CN109798646A - A kind of air quantity variable air conditioner control system and method based on big data platform - Google Patents
A kind of air quantity variable air conditioner control system and method based on big data platform Download PDFInfo
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Abstract
The invention discloses a kind of air quantity variable air conditioner control system and method based on big data platform, are related to air conditioner controlling technology field.The control system includes control host, actuator, sensor and server data platform.The running state data of actuator described in the control host transparent transmission and the sensor gives the server data platform；The data of the multiple control hosts of server data platform set, the parameter of operation neural network intelligence learning program adjusting Nonlinear Prediction Models, and it is back to the control host.Nonlinear model shape parameter described in host loads is controlled, according to control model, is based on Nonlinear Prediction Models, control program is executed and uses optimization algorithm solving optimization function, calculate actuator optimal value, complete the control to air-conditioner temperature.The present invention is easy to implement in practical projects, can meet comfort air conditioning system system simultaneously and have the demand for control of the technical grade air-conditioning system of very high-precision control requirement.
Description
Technical field
The present invention relates to air conditioner controlling technology field more particularly to a kind of air quantity variable air conditioner controls based on big data platform
System and method.
Background technique
Central air-conditioning system is a pith in Architectural Equipment, with the development of urban modernization, building energy consumption
Also it increases considerably.Therefore, the Optimization of Energy Saving control of central air-conditioning system is a kind of inexorable trend.Due to current central air conditioning system
The defect of the design concept of system, the design capacity of China's major part air conditioning system are needed significantly more than the actual load of building
It asks, in order to reduce the energy waste of this part, the Optimization of Energy Saving operation control of central air-conditioning system is necessary.
In the prior art it is a kind of with remote operation function VAV control system, control module be connected with communication module and
It is connect with client's control terminal.But this technology only can embody advantage in maintenance and debugging, simultaneously for actual control method
Do not improve.
Another prior art discloses the plan for becoming static pressure and total blast volume double control in a kind of VAV variable air volume system
Slightly, which intuitively combines the significant energy conservation for becoming static pressure with the advanced of total blast volume, total constantly to correct by becoming static pressure method
The design speed of air quantity method makes it obtain better energy-saving effect.But this Optimization of Energy Saving control device still falls within feedback and closes
Ring control.Since building system all has very strong thermal inertia, in the big amount of disturbing, this simple feedback system is easy to
There is the problems such as unstable, overshoot, lag.
A kind of existing VAV air conditioning system with variable forecast Control Algorithm neural network based, mainly establishes each
The neural network prediction model of module, comprising: air conditioning area temperature prediction model, end air-valve prediction model, air-conditioner set master
Air channel pipeline static pressure prediction model, main air duct air quantity prediction model, supply air temperature prediction model, fresh air air-valve prediction model and sky
Gas quality prediction model structure.During training, in order to obtain training data, need to arrange many sensors, this is bound to
Considerably increase the construction and operating cost of system.On the basis of these models, using optimization algorithm, these are calculated separately out
The corresponding optimal control variable of model, this is typical multiple single object optimizations, leads to multiple optimizers on-line operation simultaneously,
Calculation amount super large, moreover, the calculating error of any ring will all influence whether the fortune of other optimizers in the big amount of disturbing
Row, therefore, it is difficult to implement in practical projects.
Existing control method is mainly used for comfort air conditioning system system, for technical grade air-conditioning, there is very high precision control
System requires, this just needs more advanced system control method.
Therefore, those skilled in the art is dedicated to developing a kind of air quantity variable air conditioner control system based on big data platform
And method, the control host of the control system is built-in to be based on NARX (Dynamic neural network time series
Prediction) the room temperature prediction optimization of neural network prediction model controls program, and NARX neural network prediction model is logical
The training of server big data platform is crossed to obtain.The control system is not only able to satisfy the demand for control of comfort air conditioning system system, also can
Meet the advanced control system for having the demand for control of technical grade air-conditioning of very high precision controlling requirement.
Summary of the invention
In view of the above drawbacks of the prior art, the technical problem to be solved by the present invention is to how design one kind to be easy
Implement in Practical Project, be not only able to satisfy the needs of comfort air conditioning system system, is also able to satisfy very high precision controlling requirement
The advanced control system and method for the demand for control of technical grade air-conditioning.
To achieve the above object, the air quantity variable air conditioner control system based on big data platform that the present invention provides a kind of.
The control system includes control host, actuator, sensor and server data platform, according to control model tune
Save temperature；
The control host connects comprising CPU arithmetic unit, memory, I/O data-interface, RS485 communication interface, 4G communication
Mouth, WAN communication interface and power supply；The memory preserves the control of the room temperature prediction optimization based on Nonlinear Prediction Models
Program, the control program uses optimization algorithm solving optimization function, for calculating the optimal value of the actuator；
The server data stage+module has the neural network intelligence learning program based on big data, described for adjusting
The parameter of Nonlinear Prediction Models；
The control host is connected with the actuator by the I/O data-interface or the RS485 communication interface；
The control host is connected with the sensor by the I/O data-interface；
The control host is the 4G communication interface in a manner of wireless network and the server data platform phase
Even, or it is connected in a manner of cable network with the server data platform the WAN communication interface；
The control host has transparent transmission function, the running state data of the actuator and the sensor is passed through described
4G communication interface or the WAN communication interface are sent to the server data platform.
Further, the Nonlinear Prediction Models are NARX neural network prediction models；The NARX neural network is pre-
The parameter for surveying model includes that input vector weight coefficient and neuron bias coefficient；The optimization algorithm is particle swarm algorithm, heredity
In algorithm, differential evolution algorithm, immune algorithm, ant group algorithm, simulated annealing, tabu search algorithm and random search algorithm
One kind；
The majorized function is:
F3=ffan(k) (4)
yr(k+i)=αiy(k)+(1-αi) S, i=1 ..., P (5)
For formula (1) into formula (5), n is room number, and y (k) is real system output, i.e. room return air temperature (degree Celsius)；It is room return air temperature predicted value (degree Celsius)；yrIt (k+i) is reference locus；D is valve opening, D ∈ [0,100]；
(hundred-mark system)；ffanIt is blower frequency, ffan∈[fmin,fmax],(Hz)；P is prediction step number；W1, w2 and w3 are weight coefficients；Formula
5 give reference locus, wherein S is room temperature set point, and α is softening coefficient (0 < α < 1).
Further, the actuator includes the electric air valve actuator being integrated in VAV box and fan frequency conversion control
Device, the sensor include multiple temperature sensors；
The electric air valve actuator is connect by the I/O data-interface with the control host；
The fan frequency conversion controller is connect by the RS485 communication interface with the control host；
The temperature sensor is connect by the I/O data-interface with the control host；
One temperature sensor is set to air outlet, remaining described temperature sensor is placed in each room area.
Further, the control system provides two kinds of PREDICTIVE CONTROL modes, is high-precision control mode and energy conservation respectively
Control model.
Further, the current range that the I/O data-interface is supported is 4mA to 20mA, and the voltage range of support includes
0V to 10V and 0V to 5V.
Further, the RS485 interface supports modbusRTU, modbusASCII and PPI serial port protocol.
Further, the server data platform receives the data of one or more control host, completes big
The deployment of data platform.
The air quantity variable air conditioner control method based on big data platform that the present invention also provides a kind of is applied to described based on big
The air quantity variable air conditioner control system of data platform, comprising the following steps:
Step 1: determining the weight of the majorized function in the control host, the control model is set；
Step 2: the control host obtains the execution by the I/O data-interface or the RS485 communication interface
Device data read the sensing data by the I/O data-interface；
Step 3: the control host connects the actuator data and the sensing data by 4G communication
Mouth or the WAN communication interface are transmitted in the server data platform；
Step 4: on the server data platform, the sensing data that is uploaded using the host and described
Actuator data run the neural network intelligence learning program based on big data, carry out to the Nonlinear Prediction Models
Training obtains the parameter of the trained Nonlinear Prediction Models；
Step 5: returning the trained nonlinear prediction mould from the server data platform to the control host
The parameter of type；
Step 6: the parameter completion of the control trained Nonlinear Prediction Models of host computer is described non-linear
The configuration of prediction model is then based on the Nonlinear Prediction Models, solves the majorized function, meter using the optimization algorithm
Calculation obtains the optimal value of actuator described in subsequent time；
Step 7, the control host pass through the RS-485 communication interface and the I/O data-interface, hold described in adjustment
The optimal control of subsequent time is completed in the movement of row device.
Further, every a model training period repeating said steps two to step 5；Every a control period
Repeat step 6 and step 7.
Further, the model training period is at least 1 month；The control period is 5 minutes to 20 minutes.
In better embodiment of the invention, a kind of air quantity variable air conditioner control system based on big data platform is proposed
And method, the control host of the control system is built-in to be based on NARX (Dynamic neural network time series
Prediction) the room temperature prediction optimization of neural network prediction model controls program, and NARX neural network prediction model is logical
The training of server big data platform is crossed to obtain.Control system can need to provide according to the actual situation two kinds of PREDICTIVE CONTROL modes, i.e.,
Using temperature control precision as the high-precision control mode of target and with the Energy Saving Control mode of the minimum target of energy consumption.Using described
Control method is in the control system, so that it may the control of temperature is realized according to PREDICTIVE CONTROL mode.
Compared with prior art, the beneficial technical effect of the present invention lies in: it is put down using of the invention based on big data
The air quantity variable air conditioner control system and method for platform can facilitate the cloud big data platform deployment for realizing air conditioning system with variable, be
It establishes high-precision NARX neural network prediction model and has established big data basis.In addition, of the invention based on big data platform
Air quantity variable air conditioner control system can realize that the control of air conditioning system with variable High-accuracy indoor temperature and optimization are saved according to optimization aim
It can control, the optimal control suitable for comfort air conditioning system with variable and industrial air conditioning system with variable.
It is described further below with reference to technical effect of the attached drawing to design of the invention, specific structure and generation, with
It is fully understood from the purpose of the present invention, feature and effect.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of apparatus of the present invention embodiment.
Wherein, 1- controls host, 2-CPU arithmetic unit, 3- memory, 4-I/O data port, 5-RS485 communication interface, 6-
4G communication interface, 7-WAN communication interface, 8- power supply, 9- server data platform, 10- electric air valve actuator, 11- temperature pass
Sensor, 12- fan frequency conversion controller.
Specific embodiment
Multiple preferred embodiments of the invention are introduced below with reference to Figure of description, keep its technology contents more clear and just
In understanding.The present invention can be emerged from by many various forms of embodiments, and protection scope of the present invention not only limits
The embodiment that Yu Wenzhong is mentioned.
In the accompanying drawings, the identical component of structure is indicated with same numbers label, everywhere the similar component of structure or function with
Like numeral label indicates.The size and thickness of each component shown in the drawings are to be arbitrarily shown, and there is no limit by the present invention
The size and thickness of each component.Apparent in order to make to illustrate, some places suitably exaggerate the thickness of component in attached drawing.
As shown in Figure 1, apparatus of the present invention embodiment includes control host 1, CPU arithmetic unit 2, memory 3, I/O data terminal
4, RS485 of mouth communication interface 5,4G communication interface 6, WAN communication interface 7, power supply 8, server data platform 9, Electric air valve is held
Row device 10, temperature sensor 11 and fan frequency conversion controller 12.
Wherein, CPU arithmetic unit 2, memory 3, I/O data-interface 4, RS485 communication interface 5,4G communication interface 6, WAN are logical
Communication interface 7 and power supply 8 are placed in control host 1.Several electric air valve actuators 10 are integrated in VAV box for acquiring and controlling
Each valve area is made, and is connected with the input of I/O data port 4, output end.If (1 temperature sensor of dry temperature sensor 11
Air outlet is placed in for measuring supply air temperature, other temperature sensors are placed in each room area for measuring room temperature) and I/O
The input terminal of data port 4 is connected.Fan frequency conversion controller 12 is connected with RS485 interface 5.4G communication interface 6 passes through wireless network
Network is connected with server data platform 9, and WAN communication interface 7 is connected by cable network with server data platform 9.Control master
I/O data-interface 4 in machine 1 supports the physical signals such as 4~20mA and 0~10V/0~5V, can access various sensors and execution
The equipment such as device, RS485 interface 5 support modbusRTU, the serial port protocols such as modbusASCII, PPI, wan interface and 4G interface point
It is not communicated by wired and wireless mode and server data platform 9, to realize that the running state data of mechanical floor is saturating
Server data platform 9 is reached to be stored.A room temperature based on NARX neural network prediction model built in memory 3
Prediction optimization controls program.Server data platform 9 is equipped with the neural network intelligence learning program based on big data, for whole
Determine the input vector weight coefficient of the NARX neural network prediction model in memory 3 and the biasing coefficient of neuron.
Each temperature that the VAV box valve area signal data and temperature sensor 11 that electric air valve actuator 10 obtains obtain
It spends in the incoming control host 1 of input terminal that signal data passes through I/O data port 4, the blower frequency in fan frequency conversion controller 12
Rate signal data is by the way that in the incoming control host 1 of RS485 interface 5, the 4G communication that these signal datas pass through control host 1 is connect
6 or WAN of mouth communication interface 7 is transmitted in server data platform 9, and after data volume, which reaches certain, to be required, server data is flat
Neural network intelligence learning program in platform 9 carries out on-line study according to the data of acquisition, adjusts NARX neural network prediction mould
The input vector weight coefficient of type and the biasing coefficient of neuron, are then led to the coefficient adjusted by 4G communication interface 6 or WAN
Communication interface 7 returns to the NARX neural network prediction model in memory 3, in memory 3 based on NARX neural network prediction
The room temperature prediction optimization control program of model starts to start work, according to supply air temperature, return air temperature, blower frequency and each
The nearest historical juncture value of valve area predicts subsequent time blower frequency using optimization algorithm (such as particle swarm algorithm PSO)
With the optimal value of valve area.
In the present embodiment, in Control System Design, majorized function is obtained by following equation:
The majorized function is:
F3=ffan(k) (4)
yr(k+i)=αiy(k)+(1-αi) S, i=1 ..., P (5)
For formula (1) into formula (5), n is room number.Y (k) is real system output, i.e. room return air temperature (degree Celsius)；It is room return air temperature predicted value (degree Celsius)；yrIt (k+i) is reference locus；D is valve opening, D ∈ [0,100]；
(hundred-mark system)；ffanIt is blower frequency, ffan∈[fmin,fmax],(Hz)；P is prediction step number；W1, w2 and w3 are weight coefficients；Formula
5 give reference locus, wherein S is room temperature set point, and α is softening coefficient (0 < α < 1).
The control system of the present embodiment can need to provide according to the actual situation two kinds of PREDICTIVE CONTROL modes, i.e., controlled with temperature
Precision is for the high-precision control mode of target and with the Energy Saving Control mode of the minimum target of energy consumption.Work as w1=1, w2=0, w3=
When 0, control system is in high-precision optimized Control Mode, i.e., temperature control with high precision is optimization aim；Work as w1=0.5,
When w2=0.25, w3=0.25, control system is in optimal control for energy saving mode, i.e., with the minimum optimization mesh of system total energy consumption
Mark.
The present embodiment is controlled using method control as follows, the control method the following steps are included:
Step 1: determining the weight of the majorized function in the control host, the control model is set；
Step 2: the control host obtains the execution by the I/O data-interface or the RS485 communication interface
Device data read the sensing data by the I/O data-interface；
Step 3: the control host connects the actuator data and the sensing data by 4G communication
Mouth or the WAN communication interface are transmitted in the server data platform；
Step 4: on the server data platform, the sensing data that is uploaded using the host and described
Actuator data run the neural network intelligence learning program based on big data, carry out to the Nonlinear Prediction Models
Training obtains the parameter of the trained Nonlinear Prediction Models；
Step 5: returning the trained nonlinear prediction mould from the server data platform to the control host
The parameter of type；
Step 6: the parameter completion of the control trained Nonlinear Prediction Models of host computer is described non-linear
The configuration of prediction model is then based on the Nonlinear Prediction Models, solves the majorized function, meter using the optimization algorithm
Calculation obtains the optimal value of actuator described in subsequent time；
Step 7, the control host pass through the RS-485 communication interface and the I/O data-interface, hold described in adjustment
The optimal control of subsequent time is completed in the movement of row device.
Second step and third step are repeated every a model training period (at least one moon), every a control period (5-
20 minutes) repeat the 4th step and the 5th step.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that the ordinary skill of this field is without wound
The property made labour, which according to the present invention can conceive, makes many modifications and variations.Therefore, all technician in the art
Pass through the available technology of logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Scheme, all should be within the scope of protection determined by the claims.
Claims (10)
1. a kind of air quantity variable air conditioner control system based on big data platform, which is characterized in that the control system includes control
Host, actuator, sensor and server data platform adjust temperature according to control model；
The control host includes CPU arithmetic unit, memory, I/O data-interface, RS485 communication interface, 4G communication interface, WAN
Communication interface and power supply；The memory preserves the control program of the room temperature prediction optimization based on Nonlinear Prediction Models,
The control program uses optimization algorithm solving optimization function, for calculating the optimal value of the actuator；
The server data stage+module has the neural network intelligence learning program based on big data, described non-thread for adjusting
The parameter of property prediction model；
The control host is connected with the actuator by the I/O data-interface or the RS485 communication interface；
The control host is connected with the sensor by the I/O data-interface；
The control host is connected in a manner of wireless network with the server data platform the 4G communication interface, or
Person is connected in a manner of cable network with the server data platform the WAN communication interface；
The control host has transparent transmission function, and the running state data of the actuator and the sensor is led to by the 4G
Communication interface or the WAN communication interface are sent to the server data platform.
2. the air quantity variable air conditioner control system based on big data platform as described in claim 1, which is characterized in that
The Nonlinear Prediction Models are NARX neural network prediction models；
The parameter of the NARX neural network prediction model includes that input vector weight coefficient and neuron bias coefficient；
The optimization algorithm is particle swarm algorithm, genetic algorithm, differential evolution algorithm, immune algorithm, ant group algorithm, simulated annealing
One of algorithm, tabu search algorithm and random search algorithm；
The majorized function is:
F3=ffan(k) (4)
yr(k+i)=αiy(k)+(1-αi) S, i=1 ..., P (5)
For formula (1) into formula (5), n is room number, and y (k) is real system output, i.e. room return air temperature (degree Celsius)；
It is room return air temperature predicted value (degree Celsius)；yrIt (k+i) is reference locus；D is valve opening, D ∈ [0,100]；(percentage
System)；ffanIt is blower frequency, ffan∈[fmin,fmax],(Hz)；P is prediction step number；W1, w2 and w3 are weight coefficients；Formula 5 provides
Reference locus, wherein S is room temperature set point, α is softening coefficient (0 < α < 1).
3. the air quantity variable air conditioner control system based on big data platform as claimed in claim 1 or 2, which is characterized in that described
Actuator includes the electric air valve actuator being integrated in VAV box and fan frequency conversion controller, and the sensor includes multiple
Temperature sensor；
The electric air valve actuator is connect by the I/O data-interface with the control host；
The fan frequency conversion controller is connect by the RS485 communication interface with the control host；
The temperature sensor is connect by the I/O data-interface with the control host；
One temperature sensor is set to air outlet, remaining described temperature sensor is placed in each room area.
4. the air quantity variable air conditioner control system based on big data platform as claimed in claim 1 or 2, which is characterized in that described
Control system provides two kinds of PREDICTIVE CONTROL modes, is high-precision control mode and Energy Saving Control mode respectively.
5. the air quantity variable air conditioner control system based on big data platform as claimed in claim 1 or 2, which is characterized in that described
The current range that I/O data-interface is supported is 4mA to 20mA, and the voltage range of support includes 0V to 10V and 0V to 5V.
6. the air quantity variable air conditioner control system based on big data platform as claimed in claim 1 or 2, which is characterized in that described
RS485 interface supports modbusRTU, modbusASCII and PPI serial port protocol.
7. the air quantity variable air conditioner control system based on big data platform as claimed in claim 1 or 2, which is characterized in that described
Server data platform receives the data of one or more control host, completes the deployment of big data platform.
8. a kind of air quantity variable air conditioner control method based on big data platform, which is characterized in that be applied to such as claims 1 or 2
The air quantity variable air conditioner control system based on big data platform, comprising the following steps:
Step 1: determining the weight of the majorized function in the control host, the control model is set；
Step 2: the control host obtains the actuator number by the I/O data-interface or the RS485 communication interface
According to passing through the I/O data-interface and read the sensing data；
Step 3: the control host is by the actuator data and the sensing data, by the 4G communication interface or
The WAN communication interface is transmitted in the server data platform；
Step 4: the sensing data uploaded on the server data platform using the host and the execution
Device data run the neural network intelligence learning program based on big data, are trained to the Nonlinear Prediction Models,
Obtain the parameter of the trained Nonlinear Prediction Models；
Step 5: returning the trained Nonlinear Prediction Models from the server data platform to the control host
Parameter；
Step 6: the parameter of the control trained Nonlinear Prediction Models of host computer completes the nonlinear prediction
The configuration of model is then based on the Nonlinear Prediction Models, solves the majorized function using the optimization algorithm, calculates
To the optimal value of actuator described in subsequent time；
Step 7, the control host adjust the actuator by the RS-485 communication interface and the I/O data-interface
Movement, complete the optimal control of subsequent time.
9. the air quantity variable air conditioner control method based on big data platform as claimed in claim 8, which is characterized in that every one
Model training period repeating said steps two are to step 5；Step 6 and step 7 are repeated every a control period.
10. the air quantity variable air conditioner control method based on big data platform as claimed in claim 9, which is characterized in that the mould
Type is at least 1 month cycle of training；The control period is 5 minutes to 20 minutes.
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| CN111365828A (en) * | 2020-03-06 | 2020-07-03 | 上海外高桥万国数据科技发展有限公司 | Model prediction control method for realizing energy-saving temperature control of data center by combining machine learning |
| CN111536662A (en) * | 2020-04-25 | 2020-08-14 | 南京酷朗电子有限公司 | Network type fresh air system and regulation and control method based on big data analysis |
| CN111561772A (en) * | 2020-07-15 | 2020-08-21 | 上海有孚智数云创数字科技有限公司 | Cloud computing data center precision air conditioner energy-saving control method based on data analysis |
| CN111561772B (en) * | 2020-07-15 | 2020-10-02 | 上海有孚智数云创数字科技有限公司 | Cloud computing data center precision air conditioner energy-saving control method based on data analysis |
| CN113932351A (en) * | 2021-11-05 | 2022-01-14 | 上海理工大学 | Non-uniform temperature field real-time regulation and control system and method based on artificial intelligence algorithm |
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