Remote monitoring method based on Internet cloud

Abstract

The invention discloses a remote monitoring method based on an internet cloud, and relates to the technical field of agricultural informatization remote monitoring, and the method comprises the following steps: collecting an air temperature value, an air humidity value, an illumination intensity value and a soil humidity value in a greenhouse in real time through a data collection module; and the data are converted into digital signals which are transmitted to a cloud server through the Internet. According to the method, the environment parameters in the greenhouse can be collected in real time by adopting a high-precision intelligent sensor based on the Internet cloud technology, real-time transmission and remote monitoring of data can be realized, the influence of multiple factors such as temperature, humidity, illumination intensity, soil humidity and the like on crop growth can be comprehensively considered, and the crop growth quality is improved. According to the method, through energy consumption prediction and optimization, a manager can conveniently adjust an equipment operation strategy to reduce the energy consumption cost, and the purposes of energy conservation and consumption reduction are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural informatization remote monitoring, and particularly to a remote monitoring method based on the Internet cloud. Background Art

[0002] Remote monitoring based on the Internet cloud is an advanced monitoring technology. It utilizes cloud computing, Internet of Things, and big data technologies to collect the data of monitored objects in real time and transmit it to the cloud server. The collected data is processed, analyzed, and stored on the cloud server. At this time, users can freely access the cloud server through the Internet to achieve remote real-time monitoring, management, and control of the monitored objects. Among them, the monitoring of crops in the greenhouse helps to ensure the healthy growth of crops, thereby improving the yield and quality of crops.

[0003] However, currently existing greenhouse monitoring methods may mostly rely on manual inspections. Data collection is easily affected by human factors and is vulnerable to external interference, resulting in inaccurate data and a slow processing speed, which cannot meet the requirements of real-time monitoring. Moreover, the existing monitoring systems may focus on relatively single environmental parameters, such as temperature and humidity. Therefore, they lack the ability to comprehensively evaluate environmental parameters, such as light intensity and soil temperature and humidity, resulting in insufficient understanding of the greenhouse environment by managers and unable to make accurate decisions. Also, when predicting irrigation requirements and energy consumption, there is a lack of accurate and effective prediction models, leading to inefficient use of resources. Summary of the Invention

[0004] The purpose of the present invention is to provide a remote monitoring method based on the Internet cloud, which solves the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A remote monitoring method based on the Internet cloud, including the following monitoring methods:

[0006] Step S1: Real-time collect the air temperature value, air humidity value, light intensity value, and soil humidity value in the greenhouse through the data collection module, and convert the data into digital signals and transmit them to the cloud server through the Internet;

[0007] Step S2: Input the air temperature value, air humidity value, light intensity value, and soil humidity value into the data processing module. The data processing module cleans the input data and integrates it in chronological order to form time series data and output the actually measured air temperature value, actually measured air humidity value, actually measured light intensity value, and actual soil humidity value;

[0008] Step S3: Input the actually measured air temperature value, actually measured air humidity value, actually measured light intensity value, and actual soil humidity value into the calculation and analysis module. The calculation and analysis module outputs the comprehensive environmental parameter evaluation index, indoor irrigation demand, and indoor total energy consumption value;

[0009] Step S4: Input the comprehensive environmental parameter evaluation index, indoor irrigation demand, and indoor total energy consumption value into the monitoring and decision-making module. The monitoring and decision-making module processes the data and presents it in the form of a chart on the cloud monitoring platform for the convenience of the administrator to remotely view, analyze, and make decisions.

[0010] Optionally, the calculation and analysis module includes: an environmental evaluation sub-module, an irrigation analysis sub-module, and an energy monitoring sub-module.

[0011] Optionally, the calculation formula of the environmental evaluation sub-module is as follows:

[0012]

[0013] Where:

[0014] ECOP refers to the comprehensive environmental parameter evaluation index;

[0015] DA refers to the weight coefficient of temperature, TA refers to the actually measured air temperature value, TP refers to the suitable temperature value, Tma refers to the upper limit value of temperature, Tmi refers to the lower limit value of temperature, DB refers to the weight coefficient of humidity, HA refers to the actually measured air humidity value, HP refers to the suitable humidity value, Hma refers to the upper limit value of humidity, Hmi refers to the lower limit value of humidity, DC refers to the weight coefficient of light intensity, LA refers to the actually measured light intensity value, LP refers to the suitable light intensity value, Lma refers to the upper limit value of light intensity, Lmi refers to the lower limit value of light intensity, (TA - TP) / (Tma - Tmi) refers to the relative deviation between the actually measured air temperature value TA and the suitable temperature value TP, and Tma and Tmi are respectively the upper and lower limits of temperature for determining the standardization range;

[0016] The processing process of the environmental evaluation sub-module is as follows: Input the actually measured air temperature value TA, actually measured air humidity value HA, and actually measured light intensity value LA into the environmental evaluation sub-module, and output the comprehensive environmental parameter evaluation index ECOP based on the suitable temperature value TP, suitable humidity value HP, and suitable light intensity value LP.

[0017] Optionally, the calculation formula of the irrigation analysis sub-module is as follows:

[0018] IDDU = SA×(Iso - Isp) + SB×(TA - Tsp) + SC×HA + SD×(ECOP -

[0019] ECOPa);

[0020] Wherein:

[0021] IDDU refers to the indoor irrigation demand;

[0022] SA refers to weight factor one, ISO refers to the actual soil moisture value, Isp refers to the suitable soil moisture value, SB refers to weight factor two, Tsp refers to the reference temperature value, SC refers to weight factor three, SD refers to the influence coefficient of ECOP on irrigation demand, ECOPa refers to the ECOP value under the reference conditions, (Iso - Isp) refers to the deviation between the actual soil moisture value ISO and the suitable soil moisture value Isp, and (ECOP - ECOPa) refers to the influence of the comprehensive environmental parameter status on irrigation demand;

[0023] The processing process of the irrigation analysis sub-module is as follows: input the comprehensive environmental parameter evaluation index ECOP, the actual soil moisture value ISO, the actually measured air temperature value TA, and the actually measured air humidity value HA into the irrigation analysis sub-module, and output the indoor irrigation demand IDDU based on the suitable soil moisture value Isp.

[0024] Optionally, the calculation formula of the energy monitoring sub-module is as follows:

[0025]

[0026] Wherein:

[0027] ETLUE refers to the indoor total energy consumption value;

[0028] Eqa refers to the unit time energy consumption value of the ventilation system, Tqa refers to the operating time of the ventilation system, Ewa refers to the unit time energy consumption value of the heating system, Twa refers to the operating time of the heating system, Eza refers to the unit time energy consumption value of the lighting system, Tza refers to the operating time of the lighting system, α refers to the adjustment coefficient of the influence of ECOP on energy consumption, ECSOP refers to the ideal ECOP value, and (1 - α×|ECOP - ECSOP|) refers to the deviation between ECOP and the ideal ECSOP to adjust the total energy consumption;

[0029] The processing process of the energy monitoring sub-module is as follows: input the comprehensive environmental parameter evaluation index ECOP and the indoor irrigation demand IDDU into the energy monitoring sub-module, and output the indoor total energy consumption value ETLUE based on the unit time energy consumption value Eqa of the ventilation system and the unit time energy consumption value Ewa of the heating system.

[0030] Optionally, the data acquisition module uses an air temperature sensor, an air humidity sensor, a light intensity sensor, a soil temperature and humidity sensor, and a carbon dioxide concentration sensor.

[0031] Optionally, the data processing module includes data cleaning and data integration;

[0032] Specifically, the data cleaning is as follows: when receiving the input of data, clean the data to remove outliers and missing values in the data. The outliers are caused by sensor failures and data transmission errors and need to be removed;

[0033] Specifically, the data integration is as follows: integrate the cleaned data in chronological order to form time series data.

[0034] Optionally, the monitoring and decision-making module is specifically as follows: the manager can remotely monitor the greenhouse environment parameters, irrigation requirements and energy consumption data in real time, and based on the comprehensive evaluation index of environmental parameters, indoor irrigation demand and indoor total energy consumption value, the administrator correspondingly adjusts the greenhouse environment parameters, formulates irrigation plans, optimizes equipment operation and energy consumption management.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] First, the present invention outputs a comprehensive evaluation index of environmental parameters through the environmental evaluation sub-module. The comprehensive evaluation index of environmental parameters forms a comprehensive evaluation index by integrating multiple key environmental parameters such as temperature, humidity and light intensity. The comprehensive evaluation index of environmental parameters is updated and remotely monitored in real time through the Internet cloud, enabling the manager to immediately obtain the overall situation of the greenhouse environment. The comprehensive evaluation index of environmental parameters is remotely monitored through the Internet cloud to realize the real-time update and comprehensive evaluation of the greenhouse environment parameters, which not only improves the timeliness and accuracy of monitoring but also provides more timely and accurate decision-making support for the manager.

[0037] Second, the present invention outputs the indoor irrigation demand through the irrigation analysis sub-module. The calculation of the indoor irrigation demand is based on real-time environmental parameters, such as soil humidity, temperature, humidity and the comprehensive evaluation index of environmental parameters, to accurately predict the irrigation demand. Through the Internet cloud, the manager remotely obtains the prediction result of the irrigation demand and formulates an irrigation plan according to the prediction, reducing crop growth problems caused by improper irrigation, improving the utilization efficiency of water resources and reducing the irrigation cost. The indoor irrigation demand not only considers direct factors such as soil humidity, but also combines temperature, humidity and the comprehensive evaluation index of environmental parameters for irrigation demand prediction, making the prediction result more accurate and reducing the blindness and waste of irrigation. The indoor irrigation demand is remotely monitored through the Internet cloud to realize the accurate prediction and remote management of the irrigation demand.

[0038] III. The present invention outputs the total indoor energy consumption value through the energy monitoring sub-module. The calculation of the total energy consumption value can comprehensively consider various energy consumption factors in the greenhouse, and adjusts the energy consumption prediction based on the comprehensive evaluation index of environmental parameters, taking into account direct energy consumption factors, and also combines the indirect impact of the comprehensive evaluation index of environmental parameters on energy consumption, making the prediction result more comprehensive. Through the total indoor energy consumption value, the manager can remotely monitor the energy consumption data, promptly discover energy consumption anomalies and take corresponding optimization measures, which not only reduces energy consumption costs and improves energy utilization efficiency, but also realizes the green and sustainable development of the greenhouse environment. By remotely monitoring the total energy consumption value through the Internet cloud, the comprehensive monitoring and optimization of greenhouse energy consumption are realized, which not only helps to reduce energy consumption costs and improve energy utilization efficiency, but also helps to achieve the green and sustainable development of the greenhouse environment.

[0039] IV. The present invention iteratively circulates the actually measured air humidity value HA in the environmental assessment sub-module based on the calculated indoor irrigation demand of the irrigation analysis sub-module. The purpose of the iterative humidity is to more accurately simulate and predict the environmental changes in the greenhouse. By taking dynamic factors such as irrigation demand into account through iteration, the humidity condition in the greenhouse can be more real-time reflected, which helps to better understand the environmental condition in the greenhouse and take corresponding control measures to optimize the growth environment of crops. By iterating the humidity with the irrigation demand, the following prominent substantial features and remarkable effects can be achieved. This iterative form considers the impact of irrigation on humidity, enabling the system to more accurately simulate the humidity changes in the greenhouse, helping to more accurately understand the growth environment of crops and take corresponding management measures, and the humidity value can be updated in real-time during the iteration process, thus reflecting the real-time environmental changes in the greenhouse, helping to promptly discover and address environmental problems to ensure the healthy growth of crops. Through iteration, the impact of irrigation on humidity can be more accurately understood, thereby optimizing the irrigation strategy, which helps to reduce water resource waste and improve irrigation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the step flow chart of the remote monitoring method based on the Internet cloud;

[0041] Figure 2 is the overall structure schematic diagram of the remote monitoring method based on the Internet cloud;

[0042] Figure 3 is the structure schematic diagram of the calculation and analysis module of the remote monitoring method based on the Internet cloud. DETAILED DESCRIPTION OF THE INVENTION

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Regarding this Internet cloud-based remote monitoring method, it is different from the existing remote monitoring methods. The existing remote monitoring methods rely on manual inspections. Data collection is easily affected by human factors, and the processing speed is relatively slow, unable to meet the requirements of real-time monitoring. In addition, the existing remote monitoring methods often only focus on a single environmental parameter and lack the ability to comprehensively evaluate environmental parameters, resulting in an incomplete understanding of the greenhouse environment. And there may be a lack of an effective prediction system, thus unable to accurately predict the irrigation needs of crops, leading to over-irrigation or under-irrigation and affecting crop growth.

[0045] However, the module of this Internet cloud-based remote monitoring method can collect the environmental parameters in the greenhouse in real time through the cloud server, and based on data processing, it can achieve accurate collection and rapid processing of data, providing a reliable basis for subsequent analysis. Moreover, this method comprehensively considers the influence of multiple environmental parameters on crop growth, providing managers with a comprehensive understanding of the greenhouse environment, and can accurately predict future irrigation needs according to the specific situation of the crops and environmental factors, ensuring that the crops obtain an appropriate amount of water.

[0046] Embodiment, please refer to Figures 1 to 3 , this embodiment provides an Internet cloud-based remote monitoring method, including the following monitoring methods:

[0047] Step S1: The data acquisition module collects the air temperature value, air humidity value, light intensity value, and soil humidity value in the greenhouse in real time, and converts the data into digital signals and transmits them to the cloud server through the Internet;

[0048] Step S2: Input the air temperature value, air humidity value, light intensity value, and soil humidity value into the data processing module. The data processing module cleans the input data and integrates it in chronological order to form time series data and outputs the actually measured air temperature value, actually measured air humidity value, actually measured light intensity value, and actual soil humidity value;

[0049] Step S3: Input the actually measured air temperature value, actually measured air humidity value, actually measured light intensity value, and actual soil humidity value into the calculation and analysis module. The calculation and analysis module outputs the comprehensive evaluation index of environmental parameters, indoor irrigation demand, and indoor total energy consumption value;

[0050] Step S4: Input the comprehensive environmental parameter evaluation index, indoor irrigation demand, and indoor total energy consumption value into the monitoring and decision-making module. The monitoring and decision-making module processes the data and presents it in the form of a chart on the cloud monitoring platform for the convenience of the administrator to remotely view, analyze, and make decisions.

[0051] The calculation and analysis module includes: an environmental evaluation sub-module, an irrigation analysis sub-module, and an energy monitoring sub-module.

[0052] In this embodiment: Multiple sub-modules in the calculation and analysis module can integrate various environmental parameters from the intelligent greenhouse monitoring system, such as temperature, humidity, light intensity, irrigation demand data, and energy consumption data. Through the Internet cloud, these data can be collected, stored, and analyzed in real time to form a comprehensive and accurate understanding of the greenhouse environment. Based on the integrated data, ECOP can evaluate the overall condition of the greenhouse environment, providing decision-making support for environmental regulation to the manager. IDDU can predict irrigation demand to guide the formulation of irrigation plans to ensure that crops receive appropriate amounts of water. ETLUE can predict and optimize energy consumption to help the manager reduce operating costs and improve energy utilization efficiency. And through the Internet cloud, the manager can remotely monitor greenhouse environmental parameters, irrigation demand, and energy consumption data in real time. Therefore, once an abnormality or a situation that needs adjustment is found, the manager can respond quickly and make adjustments through the remote control system to ensure the stability of the greenhouse environment and the healthy growth of crops. The combined application of multiple groups of sub-modules makes greenhouse management more intelligent and automated. The manager can obtain various data and analysis results in real time through the Internet cloud and make decisions and adjustments without having to go to the greenhouse site in person, thus greatly improving management efficiency and reducing labor costs. Through accurate irrigation demand prediction and energy consumption prediction and optimization, the rational use of resources can be ensured, avoiding resource waste and cost increase caused by excessive or insufficient irrigation and high energy consumption, contributing to the sustainable development of agricultural production. And the stable greenhouse environment provides the best growth conditions for crops. Through precise environmental regulation and irrigation management, it can be ensured that crops receive sufficient nutrients and water, helping to improve the quality and yield of crops and meet the market demand for high-quality agricultural products.

[0053] Please refer to Figures 1 to 3 , and the processing process of the environmental evaluation sub-module is as follows:

[0054]

[0055] Among them:

[0056] ECOP refers to the comprehensive environmental parameter evaluation index;

[0057] DA refers to the weight coefficient of temperature, TA refers to the actually measured air temperature value, TP refers to the appropriate temperature value, Tma refers to the upper limit value of temperature, Tmi refers to the lower limit value of temperature, DB refers to the weight coefficient of humidity, HA refers to the actually measured air humidity value, HP refers to the appropriate humidity value, Hma refers to the upper limit value of humidity, Hmi refers to the lower limit value of humidity, DC refers to the weight coefficient of light intensity, LA refers to the actually measured light intensity value, LP refers to the appropriate light intensity value, Lma refers to the upper limit value of light intensity, Lmi refers to the lower limit value of light intensity, (TA−TP) / (Tma−Tmi) refers to the relative deviation between the actually measured air temperature value TA and the appropriate temperature value TP, and Tma and Tmi are respectively the upper and lower limits of temperature, which are used to determine the normalization range;

[0058] The processing process of the environmental assessment sub-module is as follows: Input the actually measured air temperature value TA, the actually measured air humidity value HA, and the actually measured light intensity value LA into the environmental assessment sub-module, and output the comprehensive environmental parameter assessment index ECOP based on the appropriate temperature value TP, the appropriate humidity value HP, and the appropriate light intensity value LP.

[0059] In this embodiment: ECOP forms a comprehensive assessment index by integrating multiple key environmental parameters such as temperature, humidity, and light intensity and combining weight allocation. This index is updated in real time and remotely monitored through the Internet cloud, enabling managers to instantly obtain the overall status of the greenhouse environment. It not only improves the real-time and accuracy of monitoring but also enables managers to quickly adjust the greenhouse environment to an optimal state to support crop growth according to the change of ECOP. As a comprehensive assessment index, ECOP can comprehensively reflect the overall status of the greenhouse environment. It not only considers multiple environmental parameters but also combines weight allocation to make the assessment results more accurate and reliable. Through the remote monitoring of ECOP, managers can understand the advantages and disadvantages of the greenhouse environment in real time and adjust environmental parameters in a timely manner according to the change of ECOP to support the best growth of crops, greatly improving the efficiency and accuracy of greenhouse management. Compared with the prior art, ECOP not only considers multiple environmental parameters but also combines weight allocation and a real-time update mechanism, enabling ECOP to more accurately reflect the overall status of the greenhouse environment and providing more timely and accurate decision-making support for managers. ECOP is remotely monitored through the Internet cloud to achieve real-time update and comprehensive assessment of greenhouse environmental parameters, not only improving the real-time and accuracy of monitoring but also providing more timely and accurate decision-making support for managers. The essential feature of ECOP is that it can comprehensively reflect the overall status of the greenhouse environment and provide optimization suggestions for managers.

[0060] Please refer to Figures 1 to 3 , and the processing process of the irrigation analysis sub-module is as follows:

[0061] IDDU = SA×(Iso - Isp) + SB×(TA - Tsp) + SC×HA + SD×(ECOP - ECOPa);

[0062] Where:

[0063] IDDU refers to the indoor irrigation demand;

[0064] SA refers to weight factor one, ISO refers to the actual soil moisture value, Isp refers to the suitable soil moisture value, SB refers to weight factor two, Tsp refers to the reference temperature value, SC refers to weight factor three, SD refers to the influence coefficient of ECOP on irrigation demand, ECOPa refers to the ECOP value under reference conditions, (Iso - Isp) refers to the deviation between the actual soil moisture value ISO and the suitable soil moisture value Isp, and (ECOP - ECOPa) refers to the influence of the comprehensive environmental parameter status on irrigation demand;

[0065] The processing process of the irrigation analysis sub - module is as follows: Input the comprehensive environmental parameter evaluation index ECOP, the actual soil moisture value ISO, the actually measured air temperature value TA, and the actually measured air humidity value HA into the irrigation analysis sub - module, and output the indoor irrigation demand IDDU based on the suitable soil moisture value Isp.

[0066] In this embodiment: The calculation of IDDU is based on real - time environmental parameters, such as soil moisture, temperature, humidity, and ECOP, to accurately predict irrigation demand. Through the Internet cloud, the manager can remotely obtain the irrigation demand prediction results and formulate irrigation plans according to these predictions. This not only reduces crop growth problems caused by improper irrigation but also improves the utilization efficiency of water resources and reduces irrigation costs. The calculation of IDDU can accurately predict irrigation demand based on real - time environmental parameters and ECOP. It not only considers direct factors such as soil moisture but also combines indirect factors such as temperature, humidity, and ECOP to make the prediction results more accurate and reliable. Through the remote monitoring of IDDU, the manager can remotely obtain the irrigation demand prediction results and formulate irrigation plans according to these predictions, which not only reduces the blindness and waste of irrigation but also improves the utilization efficiency of water resources. The calculation of IDDU not only considers direct factors such as soil moisture but also combines temperature, humidity, and ECOP for irrigation demand prediction, which makes the prediction results more accurate and reliable, reduces the blindness and waste of irrigation. IDDU is remotely monitored through the Internet cloud, realizing accurate prediction and remote management of irrigation demand, which not only reduces the blindness and waste of irrigation but also improves the utilization efficiency of water resources. The characteristics of IDDU can predict irrigation demand based on real - time environmental parameters and ECOP and provide irrigation plan suggestions for the manager.

[0067] Please refer to Figures 1 to 3 , the processing process of the energy monitoring sub - module is as follows:

[0068]

[0069] Wherein:

[0070] ETLUE refers to the total indoor energy consumption value;

[0071] Eqa refers to the energy consumption value per unit time of the ventilation system, Tqa refers to the operating time of the ventilation system, Ewa refers to the energy consumption value per unit time of the heating system, Twa refers to the operating time of the heating system, Eza refers to the energy consumption value per unit time of the lighting system, Tza refers to the operating time of the lighting system, α refers to the adjustment coefficient of the influence of ECOP on energy consumption, ECSOP refers to the ideal ECOP value, and (1 - α×|ECOP - ECSOP|) refers to the deviation between ECOP and the ideal ECSOP to adjust the total energy consumption;

[0072] The processing process of the energy monitoring sub-module is as follows: Input the environmental parameter comprehensive evaluation index ECOP and the indoor irrigation demand IDDU into the energy monitoring sub-module, and output the total indoor energy consumption value ETLUE based on the energy consumption value per unit time Eqa of the ventilation system and the energy consumption value per unit time Ewa of the heating system.

[0073] In this embodiment: The calculation of ETLUE comprehensively considers various energy consumption factors in the greenhouse, such as ventilation, heating, and lighting, etc., and adjusts the energy consumption prediction based on ECOP. Through the Internet cloud, the manager can remotely monitor the energy consumption data, timely discover energy consumption anomalies, and take corresponding optimization measures. This not only helps to reduce energy consumption costs and improve energy utilization efficiency, but also helps to achieve the green and sustainable development of the greenhouse environment. The calculation of ETLUE can comprehensively consider various energy consumption factors in the greenhouse and adjust the energy consumption prediction based on ECOP. It not only considers direct energy consumption factors, such as ventilation, heating, and lighting, etc., but also combines the indirect influence of ECOP on energy consumption, making the prediction results more comprehensive and accurate. Through the remote monitoring of ETLUE, the manager can remotely monitor the energy consumption data, timely discover energy consumption anomalies, and take corresponding optimization measures, which not only helps to reduce energy consumption costs and improve energy utilization efficiency, but also can achieve the green and sustainable development of the greenhouse environment. The calculation of ETLUE comprehensively considers various energy consumption factors in the greenhouse and adjusts the energy consumption prediction based on ECOP, which makes the prediction results more comprehensive and accurate, helps the manager to timely discover energy consumption anomalies, and take corresponding optimization measures. The ETLUE system conducts remote monitoring through the Internet cloud to achieve comprehensive monitoring and optimization of greenhouse energy consumption, which not only helps to reduce energy consumption costs and improve energy utilization efficiency, but also helps to achieve the green and sustainable development of the greenhouse environment. The essential feature of ETLUE is that it can comprehensively consider various energy consumption factors in the greenhouse and adjust the energy consumption prediction results to provide energy consumption optimization suggestions for the manager.

[0074] Furthermore, the calculated indoor irrigation demand IDDU of the irrigation analysis sub-module is used to perform cyclic iteration on the actually measured air humidity value HA in the environmental assessment sub-module. The iterative processing procedure is as follows:

[0075] Step 1: HA new = HA old + HSAP × γ × IDDU;

[0076] Step 2: Establish a loop termination condition;

[0077] Condition 1: |ECOP new - ECOP old | < 0.0005;

[0078] Condition 2: The number of iterations is 90 times;

[0079] Wherein:

[0080] HA new refers to the updated actually measured humidity value, HA old refers to the actually measured humidity value before update, HSAP refers to the influence coefficient of irrigation on humidity, γ refers to the adjustment factor, ECOP new refers to the updated comprehensive environmental parameter evaluation index, ECOP old refers to the comprehensive environmental parameter evaluation index before update.

[0081] In this embodiment: In greenhouse environmental monitoring, the humidity HA is usually an environmental parameter that changes in real time, rather than a fixed measured parameter. The humidity in the greenhouse is affected by various factors, including irrigation, ventilation, temperature, plant transpiration, etc. Therefore, when constructing a greenhouse environmental monitoring system, it is necessary to consider the influence of these factors on humidity and update the humidity value accordingly;

[0082] The purpose of iterating the humidity HA is to more accurately simulate and predict the environmental changes in the greenhouse. By iteration, dynamic factors such as the irrigation demand IDDU can be taken into account, so as to more real-timely reflect the humidity condition in the greenhouse. This helps to better understand the environmental condition in the greenhouse and take corresponding control measures to optimize the growth environment of crops. By iterating the humidity HA with the irrigation demand IDDU, we can achieve the following outstanding substantial features and remarkable effects;

[0083] It should be noted that when collecting data on HA, if only sensors are used for monitoring, some problems may occur. Since sensor data can be affected by various factors, such as the accuracy of the sensors themselves, environmental interference, such as dust, water droplets, etc., and the selection of sensor positions. These factors may cause certain errors in the sensor data. However, through this iterative calculation, the data from multiple sensors and known environmental parameters, such as temperature and light, can be combined to correct and fuse this data, thereby obtaining a more accurate humidity value. And sensors can only provide current humidity data but cannot predict future humidity changes. Through this iterative formula calculation, a system can be established to predict humidity changes in the future for a period of time. This helps us take measures in advance, such as adjusting the irrigation volume, ventilation volume, etc., to cope with possible environmental problems. And sensor data only reflects the current environmental state. Through iterative calculation, this method can combine the growth needs of crops and environmental parameters to formulate more reasonable management strategies for irrigation and ventilation, etc., which helps us optimize the growth environment of crops, improve yield and quality. Therefore, this iterative form is different from directly using sensors for data collection and has accuracy and rationality;

[0084] This iterative form takes into account the impact of irrigation on humidity, enabling the system to more accurately simulate humidity changes in the greenhouse, which helps us better understand the growth environment of crops and take corresponding management measures. And the iterative process can update the humidity value in real time to reflect the real-time environmental changes in the greenhouse, which helps us promptly discover and address environmental problems to ensure the healthy growth of crops. Through iteration, we can more accurately understand the impact of irrigation on humidity and thus optimize the irrigation strategy, which helps reduce water resource waste and improve irrigation efficiency. Based on the Internet cloud, the environmental parameters in the greenhouse, including humidity, temperature, light intensity, etc., can be monitored in real time. When the environmental parameters exceed the preset range, the system can automatically issue a warning to remind the manager to take corresponding measures. And the cloud monitoring platform can collect and store a large amount of environmental data. Through data analysis, the environmental change rules in the greenhouse can be understood, providing decision-making support for formulating more reasonable management strategies for irrigation and ventilation, etc. The cloud monitoring platform can realize remote control and automated management of the greenhouse environment. For example, it can automatically adjust the working state of the irrigation system according to the real-time irrigation demand, thereby achieving precise control of the greenhouse environment. Through cloud monitoring and automated management, the production efficiency of the greenhouse can be improved, and the labor cost can be reduced. At the same time, by optimizing management strategies such as irrigation, we can also reduce water resource waste and production costs.

[0085] In the specific implementation process, a remote monitoring system based on the Internet cloud is formed using multiple sub - modules in this method. By inputting the actually measured air temperature value TA, the actually measured air humidity value HA, and the actually measured light intensity value LA into the environmental assessment sub - module, the comprehensive environmental parameter assessment index ECOP is output. ECOP forms a comprehensive assessment index by integrating multiple key environmental parameters such as temperature, humidity, and light intensity. ECOP is updated in real - time and remotely monitored through the Internet cloud, enabling managers to immediately obtain the overall status of the greenhouse environment. ECOP is remotely monitored through the Internet cloud to achieve real - time update and comprehensive assessment of greenhouse environmental parameters, not only improving the timeliness and accuracy of monitoring but also providing more timely and accurate decision - making support for managers;

[0086] The comprehensive environmental parameter assessment index ECOP, the actual soil humidity value ISO, the actually measured air temperature value TA, and the actually measured air humidity value HA are input into the irrigation analysis sub - module, and the indoor irrigation demand value IDDU is output. IDDU calculates and accurately predicts irrigation requirements based on real - time environmental parameters such as soil humidity, temperature, humidity, and ECOP. Through the Internet cloud, managers can remotely obtain the irrigation demand prediction results and formulate irrigation plans according to the prediction, reducing crop growth problems caused by improper irrigation, improving the utilization efficiency of water resources, and reducing irrigation costs. IDDU not only considers direct factors such as soil humidity but also combines temperature, humidity, and ECOP for irrigation demand prediction, making the prediction results more accurate and reliable, reducing the blindness and waste of irrigation. IDDU is remotely monitored through the Internet cloud to achieve precise prediction and remote management of irrigation demand;

[0087] The comprehensive environmental parameter assessment index ECOP and the indoor irrigation demand value IDDU are input into the energy monitoring sub - module, and the indoor total energy consumption value ETLUE is output. ETLUE calculation can comprehensively consider various energy consumption factors in the greenhouse and adjust energy consumption prediction based on ECOP. It not only considers direct energy consumption factors such as ventilation, heating, and lighting but also combines the indirect impact of ECOP on energy consumption, making the prediction results more comprehensive and accurate. Through remote monitoring of ETLUE, managers can remotely monitor energy consumption data, promptly discover energy consumption anomalies, and take corresponding optimization measures, not only reducing energy consumption costs and improving energy utilization efficiency but also achieving green and sustainable development of the greenhouse environment. Through remote monitoring of ETLUE through the Internet cloud, comprehensive monitoring and optimization of greenhouse energy consumption are achieved, which not only helps reduce energy consumption costs and improve energy utilization efficiency but also helps achieve green and sustainable development of the greenhouse environment;

[0088] The indoor irrigation demand IDDU from the calculation result of the irrigation analysis sub-module is used to perform cyclic iteration on the actually measured air humidity value HA in the environmental assessment sub-module. The purpose of iterating the humidity HA is to more accurately simulate and predict the environmental changes in the greenhouse. By taking dynamic factors such as the irrigation demand IDDU into account through iteration, the humidity condition in the greenhouse can be more real-time reflected, which helps to better understand the environmental condition in the greenhouse and take corresponding control measures to optimize the growth environment of crops. By iterating the humidity HA with the irrigation demand IDDU, the following prominent substantial features and remarkable effects can be achieved. This iterative form considers the impact of irrigation on humidity, enabling the system to more accurately simulate the humidity changes in the greenhouse, helping to more accurately understand the growth environment of crops and take corresponding management measures. Moreover, the value of humidity can be updated in real-time during the iterative process, thus reflecting the real-time environmental changes in the greenhouse, helping to timely detect and address environmental problems to ensure the healthy growth of crops. Through iteration, the impact of irrigation on humidity can be more accurately understood to optimize the irrigation strategy, which helps to reduce water resource waste and improve irrigation efficiency;

[0089] Furthermore, it enables the overall multiple sub-modules to cooperate with each other pairwise in calculations, and can also perform overall cycles and iterations, making the overall system have the effect of automatic optimization and update, and thus having better self-adaptability.

[0090] Please refer to Figure 1 、 Figure 2 and Figure 3 For data collection module, an air temperature sensor, an air humidity sensor, a light intensity sensor, a soil temperature and humidity sensor, and a carbon dioxide concentration sensor are used;

[0091] The data processing module includes data cleaning and data integration; specifically, data cleaning is as follows: after receiving the input of data, data cleaning is performed to remove outliers and missing values in the data. The outliers are caused by sensor failures and data transmission errors and need to be eliminated. Specifically, data integration is as follows: the cleaned data is integrated in chronological order to form time series data;

[0092] The monitoring and decision-making module is specifically as follows: managers can remotely monitor the greenhouse environmental parameters, irrigation demand, and energy consumption data in real-time, and based on the comprehensive evaluation index of environmental parameters, indoor irrigation demand, and indoor total energy consumption value, the administrator correspondingly adjusts the greenhouse environmental parameters, formulates an irrigation plan, optimizes equipment operation, and energy consumption management.

[0093] In this embodiment: During actual use, based on the data acquisition module, sensors used in this data acquisition module are installed at multiple positions in the intelligent greenhouse to remotely monitor the internal environment of the intelligent greenhouse in real time, continuously collect data through the data acquisition module and transmit it to the cloud via the Internet, and process multiple data through the data processing module for subsequent calculations. Then, the calculation and analysis module analyzes and calculates the data. After that, the monitoring and decision-making module presents the data based on the output parameters of the calculation and analysis module and assists the administrator in running corresponding measures.

[0094] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote monitoring method based on the Internet cloud, characterized in that: The following monitoring methods are included: Step S1: The data acquisition module collects the air temperature, air humidity, light intensity and soil humidity in the greenhouse in real time, and converts the data into digital signals and transmits them to the cloud server via the Internet; Step S2: inputting the air temperature value, air humidity value, light intensity value and soil humidity value into the data processing module, the data processing module cleans the input data and integrates them in chronological order to form time series data outputs of the actually measured air temperature value, the actually measured air humidity value, the actually measured light intensity value and the actual soil humidity value; Step S3: inputting the actually measured air temperature value, the actually measured air humidity value, the actually measured light intensity value and the actual soil moisture value into the calculation and analysis module, and the calculation and analysis module outputs the comprehensive evaluation index of environmental parameters, the indoor irrigation demand and the indoor total energy consumption value; Step S4: The comprehensive evaluation index of environmental parameters, indoor irrigation demand and indoor total energy consumption value are input into the monitoring decision module. The monitoring decision module processes the data and presents it in the form of charts on the cloud monitoring platform to facilitate administrators to remotely view, analyze and make decisions.

2. The remote monitoring method based on the Internet cloud according to claim 1 is characterized in that: The calculation and analysis module includes: an environmental assessment submodule, an irrigation analysis submodule and an energy monitoring submodule.

3. The remote monitoring method based on the Internet cloud according to claim 2 is characterized in that: The calculation formula of the environmental assessment submodule is as follows: in: ECOP refers to the Comprehensive Assessment Index of Environmental Parameters; DA refers to the weight coefficient of temperature, TA refers to the actually measured air temperature value, TP refers to the appropriate temperature value, Tma refers to the upper limit value of temperature, Tmi refers to the lower limit value of temperature, DB refers to the weight coefficient of humidity, HA refers to the actually measured air humidity value, HP refers to the appropriate humidity value, Hma refers to the upper limit value of humidity, Hmi refers to the lower limit value of humidity, DC refers to the weight coefficient of light intensity, LA refers to the actually measured light intensity value, LP refers to the appropriate light intensity value, Lma refers to the upper limit value of light intensity, Lmi refers to the lower limit value of light intensity, (TA-TP) / (Tma-Tmi) refers to the relative deviation between the actually measured air temperature value TA and the appropriate temperature value TP, Tma and Tmi are the upper limit and lower limit of temperature respectively, which are used to determine the range of standardization; The processing process of the environmental assessment submodule is as follows: the actually measured air temperature value TA, the actually measured air humidity value HA, and the actually measured light intensity value LA are input into the environmental assessment submodule, and the environmental parameter comprehensive assessment index ECOP is output based on the appropriate temperature value TP, the appropriate humidity value HP, and the appropriate light intensity value LP.

4. The remote monitoring method based on the Internet cloud according to claim 3 is characterized in that: The calculation formula of the irrigation analysis submodule is as follows: IDDU=SA× ( Iso-Isp ) +SB× ( TA-Tsp ) +SC×HA+SD× ( ECOP- ECOPa ) ; in: IDDU refers to indoor irrigation demand; SA refers to weight factor one, ISO refers to the actual soil moisture value, Isp refers to the appropriate soil moisture value, SB refers to weight factor two, Tsp refers to the reference temperature value, SC refers to weight factor three, SD refers to the influence coefficient of ECOP on irrigation demand, ECOPa refers to the ECOP value under reference conditions, (Iso-Isp) refers to the deviation between the actual soil moisture value ISO and the appropriate soil moisture value Isp, (ECOP-ECOPa) refers to the influence of the comprehensive status of environmental parameters on irrigation demand; The processing process of the irrigation analysis submodule is as follows: the environmental parameter comprehensive evaluation index ECOP, the actual soil moisture value ISO, the actually measured air temperature value TA, and the actually measured air humidity value HA are input into the irrigation analysis submodule, and the indoor irrigation demand IDDU is output based on the appropriate soil moisture value Isp.

5. The remote monitoring method based on the Internet cloud according to claim 4 is characterized in that: The calculation formula of the energy monitoring submodule is as follows: in: ETLUE refers to the total indoor energy consumption value; Eqa refers to the energy consumption per unit time of the ventilation system, Tqa refers to the operating time of the ventilation system, Ewa refers to the energy consumption per unit time of the heating system, Twa refers to the operating time of the heating system, Eza refers to the energy consumption per unit time of the lighting system, Tza refers to the operating time of the lighting system, α refers to the adjustment coefficient of the impact of ECOP on energy consumption, ECSOP refers to the ideal ECOP value, and (1-α×|ECOP-ECSOP|) refers to the deviation between ECOP and the ideal ECSOP to adjust the total energy consumption; The processing process of the energy monitoring submodule is as follows: the environmental parameter comprehensive evaluation index ECOP and the indoor irrigation demand IDDU are input into the energy monitoring submodule, and the indoor total energy consumption value ETLUE is output based on the unit time energy consumption value Eqa of the ventilation system and the unit time energy consumption value Ewa of the heating system.

6. The remote monitoring method based on the Internet cloud according to claim 1, characterized in that: The data acquisition module uses an air temperature sensor, an air humidity sensor, a light intensity sensor, a soil temperature and humidity sensor, and a carbon dioxide concentration sensor.

7. The remote monitoring method based on the Internet cloud according to claim 1, characterized in that: The data processing module includes data cleaning and data integration; The data cleaning specifically includes: after receiving the data input, cleaning the data to remove abnormal values ​​and missing values ​​in the data. The abnormal values ​​are caused by sensor failure and data transmission errors, and they should be removed; The data integration specifically includes: integrating the cleaned data in chronological order to form time series data.

8. The remote monitoring method based on the Internet cloud according to claim 7 is characterized in that: The monitoring decision module is specifically as follows: the manager can remotely monitor the greenhouse environmental parameters, irrigation demand and energy consumption data in real time, and based on the comprehensive evaluation index of environmental parameters, indoor irrigation demand and indoor total energy consumption value, the administrator can adjust the greenhouse environmental parameters, formulate irrigation plans, optimize equipment operation and energy consumption management accordingly.

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