A temperature control method and device for a seedling greenhouse cultivation environment and a storage medium

Abstract

The present application relates to the technical field of temperature control, in particular to a temperature control method and device for seedling greenhouse cultivation environment and a storage medium. The method comprises the following steps: establishing a greenhouse temperature influence model, the greenhouse temperature influence model can predict the greenhouse air temperature according to the greenhouse soil temperature, and the influence of the outside air temperature on the greenhouse air temperature is considered for optimization; obtaining the target soil temperature range and the target air temperature range of the current seedling growth stage, when it is monitored that the real-time greenhouse air temperature is not in the target greenhouse air temperature range, the air temperature adjustment strategy is started, and whether the greenhouse soil temperature is in the target greenhouse soil temperature range is judged, if the greenhouse soil temperature is not in the target soil temperature range, the soil temperature is adjusted. The method greatly improves the response speed and adaptability to environmental changes, and provides more stable and suitable growth conditions for seedlings.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and more specifically, to a method, device, and storage medium for temperature control in a seedling greenhouse cultivation environment. Background Technology

[0002] In modern horticulture and agricultural cultivation, temperature control in greenhouse cultivation is a key factor in ensuring healthy crop growth. Seedling growth depends not only on suitable air temperature but also on the soil temperature surrounding the roots. Different growth stages have different temperature requirements; for example, seed germination requires higher soil temperatures to promote germination, while seedlings require more stable temperature conditions to support healthy root development and photosynthesis. However, maintaining this ideal temperature distribution is extremely challenging in practice. Due to the different heat transfer efficiencies between soil and air, coupled with changes in external climate conditions, a single overall temperature control strategy is insufficient to meet the specific needs of different parts of the seedling. The limitations of traditional temperature control systems are particularly evident in large greenhouses or under prolonged periods of severe weather. These systems typically lack the ability to finely regulate deep soil temperature and canopy microclimate, leading to problems such as excessively high canopy temperatures during heating (even when soil temperatures reach the ideal level), resulting in energy waste; or excessively rapid soil temperature drops during ventilation and cooling, affecting healthy root development. Therefore, this paper provides a method, device, and storage medium for temperature control in greenhouse cultivation of seedlings. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, and storage medium for temperature control in a seedling greenhouse cultivation environment, in order to solve the problems mentioned in the background art, where traditional temperature control systems often uniformly regulate the temperature of the entire greenhouse, leading to overheating of the canopy when heating the soil, wasting energy, and a sudden drop in soil temperature when ventilating to cool down, affecting root growth.

[0004] To achieve the above objectives, the present invention aims to provide a method for temperature control in a seedling greenhouse cultivation environment, comprising the following steps:

[0005] S1. Distributed sensors are buried at different depths in the soil to collect the soil temperature in the greenhouse in real time. At the same time, distributed sensors are deployed at the height of the plant canopy to measure the air temperature in the greenhouse.

[0006] S2. Based on the real-time collected soil temperature and air temperature inside the greenhouse, combined with historical soil temperature and air temperature inside the greenhouse, a greenhouse temperature influence model is established. The greenhouse temperature influence model can predict the greenhouse air temperature based on the greenhouse soil temperature, and the influence of the greenhouse outside air temperature on the greenhouse air temperature is considered and optimized when establishing the greenhouse temperature influence model.

[0007] S3. Based on the growth stage of the seedlings, determine the target soil temperature range and target air temperature range. By monitoring the real-time air temperature inside the greenhouse, determine whether it falls within the target range to identify the air temperature error between the target and actual greenhouse air temperatures. Based on the air temperature regulation strategy and the greenhouse temperature influence model, predict the completion time of air temperature regulation. ;

[0008] S4. During temperature regulation, the soil temperature inside the greenhouse is monitored in real time, and the time required is predicted based on the greenhouse temperature influence model. The soil temperature inside the greenhouse is then measured, and it is determined whether the soil temperature inside the greenhouse is within the range of the target soil temperature inside the greenhouse; if the soil temperature inside the greenhouse is not within the range of the target soil temperature inside the greenhouse, the soil temperature is adjusted.

[0009] As a further improvement to this technical solution, the specific steps for establishing the greenhouse temperature influence model in step S2 are as follows:

[0010] S21. Establish soil temperature With the air temperature inside the shed The dynamic relationship between them, taking into account soil moisture. The nonlinear regulating effect;

[0011] S22. Calculate the cross-correlation function using historical data to determine the lag time. and in soil temperature With the air temperature inside the shed Introduce a lag term into the dynamic relationship between them;

[0012] S23, Finally, adjust the outdoor temperature... Soil temperature was added as a linear term. With the air temperature inside the shed In the dynamic relationship between them, noise terms of both illumination fluctuations and human interference are considered. Finally, a model of the influence of temperature inside the greenhouse was generated.

[0013] As a further improvement to this technical solution, the specific steps in S2 for optimizing the model of the influence of the temperature inside the greenhouse on the temperature inside the greenhouse, taking into account the influence of the outside air temperature on the inside air temperature, are as follows:

[0014] S24. An exponential function is used to quantify the nonlinear effect of ventilation intensity on heat exchange inside and outside the shed, and a heat leakage coefficient is generated.

[0015] S25. By weighting the temperature difference between inside and outside the greenhouse over past time periods, the temperature lag effect caused by heat storage or release in the greenhouse is captured, and the attenuation factor is used to... Controlling the decay rate of historical effects;

[0016] S26, Based on the current outdoor temperature The direct effect of heat leakage and the cumulative effect of historical temperature lag are combined with the heat leakage coefficient to generate the external temperature influence coefficient. ;

[0017] S27. Based on the influence coefficient of outdoor temperature An optimized model of the influence of greenhouse temperature was generated.

[0018] As a further improvement to this technical solution, the determination of the target soil temperature range and target air temperature range for seedlings based on their growth stage in S3 is specifically as follows: real-time data on seedling growth status is collected by sensors installed in the greenhouse; the current growth stage of the seedling is determined based on the collected data; an experience module for growth stage and temperature requirements is constructed based on historical data; the experience module records the target soil temperature and target air temperature range corresponding to each growth stage of the seedling; and the target soil temperature and target air temperature range are then fine-tuned according to the current environment to finally determine the target soil temperature range and target air temperature range.

[0019] As a further improvement to this technical solution, the specific steps of the air temperature regulation strategy in S3 are as follows:

[0020] S31. Compare the real-time air temperature data inside the greenhouse at the current time point with the target air temperature range inside the greenhouse, and calculate the air temperature error. ;

[0021] S32. Based on a nonlinear mapping function, the air temperature error is... The mapping is used as the initial control force, and at the same time, the PID controller combines the output of the nonlinear mapping function with proportional, integral, and derivative control actions to ultimately generate a control signal that includes heating power and heating time. .

[0022] As a further improvement to this technical solution, in step S3, the air temperature regulation completion time is predicted based on the air temperature regulation strategy and the greenhouse temperature influence model. The specific steps are as follows:

[0023] S33, control variables including heating power Input the data into the greenhouse temperature influence model to predict the greenhouse air temperature at the next time step;

[0024] S34. Determine whether the air temperature inside the greenhouse at the next time step is within the range of the target air temperature inside the greenhouse. If it is not within the range, iteratively update the air temperature error and control signal until the predicted air temperature inside the greenhouse enters the target range.

[0025] S35. Employ the golden section search method to quickly solve for the minimum settling time. Furthermore, the prediction time is adjusted based on a compensation factor for historical prediction errors.

[0026] As a further improvement to this technical solution, in step S4, the time is predicted based on the greenhouse temperature influence model. The specific steps for controlling the soil temperature inside the greenhouse are as follows:

[0027] S41. Obtain the heat transfer coupling coefficient based on online fitting. With heat loss rate This leads to the soil temperature response time constant. and steady-state temperature ;

[0028] S42, Considering the inherent time lag decay factor based on the data from S41 A first-order inertial system model was used to predict the dynamic response of soil temperature inside the greenhouse, with a prediction time of [time value missing]. The soil temperature inside the greenhouse afterwards.

[0029] On the other hand, the present invention provides a temperature control device for a seedling greenhouse cultivation environment, including a sensor, a storage device, a processor, and a computer program stored in the storage device and executable on the processor. When the processor executes the computer program, it implements the steps of the temperature control method for a seedling greenhouse cultivation environment described above.

[0030] As a further improvement to this technical solution, the sensors include at least a soil temperature sensor, an air temperature sensor, a soil moisture sensor, a high-definition camera, and an outdoor air temperature sensor.

[0031] On the other hand, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] The temperature control method, device, and storage medium for the seedling greenhouse cultivation environment utilizes real-time and precise acquisition of soil and air temperatures within the greenhouse. By combining historical data, a dynamic model of the greenhouse temperature influences is established, fully considering the effects of soil moisture, external temperature, and light fluctuations. This not only effectively avoids energy waste and adverse effects on the root system but also optimizes the control signal through PID control and nonlinear mapping to achieve precise energy input. This significantly improves the response speed and adaptability to environmental changes, providing seedlings with more stable and suitable growth conditions. Attached Figure Description

[0034] Figure 1 This is a flowchart of the overall method of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1:

[0037] Please see Figure 1 As shown, this embodiment provides a method for temperature control in a seedling greenhouse cultivation environment, including the following steps:

[0038] S1. Distributed sensors are buried at different depths in the soil to collect soil temperature inside the greenhouse in real time. At the same time, distributed sensors are deployed at the height of the plant canopy to measure the air temperature inside the greenhouse. Specifically, thermometers are installed on the pillars on both sides of the planting area. The height of the thermometers can be adjusted to always keep them level with the top of the canopy. The infrared thermometers are installed above the gaps between the plant rows to ensure that the infrared rays are not blocked by the plant leaves. The soil temperature inside the greenhouse represents the temperature of the seedling roots, and the air temperature represents the temperature of the seedling canopy. By adjusting the root temperature and canopy temperature to meet the temperature requirements for seedling growth, the yield and efficiency of the seedlings are promoted.

[0039] S2. Based on the real-time collected soil temperature and air temperature inside the greenhouse, combined with historical soil temperature and air temperature inside the greenhouse, a greenhouse temperature influence model is established. The greenhouse temperature influence model can predict the greenhouse air temperature based on the greenhouse soil temperature, and the influence of the greenhouse outside air temperature on the greenhouse air temperature is considered and optimized when establishing the greenhouse temperature influence model.

[0040] The specific steps for establishing the greenhouse temperature influence model in S2 are as follows:

[0041] S21. Establish soil temperature With the air temperature inside the shed The dynamic relationship between them, taking into account soil moisture. The nonlinear regulating effect; through the exponential function Describing soil moisture Its moderating effect on heat conduction avoids the idealized assumptions of traditional linear models. When soil moisture is low... At that time, the regulating term approaches 0, and the heat exchange between the soil and the air is weak; after the humidity increases... The moderating term increased significantly, such as This reflects the increased thermal conductivity of moist soil. Especially in scenarios where humidity surges after irrigation, the model can more accurately predict the rapid response of air temperature.

[0042] Meanwhile, temperature difference driving term Reflecting the instantaneous thermal balance between air and soil, combined with the humidity regulation coefficient This ensures that the model conforms to the physical laws of energy transfer.

[0043] S22. Calculate the cross-correlation function using historical data to determine the lag time. and in soil temperature With the air temperature inside the shed A lag term is introduced into the dynamic relationship between them; historical data are analyzed using cross-correlation functions to determine the lag time of soil temperature to air temperature. (like (hours). After the soil absorbs heat during the day, it takes several hours for the heat to be conducted to the surface and affect the air temperature; lag term. This accurately reflects the process. The introduction of lag terms avoids treating soil and air temperatures as instantaneous synchronous changes, reducing the model's sensitivity to transient noise. Especially in environments with large diurnal temperature variations, the model can distinguish between different stages of heat storage during the day and heat release at night, avoiding large fluctuations in predicted values.

[0044] The lag time is determined by calculating the cross-correlation function from historical data. Specifically:

[0045]

[0046] In the formula, In order to be in Cross-correlation function at time; This represents the average soil temperature. Number of sampling time points; The time lag is the time delay. In time The measured value of the air temperature inside the shed; This represents the average air temperature. ;

[0047] The specific process is as follows: [Regarding...] and Standardize and calculate different The correlation coefficient is used to generate the CCF curve, and the peak value of the CCF curve is found. This is the lag time. If CCF is in If it reaches its peak value (e.g., 0.85) within an hour, then It takes about 2 hours for soil temperature changes to significantly affect air temperature.

[0048] S23, Finally, adjust the outdoor temperature... Soil temperature was added as a linear term. With the air temperature inside the shed In the dynamic relationship between them, noise terms of both illumination fluctuations and human interference are considered. The final model of the influence of temperature inside the greenhouse is generated;

[0049]

[0050] In the formula, In order to be in The temperature of the air inside the shed over time; In order to be in Soil temperature over time; The weighting of soil's influence on air temperature; Soil temperature lag time; For timestamps; This is the soil moisture regulation coefficient; In order to be in Soil moisture over time; It is the heat exchange intensity coefficient; In order to be in The temperature of the air inside the shed over time; In order to be in Soil temperature over time; In order to be in The temperature outside the shed during the time; The penetration intensity of outside temperature into the greenhouse; In order to be in Noise term in time;

[0051] In S2, the specific steps for optimizing the greenhouse temperature influence model by considering the impact of outside air temperature on inside air temperature are as follows:

[0052] S24. An exponential function is used to quantify the nonlinear effect of ventilation intensity on heat exchange between the inside and outside of the shed, generating a heat leakage coefficient; when ventilation is closed ( When heat leakage approaches zero, and with enhanced ventilation, heat exchange efficiency exhibits saturated growth, avoiding distortion of the linear model. (Covering material coefficient) Further adjustments to the heat leakage intensity are made; for example, double-layer membranes, due to their superior insulation properties, have a lower value. This design precisely characterizes the synergistic effect of ventilation and materials on heat exchange; the heat leakage coefficient is: ;

[0053] S25. By weighting the temperature difference between inside and outside the greenhouse over past time periods, the temperature lag effect caused by heat storage or release in the greenhouse is captured, and the attenuation factor is used to... The decay rate of historical effects is controlled because the heat storage capacity of the greenhouse structure causes temperature changes to lag behind those of the external environment. For example, during the day when the outside temperature is high, heat is absorbed by the soil; at night, as the outside temperature drops, the stored heat is gradually released, slowing down the temperature drop inside the greenhouse. Therefore, the temperature lag effect of the greenhouse is introduced, i.e., a weighted cumulative term of historical temperature differences: ;

[0054] S26, Based on the current outdoor temperature The direct effect of heat leakage and the cumulative effect of historical temperature lag are combined with the heat leakage coefficient to generate the external temperature influence coefficient. ;

[0055]

[0056] In the formula, In time The influence of the outside temperature of the greenhouse; The thermal resistivity of the covering material; In order to be in Ventilation intensity over time; To fit the parameters, the nonlinear response speed of the ventilation intensity is controlled; This is the scaling factor for the historical temperature difference accumulation term; It is the attenuation factor; For the past The outdoor temperature at each time step; For the past The air temperature inside the shed at each time step; The historical window length takes into account the impact of how many past time steps. ;

[0057] The temperature outside the shed drops sharply at night in winter. The greenhouse is in a semi-ventilated state. The covering material is a double-layer film. ;

[0058] Heat leakage coefficient:

[0059] Thermal inertia correction term (take) , , (The temperature difference over the past 3 hours was 2℃, 1℃, and 0℃).

[0060]

[0061] The influence of total outdoor temperature:

[0062] S27. Based on the influence coefficient of outdoor temperature Generate an optimized model of the influence of greenhouse temperature;

[0063]

[0064] In the formula, For the optimized version The temperature of the air inside the shed over time;

[0065] S3. Based on the growth stage of the seedlings, determine the target soil temperature range and target air temperature range. By monitoring the real-time air temperature inside the greenhouse, determine whether it falls within the target range to identify the air temperature error between the target and actual greenhouse air temperatures. Based on the air temperature regulation strategy and the greenhouse temperature influence model, predict the completion time of air temperature regulation. ;

[0066] In S3, the target soil temperature range and target air temperature range for seedlings are determined based on their growth stages. Specifically, this involves: real-time data collection of seedling growth status using sensors installed inside the greenhouse; determining the current growth stage of the seedlings based on this data; constructing an experience module that correlates growth stages with temperature requirements based on historical seedling growth data; recording the target soil temperature and target air temperature range for each growth stage; and then fine-tuning the target soil temperature and target air temperature ranges according to the current environment to ultimately determine the target soil temperature range and target air temperature range. The target soil temperature range is as follows: Target air temperature range: .

[0067] In S3, the specific steps for determining the air temperature error and adjusting the air temperature using an air temperature regulation strategy are as follows:

[0068] S31. Compare the real-time air temperature data inside the greenhouse at the current time point with the target air temperature range inside the greenhouse, and calculate the air temperature error. ;

[0069]

[0070] in, This represents the average value of the target air temperature range for the seedlings;

[0071] S32. Based on a nonlinear mapping function, the air temperature error is... The mapping is used as the initial control force, and at the same time, the PID controller combines the output of the nonlinear mapping function with proportional, integral, and derivative control actions to ultimately generate a control signal that includes heating power and heating time. ;

[0072]

[0073] In the formula, Temperature difference mapping value; This is a sign function to ensure that positive and negative instructions are correct; To control the degree of nonlinearity; To normalize the parameters, ensure that the output tends to saturate when there is a large error, and avoid excessive adjustment.

[0074]

[0075] In the formula, For time The regulatory signal; The gain is controlled proportionally. For integral control gain; The gain is controlled by the derivative.

[0076] In S33, the completion time of air temperature regulation is predicted based on the air temperature regulation strategy and the greenhouse temperature influence model. The specific steps are as follows:

[0077] S33, control variables including heating power Input the data into the greenhouse temperature influence model to predict the greenhouse air temperature at the next time step;

[0078]

[0079] In the formula, In order to be in Air temperature over time; To adjust the efficiency coefficient, it is determined based on historical operating data using conventional data fitting methods (such as the least squares method). Historical operating data includes, but is not limited to, the control signals applied at different times. The input and output data, along with the subsequently monitored changes in indoor air temperature (as the output variable), are fitted using linear regression methods such as least squares. The slope of the resulting regression model is the regulation efficiency coefficient. ;

[0080] S34. Determine whether the air temperature inside the greenhouse at the next time step is within the range of the target air temperature inside the greenhouse. If it is not within the range, iteratively update the air temperature error and control signal until the predicted air temperature inside the greenhouse enters the target range.

[0081] S35. Employ the golden section search method to quickly solve for the minimum settling time. Furthermore, the prediction time is adjusted based on a compensation factor for historical prediction errors.

[0082] Define the objective function:

[0083]

[0084] in, and These are the lower and upper limits of the target air temperature range, respectively.

[0085] Iterative process:

[0086] Initialize search interval ;

[0087] Calculate the midpoint ;

[0088] Predicting through models Update the search range;

[0089] when Time-based termination, output ;

[0090] Specifically:

[0091] The search range is iteratively narrowed within a preset time interval: each time, 61.8% of the interval length (the golden ratio point) is selected as the candidate time. Calculate using a predictive model The search boundary is updated based on the predicted air temperature inside the greenhouse at any given time, according to its deviation from the target interval. For example, if the predicted temperature is lower than the target lower limit, the left half of the interval is discarded; otherwise, the right boundary is adjusted. Iteration continues until the error between the predicted temperature and the target median is less than a threshold. Time-based termination, output ;

[0092] First, the average absolute error between the predicted and actual air temperatures inside the greenhouse over the most recent N time points is calculated. This average error is then normalized to the span ΔTrange of the target air temperature range to obtain the relative error ratio. This ratio is then multiplied by the compensation strength coefficient. (Default value 0.2) is then used as a dynamic correction coefficient to adjust the prediction time. For example, if the average prediction error over the past 5 minutes accounts for 10% of the target interval width, the final adjustment time will be extended to 1.02 times the original prediction value (1 + 0.2 × 0.1). This mechanism significantly improves the robustness of time prediction under different environmental conditions by learning the characteristics of system errors online.

[0093] S4. During temperature regulation, the soil temperature inside the greenhouse is monitored in real time, and the time required is predicted based on the greenhouse temperature influence model. The soil temperature inside the greenhouse is then measured, and it is determined whether the soil temperature inside the greenhouse is within the range of the target soil temperature inside the greenhouse. If the soil temperature inside the greenhouse is not within the range of the target soil temperature inside the greenhouse, the soil temperature is adjusted. In this embodiment, temperature adjustment is achieved through underground hot water pipes.

[0094] In S4, the time is predicted based on the greenhouse temperature influence model. The specific steps for controlling the soil temperature inside the greenhouse are as follows:

[0095] S41. Obtain the heat transfer coupling coefficient based on online fitting. With heat loss rate This leads to the soil temperature response time constant. and steady-state temperature ;

[0096]

[0097]

[0098] In the formula, The baseline temperature inside the greenhouse (the temperature that the soil will eventually tend to when there is no active heating or cooling of the soil).

[0099] S42, Considering the inherent time lag decay factor based on the data from S41 A first-order inertial system model was used to predict the dynamic response of soil temperature inside the greenhouse, with a prediction time of [time value missing]. The soil temperature inside the greenhouse afterwards;

[0100] The model for the first-order inertial system is as follows:

[0101]

[0102] In the formula, for Soil temperature inside the shed after a certain time; To regulate the initial soil temperature inside the greenhouse; The inherent time lag of the system is the response delay required for temperature to transfer from the air to the soil;

[0103] when When the value is small, the exponent term is close to 1, and the predicted temperature is close to the initial soil temperature. ;

[0104] when As the exponential term approaches a larger value, the system temperature approaches a steady-state value. ;

[0105] This model can utilize real-time detection. With parameters determined through online adaptive methods, the timing of control can be predicted more accurately. The subsequent soil temperature will provide a basis for further auxiliary regulation.

[0106] Example 2:

[0107] This embodiment provides a temperature control device for a seedling greenhouse cultivation environment, including a sensor, a storage device, a processor, and a computer program stored in the storage device and executable on the processor. When the processor executes the computer program, it implements the steps of the temperature control method for the seedling greenhouse cultivation environment described above.

[0108] The sensor includes at least:

[0109] Soil temperature sensor: Soil temperature sensor is used to monitor soil temperature in real time, drive model building and soil temperature regulation, ensure a suitable root environment, and optimize the accuracy of temperature prediction.

[0110] Air temperature sensor: The air temperature sensor is used to monitor the canopy air temperature in real time, verify the regulation effect and predict future temperature, and ensure air temperature stability.

[0111] Soil moisture sensor: Soil moisture sensor is used to provide soil moisture data, optimize the greenhouse temperature influence model of soil and air temperature relationship, reduce moisture interference, and improve the synergy between irrigation and temperature control.

[0112] High-definition cameras are used to identify seedling types and growth stages, triggering knowledge base queries to accurately adapt to the environmental needs of different growth stages.

[0113] Outdoor air temperature sensor is used to monitor external temperature, optimize the modeling of heat exchange and temperature difference hysteresis effects, improve the model's adaptability to changes in the external environment, and reduce prediction errors.

[0114] This embodiment also provides a storage medium storing a computer program thereon, characterized in that: when the computer program is executed by a processor, it implements the steps of the method described in any of the above embodiments.

[0115] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for temperature control in a seedling greenhouse cultivation environment, characterized in that, Includes the following steps: S1. Distributed sensors are buried at different depths in the soil to collect the soil temperature in the greenhouse in real time. At the same time, distributed sensors are deployed at the height of the plant canopy to measure the air temperature in the greenhouse. S2. Based on real-time collected soil and air temperatures inside the greenhouse, combined with historical data, a greenhouse temperature influence model is established. This model can predict the greenhouse air temperature based on the soil temperature, and the influence of outside air temperature on the greenhouse air temperature is optimized during the model establishment process. The specific steps for establishing the greenhouse temperature influence model are as follows: S21. Establish soil temperature With the air temperature inside the greenhouse The dynamic relationship between them, taking into account soil moisture. Nonlinear regulating effect; S22. Calculate the cross-correlation function using historical data to determine the lag time. and in soil temperature With the air temperature inside the greenhouse Introduce a lag term into the dynamic relationship between them; S23, Finally, adjust the outdoor temperature... Soil temperature was added as a linear term. With the air temperature inside the greenhouse In the dynamic relationship between them, noise terms of both illumination fluctuations and human interference are considered. The final model of the influence of temperature inside the greenhouse is generated; S3. Based on the growth stage of the seedlings, determine the target soil temperature range and target air temperature range. By monitoring the real-time air temperature inside the greenhouse, determine whether it falls within the target range to identify the air temperature error between the target and actual greenhouse air temperatures. Based on the air temperature regulation strategy and the greenhouse temperature influence model, predict the completion time of air temperature regulation. ; S4. During temperature regulation, the soil temperature inside the greenhouse is monitored in real time, and the time required is predicted based on the greenhouse temperature influence model. The soil temperature inside the greenhouse is then measured, and it is determined whether the soil temperature inside the greenhouse is within the range of the target soil temperature inside the greenhouse; if the soil temperature inside the greenhouse is not within the range of the target soil temperature inside the greenhouse, the soil temperature is adjusted.

2. The temperature control method for seedling greenhouse cultivation environment according to claim 1, characterized in that: In step S2, the specific steps for optimizing the model of the temperature influence inside the greenhouse by considering the influence of the outside air temperature on the inside air temperature are as follows: S24, use an exponential function to quantify the nonlinear influence of ventilation intensity on the heat exchange between the inside and outside of the greenhouse, and generate a heat leakage coefficient; S25, by weighting the temperature difference between the inside and outside of the greenhouse over past time periods, capture the temperature lag effect caused by heat storage or release in the greenhouse, and use an attenuation factor. Controlling the decay rate of historical effects; S26, based on the current outdoor temperature. The direct effect of heat leakage and the cumulative effect of historical temperature lag are combined with the heat leakage coefficient to generate the external temperature influence coefficient. This leads to the generation of an optimized model of the influence of greenhouse temperature.

3. The temperature control method for seedling greenhouse cultivation environment according to claim 2, characterized in that: In step S3, determining the target soil temperature range and target air temperature range for seedlings based on their growth stage involves: collecting real-time data on seedling growth status using sensors installed in the greenhouse; determining the current growth stage of the seedling based on the collected data; determining the target soil temperature and target air temperature range for each growth stage; adjusting the target soil temperature and target air temperature range according to the current environment; and finally determining the target soil temperature range and target air temperature range.

4. The temperature control method for seedling greenhouse cultivation environment according to claim 3, characterized in that: In step S3, the specific steps of the air temperature regulation strategy are as follows: S31, compare the real-time air temperature data inside the greenhouse at the current time point with the target air temperature range inside the greenhouse, and calculate the air temperature error. S32. Based on the nonlinear mapping function, the air temperature error is... The mapping is used as the initial control force, and at the same time, the PID controller combines the output of the nonlinear mapping function with proportional, integral, and derivative control actions to ultimately generate a control signal that includes heating power and heating time. .

5. The temperature control method for seedling greenhouse cultivation environment according to claim 4, characterized in that: In step S3, the air temperature regulation completion time is predicted based on the air temperature regulation strategy and the greenhouse temperature influence model. The specific steps are as follows: S33, control variables containing heating power Input the data into the greenhouse temperature influence model to predict the greenhouse air temperature at the next time step; S34, determine whether the greenhouse air temperature at the next time step is within the target greenhouse air temperature range. If it is not within the range, iteratively update the air temperature error and control signal until the predicted greenhouse air temperature enters the target range; S35, use the golden section search method to quickly solve for the minimum adjustment time. Furthermore, the prediction time is adjusted based on a compensation factor for historical prediction errors.

6. The temperature control method for seedling greenhouse cultivation environment according to claim 5, characterized in that: In step S4, the time is predicted based on the greenhouse temperature influence model. The specific steps for controlling the soil temperature inside the greenhouse are as follows: S41, Obtain the heat transfer coupling coefficient based on online fitting. With heat loss rate This leads to the soil temperature response time constant. and steady-state temperature S42, Based on the data from S41, consider the inherent time lag of the system. A first-order inertial system model was used to predict the dynamic response of soil temperature inside the greenhouse, with a prediction time of [time value missing]. The soil temperature inside the greenhouse afterwards.

7. A temperature control device for an intelligent seedling greenhouse cultivation environment, comprising a sensor, a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the temperature control method for the seedling greenhouse cultivation environment as described in any one of claims 1 to 6.

8. The temperature control device for the seedling greenhouse cultivation environment according to claim 7, characterized in that: The sensors include at least a soil temperature sensor, an air temperature sensor, a soil moisture sensor, a high-definition camera, and an outdoor air temperature sensor.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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