CN109695943B - Temperature and humidity control system of coating air conditioner based on big data deep learning - Google Patents
Temperature and humidity control system of coating air conditioner based on big data deep learning Download PDFInfo
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- CN109695943B CN109695943B CN201811444347.XA CN201811444347A CN109695943B CN 109695943 B CN109695943 B CN 109695943B CN 201811444347 A CN201811444347 A CN 201811444347A CN 109695943 B CN109695943 B CN 109695943B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
- F24F11/77—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by controlling the speed of ventilators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
- F24F11/84—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using valves
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Signal Processing (AREA)
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- Human Computer Interaction (AREA)
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- Mathematical Physics (AREA)
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- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a temperature and humidity control system for a coating air conditioner based on big data deep learning, which comprises temperature and humidity sensors, a temperature and humidity sensor control module and a temperature and humidity sensor control module, wherein the temperature and humidity sensor control module comprises a plurality of groups of internal temperature and humidity sensors arranged in an air conditioning unit, and external temperature and humidity sensors arranged at inlets and outlets on the inlet side and the outlet side of the air conditioning unit; big data training platform, through communications facilities with temperature and humidity sensor connects to be connected with air conditioner controller, air conditioner controller is connected with air conditioning system execution, and it has the temperature and humidity control model to embed, is used for inputing the real-time humiture data of gathering in the temperature and humidity control model, the control command of output including the control setting value after optimizing arrives air conditioner controller, by air conditioner controller responds this control command and adjusts air conditioning system executor. The invention can quickly shorten the time of the air conditioner reaching the stable state, greatly improve the control precision and effectively reduce the operation energy consumption of the air conditioning system.
Description
Technical Field
The invention relates to the technical field of temperature and humidity control of a coating air conditioner, in particular to a temperature and humidity control system of the coating air conditioner based on big data deep learning.
Background
The paint spraying system of the painting workshop has strict requirements on temperature and humidity, the traditional temperature and humidity control is mainly a control method mainly based on PID (proportion integration differentiation) regulation, for a large-lag, multi-change and multi-coupling system of an air conditioning system, the control effect of the regulation method extremely depends on the experience of debugging personnel, and the control switching of the system during seasonal variation is complicated and the paint spraying quality is easily influenced by overlarge system fluctuation. Generally, a traditional control method is long in debugging period, long time is needed for debugging and adapting in different seasons, the requirement on the stabilization time in a control system is long, an air conditioning system serving as an energy consumption consumer in a coating workshop consumes a large amount of fuel gas, cold water, hot water and the like, the debugging and stabilization time is long, and energy consumption and waste are relatively high.
Disclosure of Invention
The invention aims to provide a temperature and humidity control system of a coating air conditioner based on big data deep learning, aiming at the technical defects in the prior art.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a temperature and humidity control system for a coating air conditioner based on big data deep learning comprises temperature and humidity sensors, a temperature and humidity sensor control module and a temperature and humidity sensor control module, wherein the temperature and humidity sensor control module comprises a plurality of groups of internal temperature and humidity sensors arranged in an air conditioning unit so as to acquire real-time temperature and humidity data on a preset section in the air conditioning unit, and external temperature and humidity sensors arranged at an inlet and an outlet of the air conditioning unit at the inlet side and the;
big data training platform, through communications facilities with temperature and humidity sensor connects to be connected with air conditioner controller, air conditioner controller is connected with air conditioning system execution, and it has the temperature and humidity control model to embed, is used for inputing the real-time humiture data of gathering in the temperature and humidity control model, the control command of output including the control setting value after optimizing arrives air conditioner controller, by air conditioner controller responds this control command and adjusts air conditioning system executor.
The internal temperature and humidity sensor comprises a first group of sensors arranged between a primary heating mould section and a surface cooling mould section of the air conditioning unit, a second group of sensors arranged between the surface cooling mould section and the heating mould section, a third group of sensors arranged between a humidifying mould section and a secondary heating mould section, and a fourth group of sensors and a fifth group of sensors arranged between the secondary heating mould section and an outlet.
The temperature and humidity control model acquires temperature and humidity parameters of the air conditioning unit under the test operation working condition through the temperature and humidity sensor, collects and summarizes a large amount of collected data into a big data training platform, and trains data and simulates an optimization model under the off-line condition.
The temperature and humidity control model adopts a DBN model.
The air conditioning system actuator comprises a valve and a frequency converter configured for the air conditioning unit, and the adjustment of the air conditioning system actuator comprises the adjustment of the opening of the valve and the adjustment of the variable frequency speed regulation of the frequency converter of the pump and the fan.
According to the coating air conditioner temperature and humidity control system based on big data deep learning, a data model is established according to test data, optimized control parameters are obtained through model operation, and then each actuator unit of an air conditioner system is adjusted in real time to achieve the purpose of controlling temperature and humidity; meanwhile, the data training platform can optimize a model and an algorithm according to the online operation parameters, continuously improve the control precision and shorten the time for reaching the steady state.
The invention can reduce the experience requirements of the control system on technicians in debugging and realize the effect of intelligently learning and adjusting system parameters, and compared with the traditional method, the invention can quickly shorten the system stabilization time and save the energy consumption; for external conditions such as seasons, climate change and the like, the invention can adjust and control various optimized parameters in real time and increase the stability of the system.
The invention is additionally provided with an independent air conditioner controller for coping with extreme conditions and ensuring the stability of a temperature and humidity adjusting system.
The temperature and humidity control system acquires parameters of the air conditioner under the working condition of test operation through temperature and humidity sensors arranged at preset positions, collects and summarizes a large amount of collected data into a big data training platform, trains data and simulates an optimization model under the off-line condition, and finally obtains a reasonable control model; and then the control model is applied to actual production control, the air conditioner controller is correspondingly adjusted through the control output of the model, and finally the set values of the valve and the frequency converter are changed in real time to achieve the purpose of quickly adjusting and controlling the temperature and the humidity of the air conditioner.
The invention can quickly shorten the time of the air conditioner reaching the stable state, greatly improve the control precision and effectively reduce the operation energy consumption of the air conditioning system.
Drawings
Fig. 1 is a structural schematic diagram of a control system for coating a fresh air conditioner based on multi-model deep learning.
Fig. 2 is a control flow schematic diagram of the control system for coating the fresh air conditioner based on multi-model deep learning.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a temperature and humidity control system of a painting air conditioner based on big data deep learning includes:
the temperature and humidity sensor comprises a plurality of groups of internal temperature and humidity sensors arranged in the air conditioning unit so as to acquire real-time temperature and humidity data on a preset section in the air conditioning unit and external temperature and humidity sensors arranged at an inlet and an outlet of the air conditioning unit at the inlet side and the outlet side;
big data training platform, through communications facilities with temperature and humidity sensor connects to be connected with air conditioner controller, air conditioner controller is connected with air conditioning system execution, and it has the temperature and humidity control model to embed, is used for inputing the real-time humiture data of gathering in the temperature and humidity control model, the control command of output including the control setting value after optimizing arrives air conditioner controller, by air conditioner controller responds this control command and adjusts air conditioning system executor.
In the invention, the big data training platform can perform off-line training and data modeling on the collected data and can also optimize model parameters according to on-line data; the air conditioner controller can receive an optimized set value of the big data training platform and can also realize temperature and humidity regulation through self traditional PID control.
The air conditioning system equipment of the coating workshop is divided into an inlet air pipe, an air conditioning unit and an outlet air pipe, wherein the air conditioning unit comprises four die sections of primary heating, surface cooling, humidifying and secondary heating, external air sequentially passes through the four die sections, and the temperature and the humidity are correspondingly adjusted through respective actuators (a primary heating valve, a surface cooling valve, a humidifying pump and a secondary heating valve) of the four die sections. Therefore, in order to realize control, the inlet and outlet temperature and humidity sensors are arranged on the inlet and outlet air pipes; the air supply system is provided with a frequency converter and a frequency conversion fan; the humidifying pump is controlled by a frequency converter; the sensors in the air conditioning unit are all arranged in the space behind each mould section and used for detecting the temperature and humidity change of the air treated by the mould sections. Each module actuator of the air conditioning unit is connected with the air conditioning controller through a hard wire, each sensor is in communication connection with the big data platform through a wireless network, and the big data training platform is connected with the air conditioning controller through an industrial Ethernet.
In the invention, the off-line part of the big data training platform is trained by using a DBN network model, and real-time state feedback is carried out through an air conditioner controller system when on-line control is carried out; a feedback correction link is introduced into a big data platform, an online correction function of a trained model is realized by adding a punishment comparison function, when the deviation between an actual value and a target value exceeds a specified range, the model is subjected to related correction, the output of the model can be quickly applied to an executing mechanism by methods of shortening refreshing time, increasing regulation and control times and the like, the output can be kept within an allowable range, related regulation and control processes are automatically recorded, related data storage is formed, and the subsequent direct calling of the model is facilitated.
In the invention, the internal temperature and humidity sensor comprises a first group of sensors arranged between a primary heating mould section and a surface cooling mould section of the air conditioning unit, a second group of sensors arranged between the surface cooling mould section and the heating mould section, a third group of sensors arranged between a humidifying mould section and a secondary heating mould section, a fourth group of sensors and a fifth group of sensors arranged between the secondary heating mould section and an outlet, and other groups of sensors can be arranged.
The temperature and humidity control model acquires temperature and humidity parameters of the air conditioning unit under the test operation condition through the temperature and humidity sensor, collects and summarizes a large amount of collected data into a big data training platform, and trains data and simulates an optimization model under the offline condition.
In the invention, the temperature and humidity control model adopts a DBN model.
The air conditioning system actuator comprises a valve and a frequency converter configured for an air conditioning unit, and the adjustment of the air conditioning system actuator comprises the adjustment of the opening of the valve and the adjustment of the variable frequency speed regulation of the frequency converter of the fan.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (3)
1. Temperature and humidity control system of application air conditioner based on big data degree of depth study, its characterized in that includes:
the temperature and humidity sensor comprises a plurality of groups of internal temperature and humidity sensors arranged in the air conditioning unit so as to acquire real-time temperature and humidity data on a preset section in the air conditioning unit and external temperature and humidity sensors arranged at an inlet and an outlet of the air conditioning unit at the inlet side and the outlet side;
the big data training platform is connected with the temperature and humidity sensor through communication equipment and is connected with an air conditioner controller, the air conditioner controller is connected with an air conditioning system in an executing mode, a temperature and humidity control model is arranged in the big data training platform and is used for inputting collected real-time temperature and humidity data into the temperature and humidity control model and outputting a control instruction comprising an optimized control set value to the air conditioner controller, and the air conditioner controller responds to the control instruction to adjust an actuator of the air conditioning system, so that the set values of a valve and a frequency converter are changed in real time, and the purpose of quickly adjusting and controlling the temperature and humidity of the air conditioner is achieved;
the coating air conditioner is divided into an inlet air pipe, an air conditioning unit and an outlet air pipe, wherein the air conditioning unit comprises four die sections of primary heating, surface cooling, humidifying and secondary heating, external air sequentially passes through the four die sections, and the temperature and humidity are correspondingly adjusted through actuators of the four die sections; the actuator sequentially comprises a primary heating valve, a surface cooling valve, a humidifying pump and a secondary heating valve; the humidifying pump is controlled by a frequency converter;
each module actuator of the air conditioning unit is connected with the air conditioning controller through a hard wire, each sensor is in communication connection with the big data training platform through a wireless network, and the big data training platform is connected with the air conditioning controller through an industrial Ethernet;
the off-line part of the big data training platform is trained by using a DBN (database network) model, and real-time state feedback is carried out through an air conditioner controller system during on-line control; a feedback correction link is introduced into a big data platform, an online correction function of a trained model is realized by adding a punishment comparison function, when the deviation between an actual value and a target value exceeds a specified range, the model is subjected to related correction, the output of the model can be quickly applied to an executing mechanism by a method of shortening the refreshing time and increasing the regulation and control times, the output can be kept within an allowable range, the related regulation and control processes are automatically recorded, the related data storage is formed, and the subsequent direct calling of the model is facilitated;
the internal temperature and humidity sensor is arranged in a space behind each mould section and used for detecting the temperature and humidity change of air treated by the mould sections; the system comprises a first group of sensors arranged between a primary heating mould section and a surface cooling mould section of the air conditioning unit, a second group of sensors arranged between the surface cooling mould section and the heating mould section, a third group of sensors arranged between a humidifying mould section and a secondary heating mould section, and a fourth group of sensors and a fifth group of sensors arranged between the secondary heating mould section and an outlet.
2. The temperature and humidity control system of a coating air conditioner based on big data deep learning as claimed in claim 1, wherein the temperature and humidity control model obtains temperature and humidity parameters of the air conditioning unit under test operation conditions through the temperature and humidity sensor, collects and summarizes a large amount of collected data into a big data training platform, and trains the data and simulates an optimization model under an off-line condition.
3. The big data deep learning-based temperature and humidity control system for a coating air conditioner as claimed in claim 1, wherein the air conditioning system actuator comprises a valve and a frequency converter configured for an air conditioning unit, and the adjustment of the air conditioning system actuator comprises adjustment of the opening of the valve and adjustment of the frequency conversion and speed regulation of the frequency converter of a pump and a fan.
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| CN111397038A (en) * | 2020-03-19 | 2020-07-10 | 江苏联线环境设备有限公司 | Cabinet air conditioner and energy-saving control system thereof |
| CN114322217A (en) * | 2021-08-31 | 2022-04-12 | 海信家电集团股份有限公司 | Air conditioner and automatic temperature control method thereof |
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| US10353362B2 (en) * | 2017-05-03 | 2019-07-16 | Ul Llc | Method and system for predictive air filter maintenance for sustained indoor air quality |
| CN207741275U (en) * | 2017-09-18 | 2018-08-17 | 深圳市新环能科技有限公司 | A kind of central air-conditioning fault diagnosis system based on deep learning |
| CN207990779U (en) * | 2017-11-15 | 2018-10-19 | 广州番禺职业技术学院 | A kind of clean operating room temperature and humidity control system |
| CN108361927A (en) * | 2018-02-08 | 2018-08-03 | 广东美的暖通设备有限公司 | A kind of air-conditioner control method, device and air conditioner based on machine learning |
| CN108534308B (en) * | 2018-04-17 | 2019-09-06 | 广东德尔智慧工厂科技有限公司 | A kind of air-conditioner air outlet adjusting method based on big data |
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