Greenhouse environment low temperature early warning method, device, medium and equipment

Abstract

The invention discloses a greenhouse environment low-temperature early warning method, device, medium and equipment, and relates to the field of agricultural environment management and control, and the method comprises the steps: collecting environment data in a greenhouse; comparing the environment data with a preset threshold value, and generating temperature abnormity alarm data when the environment data exceeds a preset threshold value range; sending the environment data and the temperature abnormity alarm data to a cloud platform through wireless transmission; environment data reference values and heat preservation conditions required by greenhouse crops are generated through the cloud platform, short messages are generated from the current environment data, the temperature abnormity alarm data, the environment data reference values and the heat preservation conditions, and corresponding short messages are sent to a plurality of mobile user sides; according to the current environment data in the short message and the historical environment data in the historical short message, the greenhouse internal temperature prediction result is obtained through linear fitting, low temperature prediction and early warning are carried out based on the environment data reference value and the greenhouse internal temperature prediction result, and therefore the real-time performance of agricultural greenhouse decision making is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural environment control and management, and in particular to a greenhouse environment low temperature early warning method, device, medium and equipment. Background Art

[0002] Facility agriculture is a modern agricultural production method that uses certain facilities and Internet of Things technologies to adjust and control the growth environment of crops to create and improve environmental conditions suitable for crop growth, thereby achieving high quality, high yield and high efficiency. Among them, accurate and timely observation of environmental factors is the foundation of facility agriculture.

[0003] With the continuous progress of social economy and science and technology, people's demand for facility agriculture is increasing, but the harm of disastrous weather to agricultural facilities is obvious. For example, high temperature, low temperature, low sunlight, strong wind, heavy snow, etc. have an important impact on facility agriculture.

[0004] However, faced with these complex and changeable weather conditions, traditional manual control methods seem to be unable to cope with the situation and are unable to accurately meet the scientific and reasonable needs of crop growth. At the same time, traditional manual control methods are difficult to respond to emergencies immediately, thus missing the best time to adjust. It is also difficult for relevant decision-makers to achieve real-time response from problem discovery to response measures. It is impossible to effectively respond to environmental changes in greenhouses, and it is difficult to improve the real-time nature of agricultural greenhouse decision-making. Summary of the invention

[0005] The present invention provides a greenhouse environment low temperature early warning method, device, medium and equipment to solve the above-mentioned problem existing in the prior art, that is, how to improve the real-time performance of agricultural greenhouse decision-making in the prior art. The present invention provides a greenhouse environment low temperature early warning method, the method comprising:

[0006] Collect environmental data in the greenhouse;

[0007] Compare the acquired environmental data with the preset threshold value, and generate abnormal temperature alarm data when it exceeds the preset threshold range; send the environmental data and abnormal temperature alarm data to the cloud platform via wireless transmission;

[0008] Generate environmental data reference values ​​and insulation conditions required for different growth stages of greenhouse crops through the cloud platform, generate text messages of current environmental data, abnormal temperature alarm data, environmental data reference values ​​and insulation conditions, and send corresponding text messages to multiple mobile user terminals;

[0009] According to the current environmental data in the text message and the historical environmental data in the historical text messages, linear fitting is used to obtain the prediction results of the internal temperature of the greenhouse, and low temperature prediction and warning are carried out based on the reference value of the environmental data and the prediction results of the internal temperature of the greenhouse.

[0010] Optionally, the collecting of environmental data in the greenhouse specifically includes:

[0011] The temperature and humidity information in the greenhouse is obtained through the temperature and humidity sensor, and the light intensity information is obtained through the light intensity sensor.

[0012] Optionally, the method of using linear fitting to obtain the prediction result of the internal temperature of the greenhouse specifically includes:

[0013] The linear regression model was used to build the model, and the temperature inside the greenhouse was taken as the dependent variable. The following formula was used to obtain the prediction result of the temperature inside the greenhouse:

[0014] y = βx + α;

[0015] Among them, y is the internal temperature of the greenhouse, x is the external temperature of the greenhouse, β is the unstandardized coefficient, and α is the intercept.

[0016] The present invention provides a greenhouse environment low temperature early warning device, comprising:

[0017] The collection module is used to collect environmental data in the greenhouse;

[0018] The data analysis and comparison module is used to compare the acquired environmental data with the preset threshold value, and generate temperature abnormality alarm data when the preset threshold value range is exceeded;

[0019] The data transmission module is used to send the environmental data and abnormal temperature alarm data to the cloud platform through wireless transmission; the cloud platform generates the environmental data reference values ​​and insulation conditions required for the different growth stages of greenhouse crops, and generates text messages with the current environmental data, abnormal temperature alarm data, environmental data reference values ​​and insulation conditions, and sends the corresponding text messages to multiple mobile user terminals;

[0020] The low temperature prediction and warning module is used to obtain the greenhouse internal temperature prediction result by linear fitting according to the current environmental data in the text message and the historical environmental data in the historical text message, and to perform low temperature prediction and warning based on the environmental data reference value and the greenhouse internal temperature prediction result.

[0021] Optionally, the acquisition module includes a DHT11 digital temperature and humidity sensor and a GZ-A1 light intensity sensor, and the temperature and humidity information in the greenhouse is collected by the DHT11 digital temperature and humidity sensor, and the light intensity information is obtained by the GZ-A1 light intensity sensor.

[0022] Optionally, the data analysis and comparison module is an STM32F103C8T6 module, which is used to compare the acquired environmental data with a preset threshold value, and generate temperature abnormality alarm data when the preset threshold value range is exceeded.

[0023] Optionally, the data transmission module includes an ESP8266wifi module and an AIR724UG communication module, both of which are electrically connected to the STM32F103C8T6 module. The ESP8266wifi module transmits environmental data and temperature abnormality alarm data to the ONENET cloud platform via wireless transmission; the AIR724UG communication module is used to send text messages to multiple mobile user terminals.

[0024] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned greenhouse environment low temperature early warning method is implemented.

[0025] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned greenhouse environment low temperature early warning method is implemented.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a greenhouse environment low temperature warning method, which obtains the environmental data of the greenhouse through the temperature and humidity sensor, and transmits it to the main control module for analysis and processing, and can monitor the greenhouse environment in real time, ensuring the accuracy and timeliness of the data; by comparing the obtained environmental data with the preset threshold, it is timely judged whether the equipment needs to issue a low temperature warning. When the low temperature warning is triggered, the method can send a low temperature risk text message to the user in time under the low temperature warning condition according to the demand of crops at different growth stages in the greenhouse for the growth environment, and provide corresponding insulation It is recommended that users take insulation measures for crops according to the content of the text message and insulation suggestions, ensuring the real-time decision-making of the greenhouse; at the same time, the WeChat applet on the mobile user side uses linear fitting to obtain the low temperature prediction results based on the insulation conditions in the text message and the current environmental data, which can automatically adjust the control strategy according to the crop growth stage and weather changes, thereby realizing the refined management of the greenhouse; in addition, by integrating the greenhouse temperature, humidity and low temperature prediction and warning with the ONENET cloud platform and WeChat applet, users can monitor the greenhouse environmental information anytime and anywhere through their mobile phones, thereby ensuring the real-time monitoring of the greenhouse environmental information. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0028] Figure 1 A flow chart of a greenhouse environment low temperature early warning method provided by an embodiment of the present invention;

[0029] Figure 2A software flow chart of a greenhouse environment low temperature early warning method provided by an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of the ONENET cloud platform data display interface provided in an embodiment of the present invention;

[0031] Figure 4 A text message reminder interface of a greenhouse environment low temperature early warning method provided by an embodiment of the present invention;

[0032] Figure 5 A fitting diagram of the actual value and the predicted value provided by the embodiment of the present invention;

[0033] Figure 6 A forecast accumulation line chart provided by an embodiment of the present invention;

[0034] Figure 7 A schematic diagram of a limit-crossing warning page provided by an embodiment of the present invention;

[0035] Figure 8 A schematic diagram of a greenhouse temperature prediction page provided by an embodiment of the present invention;

[0036] Fig. 9 An overall block diagram of a low temperature early warning device for a greenhouse environment provided by an embodiment of the present invention;

[0037] Fig.10 A schematic diagram of a computer device for a greenhouse environment low temperature early warning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0040] like Figure 1 and Figure 2 As shown, this embodiment shows a greenhouse environment low temperature early warning method, including:

[0041] S1: Collect environmental data in the greenhouse.

[0042] Optionally, the temperature and humidity information in the greenhouse is obtained through a temperature and humidity sensor, and the light intensity information is obtained through a light intensity sensor.

[0043] For example, a DHT11 temperature and humidity sensor and a GZ-A1 light intensity sensor may be selected to collect temperature, humidity and light data.

[0044] S2: Compare the acquired environmental data with the preset threshold value, and generate temperature abnormality alarm data when it exceeds the preset threshold range; send the environmental data and temperature abnormality alarm data to the cloud platform via wireless transmission.

[0045] For example, the user can set the preset threshold value according to the suitable temperature required for the growth stage of the crop, and then compare the data obtained by the sensor with the preset threshold value to determine whether to issue an out-of-limit warning.

[0046] For example, the user can adjust the preset threshold value by setting the button module according to the suitable temperature required for the growth stage of the crop.

[0047] For example, the acquired environmental parameters and temperature threshold parameters can be displayed in real time by using an OLED screen circuit. By using a buzzer module, an alarm instruction can be generated based on the temperature and humidity values ​​and light intensity in the greenhouse detected by the temperature and humidity sensor and the light sensor. When the temperature exceeds the threshold, an alarm is sounded through a buzzer to remind people in the greenhouse of abnormal conditions.

[0048] S3: Generate environmental data reference values ​​and insulation conditions required for different growth stages of greenhouse crops through the cloud platform, generate text messages of current environmental data, temperature abnormality alarm data, environmental data reference values ​​and insulation conditions, and send corresponding text messages to multiple mobile user terminals.

[0049] Optionally, STM32F103C8T6 is used as the main control chip of the data processing module to transmit the environmental data to the cloud platform through electrical connection with the ESP8266wifi module.

[0050] As shown in Table 1, taking the temperature growth requirements of cucumbers in a greenhouse at different stages as an example, the temperature requirements for the cucumber growth period are set.

[0051] Table 1 Temperature requirements for cucumber growth

[0052] Growth period Lower limit Ambient temperature / ℃ suitable Upper limit Lower limit Germination period 12 28-30 35 12 Seedling stage 12 24-28 30 12 Flowering period 20 25-30 30 12 Fruit development period 20 25-30 30 12

[0053] For example, the AIR724UG communication module and the ESP8266wifi module can be selected to perform data interaction between the data processing module, the cloud platform and the mobile terminal; the ESP8266wifi module is configured to wirelessly communicate with the main control chip to receive the environmental data acquired by the data acquisition module; the cloud platform can select the ONENET cloud platform; the ESP8266wifi module is configured to wirelessly communicate with the Internet of Things cloud platform to send the environmental data from the data acquisition module to the Internet of Things cloud platform, such as Figure 3 As shown in the figure, the AIR724UG communication module sends text messages to users based on the data analysis of the STM32 microcontroller, and provides corresponding insulation suggestions based on the crops in different growth periods in the greenhouse, such as Figure 4 shown.

[0054] Generally, the ESP8266wifi module uses the TCP (socket) protocol and the Wi-Fi hotspot mode to wirelessly connect to the ONENET cloud platform intelligent distribution network, collect sensor data in real time, and synchronize it to the ONENET cloud platform for display, analysis and recording.

[0055] Exemplarily, the ESP8266wifi module, AIR724UG communication module, temperature and humidity sensor, light sensor, buzzer module and OLED screen circuit are controlled by the data processing module.

[0056] Exemplarily, the insulation conditions of the greenhouse may be, for example, insulation being used, electric heating status, and solar radiation conditions.

[0057] For example, key changes in the greenhouse environment can be quickly communicated to users through SMS notifications, helping users to respond to potential risks in a timely manner. Combining weather forecasts with greenhouse environmental data, the system implements intelligent low temperature warnings and accurate temperature predictions, providing a scientific basis for greenhouse management, improving the real-time nature of greenhouse decision-making, and ensuring the healthy growth of crops.

[0058] S4: According to the current environmental data in the text message and the historical environmental data in the historical text messages, linear fitting is used to obtain the prediction result of the internal temperature of the greenhouse, and low temperature prediction and warning are carried out based on the reference value of the environmental data and the prediction result of the internal temperature of the greenhouse.

[0059] Exemplarily, after determining the insulation conditions in the greenhouse, a linear regression model is constructed, and then sample data is determined based on the current environmental data and the insulation conditions in the greenhouse. The linear regression model is trained based on the sample data to obtain a low-temperature prediction model for predicting the temperature in the greenhouse. The low-temperature prediction model is used to predict the temperature change trend in the greenhouse in the future time period.

[0060] For example, the low temperature prediction part comprehensively considers future weather forecasts and various insulation conditions of the greenhouse. Through mathematical modeling and analysis, the low temperature prediction function is integrated into the WeChat applet using a linear regression model and the average temperature difference between the inside and outside. This function can display the weather forecast for the next 8 hours and 2 days in real time, and predict the temperature in the greenhouse at the corresponding time point based on this, providing users with an accurate temperature control reference.

[0061] For example, the outdoor temperature, indoor temperature and the use of insulation measures (such as thermal blankets, electric heating, etc.) of different greenhouses in a certain place under sunny conditions in winter are collected. When constructing a linear regression model, the collected data is used to construct a linear regression model between the outdoor temperature and the indoor temperature to evaluate the insulation effect under different insulation measures.

[0062] For example, under winter conditions, a linear regression model is used for modeling, with the temperature inside the greenhouse as the dependent variable and the temperature outside the greenhouse as the independent variable. The specific calculation formula is as follows:

[0063] y = βx + α;

[0064] Among them, y is the internal temperature of the greenhouse, x is the external temperature of the greenhouse, β is the unstandardized coefficient, and α is the intercept.

[0065] 100 data were analyzed on sunny days, covered with insulation blankets and assisted by electric heating, and the relationship between internal temperature and external temperature was effectively evaluated. The sample size was 100. The analysis showed that the external temperature had a significant positive effect on the internal temperature, and its unstandardized coefficient β was 0.52, which means that for every 1 unit increase in external temperature, the internal temperature increased by 0.52 units on average. The standard error of the coefficient was 0.059, indicating an estimated value. The standardized coefficient was 0.663, which further confirmed that the external temperature was an important factor affecting the change of internal temperature, and there was a strong linear relationship between the two. The specific calculation formula is as follows:

[0066] y=0.52x+23.5;

[0067] Among them, y is the predicted value of the temperature inside the greenhouse, and x is the predicted value of the temperature outside the greenhouse.

[0068] Using the constructed model, the temperature changes in the greenhouse are predicted according to the weather forecast for the next few days (especially the outdoor temperature forecast). If the indoor temperature may be lower than the suitable temperature for cucumbers in the forecast results, an insulation adjustment plan is formulated in advance. If the forecast results show that the suitable temperature cannot be maintained by insulation alone, consider adding electric heating. According to the predicted temperature conditions, determine the start time and power of the electric heating to ensure that the indoor temperature is stable within the suitable temperature range for cucumbers. At the same time, consider other possible insulation measures, such as adding multiple layers of coverage, adjusting vents, etc., to further improve the insulation effect.

[0069] like Figure 5 and Figure 6 As shown in the figure, it is shown that on sunny days, with insulation blankets and electric heating, the temperature difference between the inside and outside of the greenhouse is stable within the range of about 29°C (±0.5°C), and this stability is particularly significant when extreme weather (such as cold waves and strong winds) is excluded. In addition, it was found that when the outside temperature is below -12°C, the predicted temperature difference is more accurate; and from 13:00 to 17:00 every day, the temperature inside the greenhouse will increase significantly due to enhanced solar radiation.

[0070] Based on the above analysis, the low temperature prediction function is integrated into the WeChat applet using the linear regression model and the average value of the temperature difference between inside and outside. This function can display the weather forecast for the next 8 hours and 2 days in real time, and predict the temperature inside the greenhouse at the corresponding time point, providing users with an accurate temperature control reference. At the same time, the temperature difference between inside and outside the greenhouse can be calculated and displayed for five different combination scenarios, including the presence or absence of sunlight, the use of insulation blankets, and the state of electric heating, to fully meet the diverse needs of users.

[0071] For example, the mobile terminal can be a user's mobile phone. The user can use the WeChat applet to predict the temperature in the greenhouse, including three interfaces: user login, over-limit warning, and greenhouse temperature prediction. The user login page can ensure user security access; the over-limit warning page can be used for real-time weather, cloud platform connection, sensor display, and threshold warning. The user can view the environmental parameters of the greenhouse in real time on this interface, including key information such as temperature and humidity. Figure 7 As shown in the figure, the greenhouse temperature prediction page combines weather forecast and insulation measures to predict the greenhouse temperature in the next 8 hours and 2 days. Figure 8 shown.

[0072] In this embodiment, after obtaining the environmental data, hardware alarms, SMS push and WeChat applets can be combined to provide users with low temperature warnings from different aspects.

[0073] The above is a greenhouse environment low temperature early warning method provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding greenhouse environment low temperature early warning device, including:

[0074] The acquisition module is used to collect environmental data in the greenhouse.

[0075] The data analysis and comparison module is used to compare the acquired environmental data with the preset threshold value, and generate temperature abnormality alarm data when the preset threshold value range is exceeded.

[0076] The data transmission module is used to send environmental data and temperature anomaly alarm data to the cloud platform via wireless transmission; the cloud platform generates environmental data reference values ​​and insulation conditions required for different growth stages of greenhouse crops, and generates text messages with the current environmental data, temperature anomaly alarm data, environmental data reference values ​​and insulation conditions, and sends corresponding text messages to multiple mobile user terminals.

[0077] The low temperature prediction and warning module is used to obtain the greenhouse internal temperature prediction result by linear fitting according to the current environmental data in the text message and the historical environmental data in the historical text message, and to perform low temperature prediction and warning based on the environmental data reference value and the greenhouse internal temperature prediction result.

[0078] Optionally, the acquisition module includes a DHT11 digital temperature and humidity sensor and a GZ-A1 light intensity sensor, and the temperature and humidity information in the greenhouse is collected by the DHT11 digital temperature and humidity sensor, and the light intensity information is obtained by the GZ-A1 light intensity sensor.

[0079] Optionally, the data analysis and comparison module is an STM32F103C8T6 module, which is used to compare the acquired environmental data with a preset threshold value, and generate temperature abnormality alarm data when the preset threshold value range is exceeded.

[0080] Optionally, the data transmission module includes an ESP8266wifi module and an AIR724UG communication module, both of which are electrically connected to the STM32F103C8T6 module. The ESP8266wifi module transmits environmental data and temperature abnormality alarm data to the ONENET cloud platform via wireless transmission; the AIR724UG communication module is used to send text messages to multiple mobile user terminals.

[0081] like Fig. 9As shown in the figure, the STM32F103C8T6 single-chip microcomputer, with its powerful processing capability, becomes the data processing module of the whole system, and is electrically connected to the data acquisition module, power module, button module, OLED display alarm module, ESP8266wifi module and AIR724UG communication module respectively; the OLED display alarm module is used to display the collected data and alarm when the temperature exceeds the threshold; the ESP8266wifi module is used to wirelessly connect to the ONENET cloud platform intelligent distribution network, collect data from the DHT11 temperature and humidity sensor and the GZ-A1 light intensity sensor in real time, and synchronize them to the ONENET cloud platform; the mini-program monitoring module, as the user interaction terminal, can not only view real-time environmental parameters, but also has a weather temperature warning and prediction module, which combines the weather forecast with the greenhouse insulation status to predict low temperatures, and promptly remind users through text messages.

[0082] For the specific definition of the greenhouse environment low temperature warning device, please refer to the definition of the greenhouse environment low temperature warning method above, which will not be repeated here. Each module in the above-mentioned greenhouse environment low temperature warning device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0083] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program can be used to execute the above-mentioned greenhouse environment low temperature early warning method.

[0084] The present invention also provides Fig.10 The structural diagram of the computer device shown in FIG. Fig.10 As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the greenhouse environment low temperature early warning method provided in the above embodiment.

[0085] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A greenhouse environment low temperature early warning method, characterized in that: include: Collect environmental data in the greenhouse; Compare the acquired environmental data with the preset threshold value, and generate temperature abnormality alarm data when it exceeds the preset threshold range; Send environmental data and abnormal temperature alarm data to the cloud platform via wireless transmission; Generate environmental data reference values ​​and insulation conditions required for different growth stages of greenhouse crops through the cloud platform, generate text messages of current environmental data, abnormal temperature alarm data, environmental data reference values ​​and insulation conditions, and send corresponding text messages to multiple mobile user terminals; According to the current environmental data in the text message and the historical environmental data in the historical text messages, linear fitting is used to obtain the prediction results of the internal temperature of the greenhouse, and low temperature prediction and warning are carried out based on the reference value of the environmental data and the prediction results of the internal temperature of the greenhouse.

2. The greenhouse environment low temperature early warning method according to claim 1, characterized in that: The collecting of environmental data in the greenhouse specifically includes: The temperature and humidity information in the greenhouse is obtained through the temperature and humidity sensor, and the light intensity information is obtained through the light intensity sensor.

3. The greenhouse environment low temperature early warning method as claimed in claim 2, characterized in that: The method of using linear fitting to obtain the prediction result of the internal temperature of the greenhouse specifically includes: The linear regression model was used to build the model, and the temperature inside the greenhouse was taken as the dependent variable. The following formula was used to obtain the prediction result of the temperature inside the greenhouse: y = βx + α; Among them, y is the internal temperature of the greenhouse, x is the external temperature of the greenhouse, β is the unstandardized coefficient, and α is the intercept.

4. A greenhouse environment low temperature early warning device, characterized in that: include: The collection module is used to collect environmental data in the greenhouse; The data analysis and comparison module is used to compare the acquired environmental data with the preset threshold value, and generate temperature abnormality alarm data when the preset threshold value range is exceeded; The data transmission module is used to send the environmental data and abnormal temperature alarm data to the cloud platform through wireless transmission; the cloud platform generates the environmental data reference values ​​and insulation conditions required for the different growth stages of greenhouse crops, and generates text messages with the current environmental data, abnormal temperature alarm data, environmental data reference values ​​and insulation conditions, and sends the corresponding text messages to multiple mobile user terminals; The low temperature prediction and warning module is used to obtain the greenhouse internal temperature prediction result by linear fitting according to the current environmental data in the text message and the historical environmental data in the historical text message, and to perform low temperature prediction and warning based on the environmental data reference value and the greenhouse internal temperature prediction result.

5. The low temperature early warning device for greenhouse environment as claimed in claim 4, characterized in that: The acquisition module includes a DHT11 digital temperature and humidity sensor and a GZ-A1 light intensity sensor. The temperature and humidity information in the greenhouse is collected by the DHT11 digital temperature and humidity sensor, and the light intensity information is obtained by the GZ-A1 light intensity sensor.

6. The low temperature early warning device for greenhouse environment as claimed in claim 4, characterized in that: The data analysis and comparison module is an STM32F103C8T6 module, which is used to compare the acquired environmental data with a preset threshold value, and generate temperature abnormality alarm data when the preset threshold value range is exceeded.

7. The data transmission module includes an ESP8266wifi module and an AIR724UG communication module, both of which are electrically connected to the STM32F103C8T6 module. The ESP8266wifi module transmits environmental data and temperature abnormality alarm data to the ONENET cloud platform via wireless transmission; the AIR724UG communication module is used to send text messages to multiple mobile user terminals.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the greenhouse environment low temperature early warning method described in any one of claims 1 to 3 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for low temperature early warning of a greenhouse environment as described in any one of claims 1 to 3 is implemented.

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