Temperature control method and system for crop greenhouse cultivation environment

Abstract

The invention relates to the technical field of temperature control, in particular to a temperature control method and system for a crop greenhouse cultivation environment, and the method comprises the following steps: calculating the growth rates of crops under various temperature conditions based on the variety information of greenhouse crops, recognizing ideal temperature ranges of a plurality of growth stages, and calculating the temperature of the greenhouse crops according to the ideal temperature ranges; and a crop temperature management model is constructed by integrating ideal temperature range data of a plurality of stages. According to the invention, through calculation of growth rates of crops under various temperature conditions, identification of temperature requirements of the crops in multiple growth stages, calibration of control parameters of a temperature adjusting device, improvement of adaptability and efficiency of a temperature control system, and analysis of an illumination intensity change trend, prediction of internal temperature of a greenhouse is realized. The temperature regulation meets the actual illumination condition, energy is saved, the growth environment is optimized, it is ensured that the environment in the greenhouse is kept in the optimal state, and the market competitiveness of crops and the economic benefits of agricultural producers are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to a temperature control method and system for a greenhouse cultivation environment of crops. Background Art

[0002] The field of temperature control technology focuses on monitoring and regulating the temperature in the environment through a variety of control systems to achieve predetermined temperature standards, including the use of sensors to collect ambient temperature data in real time and adjust the operation of refrigeration or heating equipment through feedback mechanisms. It is used in industrial manufacturing, data centers, medical facilities, and agricultural greenhouses. It is crucial to product quality, operational safety, and biological growth. In agricultural greenhouse applications, temperature control involves the management of day and night temperature differences, seasonal temperature adjustments, and humidity control. It is combined with a variety of automation software to achieve the best environmental control effect.

[0003] Among them, a temperature control method for the greenhouse cultivation environment of crops focuses on using a variety of temperature control systems to create a stable growth environment inside the agricultural greenhouse, ensuring that the temperature inside the greenhouse remains in a temperature range suitable for crop growth, using temperature sensors to monitor the internal environmental temperature, and automatically adjusting the heating, ventilation, and cooling equipment according to the growth needs of the crops. It includes temperature sensors, control units, and actuators, and combines intelligent algorithms to optimize the temperature adjustment process, reduce energy consumption, and improve the efficiency and quality of crop growth. It aims to increase crop yields and market competitiveness, and provide agricultural producers with an effective tool to adapt to the planting challenges brought about by global climate change.

[0004] Traditional crop greenhouse temperature control methods cannot fully adapt to the specific temperature requirements of various crops. When dealing with variable environmental factors such as day and night temperature differences and seasonal changes, they lack sufficient flexibility and adaptability, and are unable to maximize energy utilization and optimize crop growth conditions. The lack of real-time data processing and prediction capabilities limits its ability to respond to sudden environmental changes, resulting in excessive or insufficient temperature adjustments, affecting crop growth or causing energy waste. In the highly competitive agricultural market, producers face the risk of unstable yields and increased costs, affecting economic benefits and market competitiveness. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a temperature control method and system for a greenhouse cultivation environment of crops.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a temperature control method for a greenhouse cultivation environment of crops, comprising the following steps: S1: Based on the information of greenhouse crop types, the ideal temperature ranges of multiple growth stages are identified by calculating the growth rates of crops under various temperature conditions, and a crop temperature management model is constructed by integrating the ideal temperature range data of multiple stages; S2: Based on the crop temperature management model, calculate the equipment working efficiency of the temperature control equipment under various operating conditions, and record the response time, analyze the performance of multiple temperature control equipment, identify the nonlinear characteristics of equipment behavior, and calculate the required control parameter compensation values ​​to generate calibrated control parameters; S3: Based on the calibrated control parameters, by collecting the temperature and light data of the target crop greenhouse, calculating the relationship between light intensity and temperature change, combining time series analysis, by predicting the change trend of light intensity, calculating the temperature prediction values ​​at multiple time points, and generating greenhouse temperature prediction data; S4: Based on the greenhouse temperature prediction data, combined with the temperature requirements of the target crops, the temperature difference is calculated, and the temperature adjustment range is identified. According to the working efficiency of various temperature adjustment devices in differentiated temperature ranges, the control parameters are adjusted to adjust the temperature inside the greenhouse, optimize energy utilization efficiency, and generate energy-saving temperature control parameters.

[0007] As a further solution of the present invention, the crop temperature management model includes growth rate calculation results, ideal temperature range prediction information for crop growth, and a multi-stage crop demand data set; the calibrated control parameters include compensation parameter calculation results, equipment response time, and equipment adjustment capability analysis results; the greenhouse temperature prediction data includes light intensity prediction data, temperature change prediction information, and light and temperature relationship analysis results; the energy-saving temperature control parameters specifically include adjustment equipment matching records, temperature adjustment demand calculation results, and equipment energy consumption records.

[0008] As a further solution of the present invention, the step of obtaining the growth rate is specifically: S111: Based on the greenhouse crop type information, the growth status data of the target crops under various temperature conditions are collected, including crop height, crop weight, and leaf chlorophyll content, to generate a crop status data set; S112: Based on the crop status dataset, the formula is used: ; Calculate the growth rate of target crops under various temperature conditions; in, For the and The average growth rate between time points, For in time The growth index measurements under For in time The growth index measurements under For the The measurement time points, for At the next measurement time point after is the index variable.

[0009] As a further solution of the present invention, the step of obtaining the crop temperature management model is specifically as follows: S121: Based on the growth rate, trend analysis is performed through the growth rate data, using the formula: ; Calculate the slope of growth rate change; in, is the growth rate change slope between two consecutive temperature points, At the temperature point The growth rate under At the temperature point The growth rate under For the The measured temperature points, To follow The next temperature point after is the index of temperature condition; S122: Using the growth rate change slope, by comparing it with a preset slope change threshold, an ideal growth temperature range of the crop in the target stage is identified, and a crop temperature management model is constructed by integrating the ideal temperature range data of the crop in multiple growth stages.

[0010] As a further solution of the present invention, the step of obtaining the equipment working efficiency is specifically as follows: S211: Based on the crop temperature management model, actual operation data of multiple temperature regulating devices under differentiated operating conditions are collected, including the duration and power consumption of each device operation from startup to reaching the target temperature, and generating device operation data records; S212: Based on the equipment operation data record, using the formula: ; Calculate the efficiency of the equipment in multiple temperature ranges; in, Indicates Working efficiency in each temperature range, is the normalization coefficient, It is Environmental variables influencing factors in the temperature range, is the adjustment coefficient, is a constant term, It is The time point when the temperature range operation starts, It is The time point when the temperature interval operation ends.

[0011] As a further solution of the present invention, the step of obtaining the control parameter compensation value is specifically as follows: S221: Based on the working efficiency of the device, analyzing the response time of the temperature regulating device under various operating conditions and the accuracy of reaching the preset temperature, and recording the energy consumption data of the device under various operating conditions to generate a device performance data set; S222: Based on the equipment performance data set, by formula: ; Calculate the control parameter compensation value to obtain the calibrated control parameter; in, represents the compensation value of the control parameter, is the adjustment coefficient, is the average working efficiency of the device under the current settings, It is the target work efficiency.

[0012] As a further solution of the present invention, the step of obtaining the relationship between light intensity and temperature change is specifically: S311: Based on the calibrated control parameters, real-time light intensity and temperature data in the crop greenhouse are collected through temperature and light sensors, time information is recorded, and light and temperature monitoring data are generated; S312: Based on the light and temperature monitoring data, the formula: ; Calculate the correlation between light intensity and temperature to obtain the relationship between light intensity and temperature change; in, is the data point index, is the average light intensity, is the average temperature, It is The light intensity at each data point, It is The temperature of the data point, is the total number of data points, is the correlation coefficient, is the summation symbol.

[0013] As a further solution of the present invention, the step of obtaining the greenhouse temperature prediction data is specifically as follows: S321: Based on the relationship between the light intensity and the temperature change, the light intensity data collected is used to simulate the light change during the day, using the formula: ; Calculate the predicted value of light intensity; in, For a specific time The predicted light intensity, For time point, is the baseline light intensity, For the The coefficients of the cosine terms, For the The coefficients of the sine terms, is the index of the number of terms in the Fourier series, is the basic frequency, is pi; S322: Utilizing the light intensity prediction value, utilizing the correlation coefficient between light intensity and temperature change, and combining with real-time meteorological conditions, including cloudy and sunny days, calculate the temperature prediction value and generate greenhouse temperature prediction data.

[0014] As a further solution of the present invention, the step of obtaining the energy-saving temperature control parameter is specifically as follows: S411: Based on the greenhouse temperature prediction data, the difference between the predicted temperature at multiple time points and the crop required temperature is calculated, and a temperature adjustment device is matched according to the temperature difference and the ideal temperature to generate temperature difference data; S412: Based on the temperature difference data, considering the response time and working efficiency of various temperature adjustment devices, the formula is adopted: ; Calculate the adjustment range of the control parameter and generate the adjusted control parameter; in, is the regulation efficiency required per unit time, is the absolute difference between the predicted temperature and the required temperature, is the working efficiency of the equipment, is the total time from now to the target time point, is the response time of the device; S413: Based on the adjusted control parameters, configure working parameters for various temperature adjustment devices, including working intensity, scheduled start time, optimize energy use, and generate energy-saving temperature control parameters.

[0015] A temperature control system for a crop greenhouse cultivation environment, the temperature control system for the crop greenhouse cultivation environment is used to execute the temperature control method for the crop greenhouse cultivation environment, the system comprises: The growth rate calculation module collects the growth rate data of various crops under differentiated temperature conditions based on the greenhouse crop type information and generates crop growth information; The temperature demand analysis module analyzes the ideal temperature ranges required for various greenhouse crops at multiple growth stages based on the crop growth information and establishes a crop temperature management model; The equipment performance management module evaluates the response time and regulation capability of multiple temperature regulation devices based on the crop temperature management model, calculates the compensation value of the equipment control parameter, and generates the calibrated control parameter; The greenhouse temperature prediction module monitors the light and temperature data in real time based on the calibrated control parameters, analyzes the correlation between the light intensity and the temperature data in the greenhouse, calculates the temperature prediction values ​​at multiple time points by predicting the change trend of the light intensity, and generates greenhouse temperature prediction data; The temperature regulation control module adjusts the control parameters of multiple temperature regulation devices based on the greenhouse temperature prediction data and the temperature requirements of the crops, including the working intensity and start-up time of the equipment, optimizes energy utilization efficiency, and generates energy-saving temperature control parameters.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by calculating the growth rate of crops under various temperature conditions, the temperature requirements of crops at multiple growth stages are identified, the control parameters of the temperature regulating equipment are calibrated, the adaptability and efficiency of the temperature control system are improved, the trend of light intensity changes is analyzed, and the internal temperature of the greenhouse is predicted, so that the temperature regulation meets the actual light conditions, energy is saved and the growth environment is optimized, ensuring that the environment in the greenhouse is maintained in the optimal state, thereby improving the market competitiveness of crops and the economic benefits of agricultural producers. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 is a flow chart of calculating growth rate of the present invention; Figure 3 A flow chart of constructing a crop temperature management model according to the present invention; Figure 4 It is a flow chart of the working efficiency of the analysis equipment of the present invention; Figure 5 It is a flow chart of calculating the control parameter compensation value of the present invention; Figure 6 A flow chart showing the relationship between light intensity and temperature change obtained in the present invention; Figure 7 It is a data flow chart for calculating greenhouse temperature prediction of the present invention; Figure 8This is a flow chart of obtaining energy-saving temperature control parameters of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1 The present invention provides a technical solution, a temperature control method for a crop greenhouse cultivation environment, comprising the following steps: S1: Based on the information of greenhouse crop types, the temperature requirements of multiple growth stages are identified by calculating the growth rates of crops under various temperature conditions. The ideal temperature ranges of multiple stages are identified by evaluating the data change trends of multiple stages, and the temperature requirement data of multiple stages are integrated to build a crop temperature management model. S2: Based on the crop temperature management model, calculate the equipment working efficiency of the temperature control equipment under various operating conditions and record the response time, analyze the performance of multiple temperature control equipment, identify the nonlinear characteristics of equipment behavior, and calculate the required control parameter compensation values ​​to generate calibrated control parameters; S3: Based on the calibrated control parameters, the temperature and light data of the target crop greenhouse are collected to calculate the relationship between light intensity and temperature change. Combined with time series analysis, the temperature prediction values ​​at multiple time points are calculated by predicting the change trend of light intensity, and the greenhouse temperature prediction data is generated; S4: Based on greenhouse temperature prediction data and combined with the temperature requirements of target crops, calculate temperature differences and identify the temperature adjustment range. According to the working efficiency of various temperature control devices in differentiated temperature ranges, including light management equipment, cooling equipment, ventilation equipment, and heating equipment, adjust the control parameters of various temperature control devices, adjust the temperature inside the greenhouse, including work intensity and start-up time, optimize energy utilization efficiency, and generate energy-saving temperature control parameters.

[0021] The crop temperature management model includes the growth rate calculation results, the ideal temperature range prediction information for crop growth, and the crop multi-stage demand data set. The calibrated control parameters include the compensation parameter calculation results, the equipment response time, and the equipment adjustment capability analysis results. The greenhouse temperature prediction data includes the light intensity prediction data, the temperature change prediction information, and the light and temperature relationship analysis results. The energy-saving temperature control parameters are specifically the adjustment equipment matching records, the temperature adjustment demand calculation results, and the equipment energy consumption records.

[0022] See also Figure 2 , the specific steps for obtaining the growth rate are: S111: Based on the greenhouse crop type information, the growth status data of the target crops under various temperature conditions are collected, including crop height, crop weight, and leaf chlorophyll content, to generate a crop status data set; In sub-step S111, based on the information of greenhouse crop types, data collectors and sensors are used to measure various physiological indicators of crops, including crop height, weight and chlorophyll content in leaves. The data are automatically transmitted to the central database, and the raw data are cleaned and normalized through data processing algorithms to ensure data quality and prepare for subsequent analysis. During the process, key parameters including temperature values, timestamps, and crop growth indicators are directly obtained through sensors. The sensors are calibrated regularly to ensure data accuracy. The processed data will constitute a crop status data set, which will support the calculation of crop growth rate and determination of growth temperature range in the next step.

[0023] S112: Based on the crop status dataset, the formula is: ; Calculate the growth rate of target crops under various temperature conditions; in, For the and The average growth rate between time points represents the change in growth rate between these two time points. For in time Growth index measurements under the following conditions include crop height, crop weight, leaf chlorophyll content, For in time The growth index measurements under For the A measurement time point is the specific time when growth data is collected. for At the next measurement time point after is an index variable, indicating the measurement and calculation intervals for marking and referencing specific measurement points and temperature intervals.

[0024] formula: ; Parameter meaning and acquisition method: : Time point The growth indicators under the condition of The growth status of : Time point The growth index measurements under the same conditions are also obtained through field monitoring; : No. The measurement time point is the specific number of days after planting, which is obtained by recording the actual calendar date; : No. A measurement time point, determined by a calendar date; :exist arrive The average growth rate over the time period; Calculation example: set up The weather and height of crops cm, The weather and height of crops cm, then: ; ; ; Formula calculation The results show that during the investigated period, the average daily growth rate of crops was 1 cm / day. The numerical value reflects the growth efficiency of crops under target temperature conditions. The calculation process is used to evaluate the growth efficiency of crops under various temperature conditions and guide the adjustment of temperature control strategies.

[0025] See also Figure 3 ,The specific steps for obtaining the crop temperature management model are: S121: Based on growth rate, trend analysis is performed through growth rate data using the formula: ; Calculate the slope of growth rate change; in, is the growth rate change slope between two consecutive temperature points, At the temperature point The growth rate under At the temperature point The growth rate under For the The measured temperature points, To follow The next temperature point after is the index of temperature condition; formula: ; Parameter meaning and acquisition method: : At temperature The growth rate under a certain temperature is obtained by actually measuring the crop growth data (such as plant height or leaf area) at that temperature; : At temperature The growth rate under the condition is obtained through actual measurement; : No. The actual temperature value set and recorded by the environmental control system; : No. Temperature measurement points are set and recorded by the environmental control system; Calculation example: Set in The growth rate measured under cm / day, in The growth rate measured under cm / day, calculate the growth rate slope: ; The calculated slope It shows that when the temperature changes from 20℃ to 22℃, the growth rate increases by 0.025cm / day for every degree increase. The results are used to analyze the effects of different temperatures on the growth rate and screen the ideal temperature range for crop growth.

[0026] S122: using the growth rate change slope to compare with a preset slope change threshold, identifying an ideal growth temperature range of the crop at the target stage, and constructing a crop temperature management model by integrating the ideal temperature range data of the crop at multiple growth stages; In sub-step S122, the ideal growth temperature range is determined based on the slope of the growth rate change, the average growth rate of the crops in each growth stage is calculated, and the change in rate is calculated from two consecutive measurements using the difference method. The change is compared with a preset slope change threshold, and the threshold is set according to the specific growth requirements of the crop type, such as the correlation analysis between temperature and growth rate. The slope calculation and threshold comparison are performed using statistical analysis software. The key parameters involved in the process include growth rate, time interval, and temperature records. The ideal temperature range requirement data required by the crops is obtained, and the ideal range temperature data of the crops in multiple growth stages are integrated to construct a crop temperature management model. The constructed model is used to guide agricultural producers to adjust temperature settings at different growth stages and optimize crop growth conditions.

[0027] See also Figure 4 , the specific steps for obtaining equipment working efficiency are: S211: Based on the crop temperature management model, actual operation data of multiple temperature regulating devices under differentiated operating conditions are collected, including the time and power consumption of each device operation from startup to reaching the target temperature, and device operation data records are generated; In sub-step S211, based on the crop temperature management model, the operating data of multiple temperature regulating devices under different operating conditions are actually collected, including the specific time and power consumption from the start of the equipment to reaching the set temperature. The start and end time of each operation are recorded in real time by sensors installed on the equipment, and the power meter records the power consumption. The data is transmitted to the central monitoring system through a wireless network. The collected information is processed using data analysis tools to analyze the equipment efficiency. Key parameters include operation time, power consumption, and the difference between the set and actual temperatures. The parameters are automatically obtained through the real-time monitoring system. The processed data is used to evaluate the energy efficiency of each device and generate equipment operation data records. The data is used to evaluate equipment performance and guide energy management strategies.

[0028] S212: Based on the equipment operation data record, use the formula: ; Calculate the efficiency of the equipment in multiple temperature ranges; in, Indicates The working efficiency of each temperature range shows how fast the equipment reaches the target temperature. is the normalization coefficient, which is used to adjust the numerical range of work efficiency. It is Environmental variables influencing factors under different temperature ranges, including humidity, air circulation, is the adjustment factor used to balance the effects of operating time and environmental variables, is a constant term used to prevent the denominator from being zero. It is The time point when the temperature range operation starts, It is The time point when the temperature interval operation ends.

[0029] formula: ; Parameter meaning and acquisition method: : Normalization coefficient, obtained through historical data analysis, is used to adjust the numerical range of work efficiency to ensure that the efficiency value is suitable for further analysis; : No. The influencing factors of environmental variables in each temperature range are obtained through the real-time environmental monitoring system, including humidity and air circulation; : Adjustment coefficient, which is set based on the equipment performance test results and is used to balance the effects of operating time and environmental variables; : Constant term, set to a small value such as , to prevent the denominator from being zero, a small offset is added; : No. The time point at which the operation of each temperature range starts is obtained through the device log record; : No. The time point at which the operation of each temperature range ends is also obtained through the device log record; Calculation Example set up , , , , , , calculate work efficiency : ; ; Calculated work efficiency It indicates the speed at which the device can reach the target temperature per hour under given environmental and operating conditions. The result is used to evaluate the performance of the device within this temperature range and provide a quantitative measure of the device's regulation ability.

[0030] See also Figure 5 , the specific steps for obtaining the control parameter compensation value are: S221: Based on the working efficiency of the equipment, analyzing the response time of the temperature control equipment under various operating conditions and the accuracy of reaching the preset temperature, and recording the energy consumption data of the equipment under various operating conditions to generate an equipment performance data set; In sub-step S221, a performance analysis is performed on the temperature regulating equipment under various operating conditions to analyze the response time and the accuracy of reaching the preset temperature. The equipment operation data is collected through a real-time data acquisition system, including the time from startup to reaching the preset temperature and the energy consumption during equipment operation. The data is provided by energy consumption monitors and temperature sensors connected to each device. Relevant data processing uses data analysis software such as SAS for statistical analysis and trend prediction to evaluate the efficiency and accuracy of the equipment under different conditions. The key parameters analyzed include equipment response time, compliance rate and energy consumption. Based on the analysis results, an equipment performance data set is generated, which is used for subsequent optimization of equipment configuration and adjustment of maintenance plans to improve overall energy efficiency and equipment reliability.

[0031] S222: Based on the equipment performance data set, through the formula: ; Calculate the control parameter compensation value to obtain the calibrated control parameter; in, represents the compensation value of the control parameter, is the adjustment coefficient, which is used to determine the degree of influence of the compensation value on the equipment adjustment. is the average working efficiency of the device under the current settings, It is the target work efficiency, which is set based on equipment design parameters and user needs.

[0032] formula: ; Parameter meaning and acquisition method: : Adjustment coefficient, obtained through equipment performance debugging, used to adjust the sensitivity of the compensation value; : The current average working efficiency of the equipment; : The target working efficiency of the equipment is set according to the equipment design parameters and user needs; Calculation example: set up , , , calculate the control parameter compensation value: ; ; ; ; result It shows that the adjustment range of the control parameter compensation value is 0.0075, which means that in order to achieve the target working efficiency, the control parameters of the equipment need to be adjusted. The compensation value is used to adjust the temperature set point and various related parameters of the equipment to optimize the performance and response time of the equipment.

[0033] See also Figure 6 , the steps for obtaining the relationship between light intensity and temperature change are as follows: S311: Based on the calibrated control parameters, the real-time light intensity and temperature data in the crop greenhouse are collected in real time through temperature and light sensors, the time information is recorded, and light and temperature monitoring data are generated; In sub-step S311, based on the calibrated control parameters, temperature sensors are used to collect temperature data in the greenhouse, and light sensors are used to continuously monitor the light intensity in the greenhouse. Sensors are installed at key locations inside the greenhouse to ensure comprehensive capture of environmental data. The collected data includes light intensity, temperature readings, and corresponding time stamps. LabVIEW is used for data recording and preliminary analysis. The recorded data reflects real-time environmental changes and provides instant data support for temperature control. The information will be transmitted to the central database to generate light and temperature monitoring data to provide accurate input data for the prediction model.

[0034] S312: Based on the light and temperature monitoring data, the formula: ; Calculate the correlation between light intensity and temperature to obtain the relationship between light intensity and temperature change; in, is the data point index, is the average light intensity, is the average temperature, It is The light intensity at each data point, It is The temperature of the data point, is the total number of data points, It is a summation symbol used to add sequence data in a formula.

[0035] formula: ; Parameter meaning and acquisition method : No. The light intensity of each data point is measured by a light sensor; : No. The temperature of each data point is measured by a temperature sensor; and : Average values ​​of light intensity and temperature; : Total data points, i.e. the total number of data points collected in a specific time period; Calculation example: Setting light intensity data points , temperature data points , calculate the correlation coefficient Calculate the average: ; ; The numerator of the calculation formula is: ; ; ; Denominator of the calculation formula: ; ; ; ; ; Calculate the correlation coefficient: ; result It shows that there is a positive correlation between light intensity and temperature, reflecting that an increase in light intensity is generally associated with an increase in temperature. The data is crucial for adjusting the environmental control system in the greenhouse.

[0036] See also Figure 7 ,The specific steps for obtaining greenhouse temperature prediction data are as follows: S321: Based on the relationship between light intensity and temperature change, the light intensity data collected is used to simulate the light changes during the day, using the formula: ; Calculate the predicted value of light intensity; in, For a specific time The predicted light intensity, For time point, Baseline light intensity, which represents the basic level of light that exists at any time of the day, is determined by the average light level of historical data. For the The coefficient of the cosine term represents one of the main periodic components of the light intensity variation over time in the Fourier series and is obtained by Fourier transform of historical data. For the The coefficients of the sine terms are Similarly, it is obtained through Fourier transform of historical data. is the index of the term number in the Fourier series, indicating the periodic term currently being considered, The value of determines the frequency of the cycle, The basic frequency is used to determine the basic period of light intensity changes during the day. It is determined by observing the periodic distribution of light intensity in the data. The angular frequency in the Fourier transform multiplied by time determines the periodic changes in the waveform of the cosine and sine functions during the day. is pi, which is used to convert time into degrees, and are cosine and sine functions; formula: ; Parameter meaning and acquisition method: : At a specific time The predicted light intensity; : Daily average light baseline intensity, calculated by averaging the light data for the whole day; , : Fourier coefficient, obtained by Fourier analysis of historical illumination data, used to describe the illumination variation components at different frequencies; : Index of the number of terms in the Fourier series, used to represent components of different frequencies; : The basic frequency of light intensity change, set to It means that the light intensity completes one cycle every 24 hours; : time of day, expressed in hours; Calculation example: Suppose the following parameter values ​​are obtained through measurement on a certain day: , , , , indicating that the light intensity completes a cycle every 24 hours, and the light intensity at 12 noon is calculated : ; ; result It shows that the predicted light intensity at 12 noon is 60 lux, which reflects the light intensity at noon. The value is used to predict and manage greenhouse lighting conditions.

[0037] S322: using the light intensity prediction value, using the correlation coefficient between light intensity and temperature change, combined with real-time meteorological conditions, including cloudy and sunny days, calculating the temperature prediction value, and generating greenhouse temperature prediction data; In sub-step S322, based on the predicted value of light intensity and its correlation with temperature changes, combined with real-time meteorological conditions data, such as sunny or cloudy conditions, a prediction algorithm is used to calculate the predicted temperature value. The prediction work adopts a time series analysis method based on machine learning, such as using the ARIMA model for trend analysis. The model is trained based on historical light and temperature data to predict future temperature changes. The parameters include historical light data, historical temperature data, and current meteorological conditions. The analysis results will generate greenhouse temperature prediction data to provide decision support for crop management, ensure that temperature control measures can effectively respond to future environmental changes, and maximize crop growth efficiency and yield.

[0038] See also Figure 8 , the specific steps for obtaining energy-saving temperature control parameters are: S411: Based on the greenhouse temperature prediction data, the difference between the predicted temperature at multiple time points and the crop required temperature is calculated, and the temperature adjustment device is matched according to the temperature difference and the ideal temperature to generate temperature difference data; In sub-step S411, the greenhouse temperature prediction data is processed. Through a data analysis platform, such as Python using the Pandas library, the difference between the predicted temperature at multiple preset time points and the crop required temperature is calculated. The parameters include predicted temperature data, crop growth required temperature data and time point markers. The temperature difference at each time point is calculated using a mathematical model using the target data. The temperature difference data is used for subsequent temperature regulation equipment selection and configuration to ensure that the equipment selected at each operation stage best meets the needs of the current crops. Data processing includes statistical analysis of the temperature difference data, and optimization of the temperature control strategy based on the statistical results to achieve the optimal crop growth environment and generate temperature difference data.

[0039] S412: Based on the temperature difference data, considering the response time and working efficiency of various temperature control devices, the formula is adopted: ; Calculate the adjustment range of the control parameter and generate the adjusted control parameter; in, is the regulation efficiency required per unit time, indicating the required control parameter adjustment range. is the absolute difference between the predicted temperature and the required temperature, indicating the temperature difference that needs to be adjusted. It is the working efficiency of the equipment, which indicates the amount of adjustment that can be achieved by the equipment per unit time. is the total time from now to the target time point, It is the response time of the device, the time from receiving the command to starting the operation; formula: ; Parameter meaning and acquisition method: : The absolute difference between the predicted and required temperatures is obtained through real-time monitoring of the temperature sensor in the greenhouse; : The working efficiency of the equipment per unit time, obtained through performance testing and analysis; : The length of time from now to the target time point, based on the scheduling plan settings; : The response time of the equipment, obtained from the technical manual or historical operation data; Calculation example: set up °C, , Hour, Hours, calculated : ; The calculation results show that the required control parameter adjustment range is 0.914. The value reflects the required temperature regulation efficiency, which is used to adjust the working parameters of the equipment to meet the temperature requirements of crops, ensure that the temperature control equipment can respond effectively according to the actual situation, optimize energy use and meet the growth conditions of crops.

[0040] S413: Based on the adjusted control parameters, configure working parameters for various temperature adjustment devices, including working intensity, scheduled start time, optimize energy use, and generate energy-saving temperature control parameters; In sub-step S413, various temperature control devices are configured based on the adjusted control parameters. The working intensity and scheduled startup time of the equipment are considered during operation, and the BMS is used to intelligently configure the equipment. Key parameters include the power setting, startup and shutdown time of the equipment. The configuration of parameters is based on energy-saving optimization algorithms, such as genetic algorithms, to achieve optimal energy use. The equipment operation strategy is adjusted according to real-time energy consumption data to optimize energy consumption. The generated energy-saving temperature control parameters ensure maximum energy efficiency. Through intelligent scheduling, equipment wear is reduced, the equipment service life is extended, and the overall performance and energy utilization efficiency of the temperature control system are improved.

[0041] A temperature control system for a crop greenhouse cultivation environment, the temperature control system for the crop greenhouse cultivation environment is used to execute the temperature control method for the crop greenhouse cultivation environment, the system comprises: The growth rate calculation module collects the growth rate data of various crops under differentiated temperature conditions based on the greenhouse crop type information and generates crop growth information; The temperature demand analysis module analyzes the ideal temperature range required by various greenhouse crops at multiple growth stages based on crop growth information and establishes a crop temperature management model; The equipment performance management module evaluates the response time and regulation capacity of multiple temperature regulation devices based on the crop temperature management model, calculates the compensation value of equipment control parameters, and generates calibrated control parameters; The greenhouse temperature prediction module monitors the light and temperature data in real time based on the calibrated control parameters, analyzes the correlation between light intensity and temperature data in the greenhouse, predicts the change trend of light intensity, calculates the temperature prediction values ​​at multiple time points, and generates greenhouse temperature prediction data; The temperature regulation control module adjusts the control parameters of multiple temperature regulation devices based on greenhouse temperature prediction data and the temperature requirements of crops, including the working intensity and start-up time of the equipment, optimizes energy utilization efficiency, and generates energy-saving temperature control parameters.

[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for controlling the temperature of a greenhouse cultivation environment for crops, characterized in that: The following steps are involved: Based on greenhouse crop information, the ideal temperature ranges for multiple growth stages are identified by calculating the growth rates of crops under various temperature conditions. The crop temperature management model is constructed by integrating the ideal temperature range data for multiple stages. Based on the crop temperature management model, the equipment working efficiency of the temperature regulating equipment under various operating conditions is calculated, and the response time is recorded, the performance of multiple temperature regulating equipment is analyzed, the nonlinear characteristics of the equipment behavior are identified, and the required control parameter compensation values ​​are calculated to generate calibrated control parameters; Based on the calibrated control parameters, the temperature and light data of the target crop greenhouse are collected to calculate the relationship between light intensity and temperature change. Combined with time series analysis, the temperature prediction values ​​at multiple time points are calculated by predicting the change trend of light intensity, and the greenhouse temperature prediction data is generated; Based on the greenhouse temperature prediction data, combined with the temperature requirements of the target crops, the temperature difference is calculated, and the temperature adjustment range is identified. According to the working efficiency of various temperature adjustment devices in differentiated temperature ranges, the control parameters are adjusted to adjust the temperature inside the greenhouse, optimize energy utilization efficiency, and generate energy-saving temperature control parameters.

2. The temperature control method for a crop greenhouse cultivation environment according to claim 1, characterized in that: The crop temperature management model includes the growth rate calculation results, the ideal temperature range prediction information for crop growth, and the crop multi-stage demand data set. The calibrated control parameters include the compensation parameter calculation results, the equipment response time, and the equipment adjustment capability analysis results. The greenhouse temperature prediction data includes the light intensity prediction data, the temperature change prediction information, and the light and temperature relationship analysis results. The energy-saving temperature control parameters specifically include the adjustment equipment matching records, the temperature adjustment demand calculation results, and the equipment energy consumption records.

3. The temperature control method for a crop greenhouse cultivation environment according to claim 1, characterized in that: The step of obtaining the growth rate is specifically as follows: Based on the greenhouse crop type information, the growth status data of the target crops under various temperature conditions are collected, including crop height, crop weight, and leaf chlorophyll content, to generate a crop status data set; Based on the crop status dataset, the formula is adopted: ； Calculate the growth rate of target crops under various temperature conditions; in, For the and The average growth rate between time points, For in time The growth index measurements under For in time The growth index measurements under For the The measurement time points, for At the next measurement time point after is the index variable.

4. The temperature control method for a crop greenhouse cultivation environment according to claim 3, characterized in that: The steps for obtaining the crop temperature management model are specifically as follows: Based on the growth rate, trend analysis was performed through the growth rate data using the formula: ； Calculate the slope of growth rate change; in, is the growth rate change slope between two consecutive temperature points, At the temperature point The growth rate under At the temperature point The growth rate under For the The measured temperature points, To follow The next temperature point after is the index of temperature condition; The growth rate change slope is used to compare with a preset slope change threshold to identify the ideal growth temperature range of the crop at the target stage, and a crop temperature management model is constructed by integrating the ideal temperature range data of the crop at multiple growth stages.

5. The temperature control method for a crop greenhouse cultivation environment according to claim 1, characterized in that: The steps for obtaining the equipment working efficiency are specifically as follows: Based on the crop temperature management model, actual operation data of multiple temperature regulating devices under differentiated operating conditions are collected, including the duration and power consumption of each device operation from startup to reaching the target temperature, and device operation data records are generated; Based on the equipment operation data record, using the formula: ； Calculate the efficiency of the equipment in multiple temperature ranges; in, Indicates Working efficiency in each temperature range, is the normalization coefficient, It is Environmental variables influencing factors in the temperature range, is the adjustment coefficient, is a constant term, It is The time point when the temperature range operation starts, It is The time point when the temperature interval operation ends.

6. The temperature control method for a crop greenhouse cultivation environment according to claim 5, characterized in that: The steps for obtaining the control parameter compensation value are specifically as follows: Based on the working efficiency of the equipment, analyzing the response time and accuracy of the temperature control equipment in reaching the preset temperature under various operating conditions, and recording the energy consumption data of the equipment under various operating conditions to generate an equipment performance data set; Based on the equipment performance data set, the formula: ； Calculate the control parameter compensation value to obtain the calibrated control parameter; in, represents the compensation value of the control parameter, is the adjustment coefficient, is the average working efficiency of the device under the current settings, It is the target work efficiency.

7. The temperature control method for a crop greenhouse cultivation environment according to claim 1, characterized in that: The steps for obtaining the relationship between the light intensity and the temperature change are specifically as follows: Based on the calibrated control parameters, the real-time light intensity and temperature data in the crop greenhouse are collected in real time through temperature and light sensors, the time information is recorded, and light and temperature monitoring data are generated; Based on the light and temperature monitoring data, the formula: ； Calculate the correlation between light intensity and temperature, and obtain the relationship between light intensity and temperature change; in, is the data point index, is the average light intensity, is the average temperature, It is The light intensity at each data point, It is The temperature of the data point, is the total number of data points, is the correlation coefficient, is the summation symbol.

8. The temperature control method for a crop greenhouse cultivation environment according to claim 7, characterized in that: The steps for obtaining the greenhouse temperature prediction data are specifically as follows: Based on the relationship between light intensity and temperature change, the light intensity data collected is used to simulate the light changes during the day, using the formula: ； Calculate the predicted value of light intensity; in, For a specific time The predicted light intensity, For time point, is the baseline light intensity, For the The coefficients of the cosine terms, For the The coefficients of the sine terms, is the index of the number of terms in the Fourier series, is the basic frequency, is pi; The light intensity prediction value is used, the correlation coefficient between light intensity and temperature change is used, and the temperature prediction value is calculated in combination with real-time meteorological conditions, including cloudy and sunny days, to generate greenhouse temperature prediction data.

9. The temperature control method for a crop greenhouse cultivation environment according to claim 1, characterized in that: The steps for obtaining the energy-saving temperature control parameters are specifically as follows: Based on the greenhouse temperature prediction data, the difference between the predicted temperature at multiple time points and the crop required temperature is calculated, and the temperature adjustment device is matched according to the temperature difference and the ideal temperature to generate temperature difference data; Based on the temperature difference data, considering the response time and working efficiency of various temperature regulating devices, the formula is adopted: ； Calculate the adjustment range of the control parameter and generate the adjusted control parameter; in, is the regulation efficiency required per unit time, is the absolute difference between the predicted temperature and the required temperature, is the working efficiency of the equipment, is the total time from now to the target time point, is the response time of the device; Based on the adjusted control parameters, operating parameters are configured for various temperature regulating devices, including operating intensity, scheduled start time, optimizing energy use, and generating energy-saving temperature control parameters.

10. A temperature control system for a greenhouse cultivation environment of crops, characterized in that: According to the temperature control method of the crop greenhouse cultivation environment according to any one of claims 1 to 9, the system comprises: The growth rate calculation module collects the growth rate data of various crops under differentiated temperature conditions based on the greenhouse crop type information and generates crop growth information; The temperature demand analysis module analyzes the ideal temperature ranges required for various greenhouse crops at multiple growth stages based on the crop growth information and establishes a crop temperature management model; The equipment performance management module evaluates the response time and regulation capability of multiple temperature regulation devices based on the crop temperature management model, calculates the compensation value of the equipment control parameter, and generates the calibrated control parameter; The greenhouse temperature prediction module monitors the light and temperature data in real time based on the calibrated control parameters, analyzes the correlation between the light intensity and the temperature data in the greenhouse, calculates the temperature prediction values ​​at multiple time points by predicting the change trend of the light intensity, and generates greenhouse temperature prediction data; The temperature regulation control module adjusts the control parameters of multiple temperature regulation devices based on the greenhouse temperature prediction data and the temperature requirements of the crops, including the working intensity and start-up time of the equipment, optimizes energy utilization efficiency, and generates energy-saving temperature control parameters.

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