Low-energy-consumption automatic greenhouse methanol warming control system

Abstract

The invention discloses a low-energy-consumption automatic greenhouse methanol warming control system, and relates to the technical field of agricultural equipment, and the low-energy-consumption automatic greenhouse methanol warming control system comprises a low-energy-consumption automatic greenhouse methanol warming control system, and the low-energy-consumption automatic greenhouse methanol warming control system comprises a low-energy-consumption automatic greenhouse methanol warming control system and a low-energy-consumption automatic greenhouse methanol warming control system. Through cooperative monitoring of multiple sensors and comprehensive acquisition of internal and external environmental parameters, in combination with a temperature prediction model based on machine learning and an intelligent control algorithm, precise prediction and dynamic adjustment of the internal temperature of the greenhouse are realized, and the system can adjust the working state of the methanol combustion system in advance according to the predicted temperature change trend, so that the system is more intelligent and reliable. According to the technical scheme, temperature lag fluctuation is avoided, so that energy consumption is effectively reduced and energy utilization efficiency is improved while environmental conditions required by crop growth are guaranteed, in addition, distributed sensor arrangement ensures that data cover different areas in the greenhouse, temperature control deviation caused by single-point errors is avoided, and the precision of environment regulation and control is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural equipment, in particular to a low-energy-consumption automatic greenhouse methanol heating control system. BACKGROUND

[0002] With the development of facility agriculture, the application of greenhouse is more and more widely, however, high energy consumption is always the "roadblock" in the development of facility agriculture. The traditional greenhouse heating methods such as electric heating, coal combustion and the like have many problems. Electric heating has high cost, and coal combustion pollutes the environment and does not meet the environmental protection requirements. Methanol as a renewable energy and under the condition of complete combustion, the emission is pollution-free, which has been widely used in civil and automobile fields, but in the field of agriculture, especially in the field of greenhouse heating, the application has not been fully developed.

[0003] Most of the existing methanol heating systems use simple on-off control or proportional control method, which is difficult to realize accurate adjustment of the temperature inside the greenhouse. Since the temperature inside the greenhouse is affected by many factors such as external temperature, light intensity, ventilation condition and the like, the change of these factors will cause the temperature inside the greenhouse to fluctuate, and the traditional control system cannot timely and accurately predict the temperature change trend. In the actual operation of the greenhouse, the management personnel often need to manage multiple greenhouses at the same time, and may not be able to stay at the greenhouse site at all times. However, most of the existing methanol heating systems lack remote monitoring and control functions, the management personnel cannot timely understand the environmental parameters and equipment running state inside the greenhouse, and also cannot remotely operate and adjust the heating system. Therefore, the present application provides a low-energy-consumption automatic greenhouse methanol heating control system. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a low-energy-consumption automatic greenhouse methanol heating control system, which solves the problem that most of the existing methanol heating systems lack remote monitoring and control functions, the management personnel cannot timely understand the environmental parameters and equipment running state inside the greenhouse, and also cannot remotely operate and adjust the heating system.

[0005] In order to achieve the above object, the present application is realized by the following technical scheme: a low-energy-consumption automatic greenhouse methanol heating control system, comprising a control system for low-energy-consumption automatic greenhouse methanol heating, the control system comprising a data acquisition module, an environmental parameter acquisition module, a temperature model establishment module, an intelligent control module, a methanol combustion system, a remote monitoring and control module, a safety protection module and a CO2 concentration closed-loop control module, the data acquisition module being used for greenhouse data acquisition, the environmental parameter acquisition module being used for greenhouse external data acquisition, the temperature model establishment module being used for greenhouse temperature model establishment, the intelligent control module being used for intelligent control algorithm combination, the methanol combustion system being controlled by the intelligent control module, the remote monitoring and control module and the safety protection module being linked with the installation protection module through remote monitoring and control, and the CO2 concentration closed-loop control module comprising a CO2 sensor, a gas supplement execution unit and linkage control.

[0006] Preferably, the data acquisition module comprises temperature sensors, humidity sensors, carbon dioxide concentration sensors and light sensors, and the sensors are arranged at different positions in the greenhouse.

[0007] Preferably, the environmental parameter acquisition module comprises external air temperature acquisition, light intensity acquisition and greenhouse heat preservation condition parameters, the external air temperature acquisition adopts a thermistor sensor, and the light intensity acquisition adopts a photosynthetically active radiation sensor.

[0008] Preferably, the temperature model establishment module comprises greenhouse temperature prediction model establishment, and the intelligent control module adopts a fuzzy PID control algorithm.

[0009] Preferably, the remote monitoring and control module adopts network communication technology, and the network communication technology utilizes a WiFi module, a user interface, a client module, storage management and a cloud server.

[0010] Preferably, the safety protection module comprises leakage detection, fire and explosion prevention and a safety unit, the leakage detection is realized by a methanol concentration sensor, the fire and explosion prevention is realized by temperature overload protection and a flame detector, and the safety unit is realized by an electromagnetic valve and a ventilation fan control.

[0011] Preferably, the methanol combustion system comprises a burner, a heat exchanger, an axial flow fan and a methanol storage pipe, the heat exchanger adopts a finned structure, and the methanol storage comprises a conveying pipeline, a valve and a fuel supply control.

[0012] Preferably, the gas supplement execution unit comprises a methanol combustion byproduct CO2 collection device and a gas supplement valve.

[0013] Beneficial effects

[0014] The application provides a low-energy-consumption automatic greenhouse methanol heating control system.

[0015] (1) The low-energy-consumption automatic greenhouse methanol heating control system realizes accurate prediction and dynamic adjustment of the temperature inside the greenhouse by means of comprehensive collection of internal and external environmental parameters through multi-sensor cooperative monitoring, in combination with a temperature prediction model based on machine learning and an intelligent control algorithm, the system can adjust the working state of the methanol combustion system in advance according to the predicted temperature change trend, avoid temperature lag fluctuation, thereby effectively reducing energy consumption and improving energy utilization efficiency while ensuring the environmental conditions required for crop growth, in addition, the distributed sensor arrangement ensures data coverage in different areas of the greenhouse, avoids temperature control deviation caused by single-point error, and further improves the accuracy of environmental regulation.

[0016] (2) The low-energy-consumption automatic greenhouse methanol heating control system realizes data transmission between the control system and the cloud through network communication technology, users can log in through the client to view real-time data and perform remote control, breaking through the geographical restrictions, realizing the management of greenhouse temperature anytime and anywhere, improving the operation and maintenance efficiency, at the same time, the safety protection module realizes real-time monitoring of safety parameters through methanol concentration sensor, temperature overload protection device, flame detector and other sensors, when detecting abnormality, it can immediately trigger the safety controller to close the electromagnetic valve, start the ventilation fan and send an alarm to the remote monitoring module, forming a "detection-response-alarm" linkage, which comprehensively safeguards the safety of the greenhouse, avoids the occurrence of methanol leakage, fire and other accidents, and guarantees the safety of personnel and crops, automatic emergency response reduces the delay of manual processing and reduces the loss of accidents. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The principle diagram of the energy supply system of the application;

[0018] Figure 2 The principle diagram of the data acquisition module of the application;

[0019] Figure 3 The principle diagram of the CO2 concentration closed-loop control module of the application;

[0020] Figure 4 The principle diagram of the methanol combustion system of the application;

[0021] Figure 5 The principle diagram of the remote monitoring and control module and the safety protection module of the application;

[0022] Figure 6 The principle diagram of the environmental parameter acquisition module of the application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0024] Please refer to Figure 1 and Figure 6 The present application provides a technical solution: a low-energy-consumption automated greenhouse methanol heating control system, which comprises a control system for low-energy-consumption automated greenhouse methanol heating. The control system comprises a data acquisition module, an environmental parameter acquisition module, a temperature model establishment module, an intelligent control module, a methanol combustion system, a remote monitoring and control module, a safety protection module, and a CO2 concentration closed-loop control module. The data acquisition module is used for greenhouse data acquisition. The environmental parameter acquisition module is used for greenhouse external data acquisition. The temperature model establishment module establishes a greenhouse temperature model. The intelligent control module combines an intelligent control algorithm. The methanol combustion system is controlled by the intelligent control module. The remote monitoring and control module and the safety protection module are linked to the installation protection module through remote monitoring and control. The CO2 concentration closed-loop control module comprises a CO2 sensor, a gas supplement execution unit, and a linkage control.

[0025] In a preferred embodiment, the data acquisition module comprises temperature sensors, humidity sensors, carbon dioxide concentration sensors, and light sensors, which are arranged at different positions in the greenhouse.

[0026] The temperature sensors monitor the temperature at each point in the greenhouse in real time, providing basic data for temperature control. The humidity sensors monitor the air humidity, assisting in adjusting the ventilation or humidification system, preventing crop diseases. The carbon dioxide concentration sensors detect the CO2 content in the greenhouse, which is used for CO2 gas supplement control and photosynthesis analysis. The light sensors monitor the light intensity in the greenhouse, combining with external light data. The sensors collect corresponding environmental parameters in real time. The data is transmitted to the intelligent control module through the remote monitoring and control module, forming a real-time environmental parameter database. Multi-parameter collaborative monitoring provides comprehensive data support for intelligent control, improves the environmental control precision, and the distributed arrangement ensures data coverage in different areas of the greenhouse, avoiding temperature control deviation caused by single-point error.

[0027] In a preferred embodiment, the environmental parameter acquisition module includes external air temperature acquisition, light intensity acquisition and greenhouse insulation condition parameters. The external air temperature acquisition uses a thermistor sensor, and the light intensity acquisition uses a photosynthetically active radiation sensor. The thermistor sensor acquires the real-time air temperature outside the greenhouse, which is used for temperature model calculation of the influence of the external environment on the temperature in the greenhouse. The photosynthetically active radiation sensor monitors the external light intensity and light quality, determines the contribution of natural light to the temperature rise in the greenhouse and the photosynthesis of crops, and records the thermal conductivity coefficient of the insulation material, the greenhouse structure and the like as the basic parameters of the temperature model.

[0028] The data collected by the thermistor sensor and the photosynthetically active radiation sensor is transmitted to the temperature model establishment module through network communication technology. The greenhouse insulation parameters are recorded in the system through pre-measurement, combined with real-time data to generate a temperature prediction model, and combined with internal and external environmental parameters to improve the prediction accuracy of the temperature model.

[0029] In a preferred embodiment, the temperature model establishment module includes establishing a greenhouse temperature prediction model. The intelligent control module uses a fuzzy PID control algorithm, is based on a machine learning algorithm, combines internal and external environmental parameters, predicts the temperature change trend in the greenhouse, compares the predicted temperature with the target temperature by the intelligent control module, adjusts the power of the burner, the start-stop time by using the fuzzy PID algorithm, forms a closed-loop control, and realizes accurate control of the methanol combustion system in combination with the prediction result of the temperature model.

[0030] The temperature model establishment module uses a machine learning algorithm, combines historical temperature data, greenhouse insulation condition parameters, external air temperature data and methanol combustion heat data, and establishes a greenhouse temperature prediction model.

[0031] Specifically, the temperature model establishment module is based on a machine learning (such as a neural network algorithm) temperature prediction model. The input parameters are external air temperature, light intensity, greenhouse insulation condition (thermal conductivity coefficient, structure) and methanol combustion heat (power, time). The output result is the predicted temperature change value at different positions in the greenhouse. Through a data processing unit, a computer or an edge computing device, the temperature model algorithm is run, historical temperature data, external environmental parameters (air temperature, light), methanol combustion data and greenhouse insulation parameters are collected, a machine learning algorithm is used to train the data, a temperature prediction model is established, current environmental parameters are input in real time, the model calculates and outputs the temperature change trend in the greenhouse, and provides a basis for intelligent control.

[0032] The intelligent control module combines temperature model prediction results, input data, temperature model prediction values, real-time temperature monitoring data, target temperature set values, and outputs instructions, start-stop, power adjustment, and combustion time control of the methanol combustion system through a fuzzy PID control algorithm. The control algorithm can be executed by a controller, a PLC (programmable logic controller), or an industrial-grade single-chip microcomputer. The communication interface includes RS485, Modbus, etc., and communicates with sensors and actuators to receive temperature model prediction values and real-time sensor data, calculate the deviation between the actual temperature and the target temperature, generate control parameters using a fuzzy PID algorithm, send instructions to the methanol combustion system to adjust the combustion state (such as power, start-stop), and achieve closed-loop temperature control. By dynamically predicting temperature changes, the combustion system can be adjusted in advance to avoid temperature lag fluctuations.

[0033] In a preferred embodiment, the remote monitoring and control module uses network communication technology, including a WiFi module, a user interface, a client module, storage management, and a cloud server. The WiFi module enables data transmission between the control system and the cloud, supports 4G / 5G expansion, the user interface provides real-time data display (temperature curve, device status) and remote control interface, and the cloud server stores historical data, supports multi-user access, and forwards remote instructions. The control system uploads real-time data to the cloud through the WiFi module, and users can view and set target temperatures and modify control parameters through the client. The cloud automatically pushes alarm information to the user, breaking geographical limitations and enabling real-time management of greenhouses from anywhere, improving operational efficiency.

[0034] In a preferred embodiment, the safety protection module includes leak detection, fire and explosion prevention, and a safety unit. The leak detection uses a methanol concentration sensor, the fire and explosion prevention uses temperature overload protection and a flame detector, and the safety unit uses an electromagnetic valve and a ventilation fan control. The methanol concentration sensor monitors the methanol leakage concentration around the storage tank and pipeline to prevent explosion risks. The temperature overload protection device detects abnormal temperature increases in the combustion system or the greenhouse to prevent equipment damage or crop burns. The flame detector monitors the flame state of the burner to identify abnormal flameout or deflagration. The electromagnetic valve immediately shuts off the methanol delivery pipeline to prevent leakage diffusion. The ventilation fan accelerates air circulation to reduce methanol concentration or heat dissipation.

[0035] Specifically, sensors monitor safety parameters in real time. When the methanol concentration exceeds the standard, the temperature overload, or the flame is abnormal, the safety controller is triggered, the controller immediately closes the electromagnetic valve, starts the ventilation fan, and sends an alarm to the remote monitoring module, forming a "detection-response-alarm" linkage. This ensures comprehensive safety monitoring, avoids methanol leakage, fires, and other accidents, ensures personnel and crop safety, reduces manual processing delays through automatic emergency response, and reduces accident losses.

[0036] In a preferred embodiment, the methanol combustion system comprises a burner, a heat exchanger, an axial flow fan and a methanol storage tube, the heat exchanger adopts a fin structure, the methanol storage includes a delivery pipeline, a valve and a control fuel supply, the burner atomizes the methanol and burns with air to generate heat, the fin heat exchanger transfers the combustion heat to air or water to improve thermal efficiency, the axial flow fan pushes the hot air circulation to ensure uniform distribution of the temperature in the shed, and the methanol storage and delivery unit stores the fuel and controls the supply amount according to the instruction.

[0037] Specifically, the intelligent control module sends an instruction to the burner to adjust the mixing ratio of methanol and air, starts the combustion, and the high-temperature flue gas generated by the combustion passes through the fin heat exchanger to transfer heat to the flowing air, and the axial flow fan sends the hot air into the shed or radiates heat through the floor heating pipeline, while the delivery pipeline valve adjusts the methanol supply amount according to the combustion power.

[0038] In a preferred embodiment, the air supplement execution unit comprises a methanol combustion byproduct CO2 collection device and an air supplement valve, a CO2 sensor for monitoring the CO2 concentration in the shed to determine whether air supplement is needed, a CO2 collection device for collecting the CO2 generated by the methanol combustion to avoid direct emission, and an air supplement valve for controlling the release amount of CO2 into the shed, and the air supplement strategy can be dynamically adjusted through a linkage control algorithm combined with the light intensity, temperature and crop growth stage.

[0039] Specifically, the CO2 sensor detects the concentration in real time, and when it is lower than the optimal value of photosynthesis, the linkage control algorithm starts the collection device to guide the CO2 generated by the combustion into the shed through the air supplement valve, automatically adjusts the air supplement amount in combination with the light intensity (high light intensity requires high demand) and the temperature (photosynthetic efficiency is high at 25-30°C), avoids excessive concentration, and when the CO2 concentration exceeds 1500ppm, the linkage ventilation system exhausts air to form a closed loop control, recycles the combustion byproduct CO2, reduces the additional CO2 fertilization cost, and precise CO2 concentration control can improve the photosynthesis efficiency, increase the crop yield by 10%-15%, and at the same time avoid the energy waste caused by ventilation and heat dissipation.

[0040] Meanwhile, the contents not described in detail in the specification all belong to the existing technology known to those skilled in the art.

[0041] In operation, the temperature sensor, humidity sensor, carbon dioxide concentration sensor and light sensor in the data acquisition module collect data of temperature, humidity, CO2 content and light intensity in the greenhouse in real time, which are transmitted to the intelligent control module through the WiFi module of the remote monitoring and control module. Meanwhile, the environmental parameter acquisition module collects data of outdoor air temperature and light intensity using the thermistor sensor and photosynthetically active radiation sensor, and combines the greenhouse insulation condition parameters inputted in the early stage to transmit to the temperature model establishment module. Based on machine learning algorithm, the temperature model establishment module establishes a temperature prediction model combining historical data and parameters such as methanol combustion heat, and outputs the temperature change trend in the greenhouse to the intelligent control module. The intelligent control module compares the predicted temperature with the target temperature, calculates the deviation, generates control instructions, and sends them to the methanol combustion system to adjust the power of the burner, start-stop time, etc. The burner atomizes methanol and mixes with air for combustion, and the heat is transferred to air or water through the finned heat exchanger. The axial flow fan sends hot air into the greenhouse or dissipates heat through the floor heating pipe. Meanwhile, the CO2 concentration sensor of the CO2 concentration closed-loop control module monitors the CO2 concentration in the greenhouse in real time, and when the CO2 concentration is lower than the optimal value for photosynthesis, the linkage control algorithm starts the CO2 collection device to guide the combustion byproduct CO2 into the greenhouse through the air supplement valve, and adjusts the air supplement amount in combination with the light intensity and temperature. When the concentration exceeds the limit, the linkage ventilation system is started to exhaust air. The remote monitoring and control module uploads real-time data to the cloud, and the user sets the temperature and modifies parameters remotely through the client. When the system is abnormal, the cloud pushes an alarm. The methanol concentration sensor, temperature overload protection device and flame detector of the safety protection module monitor safety parameters in real time, and when the parameters exceed the limit, the safety controller is triggered to close the electromagnetic valve, start the ventilation fan and alarm, thereby forming an automatic working process integrating data acquisition, model prediction, intelligent control, CO2 closed loop, remote monitoring and safety protection.

[0042] It should be noted that the relative terms such as first and second and the like are used herein solely to distinguish one entity or action from another, not necessarily in an actual, physical relationship or order. In addition, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusions, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed or inherent to such a process, method, article or device.

[0043] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, alternatives and variations can be made thereto without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low-energy consumption automated greenhouse methanol temperature increase control system, including a control system for low-energy consumption automated greenhouse methanol temperature increase, the control system including a data acquisition module, an environmental parameter acquisition module, a temperature model establishment module, an intelligent control module, a methanol combustion system, a remote monitoring and control module, a safety protection module and a CO2 concentration closed-loop control module, the data acquisition module is used for data acquisition inside the greenhouse, the environmental parameter acquisition module is used for data acquisition outside the greenhouse, the temperature model establishment module establishes a greenhouse temperature model, the intelligent control module is combined with an intelligent control algorithm, the methanol combustion system is controlled by the intelligent control module, the remote monitoring and control module and the safety protection module are linked with the installation protection module through remote monitoring and control, and the CO2 concentration closed-loop control module includes a CO2 sensor, a gas replenishment execution unit and a linkage control.

2. A low-energy consumption automated greenhouse methanol temperature control system according to claim 1, characterized in that: The data acquisition module includes a temperature sensor, a humidity sensor, a carbon dioxide concentration sensor and a light sensor, and the sensors are arranged at different positions in the greenhouse.

3. The low-energy consumption automated greenhouse methanol temperature control system according to claim 1 is characterized by: The environmental parameter acquisition module includes external temperature acquisition, light intensity acquisition and greenhouse insulation condition parameters. The external temperature acquisition adopts a thermal resistance sensor, and the light intensity acquisition adopts a photosynthetic active radiation sensor.

4. The low-energy consumption automated greenhouse methanol temperature control system according to claim 1, characterized in that: The temperature model establishment module includes establishing a greenhouse temperature prediction model, and the intelligent control module adopts a fuzzy PID control algorithm.

5. The low-energy consumption automated greenhouse methanol temperature control system according to claim 1, characterized in that: The remote monitoring and control module uses network communication technology, which utilizes a WiFi module, a user interface, a client module, storage management and a cloud server.

6. The low-energy consumption automated greenhouse methanol temperature control system according to claim 1, characterized in that: The safety protection module includes leakage detection, fire and explosion protection and safety units. The leakage detection is achieved through a methanol concentration sensor, the fire and explosion protection is achieved through temperature overload protection and a flame detector, and the safety unit is controlled by a solenoid valve and a ventilation fan.

7. The low-energy consumption automated greenhouse methanol temperature control system according to claim 1, characterized in that: The methanol combustion system includes a burner, a heat exchanger, an axial flow fan and a methanol storage pipe. The heat exchanger adopts a fin structure, and the methanol storage includes a delivery pipeline, a valve and a control fuel supply.

8. The low-energy consumption automated greenhouse methanol temperature control system according to claim 1, characterized in that: The air supply execution unit includes a CO2 collection device for a methanol combustion byproduct and an air supply valve.

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