Plant growth elicitor dose regulation method and system based on sample analysis

Abstract

This invention relates to the field of inducible hormone dosage regulation, and more particularly to a method and system for regulating plant growth inducible hormone dosage based on sample analysis. The method includes the following steps: collecting rice samples from multiple cultivation environments, monitoring photosynthesis to obtain response sequences over multiple periods; eliminating inefficient samples based on the response sequences to obtain response curves for candidate samples; identifying growth stages from the response curves of the candidate samples to obtain growth stage curves; performing inducible hormone gain analysis based on the growth stage curves to obtain inducible hormone sensitivity values ​​for different samples; and iteratively calculating and risk-screening the inducible hormone combination ratio based on the inducible hormone sensitivity values ​​of different samples to output the optimal dosage ratio. This invention improves the photosynthetic efficiency and enhances growth stability of rice.

Description

Technical Field

[0001] This invention relates to the field of inducing agent dosage regulation, and in particular to a method and system for regulating plant growth inducing agent dosage based on sample analysis. Background Technology

[0002] Inducible glycosides, as physiologically active substances in plants, have attracted widespread attention due to their ability to regulate photosynthetic mechanisms in rice, thereby improving photosynthetic rate and photosynthetic energy use efficiency. Inducible glycoside treatment can enhance photosynthetic activity and chlorophyll content in rice leaves, and optimize stomatal conductance and transpiration rate, thus improving photosynthetic efficiency. However, different rice samples exhibit varying responses to the same inducible glycosides, making it difficult to achieve optimal photosynthetic efficiency with a single dose or fixed ratio. Traditional methods often rely on empirical application or simple photosynthetic index measurements, lacking precise analysis of individual characteristics and photosynthetic changes at different growth stages, thus failing to specifically optimize inducible glycoside ratios to enhance photosynthetic effects. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method and system for regulating plant growth inducing hormone dosage based on sample analysis, thereby resolving at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, this invention provides a method for regulating plant growth inducing hormone dosage based on sample analysis, comprising the following steps: Step S1: Collect rice samples from multiple cultivation environments, monitor photosynthesis, and obtain response sequences for multiple cycles; Step S2: Eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; Step S3: Identify the growth stage of the response curves of the candidate samples to obtain the growth stage curves; Step S4: Perform inducing agent gain analysis based on the growth stage curve to obtain the inducing agent sensitivity values ​​of different samples; Step S5: Based on the inducing sensitivity values ​​of different samples, perform iterative calculation of the inducing combination ratio and risk screening, and output the optimal dose ratio.

[0005] This specification provides a plant growth inducing agent dosage regulation system based on sample analysis, used to execute the plant growth inducing agent dosage regulation method based on sample analysis as described above, including: The monitoring module is used to collect rice samples from multiple cultivation environments to monitor photosynthesis and obtain response sequences over multiple cycles. The elimination module is used to eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; The stage identification module is used to identify the growth stage of the response curve of the candidate sample and obtain the growth stage curve. The gain analysis module is used to perform inducing hormone gain analysis based on the growth stage curve to obtain the inducing hormone sensitivity values ​​of different samples. The iterative calculation module is used to perform iterative calculation of the ratio of inducing agents and risk screening based on the inducing agent sensitivity values ​​of different samples, and output the optimal dose ratio.

[0006] The beneficial effects of this invention are as follows: By collecting rice samples under multiple cultivation environments and monitoring photosynthesis, real photosynthetic response data of rice under different environmental conditions can be obtained. This process can record the changes in key parameters such as net photosynthetic rate, stomatal conductance, and transpiration rate, forming a continuous photosynthetic response sequence. By comparing the difference between the photosynthetic efficiency of the samples and the baseline curve, samples with relatively stable photosynthetic capacity are screened out and used as candidate samples for further analysis. By constructing continuous response curves from the screened data, the trend of photosynthetic changes in rice can be reflected more intuitively, key growth nodes such as tillering, jointing, booting, and grain-filling stages can be identified, and growth stage curves can be formed. In this way, the changing patterns of rice photosynthesis at different stages can be analyzed more clearly, thereby identifying the regulatory effects of inducible antibiotics at different stages and improving the pertinence and accuracy of inducible antibiotic effect analysis. By comparing the sample curves with the baseline curve, the photosynthetic gain at each growth stage is calculated, and a comprehensive inducible antibiotic sensitivity value is further obtained. This sensitivity value reflects the degree of rice's response to inducing agent combinations, thus providing a quantitative basis for evaluating the effects of different formulation schemes and helping to accurately identify the inducing agent combinations that have the most significant effect on improving rice photosynthetic efficiency. By iteratively calculating the combination ratio based on the inducing agent sensitivity value and combining it with a risk screening mechanism, the optimal dosage ratio can be screened from multiple formulation schemes. By evaluating the stability of photosynthetic response and the safety range of components, the risks caused by unreasonable ratios can be avoided. Attached Figure Description

[0007] Figure 1 This is a schematic flowchart of the steps of a plant growth inducing agent dosage regulation method based on sample analysis according to the present invention; Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0008] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0009] This application provides a method and system for regulating plant growth inducing hormone dosage based on sample analysis. The execution entities of the method and system include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices that can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio / image management system, an information management system, and a cloud data management system.

[0010] Please see Figures 1 to 3 This invention provides a method for regulating plant growth inducing hormone dosage based on sample analysis, comprising the following steps: Step S1: Collect rice samples from multiple cultivation environments, monitor photosynthesis, and obtain response sequences for multiple cycles; Step S2: Eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; Step S3: Identify the growth stage of the response curves of the candidate samples to obtain the growth stage curves; Step S4: Perform inducing agent gain analysis based on the growth stage curve to obtain the inducing agent sensitivity values ​​of different samples; Step S5: Based on the inducing sensitivity values ​​of different samples, perform iterative calculation of the inducing combination ratio and risk screening, and output the optimal dose ratio.

[0011] In the embodiments of the present invention, see Figure 1 This is a schematic flowchart of a plant growth inducing agent dosage regulation method based on sample analysis according to the present invention. In this example, the steps of the plant growth inducing agent dosage regulation method based on sample analysis include: Step S1: Collect rice samples from multiple cultivation environments, monitor photosynthesis, and obtain response sequences for multiple cycles; In this embodiment, rice samples were obtained under different cultivation conditions to ensure environmental diversity. Cultivation environments can include different soil types, moisture conditions, and light intensity conditions, such as a normal moisture environment (approximately 70% field capacity), a slightly drought environment (approximately 50% field capacity), and a high-temperature environment (daytime temperature approximately 32±2 ℃). Rice plants with consistent growth under each environment were selected as samples and treated according to a preset inducing agent ratio. Subsequently, photosynthesis was monitored on the functional leaves of each sample, with key parameters including net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), chlorophyll fluorescence parameters (Fv / Fm), and electron transport rate (ETR). Environmental parameters were uniformly controlled during the monitoring process, such as maintaining light intensity at 800 μmol·m⁻¹. -2 ·s -1 CO2 concentration was maintained at 400±10 μmol·mol-1 The temperature in the leaf chamber was controlled at 25±1 ℃. Data collection was conducted at three fixed times each day, such as 09:30, 13:30, and 17:00, to record the photosynthetic changes of rice at different time stages. Data was recorded continuously for more than 7 days, with each sample containing no fewer than 21 data points. These photosynthetic parameters were then arranged in chronological order and grouped into multiple photosynthetic response cycles, with each cycle consisting of 3 days. For example, the changing trends of parameters such as Pn, Gs, and ETR were recorded within each cycle.

[0012] Step S2: Eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; In this embodiment, the net photosynthetic rate (Pn) data of each sample are statistically analyzed to calculate the average, maximum, and minimum values ​​of Pn within each period. For example, the average Pn value of a certain sample within one period is 18 μmol CO2·m -2 ·s -1 The average value of the other sample was only 13 μmol CO2·m -2 ·s -1 If the latter's photosynthetic efficiency is relatively low, then a baseline reference curve is established, using the inducible sample as a reference, and smoothing its Pn data to form a growth baseline curve. Then, the deviation of each sample's Pn response curve from the baseline curve is calculated, for example, ΔPn = Pn(sample) - Pn(base) at each time point. By statistically analyzing the deviation values ​​at all time points, the overall photosynthetic efficiency deviation of the sample can be calculated. When the average deviation of a sample is lower than -3 μmol CO2·m -2 ·s -1 If the photosynthetic efficiency is significantly lower than the baseline level, the sample is identified as an inefficient sample and removed. For samples that are not removed, their complete photosynthetic response curves are retained, and the curves are smoothed, for example, by using a moving average method or local regression method, so that the curves change continuously on the time axis.

[0013] Step S3: Identify the growth stage of the response curves of the candidate samples to obtain the growth stage curves; In this embodiment, the response curve is analyzed in chronological order, and the rate of change between consecutive time points is calculated. For example, the increase or decrease trend of photosynthetic capacity is determined by the magnitude of the change in Pn value. When Pn continuously increases over a continuous time period and the rate of increase is greater than 0.5 μmol CO2·m -2 ·s -1 ·h -1When the growth rate reaches a certain threshold, it can be identified as a rapid growth stage; when the growth rate stabilizes, it indicates the entry into a stable growth stage. Subsequently, a comprehensive judgment is made by combining the changing trends of stomatal conductance (Gs) and electron transport rate (ETR). For example, when Gs gradually increases to 0.35–0.45 mol H₂O·m⁻¹, it indicates a stable growth stage. -2 ·s -1 And accompanied by an increase in ETR to 120 μmol electrons·m -2 ·s -1 The above values ​​typically correspond to the tillering stage. When Pn shows a significant peak and its growth rate subsequently slows down, it may correspond to the transition from the jointing stage to the heading stage. Key nodes are identified using curve inflection point detection methods; for example, when the second-order rate of change of the Pn curve changes from positive to negative, it can be identified as a turning point in the growth stage. Based on these turning points, the response curve can be divided into four stages: tillering stage, jointing stage, heading stage, and grain-filling stage, and the time range of each stage is recorded. Subsequently, curve fitting processing is performed on the Pn, ETR, and Gs data for each stage, for example, using a cubic spline function to ensure that the curve maintains a smooth change within each stage.

[0014] Step S4: Perform inducing agent gain analysis based on the growth stage curve to obtain the inducing agent sensitivity values ​​of different samples; In this embodiment, the growth stage curve of the sample without inducible hormone treatment is used as the baseline curve. Then, the stage curves of each candidate sample are compared with this baseline curve by difference calculation. For example, during the tillering stage, ΔPn = Pn(sample) - Pn(base) is calculated, and the average difference value of all time points within this stage is statistically analyzed. If the average Pn value of a sample during the tillering stage is 21 μmol CO2·m -2 ·s -1 The baseline value is 18 μmol CO2·m -2 ·s -1 The photosynthetic gain during this stage is 3 μmol CO2·m -2 ·s -1 Subsequently, photosynthetic gain was calculated for each growth stage, and a comprehensive evaluation was conducted based on changes in ETR and Gs. For example, when the ETR increase reached 10 μmol electrons·m -2 ·s -1 The above values ​​are maintained at Gs between 0.30 and 0.40 mol H2O·m -2 ·s -1When the range is within a certain range, it can be considered that the inducing factor has a significant promoting effect at this stage. Next, the gain values ​​of each stage are normalized, for example, by dividing the gain value by the average value of Pn in the baseline stage, to obtain the relative gain ratio. Then, the gain ratios of the four stages are weighted and averaged, with higher weights at the jointing stage and heading stage (e.g., 0.35 and 0.30), and lower weights at the tillering stage and grain filling stage (e.g., 0.20 and 0.15).

[0015] Step S5: Based on the inducing sensitivity values ​​of different samples, perform iterative calculation of the inducing combination ratio and risk screening, and output the optimal dose ratio.

[0016] In this embodiment, the inducible ratio parameters of all samples were compiled, including the concentration ratios of 14-hydroxybrassinosteroidol, sodium salicylate, chitosan oligosaccharide, seaweed oligosaccharide, and surfactant, and a parameter relationship was established with the corresponding sensitivity values. For example, if the sensitivity value increases from 0.12 to 0.18 when the chitosan oligosaccharide concentration increases from 0.30% to 0.60%, it indicates that this concentration change has a significant effect on promoting photosynthesis. Subsequently, multiple combination parameter groups were constructed within the existing ratio range, for example, setting 14-hydroxybrassinosteroidol in the range of 0.02%–0.04%, sodium salicylate in the range of 0.30%–0.45%, chitosan oligosaccharide in the range of 0.60%–0.90%, and seaweed oligosaccharide in the range of 0.90%–1.20%. By gradually adjusting the ratio of each component and iteratively calculating based on the trend of sensitivity value changes, when the sensitivity value increases by more than 5% after the combination adjustment, the combination is retained for the next round of optimization. After multiple rounds of iteration, several candidate formulation schemes can be obtained. These schemes are then subjected to risk screening, such as calculating the photosynthetic response stability of each formulation at different stages, where the standard deviation of Pn is less than 2 μmol CO2·m⁻¹. -2 ·s -1 Furthermore, the stomatal conductance Gs remained between 0.30 and 0.42 mol H₂O·m⁻¹. -2 ·s -1 When the concentration falls within a certain range, it indicates that the formulation has good stability. Simultaneously, check that the concentrations of each component are within safe ranges; for example, 14-hydroxybrassinosteroids should not exceed 0.05%, and sodium salicylate should not exceed 0.50%. Finally, based on a comprehensive evaluation of photosynthetic gain, stability indicators, and safe concentration ranges, the optimal dosage ratio is selected from the candidate formulations. For example, approximately 0.03% 14-hydroxybrassinosteroids, approximately 0.36%–0.40% sodium salicylate, approximately 0.70%–0.80% chitosan oligosaccharide, approximately 1.00%–1.10% seaweed oligosaccharide, and approximately 0.25%–0.30% surfactant.

[0017] In this embodiment, see Figure 2The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Rice samples were collected from multiple cultivation environments; specifically, the rice samples from multiple cultivation environments included: a first sample, a second sample, a third sample, a fourth sample, and a fifth sample. The first sample was a sample without added inducing agents; The second sample was a sample with added inducing agents, with the following concentration ratios: 0.01% 14-hydroxybrassinosteroidol, 0.20% sodium salicylate, 0.30% chitosan oligosaccharide, 0.50% seaweed oligosaccharide, and 0.30% surfactant; The third sample was a sample with added inducing agents, with the following concentration ratios: 0.02% 14-hydroxybrassinosteroidol, 0.30% sodium salicylate, 0.60% chitosan oligosaccharide, 1.00% seaweed oligosaccharide, and 0.30% surfactant. The fourth sample was a sample with added inducing agents, with the following concentration ratios: 0.03% 14-hydroxybrassinosteroidol, 0.40% sodium salicylate, 0.80% chitosan oligosaccharide, 0.80% seaweed oligosaccharide, and 0.25% surfactant. The fifth sample was a sample with added inducing agents, with the following concentration ratios: 0.04% 14-hydroxybrassinosteroidol, 0.50% sodium salicylate, 1.00% chitosan oligosaccharide, 1.50% seaweed oligosaccharide, and 0.30% surfactant.

[0018] The photosynthetic response of the rice samples was monitored, and photosynthetic response parameters were extracted; the photosynthetic response parameters included net photosynthetic rate, chlorophyll fluorescence parameters, stomatal conductance, and transpiration rate data. The photosynthetic response parameters were continuously acquired over time and divided into periods to obtain response sequences for multiple periods.

[0019] In this embodiment, samples one through five were transplanted into uniformly sized potted cultivation containers, each 25 cm in diameter and 28 cm in height. Three rice plants were retained in each container to avoid interference with photosynthetic parameters due to differences in population density. The culture medium consisted of a 2:1:2 volume ratio of peat moss, vermiculite, and field soil, with soil moisture content maintained at 70% ± 5% of field capacity. The experimental greenhouse environment was controlled at 28 ± 2 °C, relative humidity at 60%–70%, and light intensity at 800 μmol·m⁻². -2 ·s -1The photoperiod was 14 h light / 10 h dark to simulate a typical rice growth environment. Subsequently, a portable photosynthesis measurement system was used to monitor the leaves of each sample. The measurement sites were uniformly selected from functional leaves of rice (generally the second or third leaf from the bottom), and the measurement time was fixed between 09:00 and 11:30 daily to reduce the influence of the diurnal rhythm on the data. During the measurement process, a stable gas environment was maintained in a sealed leaf chamber, with the CO2 concentration set at 400 ± 10 μmol·mol⁻¹. -1 The air velocity in the leaf chamber is controlled at 500 μmol·s. -1 The leaf temperature remained stable at 25±1 ℃. Zero-point calibration and gas correction were performed on the instrument before monitoring to ensure that the measurement error was less than ±2%.

[0020] After setting up the photosynthetic monitoring environment, gas exchange parameters of each sample leaf were continuously monitored to obtain key photosynthetic indicators such as net photosynthetic rate, stomatal conductance, and transpiration rate. During the monitoring process, rice leaves were fixed in the transparent leaf chamber of the photosynthetic monitoring device, and the CO2 concentration change between the inlet and outlet of the leaf chamber was detected in real time using an infrared gas analysis module. The leaves were first kept stable in the leaf chamber environment for about 3 minutes, and the CO2 concentration fluctuation was considered stable when it was less than ±1 μmol·mol⁻¹. -1 Data recording began at that time. Net photosynthetic rate (Pn) was calculated from the CO2 absorption rate per unit leaf area, in μmol CO2·m⁻¹. -2 ·s -1 Stomatal conductance (Gs) is calculated based on the water vapor diffusion model, and the unit is mol H2O·m. -2 ·s -1 The transpiration rate (Tr) is calculated based on the water vapor concentration gradient between the leaf and the environment, and is expressed in mmol H2O·m -2 ·s -1 Data was collected continuously from each leaf for 10 minutes, with a sampling interval of 5 seconds, resulting in approximately 120 sets of continuous monitoring data. Five replicates were set for each sample, and the replicate data were averaged to improve parameter stability. During monitoring, the humidity in the leaf chamber was controlled in real time, maintaining it within 60% ± 3% to prevent deviations in transpiration rate caused by humidity changes. Samples with different inducible oligosaccharide ratios may exhibit different stomatal opening degrees during monitoring; for example, samples with a higher ratio of chitosan oligosaccharide to seaweed oligosaccharide may exhibit higher stomatal conductance, thereby increasing the net photosynthetic rate.

[0021] Functional leaves were selected from each sample, and the leaves underwent dark adaptation for approximately 20 minutes to fully open the photosystem II reaction centers, thereby obtaining an accurate initial fluorescence value F0. Subsequently, a saturated light pulse with an intensity of approximately 8000 μmol·m⁻¹ was applied. -2·s -1 The fluorescence intensity was monitored for approximately 0.8 s to obtain the maximum fluorescence value Fm. The variable fluorescence Fv was calculated using the difference between F0 and Fm, and the maximum photochemical efficiency parameter Fv / Fm was further calculated. Fv / Fm is typically used to evaluate the potential activity of photosystem II. Leaf fluorescence changes were then monitored under stable light conditions, with the light intensity set at 600 μmol·m⁻¹. -2 ·s -1 Simultaneously, the steady-state fluorescence value Fs and the maximum fluorescence value Fm′ under light-adapted conditions were recorded. These parameters can be used to further calculate the actual photochemical efficiency ΦPSII, electron transport rate ETR, and non-photochemical quenching coefficient NPQ. The ETR calculation needs to consider the leaf absorbance, which is generally taken as 0.84, while the photosystem II energy distribution coefficient is taken as 0.5. Each sample was measured five times repeatedly, and the leaf temperature at each measurement was recorded, maintained within the range of 25±1 ℃. Different inducible ratios may have different effects on the stability of photosystem II. For example, samples with higher concentrations of sodium salicylate and chitosan oligosaccharide usually maintain a higher Fv / Fm value, enabling photosystem II to maintain higher photochemical efficiency under adverse conditions.

[0022] The monitoring period was set to 7 consecutive days, with data collected daily at fixed times: 09:30, 13:30, and 17:00 to reflect the changing trends of photosynthesis at different time stages. At each time point, parameters such as net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), maximum photochemical efficiency (Fv / Fm), actual photochemical efficiency (ΦPSII), and electron transport rate (ETR) were recorded and stored using an automatic data recording module. To reduce systematic bias between different collection times, the raw data needed to be normalized. First, outlier screening was performed on the collected data; any parameter value deviating from the average by more than ±3 standard deviations was removed. Then, a moving average method was used to smooth the continuous data, with the sliding window width set to 5 sampling points to reduce the impact of random fluctuations on the overall trend. Finally, different parameters were normalized, converting all indicators to a uniform numerical range of 0 to 1, making different types of parameters comparable. At the same time, a unified data identification method is established. For example, S1-T1 represents the data record of the first sample at the first time node, and S4-T3 represents the data record of the fourth sample at the third time node.

[0023] Based on the diurnal variation of rice photosynthesis, daily data were divided into three functional phases: the morning photosynthetic enhancement phase, the midday photoinhibition phase, and the afternoon recovery phase. Data at 09:30 was assigned to the photosynthetic enhancement phase, data at 13:30 to the photoinhibition phase, and data at 17:00 to the recovery phase. Subsequently, with a 3-day analysis period, the data from seven consecutive days were divided into two complete periods and one supplementary period. Within each period, parameters such as net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), Fv / Fm, and electron transport rate (ETR) were arranged chronologically to form a multidimensional photosynthetic response sequence. For example, the response sequence for one period can be represented as {Pn1, Pn2, Pn3, Gs1, Gs2, Gs3, Tr1, Tr2, Tr3, Fv / Fm1, Fv / Fm2, Fv / Fm3…}. During sequence construction, it is also necessary to calculate the mean, standard deviation, and rate of change of each parameter within the period to reflect the differences in photosynthetic stability of samples with different inducible oligosaccharide ratios. For example, when the ratio of seaweed oligosaccharide and chitosan oligosaccharide is high, some samples show a smaller decrease in Pn during the midday light inhibition phase, thus exhibiting stronger stress resistance.

[0024] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Calculate the standard acquisition timestamp of the response sequence; Based on the standard collection timestamp, time-series statistical modeling was performed to construct photosynthetic response curves for multiple samples. Extract the photosynthetic response curve of the first sample; calculate the trend of photosynthetic rate change, the fluctuation range of photosynthetic efficiency and the characteristics of the inflection point of the growth stage of the photosynthetic response curve of the first sample, and construct a growth baseline curve. Based on the growth baseline curve, the deviation of other sample curves is calculated to obtain the photosynthetic efficiency deviation of other samples. Low-efficiency samples are eliminated based on the photosynthetic efficiency deviation to obtain the response curves of candidate samples.

[0025] In this embodiment, time information is extracted from all response sequences based on the time nodes recorded during the initial data collection. Each sample's photosynthetic parameters correspond to a specific collection time, such as three fixed time nodes: 09:30, 13:30, and 17:00, along with the specific date information. This time information is converted into continuous time identifiers. For example, the collection start date is used as the zero point, and subsequent times are continuously accumulated in hours or minutes to form a unified time coordinate system. For instance, 09:30 on day 1 is defined as T0=0 h, 13:30 on day 1 as T1=4 h, 17:00 on day 1 as T2=7.5 h, and 09:30 on day 2 as T3=24 h, and so on, forming a continuous time series. To improve the consistency of the time data, minor time deviations that may exist in each sample need to be corrected. For example, when the deviation between the collection time and the standard time node is less than ±2 minutes, it can be mapped to the standard time node using a time alignment method. Subsequently, the time information of all samples is recorded as a standard collection timestamp in a unified format and bound to the corresponding photosynthetic parameter data. To ensure the stability of subsequent analysis, it is also necessary to perform a completeness check on the time series. When data is missing at a certain time point, it can be filled in by interpolation of nearby time points, such as linear interpolation or three-point interpolation, so as to maintain the continuity of the time series.

[0026] Data on net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and chlorophyll fluorescence parameters collected at each time point for each sample are sorted according to standard timestamps to form a complete time-series data structure. These time-series data are then smoothed to reduce the impact of random fluctuations on the overall trend. Smoothing can be performed using a moving average method or a locally weighted regression method, with the sliding window width set to 3–5 sampling points. For example, under continuous 7-day monitoring conditions, the entire sequence contains 21 time points, and a moving average method with a window length of 5 can be used to smooth the Pn data. Next, a photosynthetic response curve is constructed using the timestamp as the x-axis and the photosynthetic parameter values ​​as the y-axis through curve fitting. Curve fitting can use cubic spline functions or polynomial regression methods to ensure the curve remains continuously changing across time points. A corresponding photosynthetic response curve is constructed for each sample; for example, samples S1, S2, S3, S4, and S5 each generate independent Pn and fluorescence response curves. During the modeling process, the first-order rate of change of the curve can also be calculated to reflect the trend of photosynthetic rate changes over time. For example, when Pn increases by more than 2 μmol CO2·m between two consecutive time points. -2 ·s -1 At this point, it can be determined as a stage of significantly enhanced photosynthesis.

[0027] Trend analysis was performed on the net photosynthetic rate curve of the first sample. The trend of photosynthetic rate change was obtained by calculating the first derivative of the curve at each time point. When the Pn value showed a continuous upward trend over a continuous time interval and the growth rate was greater than 0.5 μmol CO2·m⁻¹, the trend was considered positive. -2 ·s -1 ·h -1 When the growth rate reaches a certain value, it can be determined as the photosynthetic enhancement stage; when the growth rate approaches 0, it indicates the photosynthetic stability stage. The fluctuation range of photosynthetic efficiency is then calculated, which can be obtained by the difference between the maximum and minimum values ​​of Pn. For example, in a certain period, the maximum value of Pn is 24 μmol CO2·m⁻¹. -2 ·s -1 The minimum value is 16 μmol CO2·m -2 ·s -1 The fluctuation range is 8 μmol CO2·m -2 ·s -1 Next, the inflection point identification method is used to determine the inflection point of the growth stage. By calculating the second derivative of the curve, when the second derivative changes from positive to negative or from negative to positive, it indicates that the curve has reached an inflection point of the growth stage. For example, if a significant inflection point appears around T=48 h, this time point can be considered the inflection point where photosynthetic capacity transitions from a rapid growth stage to a stable stage. The above-mentioned trends in photosynthetic rate changes, efficiency fluctuations, and inflection point characteristics are comprehensively analyzed, and a standardized growth baseline curve is generated using curve fitting methods.

[0028] The photosynthetic response curves of samples two through five were time-aligned with the baseline curve according to the standard acquisition timestamp, ensuring that each curve had corresponding data points at the same time node. Then, the difference between the sample curve and the baseline curve was calculated at each time node. For example, when the baseline curve Pn value was 20 μmol CO2·m⁻¹ at a certain time point... -2 ·s -1 The corresponding value for a certain sample was 23 μmol CO2·m -2 ·s -1 The difference is then +3 μmol CO2·m -2 ·s -1 To obtain the overall deviation, it is necessary to statistically calculate the differences at all time points, which can be done using the mean square deviation method or the average deviation method. For example, the root mean square deviation can be obtained by calculating the average of the squares of the differences at each time point and then taking the square root. This indicator can comprehensively reflect the overall change in the photosynthetic efficiency of a sample relative to the baseline curve. When the mean square deviation of a sample is 2.5 μmol CO2·m -2 ·s -1When the value is within a certain range, it indicates that the overall photosynthetic efficiency is approximately 2.5 units higher or lower than the baseline curve. Simultaneously, the positive and negative deviation ratios can be calculated to determine whether the overall photosynthetic capacity of the sample has increased or decreased.

[0029] Set a photosynthetic efficiency screening threshold, for example, when the mean square deviation is negative and the absolute value is greater than 3 μmol CO2·m -2 ·s -1 When the inductive response is not met, the sample is considered low-efficiency. This indicates that the overall photosynthetic capacity of such samples is significantly lower than the baseline level without inducibles, and they may not possess good stress-resistance regulation. Subsequently, all samples are evaluated individually. Samples meeting the low-efficiency criteria are removed from the analysis dataset, and their inducible ratio information is recorded. For samples not removed, their photosynthetic response curves are marked as candidate response curves. For example, when the deviations of the second and third samples are +1.8 and +2.3 μmol CO2·m⁻¹, respectively... -2 ·s -1 When the photosynthetic efficiency is higher than the baseline curve, it indicates that the photosynthetic efficiency has improved, and therefore it can be retained as a candidate sample. To further improve the accuracy of screening, photosynthetic efficiency stability indicators can be combined, such as calculating the standard deviation of the curve. When the standard deviation is less than 2 μmol CO2·m -2 ·s -1 This indicates that the curve fluctuates relatively little and has good stability. Ultimately, the sample curves that simultaneously meet the conditions of "positive deviation of photosynthetic efficiency and good stability" are determined as candidate sample response curves.

[0030] In this embodiment, step S3 includes the following steps: The photosynthetic response characteristics of the candidate samples were analyzed, and the growth stages were identified to obtain the growth stage nodes of different curves; the growth stage nodes include the tillering stage, jointing stage, heading stage, and grain-filling stage. The growth intervals for different samples are obtained by dividing the growth stage nodes. Based on the growth interval, the response curve is mapped to obtain the growth stage curve.

[0031] In this embodiment, the net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and electron transport rate (ETR) curves of candidate samples are analyzed simultaneously, and a unified time series is established using the standard acquisition timestamp as the abscissa. Subsequently, the Pn curve is analyzed for trends, and the stage of photosynthetic capacity growth or decline is determined by calculating the rate of change between consecutive time points. When the Pn rate of change is consistently greater than 0.4 μmol CO2·m -2 ·s -1 ·h -1When the rate of change approaches 0 and the curve becomes stable, it indicates the start of a period of accelerated growth; when the rate of change approaches 0 and the curve becomes relatively flat, it indicates the start of a stable growth phase. Then, the changes in stomatal conductance and transpiration rate are combined to further assess the level of photosynthetic activity. For example, when the Gs value gradually increases to 0.35–0.45 mol H₂O·m⁻¹, the activity of photosynthesis is further assessed. -2 ·s -1 The range and the Tr value remained between 5 and 6 mmol H2O·m -2 ·s -1 When the range is defined, it typically corresponds to the rapid growth stage of tillering. Then, the turning points of different stages are identified using a curve inflection point detection method. This can be done by calculating the second-order rate of change of the curve; when the second-order rate of change changes from positive to negative or from negative to positive, it is determined as a growth stage inflection point. Based on the typical physiological characteristics of rice, the identified nodes are mapped to the tillering stage, jointing stage, booting stage, and grain-filling stage, respectively. For example, when Pn changes from rapid growth to stable growth and the ETR reaches approximately 120–140 μmol electrons·m -2 ·s -1 When the tillering stage transitions to the jointing stage, it can be determined that the tillering stage has ended; when Pn reaches a stage peak and Gs decreases to approximately 0.28 mol H2O·m -2 ·s -1 When this occurs, it can be identified as the start of the spikelet incubation period.

[0032] The four stages—tillering, jointing, booting, and grain-filling—are arranged chronologically and marked on a standard sampling timeline. For example, if the tillering stage occurs around T=72 h, the jointing stage around T=144 h, the booting stage around T=216 h, and the grain-filling stage around T=288 h, these time points can be used as boundaries for interval division. Growth intervals are then constructed based on the time range between adjacent stages. For example, T0–T1 is the tillering stage interval, T1–T2 is the jointing stage interval, T2–T3 is the booting stage interval, and T3–T4 is the grain-filling stage interval. During interval division, it is also necessary to correct for stage differences between different samples, as different inducing hormone ratios may lead to earlier or later growth stages. For example, in some samples under higher concentrations of seaweed oligosaccharides, the Pn growth rate is faster, which may cause the jointing stage to appear approximately 6–12 h earlier. To ensure consistency in interval division, a time standardization method can be used to map the time axis of each sample proportionally to a uniform growth cycle range. For example, the complete growth cycle can be uniformly normalized to the interval of 0 to 1, and each stage node can be mapped to standard positions such as 0.25, 0.50, and 0.75. This interval division method ensures that the photosynthetic response curves of different samples are comparable at the same growth stage.

[0033] Based on the growth intervals obtained in the previous step, the original photosynthetic response curves are segmented according to the interval boundaries. For example, the Pn time series is divided into four intervals: tillering stage, jointing stage, booting stage, and grain-filling stage, with each interval forming an independent data subsequence. Then, interval mapping is performed on each subsequence, that is, the time axis within the interval is remapped to a unified standard interval. For example, the tillering stage interval is mapped to the 0–0.25 time range, the jointing stage to the 0.25–0.50 interval, the booting stage to the 0.50–0.75 interval, and the grain-filling stage to the 0.75–1.00 interval. This ensures that the curves of different samples at each growth stage have a uniform time scale. Next, curve fitting is performed on the photosynthetic parameters within each interval. Cubic spline functions or locally weighted regression methods can be used to fit the Pn, Gs, and ETR data, ensuring that the curves maintain continuous variation within the interval. For example, within the tillering stage interval, when the Pn value changes from 12 μmol CO2·m -2 ·s -1 Gradually rise to 20 μmol CO2·m -2 ·s -1 At that time, the fitted curve could clearly reflect the trend of enhanced photosynthetic capacity; during the grouting period, when the Pn value gradually decreased to 15 μmol CO2·m -2 ·s -1 At this point, the curve shows a slow downward trend. Finally, the fitted curves of each interval are spliced ​​together in sequence to form a complete growth stage curve structure, while recording the average photosynthetic efficiency and rate of change of each interval. For example, the average Pn during the tillering stage can be calculated to be approximately 18 μmol CO2·m -2 ·s -1 The average Pn during the jointing stage is approximately 22 μmol CO2·m -2 ·s -1 .

[0034] In this embodiment, step S4 includes the following steps: The growth baseline curve is differentially calculated based on the growth stage curve, and the differential parameters for different growth stages are marked. Based on the differential parameters, inducible antigen gain analysis was performed to obtain inducible antigen gain coefficients at different stages; Based on the inducing agent gain coefficient, a multi-sample sensitivity analysis was performed to obtain the inducing agent sensitivity values ​​for different samples.

[0035] In this embodiment, the growth stage curves of the candidate samples are aligned with the baseline curve in four intervals: tillering stage, jointing stage, heading stage, and grain-filling stage, ensuring that the two curves have corresponding data points on the same time scale. For example, the normalized time axis interval 0-1 corresponds to the four stages, and curve parameter values ​​are extracted at fixed time intervals (e.g., 0.02 time units) within each stage. Then, at each time node, the difference between the candidate sample curve and the baseline curve is calculated. The difference calculation formula is ΔPn = Pn(sample) - Pn(base), where Pn(sample) represents the net photosynthetic rate of the candidate sample at that time node, and Pn(base) represents the photosynthetic rate of the corresponding node of the baseline curve. A complete difference sequence can be formed through continuous calculation. Next, the difference data within each growth stage are statistically summarized, and the stage average difference value, maximum difference value, and difference change rate are calculated. For example, in the tillering stage, if the average Pn value of a sample is 20 μmol CO2·m -2 ·s -1 The average value of the baseline curve was 18 μmol CO2·m -2 ·s -1 The stage average difference is then +2 μmol CO2·m -2 ·s -1 The difference sequence is then labeled with parameters, using the average difference, peak difference, and difference fluctuation amplitude at each stage as difference parameters. These parameters can be labeled as ΔPn_mean, ΔPn_max, and ΔPn_var, for example.

[0036] The average difference values ​​for each sample at different growth stages were normalized to eliminate the influence of differences in baseline photosynthetic rates at different stages. Normalization can be performed using a proportionality coefficient method, which involves dividing the difference value by the average Pn value of the baseline curve for the corresponding stage. For example, at the jointing stage, if the average Pn value of the baseline curve is 22 μmol CO2·m⁻¹, then... -2 ·s -1 The mean difference for a certain sample is +3 μmol CO2·m -2 ·s -1 The normalized gain ratio is approximately 0.136. This ratio is then defined as the inducible phenotype gain coefficient, representing the intensity of the inducible phenotype ratio's effect on photosynthetic capacity at this stage. To further improve the accuracy of the gain analysis, auxiliary parameters such as electron transport rate (ETR) and stomatal conductance (Gs) can also be considered. For example, when Pn increases at a certain stage, the increase in ETR reaches 10–15 μmol electrons·m⁻¹. -2 ·s -1 And Gs remained at 0.32–0.40 mol H2O·m -2 ·s-1 When the interval is defined, the photosynthetic gain at that stage can be considered to have high stability. Next, the gain coefficients for each stage are statistically analyzed to form an inducing hormone stage gain matrix. For example, G = {G1, G2, G3, G4} represents the gain coefficients for the tillering, jointing, heading, and grain-filling stages, respectively. By comparing the gain coefficients of different samples, it can be found that certain inducing hormone ratios have a more significant promoting effect at specific growth stages.

[0037] The stage gain coefficients of all candidate samples were compiled to construct a multi-sample gain coefficient matrix. For example, each row in the matrix represents a sample, and each column represents the gain coefficient of a growth stage. The mean and standard deviation of the gain coefficient for each sample at each stage were then calculated to assess the overall response and stability of the sample to inducible hormones. To quantify the sensitivity of a sample to changes in inducible hormone ratios, a sensitivity index S can be calculated, which is the ratio between the change in stage gain coefficient and the change in inducible hormone concentration. For example, if a sample's gain coefficient at the jointing stage increases from 0.08 to 0.15 when the chitosan oligosaccharide concentration increases from 0.30% to 0.60%, then the sensitivity index is approximately 0.07 / 0.30 ≈ 0.23. The higher this value, the more sensitive the sample is to changes in inducible hormone concentration. The sensitivity indices for each stage were then weighted and averaged. Higher weights were assigned to the jointing and booting stages (e.g., 0.35 and 0.30), while lower weights were assigned to the tillering and grain-filling stages (e.g., 0.20 and 0.15), as the mid-stages have a more significant impact on yield formation. The weighted average yielded a comprehensive inducing sensitivity value for each sample. For example, a comprehensive sensitivity value of 0.18 for a sample indicates a high responsiveness to changes in the inducing ratio.

[0038] In this embodiment, the specific steps of step S5 are as follows: Based on the inducing sensitivity values ​​of different samples, multiple sample ratio difference analysis was performed to obtain the inducing ratio difference; Based on the differences in inducible antigen ratios, a combination effect analysis was performed to generate photosynthetic gain data for the ratio parameters. Based on the photosynthetic gain data, the ratio of inducible hormone combinations is iteratively calculated to obtain the inducible hormone ratio scheme. Risk screening is performed on the inducing agent ratio scheme to output the optimal dosage ratio.

[0039] In this embodiment, the composition parameters of the inducing agents in all samples are compiled, including the mass concentration ratios of 14-hydroxybrassinosteroidol, sodium salicylate, chitosan oligosaccharide, seaweed oligosaccharide, and surfactant, and these parameters are correlated with their corresponding sensitivity values. For example, a sample parameter matrix can be constructed, where each row represents a sample and each column represents the concentration ratio of an inducing agent component, such as 0.01%, 0.02%, 0.03%, 0.04%, etc. Simultaneously, the inducing agent sensitivity values ​​of each sample are used as response indicators for correlation analysis. Subsequently, the differences in the ratio parameters between different samples are calculated, which can be achieved using parameter difference methods or ratio difference analysis methods. For example, for chitosan oligosaccharide concentration, multiple gradient changes occur between 0.30% and 1.00%. By calculating the concentration differences between different samples and combining them with the sensitivity value changes, it is determined which ratio changes have a more significant impact on photosynthetic response. For example, when the chitosan oligosaccharide concentration increases from 0.30% to 0.60%, the sensitivity value increases from 0.12 to 0.18, indicating that this concentration range has a significant photosynthetic promoting effect. Subsequently, multidimensional differential statistics were performed on each inducing component to calculate the correlation between the change gradient of each component and the change in sensitivity value. For example, the intensity of the influence was determined by calculating the correlation coefficient or the change ratio. When a 0.01% change in the concentration of a certain component can cause a change in sensitivity value exceeding 0.02, it can be determined that the component has a high regulatory ability on photosynthetic response.

[0040] Based on the ratio differences obtained in the previous step, the various inducing components are combined and arranged according to different concentration gradients. For example, the concentrations of 14-hydroxybrassinosteroidol can be set in the range of 0.01%–0.04%, sodium salicylate in the range of 0.20%–0.50%, chitosan oligosaccharide in the range of 0.30%–1.00%, and seaweed oligosaccharide in the range of 0.50%–1.50%, constructing multiple combination parameter groups within this range. Subsequently, these combination parameters are correlated with the previously obtained photosynthetic response data, focusing on analyzing the changes in net photosynthetic rate (Pn), electron transport rate (ETR), and stomatal conductance (Gs). For example, in a certain combination, when the Pn value increases by more than 3 μmol CO2·m compared to the baseline curve... -2 ·s -1 Meanwhile, ETR increases by 10 μmolelectrons·m -2 ·s -1 Based on the above, it can be determined that the combination has a significant photosynthetic enhancement effect. Next, a combination effect analysis method is used to determine the synergistic or antagonistic relationship between different inducing hormone components. For example, by calculating the comparison between the photosynthetic enhancement after combination and the enhancement of individual components, a synergistic effect can be considered to exist when the combined enhancement is greater than 80% of the sum of the enhancements of individual components. Subsequently, the photosynthetic enhancement of each combination at different growth stages is statistically analyzed to form a photosynthetic enhancement dataset.

[0041] The photosynthetic gain data obtained in the previous step was organized into a multidimensional parameter table, which includes the concentration of each inducible component and its corresponding photosynthetic gain index, such as Pn enhancement value, ETR enhancement value, and comprehensive gain index. Then, these data were iteratively optimized. Specifically, the comprehensive photosynthetic gain index was used as the optimization target, and the concentration ratio of each component was gradually adjusted. For example, the concentration of 14-hydroxybrassinosteroidol was initially fixed in the range of 0.02%–0.03%, and then gradually adjusted within the ranges of sodium salicylate (0.30%–0.40%), chitosan oligosaccharide (0.60%–0.80%), and seaweed oligosaccharide (0.80%–1.20%). After each adjustment, the photosynthetic gain index of the combination was recalculated. When the gain index increased by more than 5% compared to the previous combination, the parameters of that combination were retained as the new iterative baseline. Further refinement was then performed around this combination, for example, adjusting the chitosan oligosaccharide concentration to subdivided gradients such as 0.70%, 0.75%, and 0.80%, and comparing the changes in photosynthetic gain. When the gain exponent changes by less than 2% between two consecutive iterations, the relatively stable optimal region can be considered reached. Through this step-by-step iterative calculation, the optimal combination ratio can be gradually approximated. For example, a relatively optimal range may be obtained during the iteration process: approximately 0.03% 14-hydroxybrassinosteroidol, approximately 0.38% sodium salicylate, approximately 0.75% chitosan oligosaccharide, and approximately 1.10% seaweed oligosaccharide.

[0042] The photosynthetic response stability of candidate formulations at different growth stages can be evaluated by calculating the standard deviation of the photosynthetic rate Pn. For example, when the standard deviation of Pn for a certain formulation across four growth stages is less than 2 μmol CO2·m -2 ·s -1 When the concentration of each component is within a certain range, its photosynthetic stability can be considered good. Secondly, the safety range of each component concentration should be assessed. For example, 14-hydroxybrassinosteroids are typically controlled within the range of 0.01%–0.05%, and sodium salicylate within the range of 0.20%–0.50%. If any component exceeds this range, it may cause overstimulation and should be eliminated. Subsequently, the physiological response risk of the combination needs to be assessed. For example, if the stomatal conductance Gs is consistently higher than 0.45 mol H2O·m -2 ·s -1At this rate, excessive transpiration may occur, affecting water balance. Therefore, the proportion of sodium salicylate or seaweed oligosaccharides needs to be reduced. Next, all candidate proportions are scored and ranked based on photosynthetic gain index, stability index, and safe concentration range. For example, a comprehensive scoring model can be set, with photosynthetic gain weighting 0.5, stability weighting 0.3, and safety weighting 0.2. The proportion with the highest comprehensive score is selected as the optimal dosage ratio. For example, the final output proportion might be: approximately 0.03% 14-hydroxybrassinosteroidol, approximately 0.35%–0.40% sodium salicylate, approximately 0.70%–0.80% chitosan oligosaccharides, approximately 1.00%–1.20% seaweed oligosaccharides, and approximately 0.25%–0.30% surfactant.

[0043] In this embodiment, the specific steps for risk screening of the inducing agent ratio scheme and outputting the optimal dosage ratio are as follows: Simulations were conducted to verify the inducing agent ratio scheme, generating simulation data for multiple schemes. The photosynthetic response stability is calculated based on the simulation data to obtain the photosynthetic response stability. Risk screening is performed based on photosynthetic response stability to output the optimal dosage ratio.

[0044] In this embodiment, the inducing agent ratio parameters obtained in the previous step are used as input variables, including the mass concentration ratios of 14-hydroxybrassinosteroidol, sodium salicylate, chitosan oligosaccharide, seaweed oligosaccharide, and surfactant. For example, several candidate ratio combinations can be set, such as Scheme A (0.03%, 0.35%, 0.70%, 1.00%, 0.25%), Scheme B (0.03%, 0.40%, 0.75%, 1.10%, 0.30%), and Scheme C (0.02%, 0.38%, 0.80%, 1.20%, 0.25%). Subsequently, using the previously established photosynthetic response model, each ratio parameter is input into the photosynthetic response prediction model to predict and calculate the net photosynthetic rate Pn, electron transport rate ETR, stomatal conductance Gs, and transpiration rate Tr at different growth stages. During the simulation, uniform environmental parameters need to be set, such as a light intensity of 800 μmol·m⁻¹. -2 ·s -1 The ambient temperature was maintained at 28±2 ℃, and the air CO2 concentration was maintained at 400±10 μmol·mol⁻¹. -1The relative humidity was maintained at 60%–70%. The rice growth cycle was divided into four stages: tillering, jointing, booting, and grain filling. Several time points were set for each stage, for example, five time points were selected for simulation calculations. Through model calculations, photosynthetic parameters such as Pn, ETR, and Gs corresponding to each ratio at different time points can be obtained, thus forming complete simulated photosynthetic response data. For example, Pn for a certain ratio might remain at 22–25 μmol CO2·m⁻¹ during the jointing stage. -2 ·s -1 The concentration ranges from 16 to 18 μmol CO2·m during the grouting stage. -2 ·s -1 .

[0045] The Pn, ETR, and Gs parameters for each formulation scheme at different time points were compiled and grouped according to growth stage. For example, data from the five time points of the tillering stage were used as one stage dataset, and corresponding datasets were created for the jointing, heading, and grain-filling stages. Subsequently, statistical analysis was performed on the Pn values ​​for each stage, calculating the mean and standard deviation. For example, the average Pn value for a certain scheme at the jointing stage was 23 μmol CO2·m -2 ·s -1 The standard deviation is 1.2 μmol CO2·m -2 ·s -1 If the photosynthetic response fluctuates relatively little during this stage, then the stability index is calculated. This index is the ratio of the stage mean Pn value to the standard deviation, or expressed using the coefficient of variation (CV). The coefficient of variation is calculated by dividing the standard deviation by the mean. For example, when the CV value is less than 0.08, it indicates that the photosynthetic response is relatively stable during this stage. Subsequently, the same stability calculations are performed on the ETR and Gs parameters to ensure that the electron transport and stomatal regulation processes in photosynthesis also remain stable. For example, when the ETR is maintained at 120–135 μmol electrons·m during the heading stage... -2 ·s -1 The range is within the range and the fluctuation range is less than ±6 μmol electrons·m -2 ·s -1 When the electron transport process is stable, it can be determined that the stability index of each stage is stable. Finally, the stability index of each stage is weighted and combined. Usually, the weight of the jointing stage and the heading stage is set to be relatively high (e.g., 0.35 and 0.30), and the weight of the tillering stage and the grain filling stage is relatively low (e.g., 0.20 and 0.15), so as to calculate the comprehensive photosynthetic response stability index of each ratio scheme.

[0046] Screening thresholds were set based on stability indicators. For example, if the coefficient of variation corresponding to the comprehensive stability index was greater than 0.10, it indicated that the photosynthetic response of the formulation fluctuated significantly at different stages, potentially posing a risk to physiological regulation, and therefore the formulation needed to be eliminated. Further risk assessments were then conducted on the remaining formulations, focusing on analyzing changes in photosynthetic response under extreme conditions. For instance, during the heading stage, if a certain formulation resulted in stomatal conductance Gs consistently exceeding 0.45 mol H2O·m... -2 ·s -1 This could lead to excessively high transpiration rates and affect water use efficiency; therefore, this approach requires reducing the proportion of sodium salicylate or seaweed oligosaccharides. Secondly, the stable range of the net photosynthetic rate (Pn) needs to be examined, for example, when Pn remains at 20–25 μmol CO2·m⁻¹ during the critical phase. -2 ·s -1 The range and the fluctuation amplitude is less than ±2 μmol CO2·m -2 ·s -1 When the ratio is considered to have good photosynthetic stability, it can be assumed that the photosynthetic gain index, stability index, and safety range parameter are comprehensively scored. For example, a weight of 0.45 for photosynthetic gain, 0.35 for stability, and 0.20 for safety range are set. By scoring and ranking the various ratio schemes, the ratio with the highest comprehensive score is selected as the final optimal scheme. For example, the final determined optimal dosage ratio may be: approximately 0.03% 14-hydroxybrassinosteroidol, approximately 0.36%–0.40% sodium salicylate, approximately 0.70%–0.80% chitosan oligosaccharide, approximately 1.00%–1.10% seaweed oligosaccharide, and approximately 0.25%–0.30% surfactant.

[0047] In this embodiment, a plant growth inducing agent dosage regulation system based on sample analysis is provided for executing the plant growth inducing agent dosage regulation method based on sample analysis as described above, including: The monitoring module is used to collect rice samples from multiple cultivation environments to monitor photosynthesis and obtain response sequences over multiple cycles. The elimination module is used to eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; The stage identification module is used to identify the growth stage of the response curve of the candidate sample and obtain the growth stage curve. The gain analysis module is used to perform inducing hormone gain analysis based on the growth stage curve to obtain the inducing hormone sensitivity values ​​of different samples. The iterative calculation module is used to perform iterative calculation of the ratio of inducing agents and risk screening based on the inducing agent sensitivity values ​​of different samples, and output the optimal dose ratio.

[0048] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0049] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for regulating plant growth inducing hormone dosage based on sample analysis, characterized in that, Includes the following steps: Step S1: Collect rice samples from multiple cultivation environments, monitor photosynthesis, and obtain response sequences for multiple cycles; Step S2: Eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; Step S3: Identify the growth stage of the response curves of the candidate samples to obtain the growth stage curves; Step S4: Perform inducing agent gain analysis based on the growth stage curve to obtain the inducing agent sensitivity values ​​of different samples; Step S5: Based on the inducing sensitivity values ​​of different samples, perform iterative calculation of the inducing combination ratio and risk screening, and output the optimal dose ratio.

2. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 1, characterized in that, The specific steps of step S1 are as follows: Rice samples were collected from multiple cultivation environments; The photosynthetic response of the rice samples was monitored, and photosynthetic response parameters were extracted; the photosynthetic response parameters included net photosynthetic rate, chlorophyll fluorescence parameters, stomatal conductance, and transpiration rate data. The photosynthetic response parameters were continuously acquired over time and divided into periods to obtain response sequences for multiple periods.

3. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 2, characterized in that, The rice samples from multiple cultivation environments specifically include: the first sample, the second sample, the third sample, the fourth sample, and the fifth sample. The first sample was a sample without added inducing agents; The second sample was a sample with added inducing agents, with the following concentration ratios: 0.01% 14-hydroxybrassinosteroidol, 0.20% sodium salicylate, 0.30% chitosan oligosaccharide, 0.50% seaweed oligosaccharide, and 0.30% surfactant; The third sample was a sample with added inducing agents, with the following concentration ratios: 0.02% 14-hydroxybrassinosteroidol, 0.30% sodium salicylate, 0.60% chitosan oligosaccharide, 1.00% seaweed oligosaccharide, and 0.30% surfactant. The fourth sample was a sample with added inducing agents, with the following concentration ratios: 0.03% 14-hydroxybrassinosteroidol, 0.40% sodium salicylate, 0.80% chitosan oligosaccharide, 0.80% seaweed oligosaccharide, and 0.25% surfactant. The fifth sample was a sample with added inducing agents, with the following concentration ratios: 0.04% 14-hydroxybrassinosteroidol, 0.50% sodium salicylate, 1.00% chitosan oligosaccharide, 1.50% seaweed oligosaccharide, and 0.30% surfactant.

4. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 1, characterized in that, The specific steps of step S2 are as follows: Calculate the standard acquisition timestamp of the response sequence; Based on the standard collection timestamp, time-series statistical modeling was performed to construct photosynthetic response curves for multiple samples. Extract the photosynthetic response curve of the first sample; calculate the trend of photosynthetic rate change, the fluctuation range of photosynthetic efficiency and the characteristics of the inflection point of the growth stage of the photosynthetic response curve of the first sample, and construct a growth baseline curve. Based on the growth baseline curve, the deviation of other sample curves is calculated to obtain the photosynthetic efficiency deviation of other samples. Low-efficiency samples are eliminated based on the photosynthetic efficiency deviation to obtain the response curves of candidate samples.

5. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 1, characterized in that, Step S3 is as follows: The photosynthetic response characteristics of the candidate samples were analyzed, and the growth stages were identified to obtain the growth stage nodes of different curves; the growth stage nodes include the tillering stage, jointing stage, heading stage, and grain-filling stage. The growth intervals for different samples are obtained by dividing the growth stage nodes. Based on the growth interval, the response curve is mapped to obtain the growth stage curve.

6. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 1, characterized in that, The specific steps of step S4 are as follows: The growth baseline curve is differentially calculated based on the growth stage curve, and the differential parameters for different growth stages are marked. Based on the differential parameters, inducible antigen gain analysis was performed to obtain inducible antigen gain coefficients at different stages; Based on the inducing agent gain coefficient, a multi-sample sensitivity analysis was performed to obtain the inducing agent sensitivity values ​​for different samples.

7. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 1, characterized in that, The specific steps of step S5 are as follows: Based on the inducing sensitivity values ​​of different samples, multiple sample ratio difference analysis was performed to obtain the inducing ratio difference; Based on the differences in inducible antigen ratios, a combination effect analysis was performed to generate photosynthetic gain data for the ratio parameters. Based on the photosynthetic gain data, the ratio of inducible hormone combinations is iteratively calculated to obtain the inducible hormone ratio scheme. Risk screening is performed on the inducing agent ratio scheme to output the optimal dosage ratio.

8. The method for regulating plant growth inducing hormone dosage based on sample analysis according to claim 7, characterized in that, The specific steps for risk screening of the inducer ratio scheme and outputting the optimal dosage ratio are as follows: Simulations were conducted to verify the inducing agent ratio scheme, generating simulation data for multiple schemes. The photosynthetic response stability is calculated based on the simulation data to obtain the photosynthetic response stability. Risk screening is performed based on photosynthetic response stability to output the optimal dosage ratio.

9. A plant growth inducing agent dosage regulation system based on sample analysis, characterized in that, A method for performing plant growth inducing agent dosage regulation based on sample analysis as described in claim 1, comprising: The monitoring module is used to collect rice samples from multiple cultivation environments to monitor photosynthesis and obtain response sequences over multiple cycles. The elimination module is used to eliminate inefficient samples based on the response sequence to obtain the response curves of candidate samples; The stage identification module is used to identify the growth stage of the response curve of the candidate sample and obtain the growth stage curve. The gain analysis module is used to perform inducing hormone gain analysis based on the growth stage curve to obtain the inducing hormone sensitivity values ​​of different samples. The iterative calculation module is used to perform iterative calculation of the ratio of inducing agents and risk screening based on the inducing agent sensitivity values ​​of different samples, and output the optimal dose ratio.

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