Haematococcus pluvialis culture illumination system based on edge control and control method

Abstract

The invention discloses a haematococcus pluvialis culture illumination system based on edge control and a control method, and relates to the technical field of haematococcus pluvialis culture illumination control, and the haematococcus pluvialis culture illumination system comprises a plurality of sensor monitoring modules, a plurality of edge controllers, a plurality of illumination units and a digital twinborn monitoring platform. According to the method, the illumination intensity can be dynamically adjusted according to the actual growth state of algae, and the problem of algae growth inhibition or insufficient astaxanthin accumulation possibly caused in a traditional fixed illumination control mode is solved. All haematococcus pluvialis cultivation is divided into a plurality of areas for more precise control, an edge controller is arranged in each set area range, and the edge controllers can optimize illumination modes in the area ranges based on real-time data analysis. The digital twinborn monitoring platform establishes a three-dimensional digital model of haematococcus pluvialis growth by collecting data of the sensor monitoring module and the edge controller in real time, and real-time visual monitoring is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of illumination control for Haematococcus pluvialis cultivation, and in particular to an edge-controlled illumination system and a control method for Haematococcus pluvialis cultivation. Background Art

[0002] Haematococcus pluvialis, a single-celled green algae, has attracted significant attention in recent years in fields such as biotechnology, healthcare, and food additives due to its rich content of high-value-added bioactive substances such as astaxanthin. As a key environmental factor influencing the growth and astaxanthin synthesis of Haematococcus pluvialis, precise control of light is crucial for improving aquaculture efficiency and product quality. However, current light control technologies for Haematococcus pluvialis aquaculture still have numerous shortcomings, necessitating innovative solutions to overcome existing technical bottlenecks.

[0003] Current lighting control technologies for Haematococcus pluvialis cultivation primarily rely on traditional control methods and basic photoelectric sensing systems. These technologies can meet basic lighting requirements under specific conditions, but lack intelligent and refined control capabilities. Based on existing research data, the current status of Haematococcus pluvialis lighting control technology is mainly reflected in the following aspects: Existing lighting control systems typically use a simple combination of photoelectric sensors and timers, which cannot precisely control light intensity based on the actual growth needs of Haematococcus pluvialis. Research has shown that when light intensity is too strong, the algae move too quickly, causing damage; while when light intensity is too weak, the algae move more slowly and tend to adhere to the wall, affecting their growth efficiency. This imprecise control not only affects the normal growth of the algae but also leads to waste of resources and increased energy consumption.

[0004] Furthermore, existing systems typically control light intensity by adjusting the area of ​​the solar panels. However, to avoid obstructing other aquaculture tubes, the panels are typically kept small, resulting in low light collection efficiency. This problem is particularly pronounced at high light intensities. This method cannot precisely adjust light intensity, making it difficult to meet the lighting requirements of Haematococcus pluvialis at different growth stages.

[0005] Existing lighting control systems lack dynamic regulation capabilities and are unable to adjust lighting parameters in real time based on changing environmental conditions and algae growth status. Traditional timer control methods automatically turn lighting systems on and off based on a preset schedule, but are unable to adapt to the changing growth cycle of Haematococcus pluvialis, as light requirements vary with the algae's growth stage.

[0006] This static control method not only reduces light utilization efficiency but also can lead to energy waste. When there is sufficient light, the system may still turn on artificial lighting, resulting in unnecessary energy consumption; and when there is insufficient light, it may not provide sufficient light intensity, affecting algae growth.

[0007] Existing lighting control systems typically only provide a single spectrum of artificial light, making it impossible to adjust the spectral composition to suit the needs of Haematococcus pluvialis at different growth stages. Research has shown that different wavelengths of light have different effects on algae growth and metabolism, and that properly configuring the spectrum can significantly improve algal growth efficiency and target product yield.

[0008] However, current systems lack the ability to finely control the spectrum and are unable to dynamically adjust the spectral composition according to the algal growth state and environmental conditions, which severely limits the effectiveness of light control and the optimization of algal growth.

[0009] Therefore, it is urgent to deeply analyze the current status and defects of the lighting control technology of Haematococcus pluvialis, and propose an innovative lighting system and control method based on edge control to provide a theoretical basis and practical guidance for the technological upgrading of the Haematococcus pluvialis breeding industry. Summary of the Invention

[0010] In order to solve the above technical problems, the present invention provides a lighting system and control method for cultivating Haematococcus pluvialis based on edge control. The following technical solutions are adopted: The edge-controlled Haematococcus pluvialis cultivation lighting system includes multiple sensor monitoring modules, multiple edge controllers, multiple lighting units and a digital twin monitoring platform. The sensor monitoring modules are used to monitor the environmental parameters, growth status parameters and visual images of the Haematococcus pluvialis growth within a set area. The multiple edge controllers are respectively installed on one side of the multiple sensor monitoring modules and communicate with the sensor monitoring modules to collect environmental parameters and growth status parameters for environmental parameter analysis and growth status analysis to generate lighting control instructions. The lighting unit is used to supplement the lighting of the Haematococcus pluvialis within the area. The digital twin monitoring platform is respectively communicated with the multiple sensor monitoring modules and the multiple edge controllers to perform three-dimensional modeling of the growth status of the Haematococcus pluvialis and perform digital twin display of three-dimensional simulation images and real-time visual images.

[0011] Optionally, the sensor monitoring module includes a temperature sensor, a carbon dioxide sensor, a pH sensor, an optical sensor, a microscopic imaging module, a chlorophyll fluorescence sensor and a visual camera, wherein the temperature sensor is used to monitor the temperature of the culture solution, the carbon dioxide sensor is used to monitor the environmental carbon dioxide concentration, the pH sensor is used to monitor the pH value of the nutrient solution, the optical sensor is used to monitor the environmental lighting parameters, the microscopic imaging module and the chlorophyll fluorescence sensor are used to monitor the growth status parameters of Haematococcus pluvialis, and the visual camera is used to capture real-time visual images of Haematococcus pluvialis; the temperature sensor, carbon dioxide sensor, pH sensor, optical sensor, microscopic imaging module and chlorophyll fluorescence sensor are respectively communicatively connected to the edge controller.

[0012] By employing this technical solution, multiple sensor monitoring modules collect real-time data on Haematococcus pluvialis's environmental parameters (such as light intensity, temperature, and pH) and growth status parameters (such as cell density and astaxanthin content), transmitting these data to an edge controller for analysis. Based on this real-time data analysis, the edge controller generates lighting control commands, directly regulating the output of multiple lighting units and achieving precise adjustment of light intensity.

[0013] Haematococcus pluvialis is extremely sensitive to light conditions, with light requirements varying significantly across different growth stages. For example, during the green cell growth phase, an optimal light intensity is 100-200 μmol / m²·s, while during the astaxanthin accumulation phase, a higher light intensity (e.g., 5 μmol / m²·s) is required. This system dynamically adjusts light intensity based on the actual growth status of the algae, avoiding the potential problems of algal growth inhibition or insufficient astaxanthin accumulation that can occur with traditional fixed light control methods.

[0014] All Haematococcus pluvialis cultivation is divided into multiple areas for more precise control. An edge controller is set up within each set area. The edge controller can optimize the lighting mode within the area based on real-time data analysis. For example, the appropriate combination of red light and white light can promote the growth of Haematococcus pluvialis and increase astaxanthin production. Studies have shown that red light promotes photosynthesis and maintains the pH value of the culture medium between 8-9 by regulating the activity of carbonic anhydrase. Fixed rate increase.

[0015] The system can flexibly switch between different lighting modes to optimize photosynthesis efficiency, significantly increasing algal growth rate and astaxanthin accumulation. Under appropriate lighting conditions, astaxanthin production is significantly increased compared to traditional lighting control.

[0016] The edge controller can adjust lighting units on demand based on real-time data analysis, avoiding the energy waste caused by fixed patterns in traditional lighting control. For example, when algae are in a low-light demand phase, the system can automatically reduce light intensity, thereby reducing energy consumption.

[0017] The digital twin monitoring platform collects real-time data from sensor monitoring modules and edge controllers to build a three-dimensional digital model of Haematococcus pluvialis growth, enabling real-time visual monitoring. Furthermore, the platform predicts potential problems and issues early warnings based on historical data and machine learning algorithms.

[0018] Optionally, the lighting unit includes an LED chipset, a heat dissipation system and a PWM driver. The LED chipset is installed above the area range through a bracket and faces the Haematococcus pluvialis. The heat dissipation system is installed on the back of the LED chipset. The PWM driver is controlled and connected to the LED chipset. The edge controller controls the power and spectrum combination of the LED chipset through the PWM driver.

[0019] By employing this technical solution, a PWM (pulse-width modulation) driver precisely controls the output power of the LED chipset. This means the edge controller can adjust light intensity with millisecond precision based on real-time data from the sensor monitoring module (such as algal cell density, astaxanthin accumulation levels, and ambient light changes). Furthermore, LED chipsets typically consist of LEDs of different colors (such as red, blue, and white). The PWM driver can control the power of these different colors individually or in combination, allowing for flexible adjustment of the light spectrum.

[0020] Haematococcus pluvialis has significantly different light intensity and spectrum requirements during different growth phases (logarithmic growth phase, dormancy phase / astaxanthin accumulation phase). For example, red and blue light are essential for algal growth and astaxanthin synthesis, but the optimal ratio varies with each phase. This system can precisely adjust light intensity and spectrum in real time, providing a "tailor-made" lighting environment for the algae, maximizing growth and astaxanthin accumulation. This is difficult to achieve with traditional lighting systems with fixed spectra or simple dimming.

[0021] Optionally, the edge controller includes a multi-channel data collector, a memory, an AI chip for analyzing the growth status of Haematococcus pluvialis, and a control chip. The data input end of the multi-channel data collector is respectively communicated with each sensor of the sensor monitoring module, the memory is communicated with the data output end of the multi-channel data collector, the AI ​​chip is communicated with the memory, and the control chip is communicated with the AI ​​chip. The control chip controls the power and spectrum combination of the LED chipset through a PWM driver.

[0022] Optionally, the digital twin monitoring platform is implemented based on a computer.

[0023] By employing the aforementioned technical solution, the edge controller integrates a multi-channel data collector, memory, AI chip, and control chip. This means it can not only collect sensor data in real time, but also store and perform complex AI analysis locally (close to the data source), eliminating the need to upload all data to the cloud and wait for instructions. The AI ​​chip is specifically designed to analyze the growth status of Haematococcus pluvialis and can handle nonlinear and complex growth patterns and environmental influences.

[0024] This design significantly improves the system's response speed and decision-making accuracy. Based on historical and real-time data, the AI ​​chip can predict algae growth trends, astaxanthin accumulation rates, and even identify potential growth stresses (such as photoinhibition caused by excessive light intensity and nutrient deficiencies). Based on the AI ​​chip's analysis, the control chip quickly generates and executes precise lighting control instructions, enabling proactive, intelligent regulation of algae growth rather than a simple passive response.

[0025] The memory is used to store historical data and model parameters, which the AI ​​chip can use to continuously learn and optimize its analysis model. The control chip is responsible for executing these optimized models.

[0026] Through continuous learning, the system can gradually grasp the optimal lighting strategy for a specific batch of algae and under specific environmental conditions. For example, it might learn that a specific spectral combination and intensity profile within a certain temperature range maximizes astaxanthin production. This learning capability allows the system to increasingly understand the Haematococcus pluvialis it cultivates, continuously optimizing control effectiveness and surpassing traditional control systems based on fixed rules or simple threshold judgments.

[0027] The method for controlling illumination of Haematococcus pluvialis cultivation based on edge control adopts a Haematococcus pluvialis cultivation illumination system based on edge control to control illumination of the Haematococcus pluvialis cultivation area, comprising the following steps: Step 1: The multi-channel data collector collects data from the temperature sensor, carbon dioxide sensor, pH sensor, optical sensor, microscopic imaging module and chlorophyll fluorescence sensor at set time intervals and stores the data in the memory; Step 2: The AI ​​chip executes a lightweight YOLOv8 model to identify the microscopic imaging data obtained by the microscopic imaging module, and outputs the number of motile cells, the diameter of amoenospores, and the astaxanthin area ratio; Step 3: The AI ​​chip executes the trained growth stage determination model to output the growth stage of Haematococcus pluvialis. The input parameters are: number of motile cells, diameter of amoenospores, astaxanthin area ratio, Fv / Fm value of the chlorophyll fluorescence sensor, chlorophyll characteristic absorption peak intensity of the optical sensor, culture solution temperature value collected by the temperature sensor, and HSV value of the algal solution apparent color collected by the visual camera. Step 4: The AI ​​chip calculates the light requirement parameters based on the growth stage of Haematococcus pluvialis; Step 5: The control chip generates a light control instruction according to the light demand parameters and transmits the light control instruction to the PWM driver; Step 6: The PWM driver executes the illumination control instruction to control the LED chipset to provide additional illumination to the Haematococcus pluvialis according to the set illumination and spectrum combination.

[0028] Optionally, in step 3, the growth stage of Haematococcus pluvialis includes a green growth period and a red stress period.

[0029] Optionally, in step 4, if the growth stage of Haematococcus pluvialis is determined to be the green growth stage, a combination of red light, blue light and white light is used, and the calculation formula for the target light intensity is: ; in is the target light intensity, is the nutrient solution temperature, is the color enhancement factor; ;in Red saturation of algal fluid; The formula for determining the red light ratio is: ;in is the red light ratio, is the carbon dioxide concentration; The formula for determining the blue light ratio is: ; in is the blue light ratio, is the number of motile cells; The formula for determining the white light ratio is: .

[0030] Optionally, in step 4, if the growth stage of Haematococcus pluvialis is determined to be the red stress stage, a combination of blue light, ultraviolet light, and red light is used, and the target light intensity is calculated using the formula: base_intensity+diam_adjust; in is the target light intensity, is the basic light intensity; diam_adjust is the diameter compensation item; ; ; in It's a nutrient solution value, is the average diameter of amoenospores; The formula for determining the blue light ratio is: ; in is the current red light ratio; The formula for determining the UV ratio is: ; in is the UV ratio, is the astaxanthin area ratio; The formula for determining the red light ratio is: .

[0031] By adopting the above technical solution, steps 2 and 3 use the AI ​​chip to execute the lightweight YOLOv8 model and the trained growth stage determination model, comprehensively analyzing multi-dimensional data such as microscopic images (cell number, diameter, astaxanthin ratio), chlorophyll fluorescence (Fv / Fm), optical characteristics, temperature, and apparent color HSV value to accurately determine whether the Haematococcus pluvialis is in the green growth stage or the red stress stage.

[0032] This intelligent judgment, based on the fusion of multi-source data, is far more accurate and objective than traditional single indicators (such as visual observation or simple pH judgment). It enables the system to truly understand the current physiological state of the algae, providing a scientific basis for subsequent lighting strategy formulation, avoiding a "one-size-fits-all" control approach and achieving differentiated and precise management of different growth stages.

[0033] Differentiated and parameterized lighting strategies for growth stages: Step 4: Based on the identified growth stage, completely different light combinations (green growth phase: red light, blue light, white light; red stress phase: blue light, ultraviolet light, red light) and calculation formulas are used to derive light requirement parameters (target light intensity and the proportion of each spectrum). These formulas take into account not only the algae's own state (such as cell number, diameter, and astaxanthin content) but also environmental factors (such as temperature, pH, and carbon dioxide concentration).

[0034] Green growth period: Red light, blue light and white light are used to simulate natural light to promote photosynthesis and cell proliferation. The temperature correction factor (temp_factor) and color enhancement factor (color_enhance) are introduced into the formula, as well as the carbon dioxide concentration ( The red light ratio adjustment based on the cell number (_conc) and the blue light ratio adjustment based on the cell number (mobile_count) ensure that the lighting not only meets the basic growth needs but also can be dynamically optimized according to the environment to improve nutrient utilization efficiency.

[0035] During the red-shift stress phase, a combination of blue, UV, and red light is used, which is key to inducing astaxanthin synthesis. A base light intensity (base_intensity) and compensation terms based on pH and amoenospore diameter (diam_adjust) are incorporated into the formula to ensure adequate and appropriate light intensity stimulation under stress conditions. The blue light ratio is correlated with the red light ratio, the UV light ratio is directly linked to the astaxanthin ratio (astaxanthin_ratio), and the red light ratio is dynamically adjusted. This design aims to maximize the stress-induced effect while avoiding cell damage caused by excessive stress.

[0036] This differentiated and parameterized strategy makes light control no longer a simple switch or fixed mode, but a "customized" solution closely integrated with the physiological needs of algae and environmental conditions. It greatly improves the utilization efficiency of light resources and is expected to significantly improve the final yield and quality of astaxanthin.

[0037] Optionally, a digital twin display step is also included, in which the digital twin monitoring platform collects real-time parameters of multiple sensor monitoring modules and multiple edge controllers respectively, performs three-dimensional modeling of the growth status of Haematococcus pluvialis, and performs digital twin display of three-dimensional simulation images and real-time visual images, and the display content includes growth status and lighting control parameters.

[0038] In summary, the present invention includes at least one of the following beneficial technical effects: This invention provides an edge-controlled lighting system and control method for cultivating Haematococcus pluvialis. Multiple sensor monitoring modules collect real-time environmental and growth status parameters of the algae and transmit them to an edge controller for analysis. Based on this real-time data analysis, the edge controller generates lighting control instructions, directly regulating the output of multiple lighting units and achieving precise adjustment of light intensity.

[0039] It can dynamically adjust the light intensity according to the actual growth status of the algae, avoiding the problems of algae growth inhibition or insufficient astaxanthin accumulation that may be caused by traditional fixed light control methods.

[0040] All Haematococcus pluvialis cultivation is divided into multiple areas for more precise control. An edge controller is set up in each set area. The edge controller can optimize the lighting mode within the area based on real-time data analysis.

[0041] The digital twin monitoring platform collects data from sensor monitoring modules and edge controllers in real time to establish a three-dimensional digital model of the growth of Haematococcus pluvialis, enabling real-time visual monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1This is a schematic diagram of the communication connections of components of the edge-controlled Haematococcus pluvialis cultivation lighting system of the present invention; Figure 2 The present invention is a flow chart of a method for controlling illumination of Haematococcus pluvialis culture based on edge control.

[0043] Description of the accompanying figures: 1. Sensor monitoring module; 11. Temperature sensor; 12. Carbon dioxide sensor; 13. pH sensor; 14. Optical sensor; 15. Microscopic imaging module; 16. Chlorophyll fluorescence sensor; 17. Visual camera; 2. Edge controller; 21. Multi-channel data collector; 22. Memory; 23. AI chip; 24. Control chip; 3. Lighting unit; 31. LED chipset; 33. PWM driver; 4. Digital twin monitoring platform. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to the accompanying drawings.

[0045] The embodiments of the present invention disclose an edge-controlled lighting system and a control method for cultivating Haematococcus pluvialis.

[0046] Reference Figure 1 and Figure 2 , Example 1, an edge-controlled Haematococcus pluvialis cultivation lighting system includes multiple sensor monitoring modules 1, multiple edge controllers 2, multiple lighting units 3 and a digital twin monitoring platform 4. The sensor monitoring module 1 is used to monitor the environmental parameters, growth status parameters and visual images of the growth of Haematococcus pluvialis within a set area. The multiple edge controllers 2 are respectively installed on one side of the multiple sensor monitoring modules 1 and communicated with the sensor monitoring modules 1 to collect environmental parameters and growth status parameters for environmental parameter analysis and growth status analysis to generate lighting control instructions. The lighting unit 3 is used to supplement the lighting of the Haematococcus pluvialis within the area. The digital twin monitoring platform 4 is respectively communicated with the multiple sensor monitoring modules 1 and the multiple edge controllers 2 to perform three-dimensional modeling of the growth status of the Haematococcus pluvialis, and perform digital twin display of three-dimensional simulation images and real-time visual images.

[0047] In Example 2, the sensor monitoring module 1 includes a temperature sensor 11, a carbon dioxide sensor 12, a pH sensor 13, an optical sensor 14, a microscopic imaging module 15, a chlorophyll fluorescence sensor 16, and a visual camera 17. The temperature sensor 11 is used to monitor the temperature of the culture solution, the carbon dioxide sensor 12 is used to monitor the environmental carbon dioxide concentration, the pH sensor 13 is used to monitor the pH value of the nutrient solution, the optical sensor 14 is used to monitor the environmental lighting parameters, the microscopic imaging module 15 and the chlorophyll fluorescence sensor 16 are used to monitor the growth status parameters of Haematococcus pluvialis, and the visual camera 17 is used to capture the real-time visual image of Haematococcus pluvialis; the temperature sensor 11, the carbon dioxide sensor 12, the pH sensor 13, the optical sensor 14, the microscopic imaging module 15, and the chlorophyll fluorescence sensor 16 are respectively connected to the edge controller 2 for communication.

[0048] Multiple sensor monitoring modules 1 collect real-time data on Haematococcus pluvialis's environmental parameters (such as light intensity, temperature, and pH) and growth status parameters (such as cell density and astaxanthin content) and transmit them to an edge controller 2 for analysis. Based on this real-time data analysis, the edge controller generates lighting control instructions, directly regulating the output of multiple lighting units 3 to achieve precise adjustment of light intensity.

[0049] Haematococcus pluvialis is extremely sensitive to light conditions, with light requirements varying significantly across different growth stages. For example, during the green cell growth phase, an optimal light intensity is 100-200 μmol / m²·s, while during the astaxanthin accumulation phase, a higher light intensity (e.g., 145 μmol / m²·s) is required. This system dynamically adjusts light intensity based on the actual growth status of the algae, avoiding the potential problems of algal growth inhibition or insufficient astaxanthin accumulation that can occur with traditional fixed light control methods.

[0050] All Haematococcus pluvialis cultivation is divided into multiple areas for more precise control. An edge controller 2 is set up within each set area. The edge controller 2 can optimize the lighting mode within the area based on real-time data analysis. For example, the appropriate combination of red light and white light can promote the growth of Haematococcus pluvialis and increase astaxanthin production. Studies have shown that red light promotes photosynthesis and maintains the pH value of the culture medium between 8-9 by regulating the activity of carbonic anhydrase. Fixed rate increase.

[0051] The system can flexibly switch between different lighting modes to optimize photosynthesis efficiency, significantly increasing algal growth rate and astaxanthin accumulation. Under appropriate lighting conditions, astaxanthin production is significantly increased compared to traditional lighting control.

[0052] The edge controller 2 can adjust the lighting units 3 based on real-time data analysis, avoiding the energy waste caused by fixed patterns in traditional lighting control. For example, when algae are in a low-light demand phase, the system can automatically reduce light intensity, thereby reducing energy consumption.

[0053] The digital twin monitoring platform 4 collects real-time data from the sensor monitoring module 1 and the edge controller 2 to build a three-dimensional digital model of the Haematococcus pluvialis algae growth, enabling real-time visual monitoring. Furthermore, the platform can predict potential problems and issue early warnings based on historical data and machine learning algorithms.

[0054] In Example 3, the lighting unit 3 includes an LED chipset 31, a heat dissipation system, and a PWM driver 33. The LED chipset 31 is installed above the area range through a bracket and faces the Haematococcus pluvialis. The heat dissipation system is installed on the back of the LED chipset 31. The PWM driver 33 is controlled and connected to the LED chipset 31. The edge controller 2 controls the power and spectrum combination of the LED chipset 31 through the PWM driver 33.

[0055] The PWM (pulse width modulation) driver 33 precisely controls the output power of the LED chipset 31. This means that the edge controller 2 can adjust the light intensity with millisecond-level accuracy based on real-time data transmitted by the sensor monitoring module 1 (such as algal cell density, astaxanthin accumulation level, and ambient light changes). Furthermore, the LED chipset 31 typically consists of LEDs of different colors (such as red, blue, and white). The PWM driver can control the power of these different colors individually or in combination, thereby flexibly adjusting the spectral composition.

[0056] Haematococcus pluvialis has significantly different light intensity and spectrum requirements during different growth phases (logarithmic growth phase, dormancy phase / astaxanthin accumulation phase). For example, red and blue light are essential for algal growth and astaxanthin synthesis, but the optimal ratio varies with each phase. This system can precisely adjust light intensity and spectrum in real time, providing a "tailor-made" lighting environment for the algae, maximizing growth and astaxanthin accumulation. This is difficult to achieve with traditional lighting systems with fixed spectra or simple dimming.

[0057] In Example 4, the edge controller 2 includes a multi-channel data collector 21, a memory 22, an AI chip 23 for analyzing the growth status of Haematococcus pluvialis, and a control chip 24. The data input end of the multi-channel data collector 21 is respectively communicated with each sensor of the sensor monitoring module 1, the memory 22 is communicated with the data output end of the multi-channel data collector 21, the AI ​​chip 23 is communicated with the memory 22, and the control chip 24 is communicated with the AI ​​chip 23. The control chip 24 controls the power and spectrum combination of the LED chipset 31 through the PWM driver 33.

[0058] Example 5: The digital twin monitoring platform 4 is implemented based on a computer.

[0059] Edge controller 2 integrates a multi-channel data collector 21, memory 22, AI chip 23, and control chip 24. This means it can not only collect sensor data in real time, but also store and perform complex AI analysis locally (close to the data source), eliminating the need to upload all data to the cloud and wait for instructions. AI chip 23 is specifically designed to analyze the growth status of Haematococcus pluvialis and can handle nonlinear and complex growth patterns and environmental influences.

[0060] This design significantly improves the system's response speed and decision-making accuracy. Based on historical and real-time data, the AI ​​chip can predict algae growth trends, astaxanthin accumulation rates, and even identify potential growth stresses (such as photoinhibition caused by excessive light intensity and nutrient deficiencies). Based on the AI ​​chip's analysis results, the control chip 24 quickly generates and executes precise lighting control instructions, enabling proactive, intelligent regulation of algae growth rather than a simple passive response.

[0061] The memory 22 is used to store historical data and model parameters, which the AI ​​chip 23 can use to continuously learn and optimize its analysis model. The control chip 24 is responsible for executing these optimized models.

[0062] Through continuous learning, the system can gradually grasp the optimal lighting strategy for a specific batch of algae and under specific environmental conditions. For example, it might learn that a specific spectral combination and intensity profile within a certain temperature range maximizes astaxanthin production. This learning capability allows the system to increasingly understand the Haematococcus pluvialis it cultivates, continuously optimizing control effectiveness and surpassing traditional control systems based on fixed rules or simple threshold judgments.

[0063] Example 6, a method for controlling illumination of Haematococcus pluvialis cultivation based on edge control, employing a Haematococcus pluvialis cultivation illumination system based on edge control to control illumination of a Haematococcus pluvialis cultivation area, comprising the following steps: Step 1: The multi-channel data collector 21 collects data from the temperature sensor 11, the carbon dioxide sensor 12, the pH sensor 13, the optical sensor 14, the microscopic imaging module 15, and the chlorophyll fluorescence sensor 16 at set time intervals and stores the data in the memory 22; Step 2: The AI ​​chip 23 executes the lightweight YOLOv8 model to identify the microscopic imaging data obtained by the microscopic imaging module 15, and outputs the number of motile cells, the diameter of amoenospores, and the astaxanthin area ratio; Step 3: The AI ​​chip 23 executes the trained growth stage determination model to output the growth stage of Haematococcus pluvialis. The input parameters are: the number of motile cells, the diameter of the amoenospores, the astaxanthin area ratio, the Fv / Fm value of the chlorophyll fluorescence sensor 16, the chlorophyll characteristic absorption peak intensity of the optical sensor 14, the culture solution temperature value collected by the temperature sensor 11, and the HSV value of the algal solution apparent color collected by the visual camera 17. Step 4: The AI ​​chip 23 calculates the light requirement parameters according to the growth stage of Haematococcus pluvialis. Step 5: The control chip 24 generates a light control instruction according to the light demand parameter and transmits the light control instruction to the PWM driver 33; Step 6: The PWM driver 33 executes the illumination control instruction to control the LED chipset 31 to provide additional illumination to the Haematococcus pluvialis according to the set illumination and spectrum combination.

[0064] In Example 7, in step 3, the growth stages of Haematococcus pluvialis include a green growth period and a red stress period.

[0065] In Example 8, in step 4, if the growth stage of Haematococcus pluvialis is determined to be the green growth stage, a combination of red light, blue light and white light is used, and the calculation formula for the target light intensity is: ; in is the target light intensity, is the nutrient solution temperature, is the color enhancement factor; ;in Red saturation of algal fluid; The formula for determining the red light ratio is: ;in is the red light ratio, is the carbon dioxide concentration; The formula for determining the blue light ratio is: ; in is the blue light ratio, is the number of motile cells; The formula for determining the white light ratio is: .

[0066] In Example 9, in step 4, if the growth stage of Haematococcus pluvialis is determined to be the red stress period, a combination of blue light, ultraviolet light and red light is used, and the calculation formula for the target light intensity is: base_intensity+diam_adjust; in is the target light intensity, is the basic light intensity; diam_adjust is the diameter compensation item; ; ; in It's a nutrient solution value, is the average diameter of amoenospores; The formula for determining the blue light ratio is: ; in is the current red light ratio; The formula for determining the UV ratio is: ; in is the UV ratio, is the astaxanthin area ratio; The formula for determining the red light ratio is: .

[0067] Steps 2 and 3 use the AI ​​chip 23 to execute the lightweight YOLOv8 model and the trained growth stage determination model, comprehensively analyzing multi-dimensional data such as microscopic images (cell number, diameter, astaxanthin ratio), chlorophyll fluorescence (Fv / Fm), optical characteristics, temperature, and apparent color HSV value to accurately determine whether the Haematococcus pluvialis is in the green growth stage or the red stress stage.

[0068] This intelligent judgment, based on the fusion of multi-source data, is far more accurate and objective than traditional single indicators (such as visual observation or simple pH judgment). It enables the system to truly understand the current physiological state of the algae, providing a scientific basis for subsequent lighting strategy formulation, avoiding a "one-size-fits-all" control approach and achieving differentiated and precise management of different growth stages.

[0069] Differentiated and parameterized lighting strategies for growth stages: Step 4: Based on the identified growth stage, completely different light combinations (green growth phase: red light, blue light, white light; red stress phase: blue light, ultraviolet light, red light) and calculation formulas are used to derive light requirement parameters (target light intensity and the proportion of each spectrum). These formulas take into account not only the algae's own state (such as cell number, diameter, and astaxanthin content) but also environmental factors (such as temperature, pH, and carbon dioxide concentration).

[0070] Green growth period: Red light, blue light and white light are used to simulate natural light to promote photosynthesis and cell proliferation. The temperature correction factor (temp_factor) and color enhancement factor (color_enhance) are introduced into the formula, as well as the carbon dioxide concentration ( The red light ratio adjustment based on the cell number (_conc) and the blue light ratio adjustment based on the cell number (mobile_count) ensure that the lighting not only meets the basic growth needs but also can be dynamically optimized according to the environment to improve nutrient utilization efficiency.

[0071] During the red-shift stress phase, a combination of blue, UV, and red light is used, which is key to inducing astaxanthin synthesis. A base light intensity (base_intensity) and compensation terms based on pH and amoenospore diameter (diam_adjust) are incorporated into the formula to ensure adequate and appropriate light intensity stimulation under stress conditions. The blue light ratio is correlated with the red light ratio, the UV light ratio is directly linked to the astaxanthin ratio (astaxanthin_ratio), and the red light ratio is dynamically adjusted. This design aims to maximize the stress-induced effect while avoiding cell damage caused by excessive stress.

[0072] This differentiated and parameterized strategy makes light control no longer a simple switch or fixed mode, but a "customized" solution closely integrated with the physiological needs of algae and environmental conditions. It greatly improves the utilization efficiency of light resources and is expected to significantly improve the final yield and quality of astaxanthin.

[0073] Example 10 also includes a digital twin display step, in which the digital twin monitoring platform 4 respectively collects real-time parameters of multiple sensor monitoring modules 1 and multiple edge controllers 2, performs three-dimensional modeling of the growth status of Haematococcus pluvialis, and performs digital twin display of three-dimensional simulation images and real-time visual images, and the display content includes growth status and light control parameters.

[0074] The following specific embodiments are used to illustrate the implementation principle of the present invention: System composition: Breeding area division: A planar photobioreactor area of ​​10 meters long and 5 meters wide is divided into 5 independent sub-areas (for example, each sub-area is 2 meters x 5 meters). Each sub-area is regarded as a "set area range" to achieve more precise lighting control.

[0075] Sensor Monitoring Module 1: Install one sensor monitoring module 1 in each sub-area (5 in total). Each module contains: Temperature sensor 11: placed in the middle of the culture medium to monitor the temperature of the culture medium in real time.

[0076] Carbon dioxide sensor 12: installed above the sub-area to monitor the environment concentration.

[0077] pH sensor 13: immersed in the culture solution to monitor the pH value of the nutrient solution.

[0078] Optical sensor 14: measures ambient light parameters (such as PAR, photosynthetically active radiation).

[0079] Microscopic imaging module 15: uses a micro camera and a microscope lens to regularly capture microscopic images of Haematococcus pluvialis in the culture medium.

[0080] Chlorophyll fluorescence sensor 16: immersed in the culture medium to measure chlorophyll fluorescence parameters (such as Fv / Fm).

[0081] Visual camera 17: installed above each sub-area, regularly photographing the apparent color of the algae liquid in the entire sub-area.

[0082] Edge Controller 2: One Edge Controller 2 is installed on one side of each sub-area (5 in total). Each Edge Controller 2 contains: Multi-channel data collector 21: connects and regularly collects data from all sensors in the sensor monitoring module 1 of the corresponding sub-area.

[0083] Memory 22: stores collected original data and historical data.

[0084] AI chip 23: Runs a lightweight YOLOv8 model to identify microscopic images, obtains the number of motile cells, the diameter of immospores, and the area ratio of astaxanthin; runs a trained growth stage determination model and combines all sensor data to determine the current algal growth stage (green growth stage or red stress stage).

[0085] Control chip 24: Receives the growth stage and light requirement parameters output by AI chip 23, and generates specific light control instructions (target light intensity, proportion of each spectrum).

[0086] Lighting unit 3: A set of lighting units 3 (5 in total) is installed above each sub-area. Each lighting unit 3 contains: LED chipset 31: consists of multiple LEDs of different colors, including red LED, blue LED, white LED and ultraviolet LED, which are installed directly above the sub-area through a bracket with the illumination direction facing downward.

[0087] Heat dissipation system: installed on the back of the LED chipset 31 to effectively dissipate the heat generated when the LED is working.

[0088] PWM driver 33: receives the lighting control instruction from the control chip 24 of the corresponding sub-region edge controller 2, accurately controls the power of each color LED in the LED chipset 31, thereby adjusting the overall lighting intensity and spectrum combination.

[0089] Digital twin monitoring platform 4: It is implemented based on a high-performance computer and maintains communication connections with all sensor monitoring modules 1 and edge controllers 2 through wired or wireless networks.

[0090] System operation process (calculation process omitted): Data collection: The sensor monitoring module 1 of each sub-area starts working, and the multi-channel data collector 21 collects temperature, The data such as concentration, pH value, ambient light, microscopic image, chlorophyll fluorescence and apparent color are stored in the memory 22.

[0091] AI analysis and growth stage determination: AI chip 23 begins data processing. First, it identifies the microscopic image to determine the current number of motile cells, the diameter of the amoenospores, and the astaxanthin area ratio. Then, combining the chlorophyll fluorescence Fv / Fm value, the chlorophyll characteristic absorption peak intensity measured by the optical sensor, the culture solution temperature measured by the temperature sensor, and the HSV value of the algae liquid apparent color captured by the visual camera, AI chip 23 runs a growth stage determination model to determine whether the Haematococcus pluvialis is currently in the "green growth phase" or the "red stress phase."

[0092] Calculation of light requirement parameters: Based on the determined growth stage, the AI ​​chip 23 uses the corresponding formula (such as the formula in Example 8 or Example 9, but omitting the specific calculation steps) to calculate the currently required light parameters. For example, if the plant is in the green growth phase, the target light intensity and the ratios of red, blue, and white light are calculated. If the plant is in the red stress phase, the target light intensity and the ratios of blue, ultraviolet, and red light are calculated.

[0093] Generate and send control instructions: The control chip 24 receives the light requirement parameters calculated by the AI ​​chip 23, generates specific PWM control instructions (specifying the duty cycle and frequency of each color LED), and sends the instructions to the PWM driver 33 through the communication interface.

[0094] Execute illumination control: After receiving the instruction, the PWM driver 33 immediately adjusts the power output of each color LED in the LED chipset 31 so that the illumination intensity and spectrum combination in the sub-area reach the preset target value, thereby providing supplementary illumination for the Haematococcus pluvialis.

[0095] Digital Twin Monitoring and Display: The digital twin monitoring platform 4 receives real-time data from all sensor monitoring modules 1 and edge controllers 2. Based on this data, the platform constructs and updates a three-dimensional digital model of the Haematococcus pluvialis growth status. On the monitoring interface, operators can view a 3D simulation of each sub-area (showing algae distribution and density), real-time visual images (algae liquid color changes), the current growth stage determination, and the actual lighting control parameters (such as current light intensity and the proportion of each spectrum). The platform can also provide early warnings of potential anomalies (such as growth stagnation and insufficient light) based on historical data and models.

[0096] Through the above embodiments, the system can independently and intelligently regulate light in each sub-area, accurately matching the needs of Haematococcus pluvialis at different growth stages and environmental conditions, thereby optimizing its growth and astaxanthin accumulation, while achieving efficient energy utilization and providing intuitive visual monitoring and management through the digital twin platform.

[0097] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. The edge-controlled Haematococcus pluvialis cultivation lighting system is characterized by: The system comprises a plurality of sensor monitoring modules (1), a plurality of edge controllers (2), a plurality of lighting units (3) and a digital twin monitoring platform (4), wherein the sensor monitoring module (1) is used to monitor the environmental parameters, growth state parameters and visual images of the growth of Haematococcus pluvialis within a set area, the plurality of edge controllers (2) are respectively installed on one side of the plurality of sensor monitoring modules (1) and are in communication connection with the sensor monitoring modules (1), collecting environmental parameters and growth state parameters, performing environmental parameter analysis and growth state analysis to generate lighting control instructions, the lighting unit (3) is used to supplement lighting for the Haematococcus pluvialis within the area, the digital twin monitoring platform (4) is respectively in communication connection with the plurality of sensor monitoring modules (1) and the plurality of edge controllers (2), performing three-dimensional modeling of the growth state of the Haematococcus pluvialis, and performing digital twin display of three-dimensional simulation images and real-time visual images.

2. The edge-controlled Haematococcus pluvialis cultivation lighting system according to claim 1, characterized in that: The sensor monitoring module (1) comprises a temperature sensor (11), a carbon dioxide sensor (12), a pH sensor (13), an optical sensor (14), a microscopic imaging module (15), a chlorophyll fluorescence sensor (16) and a visual camera (17), wherein the temperature sensor (11) is used to monitor the temperature of the culture solution, the carbon dioxide sensor (12) is used to monitor the concentration of carbon dioxide in the environment, the pH sensor (13) is used to monitor the pH value of the nutrient solution, the optical sensor (14) is used to monitor the ambient light parameters, the microscopic imaging module (15) and the chlorophyll fluorescence sensor (16) are used to monitor the growth state parameters of Haematococcus pluvialis, and the visual camera (17) is used to capture the real-time visual image of Haematococcus pluvialis; the temperature sensor (11), the carbon dioxide sensor (12), the pH sensor (13), the optical sensor (14), the microscopic imaging module (15) and the chlorophyll fluorescence sensor (16) are respectively connected to the edge controller (2) for communication.

3. The edge-controlled Haematococcus pluvialis cultivation lighting system according to claim 2, characterized in that: The lighting unit (3) includes an LED chipset (31), a heat dissipation system, and a PWM driver (33). The LED chipset (31) is installed above the area range through a bracket and faces the Haematococcus pluvialis. The heat dissipation system is installed on the back of the LED chipset (31). The PWM driver (33) is connected to the LED chipset (31) for control. The edge controller (2) controls the power and spectrum combination of the LED chipset (31) through the PWM driver (33).

4. The edge-controlled Haematococcus pluvialis cultivation lighting system according to claim 3, characterized in that: The edge controller (2) includes a multi-channel data collector (21), a memory (22), an AI chip (23) for analyzing the growth status of Haematococcus pluvialis, and a control chip (24). The data input end of the multi-channel data collector (21) is respectively connected to the sensors of the sensor monitoring module (1), the memory (22) is connected to the data output end of the multi-channel data collector (21), the AI ​​chip (23) is connected to the memory (22), and the control chip (24) is connected to the AI ​​chip (23). The control chip (24) controls the power and spectrum combination of the LED chipset (31) through the PWM driver (33).

5. The edge-controlled Haematococcus pluvialis cultivation lighting system according to claim 4, characterized in that: The digital twin monitoring platform (4) is implemented based on computers.

6. A method for controlling illumination of Haematococcus pluvialis cultivation based on edge control, characterized in that: The edge-controlled Haematococcus pluvialis cultivation lighting system according to claim 5 is used to control the lighting in the Haematococcus pluvialis cultivation area, comprising the following steps: Step 1, a multi-channel data collector (21) collects data from a temperature sensor (11), a carbon dioxide sensor (12), a pH sensor (13), an optical sensor (14), a microscopic imaging module (15), and a chlorophyll fluorescence sensor (16) at set time intervals and stores the data in a memory (22); Step 2, the AI ​​chip (23) executes the lightweight YOLOv8 model to identify the microscopic imaging data obtained by the microscopic imaging module (15), and outputs the number of motile cells, the diameter of amoenospores, and the astaxanthin area ratio; Step 3, the AI ​​chip (23) executes the trained growth stage determination model to output the growth stage of Haematococcus pluvialis, and the input parameters are: the number of motile cells, the diameter of immospores, the area ratio of astaxanthin, the Fv / Fm value of the chlorophyll fluorescence sensor (16), the chlorophyll characteristic absorption peak intensity of the optical sensor (14), the culture solution temperature value collected by the temperature sensor (11), and the HSV value of the algae solution apparent color collected by the visual camera (17); Step 4, the AI ​​chip (23) calculates the light requirement parameters according to the growth stage of Haematococcus pluvialis; Step 5: The control chip (24) generates a light control instruction according to the light demand parameter, and transmits the light control instruction to the PWM driver (33); Step 6: The PWM driver (33) executes the illumination control instruction to control the LED chipset (31) to provide additional illumination to the Haematococcus pluvialis according to the set illumination and spectrum combination.

7. The method for controlling illumination of Haematococcus pluvialis cultivation based on edge control according to claim 6, wherein: In step 3, the growth stages of Haematococcus pluvialis include a green growth period and a red stress period.

8. The method for controlling illumination of Haematococcus pluvialis cultivation based on edge control according to claim 6, wherein: In step 4, if the growth stage of Haematococcus pluvialis is determined to be the green growth stage, a combination of red light, blue light and white light is used, and the calculation formula for the target light intensity is: ； in is the target light intensity, is the nutrient solution temperature, is the color enhancement factor; ;in Red saturation of algal fluid; The formula for determining the red light ratio is: ;in is the red light ratio, is the carbon dioxide concentration; The formula for determining the blue light ratio is: ； in is the blue light ratio, is the number of motile cells; The formula for determining the white light ratio is: 。 9. The method for controlling illumination of Haematococcus pluvialis cultivation based on edge control according to claim 6, wherein: In step 4, if the growth stage of Haematococcus pluvialis is judged to be the red stress period, a combination of blue light, ultraviolet light and red light is used, and the calculation formula for the target light intensity is: base_intensity+diam_adjust; in is the target light intensity, is the basic light intensity; diam_adjust is the diameter compensation item; ； ； in It's a nutrient solution value, is the average diameter of amoenospores; The formula for determining the blue light ratio is: ； in is the current red light ratio; The formula for determining the UV ratio is: ； in is the UV ratio, is the astaxanthin area ratio; The formula for determining the red light ratio is: 。 10. The method for controlling illumination of Haematococcus pluvialis cultivation based on edge control according to claim 9, wherein: The method also includes a digital twin display step, in which the digital twin monitoring platform (4) respectively collects real-time parameters of multiple sensor monitoring modules (1) and multiple edge controllers (2), performs three-dimensional modeling of the growth status of Haematococcus pluvialis, and performs digital twin display of three-dimensional simulation images and real-time visual images, and the display content includes growth status and light control parameters.

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