A greenhouse control method, apparatus, system, equipment, and storage medium

Abstract

This invention provides a greenhouse control method, apparatus, system, equipment, and storage medium. The method includes: collecting current environmental data in the greenhouse, including at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH; acquiring a plant growth model; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse; determining whether the current environmental data meets the plant growth requirements based on the plant growth model; if the current environmental data does not meet the plant growth requirements, determining target environmental data in the greenhouse based on the plant growth model, and adjusting the current environmental data according to the target environmental data. Compared with current traditional farming methods and threshold-based control methods, this invention effectively improves the intelligence and precision of environmental control.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a greenhouse control method, apparatus, system, device, and storage medium. Background Technology

[0002] In recent years, with the development of urbanization and agricultural modernization, my country has faced a severe shortage of labor for agricultural production, coupled with outdated production technology and low efficiency. Therefore, intelligent technologies have gradually been applied to agricultural production, significantly improving production efficiency and reducing the burden on farmers. In intelligent agricultural production, most methods rely on experience to set thresholds to control the greenhouse environment; however, simple threshold control cannot meet the requirements of precision crop production. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a greenhouse control method, apparatus, system, device and storage medium that overcomes or at least partially solves the above problems.

[0004] To address the above problems, this invention discloses a greenhouse control method, the method comprising:

[0005] Collect current environmental data in the greenhouse, including at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value;

[0006] A plant growth model is obtained; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse.

[0007] Based on the plant growth model, determine whether the current environmental data meets the requirements for plant growth;

[0008] If the current environmental data does not meet the plant growth requirements, then the target environmental data in the greenhouse is determined according to the plant growth model, and the current environmental data is adjusted according to the target environmental data.

[0009] Optionally, the plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the method further includes:

[0010] For each plant, the neural network model is trained by using multiple sets of different environmental data as input and the chlorophyll content of each plant as output, to obtain a plant growth model for each plant.

[0011] Optionally, determining whether the current environmental data meets the plant growth requirements based on the plant growth model includes:

[0012] The current environmental data is input into the plant growth model to obtain the target plant growth data;

[0013] Obtain historical plant growth data;

[0014] Based on the historical plant growth data, reference plant growth data are determined;

[0015] By comparing the growth data of the target plant with the growth data of the reference plant, a first comparison result is obtained;

[0016] Based on the first comparison result, determine whether the current environmental data meets the requirements for plant growth.

[0017] Optionally, determining whether the current environmental data meets the plant growth requirements based on the plant growth model includes:

[0018] Acquire multiple sets of environmental data;

[0019] The multiple sets of environmental data are input into the plant growth model to obtain multiple plant growth data corresponding to the multiple sets of environmental data.

[0020] Based on the aforementioned plant growth data, reference environmental data were determined;

[0021] A second comparison result is obtained by comparing the current environmental data with the reference environmental data;

[0022] Based on the second comparison result, it is determined whether the current environmental data meets the requirements for plant growth.

[0023] Optionally, the environmental data to be adjusted in the target environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH. The greenhouse includes a water pump, a temperature control module, supplemental lighting, a shade net, an air pump, and a soil nutrient supply module. Adjusting the current environmental data based on the target environmental data includes:

[0024] Based on the target environmental data, the system controls at least one of the following: a water pump, a temperature control module, a supplemental light, a shade net, an air pump, and a soil nutrient supply module, to adjust the current environmental data so that the current environmental data reaches the target environmental data.

[0025] The present invention also discloses a greenhouse control device, the device comprising:

[0026] The data acquisition module is used to collect current environmental data in the greenhouse, including at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value.

[0027] The acquisition module is used to acquire a plant growth model; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse.

[0028] The judgment module is used to determine whether the current environmental data meets the requirements for plant growth based on the plant growth model.

[0029] An adjustment module is used to determine target environmental data in the greenhouse based on the plant growth model if the current environmental data does not meet the plant growth requirements, and to adjust the current environmental data based on the target environmental data.

[0030] The present invention also discloses a greenhouse control system, the system comprising a data acquisition module, a hardware control module, a hardware module, a decision-making module, a power supply module, and a wireless radio frequency module;

[0031] The power module is connected to the hardware control module, and the power module is used to supply power to the hardware control module;

[0032] The decision module is connected to the hardware control module through the wireless radio frequency module. The decision module is used to send an environmental data acquisition signal to the hardware control module when the power module supplies power to the hardware control module.

[0033] The wireless radio frequency module is used to establish a wireless connection between the decision module and the hardware control module.

[0034] The hardware control module is connected to the acquisition module, and the hardware control module is used to control the acquisition module to acquire current environmental data and transmit it to the decision module according to the environmental data acquisition signal.

[0035] The decision module is used to acquire a plant growth model and determine whether the current environmental data meets the plant growth requirements. If the current environmental data does not meet the plant growth requirements, the target environmental data in the greenhouse is determined according to the plant growth model, and an adjustment signal is sent to the hardware control module. The adjustment signal includes the target environmental data. The plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data in the greenhouse.

[0036] The hardware control module is used to control the hardware module to adjust the current environmental data according to the adjustment signal, so that the current environmental data reaches the target environmental data.

[0037] Optionally, the environmental data includes at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH.

[0038] The data acquisition module includes a carbon dioxide sensor, a temperature and humidity sensor, a light sensor, a nutrient solution temperature sensor, and a soil sensor. The carbon dioxide sensor is used to collect the carbon dioxide concentration in the environment, the temperature and humidity sensor is used to collect the temperature and humidity in the environment, the light sensor is used to collect the light coefficient in the environment, the nutrient solution temperature sensor is used to collect the temperature of the nutrient solution in the greenhouse, and the soil sensor is used to collect the soil nutrients and soil pH value in the greenhouse.

[0039] Optionally, the hardware module includes at least one of a heating module, a cooling module, a water replenishment module, a supplemental lighting module, a shading module, an aeration module, and a soil nutrient replenishment module;

[0040] The heating module is used to raise the temperature of the greenhouse, the cooling module is used to lower the temperature of the greenhouse, the water replenishment module is used to increase the humidity of the greenhouse, the supplemental lighting module is used to enhance the light index of the greenhouse, the shading module is used to reduce the light index of the greenhouse, the gas replenishment module is used to replenish the carbon dioxide concentration of the greenhouse, and the soil nutrient replenishment module is used to replenish the soil nutrients and adjust the soil pH value.

[0041] Optionally, the plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the method for determining the plant growth model includes:

[0042] For each plant, the neural network model is trained by using multiple sets of different environmental data as input and the chlorophyll content of each plant as output, to obtain a plant growth model for each plant.

[0043] Optionally, the system further includes a control platform, and the acquisition module is also used to acquire plant growth data. The control platform is connected to at least one decision module.

[0044] The control platform is used to send a second acquisition signal to the at least one decision module. The second acquisition signal is used to acquire environmental data and plant growth data of the greenhouse where the at least one decision module is located, and to update the plant growth model of the greenhouse where the at least one decision module is located based on the environmental data and plant growth data sent by the at least one decision module.

[0045] Optionally, the control platform is further configured to, if the environmental data sent by the target decision module in the at least one decision module does not meet the plant growth requirements, determine the target environmental data in the greenhouse where the target decision module is located according to the platform's plant growth model, and send the target environmental data to the target decision module, so that the target decision module adjusts the environmental data in the greenhouse where the target decision module is located, so that the environmental data in the greenhouse where the target decision module is located reaches the target environmental data; the platform's plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data in the greenhouse where the at least one decision module is located.

[0046] The present invention also discloses an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the greenhouse control method as described above.

[0047] The present invention also discloses a non-volatile readable storage medium on which a computer program is stored, and which, when executed by a processor, implements the steps of the greenhouse control method described above.

[0048] The embodiments of the present invention have the following advantages:

[0049] This invention can collect current environmental data from a greenhouse and then determine whether the current environmental data meets the requirements for plant growth based on a plant growth model. Since the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse, the most suitable target environmental data can be determined based on the plant growth model. Therefore, when it is determined that the current environmental data does not meet the requirements for plant growth, the current environmental data can be adjusted based on the target environmental data, thereby achieving intelligent control of the greenhouse environment. Compared with the current traditional farming mode and the control mode based on thresholds, this invention effectively improves the intelligence and accuracy of environmental control. Attached Figure Description

[0050] Figure 1 This is a flowchart of the steps of a greenhouse control method provided in an embodiment of the present invention;

[0051] Figure 2 This is a flowchart of another greenhouse control method provided in an embodiment of the present invention;

[0052] Figure 3 This is a structural block diagram of a greenhouse control device provided in an embodiment of the present invention;

[0053] Figure 4 This is a structural block diagram of a greenhouse control system provided in an embodiment of the present invention;

[0054] Figure 5 This is a structural block diagram of another greenhouse control system provided in an embodiment of the present invention;

[0055] Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention;

[0056] Figure 7 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] In recent years, with the development of urbanization and agricultural modernization, my country has faced a severe shortage of labor for agricultural production, coupled with outdated production technologies and low efficiency. Therefore, intelligent technologies have gradually been applied to agricultural production, significantly improving production efficiency and reducing the burden on farmers.

[0059] The production of greenhouse vegetables largely relies on mechanization, automation, and intelligent technologies to regulate their growing environment. Most technologies and equipment control factors such as temperature, light, water, gaseous conditions, and fertilizer nutrient levels to meet the crop's growth needs. This cultivation method allows for both cyclical and off-season production, reducing dependence on the natural environment. Simultaneously, this method significantly improves crop yield and quality, thereby increasing economic benefits and promoting my country's agricultural modernization. Since the 1990s, my country's greenhouse vegetable industry has experienced rapid development. Due to its high technology, high investment, high quality, high yield, and high returns, it has become one of the most dynamic forms of modern agriculture in my country. In 2020 alone, the scale of greenhouse vegetable production in my country reached 25.488 million hectares, accounting for 21.5% of the total vegetable planting area in the country; the output of greenhouse vegetables reached 918.349 million tons, accounting for 30.5% of the total vegetable production in the country; and the output value reached two trillion yuan, accounting for 62.7% of the total output value of vegetables in the country. At present, my country's greenhouse vegetable industry ranks first in the world in both area and output.

[0060] Related intelligent agricultural technologies include monitoring environmental factors such as temperature, light, water, gas environment, and fertilizer, and controlling the threshold of these environmental factors. However, in intelligent agricultural production, most greenhouse environments are controlled by setting thresholds based on experience. But due to the different seasons and plant growth stages, the demand for growth environment and nutrients varies greatly. A single threshold control cannot meet the requirements of precision crop production and leads to a large waste of resources.

[0061] Based on this, the present invention creatively proposes a greenhouse control method. The present invention can collect current environmental data of the greenhouse, and then determine whether the current environmental data meets the requirements for plant growth based on a plant growth model. Since the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data of the greenhouse, the most suitable target environmental data can be determined based on the plant growth model. Therefore, when it is determined that the current environmental data does not meet the requirements for plant growth, the current environmental data can be adjusted according to the target environmental data, thereby realizing intelligent control of the greenhouse environment. Compared with the current traditional farming mode and the control mode with threshold as the target, the present invention effectively improves the intelligence and accuracy of environmental control.

[0062] Reference Figure 1 The diagram illustrates a flowchart of a greenhouse control method according to an embodiment of the present invention, which may include the following steps:

[0063] Step 101: Collect current environmental data in the greenhouse. The current environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value.

[0064] In this embodiment of the invention, the current environmental data refers to the relevant parameters that affect the growth of plants in the greenhouse. The current environmental data may include at least one of the following: carbon dioxide concentration, temperature, humidity, light coefficient, nutrient solution temperature, soil nutrients, and soil pH value.

[0065] Sensors can be installed in the greenhouse to collect current environmental data. For example, temperature sensors can collect the temperature and nutrient solution temperature, carbon dioxide sensors can collect the carbon dioxide concentration, light sensors can collect the light coefficient, pH indicators can detect the soil pH, and temperature and humidity sensors can collect the humidity.

[0066] In one example, the carbon dioxide concentration in the greenhouse was measured to be 110 ppm by a carbon dioxide sensor, and the temperature in the greenhouse was measured to be 18 degrees Celsius by a temperature sensor.

[0067] Step 102: Obtain the plant growth model; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse.

[0068] In this embodiment of the invention, plant growth data refers to data representing the rate of plant growth, which may include plant growth charts and chlorophyll content. The specific type is not limited here.

[0069] The plant growth model is trained based on multiple sets of different environmental data and plant growth data in a greenhouse. Specifically, multiple sets of environmental data can be used as the input to the model, and the growth data corresponding to each set of environmental data can be used as the output of the model. This training process yields the growth model, allowing researchers to obtain the most suitable environmental data based on the model.

[0070] Step 103: Based on the plant growth model, determine whether the current environmental data meets the requirements for plant growth.

[0071] In this embodiment of the invention, plant growth requirements refer to whether the greenhouse environmental data can meet the grower's requirements. These requirements can be a certain height of the plant in meters or a certain chlorophyll content of the plant. The specific type is not limited here.

[0072] Since the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse, it can be used to determine whether the current environmental data meets the growth requirements.

[0073] Step 104: If the current environmental data does not meet the requirements for plant growth, then determine the target environmental data in the greenhouse based on the plant growth model, and adjust the current environmental data according to the target environmental data.

[0074] In this embodiment of the invention, the target environmental data refers to the environmental data most suitable for plant growth. If the current environmental data does not meet the requirements for plant growth, that is, the current environmental data is not the most suitable environment for plant growth in the greenhouse, the target environmental data of the greenhouse can be calculated by the plant growth model, and then the current environment can be adjusted so that the environment in the greenhouse is the most suitable environment for plant growth. In one example, the current carbon dioxide concentration in the greenhouse is 300 ppm, and the current carbon dioxide concentration does not meet the requirements for plant growth. According to the plant growth model, the target growth model in the greenhouse is calculated to be 120 ppm. At this time, the carbon dioxide concentration in the greenhouse can be adjusted so that the carbon dioxide concentration in the greenhouse reaches 120 ppm.

[0075] This invention can collect current environmental data from a greenhouse and then determine whether the current environmental data meets the requirements for plant growth based on a plant growth model. Since the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse, the most suitable target environmental data can be determined based on the plant growth model. Therefore, when it is determined that the current environmental data does not meet the requirements for plant growth, the current environmental data can be adjusted based on the target environmental data, thereby achieving intelligent control of the greenhouse environment. Compared with the current traditional farming mode and the control mode based on thresholds, this invention effectively improves the intelligence and accuracy of environmental control.

[0076] Reference Figure 2 The diagram illustrates a flowchart of another greenhouse control method provided by an embodiment of the present invention, which may include the following steps:

[0077] Step 201: Collect current environmental data in the greenhouse. The current environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value.

[0078] Step 202: Obtain the plant growth model; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse.

[0079] In one embodiment of the present invention, the plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the method may further include:

[0080] For each plant species, multiple sets of different environmental data were used as input to the neural network model, and the chlorophyll content of each plant species was used as the output of the neural network model. The models were then trained to obtain plant growth models for each plant species.

[0081] In this embodiment of the invention, plant growth data can include chlorophyll content of various plants collected under different greenhouse environmental conditions. For example, in the past year, various plants were grown in the greenhouse, including tomatoes, cucumbers, and pumpkins. Researchers conducted experiments in multiple greenhouses, using different carbon dioxide concentrations, temperatures, humidity, light intensities, and nutrient solution temperatures as environmental data for each greenhouse. The environmental data from each greenhouse was then used as input to a neural network model, and the chlorophyll content of each plant measured in each greenhouse was used as output to train the neural network model, thereby obtaining a plant growth model for each plant. In other words, in this embodiment of the invention, plant growth models for tomatoes, cucumbers, and pumpkins can be obtained. When planting the corresponding plants, the matching plant growth model can be selected for use.

[0082] In one embodiment of the present invention, the plant grown in the greenhouse can be a single species, and the plant growth data can be the chlorophyll content of the single plant species collected under different environmental data in the greenhouse. For example, if the plant grown in the greenhouse is a chili pepper, the experimenter can conduct experiments in multiple greenhouses, using different greenhouse temperatures, carbon dioxide concentrations, and light coefficients as environmental data for each greenhouse. Then, the environmental data in each greenhouse is used as the input to a neural network model, and the final measured plant growth data in each greenhouse is used as the output of the neural network model. The model is then trained to obtain a plant growth model.

[0083] Step 203: Input the current environmental data into the plant growth model to obtain the target plant growth data.

[0084] In this embodiment of the invention, if the plant grown in the greenhouse is a pumpkin, the pumpkin plant growth model is first obtained, and then the current environmental data is input into the plant growth model. The final chlorophyll content of the pumpkin can be output. In one example, the current environmental data includes a temperature of 17 degrees Celsius. After inputting into the plant growth model, the target growth data output is a chlorophyll content of 100 mg / g.

[0085] Step 204: Obtain historical plant growth data.

[0086] In this embodiment of the invention, historical plant growth data refers to plant growth data obtained from growing plants in a greenhouse at historical times. For example, researchers can record plant growth data corresponding to different environmental data in the greenhouse over the past year.

[0087] In one embodiment of the present invention, historical plant growth data may also be plant growth data recorded by researchers over the past few years. The specific method used to determine historical plant data is not limited here.

[0088] Step 205: Determine reference plant growth data based on historical plant growth data.

[0089] In this embodiment of the invention, the acquired historical plant growth data includes 20 data points, and the largest plant growth data point among the 20 can be used as the reference plant growth data.

[0090] Step 206: Compare the growth data of the target plant with the growth data of the reference plant to obtain the first comparison result.

[0091] In this embodiment of the invention, when the plant growth data includes chlorophyll content, the size between the target plant growth data and the reference plant growth data can be determined. If the target plant growth data is greater than or equal to the reference plant growth data, it indicates that the current environmental data meets the plant growth requirements. If the target plant growth data is less than the reference plant growth data, it indicates that the current environmental data does not meet the plant growth requirements.

[0092] When plant growth data includes plant height, it is also possible to compare the size between target plant growth data and reference plant growth data, and then based on the first comparison result, the first comparison result includes the target plant height being greater than or equal to the reference plant height, and the first comparison result may also include the target plant height being less than the reference plant height.

[0093] Step 207: Based on the first comparison result, determine whether the current environmental data meets the requirements for plant growth.

[0094] In one embodiment of the present invention, step 103 may include:

[0095] Acquire multiple sets of environmental data; input the multiple sets of environmental data into the plant growth model to obtain multiple plant growth data corresponding to the multiple sets of environmental data; determine reference environmental data based on the multiple plant growth data; compare the current environmental data and the reference environmental data to obtain a second comparison result; and determine whether the current environmental data meets the requirements for plant growth based on the second comparison result.

[0096] In this embodiment of the invention, since the plant growth model is trained based on multiple sets of different environmental data and plant growth data of the greenhouse, multiple sets of different environmental data can be obtained, such as obtaining greenhouse temperatures from 10 degrees Celsius to 37 degrees Celsius. Then, these multiple sets of temperature values ​​are input into the plant growth model to obtain the plant growth data corresponding to each set of temperature values. Then, reference environmental data can be determined based on multiple sets of plant growth data.

[0097] Since plant growth data can include plant growth maps, the best plant growth map among multiple plant growth data can be used as the reference plant growth data. Since the plant growth data is obtained based on the plant growth model, the reference environmental data corresponding to the reference plant growth data can be obtained. At this time, comparing the current environmental data and the reference environmental data can yield a second comparison result. The second comparison result can include the difference between the greenhouse temperature of the current environmental data and the greenhouse temperature of the reference environmental data being less than a preset value, and can also include the difference between the greenhouse temperature of the current environmental data and the greenhouse temperature of the reference environmental data being greater than a preset value.

[0098] When plant growth data includes chlorophyll content, the plant with the highest chlorophyll content among multiple plant growth data can be used as the reference environmental data. Similarly, since plant growth data is obtained based on plant growth models, reference environmental data corresponding to the reference plant growth data can be obtained. At this time, comparing the current environmental data and the reference environmental data can yield a second comparison result. The second comparison result can include the difference between the greenhouse temperature of the current environmental data and the greenhouse temperature of the reference environmental data being less than a preset value, and can also include the difference between the greenhouse temperature of the current environmental data and the greenhouse temperature of the reference environmental data being greater than a preset value.

[0099] It should be noted that the preset values ​​can be set according to user needs, and there are no restrictions here.

[0100] Step 208: If the current environmental data does not meet the requirements for plant growth, then determine the target environmental data in the greenhouse according to the plant growth model, and adjust the current environmental data according to the target environmental data.

[0101] In this embodiment of the invention, if the first comparison result includes that the target plant height is less than the reference plant height, or the second comparison result includes that the greenhouse temperature of the current environmental data and the greenhouse temperature of the reference environmental data are greater than a preset value, it indicates that the current environmental data does not meet the requirements for plant growth. At this time, the target environmental data corresponding to the optimal plant growth data can be calculated according to the plant growth model. In one example, the current greenhouse temperature is 17 degrees Celsius. By inputting multiple sets of temperature values ​​into the plant growth model, multiple sets of plant growth data can be obtained, and the chlorophyll content is highest at a temperature of 20 degrees Celsius. At this time, the current environmental temperature value can be adjusted from 17 to 20 degrees Celsius.

[0102] In one embodiment of the present invention, the environmental data to be adjusted in the target environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH. The greenhouse includes a water pump, a temperature control module, supplemental lighting, a shade net, an air pump, and a soil nutrient supply module. Adjusting the current environmental data according to the target environmental data may include:

[0103] Based on the target environmental data, control the target environmental data by controlling at least one of the following: water pump, temperature control module, supplemental lighting, shade net, air pump, and soil nutrient supply module, to adjust the current environmental data so that the current environmental data reaches the target environmental data.

[0104] In this embodiment of the invention, the greenhouse may include a water pump, a temperature control module, supplemental lighting, a shade net, an air pump, and a soil nutrient supply module. If the measured current environmental data includes carbon dioxide concentration, temperature, humidity, and light index, and it is determined that the target environmental data includes carbon dioxide concentration, temperature, humidity, and light index, and that the light index needs to be enhanced, then the air pump can be controlled to adjust the carbon dioxide concentration, the temperature control module can be controlled to adjust the temperature, and the supplemental lighting can be controlled to adjust the light index.

[0105] This invention can collect current environmental data from a greenhouse and then determine whether the current environmental data meets the requirements for plant growth based on a plant growth model. Since the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse, the most suitable target environmental data can be determined based on the plant growth model. Therefore, when it is determined that the current environmental data does not meet the requirements for plant growth, the current environmental data can be adjusted based on the target environmental data, thereby achieving intelligent control of the greenhouse environment. Compared with the current traditional farming mode and the control mode based on thresholds, this invention effectively improves the intelligence and accuracy of environmental control.

[0106] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0107] like Figure 3 The diagram shows a structural block diagram of a greenhouse control device according to an embodiment of the present invention. The device may include:

[0108] The data acquisition module 301 is used to acquire current environmental data in the greenhouse, including at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value.

[0109] The acquisition module 302 is used to acquire a plant growth model; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data of the greenhouse.

[0110] The judgment module 303 is used to determine whether the current environmental data meets the requirements for plant growth based on the plant growth model.

[0111] The adjustment module 304 is used to determine the target environmental data in the greenhouse according to the plant growth model if the current environmental data does not meet the plant growth requirements, and to adjust the current environmental data according to the target environmental data.

[0112] This invention discloses a greenhouse control device. This device can collect current environmental data from the greenhouse and then, based on a plant growth model, determine whether the current environmental data meets the requirements for plant growth. Since the plant growth model is pre-trained based on multiple sets of different environmental and plant growth data from the greenhouse, it can determine the most suitable target environmental data. Therefore, when it is determined that the current environmental data does not meet the plant growth requirements, the current environmental data can be adjusted according to the target environmental data, thereby achieving intelligent control of the greenhouse environment. Compared with current traditional farming methods and threshold-based control methods, this invention effectively improves the intelligence and precision of environmental control.

[0113] In one embodiment of the present invention, the plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the device may further include:

[0114] The training module is used to train each plant separately, using multiple sets of different environmental data as input to the neural network model and the chlorophyll content corresponding to each plant as the output of the neural network model, to obtain a plant growth model for each plant.

[0115] In one embodiment of the present invention, the determining module 303 may further include:

[0116] The first input submodule is used to input the current environmental data into the plant growth model to obtain the target plant growth data.

[0117] The first acquisition submodule is used to acquire historical plant growth data.

[0118] The first determination submodule is used to determine reference plant growth data based on historical plant growth data.

[0119] The first comparison submodule is used to compare the growth data of the target plant and the growth data of the reference plant to obtain the first comparison result.

[0120] The first judgment submodule is used to determine whether the current environmental data meets the requirements for plant growth based on the first comparison result.

[0121] In one embodiment of the present invention, the determining module 303 may further include:

[0122] The second acquisition submodule is used to acquire multiple sets of environmental data.

[0123] The second input submodule is used to input multiple sets of environmental data into the plant growth model to obtain multiple plant growth data corresponding to the multiple sets of environmental data.

[0124] The second determination submodule is used to determine reference environmental data based on multiple plant growth data.

[0125] The second comparison submodule is used to compare the current environment data with the reference environment data to obtain a second comparison result.

[0126] The second judgment submodule is used to determine whether the current environmental data meets the requirements for plant growth based on the second comparison result.

[0127] In one embodiment of the present invention, the environmental data to be adjusted in the target environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value. The greenhouse includes a water pump, a temperature control module, a supplemental light, a shading net, an air pump, and a soil nutrient supply module. The adjustment module 304 may include:

[0128] The control submodule is used to control the target environmental data based on the target environmental data, and to control at least one of the following: water pump, temperature control module, supplemental lighting, shade net, air pump, and soil nutrient supply module, to adjust the current environmental data so that the current environmental data reaches the target environmental data.

[0129] This invention discloses a greenhouse control device. This device can collect current environmental data from the greenhouse and then, based on a plant growth model, determine whether the current environmental data meets the requirements for plant growth. Since the plant growth model is pre-trained based on multiple sets of different environmental and plant growth data from the greenhouse, it can determine the most suitable target environmental data. Therefore, when it is determined that the current environmental data does not meet the plant growth requirements, the current environmental data can be adjusted according to the target environmental data, thereby achieving intelligent control of the greenhouse environment. Compared with current traditional farming methods and threshold-based control methods, this invention effectively improves the intelligence and precision of environmental control.

[0130] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0131] Reference Figure 4 The diagram shows a structural block diagram of a greenhouse control system 40 provided in an embodiment of the present invention. The system includes a data acquisition module 401, a hardware control module 402, a hardware module 403, a decision module 404, a power supply module 405, and a wireless radio frequency module 406.

[0132] The power module 405 is connected to the hardware control module 402, and the power module 405 is used to supply power to the hardware control module 402;

[0133] The decision module 404 is connected to the hardware control module 402 via the wireless radio frequency module 405. The decision module 404 is used to send an environmental data acquisition signal to the hardware control module 402 when the power module 405 supplies power to the hardware control module 402.

[0134] In this embodiment of the invention, the decision-making module can be a Raspberry Pi.

[0135] The wireless radio frequency module 405 is used to establish a wireless connection between the decision module 404 and the hardware control module 402;

[0136] The hardware control module 402 is connected to the acquisition module 401. The hardware control module 402 is used to control the acquisition module 401 to acquire current environmental data and transmit it to the decision module 404 based on the environmental data acquisition signal.

[0137] The decision module 404 is used to acquire the plant growth model and determine whether the current environmental data meets the requirements for plant growth. If the current environmental data does not meet the requirements for plant growth, the target environmental data in the greenhouse is determined according to the plant growth model, and an adjustment signal is sent to the hardware control module 402. The adjustment signal includes the target environmental data. The plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data in the greenhouse.

[0138] The hardware control module 402 is used to control the hardware module 403 to adjust the current environmental data according to the adjustment signal, so that the current environmental data reaches the target environmental data.

[0139] In one embodiment of the present invention, the environmental data includes at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH.

[0140] The data acquisition module 401 includes a carbon dioxide sensor, a temperature and humidity sensor, a light sensor, a nutrient solution temperature sensor, and a soil sensor. The carbon dioxide sensor is used to collect the carbon dioxide concentration in the environment, the temperature and humidity sensor is used to collect the temperature and humidity in the environment, the light sensor is used to collect the light coefficient in the environment, the nutrient solution temperature sensor is used to collect the nutrient solution temperature in the greenhouse, and the soil sensor is used to collect the soil nutrients and soil pH value in the greenhouse.

[0141] In this embodiment of the invention, when the acquisition module 401 receives the environmental data acquisition signal forwarded by the hardware control module 402, it can activate the corresponding sensor to acquire the current environmental data. For example, if the current environmental data includes temperature, humidity, and illuminance, the temperature and humidity sensor can be controlled to acquire the temperature and humidity in the environment, and the illuminance sensor can be controlled to acquire the illuminance in the environment.

[0142] In one embodiment of the present invention, the hardware module 403 includes at least one of a heating module, a cooling module, a water replenishment module, a light replenishment module, a shading module, a gas replenishment module, and a soil nutrient replenishment module;

[0143] The heating module is used to raise the temperature of the greenhouse, the cooling module is used to lower the temperature of the greenhouse, the water supply module is used to increase the humidity of the greenhouse, the supplemental lighting module is used to enhance the light index of the greenhouse, the shading module is used to reduce the light index of the greenhouse, the gas supply module is used to supplement the carbon dioxide concentration of the greenhouse, and the soil nutrient supply module is used to supplement the soil nutrients and adjust the soil pH value.

[0144] In this embodiment of the invention, when the hardware module 403 receives the adjustment signal sent by the hardware control module, it can be determined from the target environmental data in the adjustment signal that it is necessary to supplement the carbon dioxide concentration, increase the light coefficient, and reduce the greenhouse temperature. Then, the supplementary lighting module is controlled to enhance the light coefficient of the greenhouse. The supplementary lighting module can be a supplementary lamp. The cooling module can be controlled to cool the greenhouse. The gas supply module is controlled to supplement the carbon dioxide concentration of the greenhouse. The gas supply module can be an air pump.

[0145] In one embodiment of the present invention, the plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the method for determining the plant growth model includes:

[0146] For each plant species, multiple sets of different environmental data were used as input to the neural network model, and the chlorophyll content of each plant species was used as the output of the neural network model. The models were then trained to obtain plant growth models for each plant species.

[0147] In one embodiment of the present invention, the system further includes a control platform, and the acquisition module 401 is also used to acquire plant growth data. The control platform is connected to at least one decision module 404.

[0148] like Figure 5 This diagram illustrates a structural block diagram of a greenhouse control system 40 according to an embodiment of the present invention, including a control platform 407 connected to at least one decision module 404, such as... Figure 5 As shown, the control platform can be connected to decision module 4041-decision module 404 n connect.

[0149] In this embodiment of the invention, the control platform 407 can communicate with at least one decision module via Ethernet. Based on the Ethernet network, the park can be managed in an intensive manner, thereby improving production efficiency.

[0150] The control platform is used to send a second acquisition signal to at least one decision module 404. The second acquisition signal is used to acquire environmental data and plant growth data of the greenhouse where at least one decision module 404 is located, and to update the plant growth model of the greenhouse where at least one decision module 404 is located based on the environmental data and plant growth data sent by at least one decision module 404.

[0151] In this embodiment of the invention, the control platform can receive real-time environmental data and plant growth data from each greenhouse, and then train the plant growth model of each greenhouse based on the current environmental data and plant growth data.

[0152] In one embodiment of the present invention, the control platform is further configured to, if the environmental data sent by the target decision module in at least one decision module does not meet the requirements for plant growth, determine the target environmental data in the greenhouse where the target decision module is located according to the platform's plant growth model, and send the target environmental data to the target decision module, so that the target decision module adjusts the environmental data in the greenhouse where the target decision module is located, so that the environmental data in the greenhouse where the target decision module is located reaches the target environmental data; the platform's plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data in the greenhouse where at least one decision module is located.

[0153] In this embodiment of the invention, the platform plant growth model is trained based on multiple sets of different environmental data and plant growth data from the greenhouses where all decision modules connected to the platform are located. If the platform detects an abnormal environment in the greenhouse where a certain decision module is located, the platform can send an adjustment control command to the corresponding Raspberry Pi. For example, if the platform is connected to 10 decision modules, and after receiving the environmental data sent by the 3rd decision module, it determines that the environmental data sent by the 3rd decision module does not meet the requirements for plant growth, then it calculates the corresponding target environmental data according to the platform plant growth model and sends it to the 3rd decision module to adjust the environment of the greenhouse where the 3rd decision module is located.

[0154] like Figure 6 The diagram illustrates a structural block diagram of an electronic device 60 provided by an embodiment of the present invention, comprising:

[0155] The processor 601, the memory 602, and the computer program 6021 stored in the memory 602 and capable of running on the processor 601, when the computer program 6021 is executed by the processor 601, implement the various processes of the above-described greenhouse control method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0156] like Figure 5 This diagram illustrates a structural block diagram of a non-volatile readable storage medium 70 provided in an embodiment of the present invention. A computer program 701 is stored on the computer program 700. When executed by a processor, the computer program 701 implements the various processes of the above-described greenhouse control method embodiments and achieves the same technical effects. To avoid repetition, further details are omitted here. The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0157] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0158] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0163] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0164] The above provides a detailed description of a greenhouse control method, apparatus, system, equipment, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A greenhouse regulation method, characterized in that, The method includes: Collect current environmental data in the greenhouse, including at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value; the greenhouse includes a water pump, a temperature control module, supplemental lighting, a shade net, an air pump, and a soil nutrient supply module; A plant growth model is obtained; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse. Based on the plant growth model, determine whether the current environmental data meets the requirements for plant growth; If the current environmental data does not meet the plant growth requirements, then the target environmental data in the greenhouse is determined according to the plant growth model, and the current environmental data is adjusted according to the target environmental data. The step of determining whether the current environmental data meets the requirements for plant growth based on the plant growth model includes: Acquire multiple sets of environmental data; The multiple sets of environmental data are input into the plant growth model to obtain multiple plant growth data corresponding to the multiple sets of environmental data. Based on the aforementioned plant growth data, reference environmental data were determined; A second comparison result is obtained by comparing the current environmental data with the reference environmental data; Based on the second comparison result, it is determined whether the current environmental data meets the requirements for plant growth; The step of determining the target environmental data in the greenhouse based on the plant growth model includes: using the reference environmental data as the target environmental data.

2. The method according to claim 1, characterized in that, The plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the method further includes: For each plant, the neural network model is trained by using multiple sets of different environmental data as input and the chlorophyll content of each plant as output, to obtain a plant growth model for each plant.

3. The method according to claim 1, characterized in that, The step of determining whether the current environmental data meets the requirements for plant growth based on the plant growth model includes: The current environmental data is input into the plant growth model to obtain the target plant growth data; Obtain historical plant growth data; Based on the historical plant growth data, reference plant growth data are determined; By comparing the growth data of the target plant with the growth data of the reference plant, a first comparison result is obtained; Based on the first comparison result, determine whether the current environmental data meets the requirements for plant growth.

4. The method according to claim 1, characterized in that, The environmental data requiring adjustment in the target environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH. Adjusting the current environmental data based on the target environmental data includes: Based on the target environmental data, at least one of the following is controlled: water pump, temperature control module, supplemental lighting, shade net, air pump, and soil nutrient supply module, to adjust the current environmental data so that the current environmental data reaches the target environmental data.

5. A greenhouse control device, characterized in that, The device includes: The data acquisition module is used to collect current environmental data in the greenhouse, including at least one of carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH value; the greenhouse includes a water pump, a temperature control module, supplemental lighting, a shade net, an air pump, and a soil nutrient supply module. The acquisition module is used to acquire a plant growth model; the plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data from the greenhouse. The judgment module is used to determine whether the current environmental data meets the requirements for plant growth based on the plant growth model. An adjustment module is used to determine target environmental data in the greenhouse based on the plant growth model if the current environmental data does not meet the plant growth requirements, and to adjust the current environmental data based on the target environmental data. The judgment module includes: The second acquisition submodule is used to acquire multiple sets of environmental data; The second input submodule is used to input multiple sets of environmental data into the plant growth model to obtain multiple plant growth data corresponding to the multiple sets of environmental data. The second determination submodule is used to determine reference environmental data based on multiple plant growth data. The second comparison submodule is used to compare the current environment data with the reference environment data to obtain a second comparison result. The second judgment submodule is used to determine whether the current environmental data meets the requirements for plant growth based on the second comparison result; The step of determining the target environmental data in the greenhouse based on the plant growth model includes: using the reference environmental data as the target environmental data.

6. A greenhouse control system, characterized in that, The system includes a data acquisition module, a hardware control module, a hardware module, a decision-making module, a power supply module, and a wireless radio frequency module. The power module is connected to the hardware control module, and the power module is used to supply power to the hardware control module; The decision module is connected to the hardware control module through the wireless radio frequency module. The decision module is used to send an environmental data acquisition signal to the hardware control module when the power module supplies power to the hardware control module. The wireless radio frequency module is used to establish a wireless connection between the decision module and the hardware control module. The hardware control module is connected to the acquisition module, and the hardware control module is used to control the acquisition module to acquire current environmental data and transmit it to the decision module according to the environmental data acquisition signal. The decision module is used to acquire a plant growth model and determine whether the current environmental data meets the plant growth requirements. If the current environmental data does not meet the plant growth requirements, the module determines the target environmental data in the greenhouse based on the plant growth model and sends an adjustment signal to the hardware control module. The adjustment signal includes the target environmental data. The plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data in the greenhouse. Determining whether the current environmental data meets the plant growth requirements includes: acquiring multiple sets of environmental data, inputting the multiple sets of environmental data into the plant growth model to obtain multiple plant growth data corresponding to the multiple sets of environmental data, determining reference environmental data based on the multiple plant growth data, comparing the current environmental data and the reference environmental data to obtain a second comparison result, and determining whether the current environmental data meets the plant growth requirements based on the second comparison result. Determining the target environmental data in the greenhouse based on the plant growth model includes: using the reference environmental data as the target environmental data. The hardware control module is used to control the hardware module to adjust the current environmental data according to the adjustment signal, so that the current environmental data reaches the target environmental data.

7. The system according to claim 6, characterized in that, The environmental data includes at least one of the following: carbon dioxide concentration, temperature, humidity, light index, nutrient solution temperature, soil nutrients, and soil pH. The data acquisition module includes a carbon dioxide sensor, a temperature and humidity sensor, a light sensor, a nutrient solution temperature sensor, and a soil sensor. The carbon dioxide sensor is used to collect the carbon dioxide concentration in the environment, the temperature and humidity sensor is used to collect the temperature and humidity in the environment, the light sensor is used to collect the light coefficient in the environment, the nutrient solution temperature sensor is used to collect the temperature of the nutrient solution in the greenhouse, and the soil sensor is used to collect the soil nutrients and soil pH value in the greenhouse.

8. The system according to claim 6, characterized in that, The hardware module includes at least one of the following: heating module, cooling module, water replenishment module, supplemental lighting module, shading module, gas replenishment module, and soil nutrient replenishment module; The heating module is used to raise the temperature of the greenhouse, the cooling module is used to lower the temperature of the greenhouse, the water replenishment module is used to increase the humidity of the greenhouse, the supplemental lighting module is used to enhance the light index of the greenhouse, the shading module is used to reduce the light index of the greenhouse, the gas replenishment module is used to replenish the carbon dioxide concentration of the greenhouse, and the soil nutrient replenishment module is used to replenish the soil nutrients and adjust the soil pH value.

9. The system according to claim 6, characterized in that, The plant growth data includes chlorophyll content of various plants collected under different greenhouse environmental conditions; the plant growth model is determined by the following methods: For each plant, the neural network model is trained by using multiple sets of different environmental data as input and the chlorophyll content of each plant as output, to obtain a plant growth model for each plant.

10. The system according to claim 6, characterized in that, The system also includes a control platform, and the acquisition module is further used to collect plant growth data. The control platform is connected to at least one decision module. The control platform is used to send a second acquisition signal to the at least one decision module. The second acquisition signal is used to acquire environmental data and plant growth data of the greenhouse where the at least one decision module is located, and to update the plant growth model of the greenhouse where the at least one decision module is located based on the environmental data and plant growth data sent by the at least one decision module.

11. The system according to claim 10, characterized in that, The control platform is further configured to, if the environmental data sent by the target decision module in the at least one decision module does not meet the plant growth requirements, determine the target environmental data in the greenhouse where the target decision module is located based on the platform's plant growth model, and send the target environmental data to the target decision module, so that the target decision module adjusts the environmental data in the greenhouse where the target decision module is located to achieve the target environmental data; the platform's plant growth model is pre-trained based on multiple sets of different environmental data and plant growth data in the greenhouse where the at least one decision module is located.

12. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executed, implements the steps of the greenhouse control method as described in any one of claims 1-4.

13. A non-volatile readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the greenhouse control method as described in any one of claims 1-4.

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