A smart temperature control system for greenhouses

Abstract

This invention discloses an intelligent temperature control system for greenhouses, relating to the field of greenhouse temperature control technology. The intelligent temperature control system includes a data acquisition module, a crop growth database module, a data analysis and decision-making module, a remote monitoring and alarm module, an intelligent control module, and a user interface module, wherein the modules are electrically connected. This invention monitors the temperature environment inside the greenhouse in real time and precisely adjusts it according to the crop's growth needs and current environmental conditions, ensuring that the crop is always within the optimal growth temperature range. This promotes healthy growth and development of the crop. Compared with traditional manual control methods, it can more accurately control temperature fluctuations, avoiding problems such as stunted crop growth and increased pests and diseases caused by unsuitable temperatures. Furthermore, it can automatically adjust the temperature setpoint according to different growth stages and types of crops, further optimizing the growth environment and improving crop yield and quality.

Description

Technical Field

[0001] This invention relates to the field of greenhouse temperature control technology, specifically to an intelligent temperature control system for greenhouses. Background Technology

[0002] Agricultural greenhouses, as an important agricultural production method, are characterized by controllable facility environment, energy and water conservation, and high production efficiency. With the continuous development of agricultural technology, traditional agricultural production methods are gradually transforming into modernization and intelligence. In greenhouse cultivation, temperature is one of the key factors affecting crop growth. Different crops have strict temperature requirements at different growth stages. By controlling the temperature inside the greenhouse, the most suitable growth environment can be provided for crops. This is an important means to improve crop yield and quality in the context of agricultural modernization.

[0003] For example, a control system for intelligently adjusting greenhouse temperature, as disclosed in Chinese Patent Application Publication No. CN113687671A, includes a controller, a top ventilation device, multiple side ventilation devices, multiple internal fans, a water curtain, and multiple negative pressure fans. The controller determines whether the current greenhouse temperature detection value is higher than a preset first temperature threshold. If so, it controls one of the internal fans to turn on. If the greenhouse temperature detection value shows an upward trend, it controls at least one of the remaining internal fans to turn on.

[0004] In existing technologies, greenhouse temperature is controlled by combining meteorological information and monitoring factors affecting temperature changes within the greenhouse using temperature and light sensors. This efficiently regulates the greenhouse temperature, solving the problems of lag and low efficiency associated with manual temperature control. However, different crops at different growth stages have significantly different sensitivities and requirements for temperature, making it difficult to adjust the temperature according to the crop's needs. This makes it difficult to dynamically match the crop's growth requirements in practical applications, thus affecting crop yield and quality. Therefore, how to dynamically adjust and optimize temperature control based on crop planting data to meet the crop's temperature requirements is the problem we need to solve. To this end, we propose an intelligent temperature control system for greenhouses. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent temperature control system for greenhouses to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] An intelligent temperature control system for greenhouses includes a data acquisition module, a crop growth database module, a data analysis and decision-making module, a remote monitoring and alarm module, an intelligent control module, and a user interface module, wherein the modules are electrically connected.

[0008] The data acquisition module collects environmental data through various sensors deployed in the greenhouse, senses changes in ambient temperature, ensures the accuracy and real-time nature of the data, and provides a reliable foundation for subsequent analysis and control.

[0009] The crop growth database module constructs a crop growth database, pre-stores planting data on the temperature, humidity, and light requirements of different crops at various growth stages. The data comes from long-term agricultural scientific research practices and planting experience summaries, and constructs a crop growth model to predict the suitable temperature range for crops at different stages, providing a basis for intelligent control.

[0010] The data analysis and decision-making module receives environmental data acquired by the data acquisition module, compares and analyzes it with the crop growth model, calculates the deviation value of environmental parameters, determines whether to adjust the temperature inside the greenhouse, and determines the target value for adjustment.

[0011] The remote monitoring and alarm module remotely monitors the environmental data inside the greenhouse, assesses the overall environmental deviation based on the deviation of the environmental data, and sends alarm information to the grower so that timely measures can be taken for manual intervention.

[0012] The intelligent control module controls the temperature control equipment in the greenhouse based on the output of the remote monitoring and alarm module, and dynamically adjusts the temperature inside the greenhouse. The temperature control equipment includes ventilation fans, shade nets, and heating equipment to achieve precise temperature control and ensure that crops are in the best growing environment.

[0013] The user interface module, combined with the output of the intelligent control module, sends various control parameters inside the greenhouse to the grower, displays real-time environmental data, crop growth status, and operating status of temperature control equipment inside the greenhouse, and allows users to manually adjust temperature control parameters or view historical data and reports, enhancing the system's flexibility and user participation.

[0014] A further improvement to the technical solution of the present invention is that the environmental data acquisition process in the data acquisition module includes:

[0015] Various sensors, including temperature sensors, humidity sensors, and light sensors, are deployed inside the greenhouse. The distribution of the sensors is planned according to the area and shape of the greenhouse to ensure that the sensors can fully cover the interior area of ​​the greenhouse and collect environmental data without omission. Fixed brackets are used to install the sensors at different heights and positions inside the greenhouse to obtain more comprehensive environmental information.

[0016] Initialize and configure various sensors, including calibration, setting sampling frequency, and configuring communication parameters, to ensure that the sensors can work normally and transmit data accurately. The sensors collect environmental data in the greenhouse in real time according to the preset sampling frequency. The environmental data includes temperature, humidity, and light intensity.

[0017] The sensor converts the raw data it collects into digital signals through an internal analog-to-digital converter (ADC) and preprocesses the collected environmental data, including data smoothing, noise reduction, and filtering.

[0018] The sensor transmits the pre-processed environmental data to the data center of the intelligent temperature control system via a communication interface, and a data warehouse is built in the data center to store the pre-processed environmental data.

[0019] A further improvement to the technical solution of this invention lies in that: the construction process of the crop growth database in the crop growth database module includes:

[0020] Based on long-term agricultural scientific research practices, planting experience summaries, agricultural expert consultations, agricultural literature and journals, etc., data from different sources are uniformly formatted and standardized, integrating data on the temperature, humidity and light requirements of different crops at various growth stages (sowing period, growth period, flowering period, fruiting period, etc.), as well as crop growth cycle, growth rate and yield information.

[0021] A crop growth database is constructed, and the integrated data is entered into the database according to crop type, growth stage, and environmental parameters for classified storage. This ensures the accuracy and integrity of the data, and the data is verified and cleaned to remove outliers and duplicate data.

[0022] Based on the characteristics and patterns of crop growth, a crop growth model is constructed by combining data from a crop growth database with a neural network model to analyze the relationship between crop growth and environmental parameters.

[0023] Based on the current time, crop type, and growth stage, relevant environmental parameter requirements data are obtained from the crop growth database. The input data is then fed into the trained crop growth model for prediction and calculation. The model outputs suitable temperature range, humidity range, and light intensity environmental parameters for the crop at the current growth stage. The prediction results are analyzed and interpreted to understand the crop's growth status and requirements under the current environmental conditions.

[0024] A further improvement to the technical solution of this invention lies in that: in the data analysis and decision-making module, the calculation process of the environmental parameter deviation value includes:

[0025] The data warehouse extracts preprocessed environmental data for various environmental parameters (temperature, humidity, light intensity), and based on the current time, crop type, and growth stage, extracts the optimal range of environmental parameters corresponding to the current crop type and growth stage from the crop growth database, including the ideal values ​​and acceptable ranges of temperature, humidity, and light intensity.

[0026] The extracted environmental data is compared and analyzed with the optimal environmental parameters in the crop growth model. The deviation values ​​of the environmental parameters are calculated, namely the temperature deviation value, humidity deviation value, and light intensity deviation value, that is, the difference between the current value and the ideal value. The magnitude of the deviation value is analyzed to determine the degree of difference between the current environmental data and the ideal crop growth conditions.

[0027] Based on the characteristics of crop types and growth stages, set deviation thresholds for each environmental parameter. Compare the deviation values ​​of each environmental parameter with the deviation thresholds to determine whether the temperature inside the greenhouse needs to be adjusted. If adjustment is required, determine the target value to be adjusted, i.e., which environmental parameter value can be adjusted to meet the optimal growth conditions for the crop.

[0028] A further improvement to the technical solution of the present invention is that the formula for calculating the temperature deviation value is:

[0029]

[0030] Among them, T d T represents the temperature deviation value. cu T represents the current temperature value. id For the ideal temperature value, T r For the acceptable temperature range, T d The value range is from 0 to 1, where 0 indicates that the current temperature is exactly the ideal temperature, and 1 indicates that the current temperature is outside the acceptable range.

[0031] The formula for calculating the humidity deviation value is:

[0032]

[0033] Among them, H d H represents the humidity deviation value. cu The current humidity value, H id For the ideal humidity value, H r For acceptable humidity levels, H d The value range is from 0 to 1, where 0 indicates that the current humidity is exactly the ideal humidity, and 1 indicates that the current humidity is outside the acceptable range.

[0034] The formula for calculating the light intensity deviation value is:

[0035]

[0036] Among them, L d L represents the light intensity deviation value. cu L represents the current light intensity value. id For the ideal light intensity value, L r For light intensity within an acceptable range, L d The value range is from 0 to 1, where 0 indicates that the current light intensity is exactly the same as the ideal light intensity, and 1 indicates that the current light intensity exceeds the acceptable range.

[0037] A further improvement to the technical solution of this invention lies in the fact that the evaluation process of the overall environmental deviation degree inside the greenhouse in the remote monitoring alarm module includes:

[0038] By analyzing the environmental data collected in real time by various types of sensors (temperature sensor, humidity sensor, light sensor) inside the greenhouse, the environmental status inside the greenhouse can be monitored.

[0039] By combining the data provided by the data analysis and decision-making module, the deviation values ​​of various environmental parameters are obtained, and the influence weights of temperature, humidity, and light intensity are set according to the degree of influence of different environmental parameters on crop growth.

[0040] By comprehensively analyzing the deviation values ​​of various environmental parameters and their weights in relation to crop growth, and combining the maximum acceptable deviation values ​​of temperature, humidity, and light intensity, an environmental deviation evaluation coefficient is calculated to assess the degree of deviation of the overall environment inside the greenhouse.

[0041] Based on the crop type, growth stage, and grower's actual needs, a dynamic alarm threshold for the environmental deviation evaluation coefficient is set. Based on the environmental deviation evaluation coefficient, the degree of deviation of the environmental data in the greenhouse is analyzed to see if it exceeds the preset alarm threshold. When the environmental deviation evaluation coefficient exceeds the set alarm threshold, the alarm mechanism is triggered.

[0042] Once the alarm mechanism is triggered, the remote monitoring module automatically generates alarm information, including the greenhouse number, deviation values ​​of various environmental parameters, environmental deviation evaluation coefficient, and suggested intervention measures. The generated alarm information is then sent to the grower via SMS, email, and APP push to ensure that the grower can receive the alarm information in a timely manner and understand the environmental conditions inside the greenhouse.

[0043] A further improvement to the technical solution of the present invention is that the dynamic alarm threshold specifically includes:

[0044] Based on alarm thresholds for crop growth stages, which include sowing, growing, flowering, and fruiting stages, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.3 during the sowing stage, 0.4 during the growing stage, 0.2 during the flowering stage (because the flowering stage is very sensitive to environmental changes), and 0.35 during the fruiting stage.

[0045] Based on crop type, alarm thresholds are set as follows: for heat-resistant crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.4; for cold-resistant crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.25; and for crops sensitive to humidity, the humidity deviation value has a higher weight, and an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.3.

[0046] Based on grower needs, alarm thresholds are set up so that for high-value crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.2 to ensure optimal crop growth conditions. For large-scale crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.45 to reduce false alarms and unnecessary interventions.

[0047] A further improvement to the technical solution of this invention is that the calculation formula for the environmental deviation evaluation coefficient is:

[0048]

[0049] Among them, E c T is the environmental deviation evaluation coefficient. d H represents the temperature deviation value, indicating the difference between the current temperature and the ideal temperature. d L represents the humidity deviation value, indicating the difference between the current humidity and the ideal humidity. d The light intensity deviation value represents the difference between the current light intensity and the ideal light intensity, w. T w H w L These represent the influence weights of temperature, humidity, and light intensity, reflecting the importance of each parameter to crop growth. B is an adjustment parameter, a constant used to adjust the sensitivity of the exponential function. T max H max L max These represent the maximum acceptable deviations for temperature, humidity, and light intensity, respectively, E. c The value range is limited to 0 to 1, where 0 indicates that the environmental parameter is completely consistent with the ideal value, and 1 indicates that the environmental parameter deviates greatly.

[0050] A further improvement to the technical solution of this invention lies in that: the process of dynamically adjusting the temperature inside the greenhouse in the intelligent control module includes:

[0051] The intelligent control module receives data from various sensors through the input interface and also receives signals sent by the remote monitoring and alarm module.

[0052] Analyze the temperature data, environmental deviation evaluation coefficient, and alarm status in the signal to determine whether the current temperature is within a suitable range. If it is within a suitable range, further analyze the temperature change trend to predict whether it will soon exceed the suitable range and make adjustments in advance.

[0053] If the current temperature has exceeded the suitable range, determine whether it is higher or lower than the optimal temperature range, and the extent of the deviation. At the same time, combine the temperature change trend to comprehensively assess the adjustment measures that need to be taken to bring the temperature back to the suitable range and ensure that the crops are in the best growing environment.

[0054] If the current temperature is higher than the suitable temperature range for the current growth stage of the crop, and the temperature is judged to be rising based on the trend, the intelligent control module generates corresponding control decision instructions. It prioritizes increasing the speed of the ventilation fan to accelerate air circulation and enhance heat dissipation. If the temperature drop after ventilation adjustment is not ideal, it further adjusts the opening and closing degree of the shade net to increase the shading area, reduce the heat brought by direct sunlight, and reduce the power of the heating equipment or turn off the heating equipment. Through the coordinated action of multiple devices, the temperature inside the greenhouse is quickly reduced.

[0055] If the current temperature is below the suitable range and the temperature is trending downward, the intelligent control module decides to start the heating equipment (the power level of the heating equipment is determined according to the difference between the temperature and the suitable range; the larger the difference, the higher the starting power). At the same time, the speed of the ventilation fan is reduced to reduce heat loss, and some of the shade netting is retracted to increase sunlight exposure for heat replenishment, so as to raise the temperature inside the greenhouse to the suitable range as soon as possible.

[0056] If the temperature is currently within a suitable range but shows a tendency to exceed it, specifically a slow rise approaching the upper limit or a slow drop approaching the lower limit, the intelligent control module will make a fine-tuning decision in advance, adjusting the speed of the ventilation fan or the opening and closing degree of the shade net to prevent the temperature from exceeding the optimal range, maintain a relatively stable temperature, and ensure that the crops are always in a good growing temperature environment.

[0057] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0058] 1. This invention provides an intelligent temperature control system for greenhouses. By monitoring the temperature environment inside the greenhouse in real time and making precise adjustments based on the growth needs of crops and current environmental conditions, it ensures that crops are always within the most suitable growth temperature range, thereby promoting healthy growth and development of crops. Compared with traditional manual control methods, it can more accurately control temperature fluctuations and avoid problems such as stunted crop growth and increased pests and diseases caused by unsuitable temperatures. In addition, it can automatically adjust the temperature setpoint according to different growth stages and types of crops, further optimizing the growth environment and improving crop yield and quality.

[0059] 2. This invention provides an intelligent temperature control system for greenhouses. By monitoring and analyzing temperature data in the greenhouse in real time, it accurately controls the operation of temperature control equipment, avoids unnecessary energy waste, and can detect abnormal changes in the temperature environment inside the greenhouse in advance and issue early warning signals, enabling growers to promptly identify and deal with potential problems, avoid crop losses caused by abnormal temperatures, ensure the stable operation of the system, and improve the production efficiency of the greenhouse. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0061] Figure 1 This is a block diagram of the present invention;

[0062] Figure 2 This is a flowchart illustrating the calculation of environmental parameter deviation values ​​in this invention.

[0063] Figure 3 This is a flowchart for assessing the overall environmental deviation within the greenhouse according to the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Example 1, as Figure 1 , Figure 2As shown, the present invention provides an intelligent temperature control system for greenhouses. The intelligent temperature control system includes a data acquisition module, a crop growth database module, a data analysis and decision-making module, a remote monitoring and alarm module, an intelligent control module, and a user interface module, wherein the modules are electrically connected.

[0066] The data acquisition module collects environmental data through various sensors deployed within the greenhouse, sensing changes in ambient temperature to ensure data accuracy and real-time performance. This provides a reliable foundation for subsequent analysis and control. Various sensors, including temperature, humidity, and light sensors, are deployed within the greenhouse. The sensor distribution is planned according to the greenhouse's area and shape to ensure comprehensive coverage of the interior area and complete data collection. Fixed brackets are used to install the sensors at different heights and positions within the greenhouse to obtain more comprehensive environmental information. Initialization settings for each sensor are performed, including calibration, setting the sampling frequency, and configuring communication parameters, ensuring proper functioning and accurate data transmission. The sensors operate according to preset sampling frequencies. The system collects environmental data from inside the greenhouse in real time, including temperature, humidity, and light intensity. The sensor converts the raw data into digital signals using an internal analog-to-digital converter (ADC). The collected environmental data is preprocessed, including data smoothing, noise reduction, and filtering. Smoothing reduces data fluctuations caused by sensor noise or environmental interference. Noise reduction removes outliers or noise points to improve data accuracy. Filtering algorithms further smooth the data and improve its reliability. The sensor transmits the preprocessed environmental data to the data center of the intelligent temperature control system via a communication interface. A data warehouse is built in the data center to store the preprocessed environmental data.

[0067] The crop growth database module constructs a crop growth database, pre-storing planting data on the temperature, humidity, and light requirements of different crops at various growth stages. This data originates from long-term agricultural research practices and planting experience summaries. A crop growth model is also built to predict the suitable temperature range for crops at different stages, providing a basis for intelligent control. Based on long-term agricultural research practices, planting experience summaries, agricultural expert consultations, agricultural literature, and journals, data from different sources are uniformly formatted and standardized. The module integrates data on the temperature, humidity, and light requirements of different crops at various growth stages (sowing period, growing period, flowering period, fruiting period, etc.), as well as crop growth cycle, growth rate, and yield information, constructing a crop growth database. The integrated data is then categorized and stored in the database according to crop type, growth stage, and environmental parameters, ensuring data accuracy and completeness. The data is then further processed. The process involves validation and cleaning to remove outliers and duplicate data. Based on the characteristics and patterns of crop growth, a crop growth model is constructed using data from a crop growth database combined with a neural network model. This model analyzes the relationship between crop growth and environmental parameters, and trains and optimizes the model. By adjusting the model's parameters and structure, the predictive accuracy and generalization ability are improved. The trained model is validated and evaluated using an independent test dataset. Based on the validation results, the model is adjusted and improved. According to the current time, crop type, and growth stage, relevant environmental parameter requirement data is obtained from the crop growth database. This input data is then fed into the trained crop growth model for prediction calculations. The model outputs suitable temperature range, humidity range, and light intensity environmental parameters for the crop at the current growth stage. The prediction results are analyzed and interpreted to understand the crop's growth status and requirements under the current environmental conditions.

[0068] The data analysis and decision-making module receives environmental data acquired by the data acquisition module, compares and analyzes it with the crop growth model, calculates environmental parameter deviation values, determines whether to adjust the temperature inside the greenhouse, and identifies the target adjustment value. It extracts pre-processed environmental data (temperature, humidity, light intensity) from the data warehouse and, based on the current time, crop type, and growth stage, extracts the optimal environmental parameter ranges corresponding to the current crop type and growth stage from the crop growth database, including ideal and acceptable values ​​for temperature, humidity, and light intensity. It compares and analyzes the extracted environmental data with the optimal environmental parameters in the crop growth model, calculating environmental parameter deviation values, namely temperature deviation, humidity deviation, and light intensity deviation—that is, the difference between the current value and the ideal value. It analyzes the magnitude of the deviation values ​​to determine the degree of difference between the current environmental data and the ideal crop growth conditions. Based on the characteristics of the crop type and growth stage, it sets deviation thresholds for each environmental parameter. By comparing the deviation values ​​of each environmental parameter with the environmental parameter deviation thresholds, it determines whether the temperature inside the greenhouse needs to be adjusted. If adjustment is required, it identifies the target adjustment value—that is, the environmental parameter value adjusted to meet the optimal growth conditions for the crop.

[0069] Furthermore, the formula for calculating the temperature deviation value is:

[0070]

[0071] Among them, T d T represents the temperature deviation value. cu T represents the current temperature value. id For the ideal temperature value, T r For the acceptable temperature range, T d The value range is from 0 to 1, where 0 indicates that the current temperature is exactly the ideal temperature, and 1 indicates that the current temperature is outside the acceptable range.

[0072] The formula for calculating the humidity deviation value is:

[0073]

[0074] Among them, H d H represents the humidity deviation value. cu The current humidity value, H id For the ideal humidity value, H r For acceptable humidity levels, H d The value range is from 0 to 1, where 0 indicates that the current humidity is exactly the ideal humidity, and 1 indicates that the current humidity is outside the acceptable range.

[0075] The formula for calculating the light intensity deviation is:

[0076]

[0077] Among them, L d L represents the light intensity deviation value. cu L represents the current light intensity value. id For the ideal light intensity value, L r For light intensity within an acceptable range, L d The value range is from 0 to 1, where 0 indicates that the current light intensity is exactly the same as the ideal light intensity, and 1 indicates that the current light intensity exceeds the acceptable range;

[0078] The remote monitoring and alarm module remotely monitors environmental data inside the greenhouse, assesses the overall environmental deviation based on the degree of deviation of the environmental data, and sends alarm information to the grower so that timely measures can be taken for manual intervention.

[0079] The intelligent control module, based on the output of the remote monitoring and alarm module, controls the temperature control equipment in the greenhouse to dynamically adjust the temperature inside the greenhouse. The temperature control equipment includes ventilation fans, shade nets, and heating equipment to achieve precise temperature control and ensure that crops are in the best growing environment.

[0080] The user interface module, combined with the output of the intelligent control module, sends various control parameters inside the greenhouse to growers, displays real-time environmental data, crop growth status, and the operating status of temperature control equipment, and allows users to manually adjust temperature control parameters or view historical data and reports, enhancing the system's flexibility and user engagement.

[0081] Example 2, as Figure 3 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, in the remote monitoring alarm module, the assessment process of the overall environmental deviation degree inside the greenhouse includes:

[0082] By analyzing real-time environmental data collected by various sensors (temperature, humidity, and light) within the greenhouse, the system monitors the environmental conditions inside the greenhouse. Combined with data provided by the data analysis and decision-making module, it obtains the deviation values ​​of various environmental parameters. Based on the degree of influence of different environmental parameters on crop growth, it sets the influence weights for temperature, humidity, and light intensity. It comprehensively analyzes the deviation values ​​of each environmental parameter and their influence weights on crop growth, and calculates the environmental deviation evaluation coefficient by combining the maximum acceptable deviation values ​​of temperature, humidity, and light intensity. This assesses the overall environmental deviation within the greenhouse. Based on the crop type, growth stage, and the grower's actual needs, it sets a dynamic alarm threshold for the environmental deviation evaluation coefficient. Based on the environmental deviation evaluation coefficient, it analyzes whether the deviation of the environmental data within the greenhouse exceeds the preset alarm threshold. When the environmental deviation evaluation coefficient exceeds the set alarm threshold, an alarm mechanism is triggered. Once the alarm mechanism is triggered, the remote monitoring module automatically generates alarm information, including the greenhouse number, the deviation values ​​of each environmental parameter, the environmental deviation evaluation coefficient, and suggested intervention measures. The generated alarm information is sent to the grower via SMS, email, and APP push notifications to ensure that the grower receives the alarm information promptly and understands the environmental conditions inside the greenhouse.

[0083] Furthermore, the dynamic alarm threshold specifically includes:

[0084] Alarm thresholds are based on crop growth stages, including sowing, growth, flowering, and fruiting. An alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.3 during sowing, 0.4 during growth, 0.2 during flowering (as flowering is highly sensitive to environmental changes), and 0.35 during fruiting. Alarm thresholds are also based on crop type: for heat-resistant crops, an environmental deviation evaluation coefficient exceeding 0.4 triggers an alarm; for cold-resistant crops, it exceeds 0.25; for humidity-sensitive crops, humidity deviation values ​​are weighted higher, triggering an alarm when the environmental deviation evaluation coefficient exceeds 0.3. Finally, alarm thresholds are based on grower needs: for high-value crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.2 to ensure optimal growth conditions; for large-scale crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.45 to reduce false alarms and unnecessary intervention.

[0085] Furthermore, the formula for calculating the environmental deviation evaluation coefficient is as follows:

[0086]

[0087] Among them, E c T is the environmental deviation evaluation coefficient. dH represents the temperature deviation value, indicating the difference between the current temperature and the ideal temperature. d L represents the humidity deviation value, indicating the difference between the current humidity and the ideal humidity. d The light intensity deviation value represents the difference between the current light intensity and the ideal light intensity, w. T w H w L These represent the influence weights of temperature, humidity, and light intensity, reflecting the importance of each parameter to crop growth. B is an adjustment parameter, a constant used to adjust the sensitivity of the exponential function. T max H max L max These represent the maximum acceptable deviations for temperature, humidity, and light intensity, respectively, E. c The value of E is limited to between 0 and 1, where 0 indicates that the environmental parameter is exactly the same as the ideal value, and 1 indicates that the environmental parameter deviates greatly. As the deviation of the environmental parameter increases, E... c An increase in the value indicates that the environmental conditions deviate further from the ideal state; when the deviation of the environmental parameter decreases, E... c A decrease in the value indicates that the environmental conditions are closer to the ideal state;

[0088] The process of dynamically adjusting the temperature inside the greenhouse in the intelligent control module includes:

[0089] The intelligent control module receives data from various sensors via the input interface and signals from the remote monitoring and alarm module. It analyzes temperature data, environmental deviation evaluation coefficients, and alarm status in the signals to determine if the current temperature is within a suitable range. If it is, it further analyzes the temperature trend to predict whether it will soon exceed the suitable range and prepares for adjustments in advance. If the current temperature has already exceeded the suitable range, it determines whether it is higher or lower than the optimal temperature range and the extent of the exceedance. Simultaneously, it comprehensively evaluates the necessary adjustment measures based on the temperature trend to bring the temperature back to the suitable range and ensure the crop is in an optimal growth environment. If the current temperature is higher than the suitable temperature range for the crop's current growth stage and the trend indicates that the temperature is continuing to rise, the intelligent control module generates corresponding control decision commands. It prioritizes increasing the speed of the ventilation fan to accelerate air circulation and enhance heat dissipation. If the temperature drop after ventilation adjustment is not ideal, it further adjusts the shading... The opening and closing of the shade netting increases the shading area, reduces the heat from direct sunlight, and lowers or shuts down the heating equipment. Through the coordinated action of multiple devices, the temperature inside the greenhouse is quickly reduced. If the current temperature is below the suitable range and is trending downwards, the intelligent control module decides to activate the heating equipment (determining the power level of the heating equipment based on the difference between the temperature and the suitable range; the larger the difference, the higher the activation power). At the same time, the speed of the ventilation fans is reduced to minimize heat loss, and part of the shade netting is retracted to increase sunlight exposure for heat replenishment, so as to quickly raise the temperature inside the greenhouse to the suitable range. If the temperature is currently in the suitable range but shows a trend of exceeding it, specifically a slow rise approaching the upper limit or a slow fall approaching the lower limit, the intelligent control module makes a fine-tuning decision in advance, adjusting the speed of the ventilation fans or the opening and closing of the shade netting to prevent the temperature from exceeding the optimal range, maintain a relatively stable temperature, and ensure that the crops are always in a good growing temperature environment.

[0090] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent temperature control system for greenhouses, characterized in that: The intelligent temperature control system includes a data acquisition module, a crop growth database module, a data analysis and decision-making module, a remote monitoring and alarm module, an intelligent control module, and a user interface module, wherein the modules are connected by electrical signals. The data acquisition module collects environmental data through various sensors deployed inside the greenhouse; The crop growth database module constructs a crop growth database, pre-stores planting data required by different crops at various growth stages, and constructs a crop growth model to predict the suitable temperature range for crops at different stages. The data analysis and decision-making module receives environmental data, compares and analyzes it with the crop growth model, calculates the deviation value of environmental parameters, determines whether to adjust the temperature inside the greenhouse, and determines the target value for adjustment. The remote monitoring and alarm module remotely monitors environmental data within the greenhouse, assesses the degree of deviation in the overall environment, and sends alarm information to the grower. The assessment process for the degree of deviation in the overall environment within the greenhouse includes: By analyzing the environmental data collected in real time by various types of sensors inside the greenhouse, the environmental status inside the greenhouse can be monitored. By combining the data provided by the data analysis and decision-making module, the deviation values ​​of various environmental parameters are obtained, and the influence weights of temperature, humidity, and light intensity are set according to the degree of influence of different environmental parameters on crop growth. By comprehensively analyzing the deviation values ​​of various environmental parameters and their weights in relation to crop growth, and combining the maximum acceptable deviation values ​​of temperature, humidity, and light intensity, an environmental deviation evaluation coefficient is calculated to assess the degree of deviation of the overall environment inside the greenhouse. Based on the crop type, growth stage, and grower's actual needs, a dynamic alarm threshold for the environmental deviation evaluation coefficient is set. Based on the environmental deviation evaluation coefficient, the degree of deviation of the environmental data in the greenhouse is analyzed to see if it exceeds the preset alarm threshold. When the environmental deviation evaluation coefficient exceeds the set alarm threshold, the alarm mechanism is triggered. The dynamic alarm threshold specifically includes: Based on alarm thresholds for crop growth stages, which include sowing, growing, flowering, and fruiting stages, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.3 during the sowing stage, 0.4 during the growing stage, 0.2 during the flowering stage, and 0.35 during the fruiting stage. Based on crop type, alarm thresholds are set as follows: for heat-resistant crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.4; for cold-resistant crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.25; and for humidity-sensitive crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.

3. Based on grower needs, alarm thresholds are set up so that for high-value crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.2, and for large-scale crops, an alarm is triggered when the environmental deviation evaluation coefficient exceeds 0.

45.

2. The intelligent temperature control system for greenhouses according to claim 1, characterized in that: The environmental data acquisition process in the data acquisition module includes: Various sensors, including temperature sensors, humidity sensors, and light sensors, are deployed inside the greenhouse. The distribution of the sensors is planned according to the area and shape of the greenhouse, and fixed brackets are used to install the sensors at different heights and positions inside the greenhouse. Initialize and configure various sensors, including calibration, setting sampling frequency, and configuring communication parameters. The sensors collect environmental data in the greenhouse in real time according to the preset sampling frequency. The environmental data includes temperature, humidity, and light intensity. The sensor converts the raw data it collects into digital signals through an internal analog-to-digital converter and preprocesses the collected environmental data, including data smoothing, noise reduction, and filtering. The sensor transmits the pre-processed environmental data to the data center of the intelligent temperature control system via a communication interface, and a data warehouse is built in the data center to store the pre-processed environmental data.

3. The intelligent temperature control system for greenhouses according to claim 2, characterized in that: The crop growth database module includes the following construction process: Based on agricultural scientific research practices, planting experience summaries, and agricultural literature, data from different sources are formatted and standardized in a unified manner, integrating data on the temperature, humidity, and light requirements of different crops at various growth stages, as well as information on crop growth cycle, growth rate, and yield. A crop growth database is constructed, and the integrated data is entered into the database according to crop type, growth stage, and environmental parameters for classification and storage. The data is then verified and cleaned. Based on the characteristics and patterns of crop growth, a crop growth model is constructed by combining data from a crop growth database with a neural network model to analyze the relationship between crop growth and environmental parameters. Based on the current time, crop type, and growth stage, the system retrieves the required environmental parameters from the crop growth database, inputs the data into the trained crop growth model, performs prediction calculations, and outputs the suitable temperature range, humidity range, and light intensity environmental parameters for the crop at the current growth stage.

4. The intelligent temperature control system for greenhouses according to claim 3, characterized in that: The calculation process for environmental parameter deviation values ​​in the data analysis and decision-making module includes: The preprocessed environmental data of each environmental parameter is extracted from the data warehouse, and the optimal environmental parameter range corresponding to the current crop type and growth stage is extracted from the crop growth database based on the current time, crop type and growth stage, including the ideal value and acceptable range of temperature, humidity and light intensity. The extracted environmental data is compared and analyzed with the optimal environmental parameters in the crop growth model. The deviation values ​​of the environmental parameters are calculated, namely temperature deviation, humidity deviation and light intensity deviation. The magnitude of the deviation values ​​is analyzed to determine the degree of difference between the current environmental data and the ideal crop growth conditions. Based on the characteristics of crop types and growth stages, set deviation thresholds for each environmental parameter, compare the deviation values ​​of each environmental parameter with the environmental parameter deviation thresholds, and determine whether the temperature inside the greenhouse needs to be adjusted. If adjustment is required, determine the target value to be adjusted.

5. The intelligent temperature control system for greenhouses according to claim 4, characterized in that: The formula for calculating the temperature deviation value is: Among them, T d T represents the temperature deviation value. cu T represents the current temperature value. id For the ideal temperature value, T r For the acceptable temperature range, T d The value range is from 0 to 1, where 0 indicates that the current temperature is exactly the ideal temperature, and 1 indicates that the current temperature is outside the acceptable range. The formula for calculating the humidity deviation value is: Among them, H d H represents the humidity deviation value. cu The current humidity value, H id For the ideal humidity value, H r For acceptable humidity levels, H d The value range is from 0 to 1, where 0 indicates that the current humidity is exactly the ideal humidity, and 1 indicates that the current humidity is outside the acceptable range. The formula for calculating the light intensity deviation value is: Among them, L d L represents the light intensity deviation value. cu L represents the current light intensity value. id For the ideal light intensity value, L r For light intensity within an acceptable range, L d The value range is from 0 to 1, where 0 indicates that the current light intensity is exactly the same as the ideal light intensity, and 1 indicates that the current light intensity exceeds the acceptable range.

6. The intelligent temperature control system for greenhouses according to claim 5, characterized in that: The formula for calculating the environmental deviation evaluation coefficient is as follows: Among them, E c T is the environmental deviation evaluation coefficient. d H represents the temperature deviation value. d L represents the humidity deviation value. d w represents the light intensity deviation value. T w H w L These represent the influence weights of temperature, humidity, and light intensity, respectively. B is an adjustment parameter, a constant used to adjust the sensitivity of the exponential function. T max H max L max These represent the maximum acceptable deviations for temperature, humidity, and light intensity, respectively, E. c The value of is limited to the range between 0 and 1.

7. The intelligent temperature control system for greenhouses according to claim 6, characterized in that: The intelligent control module includes the following process for dynamically adjusting the temperature inside the greenhouse: The intelligent control module receives data from various sensors through the input interface and also receives signals sent by the remote monitoring and alarm module. Analyze the temperature data, environmental deviation evaluation coefficient, and alarm status in the signal to determine whether the current temperature is within a suitable range. If it is within a suitable range, further analyze the temperature change trend to predict whether it will soon exceed the suitable range. If the current temperature has exceeded the suitable range, determine whether it is higher or lower than the optimal temperature range, and the extent of the deviation. At the same time, combine the temperature change trend to comprehensively evaluate the adjustment measures that need to be taken to bring the temperature back to the suitable range. If the current temperature is higher than the suitable temperature range for the current growth stage of the crop, and the temperature is judged to be rising continuously based on the trend, the intelligent control module generates corresponding control decision instructions, prioritizing the increase of the speed of the ventilation fan to accelerate air circulation. If the temperature drop after ventilation adjustment is not ideal, the opening and closing degree of the shade net is further adjusted to increase the shading area, and the power of the heating equipment is reduced or the heating equipment is turned off. If the current temperature is below the suitable range and the temperature is trending downwards, the intelligent control module decides to start the heating equipment, while reducing the speed of the ventilation fan and retracting part of the shade net to increase sunlight exposure for heat replenishment. If the temperature is currently within a suitable range but shows a tendency to exceed it, specifically a slow rise towards the upper limit or a slow drop towards the lower limit, the intelligent control module will make a fine-tuning decision in advance, adjusting the speed of the ventilation fan or the opening and closing degree of the shade net to maintain a relatively stable temperature.

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