soil water potential data at 30-minute intervals. Taking the morning of April 9th ​​as an example, the raw data collected is as follows: Table 1. Raw monitoring data of micrometeorological conditions and soil water potential (morning of April 9th)

[0069] Since some tensiometers directly collect soil volumetric moisture content data, it is necessary to convert it using a pre-calibrated soil moisture characteristic curve. The clay soil of plot A01 was calibrated by indoor pressure plate tests to obtain van Genuchten model parameters, and the correspondence between moisture content and water potential was established.

[0070] Table 2 Examples of soil moisture content to water potential conversion

[0071] The micrometeorological time series dataset and soil water potential time series data are fully aligned on the time axis, and each sampling time contains a complete set of 7 monitoring parameters, providing basic data for subsequent calculation of canopy evapotranspiration resistance and soil-root interface resistance.

[0072] Step 2: Calculate the time-series curve of canopy transpiration resistance.

[0073] Using data from 09:00 on April 9th ​​as an example, the calculation process of canopy transpiration resistance is shown in detail. At this time, winter wheat is in the jointing stage, the canopy height is 0.52m, and the wind speed measurement height is 2.0m.

[0074] First, calculate the aerodynamic drag. Based on the canopy height... m, determine the zero-plane displacement height m, momentum roughness length m. Substitute wind speed m / s, calculate aerodynamic drag: ; Then calculate the sensible heat flux. Air density. Based on an air temperature of 16.3°C, the following calculations were performed using the ideal gas law. kg / m³, specific heat capacity of air at constant pressure J / (kg·K), the difference between canopy temperature and air temperature is °C. Substitute into the formula: ; Next, we calculate the latent heat flux. Net radiation. W / m², daytime soil heat flux is taken as W / m², latent heat of vaporization of water MJ / kg. From the energy balance equation: ; Finally, the canopy evapotranspiration resistance was calculated. The saturated vapor pressure corresponding to a canopy temperature of 18.9°C was calculated using the Tetens formula. kPa, the saturated vapor pressure corresponding to an air temperature of 16.3°C kPa, actual water vapor pressure kPa, wet / dry constant kPa / °C. Substitute into the formula: ; Table 3. Data for the calculation of canopy transpiration resistance (morning session, April 9th)

[0075] The canopy transpiration resistance values ​​at all 144 moments within the monitoring period were arranged chronologically to generate a 72-hour canopy transpiration resistance time-series curve. The curve shows that canopy transpiration resistance initially decreases and then increases during the day, while significantly increasing at night due to near-zero net radiation.

[0076] Step 3: Calculate the timing data of the total transmission resistance of the SPAC system.

[0077] Continuing with the example of 09:00 on April 9th, we calculate the soil-root interface resistance. At this time, winter wheat is in the jointing stage, with vigorous root activity. The root activity correction coefficient is taken as... Soil water potential kPa, absolute value kPa. Reference transpiration rate. Calculated using the FAO Penman-Monteith formula based on meteorological data from the same period. mm / d, converted to mm / s. Substitute into the formula: ; Due to canopy transpiration resistance s / m and soil-root interface resistance Although s / m are dimensionlessly consistent, their numerical values ​​differ significantly and require standardization. The mean value was calculated from the time-series data of canopy transpiration resistance over a 72-hour monitoring period. s / m, standard deviation s / m; the mean value was calculated from the time-series data of soil-root interface resistance. s / m, standard deviation s / m.

[0078] Z-score standardization method is used: ; ; Total transmission resistance of the standardized SPAC system: ; Table 4. Calculation data of total transmission resistance of SPAC system (morning session, April 9th)

[0079] The standardized total transport resistance values ​​of the SPAC system at all 144 time points within the monitoring period were arranged chronologically to generate complete time-series data on the total transport resistance of the SPAC system. The data shows that the total transport resistance of the SPAC system increases with rising diurnal transpiration demand and decreasing soil water potential.

[0080] Step 4: Identify events of abnormally high resistance.

[0081] The mean value was calculated from the time-series data of the total transmission resistance of the SPAC system over a 72-hour monitoring period. Standard deviation Based on the moderate sensitivity of winter wheat to waterlogging, a threshold coefficient was set. Calculate the resistance anomaly threshold: ; Morlet wavelet was selected as the mother wavelet function to perform continuous wavelet transform on the total transmission resistance time series data of the SPAC system. The scale parameter s was set to range from 2 to 64 (corresponding to time scales from 1 hour to 32 hours), and the shift parameter tau covered the entire monitoring period. The wavelet coefficient matrix was calculated, and then the wavelet power spectrum was calculated.

[0082] Table 5. Results of wavelet power spectrum energy concentration region identification

[0083] At the 4-hour scale, a short-term anomaly in drag was identified from 10:30 to 18:00 on April 9, corresponding to the daytime transpiration peak. At the 8-hour and 16-hour scales, persistent drag anomalies spanning day and night were identified. At the 32-hour scale, a long-term drag anomaly trend spanning two complete diurnal cycles was identified.

[0084] The abnormal time intervals extracted from each time scale are merged, and the union on the time axis is taken to generate a record of abnormal resistance events. Table 6 Collection of Resistance Anomaly Event Records

[0085] Three abnormal resistance increases were identified through multi-scale wavelet analysis. Among them, Event02 was the longest-lasting and largest-amplitude abnormal event, corresponding to the period of waterlogging risk on April 9th, when continuous rain, declining soil water potential, and impaired root water absorption occurred.

[0086] Step 5 Implementation: Generating the critical reproductive stages of drainage and their risk levels

[0087] Based on the division of winter wheat growth stages, the monitoring period from March 15 to June 10, 20XX, was divided into 5 growth stages: greening stage (March 15-March 28), jointing stage (March 29-April 25), booting stage (April 26-May 10), heading and flowering stage (May 11-May 25), and grain filling stage (May 26-June 10).

[0088] All abnormal resistance events identified during the monitoring period were overlaid with the time intervals of the reproductive stages for analysis, and the abnormal resistance characteristics of each reproductive stage were statistically analyzed: Table 7. Statistics of abnormal events hindering childbirth at each stage.

[0089] Taking the average overthreshold value calculation during the jointing stage as an example, a total of 7 abnormal resistance events occurred during this stage. The cumulative overthreshold intensity of each event was calculated using the trapezoidal integral method. The duration of the first event was 14.2 hours, the cumulative integral value was 9.85, and the average overthreshold amplitude was... The second event lasted 18.5 hours, with a cumulative score of 13.24 and an average exceedance threshold. After calculating the average overthreshold amplitude of 7 events in sequence, the arithmetic mean was obtained to obtain the average overthreshold amount during the jointing period as 0.68.

[0090] The occurrence frequency, average duration, and average threshold exceedance were normalized using range normalization. The range of the occurrence frequency is... The range of average duration is The range of the average overthreshold quantity over hours is .

[0091] Table 8. Normalized parameters and comprehensive score of waterlogging risk

[0092] Calculate the comprehensive risk score of waterlogging at each growth stage, taking the jointing stage as an example:

[0093] Risk level classification thresholds are set: a comprehensive waterlogging risk score greater than 0.60 is considered high risk, between 0.20 and 0.60 is medium risk, and less than 0.20 is low risk. The risk levels are then ranked and classified according to the comprehensive waterlogging risk score. Table 9 Key Fertility Stages and Risk Levels of Drainage

[0094] Data flow summary

[0095] The data flow throughout the detection process reflects a step-by-step transformation from raw monitoring data to drainage decision-making information: First, raw micro-meteorological data and soil water potential data collected by infrared temperature sensors, weather stations, and tensiometers are converted into a time-synchronized micro-meteorological time-series dataset after unit conversion and coordinate alignment; second, meteorological elements such as canopy temperature difference and net radiation are converted into canopy transpiration resistance through energy balance equations, while soil water potential and root activity parameters are converted into soil-root interface resistance. These two data, after standardization, are cascaded and superimposed to obtain the total transmission resistance time-series data of the SPAC system. This data integrates the resistance status of the complete transmission link from soil to atmosphere; then, through... The total transmission resistance time-series data of the SPAC system was decomposed into multiple scales using continuous wavelet transform. In the time-frequency domain, events with abnormally high resistance in short-term fluctuations and long-term trends were identified. Each event included characteristic parameters such as start time, end time, and duration. Finally, the abnormal resistance events were overlaid with the time intervals of the growth stages for analysis. Risk characteristics were statistically analyzed in three dimensions: occurrence frequency, average duration, and average exceedance amount for each growth stage. After normalization, a comprehensive waterlogging risk score was calculated. Based on the score ranking and threshold classification, the final output identified the key growth stages for drainage as high-risk (heading and flowering stage), medium-risk (jointing stage), and low-risk (booting stage). Throughout the data flow, the output data of each step served as the input data for the next step, forming a complete data chain from bottom-level physical monitoring to upper-level decision support, ensuring the scientific validity and traceability of the key drainage period identification results.

[0096] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0097] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0098] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0099] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0101] The units described as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should 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.

[0104] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for determining the critical growth stages of crops in farmland in plain areas, characterized in that, include: Acquire micro-meteorological time-series datasets and root zone soil water potential time-series data for the target farmland during the monitoring period. The micro-meteorological time-series datasets include canopy temperature data, air temperature data, relative humidity data, net radiation data, and wind speed data. Based on the aforementioned micrometeorological time series dataset, the canopy evapotranspiration resistance is calculated at each sampling time using the energy balance principle, generating a time series curve of canopy evapotranspiration resistance. At each sampling time, the canopy evaporation resistance is superimposed in series with the soil-root interface resistance calculated from the soil water potential time series data to calculate the total transmission resistance of the SPAC system and generate the time series data of the total transmission resistance of the SPAC system. Continuous wavelet transform is performed on the total transmission resistance time series data of the SPAC system to conduct multi-timescale analysis, identify events of abnormal resistance increase, and generate a set of records of abnormal resistance events. Obtain the growth stage division information of the target crop, overlay the set of resistance abnormal event records with the time interval of the growth stage for analysis, and statistically analyze the occurrence frequency, average duration and average exceedance of resistance abnormal events in each growth stage. Calculate the comprehensive waterlogging risk score for each growth stage, and determine the key growth stage for drainage and its corresponding risk level based on the comprehensive waterlogging risk score.

2. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, The acquisition of the root zone soil water potential time series data includes: when the data directly collected by the sensor is soil volumetric water content, the soil volumetric water content is converted into the corresponding soil water potential value using a pre-calibrated soil moisture characteristic curve. The soil moisture characteristic curve is the correspondence between water content and water potential obtained in advance through indoor experiments based on the soil texture of the target farmland.

3. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, The calculation of canopy transpiration resistance includes the following sub-steps: Based on the difference between canopy temperature and air temperature and aerodynamic drag, the sensible heat flux is calculated by multiplying the product of air density and air specific heat capacity at constant pressure by the difference between canopy temperature and air temperature, and then dividing by aerodynamic drag; based on the energy balance equation, the latent heat flux is calculated by subtracting soil heat flux and sensible heat flux from net radiation; based on the latent heat flux, the difference between the saturated vapor pressure and actual vapor pressure corresponding to canopy temperature, the canopy transpiration resistance is calculated by multiplying the product of air density and air specific heat capacity at constant pressure by the difference between saturated vapor pressure and actual vapor pressure, dividing by the product of ecliptic constant and latent heat flux, and then subtracting aerodynamic drag; wherein, the saturated vapor pressure corresponding to canopy temperature is calculated using the Tetens formula, and the actual vapor pressure is calculated by multiplying the saturated vapor pressure corresponding to air temperature by the relative humidity.

4. The method for determining the critical growth stage of crops in plain farmland according to claim 3, characterized in that, The aerodynamic drag is estimated based on wind speed and crop canopy height using a logarithmic wind speed profile formula. Specifically, the difference between the wind speed measurement height and the zero-plane displacement height is divided by the momentum roughness length, and the natural logarithm is taken. The square of this natural logarithm is then divided by the product of the square of the von Kármán constant and the wind speed to obtain the aerodynamic drag. The zero-plane displacement height and momentum roughness length are determined empirically based on the crop canopy height.

5. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, The method for calculating the soil-root interface resistance is as follows: the absolute value of the soil water potential is divided by the product of the root activity correction coefficient and the reference transpiration rate to obtain the soil-root interface resistance; wherein, the root activity correction coefficient is greater than zero and less than or equal to one, and the root activity correction coefficient is preset according to the root growth characteristics of the target crop at different growth stages; the reference transpiration rate is the reference crop evapotranspiration calculated using the FAO Penman-Monteith formula based on the same period's meteorological data under no water stress conditions.

6. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, Before calculating the total transport resistance of the SPAC system, the time-series data of the canopy evaporation resistance and the soil-root interface resistance are standardized using the Z-score standardization method. The standardized canopy evaporation resistance and the standardized soil-root interface resistance are then cascaded and superimposed to obtain the standardized total transport resistance of the SPAC system.

7. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, The specific process of the continuous wavelet transform includes: selecting the Morlet wavelet as the mother wavelet function, performing a continuous wavelet transform on the total transmission resistance time series data of the SPAC system, and calculating the wavelet coefficient matrix at each time scale; based on the wavelet coefficient matrix, calculating the wavelet power spectrum at each time scale, where the wavelet power spectrum is the square of the modulus of the wavelet coefficients; setting power spectrum thresholds at each time scale, and extracting the time-frequency domain regions where the wavelet power spectrum values ​​exceed the corresponding power spectrum thresholds as time-frequency domain feature patterns of abnormal resistance increases; merging the abnormal time intervals extracted at each time scale, taking the union of the abnormal time intervals that overlap on the time axis, generating the resistance abnormal event record set, and recording the start time, end time, and duration of each resistance abnormal increase event.

8. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, The power spectrum threshold is determined based on the statistical characteristics of the time-series data of the total transmission resistance of the SPAC system within the monitoring period. Specifically, it is the product of the arithmetic mean of the time-series data of the total transmission resistance of the SPAC system and the threshold coefficient and the standard deviation. The threshold coefficient is used to control the sensitivity of anomaly detection, and the threshold coefficient is determined according to the sensitivity of the target crop to waterlogging.

9. The method for determining the critical growth stage of crops in plain farmland according to claim 1, characterized in that, The calculation of the comprehensive water damage risk score includes: The occurrence frequency, average duration, and average threshold exceedance of each fertility stage were normalized across all fertility stages using a range-based mean normalization method. The normalized occurrence frequency, average duration, and average threshold exceedance were then multiplied to obtain the comprehensive waterlogging risk score for each fertility stage. The average threshold exceedance was calculated as the arithmetic mean of the time integral of the total transmission resistance of the SPAC system exceeding the resistance anomaly threshold during each resistance anomaly event within the corresponding fertility stage, divided by the event duration. The comprehensive waterlogging risk scores for all fertility stages were sorted from highest to lowest. Fertility stages with comprehensive waterlogging risk scores exceeding a preset risk threshold were identified as critical fertility stages for drainage. These critical fertility stages were then categorized into high-risk, medium-risk, and low-risk levels based on the range of their comprehensive waterlogging risk scores.

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