LED multi-spectrum dynamic self-adaptive regulation method and system based on plant growth cycle

Abstract

The application discloses an LED multi-spectrum dynamic self-adaptive regulation method and system based on a plant growth cycle, relates to the technical field of plant cultivation light control, and comprises the following steps: acquiring leaf optical characteristics, photosynthetic response characteristics and growth morphological characteristic data of a target plant, identifying a current growth stage, calculating a physiological development rate, predicting a stage evolution track in a future time window, and obtaining a spectrum demand sequence; extracting multi-band photosynthetic contribution and establishing a coupling equation with light intensity, fitting to determine spectrum-photosynthetic response characteristic parameters; taking the predicted spectrum demand as an optimization target, taking the response characteristic parameters, LED output capacity and power conservation as constraint conditions, solving a multi-band light intensity distribution optimization problem, and generating driving current parameter control to control LED output. The application realizes intelligent spectrum regulation based on the real-time physiological state and future demand of plants, and improves light energy utilization efficiency and plant growth regulation precision.

Description

Technical Field

[0001] This invention relates to the field of light control technology for plant cultivation, specifically to a method and system for dynamic adaptive regulation of LED multispectral light based on the plant growth cycle. Background Technology

[0002] With the rapid development of facility agriculture and plant factories, artificial light sources are increasingly widely used in crop production. LED light sources, due to their advantages such as adjustable spectrum, high energy efficiency, and long lifespan, have become the mainstream choice for plant supplemental lighting and fully artificial light cultivation. However, existing LED plant lighting systems generally suffer from static spectral configurations, making it difficult to adapt to the dynamic changes in light environment requirements of plants at different growth stages.

[0003] Plants exhibit significantly different spectral requirements at different growth stages. For example, the vegetative growth stage requires a higher proportion of blue light to promote leaf development, while the reproductive growth stage requires an increased proportion of red and far-red light to promote flowering and fruiting. Traditional fixed spectral configurations cannot be dynamically adjusted according to the actual growth status of plants, resulting in low light energy utilization efficiency, energy waste, and an inability to achieve optimal plant growth control.

[0004] Currently, some studies attempt to adjust spectral configurations through preset time programs or simple growth stage divisions. However, these methods lack real-time perception of the individual plant's physiological state and cannot accurately grasp the actual developmental process of the plant. Existing methods often ignore the band specificity of plant photosynthetic response during spectral optimization, failing to establish a quantitative relationship between light intensity and photosynthetic efficiency in different bands, resulting in a lack of scientific basis for spectral configuration. Furthermore, the output capability limitations and total power constraints of multi-band LED light sources are often simplified during the optimization process, making it difficult to effectively implement the optimization results in practical systems.

[0005] Furthermore, most systems can only adjust the spectrum based on the current state, lacking the ability to predict the evolution trend of future growth stages. This results in a lag in spectral regulation, failing to prepare the light environment in advance for the plant's upcoming growth stage. This passive regulation strategy limits the full realization of the plant's growth potential and is also detrimental to the precise management of the production cycle. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for dynamic adaptive control of LED multispectral spectrum based on plant growth cycle, aiming to solve at least one of the technical problems existing in the prior art.

[0007] The technical solution of this invention is: a dynamic adaptive control method for LED multispectral spectrum based on plant growth cycle, comprising the following steps: Acquire leaf optical characteristics, photosynthetic response characteristics, and growth morphology characteristics of the target plant; The current growth stage is identified based on leaf optical feature data and growth morphology feature data. The physiological development rate is calculated based on the current growth stage and growth morphology feature data. The stage evolution trajectory within the future time window is predicted based on the physiological development rate, and the stage evolution spectral demand sequence is obtained. The photosynthetic contribution of multiple bands in the photosynthetic response characteristic data is extracted, and the coupling equation between the photosynthetic contribution of each band and the light intensity of the corresponding band is established. The coefficient parameters in the coupling equation are determined by fitting the relationship between the photosynthetic contribution and the light intensity of the band, and the spectral-photosynthetic response characteristic parameters are obtained. Taking the spectral demand corresponding to the future time window in the stage-evolution spectral demand sequence as the optimization objective, and taking the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints and total power conservation constraints as constraints, a constrained optimization problem of multi-band light intensity allocation is constructed and solved to obtain the target light intensity allocation scheme. The driving current parameters are generated according to the target light intensity allocation scheme, and the driving current parameters are sent to the LED light source to output the modulated spectrum.

[0008] The current growth stage is identified based on leaf optical and morphological data. The physiological development rate is calculated based on the current growth stage and morphological data. The stage evolution trajectory within a future time window is predicted based on the physiological development rate, resulting in a stage evolution spectral demand sequence including: Extract physiological state features from leaf optical and growth morphology data, and identify the current growth stage based on these features. The morphological parameter sequence of continuous time points is extracted from the growth morphological characteristic data. The measured developmental rate is obtained by the time differentiation of the morphological parameter sequence. The average developmental rate of the same growth stage is statistically analyzed from historical growth data based on the current growth stage. The physiological developmental rate is obtained by calculating the ratio of the measured developmental rate to the average developmental rate. Based on the current growth stage, the average duration of each subsequent growth stage is statistically analyzed from historical growth data. The predicted duration is calculated based on the average duration and the physiological development rate. Starting from the current moment, the predicted duration is accumulated sequentially to determine the start and end times of each growth stage, thus obtaining the stage evolution trajectory. Extract the identifiers and corresponding start and end times of each growth stage from the stage evolution trajectory, and extract the baseline value of photosynthetic rate from historical photosynthetic response characteristic data based on the identifiers. Based on the response relationship between light intensity and photosynthetic rate in each band of photosynthetic response characteristic data, the light intensity values ​​of each band that reach the baseline value of photosynthetic rate are calculated. The light intensity values ​​of each band are associated with the corresponding start and end times and arranged in chronological order to obtain the stage evolution spectral demand sequence.

[0009] The photosynthetic contribution of multiple bands in the photosynthetic response characteristic data is extracted, and a coupling equation between the photosynthetic contribution of each band and the corresponding light intensity is established. By fitting the relationship between the photosynthetic contribution and the light intensity of the band, the coefficient parameters in the coupling equation are determined, and the spectral-photosynthetic response characteristic parameters are obtained, including: Photosynthetic rate measurements of multiple bands are extracted from photosynthetic response characteristic data, and the photosynthetic contribution of each band is obtained by performing inter-band difference calculation on the photosynthetic rate measurements. Calculate the differential value of the photosynthetic contribution of each band with respect to the light intensity of the corresponding band. Based on the differential value, divide multiple bands into dominant bands and auxiliary bands. Multiply the light intensity of the band corresponding to the dominant band with the light intensity of the band corresponding to the auxiliary band to obtain the band product term. Multiply the light intensity of each band by itself and then calculate the ratio with the corresponding photosynthetic contribution to obtain the band ratio term. A coupled equation was established with light intensity, band product term and band ratio term of each band as independent variables and photosynthetic contribution of each band as dependent variable. Multiple sets of sample data of light intensity and photosynthetic contribution of each band were collected and substituted into the coupled equation to obtain the coefficient parameters corresponding to each independent variable. Normalize the coefficient parameters to obtain the normalized weights of each independent variable. Arrange the normalized weights in descending order of value and calculate the cumulative sum of each term. Determine the position corresponding to the first time the cumulative sum reaches the preset coverage ratio. Retain the independent variables and corresponding coefficient parameters before the position. Construct a simplified coupling equation based on the retained independent variables and corresponding coefficient parameters. Use the coefficient parameters in the simplified coupling equation as the spectral-photosynthetic response characteristic parameters.

[0010] Taking the spectral demand corresponding to the future time window in the stage-evolved spectral demand sequence as the optimization objective, and using the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints, and total power conservation constraints as constraints, a constrained optimization problem for multi-band light intensity allocation is constructed and solved, resulting in the following target light intensity allocation schemes: The spectral demand corresponding to the future time window is extracted from the spectral demand sequence of the stage evolution. The spectral demand of each band in the spectral demand is weighted and averaged in time order to obtain the expected light intensity value of each band. An objective function is constructed with the goal of minimizing the sum of squares of the deviation between the actual allocated light intensity of each band and the expected light intensity value of each band. Based on the spectral-photosynthetic response characteristic parameters, the expected photosynthetic contribution corresponding to the expected light intensity value of each band and the actual photosynthetic contribution corresponding to the actual distributed light intensity of each band are calculated, and the spectral-photosynthetic response characteristic parameter constraint condition is constructed with the inequality constraint that the actual photosynthetic contribution is not less than the expected photosynthetic contribution. Obtain the maximum and minimum output capability values ​​of each band of the LED light source, and construct the LED light source output capability constraint condition with the actual distributed light intensity of each band located between the corresponding minimum and maximum output capability values ​​as the range constraint. Obtain the system's preset total power value and construct a total power conservation constraint condition with the equation that the sum of the actual distributed light intensities of each band equals the total power value. Using the objective function as the optimization objective and the constraints of spectral-photosynthetic response characteristic parameters, LED light source output capability, and total power conservation as constraints, a constrained optimization problem for multi-band light intensity allocation is constructed. The constrained optimization problem is solved iteratively using a sequential quadratic programming algorithm, and the convergent value of the actual allocated light intensity of each band that minimizes the objective function is obtained as the target light intensity allocation scheme.

[0011] Based on the target light intensity allocation scheme, drive current parameters are generated, and the drive current parameters are sent to the LED light source to output a modulated spectrum, including: The target allocated light intensity value of each band is extracted from the target light intensity allocation scheme. Based on the mapping relationship between the light intensity and driving current of each band light-emitting chip at standard temperature, the initial driving current value of each band light-emitting chip is obtained by reverse lookup of the target allocated light intensity value of each band. The heating power of each light-emitting chip is determined based on the initial driving current value of each light-emitting chip. The operating temperature of each light-emitting chip is determined based on the relationship between the heating power of each light-emitting chip and the heat conduction between the chips. The light intensity attenuation of each light-emitting chip is determined based on the degree of temperature deviation between the operating temperature and the standard temperature. The compensation light intensity value for each band is determined based on the target light intensity value allocated to each band and the light intensity attenuation of the corresponding light-emitting chip in the band. Based on the mapping relationship between the light intensity and driving current of the light-emitting chip in each band at the standard temperature, the compensation light intensity value for each band is reverse-searched to obtain the compensation driving current value of the light-emitting chip in each band. The compensation drive current values ​​of each band of light-emitting chips are encapsulated to generate drive current parameters, which are then sent to the drive circuit of the LED light source. The drive circuit adjusts the operating current of each band of light-emitting chips according to the drive current parameters to output a modulated spectrum.

[0012] The operating temperature of each band of light-emitting chips is determined based on the relationship between the heat generation power of each chip and the thermal conductivity between them. A heat source distribution matrix is ​​constructed based on the heating power of each band of light-emitting chips, and a heat conduction coupling matrix is ​​constructed based on the spatial position relationship of each band of light-emitting chips on the substrate. The heat exchange flux distribution between each band of light-emitting chips is obtained through matrix operation of the heat source distribution matrix and the heat conduction coupling matrix. The temperature offset of each light-emitting chip is calculated based on the heat exchange flux distribution between the chips in each band. The temperature offset of each light-emitting chip is then dynamically corrected according to the time delay characteristics of the heat conduction path to obtain the transient temperature response value of each light-emitting chip. The current operating temperature of each light-emitting chip is calculated based on the transient temperature response value of each band and the reference temperature of the substrate. Update the current operating temperature of each band of light-emitting chips to the heat source distribution matrix, recalculate the heat exchange flux distribution between each band of light-emitting chips, and update the current operating temperature of each band of light-emitting chips. Repeat the update until the change in the current operating temperature of each band of light-emitting chip is less than the preset convergence threshold, and then use the current operating temperature of each band of light-emitting chip after convergence as the operating temperature of each band of light-emitting chip.

[0013] This invention provides an LED multispectral dynamic adaptive control system based on plant growth cycles, the system comprising: The data acquisition module is used to acquire leaf optical characteristic data, photosynthetic response characteristic data, and growth morphology characteristic data of the target plant. The stage prediction module is used to identify the current growth stage based on leaf optical feature data and growth morphology feature data, calculate the physiological development rate based on the current growth stage and growth morphology feature data, predict the stage evolution trajectory within the future time window based on the physiological development rate, and obtain the stage evolution spectral demand sequence. The response modeling module is used to extract the photosynthetic contribution of multiple bands in the photosynthetic response feature data, establish the coupling equation between the photosynthetic contribution of each band and the light intensity of the corresponding band, determine the coefficient parameters in the coupling equation by fitting the relationship between the photosynthetic contribution and the light intensity of the band, and obtain the spectral-photosynthetic response characteristic parameters. The light intensity allocation module is used to construct and solve the constrained optimization problem of multi-band light intensity allocation with the spectral demand corresponding to the future time window in the stage evolution spectral demand sequence as the optimization objective, and the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints and total power conservation constraints as constraints, to obtain the target light intensity allocation scheme. The spectral control module is used to generate drive current parameters according to the target light intensity allocation scheme, and send the drive current parameters to the LED light source to output the controlled spectrum.

[0014] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0015] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps in any of the aforementioned methods.

[0016] This invention accurately identifies the current growth stage and predicts future evolution trajectories by real-time acquisition of plant leaf optical characteristics and growth morphology data, achieving a shift from passive response to active prediction and effectively solving the lag problem of traditional spectral regulation. By establishing a quantitative coupling equation between multi-band photosynthetic contribution and light intensity, the specific contribution patterns of different bands to photosynthesis are scientifically revealed, avoiding light energy waste caused by empirical configuration. In optimizing light intensity allocation, both LED output capacity constraints and total power conservation constraints are considered simultaneously, ensuring the feasibility and optimal energy efficiency of the optimization scheme. This invention achieves precise and dynamic spectral regulation based on the individual physiological state of plants, significantly improving light energy utilization efficiency, optimizing plant growth regulation effects, and providing effective technical support for intelligent management of facility agriculture. Attached Figure Description

[0017] Figure 1 A flowchart of an LED multispectral dynamic adaptive control method based on plant growth cycle provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the LED multispectral dynamic adaptive control system based on the plant growth cycle according to an embodiment of the present invention. Detailed Implementation

[0018] like Figure 1 As shown, Figure 1 A flowchart of an LED multispectral dynamic adaptive control method based on plant growth cycle provided in an embodiment of the present invention is shown. The method includes the following steps: Step 101: Obtain leaf optical characteristic data, photosynthetic response characteristic data, and growth morphology characteristic data of the target plant.

[0019] Step 102: Identify the current growth stage based on leaf optical feature data and growth morphology feature data; calculate the physiological development rate based on the current growth stage and growth morphology feature data; predict the stage evolution trajectory within the future time window based on the physiological development rate; and obtain the stage evolution spectral demand sequence.

[0020] In some embodiments of the present invention, step 102 may specifically include the following sub-steps: Sub-step 1021: Extract the feature quantities representing physiological state from the leaf optical feature data and growth morphology feature data, and identify the current growth stage based on the feature quantities; Sub-step 1022: Extract the morphological parameter sequence of continuous time points from the growth morphological characteristic data, perform time differentiation on the morphological parameter sequence to obtain the measured developmental rate, and calculate the average developmental rate of the same growth stage from historical growth data based on the current growth stage, and calculate the ratio of the measured developmental rate to the average developmental rate to obtain the physiological developmental rate. Sub-step 1023: Based on the current growth stage, calculate the average duration of each subsequent growth stage from historical growth data, calculate the predicted duration based on the average duration and physiological development rate, and sequentially accumulate the predicted duration from the current moment to determine the start and end times of each growth stage, thus obtaining the stage evolution trajectory. Sub-step 1024: Extract the identifiers and corresponding start and end times of each growth stage from the stage evolution trajectory, and extract the photosynthetic rate baseline value from the historical photosynthetic response characteristic data based on the identifiers. Sub-step 1025: Based on the response relationship between light intensity and photosynthetic rate in each band of photosynthetic response characteristic data, calculate the light intensity value of each band that reaches the baseline value of photosynthetic rate, associate the light intensity value of each band with the corresponding start and end time and arrange them in chronological order to obtain the stage evolution spectral demand sequence.

[0021] After acquiring leaf optical characteristics, photosynthetic response characteristics, and growth morphology characteristics of the target plant, these data need to be comprehensively analyzed to achieve dynamic spectral regulation. Leaf optical characteristics include reflectance and transmittance at different wavelengths, collected using a spectrometer in the 400nm to 800nm ​​range. Photosynthetic response characteristics record the photosynthetic rate variation curves of the plant under different light intensities, reflecting the response of photosynthesis to light conditions. Growth morphology characteristics cover measurable morphological indicators such as plant height, leaf area, and stem diameter, which exhibit specific change patterns at different growth stages.

[0022] The reflectance ratio of specific wavelengths in leaf optical characteristic data is extracted as a characterization of physiological state. The reflectance ratio of the red light band to the near-infrared band can effectively reflect chlorophyll content and leaf maturity. When the ratio is below 0.6, it usually corresponds to the vegetative growth stage; a ratio between 0.6 and 0.9 corresponds to the reproductive growth stage; and a ratio above 0.9 indicates the maturation stage. Combining this with the leaf area expansion rate in growth morphology data can further verify the accuracy of the growth stage determination. The daily leaf area growth rate during the vegetative growth stage is typically around 3 cm. 2 up to 5cm 2 Between these two stages, the growth rate gradually decreases to 1 cm during the reproductive growth stage. 2 The following method, through cross-validation of optical and morphological features, allows for accurate identification of the current specific growth stage.

[0023] After determining the current growth stage, the physiological development rate needs to be calculated from the time series of growth morphology data. Plant height data measured daily over seven consecutive days is selected, and the average difference between adjacent days' heights is calculated as the measured development rate. Taking a tomato plant as an example, if the height on the first day is 28 cm and on the seventh day is 35 cm, the measured development rate is 1.0 cm / day. The average development rate of the same variety during the vegetative growth stage is retrieved from a historical growth database. Assuming historical data shows an average development rate of 0.8 cm / day for this stage, the physiological development rate is calculated as the ratio of the measured value to the historical average, i.e., 1.25 times the standard rate. This ratio reflects the speed of plant development under current environmental conditions relative to standard conditions.

[0024] The duration of each growth stage is predicted based on the physiological developmental rate. Historical data shows that the reproductive growth stage, following the vegetative growth stage, lasts an average of 28 days, and the maturation stage lasts an average of 14 days. Dividing these average durations by the physiological developmental rate of 1.25 yields predicted durations of 22.4 days and 11.2 days, respectively. Starting from the current moment, the predicted durations of each stage are sequentially added together, determining that the reproductive growth stage will begin in 22.4 days, and the maturation stage will begin in 33.6 days. This establishes a complete stage evolution trajectory and clarifies the transition time points for each growth stage within the future time window.

[0025] Based on the identifiers of each growth stage in the evolutionary trajectory, corresponding baseline photosynthetic rate values ​​are extracted from historical photosynthetic response characteristic data. The baseline photosynthetic rate for the vegetative growth stage is typically set at 18 μmol / (m²). 2 ·s), the reproductive growth stage increased to 25 μmol / (m 2 During the mature stage, the concentration of mol / (m·s) decreases to 15 μmol / (m·s). 2 These benchmark values ​​are optimal photosynthetic efficiency reference values ​​derived from a large amount of historical planting data, and can guide the formulation of spectral configuration schemes.

[0026] Using the response relationship between light intensity and photosynthetic rate recorded in the current photosynthetic response characteristic data, the light intensity configuration required for each wavelength band to reach the baseline photosynthetic rate is calculated. The photosynthetic response characteristic data includes the measured photosynthetic rates of major wavelength bands such as red, blue, and far-red light under different light intensities. Taking the vegetative growth stage as an example, the baseline photosynthetic rate is set at 18 μmol CO2 / (m 2 By consulting the photosynthetic response curve in the red light band, as the red light intensity gradually increases from 0 to 120 μmol / (m²), the results show that... 2 At a time ·s), the photosynthetic rate increases from 0 to near saturation. The response curve shows that the photosynthetic rate exactly reaches 18 μmol CO2 / (m²). 2The light intensity point corresponding to ·s) is where the red light intensity is 120 μmol / (m 2 The same method was applied to the blue and far-infrared light bands, and the required concentration of blue light was found to be 40 μmol / (m²) from their respective response curves. 2 Far-red light requires 20 μmol / (m 2 ·s). For the reproductive growth stage, the baseline photosynthetic rate is increased to 25 μmol CO2 / (m 2 To find the light intensity value corresponding to a higher photosynthetic rate on the response curve, interpolation calculations show that red light needs to be increased to 150 μmol / (m²). 2 Blue light increased to 50 μmol / (m 2 ·s), far-red light increased to 30μmol / (m 2 These light intensities (·s) enable plants to achieve higher photosynthetic efficiency during the reproductive growth stage. The baseline photosynthetic rate at maturity decreases to 15 μmol CO2 / (m²·s). 2 ·s), and the corresponding light intensity configuration is also reduced to 100 μmol / (m²) for red light. 2 ·s), blue light 30μmol / (m 2 ·s), far-red light 15μmol / (m 2 ·s).

[0027] The calculated light intensity values ​​for each band are correlated with the start and end times in the stage evolution trajectory. The current light intensity configuration scheme for the vegetative growth stage corresponds to days 0 to 22.4, the configuration scheme for the reproductive growth stage corresponds to days 22.4 to 33.6, and the configuration scheme for the maturity stage corresponds to days 33.6 and beyond. The spectral requirement parameters for each stage are arranged chronologically to form a sequence data structure containing time labels and light intensity configurations; this sequence is the stage evolution spectral requirement sequence. This sequence not only includes the spectral configuration schemes that should be adopted at each future time point but also reflects the dynamic changes in spectral parameters as the growth stage progresses.

[0028] This invention, through the aforementioned technical solution, achieves dynamic prediction of spectral requirements based on plant physiological state and developmental process. This method can accurately identify the current growth stage of a plant, quantify its physiological development rate, and then predict future stage transition time points and corresponding spectral configuration requirements. Compared to a fixed spectral configuration scheme, this dynamic prediction mechanism can synchronize light conditions with the actual physiological needs of the plant, avoiding light energy waste and growth inhibition, and improving light energy utilization efficiency and crop yield.

[0029] Step 103: Extract the photosynthetic contribution of multiple bands from the photosynthetic response characteristic data, establish the coupling equation between the photosynthetic contribution of each band and the light intensity of the corresponding band, determine the coefficient parameters in the coupling equation by fitting the relationship between the photosynthetic contribution and the light intensity of the band, and obtain the spectral-photosynthetic response characteristic parameters.

[0030] In some embodiments of the present invention, step 103 may specifically include the following sub-steps: Sub-step 1031: Extract photosynthetic rate measurements from multiple bands from photosynthetic response characteristic data, and perform inter-band difference calculations on the photosynthetic rate measurements to obtain the photosynthetic contribution of each band. Sub-step 1032: Calculate the differential value of the photosynthetic contribution of each band with respect to the light intensity of the corresponding band. Based on the differential value, divide multiple bands into dominant bands and auxiliary bands. Multiply the light intensity of the band corresponding to the dominant band with the light intensity of the band corresponding to the auxiliary band to obtain the band product term. Multiply the light intensity of each band by itself and then perform a ratio operation with the corresponding photosynthetic contribution to obtain the band ratio term. Sub-step 1033: Establish a coupling equation with light intensity, band product term and band ratio term of each band as independent variables and photosynthetic contribution of each band as dependent variable. Collect multiple sets of sample data of light intensity and photosynthetic contribution of each band and substitute them into the coupling equation to obtain the coefficient parameters corresponding to each independent variable. Sub-step 1034: Normalize the coefficient parameters to obtain the normalized weights of each independent variable, arrange the normalized weights in descending order of value and calculate the cumulative sum of each term, determine the position corresponding to the first time the cumulative sum reaches the preset coverage ratio, retain the independent variables and corresponding coefficient parameters before the position, construct a simplified coupling equation based on the retained independent variables and corresponding coefficient parameters, and use the coefficient parameters in the simplified coupling equation as the spectral-photosynthetic response characteristic parameters.

[0031] Photosynthetic rate measurements across multiple wavelengths were extracted from photosynthetic response characteristic data. These measurements were taken under different wavelengths of light. The photosynthetic rate measured under red light alone was recorded as the photosynthetic rate with red light contribution, the photosynthetic rate measured under blue light alone was recorded as the photosynthetic rate with blue light contribution, and the photosynthetic rate measured under far-red light alone was recorded as the photosynthetic rate with far-red light contribution. Under combined light conditions with simultaneous red and blue light illumination, the measured total photosynthetic rate was higher than the sum of the photosynthetic rates under individual wavelength illumination. The actual photosynthetic contribution of each wavelength was identified through inter-wavelength difference calculations. The difference between the total photosynthetic rate under combined illumination and the sum of the photosynthetic rates under individual wavelength illumination was the additional contribution from inter-wavelength synergy. This difference calculation can reveal the impact of interactions between different wavelengths on photosynthetic efficiency and quantify the strength of the inter-wavelength synergy effect.

[0032] The response sensitivity is assessed by calculating the derivative of the photosynthetic contribution of each wavelength band with respect to the corresponding wavelength intensity. The photosynthetic response characteristic data includes photosynthetic rate measurement points for each wavelength band at different intensity levels; these discrete measurement points constitute the photosynthetic response line. A function is fitted to the photosynthetic response curve of the red wavelength band, selecting an exponentially saturated function form suitable for the photosynthetic law to obtain a continuous function expression of the photosynthetic contribution with respect to red light intensity. The derivative of this fitted function is obtained, yielding the derivative function of the photosynthetic contribution with respect to red light intensity. The value of this derivative function is calculated at the current red light operating intensity, which is the derivative value for the red wavelength band. The same method is applied to the blue and far-red wavelength bands, fitting and differentiating their respective photosynthetic response curves, and calculating the derivative values ​​at their respective current operating intensities. Based on the magnitude of the differential value of each band, the bands are divided into dominant bands and auxiliary bands. Bands with larger differential values ​​indicate that the photosynthetic rate is more sensitive to changes in light intensity under the current light intensity conditions, and are therefore classified as dominant bands. Bands with smaller differential values ​​are classified as auxiliary bands.

[0033] Inter-band interaction terms are constructed to characterize the synergistic effect between different bands. The light intensity of the dominant band is multiplied by the light intensity of each auxiliary band, resulting in multiple band product terms. These product terms reflect the potential synergistic enhancement effect when different bands of illumination coexist, capturing the nonlinear interactions between bands. Simultaneously, band ratio terms are constructed to characterize the saturation characteristics of each band. The light intensity of each band is multiplied by itself and then ratioed to the corresponding photosynthetic contribution. These ratio terms reflect the variation of photosynthetic efficiency at different light intensity levels, particularly the photosynthetic saturation phenomenon that may occur under high light intensity conditions. By introducing band product terms and band ratio terms, the complex relationship between band light intensity and photosynthetic contribution can be described more comprehensively.

[0034] A coupled equation was established using light intensity, band product, and band ratio terms as independent variables, and the corresponding photosynthetic contribution as the dependent variable. This coupled equation is in the form of a multiple linear combination, expressed as the photosynthetic contribution equal to the weighted sum of each independent variable multiplied by its corresponding coefficient. Multiple sets of sample data were collected under different illumination configurations, each set containing light intensity for each band and the corresponding measured photosynthetic contribution. These sample data were substituted into the coupled equation, and the coefficient parameters corresponding to each independent variable were obtained through least squares fitting. These coefficient parameters quantitatively reflect the degree of influence of each independent variable on the photosynthetic contribution; the larger the absolute value of the coefficient, the more significant the influence of that independent variable. During the fitting process, it is necessary to ensure that the sample data covers the typical operating range of light intensity for each band to guarantee the representativeness and reliability of the coefficient parameters.

[0035] The fitted coefficients are normalized to assess the relative importance of each independent variable. The absolute values ​​of all coefficients are summed, and the absolute value of each coefficient is divided by this sum to obtain the normalized weight. The normalized weight reflects the relative contribution of each independent variable to the coupled equation; a larger weight indicates a stronger explanatory power for the photosynthetic contribution. These normalized weights are arranged in descending order of value, and the cumulative sum is calculated for each term, observing its trend as the independent variable increases. A preset coverage ratio is set as a threshold; accumulation stops when the cumulative sum first reaches or exceeds this threshold, and the corresponding position is recorded. All independent variables before this position and their corresponding coefficients are retained, while those after are discarded. This screening method simplifies the model structure and removes redundant independent variables with little impact on the photosynthetic contribution while maintaining the main predictive power. A simplified coupled equation is constructed based on the retained independent variables and coefficients. This simplified equation contains only a few key independent variables, whose coefficients are the spectral-photosynthetic response characteristic parameters.

[0036] This invention establishes a quantitative coupling relationship between wavelength light intensity and photosynthetic contribution through the aforementioned technical solution, identifying the independent contributions and synergistic effects of different wavelengths on photosynthesis. By using normalized weights to screen and retain key independent variables, the simplified coupling equation reduces computational complexity while maintaining prediction accuracy, providing accurate response characteristic parameters for subsequent dynamic spectral regulation, and enabling spectral configuration to more precisely match the photosynthetic needs of plants.

[0037] Step 104: Taking the spectral demand corresponding to the future time window in the stage evolution spectral demand sequence as the optimization objective, and taking the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints and total power conservation constraints as constraints, construct and solve the constraint optimization problem of multi-band light intensity allocation to obtain the target light intensity allocation scheme.

[0038] In some embodiments of the present invention, step 104 may specifically include the following sub-steps: Sub-step 1041: Extract the spectral demand corresponding to the future time window from the stage evolution spectral demand sequence, and obtain the expected light intensity value of each band by weighted average of the spectral demand of each band in time order. Construct an objective function with the goal of minimizing the sum of squares of the deviation between the actual allocated light intensity of each band and the expected light intensity value of each band. Sub-step 1042: Calculate the expected photosynthetic contribution corresponding to the expected light intensity value of each band and the actual photosynthetic contribution corresponding to the actual distributed light intensity of each band based on the spectral-photosynthetic response characteristic parameters, and construct the spectral-photosynthetic response characteristic parameter constraint condition with the inequality constraint that the actual photosynthetic contribution is not less than the expected photosynthetic contribution. Sub-step 1043: Obtain the maximum and minimum output capability values ​​of each band of the LED light source, and construct the LED light source output capability constraint condition with the actual distributed light intensity of each band located between the corresponding minimum and maximum output capability values ​​as the range constraint. Sub-step 1044: Obtain the system's preset total power value and construct a total power conservation constraint condition with the sum of the actual distributed light intensities of each band equal to the total power value as the equation constraint. Sub-step 1045: With the objective function as the optimization objective and the constraints of spectral-photosynthetic response characteristic parameters, LED light source output capability, and total power conservation as constraints, a constrained optimization problem for multi-band light intensity allocation is constructed. The constrained optimization problem is solved iteratively using a sequential quadratic programming algorithm to obtain the convergence value of the actual allocated light intensity of each band that minimizes the objective function, which is then used as the target light intensity allocation scheme.

[0039] Spectral demand data corresponding to future time windows are extracted from the stage-evolution spectral demand sequence. The future time window can be set to the next 7 days, which may span multiple growth stages or be within the same growth stage. The stage-evolution spectral demand sequence records the light intensity demand values ​​for each band (red, blue, far-red, etc.) at each time point. The daily light intensity demand values ​​for each band within the next 7 days are extracted, and the demand values ​​for the same band at different time points are weighted and averaged. The weighting coefficients are determined based on the distance from the current time; time points closer to the current time have a higher weight, and time points farther away have a lower weight. The demand values ​​for the red band within the next 7 days are multiplied by their respective time weights, summed, and then divided by the total weights to obtain the expected light intensity value for the red band. The blue and far-red bands are calculated using the same method to obtain their respective expected light intensity values.

[0040] An objective function is constructed to minimize the deviation between the actual allocated light intensity and the desired light intensity value for each band. This objective function adopts the form of a sum of squared deviations, which can penalize both positive and negative deviations simultaneously, preventing the actual allocated light intensity from deviating excessively from the desired value. The objective function can be expressed as: Where F is the objective function value, n is the total number of bands, and I i For the actual allocated light intensity of the i-th band, I i，t Let be the desired light intensity value for the i-th band. The smaller this objective function value, the closer the actual allocated light intensity is to the desired light intensity value, and the more the spectral configuration scheme meets the needs of plant growth.

[0041] The expected photosynthetic contribution corresponding to the expected light intensity value for each band is calculated based on the spectral-photosynthetic response characteristic parameters. The expected light intensity values ​​for red, blue, and far-red light are substituted into the simplified coupling equation to calculate the corresponding photosynthetic contributions for red, blue, and far-red light, respectively. These single-band photosynthetic contributions are added to the contribution of the interaction term in the coupling equation to obtain the total expected photosynthetic contribution corresponding to the expected light intensity value. The same method is applied to the actual allocated light intensity; the actual allocated light intensity for each band is substituted into the coupling equation to calculate the actual photosynthetic contribution. An inequality constraint is constructed requiring that the actual photosynthetic contribution is not less than the expected photosynthetic contribution, ensuring that the actual spectral configuration at least meets the basic photosynthetic needs of the plant. This constraint ensures that photosynthetic efficiency is not sacrificed to reduce deviation during the optimization process, avoiding situations where the actual light intensity is close to the expected value but the photosynthetic effect is insufficient.

[0042] Obtain the achievable light intensity range parameters for each wavelength band of the LED light source under the current illumination conditions. Based on the LED light source configuration and illumination conditions, the achievable light intensity range for the red light band is 10 to 200 μmol / (m²). 2 The achievable light intensity range in the blue light band is 5 to 100 μmol / (m²). 2 The light intensity range achievable in the far-infrared band is 2 to 50 μmol / (m²). 2 •s). The upper limit of the luminous intensity range is limited by the rated power and heat dissipation capacity of the LED chips; exceeding this value will cause the LED to overheat or be damaged. The lower limit of the luminous intensity range is limited by the dimming range of the LED driver circuit; below this value, the LED may not work stably or may turn off. Constructing constraints on the output capability of the LED light source requires that the actual luminous intensity allocated to each wavelength band must be within the corresponding luminous intensity range, ensuring that the luminous intensity allocation scheme can be practically implemented within the LED hardware capabilities.

[0043] Obtain a preset total power value, which limits the total energy consumption when all LED bands operate simultaneously. The total power value can be determined comprehensively based on factors such as planting scale, power supply capacity, and operating costs. Convert the light intensity of each band into corresponding electrical power. The photoelectric conversion efficiency of LEDs in different bands (red, blue, far-red, etc.) varies and needs to be converted according to the luminous efficacy parameters of each band. The total power conservation constraint is expressed as: , where I i Let A be the actual light intensity allocated to the i-th band, A be the irradiated area, η be the luminous efficacy parameter of the LED in the i-th band, and P be the actual light intensity allocated to the i-th band. total This represents the total power value. This constraint ensures that the optimized light intensity distribution scheme will not exceed the total power limit, thus avoiding excessive energy consumption or power supply system overload.

[0044] A constrained optimization problem for multi-band light intensity allocation is constructed, with the objective function as the optimization target and constraints including spectral-photosynthetic response characteristic parameters, LED light source output capability constraints, and total power conservation constraints. This optimization problem is a nonlinear constrained optimization problem because the spectral-photosynthetic response characteristic parameter constraints include nonlinear terms such as band product terms and band ratio terms. An iterative solution method is used to numerically solve this constrained optimization problem. Starting from the initial light intensity allocation values, the light intensity values ​​of each band are adjusted in each iteration according to the gradient of the objective function and the boundaries of the constraints. During the iteration process, the degree to which the objective function value corresponding to the current light intensity allocation is satisfied with each constraint is continuously calculated, and the light intensity values ​​are updated along the direction that reduces the objective function and satisfies all constraints. The algorithm is considered to have converged when the change in light intensity value between two consecutive iterations is less than a set threshold, and the actual allocated light intensity of each band at this time is the target light intensity allocation scheme. This scheme simultaneously satisfies multiple requirements such as minimizing deviation, ensuring photosynthetic efficiency, hardware capability limitations, and total power constraints.

[0045] This invention achieves optimized allocation of multi-band light intensity for future time windows through the aforementioned technical solution. This method comprehensively considers plant photosynthetic requirements, LED hardware capabilities, and energy consumption constraints. By constructing a constrained optimization problem and iteratively solving it, it obtains an allocation scheme that best approximates the desired spectral configuration under various constraints, balancing photosynthetic efficiency and energy consumption control, and providing a decision-making basis for LED multispectral lighting control.

[0046] Step 105: Generate driving current parameters according to the target light intensity allocation scheme, and send the driving current parameters to the LED light source to output the modulated spectrum.

[0047] In some embodiments of the present invention, step 105 may specifically include the following sub-steps: Sub-step 1051: Extract the target allocated light intensity value of each band from the target light intensity allocation scheme. Based on the mapping relationship between the light intensity and driving current of each band light-emitting chip at standard temperature, reverse lookup is performed on the target allocated light intensity value of each band to obtain the initial driving current value of each band light-emitting chip. Sub-step 1052: Determine the heating power of each light-emitting chip based on the initial driving current value of each light-emitting chip, determine the operating temperature of each light-emitting chip based on the relationship between the heating power of each light-emitting chip and the heat conduction between the light-emitting chips, and determine the light intensity attenuation of each light-emitting chip based on the degree of temperature deviation between the operating temperature of each light-emitting chip and the standard temperature. Sub-step 1053: Determine the compensation light intensity value of each band based on the target allocated light intensity value of each band and the light intensity attenuation of the corresponding light-emitting chip in the band. Based on the mapping relationship between the light intensity and driving current of the light-emitting chip in each band at the standard temperature, reverse lookup is performed on the compensation light intensity value of each band to obtain the compensation driving current value of the light-emitting chip in each band. In sub-step 1054, the compensation drive current values ​​of each band of light-emitting chip are encapsulated to generate drive current parameters, and the drive current parameters are sent to the drive circuit of the LED light source. The drive circuit adjusts the operating current of each band of light-emitting chip according to the drive current parameters to output the modulated spectrum.

[0048] The target light intensity values ​​for each wavelength band are extracted from the target light intensity allocation scheme. This scheme specifies the achievable light intensity for the red, blue, and far-infrared wavelengths. These target light intensity values ​​are used as the basis for subsequent drive current calculations. LED chips have a defined mapping relationship between light intensity and drive current at standard temperatures, recording the output light intensity corresponding to different drive currents. The standard temperature is typically set at 25°C, serving as the reference temperature for testing LED technical parameters. By consulting the mapping relationship table for red LED chips, the drive current corresponding to the light intensity value closest to the target red light intensity is found and used as the initial drive current value for the red LED chip. The same method is used for blue and far-infrared LED chips, respectively, consulting their respective mapping relationship tables to obtain the corresponding initial drive current values.

[0049] The heating power is calculated based on the initial driving current of the LED chips for each wavelength band. When an LED chip operates, it converts electrical energy into light and heat energy; the portion converted to heat constitutes the heating power. Heating power is related to the driving current and the chip's voltage drop; the higher the driving current, the higher the heating power. The heating power of a red LED chip at the initial driving current can be obtained by multiplying the chip's voltage drop by the driving current and then subtracting the light output power. The heating power of blue and far-red LED chips is calculated using the same method. In an LED light source, multiple wavelength LED chips are typically mounted on the same heat sink, and heat conduction exists between the chips. The heat emitted by the red chip is conducted through the substrate to the blue and far-red chips, and similarly, the heat from the blue and far-red chips is conducted between them. A heat conduction model between the chips is established, describing the relationship between the temperature at each chip location and the heating power of each chip. The heating power of each wavelength LED chip is substituted into the heat conduction model to calculate the operating temperature reached at each chip location.

[0050] Determine the light intensity attenuation of LED chips for each wavelength band. The light output efficiency of LED chips decreases with increasing temperature, and light intensity attenuation occurs when the operating temperature exceeds the standard temperature. The amount of light intensity attenuation is related to the degree of temperature deviation; the greater the temperature deviation, the more severe the light intensity attenuation. The difference between the operating temperature and the standard temperature of the red LED chip is the temperature deviation. Multiplying this temperature deviation by the temperature coefficient of the red LED chip yields the light intensity attenuation ratio. Multiplying this ratio by the target light intensity value gives the light intensity attenuation of the red LED. The temperature coefficient reflects the sensitivity of the chip's light output to temperature changes, and the temperature coefficients differ for chips in different wavelength bands. The light intensity attenuation of blue and far-red LED chips is calculated using the same method, determined based on their respective operating temperatures, temperature deviations, and temperature coefficients.

[0051] The compensation light intensity value is determined based on the target allocated light intensity value and the corresponding light intensity attenuation for each band. The compensation light intensity value equals the target allocated light intensity value plus the light intensity attenuation. This compensation light intensity value takes into account the light intensity decrease caused by temperature, ensuring that the light intensity output by the LED chip at the actual operating temperature reaches the target value. The compensation light intensity value for the red light band is the sum of the target allocated red light intensity value and the red light intensity attenuation. The compensation light intensity values ​​for the blue and far-infrared light bands are calculated using the same method. The compensation light intensity value for each band is then substituted back into the mapping relationship between the light intensity and driving current of the LED chip at standard temperature for each band for a reverse lookup. The driving current corresponding to the red light compensation light intensity value is found, yielding the compensation driving current value for the red LED chip. This compensation driving current value is higher than the initial driving current value; the additional current is used to compensate for the light intensity attenuation caused by temperature. The compensation driving current values ​​for the blue and far-infrared LED chips are obtained using the same method.

[0052] The compensated drive current values ​​of each band of light-emitting chips are encapsulated into drive current parameters. These parameters include current setting information for multiple bands, such as red, blue, and far-infrared drive current values, organized using a specific data format. This data format defines the position and encoding method of each band's current value within the parameters, facilitating parsing by the drive circuit. The drive current parameters are sent to the LED light source's drive circuit via a communication interface. Upon receiving the drive current parameters, the drive circuit parses them and extracts the current setting values ​​for each band. The drive circuit internally contains multiple constant current sources, supplying power to the red, blue, and far-infrared chips. Based on the parsed current setting values, the drive circuit adjusts the output current of each constant current source, ensuring that the red chip operates under red compensated drive current, the blue chip under blue compensated drive current, and the far-infrared chip under far-infrared compensated drive current. Driven by the compensated drive current, each band of light-emitting chip emits light of its corresponding band, and the mixing of multiple bands forms a controlled spectrum. Since the driving current has taken into account and compensated for the light intensity attenuation caused by temperature, the actual light intensity of each band in the controlled spectrum can accurately reach the target value set in the target light intensity allocation scheme.

[0053] This invention achieves precise conversion from the target light intensity allocation scheme to the actual driving current parameters through the above-described technical solution. This method considers the temperature rise effect and light intensity attenuation characteristics of the LED light-emitting chip, predicts the operating temperature by establishing a heat conduction model between chips, and calculates the compensation driving current, ensuring that the light intensity of each band of the output spectrum accurately matches the target value, thus improving the accuracy and stability of spectral control.

[0054] In sub-step 1052, determining the operating temperature of each band of light-emitting chip based on the relationship between the heating power of each band of light-emitting chip and the thermal conduction between the light-emitting chips also includes: A heat source distribution matrix is ​​constructed based on the heating power of each band of light-emitting chips, and a heat conduction coupling matrix is ​​constructed based on the spatial position relationship of each band of light-emitting chips on the substrate. The heat exchange flux distribution between each band of light-emitting chips is obtained through matrix operation of the heat source distribution matrix and the heat conduction coupling matrix. The temperature offset of each light-emitting chip is calculated based on the heat exchange flux distribution between the chips in each band. The temperature offset of each light-emitting chip is then dynamically corrected according to the time delay characteristics of the heat conduction path to obtain the transient temperature response value of each light-emitting chip. The current operating temperature of each light-emitting chip is calculated based on the transient temperature response value of each band and the reference temperature of the substrate. Update the current operating temperature of each band of light-emitting chips to the heat source distribution matrix, recalculate the heat exchange flux distribution between each band of light-emitting chips, and update the current operating temperature of each band of light-emitting chips. Repeat the update until the change in the current operating temperature of each band of light-emitting chip is less than the preset convergence threshold, and then use the current operating temperature of each band of light-emitting chip after convergence as the operating temperature of each band of light-emitting chip.

[0055] A heat source distribution matrix is ​​constructed based on the heating power of the light-emitting chips in each wavelength band. This matrix records the heating power of each light-emitting chip as a heat source. The heating power of the red light-emitting chip is the first element of the matrix, the heating power of the blue light-emitting chip is the second element, and the heating power of the far-red light-emitting chip is the third element. The dimension of the matrix corresponds to the number of light-emitting chips; the three wavelength bands correspond to a three-dimensional matrix. The spatial coordinates of the light-emitting chips on the substrate are obtained, with the red chip located at a certain point on the substrate, and the blue and far-red chips located at other points. A thermal conduction coupling matrix is ​​constructed based on the distance between the chips. This matrix describes the thermal conduction capability between any two chips. The closer the two chips are, the stronger the thermal conduction capability, and the larger the value of the corresponding matrix element. The thermal conduction capability is also related to the thermal conductivity of the substrate material; substrate materials with higher thermal conductivity correspond to stronger thermal conduction capabilities. The thermal conduction coupling matrix is ​​a symmetric matrix, and the thermal conduction capability from the red chip to the blue chip is equal to the thermal conduction capability from the blue chip to the red chip.

[0056] The heat exchange flux distribution is obtained by performing matrix operations on the heat source distribution matrix and the heat conduction coupling matrix. During the matrix operation, the heating power of each chip is multiplied by its heat conduction capacity to other chips to obtain the heat transferred from that chip to other chips. The heat transferred from the red light chip to the blue light chip is equal to the red light chip's heating power multiplied by the red-to-blue light heat conduction capacity, and the heat transferred from the red light chip to the far-red light chip is also equal to the red light chip's heating power multiplied by the red-to-far-red light heat conduction capacity. The heat transferred from the blue light chip and the far-red light chip to other chips is calculated using the same method. The heat transfer relationships between all chips are summarized to form a complete heat exchange flux distribution. This distribution clearly shows the actual amount of heat exchanged between each pair of chips.

[0057] The temperature offset of each wavelength-band light-emitting chip is calculated based on the heat exchange flux distribution. The red light chip receives heat from the blue and far-infrared light chips, and its own heat generation also causes its temperature to rise. The temperature offset of the red light chip is obtained by dividing the total heat received by the red light chip by its heat capacity. Heat capacity reflects the ease with which the chip's temperature rises; the larger the heat capacity, the smaller the temperature rise caused by the same amount of heat. The temperature offsets of the blue and far-infrared light chips are calculated using the same method, determined respectively based on their total received heat and their respective heat capacities.

[0058] Heat conduction within the substrate involves a time delay; heat needs time to travel from one chip location to another. This time delay is related to the distance between chips and the thermal conductivity of the substrate. Based on the distance between the red and blue LED chips and the thermal conductivity, the time delay required for heat emitted from the red chip to reach the blue chip is calculated. This time delay is then applied to the heat exchange flux to dynamically correct the calculated temperature offset of the blue chip. The corrected temperature offset reflects the transient temperature response characteristics of the chip after considering the conduction delay. The time delay characteristics must be considered for all heat conduction paths between chips, and the temperature offsets of LEDs in each wavelength band are comprehensively corrected to obtain the transient temperature response values ​​for each wavelength LED chip.

[0059] The substrate has a reference temperature, which is determined by both the ambient temperature and heat dissipation conditions. The current operating temperature of the red LED chip is obtained by adding the substrate's reference temperature to the transient temperature response value of the red LED chip. The current operating temperatures of the blue LED chip and the far-red LED chip are calculated using the same method, by adding the substrate's reference temperature to their respective transient temperature response values. The current operating temperature reflects the temperature level achieved by the chip under actual operating conditions.

[0060] An increase in chip operating temperature alters the chip's thermal characteristics, thus affecting heat exchange between chips. The current operating temperature of each wavelength LED chip is updated in the heat source distribution matrix, and the heating power of each wavelength LED chip is adjusted based on the impact of temperature on photoelectric conversion efficiency. The updated heat source distribution matrix is ​​used to recalculate the heat exchange flux distribution. Based on the new heat exchange flux distribution, the temperature offset, transient temperature response value, and current operating temperature of each wavelength LED chip are recalculated using the aforementioned method, completing one iteration update.

[0061] The changes in the current operating temperature of each wavelength-emitting chip before and after the update are compared. The difference between the operating temperature of the red chip before and after the update is the temperature change of the red chip. The temperature changes of the blue chip and the far-red chip are determined using the same method. A preset convergence threshold of 0.1℃ is set, and it is determined whether the temperature changes of all chips are less than this threshold. If the temperature change of the red chip is 0.15℃, exceeding the convergence threshold, the next iteration is performed. If, after multiple iterations, the temperature change of the red chip drops to 0.08℃, the temperature change of the blue chip is 0.06℃, and the temperature change of the far-red chip is 0.07℃, and the temperature changes of all chips are less than the convergence threshold, the iteration stops. The current operating temperature of each wavelength-emitting chip after convergence is taken as the final determined operating temperature and used for subsequent calculations of light intensity attenuation and compensation drive current.

[0062] This invention achieves accurate prediction of the operating temperature of LED light-emitting chips through the above-described technical solution. The method establishes a heat exchange model that considers chip spatial location, thermal conduction coupling, and time delay. Through iterative calculations, it accurately reflects the actual thermal environment when multiple chips are operating in parallel, providing a reliable basis for temperature compensation and ensuring the stability and consistency of spectral modulation.

[0063] like Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of an LED multispectral dynamic adaptive control system based on the plant growth cycle provided in an embodiment of the present invention. The system includes: The data acquisition module 201 is used to acquire leaf optical characteristic data, photosynthetic response characteristic data and growth morphology characteristic data of the target plant; The stage prediction module 202 is used to identify the current growth stage based on leaf optical feature data and growth morphology feature data, calculate the physiological development rate based on the current growth stage and growth morphology feature data, predict the stage evolution trajectory within the future time window based on the physiological development rate, and obtain the stage evolution spectral demand sequence. The response modeling module 203 is used to extract the photosynthetic contribution of multiple bands in the photosynthetic response feature data, establish the coupling equation between the photosynthetic contribution of each band and the light intensity of the corresponding band, determine the coefficient parameters in the coupling equation by fitting the relationship between the photosynthetic contribution and the light intensity of the band, and obtain the spectral-photosynthetic response characteristic parameters. The light intensity allocation module 204 is used to construct and solve the constraint optimization problem of multi-band light intensity allocation with the spectral demand corresponding to the future time window in the stage evolution spectral demand sequence as the optimization objective, and the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints and total power conservation constraints as constraints, to obtain the target light intensity allocation scheme. The spectral control module 205 is used to generate driving current parameters according to the target light intensity allocation scheme and send the driving current parameters to the LED light source to output the controlled spectrum.

[0064] One technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0065] One technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein the processor executes the computer program to implement the steps in any of the aforementioned methods.

[0066] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A dynamic adaptive control method for LED multispectral modulation based on plant growth cycle, characterized in that, Includes the following steps: Acquire leaf optical characteristics, photosynthetic response characteristics, and growth morphology characteristics of the target plant; The current growth stage is identified based on leaf optical feature data and growth morphology feature data. The physiological development rate is calculated based on the current growth stage and growth morphology feature data. The stage evolution trajectory within the future time window is predicted based on the physiological development rate, and the stage evolution spectral demand sequence is obtained. The photosynthetic contribution of multiple bands in the photosynthetic response characteristic data is extracted, and the coupling equation between the photosynthetic contribution of each band and the light intensity of the corresponding band is established. The coefficient parameters in the coupling equation are determined by fitting the relationship between the photosynthetic contribution and the light intensity of the band, and the spectral-photosynthetic response characteristic parameters are obtained. Taking the spectral demand corresponding to the future time window in the stage-evolution spectral demand sequence as the optimization objective, and taking the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints and total power conservation constraints as constraints, a constrained optimization problem of multi-band light intensity allocation is constructed and solved to obtain the target light intensity allocation scheme. The driving current parameters are generated according to the target light intensity allocation scheme, and the driving current parameters are sent to the LED light source to output the modulated spectrum.

2. The method according to claim 1, characterized in that, The current growth stage is identified based on leaf optical and morphological data. The physiological development rate is calculated based on the current growth stage and morphological data. The stage evolution trajectory within a future time window is predicted based on the physiological development rate, resulting in a stage evolution spectral demand sequence including: Extract physiological state features from leaf optical and growth morphology data, and identify the current growth stage based on these features. The morphological parameter sequence of continuous time points is extracted from the growth morphological characteristic data. The measured developmental rate is obtained by the time differentiation of the morphological parameter sequence. The average developmental rate of the same growth stage is statistically analyzed from historical growth data based on the current growth stage. The physiological developmental rate is obtained by calculating the ratio of the measured developmental rate to the average developmental rate. Based on the current growth stage, the average duration of each subsequent growth stage is statistically analyzed from historical growth data. The predicted duration is calculated based on the average duration and the physiological development rate. Starting from the current moment, the predicted duration is accumulated sequentially to determine the start and end times of each growth stage, thus obtaining the stage evolution trajectory. Extract the identifiers and corresponding start and end times of each growth stage from the stage evolution trajectory, and extract the baseline value of photosynthetic rate from historical photosynthetic response characteristic data based on the identifiers. Based on the response relationship between light intensity and photosynthetic rate in each band of photosynthetic response characteristic data, the light intensity values ​​of each band that reach the baseline value of photosynthetic rate are calculated. The light intensity values ​​of each band are associated with the corresponding start and end times and arranged in chronological order to obtain the stage evolution spectral demand sequence.

3. The method according to claim 1, characterized in that, The photosynthetic contribution of multiple bands in the photosynthetic response characteristic data is extracted, and a coupling equation between the photosynthetic contribution of each band and the corresponding light intensity is established. By fitting the relationship between the photosynthetic contribution and the light intensity of the band, the coefficient parameters in the coupling equation are determined, and the spectral-photosynthetic response characteristic parameters are obtained, including: Photosynthetic rate measurements of multiple bands are extracted from photosynthetic response characteristic data, and the photosynthetic contribution of each band is obtained by performing inter-band difference calculation on the photosynthetic rate measurements. Calculate the differential value of the photosynthetic contribution of each band with respect to the light intensity of the corresponding band. Based on the differential value, divide multiple bands into dominant bands and auxiliary bands. Multiply the light intensity of the band corresponding to the dominant band with the light intensity of the band corresponding to the auxiliary band to obtain the band product term. Multiply the light intensity of each band by itself and then calculate the ratio with the corresponding photosynthetic contribution to obtain the band ratio term. A coupled equation was established with light intensity, band product term and band ratio term of each band as independent variables and photosynthetic contribution of each band as dependent variable. Multiple sets of sample data of light intensity and photosynthetic contribution of each band were collected and substituted into the coupled equation to obtain the coefficient parameters corresponding to each independent variable. Normalize the coefficient parameters to obtain the normalized weights of each independent variable. Arrange the normalized weights in descending order of value and calculate the cumulative sum of each term. Determine the position corresponding to the first time the cumulative sum reaches the preset coverage ratio. Retain the independent variables and corresponding coefficient parameters before the position. Construct a simplified coupling equation based on the retained independent variables and corresponding coefficient parameters. Use the coefficient parameters in the simplified coupling equation as the spectral-photosynthetic response characteristic parameters.

4. The method according to claim 1, characterized in that, Taking the spectral demand corresponding to the future time window in the stage-evolved spectral demand sequence as the optimization objective, and using the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints, and total power conservation constraints as constraints, a constrained optimization problem for multi-band light intensity allocation is constructed and solved, resulting in the following target light intensity allocation schemes: The spectral demand corresponding to the future time window is extracted from the spectral demand sequence of the stage evolution. The spectral demand of each band in the spectral demand is weighted and averaged in time order to obtain the expected light intensity value of each band. An objective function is constructed with the goal of minimizing the sum of squares of the deviation between the actual allocated light intensity of each band and the expected light intensity value of each band. Based on the spectral-photosynthetic response characteristic parameters, the expected photosynthetic contribution corresponding to the expected light intensity value of each band and the actual photosynthetic contribution corresponding to the actual distributed light intensity of each band are calculated, and the spectral-photosynthetic response characteristic parameter constraint condition is constructed with the inequality constraint that the actual photosynthetic contribution is not less than the expected photosynthetic contribution. Obtain the maximum and minimum output capability values ​​of each band of the LED light source, and construct the LED light source output capability constraint condition with the actual distributed light intensity of each band located between the corresponding minimum and maximum output capability values ​​as the range constraint. Obtain the system's preset total power value and construct a total power conservation constraint condition with the equation that the sum of the actual distributed light intensities of each band equals the total power value. Using the objective function as the optimization objective and the constraints of spectral-photosynthetic response characteristic parameters, LED light source output capability, and total power conservation as constraints, a constrained optimization problem for multi-band light intensity allocation is constructed. The constrained optimization problem is solved iteratively using a sequential quadratic programming algorithm, and the convergent value of the actual allocated light intensity of each band that minimizes the objective function is obtained as the target light intensity allocation scheme.

5. The method according to claim 1, characterized in that, Based on the target light intensity allocation scheme, drive current parameters are generated, and the drive current parameters are sent to the LED light source to output a modulated spectrum, including: The target allocated light intensity value of each band is extracted from the target light intensity allocation scheme. Based on the mapping relationship between the light intensity and driving current of each band light-emitting chip at standard temperature, the initial driving current value of each band light-emitting chip is obtained by reverse lookup of the target allocated light intensity value of each band. The heating power of each light-emitting chip is determined based on the initial driving current value of each light-emitting chip. The operating temperature of each light-emitting chip is determined based on the relationship between the heating power of each light-emitting chip and the heat conduction between the chips. The light intensity attenuation of each light-emitting chip is determined based on the degree of temperature deviation between the operating temperature and the standard temperature. The compensation light intensity value for each band is determined based on the target light intensity value allocated to each band and the light intensity attenuation of the corresponding light-emitting chip in the band. Based on the mapping relationship between the light intensity and driving current of the light-emitting chip in each band at the standard temperature, the compensation light intensity value for each band is reverse-searched to obtain the compensation driving current value of the light-emitting chip in each band. The compensation drive current values ​​of each band of light-emitting chips are encapsulated to generate drive current parameters, which are then sent to the drive circuit of the LED light source. The drive circuit adjusts the operating current of each band of light-emitting chips according to the drive current parameters to output a modulated spectrum.

6. The method according to claim 5, characterized in that, The operating temperature of each band of light-emitting chips is determined based on the relationship between the heat generation power of each chip and the thermal conductivity between them. A heat source distribution matrix is ​​constructed based on the heating power of each band of light-emitting chips, and a heat conduction coupling matrix is ​​constructed based on the spatial position relationship of each band of light-emitting chips on the substrate. The heat exchange flux distribution between each band of light-emitting chips is obtained through matrix operation of the heat source distribution matrix and the heat conduction coupling matrix. The temperature offset of each light-emitting chip is calculated based on the heat exchange flux distribution between the chips in each band. The temperature offset of each light-emitting chip is then dynamically corrected according to the time delay characteristics of the heat conduction path to obtain the transient temperature response value of each light-emitting chip. The current operating temperature of each light-emitting chip is calculated based on the transient temperature response value of each band and the reference temperature of the substrate. Update the current operating temperature of each band of light-emitting chips to the heat source distribution matrix, recalculate the heat exchange flux distribution between each band of light-emitting chips, and update the current operating temperature of each band of light-emitting chips. Repeat the update until the change in the current operating temperature of each band of light-emitting chip is less than the preset convergence threshold, and then use the current operating temperature of each band of light-emitting chip after convergence as the operating temperature of each band of light-emitting chip.

7. A multispectral dynamic adaptive control system for LEDs based on plant growth cycles, used to implement the method described in any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to acquire leaf optical characteristic data, photosynthetic response characteristic data, and growth morphology characteristic data of the target plant. The stage prediction module is used to identify the current growth stage based on leaf optical feature data and growth morphology feature data, calculate the physiological development rate based on the current growth stage and growth morphology feature data, predict the stage evolution trajectory within the future time window based on the physiological development rate, and obtain the stage evolution spectral demand sequence. The response modeling module is used to extract the photosynthetic contribution of multiple bands in the photosynthetic response feature data, establish the coupling equation between the photosynthetic contribution of each band and the light intensity of the corresponding band, determine the coefficient parameters in the coupling equation by fitting the relationship between the photosynthetic contribution and the light intensity of the band, and obtain the spectral-photosynthetic response characteristic parameters. The light intensity allocation module is used to construct and solve the constrained optimization problem of multi-band light intensity allocation with the spectral demand corresponding to the future time window in the stage evolution spectral demand sequence as the optimization objective, and the spectral-photosynthetic response characteristic parameters, LED light source output capability constraints and total power conservation constraints as constraints, to obtain the target light intensity allocation scheme. The spectral control module is used to generate drive current parameters according to the target light intensity allocation scheme, and send the drive current parameters to the LED light source to output the controlled spectrum.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

Images