A Method and System for Optimizing Plant Growth Based on LED Spectrum Control

Abstract

The present invention discloses a plant growth optimization method and system based on LED spectrum control, which relates to the field of plant growth optimization technology, and includes the following steps: using a sensor or monitoring equipment to obtain the actual flicker frequency of an LED light source in real time; after data acquisition, using spectrum analysis technology to analyze the flicker frequency of the LED light source under the monitoring window, and preliminarily identifying the abnormality of the flicker frequency. The present invention obtains the flicker frequency of the LED light source in real time through a sensor, combines spectrum analysis with digital signal processing technology, quickly identifies frequency anomalies and quantifies frequency differences, reduces detection lag, ensures the stability of the light cycle, automatically adjusts the current through dynamic analysis of deviations, quickly corrects the flicker frequency anomalies, restores normal frequency, avoids irreversible damage to plants, and thus improves plant growth efficiency and yield.

Description

Technical Field

[0001] The present invention relates to the technical field of plant growth optimization, and in particular to a plant growth optimization method and system based on LED spectrum control. Background Art

[0002] Plant growth optimization based on LED spectrum control refers to adjusting the proportion and intensity of the spectral components of LED light sources (such as red light, blue light, green light, etc.) to simulate natural light or create specific lighting conditions, thereby promoting plant photosynthesis and growth and development. Different wavelengths of light have different effects on the physiological processes of plants (such as germination, leaf growth, flowering and fruit ripening). Therefore, by intelligently controlling the LED spectrum, it is possible to provide the best lighting environment for plants at different growth stages, improve the efficiency of light energy utilization, and optimize the growth rate, quality and yield of plants.

[0003] The prior art has the following deficiencies:

[0004] When optimizing plant growth based on LED spectrum control, problems with the pulse frequency control of the LED light source may cause the light source to flicker at a frequency that is too fast or too slow. Since this flicker usually occurs at a frequency that is difficult to detect with the naked eye, existing technologies cannot detect it in time. Existing technologies can usually only discover the problem when there is an obvious abnormality in the flicker frequency of the light source. There is a serious lag in discovering the problem. Before the problem is discovered and repaired, the abnormal frequency flicker may have already caused damage to plant growth. Since plants are extremely sensitive to photoperiods and light stability, long-term flickering can lead to decreased photosynthesis efficiency, growth disorders, abnormal leaf development, etc., and may even cause irreversible effects on the entire growth cycle of the plant. While waiting for maintenance, the losses may continue to increase;

[0005] When the light flashes too quickly, the plant may not be able to effectively use the light for photosynthesis, similar to a growth process that is constantly "interrupted";

[0006] When the flashing is too slow, the plants may not get enough light time to maintain normal growth.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The purpose of the present invention is to provide a plant growth optimization method and system based on LED spectrum control, which obtains the actual flickering frequency of LED light sources in real time through sensors and monitoring equipment, and uses spectrum analysis and digital signal processing technology for in-depth analysis, which can quickly identify frequency anomalies and quantify the difference between the actual frequency and the expected frequency, reduce detection hysteresis, effectively reduce the risk of plants being exposed to abnormal light, and ensure the stability of the light cycle. By dynamically analyzing frequency deviations, the system automatically adjusts the LED drive current, quickly corrects excessively fast or slow flickering, restores normal frequency, and avoids irreversible damage to plants caused by long-term light abnormalities, thereby improving plant growth efficiency and yield, so as to solve the problems in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a plant growth optimization method based on LED spectrum control, comprising the following steps:

[0010] Use sensors or monitoring equipment to obtain the actual flickering frequency of the LED light source in real time;

[0011] After data collection, the flicker frequency of the LED light source is analyzed using spectrum analysis technology in the monitoring window to preliminarily identify abnormalities in the flicker frequency;

[0012] After initially identifying the abnormal flicker frequency of the LED light source, digital signal processing technology is used to further analyze the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window to quantify the difference between the actual flicker frequency and the expected flicker frequency;

[0013] After the deviation analysis is completed, the flicker frequency conditions under the monitoring window are classified into too fast flicker frequency, too slow flicker frequency and normal flicker frequency;

[0014] By dynamically analyzing the deviation between the current flickering frequency and the expected flickering frequency and adjusting the current in the LED drive circuit, the pulse frequency of the excessively fast and excessively slow flickering frequencies is corrected, and the pulse frequency of the light source is precisely controlled to restore the expected flickering frequency.

[0015] Preferably, under the monitoring window, the flicker frequency of the LED light source is analyzed using spectrum analysis technology to generate a flicker frequency concealment coefficient, and the flicker frequency abnormality is intelligently sensed through the flicker frequency concealment coefficient;

[0016] The generation logic of the flicker frequency concealment coefficient is as follows:

[0017] The light intensity data of the LED light source is collected in real time by the sensor and expressed in the time domain as ,in Represents the light intensity at time t, converts the time domain signal into a frequency domain signal, and uses discrete Fourier transform to convert the collected light intensity data from the time domain to the frequency domain to obtain the frequency domain signal , where f is the frequency, the expression for the discrete Fourier transform is: , where N represents the number of sampling points, n is the point in the sampling sequence, i is the imaginary unit, and the frequency domain signal is the complex number representation of frequency f, containing frequency and phase information;

[0018] In the frequency domain signal After that, we need to extract the significant frequency components in the frequency distribution by calculating the spectral energy density , quantify the energy contribution at different frequencies, and the calculation expression of spectral energy density is: ,in is the amplitude of the frequency domain signal, indicating the intensity of the frequency component;

[0019] Calculate the spectrum smoothness S. The spectrum smoothness reflects the concentration or dispersion of the frequency components. The calculation expression is: , where f represents the frequency, is the frequency at which the energy density peaks;

[0020] Based on the spectrum smoothness, the flicker frequency concealment coefficient is calculated to quantify the flicker frequency state of the LED light source. A nonlinear adjustment factor is introduced to amplify the influence of the frequency abnormal signal. The calculation expression of the flicker frequency concealment coefficient is: , where Indicates the flicker hiding coefficient, represents the adjustment factor, Indicates the reference value of spectrum smoothness, which is used to determine whether there is a frequency anomaly.

[0021] Preferably, the flicker frequency concealment coefficient generated by analyzing the flicker frequency of the LED light source using spectral analysis technology under the monitoring window is compared with a preset flicker frequency concealment coefficient reference threshold to preliminarily identify potential abnormalities in the flicker frequency of the LED light source. The specific steps are as follows:

[0022] If the flicker frequency concealment coefficient is greater than or equal to the flicker frequency concealment coefficient reference threshold, the flicker frequency of the LED light source in the monitoring window is marked as a potential abnormality;

[0023] If the flicker frequency concealment coefficient is less than the flicker frequency concealment coefficient reference threshold, the flicker frequency of the LED light source in the monitoring window is marked as normal.

[0024] Preferably, when the flickering frequency of the LED light source under the monitoring window is marked as a potential abnormality, digital signal processing technology is used to further analyze the deviation between the actual flickering frequency and the expected flickering frequency, generate an expected flickering frequency deviation index, and quantify the difference between the actual flickering frequency and the expected flickering frequency through the expected flickering frequency deviation index.

[0025] Preferably, the generation logic of the expected flicker frequency deviation index is as follows:

[0026] In the monitoring window, the actual flicker frequency and expected flicker frequency of the LED light source at several time points are collected, and the actual flicker frequency and expected flicker frequency are respectively expressed as and To express, is the sampling time point, , M is the number of samples;

[0027] At each sampling point , calculate the instantaneous frequency deviation between the actual flicker frequency and the expected flicker frequency, the calculation expression is: , where It indicates the instantaneous frequency deviation between the actual flicker frequency and the expected flicker frequency. When the actual flashing frequency of the LED light source is higher than the expected flashing frequency, that is, the flashing frequency is too fast. When , it means that the actual flashing frequency is lower than the expected flashing frequency, that is, the flashing frequency is too slow momentarily;

[0028] Calculate the expected flicker frequency deviation index. The calculation expression is: , where represents the expected flicker frequency deviation index, represents the instantaneous weight function, which is used to assign different importances to the instantaneous frequency deviation at different time points. k represents the nonlinear amplification factor, which is used to enhance the influence of the instantaneous frequency deviation on the expected flicker frequency deviation index.

[0029] Preferably, after the deviation analysis is completed, the expected flicker frequency deviation index generated after analyzing the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window is compared and analyzed with the preset expected flicker frequency deviation reference range, and the flicker frequency situation under the monitoring window is classified. The specific classification steps are as follows:

[0030] If the expected flicker frequency deviation index is greater than the maximum value of the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as excessive flicker frequency;

[0031] If the expected flicker frequency deviation index is less than the minimum value of the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as too slow flicker frequency;

[0032] If the expected flicker frequency deviation index is within the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as normal flicker frequency.

[0033] Preferably, the deviation between the current flashing frequency and the expected flashing frequency is dynamically analyzed, and the current in the LED driving circuit is adjusted to correct the pulse frequency of the too fast flashing frequency and the too slow flashing frequency. The specific steps are as follows:

[0034] Based on the expected flicker frequency deviation index, calculate the current correction amount that needs to be adjusted , the current correction will correct the frequency anomaly by adjusting the current of the drive circuit. The calculation expression is: , where Indicates the instantaneous current adjustment amount, indicating the current correction value that should be adjusted. To control the gain coefficient, determine the intensity of the correction current, and adjust the system response speed, To adjust the sensitivity parameter, the nonlinear response of the current adjustment amount is controlled.

[0035] Preferably, for the pulse frequency adjustment of the over-fast flash frequency, the calculated current correction amount is fed back to the LED driving circuit, and the adjusted current is calculated. , the adjusted current represents the reduced current, and the calculation expression is: , where Indicates the current after the pulse frequency of the overfast flash is adjusted; Indicates the actual current value of the current driving circuit;

[0036] For the pulse frequency adjustment of the too slow flash frequency, the calculated current correction amount is fed back to the LED drive circuit to calculate the adjusted current , the adjusted current represents the increased current, and the calculation expression is: , where Indicates the current after the pulse frequency of the slow flash is adjusted.

[0037] Preferably, after the current is adjusted, the expected flicker frequency deviation index generated between the actual flicker frequency of the LED and the expected flicker frequency is continuously monitored. If the expected flicker frequency deviation index has not yet recovered to the expected flicker frequency deviation reference range, a new current adjustment amount is calculated again through the pulse frequency adjustment step until the expected flicker frequency deviation index recovers to the expected flicker frequency deviation reference range. The feedback loop formula is: , where Represents the feedback coefficient, which is used to control the actual flicker frequency adjustment speed. Indicates the flickering frequency of the LED light source after adjustment.

[0038] A plant growth optimization system based on LED spectrum control, including a real-time frequency acquisition module, a frequency anomaly recognition module, a frequency deviation quantification module, a frequency classification module and a frequency adjustment module;

[0039] Real-time frequency acquisition module, using sensors or monitoring equipment to obtain the actual flickering frequency of the LED light source in real time;

[0040] Frequency anomaly identification module: after data collection, the flicker frequency of the LED light source is analyzed using spectrum analysis technology in the monitoring window to preliminarily identify the anomaly of the flicker frequency;

[0041] The frequency deviation quantification module, after initially identifying the abnormal flicker frequency of the LED light source, further analyzes the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window using digital signal processing technology to quantify the difference between the actual flicker frequency and the expected flicker frequency;

[0042] The frequency classification module, after the deviation analysis is completed, classifies the flicker frequency under the monitoring window into too fast flicker frequency, too slow flicker frequency and normal flicker frequency;

[0043] The frequency adjustment module dynamically analyzes the deviation between the current flickering frequency and the expected flickering frequency, and adjusts the current in the LED drive circuit to correct the pulse frequency of the too fast flickering frequency and the too slow flickering frequency, and accurately controls the pulse frequency of the light source to restore the expected flickering frequency.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] The present invention obtains the actual flickering frequency of the LED light source in real time through sensors and monitoring equipment, and uses spectral analysis and digital signal processing technology to conduct in-depth analysis of the frequency. It can not only quickly identify potential frequency anomalies, but also quantify the difference between the actual frequency and the expected frequency. Compared with the traditional method that relies on manual or visual detection, it can capture the anomaly in time when it just occurs, and provide accurate feedback through data analysis, which greatly reduces the detection lag. This helps to reduce the risk of plants being exposed to abnormal light for a long time, ensure the stability of the photoperiod, and effectively avoid adverse consequences such as plant growth disorders and abnormal leaf development.

[0046] Through dynamic analysis of frequency deviation, the present invention can automatically adjust the current in the LED drive circuit according to the actual too fast flashing frequency or too slow flashing frequency, correct the frequency anomaly, and utilize the precise adjustment of current to enable the system to quickly restore the normal flashing frequency of the LED light source, thereby avoiding irreversible damage to plants caused by long-term abnormal lighting. The process is fully automated, ensuring that the system can respond quickly when abnormal frequency occurs, and continuously monitors and adjusts the current through a feedback mechanism to keep the LED light source within the expected frequency range, thereby improving the lighting optimization effect for plant growth. This precise control can continuously maintain an ideal lighting environment and optimize the growth rate, health and yield of plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 The present invention is a flow chart of the plant growth optimization method based on LED spectrum control.

[0049] Figure 2 It is a module schematic diagram of the plant growth optimization system based on LED spectrum control of the present invention. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0051] Embodiment 1: This embodiment provides as Figure 1 The plant growth optimization method based on LED spectrum control shown includes the following steps:

[0052] Use sensors or monitoring equipment to obtain the actual flickering frequency of the LED light source in real time;

[0053] In the prior art, light intensity sensors, spectrum sensors or high-precision optical devices are often used to capture the pulse frequency of LED light sources. These devices can accurately detect the frequency characteristics of the light source output. This step provides basic data for subsequent frequency analysis and adjustment, and avoids the lag problem of relying solely on the naked eye to judge light source abnormalities.

[0054] After data collection, the flicker frequency of the LED light source is analyzed using spectrum analysis technology in the monitoring window to preliminarily identify abnormalities in the flicker frequency;

[0055] In the monitoring window, the flicker frequency of the LED light source is analyzed using spectrum analysis technology to generate a flicker frequency concealment coefficient. The flicker frequency concealment coefficient is used to intelligently sense the flicker frequency anomaly. The generation logic of the flicker frequency concealment coefficient is as follows:

[0056] The light intensity data of the LED light source is collected in real time through sensors (light intensity sensor, spectrum sensor), and expressed in the time domain as ,in Represents the light intensity at time t. In order to better analyze the frequency characteristics, it is necessary to convert the time domain signal into a frequency domain signal. The discrete Fourier transform (DFT) is used to convert the collected light intensity data from the time domain to the frequency domain to obtain the frequency domain signal , where f is the frequency, the expression for the discrete Fourier transform is: , where N represents the number of sampling points, n is the point in the sampling sequence, i is the imaginary unit, and the frequency domain signal is the complex number representation of frequency f, containing frequency and phase information;

[0057] In the frequency domain signal After that, we need to extract the significant frequency components in the frequency distribution by calculating the spectral energy density , quantify the energy contribution at different frequencies, and the calculation expression of spectral energy density is: ,in is the amplitude of the frequency domain signal, indicating the intensity of the frequency component. In order to identify the flickering characteristics of the LED light source, the frequency corresponding to the energy density peak is selected , and use it as the basis for subsequent calculations;

[0058] When analyzing the flicker characteristics of LED light sources, we must first find the frequency with the highest energy density in the spectrum, that is, the frequency with the strongest flicker. This frequency is called LED light sources have different energy distributions at different frequencies, and energy density represents the intensity of a certain frequency component. By selecting the frequency with the highest energy density , the dominant flicker frequency of the light source can be determined.

[0059] Calculate the spectrum smoothness S. The spectrum smoothness reflects the concentration or dispersion of the frequency components. The calculation expression is: , where f represents the frequency, It is the frequency where the energy density peak is located. The larger the spectrum smoothness S is, the more concentrated the frequency components are and the more stable the flicker is. On the contrary, the smaller the spectrum smoothness S is, the more dispersed the frequency components are and abnormal flicker may occur.

[0060] Based on the spectrum smoothness, the flicker frequency concealment coefficient is calculated to quantify the flicker frequency state of the LED light source. A nonlinear adjustment factor is introduced to amplify the influence of the frequency abnormal signal. The calculation expression of the flicker frequency concealment coefficient is: , where Indicates the flicker hiding coefficient, represents the adjustment factor, Indicates the reference value of spectrum smoothness, which is used to determine whether there is frequency anomaly;

[0061] The reference value of spectrum smoothness is a reference standard used to measure whether the flicker frequency of LED light sources is normal. It represents the smoothness of the spectrum under normal operation, that is, the concentration of frequency components. When the actual calculated spectrum smoothness is compared with this reference value, if the difference is large, it means that the frequency of the light source may be abnormal. In simple terms, the reference value is a reference point for normal frequency distribution, which is used to determine whether the frequency components in the spectrum are stable or scattered, thereby identifying frequency anomalies;

[0062] This formula is a Sigmoid function. When the spectrum smoothness S deviates significantly When the flicker frequency hiding coefficient Will change significantly, indicating an abnormal frequency.

[0063] The larger the performance value of the flicker concealment coefficient generated by analyzing the flicker frequency of the LED light source using spectral analysis technology, the greater the risk of potential abnormal flicker frequency in the LED light source. The flicker concealment coefficient is a key indicator extracted based on the light source frequency data, which aims to quantify the potential risk of frequency abnormality. When the frequency of the LED light source is abnormal (such as a flicker frequency that is too fast or too slow), the frequency distribution in the spectrum will change, resulting in a shift in the peak frequency of the energy density or an increase in the dispersion of the spectrum. At this time, the flicker concealment coefficient will increase significantly, indicating that the frequency stability of the light source has been affected and there is a greater risk of potential flicker abnormality. Conversely, if the flicker concealment coefficient is low, it means that the frequency performance of the light source is stable, the spectrum energy is concentrated, and the potential risk of frequency abnormality is small. Therefore, the flicker concealment coefficient can be used as an intelligent indicator of the health of the flicker frequency of the LED light source to warn of the possibility of frequency abnormality.

[0064] The flicker frequency concealment coefficient generated by analyzing the flicker frequency of the LED light source using spectral analysis technology under the monitoring window is compared with the pre-set flicker frequency concealment coefficient reference threshold to preliminarily identify potential abnormalities in the flicker frequency of the LED light source. The specific steps are as follows:

[0065] If the flicker frequency concealment coefficient is greater than or equal to the flicker frequency concealment coefficient reference threshold, the flicker frequency of the LED light source in the monitoring window is marked as a potential abnormality;

[0066] If the flicker frequency concealment coefficient is less than the flicker frequency concealment coefficient reference threshold, the flicker frequency of the LED light source in the monitoring window is marked as normal.

[0067] After initially identifying the abnormal flicker frequency of the LED light source, digital signal processing technology is used to further analyze the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window to quantify the difference between the actual flicker frequency and the expected flicker frequency;

[0068] When the flicker frequency of the LED light source under the monitoring window is marked as a potential abnormality, the digital signal processing technology is used to further analyze the deviation between the actual flicker frequency and the expected flicker frequency to generate an expected flicker frequency deviation index. The expected flicker frequency deviation index is used to quantify the difference between the actual flicker frequency and the expected flicker frequency. The generation logic of the expected flicker frequency deviation index is as follows:

[0069] In the monitoring window, the actual flicker frequency and expected flicker frequency of the LED light source at several time points are collected, and the actual flicker frequency and expected flicker frequency are respectively expressed as and To express, is the sampling time point, , M is the number of samples;

[0070] These flicker frequency data will be used for subsequent flicker frequency deviation calculations. For higher accuracy, the collected flicker frequency data can be high-frequency real-time data that can capture subtle changes in flicker frequency.

[0071] At each sampling point , calculate the instantaneous frequency deviation between the actual flicker frequency and the expected flicker frequency, the calculation expression is: , where It indicates the instantaneous frequency deviation between the actual flicker frequency and the expected flicker frequency. When the actual flashing frequency of the LED light source is higher than the expected flashing frequency, that is, the flashing frequency is too fast. When , it means that the actual flashing frequency is lower than the expected flashing frequency, that is, the flashing frequency is too slow momentarily;

[0072] Through instantaneous deviation calculation, the instantaneous frequency deviation of each sampling point can be obtained, which lays the foundation for further generating the deviation index.

[0073] Calculate the expected flicker frequency deviation index. The calculation expression is: , where represents the expected flicker frequency deviation index, represents the instantaneous weight function, which is used to assign different importance to the instantaneous frequency deviation at different time points. The weight can be set according to the actual scenario. Some time points (such as stable period) may be more important than other time points (such as transition period). k represents the nonlinear amplification factor, which is usually 2 or 3, and is used to enhance the impact of large instantaneous frequency deviation on the expected flicker frequency deviation index. Points with large deviation amplitudes are amplified to ensure that large deviations are more sensitive to the system.

[0074] After the deviation analysis is completed, the flicker frequency conditions under the monitoring window are classified into too fast flicker frequency, too slow flicker frequency and normal flicker frequency;

[0075] After the deviation analysis is completed, the expected flicker frequency deviation index generated by analyzing the deviation between the actual flicker frequency and the expected flicker frequency in the monitoring window is compared with the preset expected flicker frequency deviation reference range to classify the flicker frequency in the monitoring window. The specific classification steps are as follows:

[0076] If the expected flicker frequency deviation index is greater than the maximum value of the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as excessive flicker frequency;

[0077] If the expected flicker frequency deviation index is less than the minimum value of the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as too slow flicker frequency;

[0078] If the expected flicker frequency deviation index is within the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as normal flicker frequency. This situation may be that the flicker frequency of the LED light source has a sudden abnormality and then returns to normal after a short time.

[0079] When the expected flicker frequency deviation index exceeds the maximum value of the expected flicker frequency deviation reference range, it means that the actual flicker frequency of the LED light source is much higher than the expected frequency. This excessively fast flicker frequency may cause excessively frequent switching of light, making it difficult for plants to adapt to the rapid changes in light, thus affecting the efficiency of photosynthesis. When the frequency is too fast, the flicker cycle of the light source becomes shorter, and the alternation between light and dark is too rapid, making it impossible for plants to fully utilize each light cycle to absorb light energy and complete physiological processes. Long-term excessively fast flicker frequency may have a negative impact on the growth, development and photoperiod regulation of plants. Therefore, when the system detects such a situation, it is necessary to adjust the frequency in time to restore normal conditions.

[0080] When the expected flicker frequency deviation index is lower than the minimum value of the expected flicker frequency deviation reference range, it means that the actual flicker frequency of the LED light source is significantly lower than expected. This situation is called too slow flicker, which means that the flicker cycle of the light source is prolonged, and the amount of light received by the plant during the light cycle is insufficient, affecting normal physiological activities. Too slow flicker of the light source will cause the plant to spend too long in the dark period and not be able to fully access sufficient light, which may affect the plant's growth rate, leaf expansion, and even the generation and accumulation of photosynthetic products. Therefore, too slow flicker needs to be paid attention to to avoid irreversible damage to the plant due to long-term insufficient light.

[0081] When the expected flicker frequency deviation index is between the minimum and maximum values ​​of the expected flicker frequency deviation reference range, it means that the actual flicker frequency of the LED light source is relatively consistent with the expected frequency, and the frequency situation at this time is classified as normal flicker frequency. However, this does not mean that there were no abnormalities before. It may be that the LED light source has a sudden frequency abnormality, and then it recovers to normal after a short period of time. The system will not trigger the processing mechanism in this case, because the flicker frequency has returned to the normal working range, but it is necessary to pay close attention to subsequent frequency fluctuations to prevent similar abnormalities from occurring frequently and affecting the stability of plant growth.

[0082] By dynamically analyzing the deviation between the current flickering frequency and the expected flickering frequency and adjusting the current in the LED drive circuit, the pulse frequency of the too-fast flickering frequency and the too-slow flickering frequency is corrected, and the pulse frequency of the light source is accurately controlled to restore the expected flickering frequency;

[0083] By dynamically analyzing the deviation between the current flashing frequency and the expected flashing frequency and adjusting the current in the LED drive circuit, the pulse frequency of the too fast flashing frequency and the too slow flashing frequency can be corrected. The specific steps are as follows:

[0084] Based on the expected flicker frequency deviation index, calculate the current correction amount that needs to be adjusted , the current correction will correct the frequency anomaly by adjusting the current of the drive circuit. The calculation expression is: , where Indicates the instantaneous current adjustment amount, indicating the current correction value that should be adjusted. To control the gain coefficient, determine the intensity of the correction current, and adjust the system response speed, To adjust the sensitivity parameter and control the nonlinear response of the current adjustment amount, this parameter is used to reduce the adjustment range when the deviation is small to avoid over-adjustment;

[0085] For the pulse frequency adjustment of the over-fast flash frequency, the calculated current correction amount is fed back to the LED drive circuit to calculate the adjusted current , the adjusted current represents the reduced current, and the calculation expression is: , where Indicates the current after the pulse frequency of the overfast flash is adjusted; Indicates the actual current value of the current driving circuit;

[0086] For the pulse frequency adjustment of the too slow flash frequency, the calculated current correction amount is fed back to the LED drive circuit to calculate the adjusted current , the adjusted current represents the increased current, and the calculation expression is: , where Indicates the current after the pulse frequency of the slow flash is adjusted.

[0087] After the current is adjusted, the expected flicker frequency deviation index generated between the actual flicker frequency of the LED and the expected flicker frequency is continuously monitored. If the expected flicker frequency deviation index has not yet recovered to the expected flicker frequency deviation reference range, a new current adjustment amount will be calculated again through the pulse frequency adjustment step until the expected flicker frequency deviation index recovers to the expected flicker frequency deviation reference range. The feedback loop formula is: , where Represents the feedback coefficient, which is used to control the actual flicker frequency adjustment speed. Indicates the flickering frequency of the LED light source after adjustment;

[0088] The feedback factor refers to the parameter used to control the actual flashing frequency adjustment speed of the LED light source in the regulation system. It determines the response speed and amplitude of the system to adjust the current when the frequency deviation occurs. A larger feedback factor will cause the system to make adjustments quickly and correct the frequency deviation quickly, but it may cause over-correction and system instability; a smaller feedback factor will make the adjustment slower, and although the stability is higher, it may delay the frequency recovery time. The feedback factor should be set according to the system's stability requirements, response time requirements, and the size of the frequency deviation. It is usually optimized through experiments or system debugging to balance the adjustment speed and stability.

[0089] The present invention obtains the actual flickering frequency of the LED light source in real time through sensors and monitoring equipment, and uses spectral analysis and digital signal processing technology to conduct in-depth analysis of the frequency. It can not only quickly identify potential frequency anomalies, but also quantify the difference between the actual frequency and the expected frequency. Compared with the traditional method that relies on manual or visual detection, it can capture the anomaly in time when it just occurs, and provide accurate feedback through data analysis, which greatly reduces the detection lag. This helps to reduce the risk of plants being exposed to abnormal light for a long time, ensure the stability of the photoperiod, and effectively avoid adverse consequences such as plant growth disorders and abnormal leaf development.

[0090] Through dynamic analysis of frequency deviation, the present invention can automatically adjust the current in the LED drive circuit according to the actual too fast flashing frequency or too slow flashing frequency, correct the frequency anomaly, and utilize the precise adjustment of current to enable the system to quickly restore the normal flashing frequency of the LED light source, thereby avoiding irreversible damage to plants caused by long-term abnormal lighting. The process is fully automated, ensuring that the system can respond quickly when abnormal frequency occurs, and continuously monitors and adjusts the current through a feedback mechanism to keep the LED light source within the expected frequency range, thereby improving the lighting optimization effect for plant growth. This precise control can continuously maintain an ideal lighting environment and optimize the growth rate, health and yield of plants.

[0091] Example 2: This example provides Figure 2 The plant growth optimization system based on LED spectrum control shown is used to implement the plant growth optimization method based on LED spectrum control of the above-mentioned embodiment 1, and the system includes a real-time frequency acquisition module, a frequency anomaly recognition module, a frequency deviation quantification module, a frequency classification module and a frequency adjustment module;

[0092] Real-time frequency acquisition module, using sensors or monitoring equipment to obtain the actual flickering frequency of the LED light source in real time;

[0093] Frequency anomaly identification module: after data collection, the flicker frequency of the LED light source is analyzed using spectrum analysis technology in the monitoring window to preliminarily identify the anomaly of the flicker frequency;

[0094] The frequency deviation quantification module, after initially identifying the abnormal flicker frequency of the LED light source, further analyzes the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window using digital signal processing technology to quantify the difference between the actual flicker frequency and the expected flicker frequency;

[0095] The frequency classification module, after the deviation analysis is completed, classifies the flicker frequency under the monitoring window into too fast flicker frequency, too slow flicker frequency and normal flicker frequency;

[0096] The frequency adjustment module dynamically analyzes the deviation between the current flickering frequency and the expected flickering frequency, and adjusts the current in the LED drive circuit to correct the pulse frequency of the too fast flickering frequency and the too slow flickering frequency, and accurately controls the pulse frequency of the light source to restore the expected flickering frequency;

[0097] The plant growth optimization method based on LED spectrum control provided in an embodiment of the present invention is realized by the above-mentioned plant growth optimization system based on LED spectrum control. The specific methods and processes of the plant growth optimization system based on LED spectrum control are detailed in the above-mentioned embodiment of the plant growth optimization method based on LED spectrum control, which will not be repeated here.

[0098] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

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

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

[0101] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0104] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A plant growth optimization method based on LED spectrum control, characterized in that: The following steps are involved: Use sensors or monitoring equipment to obtain the actual flickering frequency of the LED light source in real time; After data collection, the flicker frequency of the LED light source is analyzed using spectrum analysis technology in the monitoring window to preliminarily identify abnormal flicker frequency. The specific steps include: In the monitoring window, the flicker frequency of the LED light source is analyzed using spectrum analysis technology to generate a flicker frequency concealment coefficient, which can be used to intelligently perceive flicker frequency anomalies. The generation logic of the flicker frequency concealment coefficient is as follows: The light intensity data of the LED light source is collected in real time by the sensor and expressed in the time domain as ,in Represents the light intensity at time t, converts the time domain signal into a frequency domain signal, and uses discrete Fourier transform to convert the collected light intensity data from the time domain to the frequency domain to obtain the frequency domain signal , where f is the frequency, the expression for the discrete Fourier transform is: , where N represents the number of sampling points, n is the point in the sampling sequence, i is the imaginary unit, and the frequency domain signal is the complex number representation of frequency f, containing frequency and phase information; In the frequency domain signal After that, we need to extract the significant frequency components in the frequency distribution by calculating the spectral energy density , quantify the energy contribution at different frequencies, and the calculation expression of spectral energy density is: ,in is the amplitude of the frequency domain signal, indicating the intensity of the frequency component; Calculate the spectrum smoothness S. The spectrum smoothness reflects the concentration or dispersion of the frequency components. The calculation expression is: , where f represents the frequency, is the frequency at which the energy density peaks; Based on the spectrum smoothness, the flicker frequency concealment coefficient is calculated to quantify the flicker frequency state of the LED light source. A nonlinear adjustment factor is introduced to amplify the influence of the frequency abnormal signal. The calculation expression of the flicker frequency concealment coefficient is: , where Indicates the flicker hiding coefficient, represents the adjustment factor, Indicates the reference value of spectrum smoothness, which is used to determine whether there is frequency anomaly; After initially identifying the abnormal flicker frequency of the LED light source, digital signal processing technology is used to further analyze the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window to quantify the difference between the actual flicker frequency and the expected flicker frequency; After the deviation analysis is completed, the flicker frequency conditions under the monitoring window are classified into too fast flicker frequency, too slow flicker frequency and normal flicker frequency; By dynamically analyzing the deviation between the current flickering frequency and the expected flickering frequency and adjusting the current in the LED drive circuit, the pulse frequency of the excessively fast and excessively slow flickering frequencies is corrected, and the pulse frequency of the light source is precisely controlled to restore the expected flickering frequency.

2. The plant growth optimization method based on LED spectrum control according to claim 1, characterized in that: The flicker frequency concealment coefficient generated by analyzing the flicker frequency of the LED light source using spectral analysis technology under the monitoring window is compared with the pre-set flicker frequency concealment coefficient reference threshold to preliminarily identify potential abnormalities in the flicker frequency of the LED light source. The specific steps are as follows: If the flicker frequency concealment coefficient is greater than or equal to the flicker frequency concealment coefficient reference threshold, the flicker frequency of the LED light source in the monitoring window is marked as a potential abnormality; If the flicker frequency concealment coefficient is less than the flicker frequency concealment coefficient reference threshold, the flicker frequency of the LED light source in the monitoring window is marked as normal.

3. The plant growth optimization method based on LED spectrum control according to claim 2, characterized in that: When the flicker frequency of the LED light source under the monitoring window is marked as a potential abnormality, digital signal processing technology is used to further analyze the deviation between the actual flicker frequency and the expected flicker frequency to generate an expected flicker frequency deviation index. The difference between the actual flicker frequency and the expected flicker frequency is quantified by the expected flicker frequency deviation index.

4. The plant growth optimization method based on LED spectrum control according to claim 3, characterized in that: The generation logic of the expected flicker frequency deviation index is as follows: In the monitoring window, the actual flicker frequency and expected flicker frequency of the LED light source at several time points are collected, and the actual flicker frequency and expected flicker frequency are respectively expressed as and To express, is the sampling time point, , M is the number of samples; At each sampling point , calculate the instantaneous frequency deviation between the actual flicker frequency and the expected flicker frequency, the calculation expression is: , where It indicates the instantaneous frequency deviation between the actual flicker frequency and the expected flicker frequency. When the actual flashing frequency of the LED light source is higher than the expected flashing frequency, that is, the flashing frequency is too fast. When , it means that the actual flashing frequency is lower than the expected flashing frequency, that is, the flashing frequency is too slow momentarily; Calculate the expected flicker frequency deviation index. The calculation expression is: , where represents the expected flicker frequency deviation index, represents the instantaneous weight function, which is used to assign different importances to the instantaneous frequency deviation at different time points. k represents the nonlinear amplification factor, which is used to enhance the influence of the instantaneous frequency deviation on the expected flicker frequency deviation index.

5. The plant growth optimization method based on LED spectrum control according to claim 3, characterized in that: After the deviation analysis is completed, the expected flicker frequency deviation index generated by analyzing the deviation between the actual flicker frequency and the expected flicker frequency in the monitoring window is compared with the preset expected flicker frequency deviation reference range to classify the flicker frequency in the monitoring window. The specific classification steps are as follows: If the expected flicker frequency deviation index is greater than the maximum value of the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as excessive flicker frequency; If the expected flicker frequency deviation index is less than the minimum value of the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as too slow flicker frequency; If the expected flicker frequency deviation index is within the expected flicker frequency deviation reference range, the flicker frequency situation under the monitoring window is classified as normal flicker frequency.

6. The plant growth optimization method based on LED spectrum control according to claim 4, characterized in that: By dynamically analyzing the deviation between the current flashing frequency and the expected flashing frequency and adjusting the current in the LED drive circuit, the pulse frequency of the too fast flashing frequency and the too slow flashing frequency can be corrected. The specific steps are as follows: Based on the expected flicker frequency deviation index, calculate the current correction amount that needs to be adjusted , the current correction will correct the frequency anomaly by adjusting the current of the drive circuit. The calculation expression is: , where Indicates the instantaneous current adjustment amount, indicating the current correction value that should be adjusted. To control the gain coefficient, determine the intensity of the correction current, and adjust the system response speed, To adjust the sensitivity parameter, the nonlinear response of the current adjustment amount is controlled.

7. The plant growth optimization method based on LED spectrum control according to claim 6, characterized in that: For the pulse frequency adjustment of the too fast flash frequency, the calculated current correction amount is fed back to the LED drive circuit to calculate the adjusted current , the adjusted current represents the reduced current, and the calculation expression is: , where Indicates the current after the pulse frequency of the overfast flash is adjusted. Indicates the actual current value of the current driving circuit; For the pulse frequency adjustment of the too slow flash frequency, the calculated current correction amount is fed back to the LED drive circuit to calculate the adjusted current , the adjusted current represents the increased current, and the calculation expression is: , where Indicates the current after the pulse frequency of the slow flash is adjusted.

8. The plant growth optimization method based on LED spectrum control according to claim 7, characterized in that: After the current is adjusted, the expected flicker frequency deviation index generated between the actual flicker frequency of the LED and the expected flicker frequency is continuously monitored. If the expected flicker frequency deviation index has not yet recovered to the expected flicker frequency deviation reference range, a new current adjustment amount will be calculated again through the pulse frequency adjustment step until the expected flicker frequency deviation index recovers to the expected flicker frequency deviation reference range. The feedback loop formula is: , where Represents the feedback coefficient, which is used to control the actual flicker frequency adjustment speed. Indicates the flickering frequency of the LED light source after adjustment.

9. A plant growth optimization system based on LED spectrum control, used to implement the plant growth optimization method based on LED spectrum control as described in any one of claims 1 to 8, characterized in that: It includes real-time frequency acquisition module, frequency anomaly identification module, frequency deviation quantification module, frequency classification module and frequency adjustment module; Real-time frequency acquisition module, using sensors or monitoring equipment to obtain the actual flickering frequency of the LED light source in real time; Frequency anomaly identification module: after data collection, the flicker frequency of the LED light source is analyzed using spectrum analysis technology in the monitoring window to preliminarily identify the anomaly of the flicker frequency; The frequency deviation quantification module, after initially identifying the abnormal flicker frequency of the LED light source, further analyzes the deviation between the actual flicker frequency and the expected flicker frequency under the monitoring window using digital signal processing technology to quantify the difference between the actual flicker frequency and the expected flicker frequency; The frequency classification module, after the deviation analysis is completed, classifies the flicker frequency under the monitoring window into too fast flicker frequency, too slow flicker frequency and normal flicker frequency; The frequency adjustment module dynamically analyzes the deviation between the current flickering frequency and the expected flickering frequency, and adjusts the current in the LED drive circuit to correct the pulse frequency of too fast flickering frequency and too slow flickering frequency, and accurately controls the pulse frequency of the light source to restore the expected flickering frequency.

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