Crop growth controllable agricultural greenhouse intelligent environment control system and method based on big data

Abstract

The invention relates to the technical field of greenhouse temperature control, and discloses a crop growth controllable agricultural greenhouse intelligent environment control system and method based on big data, and the system comprises a central processing unit which is used for processing data and coordinating the operation of all modules; the weather forecast system comprises a data extraction module, a data processing module and a data analysis module, the data extraction module is used for acquiring local weather forecast information in real time, and the data processing module performs standardization processing on weather information and extracts key parameters; and the data analysis module predicts a cold current cooling time node in combination with historical meteorological data. According to the invention, weather forecast information sent by a local weather forecast system is processed and analyzed, key information is obtained, a time node during cold current cooling is obtained, and a geothermal line or an air heater is started in advance before the node to be cooled arrives, so that the geothermal line or the air heater works at a low temperature for preheating; power can be conveniently and rapidly increased to achieve the heat preservation purpose.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse temperature control, and specifically to an intelligent environmental control system and method for an agricultural greenhouse with controllable crop growth based on big data. Background Art

[0002] A greenhouse, also known as a hothouse, refers to a room equipped with cold-proof, heating and light-transmitting facilities for cultivating thermophilic plants in winter. It can provide a growth period and increase yield in seasons that are not suitable for plant growth. It is mostly used for cultivating or raising seedlings of thermophilic vegetables, flowers, trees and other plants in low temperature seasons. A greenhouse refers to a building that can control or partially control the growth environment of plants. It is mainly used for non-seasonal or non-regional plant cultivation, scientific research, generation breeding and ornamental plant cultivation.

[0003] The temperature in the greenhouse during the day is usually reduced or increased by changing the ventilation status. At night, the temperature is mainly controlled by geothermal lines and air heaters. Especially when raising seedlings, the requirements for nighttime temperature control are higher. The existing technology relies on temperature sensors in the greenhouse to detect the temperature of the greenhouse. When the temperature drops, the geothermal lines or air heaters are activated to achieve the purpose of heating and heat preservation.

[0004] However, even in greenhouses, internal temperatures are significantly affected by fluctuations in the outside temperature. Relying solely on temperature sensors to detect temperature is prone to lag. For example, during a cold snap, the outside temperature can drop rapidly in a short period of time. Activating the geothermal heating lines or air heaters only after the greenhouse detects a temperature drop can cause a lag in temperature increase due to the long preheating time of these lines and air heaters, impacting the normal growth of crops. This requires a system that preheats in advance based on weather forecasts to ensure timely temperature increases and control. Therefore, we propose a big data-based intelligent environmental control system and method for agricultural greenhouses with controllable crop growth. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent environmental control system and method for a crop growth-controllable agricultural greenhouse based on big data. By processing and analyzing the weather forecast information issued by the local weather forecast system, key information and the time node when the cold current drops the temperature are obtained. Before the node when the temperature is about to drop arrives, the geothermal line or air heater is started in advance to make it work at a low temperature and preheat it. When the temperature starts to drop, the power can be easily and quickly increased to achieve the purpose of insulation, thereby solving the problems raised in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a big data-based intelligent environmental control system and method for crop growth-controllable agricultural greenhouses, comprising:

[0007] Central processing unit, used to process data and coordinate the operation of various modules;

[0008] The weather forecast system includes a data extraction module, a data processing module, and a data analysis module. The data extraction module is used to obtain local weather forecast information in real time. The data processing module standardizes the weather information and extracts key parameters. The data analysis module combines historical weather data to predict the time node of cold front and temperature drop.

[0009] An early warning planning module, connected to the central processing unit, includes a clock module, an input terminal, a regulation management module, and a storage module. The clock module is used to calibrate time nodes, the input terminal is used to input crop types and temperature threshold parameters, the regulation management module generates preheating instructions based on predicted cooling time nodes, and the storage module is used to store temperature control parameters for different crops.

[0010] The temperature control module is connected to the geothermal line and the air heater, receives instructions from the early warning planning module, preheats the geothermal line and the air heater at low power 1-2 hours before the temperature drop node, and increases the power to maintain the greenhouse temperature when the temperature drops;

[0011] The monitoring module uses multiple soil temperature sensors and internal temperature sensors to collect real-time air temperature and soil temperature data in the greenhouse and feed it back to the central processor, dynamically adjusting the heating power through the temperature control module.

[0012] As a preferred embodiment of the present invention, it also includes:

[0013] Outdoor temperature sensor, used to monitor outdoor temperature changes in real time;

[0014] Anemometer, used to measure wind speed and direction;

[0015] The central processor combines data from the external temperature sensor and the wind speed and direction meter to predict the actual arrival time of the cold wave. When the outdoor temperature drops sharply at a rate exceeding a threshold or the wind speed and direction meet the preset conditions, preheating is started in advance.

[0016] As a preferred embodiment of the present invention, multiple external temperature sensors and anemometers are provided, and abnormal data are eliminated by averaging.

[0017] As a preferred embodiment of the present invention, the early warning planning module is used to generate a preheating instruction, which includes the preheating time point, heating power and duration, etc., and coordinate the execution of each module.

[0018] As a preferred embodiment of the present invention, it also includes:

[0019] A database, connected to the Internet, is used to store complete data of each temperature control process, including weather forecast accuracy, heating power curve and temperature deviation value;

[0020] The solution summary module is used to analyze the data in the database, optimize the temperature control strategy and realize the coordinated control of multiple greenhouses through the Internet.

[0021] As a preferred embodiment of the present invention, the database supports remote data synchronization and algorithm update, and trains a big data model based on historical temperature control data to optimize prediction accuracy.

[0022] As a preferred embodiment of the present invention, multiple soil temperature sensors and internal temperature sensors are provided. When the temperature difference between sensors exceeds a threshold, the abnormal data is discarded and the temperature value is corrected by averaging the values ​​of the remaining sensors.

[0023] As a preferred embodiment of the present invention, a plurality of external temperature sensors are provided. When the temperature difference between the sensors exceeds a threshold, the abnormal data is discarded and the temperature value is corrected by averaging the values ​​of the remaining sensors.

[0024] As a preferred embodiment of the present invention, the following steps are included:

[0025] Step 1: Obtain meteorological data in real time through the weather forecast system to extract the time nodes of cold current cooling and the temperature change range;

[0026] Step 2: Combine historical meteorological data with real-time data from outdoor temperature sensors and wind speed and direction meters to revise the cold front arrival time forecast;

[0027] Step 3: 1-2 hours before the predicted cooling node, start the geothermal line or air heater at low power for preheating;

[0028] Step 4: Use soil temperature sensors and internal temperature sensors to monitor the ambient temperature in the greenhouse in real time. When the temperature drops, dynamically adjust the heating power to the set threshold.

[0029] Step 5: Store each temperature control data in the database and use the solution summary module to optimize subsequent temperature control strategies.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The big data-based intelligent environmental control system and method for crop growth-controllable agricultural greenhouses of the present invention processes and analyzes weather forecast information issued by the local weather forecast system to obtain key information and determine the time nodes when cold currents and temperature drops. Before the temperature drops, the geothermal lines or air heaters are started in advance to work at low temperatures for preheating. When the temperature starts to drop, the power can be quickly and conveniently increased to achieve the purpose of heat preservation.

[0032] 2. The intelligent environmental control system and method for a controllable agricultural greenhouse for crop growth based on big data of the present invention are also provided with an outdoor temperature sensor and an anemometer, which can predict outdoor temperature changes based on outdoor temperature changes and wind speed and direction. Therefore, when there is an error in the forecast time node in the weather forecast and there is a possibility of early cooling, the geothermal wire and air heater can be preheated in advance to ensure the accuracy of the temperature control time.

[0033] 3. The intelligent environmental control system and method for crop growth controllable agricultural greenhouses based on big data of the present invention is equipped with a program summary module machine database, which can summarize the data of each temperature control and store it in the database for continuous optimization of the system temperature control program. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0035] Figure 1 This is a schematic diagram of the overall structure of the intelligent environmental control system and method for controllable crop growth in agricultural greenhouses based on big data of the present invention;

[0036] Figure 2 This is a structural schematic diagram of the early warning planning module of the intelligent environmental control system and method for controllable crop growth in agricultural greenhouses based on big data of the present invention.

[0037] In the figure: 1. Central processing unit; 2. Early warning planning module; 21. Clock module; 22. Input terminal; 23. Adjustment management module; 24. Storage module; 3. Weather forecast system; 31. Data extraction module; 32. Data processing module; 33. Data analysis module; 4. Database; 41. Solution summary module; 42. Internet; 5. Temperature control module; 51. Geothermal line; 52. Air heater; 6. External temperature sensor; 61. Internal temperature sensor; 62. Soil temperature sensor; 7. Anemometer; 8. Monitoring module. DETAILED DESCRIPTION

[0038] Example 1

[0039] See also Figure 1-2 The present invention provides a technical solution: a crop growth controllable agricultural greenhouse intelligent environmental control system and method based on big data, which mainly includes a central processing unit 1, an early warning planning module 2, a weather forecast system 3, a temperature control module 5, etc., wherein the central processing unit 1 serves as the center of the entire system and is used to process various data.

[0040] The weather forecast system 3 receives weather forecast information issued by the local meteorological department in real time. The data extraction module 31 extracts key meteorological parameters (such as the time node of the cold wave cooling, the predicted temperature change range, etc.) from it, and the data processing module 32 standardizes the format and extracts key parameters (such as the cooling range and duration). The data analysis module 33 combines historical meteorological data to analyze the cooling time node and predict the impact of the cold wave on the greenhouse.

[0041] The early warning planning module 2 is used to generate preheating instructions, which include the preheating time point, heating power and duration, etc., and coordinate the execution of various modules. The early warning planning module 2 consists of a clock module 21, an input terminal 22, an adjustment management module 23 and a storage module 24. The clock module 21 is used to accurately calibrate the time to ensure that the preheating instructions are synchronized with the weather forecast time node. The input terminal 22 is used to manually input parameters such as crop type and temperature threshold to personalize the temperature control strategy. The storage module 24 is used to store the set parameters for different crops. When continuing to input, the corresponding set parameters can be directly matched according to the crop name without having to enter each data in detail each time. The adjustment management module 23 is used to set data such as the start time, start power and duration of the heating equipment, where the heating equipment includes a geothermal line 51 for heating the soil and an air heater 52 for heating the air in the greenhouse. It also includes a temperature control module 5 for controlling the geothermal line 51 and the air heater 52.

[0042] 1-2 hours before the cooling node, the geothermal line 51 or the air heater 52 is started at low power for preheating, and the power of the geothermal line 51 and the air heater 52 is prevented from being too high, which may cause the soil or air temperature in the greenhouse to rise. The temperature of the geothermal line 51 and the air heater 52 is gradually increased to close to the base temperature of the soil and air and kept warm. In addition, a soil temperature sensor 62 is inserted into the soil to monitor the soil temperature, and an internal temperature sensor 61 detects the air temperature in the greenhouse. When the internal temperature sensor 61 or the soil temperature sensor 62 detects that the soil or air temperature in the greenhouse begins to drop, the central processing unit 1 controls the geothermal line 51 or the air heater 52 to increase the heating power according to the set parameters of the early warning planning module 2 through the temperature control module 5 to ensure that the greenhouse temperature is stable within the set threshold. A monitoring module 8 is also provided, which uses the soil temperature sensor 62 and the internal temperature sensor 61 to detect the air temperature and soil temperature in the greenhouse in real time, and transmits the data to the central processing unit 1. The power of the air heater 52 and the geothermal line 51 is dynamically controlled through the temperature control module 5 to avoid excessive temperature fluctuations.

[0043] In order to ensure the accuracy of monitoring, the present invention is provided with multiple soil temperature sensors 62 and internal temperature sensors 61, and the temperature is judged by the average value of multiple sensors. When the temperature difference of a temperature sensor is large, the detected temperature data of the temperature sensor should be discarded to avoid erroneous data caused by sensor failure.

[0044] Example 2

[0045] Since the weather forecast is the weather conditions predicted by the local meteorological bureau, it is well known that the weather forecast is not completely accurate, that is, there may be forecast errors, that is, the actual cooling node is earlier than the predicted node or later than the predicted node. When the actual cooling node is later than the predicted cooling node, the insulation time of the geothermal line 51 or the air heater 52 needs to be extended until the actual temperature drops, and the heating power will be increased, which will only lead to increased power consumption and the actual impact on crops can be ignored; but when the actual cooling node is advanced, the geothermal line 51 and the air heater 52 cannot be preheated in advance. Then, when the actual cooling node arrives and the temperature drops, the geothermal line 51 and the air heater 52 are not preheated to the required temperature, and there is a disadvantage that the interior of the greenhouse cannot be heated and insulated in time. In order to solve this problem, the present invention proposes a second embodiment.

[0046] See also Figure 1 Based on the first embodiment, additional outdoor environmental parameters are set to optimize temperature control. The specific modules include:

[0047] The external temperature sensor 6 is used to monitor the outdoor temperature changes in real time and detect whether the cold wave arrives in advance. The temperature prediction module is used to predict the cooling time node when the actual cold wave arrives. When the temperature drop rate exceeds the threshold (such as 2°C / hour), it can be judged that the cold wave arrives in advance, so that the early warning planning module 2 can control the temperature control module 5 to start the geothermal line 51 or the air heater 52 in advance for preheating.

[0048] However, if a single external temperature sensor 6 is provided, the detection accuracy may be insufficient. Therefore, multiple external temperature sensors 6 need to be provided to avoid obtaining erroneous data. The principle is the same as that of multiple internal temperature sensors 61 and multiple soil temperature sensors 62.

[0049] In order to further improve the prediction accuracy, an additional anemometer 7 is provided for measuring wind speed and wind direction, so that the central processor 1 can predict the heat loss rate based on the wind speed and wind direction data.

[0050] For example, when the temperature drop rate exceeds a threshold, and a cold current comes from the north, and the north wind continues, it can be determined that the cold current may arrive early, so that the regulation management module 23 starts the geothermal line 51 and the air heater 52 through the temperature control module 5 in advance to improve preheating.

[0051] Example 3

[0052] In order to provide a reference for solutions in subsequent cold weather and to achieve the purpose of continuously optimizing temperature control strategies, the present invention proposes a third embodiment, which is different from the first and second embodiments in that a database 4 for storing implementation plans or strategies is also provided.

[0053] The other database 4 is also connected to the Internet 42, which can synchronize data and update algorithms remotely, realize multi-greenhouse collaborative optimization, and use the Internet 42 to share solutions and strategies.

[0054] A solution summary module 41 is also provided for classifying and storing complete data of each temperature control process (such as weather forecast accuracy, heating power curve, temperature deviation value), and providing label data for training of the big data model.

[0055] Through the above implementation methods, all modules of the system can fully exert their design functions to achieve efficient, accurate and adaptive greenhouse environment control.

Claims

1. A crop growth controllable agricultural greenhouse intelligent environmental control system based on big data, characterized by: include: Central processing unit (1), used to process data and coordinate the operation of various modules; A weather forecast system (3) includes a data extraction module (31), a data processing module (32), and a data analysis module (33), wherein the data extraction module (31) is used to obtain local weather forecast information in real time, the data processing module (32) performs standardization processing on the weather information and extracts key parameters, and the data analysis module (33) predicts a cold snap temperature drop time node based on historical weather data; The early warning planning module (2) is connected to the central processing unit (1), and includes a clock module (21), an input terminal (22), a regulation management module (23), and a storage module (24). The clock module (21) is used to calibrate the time node, the input terminal (22) is used to input the crop type and temperature threshold parameters, the regulation management module (23) generates a preheating instruction according to the predicted cooling time node, and the storage module (24) is used to store temperature control parameters for different crops. The temperature control module (5) is connected to the geothermal line (51) and the air heater (52), receives the instruction of the early warning planning module (2), preheats the geothermal line (51) and the air heater (52) at low power 1-2 hours before the temperature drop node, and increases the power to maintain the greenhouse temperature when the temperature drops; The monitoring module (8) uses a plurality of soil temperature sensors (62) and an internal temperature sensor (61). The internal temperature sensor (61) and the soil temperature sensor (62) collect the air temperature and soil temperature data in the greenhouse in real time and feed them back to the central processing unit (1). The heating power is dynamically adjusted through the temperature control module (5).

2. The intelligent environmental control system for crop growth controllable agricultural greenhouses based on big data according to claim 1 is characterized by: Also includes: An external temperature sensor (6) is used to monitor outdoor temperature changes in real time; Anemometer (7), used to measure wind speed and direction; The central processing unit (1) combines data from the external temperature sensor (6) and the wind speed and direction meter (7) to predict the actual arrival time of the cold wave, and starts preheating in advance when the outdoor temperature drop rate exceeds a threshold or the wind speed and direction meet preset conditions.

3. The intelligent environmental control system for crop growth controllable agricultural greenhouses based on big data according to claim 2, characterized in that: Multiple external temperature sensors (6) and anemometers (7) are provided, and abnormal data are eliminated through mean value calculation.

4. The intelligent environmental control system for crop growth controllable agricultural greenhouses based on big data according to claim 1 is characterized by: The early warning planning module (2) is used to generate a preheating instruction, which includes the preheating time point, heating power and duration, etc., and coordinate the execution of each module.

5. The intelligent environmental control system for crop growth controllable agricultural greenhouse based on big data according to claim 1 is characterized by: Also includes: A database (4), connected to the Internet (42), for storing complete data of each temperature control process, including weather forecast accuracy, heating power curve and temperature deviation value; The solution summary module (41) is used to analyze the data in the database (4), optimize the temperature control strategy and realize multi-greenhouse coordinated control through the Internet (42).

6. The intelligent environmental control system for crop growth controllable agricultural greenhouses based on big data according to claim 5 is characterized by: The database (4) supports remote data synchronization and algorithm update, and trains a big data model based on historical temperature control data to optimize prediction accuracy.

7. The intelligent environmental control system for crop growth controllable agricultural greenhouse based on big data according to claim 1, characterized in that: A plurality of soil temperature sensors (62) and internal temperature sensors (61) are provided. When the temperature difference between the sensors exceeds a threshold value, the abnormal data is discarded and the temperature value is corrected by averaging the values ​​of the remaining sensors.

8. The intelligent environmental control system for crop growth controllable agricultural greenhouse based on big data according to claim 2, characterized in that: A plurality of external temperature sensors (6) are provided. When the temperature difference between the sensors exceeds a threshold value, the abnormal data is discarded and the temperature value is corrected by averaging the values ​​of the remaining sensors.

9. The method for intelligent environmental control of a crop growth-controllable agricultural greenhouse based on big data according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Obtain meteorological data in real time through the weather forecast system (3) to extract the cold current cooling time nodes and temperature change amplitude; Step 2: Combine historical meteorological data with real-time data from the outdoor temperature sensor (6) and the wind speed and direction meter (7) to revise the cold front arrival time prediction; Step 3: 1-2 hours before the predicted cooling node, start the geothermal line (51) or air heater (52) at low power for preheating; Step 4: Real-time monitoring of the ambient temperature in the greenhouse via the soil temperature sensor (62) and the internal temperature sensor (61), and dynamically adjusting the heating power to a set threshold when the temperature drops; Step 5: Store each temperature control data into the database (4), and use the solution summary module (41) to optimize the subsequent temperature control strategy.

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