Room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod

Abstract

The present invention relates to the technical field of seedling cultivation, and in particular to a room temperature supplementary light temperature control device for seedling cultivation based on controllable light intensity and photoperiod. The device comprises a hardware module, a data processing module, a growth data prediction module, a growth simulation and strategy optimization module, and a growth data adjustment module. The hardware module constructs a cultivation environment, and adjusts light intensity, temperature, and humidity through a laser radar and an LED supplementary light. The data processing module eliminates anomalies, performs time series alignment, and performs standardization processing on collected temperature and humidity growth data. The growth data prediction module uses an LSTM network to predict environmental changes in the next hour. The growth simulation and strategy optimization module constructs a three-dimensional growth model of a poplar seedling, and virtually previews a control strategy based on the predicted data to determine whether adjustment is required. The growth data adjustment module dynamically adjusts environmental parameters according to single-parameter, double-parameter, and triple-parameter abnormalities based on the prediction results and the growth cycle, thereby achieving energy saving and precise management.

Description

Technical Field

[0001] The invention relates to the technical field of seedling cultivation, and in particular to a room temperature light supplement and temperature control device for seedling cultivation based on controllable light intensity and photoperiod. Background Art

[0002] The present invention relates to the seedling cultivation technology. In the existing seedling cultivation technology, especially in the seedling cultivation scenario of fast-growing woody trees, there are many technical bottlenecks:

[0003] First, traditional cultivation equipment makes it difficult to precisely control light intensity, photoperiod, and light quality. It's impossible to independently study the effects of artificial lighting on seedling growth when natural light interferes. Furthermore, fixed LED light stands can't adjust the light distance as the seedlings grow taller, resulting in insufficient light for edge plants, which can easily lead to premature topping or growth stagnation.

[0004] Second, the regulation of environmental parameters lacks a dynamic response mechanism. The monitoring and regulation of indicators such as temperature, humidity, and carbon dioxide concentration lag behind, making it impossible to dynamically optimize according to the seedling growth cycle, affecting the efficiency of photosynthesis and transpiration.

[0005] Third, data processing methods are simple and lack time series alignment, anomaly elimination, and standardization of multi-source sensor data, making it difficult to support accurate environmental prediction and control.

[0006] Fourth, energy efficiency is low. Traditional devices often rely on mains electricity, resulting in high energy consumption and failing to meet energy conservation and emission reduction requirements. To address these issues, the present invention provides a room-temperature supplemental lighting and temperature control device that integrates controllable light intensity and photoperiod, intelligently regulates environmental parameters, and enables data-driven decision-making to achieve precise and intelligent management of the seedling cultivation environment.

[0007] To this end, the present invention provides a room temperature light supplement and temperature control device for seedling cultivation based on controllable light intensity and photoperiod. Summary of the Invention

[0008] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0009] The technical solution adopted by the present invention to solve its technical problem is:

[0010] In a first aspect, the present invention provides a room temperature light supplementation and temperature control device for seedling cultivation based on controllable light intensity and photoperiod, comprising:

[0011] Hardware module: Build a room temperature light supplement and temperature control device for seedling cultivation;

[0012] Data processing module: processes the growth data collected by the hardware module, performs time series alignment on the processed growth data, and standardizes the time series aligned growth data to obtain a growth data set;

[0013] Growth data prediction module: establishes a growth data prediction model based on the growth data set;

[0014] Growth simulation and strategy optimization module: By constructing a three-dimensional growth model of poplar seedlings and combining it with environmental parameter prediction data to conduct a virtual control strategy preview, it is determined whether the growth data predicted by the growth data prediction module within the next hour needs to be adjusted;

[0015] Growth data adjustment module: If adjustment is necessary, the temperature data, humidity data, and carbon dioxide concentration data are adjusted according to the changes in temperature data, humidity data, and carbon dioxide concentration data predicted by the growth data prediction model and the growth cycle of the poplar seedlings.

[0016] As a further improvement of the present invention, the room temperature supplementary light temperature control device specifically includes:

[0017] Greenhouse seedbeds, LED fill lights, adjustable sunshade nets, LED light stands, ventilation fans, automatic spray devices, solar power supply systems, carbon dioxide concentration sensors, temperature and humidity sensors, heating devices, humidification devices, and lidar.

[0018] As a further improvement of the present invention, the growth data specifically includes:

[0019] Temperature data, humidity data, carbon dioxide concentration data.

[0020] As a further improvement of the present invention, the specific process of processing the growth data collected by the hardware module is as follows:

[0021] The IQR interquartile range method was used to eliminate obviously abnormal data. First, the outliers were identified and the temperature values ​​with temperature, humidity and carbon dioxide concentration exceeding the suitable growth range of poplar seedlings were eliminated;

[0022] Combined with the ventilation fan startup data, sudden jump data is identified. For short-term missing growth data, linear interpolation or adjacent value filling is used; long-term missing growth data is marked as invalid to avoid affecting subsequent analysis.

[0023] As a further improvement of the present invention, the specific process of performing temporal alignment on the processed growth data is as follows:

[0024] First, time synchronization is performed. For sensors with different acquisition frequencies, the sensor with the highest acquisition frequency is used as the benchmark. The low-frequency data is interpolated into the high-frequency time series through resampling to ensure that the timestamps of all data points are consistent.

[0025] Then, timestamp calibration is performed to eliminate the time misalignment caused by clock deviation among different sensors, and the data timing consistency is ensured through a synchronous trigger mechanism.

[0026] As a further improvement of the present invention, the specific process of establishing the growth data prediction model is as follows:

[0027] Using the long short-term memory network prediction framework, a growth data prediction model is established. The data in the growth data set is input into the input layer, and a two-layer LSTM unit is designed in the hidden layer to learn the nonlinear coupling relationship between parameters. The output layer outputs the temperature data, humidity data, and carbon dioxide concentration data changes for the next hour;

[0028] If the validation set loss decreases by less than 0.5% in 50 consecutive iterations, the growth data prediction model is considered to be established.

[0029] As a further improvement of the present invention, the specific process of constructing the three-dimensional growth model of poplar seedlings is as follows:

[0030] Based on the physiological characteristics of poplar seedlings, a 3D digital twin of the trunk, branches, and leaves was constructed by combining the LIGNUM model with a physics engine. The model parameters include:

[0031] Morphological parameters: plant height, stem diameter, and leaf area index, generated by fitting seedling height data monitored in real time by lidar with historical growth data;

[0032] Physiological parameters: photosynthetic rate, transpiration rate, respiration rate, simulated based on the Michaelis-Menten equation Effects of concentration and temperature on enzyme activity;

[0033] Environmental response parameters: light intensity photosynthetic curve, temperature-humidity coupling coefficient, and historical growth data collected by the hardware module are used to train a three-dimensional growth model of poplar seedlings.

[0034] As a further improvement of the present invention, the specific process of determining whether the data predicted by the growth prediction module within the next hour needs to be adjusted is as follows:

[0035] The hardware module obtains the seedling morphological data monitored by the lidar and the real-time environmental parameters of the temperature and humidity sensors, and the temperature, humidity, Concentration prediction value;

[0036] The above data is input into the seedling three-dimensional growth model to determine whether the data predicted by the growth prediction module within the next hour needs to be adjusted;

[0037] Based on the growth data of the next hour output by the growth prediction module, N possible disturbance scenarios are generated, and the disturbance scenarios cover fluctuations within the prediction error range;

[0038] According to the Farquhar model, combined with the predicted concentration and temperature, calculate stomatal conductance and carboxylation rate, if the prediction When the concentration is lower than 800ppm, the photosynthetic rate decreases, and it is judged that adjustment is necessary at this time;

[0039] Based on the temperature growth accumulated temperature model, if the predicted temperature exceeds 30℃, the elongation rate of the seedling stem tip cells will decrease, resulting in a slowdown in plant height growth, and it is judged that adjustment is necessary at this time.

[0040] As a further improvement of the present invention, the specific process of adjusting the temperature data, humidity data, and carbon dioxide concentration data is as follows:

[0041] Based on the growth data prediction model, the temperature data, humidity data, and carbon dioxide concentration data changes in the next hour are predicted. First, the hard threshold is set. In the present invention, the hard threshold is set as follows: temperature>30℃, humidity<60%, <800ppm;

[0042] When the growth data prediction model predicts that one of the temperature data, humidity data, and carbon dioxide concentration data for the next hour does not conform to the suitable growth range for poplar seedlings, the data that does not conform to the suitable growth range for poplar seedlings is adjusted separately;

[0043] When the growth data prediction model predicts that two of the temperature data, humidity data, and carbon dioxide concentration data for the next hour do not conform to the suitable growth range for poplar seedlings, the two data that do not conform to the suitable growth range for poplar seedlings are adjusted simultaneously.

[0044] As a further improvement of the present invention, the specific process of adjusting the temperature data, humidity data, and carbon dioxide concentration data is as follows:

[0045] When the growth data prediction model predicts that three of the temperature data, humidity data, and carbon dioxide concentration data for the next hour do not conform to the suitable growth range for poplar seedlings, the temperature data, humidity data, and carbon dioxide concentration data are adjusted in combination with the growth cycle of the poplar seedlings. The specific adjustment process is as follows:

[0046] If the poplar seedlings are in the seedling stage, the temperature data is adjusted first; if the poplar seedlings are in the growth period, the carbon dioxide concentration and humidity are adjusted first.

[0047] In a second aspect, the present invention provides a method for controlling temperature by supplementing light at room temperature for seedling cultivation based on controllable light intensity and photoperiod, comprising:

[0048] S1: Construct a room temperature light supplement temperature control device for seedling cultivation;

[0049] S2: Processing the growth data collected by the hardware module, performing time series alignment on the processed growth data, and standardizing the time series aligned growth data to obtain a growth data set;

[0050] S3: Based on the growth data set, a growth data prediction model is established;

[0051] S4: Growth simulation and strategy optimization module: By constructing a three-dimensional growth model of poplar seedlings and combining it with environmental parameter prediction data to conduct a virtual control strategy preview, it is determined whether the growth data predicted by the growth data prediction module within the next hour needs to be adjusted;

[0052] S5: According to the changes in temperature data, humidity data, and carbon dioxide concentration data predicted by the growth data prediction model and in combination with the growth cycle of the poplar seedlings, the temperature data, humidity data, and carbon dioxide concentration data are adjusted.

[0053] Beneficial effects of the present invention:

[0054] LiDAR monitors seedling height in real time, and LED light stands automatically adjust the distance and angle of fill light, solving the problem of insufficient light at the edges of plants caused by traditional fixed light stands. This improves the uniformity of light exposure for seedlings and prevents premature capping or growth stagnation. Furthermore, temperature and humidity sensors monitor environmental parameters in real time, and combined with other devices, these parameters are stabilized within an optimal range, improving the efficiency of seedling transpiration and photosynthesis.

[0055] The data processing module removes anomalies, aligns time series, and standardizes multi-source sensor data, providing support for precise environmental prediction and control. The growth data prediction module uses an LSTM network to predict future environmental changes. The growth simulation and strategy optimization module constructs a three-dimensional growth model for virtual rehearsals, enabling intelligent decision-making through simulation-first, then execution, to improve the accuracy and timeliness of control.

[0056] The growth data adjustment module combines the growth cycle of poplar seedlings and adjusts the temperature, humidity, and carbon dioxide concentration data according to different situations. For example, during the seedling stage, the temperature is adjusted first to promote stem and leaf differentiation, and during the growth period, the carbon dioxide concentration and humidity are adjusted first to improve photosynthetic efficiency, realizing the dynamic optimization of environmental parameters according to the seedling growth cycle.

[0057] The entire device is powered by a solar power supply system, achieving the effect of energy conservation and emission reduction and improving energy utilization efficiency. Through the synergistic effect of the above modules, the device realizes precise and intelligent management of the seedling cultivation environment, and improves the cultivation quality and growth efficiency of the seedlings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 This is a module schematic diagram of a room temperature light supplement and temperature control device for seedling cultivation based on controllable light intensity and photoperiod provided in Example 1 of the present invention;

[0060] Figure 2 This is a method flow chart of a room temperature light supplementation and temperature control method for seedling cultivation based on controllable light intensity and photoperiod provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0062] Example 1

[0063] like Figure 1 As shown, the room temperature supplementary light and temperature control device for seedling cultivation based on controllable light intensity and photoperiod provided by the embodiment of the present invention includes the following modules:

[0064] Hardware module: Build a room temperature light supplement and temperature control device for seedling cultivation;

[0065] The room temperature supplementary light temperature control device specifically comprises:

[0066] Greenhouse seedling beds, LED fill lights, adjustable sunshade nets, LED light stands, ventilation fans, automatic spray devices, solar power supply systems, carbon dioxide concentration sensors, temperature and humidity sensors, heating devices, humidification devices, and lidar;

[0067] Greenhouse seedbeds are the carriers for cultivating seedlings. When cultivating fast-growing woody trees, for example, poplar seedlings, greenhouse seedbeds are indispensable carriers. When studying the effects of light on the growth of poplar seedlings, artificial lighting is often used to control the light intensity, duration, and quality. Therefore, adjustable sunshade nets are used to isolate natural light and eliminate the effects of natural light on poplar seedlings.

[0068] After blocking out natural light, the poplar seedlings are illuminated by LED supplementary lights, which facilitates the study of the effects of light intensity, duration, and quality on the growth of poplar seedlings. The seedling cultivation room is fully enclosed by an adjustable sunshade net.

[0069] Due to the greenhouse effect and the heat dissipation of the LED fill light itself, the temperature in the seedling cultivation room is too high. Excessive temperature will affect the growth and development of poplar seedlings. When the temperature and humidity sensor detects that the temperature is too high, the system starts the ventilation fan to cool the seedling cultivation room.

[0070] During the poplar seedling cultivation process, low temperature and humidity affect the growth and development of poplar seedlings. When the temperature and humidity sensors detect that the temperature or humidity is too low, the system activates the heating device and humidification device to increase the temperature and humidity of the seedling cultivation room accordingly.

[0071] During the growth process of poplar trees, as the poplar seedlings develop, the height of the poplar seedlings will gradually increase, and the distance between the seedlings and the light source will gradually shorten. The LED light stand is used to adjust the LED fill light. When the laser radar detects that the distance to the poplar seedlings is less than the preset distance, the LED light stand starts the servo motor to adjust the distance between the LED fill light and the poplar seedlings. At the same time, the LED light stand can also make real-time adjustments to the uneven growth position of the poplar seedlings based on the data information collected by the laser radar. For poplar seedlings that grow too slowly, the light angle and light distance can be adjusted to ensure that the slow-growing poplar seedlings receive sufficient light.

[0072] The entire room temperature supplementary lighting and temperature control device is powered by a solar power supply system to achieve energy conservation and emission reduction;

[0073] By using LiDAR to monitor the height of seedlings in real time, the LED light stand automatically adjusts the fill light distance and angle to ensure that the seedlings maintain the optimal lighting distance from the light source throughout the entire lighting cycle. This solves the problem of insufficient lighting for edge plants caused by traditional fixed light stands, prevents seedlings from capping out prematurely or stagnating in growth, and improves the uniformity of light received by seedlings.

[0074] The temperature and humidity sensors monitor environmental parameters in real time. When the temperature is too high, the ventilation fan and spray cooling are activated. When the humidity is too low, humidification is automatically added. When the temperature is too low, the heating device is activated to stabilize the temperature and humidity within the appropriate range, thereby improving the transpiration and photosynthesis efficiency of the seedlings.

[0075] Example 2

[0076] like Figure 1 As shown, based on Example 1, the present invention provides a room temperature supplementary light temperature control device for seedling cultivation based on controllable light intensity and photoperiod, which also includes the following modules:

[0077] Data processing module: processes the growth data collected by the hardware module, performs time series alignment on the processed growth data, and standardizes the time series aligned growth data to obtain a growth data set;

[0078] The growth data specifically includes: temperature data, humidity data, and carbon dioxide concentration data;

[0079] The specific process of processing the growth data collected by the hardware module is as follows:

[0080] The IQR interquartile range method was used to eliminate obviously abnormal data. First, the outliers were identified and the temperature values ​​with temperature, humidity and carbon dioxide concentration exceeding the suitable growth range of poplar seedlings were eliminated;

[0081] Combined with ventilation fan startup data, sudden jump data is identified. Short-term missing growth data is filled with linear interpolation or adjacent values. Long-term missing growth data is marked as invalid to avoid affecting subsequent analysis.

[0082] The specific process of performing temporal alignment on the processed growth data is as follows:

[0083] First, time synchronization is performed. For sensors with different acquisition frequencies, the sensor with the highest acquisition frequency is used as the benchmark. The low-frequency data is interpolated into the high-frequency time series through resampling to ensure that the timestamps of all data points are consistent.

[0084] Then, timestamp calibration is performed to eliminate time misalignment caused by clock deviation between different sensors, and data timing consistency is ensured through a synchronous trigger mechanism.

[0085] The specific process of normalizing the temporally aligned growth data is as follows:

[0086] First, unify the dimensions and perform Z-Score processing on data of different dimensions to facilitate subsequent modeling and analysis;

[0087] For example, Z-Score processing is performed: ,in, is the growth data after time series alignment, is the mean of the growth data after time series alignment, is the variance of the growth data after temporal alignment;

[0088] The data after Z-Score processing is then smoothed, and moving average filtering is used to reduce high-frequency noise, highlight data trends, and avoid the interference of short-term fluctuations on subsequent regulatory decisions;

[0089] The specific process of obtaining the growth data set is:

[0090] The growth data after IQR interquartile range method, time series alignment and Z-Score standardization are used to establish a data set to obtain a growth data set;

[0091] Growth data prediction module: establishes a growth data prediction model based on the growth data set;

[0092] Specifically, the specific process of establishing the growth data prediction model is as follows:

[0093] Using the long short-term memory network prediction framework, a growth data prediction model is established. The data in the growth data set is input into the input layer, and a two-layer LSTM unit is designed in the hidden layer to learn the nonlinear coupling relationship between parameters. The output layer outputs the temperature data, humidity data, and carbon dioxide concentration data changes for the next hour;

[0094] If the validation set loss decreases by less than 0.5% in 50 consecutive iterations, the growth data prediction model is considered to be established;

[0095] Growth simulation and strategy optimization module: By constructing a three-dimensional growth model of poplar seedlings and combining it with environmental parameter prediction data to conduct a virtual control strategy preview, it is determined whether the growth data predicted by the growth data prediction module within the next hour needs to be adjusted;

[0096] The specific process of constructing the three-dimensional growth model of poplar seedlings is as follows:

[0097] Based on the physiological characteristics of poplar seedlings, a 3D digital twin of the trunk, branches, and leaves was constructed by combining the LIGNUM model with a physics engine. The model parameters include:

[0098] Morphological parameters: plant height, stem diameter, and leaf area index, generated by fitting seedling height data monitored in real time by lidar with historical growth data;

[0099] Physiological parameters: photosynthetic rate, transpiration rate, respiration rate, simulated based on the Michaelis-Menten equation Effects of concentration and temperature on enzyme activity;

[0100] Environmental response parameters: light intensity photosynthetic curve, temperature-humidity coupling coefficient, and a three-dimensional growth model of poplar seedlings trained with historical growth data collected by the hardware module;

[0101] The specific process of determining whether the data predicted by the growth prediction module within the next hour needs to be adjusted is as follows:

[0102] The hardware module obtains the seedling morphological data monitored by the lidar and the real-time environmental parameters of the temperature and humidity sensors, and the temperature, humidity, Concentration prediction value;

[0103] The above data is input into the seedling three-dimensional growth model to determine whether the data predicted by the growth prediction module within the next hour needs to be adjusted;

[0104] Based on the growth data of the next hour output by the growth prediction module, N possible disturbance scenarios are generated, and the disturbance scenarios cover fluctuations within the prediction error range;

[0105] According to the Farquhar model, combined with the predicted concentration and temperature, calculate stomatal conductance and carboxylation rate, if the prediction When the concentration is lower than 800ppm, the photosynthetic rate decreases, and it is judged that adjustment is necessary at this time;

[0106] Based on the temperature growth accumulated temperature model, if the predicted temperature exceeds 30°C, the elongation rate of the seedling stem tip cells will decrease, resulting in a slowdown in plant height growth, indicating that adjustment is necessary at this time;

[0107] Growth data adjustment module: If adjustment is necessary, the temperature data, humidity data, and carbon dioxide concentration data are adjusted according to the changes in temperature data, humidity data, and carbon dioxide concentration data predicted by the growth data prediction model and the growth cycle of the poplar seedlings;

[0108] The specific process of adjusting the temperature data, humidity data, and carbon dioxide concentration data is as follows:

[0109] Based on the growth data prediction model, the temperature data, humidity data, and carbon dioxide concentration data changes in the next hour are predicted. First, the hard threshold is set. In the present invention, the hard threshold is set as follows: temperature>30℃, humidity<60%, <800ppm;

[0110] When the growth data prediction model predicts that one of the temperature data, humidity data, and carbon dioxide concentration data for the next hour does not conform to the suitable growth range for poplar seedlings, the data that does not conform to the suitable growth range for poplar seedlings is adjusted separately;

[0111] When the growth data prediction model predicts that two of the temperature data, humidity data, and carbon dioxide concentration data for the next hour do not conform to the suitable growth range for poplar seedlings, the two data that do not conform to the suitable growth range for poplar seedlings are adjusted simultaneously;

[0112] When the growth data prediction model predicts that three of the temperature data, humidity data, and carbon dioxide concentration data for the next hour do not conform to the suitable growth range for poplar seedlings, the temperature data, humidity data, and carbon dioxide concentration data are adjusted in combination with the growth cycle of the poplar seedlings. The specific adjustment process is as follows:

[0113] If the poplar seedlings are in the seedling stage, the priority is to adjust the temperature data to promote stem and leaf differentiation; if the poplar seedlings are in the growth stage, the priority is to adjust the carbon dioxide concentration and humidity to improve photosynthetic efficiency;

[0114] The technical solution of the embodiment of the present invention is:

[0115] It consists of a greenhouse seedling bed, LED fill lights, adjustable sunshade nets, etc. It can build a cultivation environment, monitor the height of seedlings through lidar, automatically adjust the distance and angle of LED fill lights, and monitor environmental parameters in real time. It can also activate corresponding devices for adjustment according to temperature and humidity conditions, and use a solar power supply system for power.

[0116] For the collected growth data such as temperature, humidity, and carbon dioxide concentration, the IQR interquartile range method was used to eliminate outliers, and time series alignment was performed based on the highest frequency sensor. Standardization was completed through Z-Score processing and moving average filtering to obtain the growth data set.

[0117] Based on the growth data set, a model is established using the long short-term memory network prediction framework. After the data is input, the nonlinear coupling relationship between parameters is learned through a double-layer LSTM unit to output the temperature, humidity, and carbon dioxide concentration data changes in the next hour.

[0118] A three-dimensional growth model of poplar seedlings was constructed, and virtual control strategies were previewed in combination with environmental parameter prediction data. Relevant data was obtained from the hardware module and input into the model to generate disturbance scenarios. The need for adjustment was determined based on the photosynthetic rate and seedling growth.

[0119] If adjustment is necessary, the temperature, humidity, and carbon dioxide concentration data will be adjusted in combination with the growth cycle of the poplar seedlings. A hard threshold will be set. If a single data does not meet the requirements, it will be adjusted separately. If two data do not meet the requirements, they will be adjusted simultaneously. If three data do not meet the requirements, the temperature will be adjusted first during the seedling stage, and the carbon dioxide concentration and humidity will be adjusted first during the growth stage.

[0120] Example 3

[0121] like Figure 2As shown, based on Example 1 and Example 2, the present invention also provides a room temperature light supplementation and temperature control method for seedling cultivation based on controllable light intensity and photoperiod, comprising the following steps:

[0122] S1: Construct a room temperature light supplement temperature control device for seedling cultivation;

[0123] S2: Processing the growth data collected by the hardware module, performing time series alignment on the processed growth data, and standardizing the time series aligned growth data to obtain a growth data set;

[0124] S3: Based on the growth data set, a growth data prediction model is established;

[0125] S4: Growth simulation and strategy optimization module: By constructing a three-dimensional growth model of poplar seedlings and combining it with environmental parameter prediction data to conduct a virtual control strategy preview, it is determined whether the growth data predicted by the growth data prediction module within the next hour needs to be adjusted;

[0126] S5: According to the changes in temperature data, humidity data, and carbon dioxide concentration data predicted by the growth data prediction model and in combination with the growth cycle of the poplar seedlings, the temperature data, humidity data, and carbon dioxide concentration data are adjusted.

[0127] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A room temperature light supplement and temperature control device for seedling cultivation based on controllable light intensity and photoperiod, characterized in that: include: Hardware module: Build a room temperature light supplement and temperature control device for seedling cultivation; Data processing module: processes the growth data collected by the hardware module, performs time series alignment on the processed growth data, and standardizes the time series aligned growth data to obtain a growth data set; Growth data prediction module: establishes a growth data prediction model based on the growth data set; Growth simulation and strategy optimization module: By constructing a three-dimensional growth model of poplar seedlings and combining it with environmental parameter prediction data to conduct a virtual control strategy preview, it is determined whether the growth data predicted by the growth data prediction module within the next hour needs to be adjusted; Growth data adjustment module: If adjustment is necessary, the temperature data, humidity data, and carbon dioxide concentration data are adjusted according to the changes in temperature data, humidity data, and carbon dioxide concentration data predicted by the growth data prediction model and the growth cycle of the poplar seedlings; The specific process of establishing the growth data prediction model is as follows: Using the long short-term memory network prediction framework, a growth data prediction model is established. The data in the growth data set is input into the input layer, and a two-layer LSTM unit is designed in the hidden layer to learn the nonlinear coupling relationship between parameters. The output layer outputs the temperature data, humidity data, and carbon dioxide concentration data changes for the next hour. If the validation set loss decreases by less than 0.5% in 50 consecutive iterations, the growth data prediction model is considered complete. The specific process of constructing the three-dimensional growth model of poplar seedlings is as follows: Based on the physiological characteristics of poplar seedlings, a 3D digital twin of the trunk, branches, and leaves was constructed by combining the LIGNUM model with a physics engine. The model parameters include: Morphological parameters: plant height, stem diameter, and leaf area index, generated by fitting seedling height data monitored in real time by lidar with historical growth data; Physiological parameters: photosynthetic rate, transpiration rate, respiration rate, and the effects of CO2 concentration and temperature on enzyme activity were simulated based on the Michaelis-Menten equation; Environmental response parameters: light intensity photosynthetic curve, temperature-humidity coupling coefficient, and a three-dimensional growth model of poplar seedlings trained with historical growth data collected by the hardware module; The specific process of determining whether the data predicted by the growth prediction module within the next hour needs to be adjusted is as follows: The hardware module obtains seedling morphological data monitored by the lidar and real-time environmental parameters of the temperature and humidity sensors, and the predicted values ​​of temperature, humidity, and CO2 concentration for the next hour from the growth data prediction module; The above data is input into the seedling three-dimensional growth model to determine whether the data predicted by the growth prediction module within the next hour needs to be adjusted; Based on the growth data of the next hour output by the growth prediction module, N possible disturbance scenarios are generated, and the disturbance scenarios cover fluctuations within the prediction error range; According to the Farquhar model, combined with the predicted CO2 concentration and temperature, stomatal conductance and carboxylation rate are calculated. If the predicted CO2 concentration is lower than 800 ppm, the photosynthetic rate will decrease, indicating that adjustment is necessary. Based on the temperature growth accumulated temperature model, if the predicted temperature exceeds 30℃, the elongation rate of the seedling stem tip cells will decrease, resulting in a slowdown in plant height growth, and it is judged that adjustment is necessary at this time.

2. The room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod according to claim 1, characterized in that: The room temperature supplementary light temperature control device specifically comprises: Greenhouse seedbeds, LED fill lights, adjustable sunshade nets, LED light stands, ventilation fans, automatic spray devices, solar power supply systems, carbon dioxide concentration sensors, temperature and humidity sensors, heating devices, humidification devices, and lidar.

3. The room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod according to claim 1, characterized in that: The growth data specifically includes: Temperature data, humidity data, carbon dioxide concentration data.

4. The room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod according to claim 1, characterized in that: The specific process of processing the growth data collected by the hardware module is as follows: The IQR interquartile range method was used to eliminate obviously abnormal data. First, the outliers were identified and the temperature values ​​with temperature, humidity and carbon dioxide concentration exceeding the suitable growth range of poplar seedlings were eliminated; Combined with the ventilation fan startup data, sudden jump data is identified. For short-term missing growth data, linear interpolation or adjacent value filling is used; long-term missing growth data is marked as invalid to avoid affecting subsequent analysis.

5. The room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod according to claim 1, characterized in that: The specific process of performing temporal alignment on the processed growth data is as follows: First, time synchronization is performed. For sensors with different acquisition frequencies, the sensor with the highest acquisition frequency is used as the benchmark. The low-frequency data is interpolated into the high-frequency time series through resampling to ensure that the timestamps of all data points are consistent. Then, timestamp calibration is performed to eliminate the time misalignment caused by clock deviation among different sensors, and the data timing consistency is ensured through a synchronous trigger mechanism.

6. The room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod according to claim 1, characterized in that: The specific process of adjusting the temperature data, humidity data, and carbon dioxide concentration data is as follows: Based on the growth data prediction model, the temperature data, humidity data, and carbon dioxide concentration data changes for the next hour are predicted. First, hard thresholds are set. In the present invention, the hard thresholds are set as follows: temperature>30°C, humidity<60%, and CO2<800ppm; When the growth data prediction model predicts that one of the temperature data, humidity data, and carbon dioxide concentration data for the next hour does not conform to the suitable growth range for poplar seedlings, the data that does not conform to the suitable growth range for poplar seedlings is adjusted separately; When the growth data prediction model predicts that two of the temperature data, humidity data, and carbon dioxide concentration data for the next hour do not conform to the suitable growth range for poplar seedlings, the two data that do not conform to the suitable growth range for poplar seedlings are adjusted simultaneously.

7. The room temperature supplementary light control device for seedling cultivation based on controllable light intensity and photoperiod according to claim 6, characterized in that: The specific process of adjusting the temperature data, humidity data, and carbon dioxide concentration data is as follows: When the growth data prediction model predicts that three of the temperature data, humidity data, and carbon dioxide concentration data for the next hour do not conform to the suitable growth range for poplar seedlings, the temperature data, humidity data, and carbon dioxide concentration data are adjusted in combination with the growth cycle of the poplar seedlings. The specific adjustment process is as follows: If the poplar seedlings are in the seedling stage, the temperature data is adjusted first; if the poplar seedlings are in the growth period, the carbon dioxide concentration and humidity are adjusted first.

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