CN109695943A - The temperature and humidity control system of coating air-conditioning based on big data deep learning - Google Patents
The temperature and humidity control system of coating air-conditioning based on big data deep learning Download PDFInfo
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- CN109695943A CN109695943A CN201811444347.XA CN201811444347A CN109695943A CN 109695943 A CN109695943 A CN 109695943A CN 201811444347 A CN201811444347 A CN 201811444347A CN 109695943 A CN109695943 A CN 109695943A
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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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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)
- Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Fluid Mechanics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The present invention discloses the temperature and humidity control system of the coating air-conditioning based on big data deep learning, including Temperature Humidity Sensor, including the multiple groups inside Temperature Humidity Sensor being located in air-conditioner set, and it is located at the external Temperature Humidity Sensor of the entrance side of air-conditioner set and the entrance of outlet side；Big data training platform, it is connect by communication apparatus with the Temperature Humidity Sensor, and it is connect with air-conditioner controller, the air-conditioner controller and air-conditioning system execute connection, it is built-in with Temperature and Humidity Control model, for the real time humiture data of acquisition to be input in the Temperature and Humidity Control model, output includes the control instruction of the control setting value after optimization to the air-conditioner controller, responds the control instruction by the air-conditioner controller and air-conditioning system actuator is adjusted.The present invention can quickly shorten the time that air-conditioning reaches stable state, greatly improve the precision of control, can be effectively reduced the operation energy consumption of air-conditioning system.
Description
Technical field
The present invention relates to coating air-conditioning temperature and humidity control technical fields, are based on big data deep learning more particularly to one kind
Coating air-conditioning temperature and humidity control system.
Background technique
Painting Shop paint spraying system has temperature and humidity more stringent demand, and traditional Temperature and Humidity Control is mainly
Control method based on PID adjusting, such adjusting method large time delay this for air-conditioning system, changeableization, multiple coupled system
For, control effect is very dependent on commissioning staff's experience, and control switching of the system during seasonal variations it is relatively complicated and
Easily cause the excessive influence painting quality of system fluctuation.Usually debugging cycle is more very long for the control method of tradition application, right
The debugging of Various Seasonal and the long period is met the needs of, the stabilization time demand in control system is longer, and air-conditioning system conduct
The major power consumer of Painting Shop, more huge for the consumption of combustion gas, cold water, hot water etc., debugging and stable time are longer,
Energy consumption and waste are relatively more.
Summary of the invention
In view of the technical drawbacks of the prior art, it is an object of the present invention to provide one kind to be based on big data depth
The temperature and humidity control system of the coating air-conditioning of habit.
The technical solution adopted to achieve the purpose of the present invention is:
A kind of temperature and humidity control system of the coating air-conditioning based on big data deep learning, including Temperature Humidity Sensor, packet
Temperature Humidity Sensor inside the multiple groups being located in air-conditioner set is included, to acquire the real time humiture in air-conditioner set on predetermined section
Data, and it is located at the external Temperature Humidity Sensor of the entrance side of air-conditioner set and the entrance of outlet side；
Big data training platform is connect with the Temperature Humidity Sensor by communication apparatus, and connect with air-conditioner controller,
The air-conditioner controller and air-conditioning system execute connection, are built-in with Temperature and Humidity Control model, the real time humiture for will acquire
Data are input in the Temperature and Humidity Control model, control instruction of the output including the control setting value after optimization to the air-conditioning
Controller responds the control instruction by the air-conditioner controller and air-conditioning system actuator is adjusted.
The internal Temperature Humidity Sensor includes first be located between the primary heating mold segment of air-conditioner set and the cold mold segment of table
Group sensor is located at the cold mold segment of table and heats the second group sensor between mold segment, is located between humidification mold segment and reheating mold segment
Third group sensor, and be located at reheating mold segment and outlet between the 4th group of sensor and the 5th group of sensor.
The Temperature and Humidity Control model obtains air-conditioner set in test run work feelings by the Temperature Humidity Sensor
Temperature and humidity parameter under condition, and the acquisition of the mass data of collection is summarized into big data training platform, it is right in off-line case
Data are trained and simulation-optimization model.
The Temperature and Humidity Control model uses DBN model.
The air-conditioning system actuator includes the valve and frequency converter of air-conditioner set configuration, described to execute to air-conditioning system
The adjusting of adjusting and the frequency control of pump, the frequency converter of blower including valve opening size is adjusted in device.
Coating air-conditioning temperature and humidity control system proposed by the present invention based on big data deep learning is built with test data
Vertical data model, the control parameter optimized by the operation of model, and then come to each actuator unit of air-conditioning system into
Row is adjusted in real time, to achieve the purpose that control temperature and humidity；Meanwhile data training platform can optimize mould according to on-line operation parameter
Type and algorithm are continuously improved control precision and shorten arrival steady state time.
Control system debugging can be reduced through the invention to the experience needs of technical staff, realized intelligence learning and adjusted system
The effect for parameter of uniting, the present invention can quickly shorten system relative to conventional method and stablize the time, save energy consumption；For season
The external conditions such as section, climate change, the present invention can adjust each Optimal Parameters of control in real time, increase system stability.
The present invention is separately equipped with Independent air conditioning controller, to cope with extreme case, it is ensured that temperature and humidity adjustment system it is steady
It is fixed.
Temperature and humidity control system of the invention obtains air-conditioning by the Temperature Humidity Sensor that predetermined position is arranged and transports in test
Parameter under row working condition, and the acquisition of the mass data of collection is summarized into big data training platform, it is right under off-line case
Data are trained and simulation-optimization model, finally obtain reasonable Controlling model；Controlling model is applied to actual production again
It in control, is exported by the control of model, corresponding adjusting is carried out to air-conditioner controller, it is final to change valve and frequency conversion in real time
The setting value of device, to achieve the purpose that quickly to adjust control air-conditioning temperature and humidity.
The present invention can quickly shorten the time that air-conditioning reaches stable state, greatly improve the precision of control, can be effective
The operation energy consumption of ground reduction air-conditioning system.
Detailed description of the invention
Fig. 1 is that the present invention is based on the structure principle charts of the control system of the coating fresh air conditioner of multi-model deep learning.
Fig. 2 is that the present invention is based on the signals of the control flow of the control system of the coating fresh air conditioner of multi-model deep learning
Figure.
Specific embodiment
The present invention is described in further detail below in conjunction with the drawings and specific embodiments.It should be appreciated that described herein
Specific embodiment be only used to explain the present invention, be not intended to limit the present invention.
It is shown in Figure 1, a kind of temperature and humidity control system of the coating air-conditioning based on big data deep learning, comprising:
Temperature Humidity Sensor, including the multiple groups inside Temperature Humidity Sensor being located in air-conditioner set, to acquire air-conditioner set
Real time humiture data on interior predetermined section, and it is located at the external temperature of the entrance side of air-conditioner set and the entrance of outlet side
Humidity sensor；
Big data training platform is connect with the Temperature Humidity Sensor by communication apparatus, and connect with air-conditioner controller,
The air-conditioner controller and air-conditioning system execute connection, are built-in with Temperature and Humidity Control model, the real time humiture for will acquire
Data are input in the Temperature and Humidity Control model, control instruction of the output including the control setting value after optimization to the air-conditioning
Controller responds the control instruction by the air-conditioner controller and air-conditioning system actuator is adjusted.
In the present invention, the big data training platform can carry out off-line training to the data being collected into and data are built
Mould, can also be according to online data Optimized model parameter；The air-conditioner controller can receive the optimization of big data training platform
Setting value can also realize temperature and humidity adjustment by the traditional PID control of itself.
Since Painting Shop air-conditioning system equipment is divided into entrance air hose, air-conditioner set, outlet air hose, wherein in air-conditioner set
Comprising primary heating, table is cold, humidify and four mold segments of reheating, external wind pass sequentially through four mold segments, passes through four mold segments
Respective actuator (primary heating valve, table low temperature valve, humidification pump and reheating valve) to carry out corresponding adjusting to temperature and humidity.Cause
This, in order to realize control, entrance Temperature Humidity Sensor is mounted on entrance air hose；Supply air system is equipped with frequency converter and change
Frequency blower；Humidification pump is controlled by frequency converter；Air-conditioner set inner sensor is arranged in the space after each mold segment,
For detect handled by mold segment after air temperature and humidity variation.Lead between each mold segment actuator of air-conditioner set and air-conditioner controller
Cross rigid line connection, between each sensor and big data platform by wireless network carry out communication connection, big data training platform and
It is connected between air-conditioner controller by Industrial Ethernet.
In the present invention, partially training is carried out big data training platform using DBN network model offline, is carrying out On-line Control
When, real-time state feedback is carried out by air-conditioner controller system；Feedback compensation link is introduced in big data platform, passes through increasing
Punishment contrast function is added to realize the on-line correction function to training pattern, when actual value and target value deviation are more than specified model
When enclosing, correlation-corrected is carried out to model, by shortening refresh time, increasing the methods of times of regulate and control come so that model output can
It is rapidly applied in executing agency, is positively retained in allowed band, and automatically record relevant regulation process, formed relevant
Data storage, facilitates model is subsequent to call directly.
In the present invention, the internal Temperature Humidity Sensor includes the primary heating mold segment and the cold mold segment of table for being located at air-conditioner set
Between first group of sensor, be located at the cold mold segment of table and heat mold segment between second group sensor, be located at humidification mold segment and it is secondary plus
Third group sensor between hot-die section, and the 4th group of sensor being located between reheating mold segment and outlet and the 5th group of sensing
Device, naturally it is also possible to be arranged to other groups of form.
The Temperature and Humidity Control model of the invention obtains air-conditioner set by the Temperature Humidity Sensor and transports in test
Temperature and humidity parameter under row working condition, and the acquisition of the mass data of collection is summarized into big data training platform, offline
In the case of data are trained and simulation-optimization model.
In the present invention, the Temperature and Humidity Control model uses DBN model.
Wherein, the air-conditioning system actuator includes the valve and frequency converter of air-conditioner set configuration, described to air-conditioning system
The adjusting of the adjusting including valve opening size and the frequency control of the frequency converter of blower is adjusted in system actuator.
The above is only a preferred embodiment of the present invention, it is noted that for the common skill of the art
For art personnel, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications
Also it should be regarded as protection scope of the present invention.
Claims (5)
1. the temperature and humidity control system of the coating air-conditioning based on big data deep learning characterized by comprising
Temperature Humidity Sensor, it is pre- in air-conditioner set to acquire including the multiple groups inside Temperature Humidity Sensor being located in air-conditioner set
Determine the real time humiture data on section, and is located at the external temperature and humidity of the entrance side of air-conditioner set and the entrance of outlet side
Sensor；
Big data training platform is connect with the Temperature Humidity Sensor by communication apparatus, and connect with air-conditioner controller, described
Air-conditioner controller and air-conditioning system execute connection, are built-in with Temperature and Humidity Control model, the real time humiture data for that will acquire
It is input in the Temperature and Humidity Control model, control instruction of the output including the control setting value after optimization to the airconditioning control
Device responds the control instruction by the air-conditioner controller and air-conditioning system actuator is adjusted.
2. the temperature and humidity control system of the coating air-conditioning based on big data deep learning as described in claim 1, which is characterized in that
The internal Temperature Humidity Sensor includes the first group of sensor being located between the primary heating mold segment of air-conditioner set and the cold mold segment of table,
It is located at the cold mold segment of table and heats the second group sensor between mold segment, the third group being located between humidification mold segment and reheating mold segment passes
Sensor, and the 4th group of sensor and the 5th group of sensor that are located between reheating mold segment and outlet.
3. the temperature and humidity control system of the coating air-conditioning based on big data deep learning as described in claim 1, which is characterized in that
The Temperature and Humidity Control model obtains temperature of the air-conditioner set under test run working condition by the Temperature Humidity Sensor
Humidity parameter, and the acquisition of the mass data of collection is summarized into big data training platform, data are carried out in off-line case
Trained and simulation-optimization model.
4. the temperature and humidity control system of the coating air-conditioning based on big data deep learning as described in claim 1, which is characterized in that
The Temperature and Humidity Control model uses DBN model.
5. the temperature and humidity control system of the coating air-conditioning based on big data deep learning as described in claim 1, which is characterized in that
The air-conditioning system actuator includes the valve and frequency converter of air-conditioner set configuration, described to adjust to air-conditioning system actuator
Section includes the adjusting of the adjusting and the frequency control of pump, the frequency converter of blower of valve opening size.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811444347.XA CN109695943B (en) | 2018-11-29 | 2018-11-29 | Temperature and humidity control system of coating air conditioner based on big data deep learning |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811444347.XA CN109695943B (en) | 2018-11-29 | 2018-11-29 | Temperature and humidity control system of coating air conditioner based on big data deep learning |
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| CN109695943A true CN109695943A (en) | 2019-04-30 |
| CN109695943B CN109695943B (en) | 2021-04-06 |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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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