CN113283165A - Greenhouse data synchronous simulation system and method based on cloud computing - Google Patents
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Abstract
The invention relates to a greenhouse data synchronous simulation system and method based on cloud computing, which comprises the following steps: the system comprises an outdoor weather station, an indoor environment data sensor, a cloud computing service platform, a synchronous simulation module and a parameter calibration module, wherein the outdoor weather station is used for collecting environment weather data; the indoor environment data sensor is used for acquiring environment data in the greenhouse; the cloud computing service platform generates data which cannot be directly measured through a sensor according to environmental meteorological data and indoor environmental data, and inputs all the data into a model to simulate the greenhouse environment and the crop state; the synchronous simulation module is used for comparing the simulation result with the actual measurement result, calculating the error between the simulation result and the actual measurement result and generating a parameter adjusting command; and the parameter calibration module is used for calibrating the parameters of the model according to the adjustment command. The greenhouse simulation system is relatively accurate in greenhouse simulation system acquisition by measuring a small amount of parameters, simple to operate, high in accuracy and low in cost, and meanwhile provides more data support for greenhouse state analysis.
Description
Technical Field
The invention relates to a greenhouse data synchronous simulation system and method based on cloud computing, belongs to the technical field of agricultural informatization, and particularly relates to the technical field of greenhouse data simulation.
Background
The intelligent greenhouse has a comprehensive environment control system, can directly adjust a plurality of factors such as indoor temperature, light, water, fertilizer, gas and the like, can realize annual high yield and stable fine vegetables and flowers, and has good economic benefit. According to information collected by sensors such as temperature and humidity, soil moisture and soil temperature in the greenhouse, the information of the sensors is transmitted to the converter by the RS485 bus and is received by an upper computer to be displayed, alarmed and inquired. The monitoring center displays and stores the received sampling data in a table form, then compares the sampling data with a set alarm value, and if the measured value exceeds the set range, the monitoring center displays an alarm or gives an alarm through a screen.
However, the intelligent greenhouse needs to detect multiple factors such as indoor and outdoor temperature, light, moisture and the like, and a plurality of sensors are needed, so that the intelligent greenhouse is high in cost, the application range of the intelligent greenhouse is limited, parameters such as the water vapor condensation amount of the glass surface and the temperature of the glass are included, and even if the corresponding sensors are arranged, specific and accurate numerical values are difficult to obtain. Therefore, if a greenhouse system needs to be accurately simulated, a plurality of sensors are needed, the test cost is very high, and the realization difficulty is high; if the using amount of the sensors is reduced, the simulated greenhouse system is greatly different from an actual system, the actual system cannot be well guided, and planting strategies and reason tracing cannot be carried out on a plurality of optimization schemes.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a greenhouse data synchronous simulation system and method based on cloud computing, which obtain a relatively accurate greenhouse simulation system by measuring a small number of parameters, and have the advantages of simple operation, high accuracy and low cost, and provide more data support for analyzing greenhouse status.
In order to achieve the purpose, the invention adopts the following technical scheme: a greenhouse data synchronous simulation system based on cloud computing comprises: the system comprises an outdoor weather station, an indoor environment data sensor, a cloud computing service platform, a synchronous simulation module and a parameter calibration module, wherein the outdoor weather station is used for collecting environment weather data; the indoor environment data sensor is used for acquiring environment data in the greenhouse; the cloud computing service platform generates data which cannot be directly measured through the sensor according to the environmental meteorological data and the indoor environmental data, and inputs all the data into the model to simulate the greenhouse environment and the crop state; the synchronous simulation module is used for comparing a simulation result and an actual measurement result of the cloud computing service platform, calculating an error between the simulation result and the actual measurement result, and generating a parameter adjusting command according to the error; and the parameter calibration module is used for calibrating the parameters of the model according to the adjustment command.
Further, the data acquisition of the outdoor weather station comprises: air temperature, relative humidity, total solar radiation intensity, wind direction, wind speed and CO2And (4) concentration.
Further, the data of the indoor environment data sensor test comprises: the air temperature and relative humidity in the room.
Further, data that cannot be measured directly by sensors: water vapor condensation amount of glass surface, glass temperature, surface temperature, crop canopy temperature, canopy transpiration amount and indoor CO2Concentration, leaf area index, dry and fresh weight of crop roots, stems, leaves, fruits and yield.
Further, data that cannot be directly measured by the sensor is calculated by introducing indoor environmental data measured by the sensor into the environmental model, the crop growth model, and the mechanism model.
Furthermore, the earth surface temperature adopts the law of heat transfer science for calculating the temperature of each part in the greenhouse, convection heat exchange is carried out between the greenhouse ground and the air by calculating the downward heat conduction of the greenhouse ground, outward long-wave radiation heat exchange is carried out on the greenhouse ground, the latent heat loss of evaporation of earth surface moisture is solved, the variation of the greenhouse ground temperature is solved, and then the greenhouse earth surface temperature obtained by a differential equation is solved.
Further, in the crop model, crop yield, canopy temperature and leaf area index are obtained by simulating the indoor crop growth state, and CO is calculated by combining the canopy temperature and the photosynthetic effective radiation quantity above the photosphere2Concentration, bindingAnd (3) calculating a water limiting factor, calculating the photosynthesis rate, solving the material flow from the photosynthetic product to roots, stems, leaves and fruits through a source-base relation, and solving the dry and fresh weight of the roots, stems, leaves and fruits of the crops through plant dry matter balance.
Furthermore, parameters in the mechanism model dynamically change along with the greenhouse structure and the crop state and time, sensitivity and relative sensitivity instantaneous values of the model parameters are synchronously calculated through continuous iterative calculation, when the error between a simulation value and an actual measurement value is larger than a threshold value, automatic correction of the maximum sensitivity parameter is carried out, and the relative root mean square error is reduced by combining the actual measurement value.
Further, the threshold is greater than 10% relative root mean square error.
The invention also discloses a greenhouse data synchronous simulation method based on cloud computing, which comprises the following steps: s1, acquiring environmental meteorological data and greenhouse internal environmental data; s2, generating data which can not be directly measured by a sensor according to environmental meteorological data and indoor environmental data, and inputting all the data into a model to simulate the greenhouse environment and the crop state; s3, comparing the simulation result and the actual measurement result in the step S2, calculating the error between the simulation result and the actual measurement result, and generating a parameter adjusting command according to the error; s4 calibrates the parameters of the model according to the adjustment command.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. the invention can realize the synchronous simulation with the greenhouse and quantize the environmental states of the greenhouse and crops. 2. The invention can simulate the parameters which can be measured by common sensors and also comprises the parameters which can not be measured or are difficult to measure by the sensors. 3. The invention can provide a theoretical platform for digital agriculture, realize greenhouse environment control, cultivation management, scheme optimization and evaluation, and trace the reason.
Drawings
Fig. 1 is a schematic diagram of a greenhouse data synchronization simulation system based on cloud computing according to an embodiment of the present invention.
Detailed Description
The present invention is described in detail by way of specific embodiments in order to better understand the technical direction of the present invention for those skilled in the art. It should be understood, however, that the detailed description is provided for a better understanding of the invention only and that they should not be taken as limiting the invention. In describing the present invention, it is to be understood that the terminology used is for the purpose of description only and is not intended to be indicative or implied of relative importance.
The invention discloses a greenhouse data synchronous simulation system and method based on cloud computing, which generate data which cannot be directly measured through sensors through environmental meteorological data and indoor environmental data, input all the data into a crop growth model for training, can automatically predict the optimal environmental data for crop growth, do not need to arrange a plurality of sensors, can obtain the data which cannot be calculated by the sensors, have low cost and high prediction precision, store a plurality of intelligent greenhouse prediction models through a cloud computing service platform, realize remote access and data intercommunication and reduce the investment cost of a local server.
Example one
The embodiment discloses a greenhouse data synchronization simulation system based on cloud computing, as shown in fig. 1, including: the system comprises an outdoor weather station, an indoor environment data sensor, a cloud computing service platform, a synchronous simulation module and a parameter calibration module;
outdoor weather station for gather environment meteorological data, outdoor weather station data collection includes: air temperature, relative humidity, total solar radiation intensity, wind direction, wind speed and CO2And (4) concentration.
Indoor environmental data sensor for gather greenhouse internal environment data, the data of indoor environmental data sensor test include: the air temperature and relative humidity in the room.
And the cloud computing service platform generates data which cannot be directly measured through the sensor according to the environmental meteorological data and the indoor environmental data, and inputs all the data into the model to simulate the greenhouse environment and the crop state. Data that cannot be measured directly by the sensors include, but is not limited to: water vapor condensation amount of glass surface, glass temperature, surface temperature, crop canopy temperature, canopy transpiration amount and indoor CO2Concentration, leaf area index, dry and fresh weight of crop root, stem, leaf and fruitAnd yield, etc. Such data can be calculated by the following formula:
wherein S isgreenhouseAs greenhouse state variables, e.g. air temperature, air relative humidity, glass temperature, ground temperature and CO in the air2Concentration, etc., wherein the air temperature is divided into two states of upper part and lower part of the inner heat preservation curtain cloth; scropIs a plant state variable, including canopy temperature, dry matter content of roots, stems, leaves and fruits, leaf area index and the like; d is a greenhouse design parameter, including a greenhouse key node coordinate CgreenhouseShape type G of greenhouse covering surfacecoverGeographical coordinates of greenhouse LgreenhouseAnd greenhouse parts material and physical property Pgreenhouse(ii) a u is the control state of the environment control system, and comprises an inner heat-preservation curtain cloth, a light supplement lamp, a heating pipeline, natural ventilation, high-pressure spraying and CO2Control states of supplementary systems, etc., d is outdoor meteorological data including air temperature, relative humidity, solar radiation intensity, wind direction, wind speed and CO2Concentration; p is a constant in the model. The greenhouse state and the plant state can be calculated according to the secondary dynamic mechanism model through outdoor meteorological data.
For example, the water vapor condensation amount MV (kg m) of the glass surface-2s-1) Can be calculated by the following formula:
MV＝6.4·10-9C(VP1-VP2)
wherein C is the convection heat transfer coefficient between the inner surface of the glass and the greenhouse air, VP1For the actual water vapor pressure of air, VP2Is the saturated water vapor pressure of air at the current temperature of the glass, as VP1＞VP2In time, condensation of water vapor can form on the glass surface. The crop canopy transpiration amount is calculated by adopting a similar formula:
MVtranspiration＝CCanopy(VPCanopy-VP1)
CCanopy＝(2ρcLAI)/(ΔHγ(rb+rs))
VPCanopyIs the saturated water vapor pressure of air under the condition of canopy temperature, rho is the air density, c is the air specific heat capacity, LAI is the leaf area index, Delta H is the latent heat exchange coefficient, Gamma is a constant, rbIs the surface resistance coefficient of canopy transpiration, rsThe resistance coefficient of the leaf surface stomata is shown.
The glass temperature, the surface temperature, the crop canopy temperature and the like are obtained by calculating the heat transfer in the greenhouse through heat transfer science. Indoor CO2Concentrations can be calculated by calculating ventilation, photosynthesis consumption and respiration production. The dry weight of the dry substance can be calculated according to the distribution rule of the dry substances of the roots, the stems, the leaves and the fruits of the crops, the fresh weight of the dry substance can be calculated according to the water content parameter, the leaf area can be calculated by utilizing the parameters of the fresh weight of the leaves and the thickness of the leaves, and further the leaf area index can be calculated.
Photosynthesis calculation formula:
P＝J·(CO2-Γ)/(4·(CO2+2Γ))
j is the electron transfer rate, CO2Gamma is CO, the intercellular carbon dioxide concentration2A compensation point.
The formula for the calculation of respiration is Q10I.e. the temperature on the respiration influence coefficient.
The cloud computing service platform stores various intelligent greenhouse prediction models, remote access and data intercommunication can be achieved, and investment cost of a local server is reduced.
And the synchronous simulation module is used for comparing the simulation result of the cloud computing service platform with the actual measurement result, calculating the error of the simulation result and the actual measurement result, generating a parameter adjustment command according to the error, realizing the simulation of the environment and the crop state in the greenhouse under the condition that the historical data of the outdoor environment is synchronously updated and the control state of the greenhouse is consistent, and synchronizing the simulation value with the actual situation in the actual greenhouse.
And the parameter calibration module is used for calibrating the parameters of the model according to the adjustment command. In the iterative calculation process, the model parameters are gradually updated, so that the calculation result is closer to the actual environment measured value. The method can be used for guiding greenhouse environment decision making, yield prediction, greenhouse and crop potential evaluation, theoretical basis of facility cultivation system optimization schemes, production error reason tracing and the like.
The data that cannot be directly measured by the sensor in this embodiment is calculated by introducing the indoor environmental data measured by the sensor into the environmental model, the crop growth model, and the mechanism model.
In the environment model, the earth surface temperature adopts the law of heat transfer science for calculating the temperature of each part in the greenhouse, the greenhouse earth surface temperature is obtained by calculating the downward heat conduction of the greenhouse earth surface, the convection heat exchange between the greenhouse earth surface and the air, the outward long-wave radiation heat exchange of the greenhouse earth surface, the evaporation latent heat loss of the earth surface moisture, solving the variation of the greenhouse earth surface temperature and further solving the differential equation.
In the crop model, crop yield, canopy temperature and leaf area index are obtained by simulating the growth state of indoor crops, and CO is calculated by combining the canopy temperature and the photosynthetic effective radiation quantity above the photosphere2And (3) calculating the photosynthesis rate by combining the concentration with a water limiting factor, solving the material flow from the photosynthetic product to roots, stems, leaves and fruits through a source-base relation, and solving the dry and fresh weight of the roots, stems, leaves and fruits of the crops through the balance of plant dry matters.
The parameters in the mechanism model dynamically change along with the greenhouse structure, the crop state and the time, the sensitivity and the relative sensitivity instantaneous value of the model parameters are synchronously calculated through continuous iterative calculation, when the error between the simulation value and the measured value is larger than a threshold value, the automatic correction of the maximum sensitivity parameter is carried out, and the relative root mean square error is reduced by combining the measured value. In this embodiment, the threshold is greater than 10% relative rms error.
Example two
The embodiment discloses a greenhouse data synchronous simulation method based on cloud computing, which comprises the following steps:
s1, acquiring environmental meteorological data and greenhouse internal environmental data;
s2, generating data which can not be directly measured by a sensor according to environmental meteorological data and indoor environmental data, and inputting all the data into a model to simulate the greenhouse environment and the crop state;
s3, comparing the simulation result and the actual measurement result in the step S2, calculating the error between the simulation result and the actual measurement result, and generating a parameter adjusting command according to the error;
s4 calibrates the parameters of the model according to the adjustment command.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims. The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application should be defined by the claims.
Claims (10)
1. A greenhouse data synchronous simulation system based on cloud computing is characterized by comprising: an outdoor weather station, an indoor environment data sensor, a cloud computing service platform, a synchronous simulation module and a parameter calibration module,
the outdoor weather station is used for acquiring environmental weather data;
the indoor environment data sensor is used for acquiring environment data in the greenhouse;
the cloud computing service platform generates data which cannot be directly measured through a sensor according to the environmental meteorological data and the indoor environmental data, and inputs all the data into a model to simulate the greenhouse environment and the crop state;
the synchronous simulation module is used for comparing a simulation result and an actual measurement result of the cloud computing service platform, calculating an error between the simulation result and the actual measurement result, and generating a parameter adjusting command according to the error;
and the parameter calibration module calibrates the parameters of the model according to the adjustment command.
2. The cloud-computing-based greenhouse data synchronization simulation system of claim 1, wherein the outdoor weather station acquiring data comprises: air temperature, relative humidity, total solar radiation intensity, wind direction, wind speed and CO2And (4) concentration.
3. The cloud-computing-based greenhouse data synchronization simulation system of claim 1, wherein the data for the indoor environmental data sensor test comprises: the air temperature and relative humidity in the room.
4. The cloud-computing-based greenhouse data synchronization simulation system of claim 1, wherein data that cannot be directly measured by sensors: water vapor condensation amount of glass surface, glass temperature, surface temperature, crop canopy temperature, canopy transpiration amount and indoor CO2Concentration, leaf area index, dry and fresh weight of crop roots, stems, leaves, fruits and yield.
5. The cloud-computing-based greenhouse data synchronization simulation system of claim 4, wherein the data that cannot be directly measured by the sensors is calculated by introducing indoor environmental data measured by the sensors into an environmental model, a crop growth model, and a mechanism model.
6. The cloud-computing-based greenhouse data synchronous simulation system as claimed in claim 5, wherein the surface temperature is calculated by using the law of heat transfer theory through temperature calculation of each part in the greenhouse, and the greenhouse surface temperature is obtained by calculating heat conduction of the greenhouse ground facing downwards, convection heat exchange between the greenhouse ground and the air, outward long-wave radiant heat exchange of the greenhouse ground, loss of surface moisture evaporation latent heat, solving greenhouse ground temperature variation and further solving a differential equation.
7. The cloud-computing-based greenhouse data synchronized simulation system of claim 5, wherein in the crop model, crop yield, canopy temperature, and leaf area index are obtained by simulating indoor crop growth conditions, and CO is calculated from canopy temperature in combination with photosynthetically active radiation dose over a photosphere2And (3) calculating the photosynthesis rate by combining the concentration with a water limiting factor, solving the material flow from the photosynthetic product to roots, stems, leaves and fruits through a source-base relation, and solving the dry and fresh weight of the roots, stems, leaves and fruits of the crops through the balance of plant dry matters.
8. The cloud-computing-based greenhouse data synchronization simulation system according to claim 5, wherein the parameters in the mechanism model dynamically change with the greenhouse structure, the crop status and the time, the sensitivity and the relative sensitivity instantaneous values of the model parameters are synchronously calculated through continuous iterative computation, when the error between the simulation value and the measured value is larger than a threshold value, the automatic correction of the maximum sensitivity parameter is performed, and the relative root mean square error is reduced by combining the measured value.
9. The cloud-computing-based greenhouse data synchronization simulation system of claim 8, wherein the threshold is a relative root mean square error of greater than 10%.
10. A greenhouse data synchronous simulation method based on cloud computing is characterized by comprising the following steps:
s1, acquiring environmental meteorological data and greenhouse internal environmental data;
s2, generating data which can not be directly measured by a sensor according to the environmental meteorological data and the indoor environmental data, and inputting all the data into a model to simulate the greenhouse environment and the crop state;
s3, comparing the simulation result and the actual measurement result in the step S2, calculating the error between the simulation result and the actual measurement result, and generating a parameter adjusting command according to the error;
s4, calibrating the parameters of the model according to the adjusting command.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656617A (en) * | 2015-01-15 | 2015-05-27 | 青岛智能产业技术研究院 | System and method for regulating and controlling greenhouse environment based on Internet of Things and cloud computing technology |
| CN107036652A (en) * | 2017-04-12 | 2017-08-11 | 林波荣 | The indoor environment monitoring system and method for a kind of combination architectural environment simulation |
| CN110377082A (en) * | 2019-07-16 | 2019-10-25 | 北京水木九天科技有限公司 | A kind of automatic control system in greenhouse |
| CN112527037A (en) * | 2020-12-24 | 2021-03-19 | 江苏省农业科学院 | Greenhouse environment regulation and control method and system with environment factor prediction function |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656617A (en) * | 2015-01-15 | 2015-05-27 | 青岛智能产业技术研究院 | System and method for regulating and controlling greenhouse environment based on Internet of Things and cloud computing technology |
| CN107036652A (en) * | 2017-04-12 | 2017-08-11 | 林波荣 | The indoor environment monitoring system and method for a kind of combination architectural environment simulation |
| CN110377082A (en) * | 2019-07-16 | 2019-10-25 | 北京水木九天科技有限公司 | A kind of automatic control system in greenhouse |
| CN112527037A (en) * | 2020-12-24 | 2021-03-19 | 江苏省农业科学院 | Greenhouse environment regulation and control method and system with environment factor prediction function |
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Application publication date: 20210820 |
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