CN113757833A - Production mode and duty mode switching method and system based on data driving - Google Patents

Production mode and duty mode switching method and system based on data driving Download PDF

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CN113757833A
CN113757833A CN202111083670.0A CN202111083670A CN113757833A CN 113757833 A CN113757833 A CN 113757833A CN 202111083670 A CN202111083670 A CN 202111083670A CN 113757833 A CN113757833 A CN 113757833A
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air
clean room
mode
data
air supply
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CN113757833B (en
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李春旺
程煊锐
马晓钧
杨志成
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Beijing Union University
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Beijing Union University
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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
    • F24F3/00—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
    • F24F3/12—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the treatment of the air otherwise than by heating and cooling
    • F24F3/16—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems characterised by the treatment of the air otherwise than by heating and cooling by purification, e.g. by filtering; by sterilisation; by ozonisation
    • F24F3/167—Clean rooms, i.e. enclosed spaces in which a uniform flow of filtered air is distributed
    • 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
    • F24F11/65—Electronic processing for selecting an operating mode
    • 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
    • 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
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/40—Pressure, e.g. wind pressure
    • 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)
  • Physics & Mathematics (AREA)
  • Fluid Mechanics (AREA)
  • Signal Processing (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Air Conditioning Control Device (AREA)
  • Ventilation (AREA)

Abstract

The invention discloses a production mode and duty mode switching method and system based on data driving, which are used for a clean space formed by a plurality of clean rooms, wherein the clean space is provided with an air conditioning system, the air conditioning system comprises a blower and a frequency converter, and the method mainly comprises the following steps: debugging a plurality of working conditions of a plurality of air conditioning systems, and collecting working condition data in the debugging process; constructing a pressure gradient steady-state data set based on the working condition data, and constructing a pressure control data relation model corresponding to each clean room based on the pressure gradient steady-state data set; in the mode switching process, the air returning quantity of each clean room is accurately controlled through the stepping output of the frequency of the air feeder, the air supply quantity of each clean room and the output of the pressure control data relation model.

Description

Production mode and duty mode switching method and system based on data driving
Technical Field
The invention relates to the technical field of pressure gradient control of a clean room, in particular to a method and a system for switching a production mode and an on-duty mode based on data driving.
Background
High-grade clean rooms are a key infrastructure in the fields of pharmacy, chip manufacturing, operating rooms, biosafety, and the like. Due to the stringent cleanliness requirements, high-grade cleanrooms must maintain high air exchange times and require continuous operation. If the clean air conditioning system is stopped and started again in the non-production period, the verification needs to be carried out again, and the cost is far higher than that of continuous operation. Therefore, the operation of the clean air conditioning system in the non-production period causes a great waste of operation energy consumption, and increases the production cost.
The duty mode is a mode in which the clean air conditioning system greatly reduces the air supply amount of the clean room during the non-production period and maintains the cleanliness. The clean air conditioning system is switched from the production mode to the duty mode in the non-production period, so that the operation energy consumption can be greatly reduced. However, the bidirectional switching between the production mode and the duty mode is limited by the fact that an ordered pressure gradient must be maintained between the clean rooms in the switching process, if the pressure gradient is damaged, a serious risk of cross contamination is caused, the production safety is seriously affected, and the problem of the pressure gradient of the clean rooms becomes a bottleneck problem restricting the bidirectional switching between the production mode and the duty mode.
Accordingly, there is a need in the art for a new method for switching between a clean room production mode and a duty mode to solve the above problems.
Disclosure of Invention
The invention aims to provide a method and a system for switching a production mode and an on-duty mode based on data driving, which can realize the switching between the production mode and the on-duty mode, keep the pressure gradient between clean rooms stable and lay a foundation for the energy-saving operation of the clean rooms in a non-working state.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data-driven switching method between a production mode and an on-duty mode, which is used for a clean space consisting of a plurality of clean rooms, wherein the clean space is provided with an air conditioning system, the air conditioning system comprises a blower and a frequency converter, and the method comprises the following steps:
a model establishing step:
debugging m groups of working conditions of the air conditioning system and recording working condition data in the debugging process, wherein the working condition data at least comprise air supply volume, air return volume, pressure value and time parameter corresponding to each clean room;
dividing the collected working condition data into m working condition parameter matrixes according to the corresponding relation between the collected working condition data and m groups of working conditions;
selecting steady-state data in the working condition parameter matrix to form m steady-state working condition parameter matrices, wherein the steady-state data are working condition data in a time period meeting pressure gradient requirements among the clean rooms;
comparing the pressure values in the m steady-state working condition parameter matrixes with a preset pressure fluctuation range, and eliminating the pressure values which do not belong to the pressure fluctuation range and other parameters corresponding to the pressure values to form a pressure gradient steady-state data set;
removing abnormal points in the pressure gradient steady-state data set, and calculating the mean value of parameters corresponding to each working condition to form a working condition mean value parameter set corresponding to each working condition;
constructing a pressure control data relation model corresponding to each clean room based on the working condition mean parameter sets of all working conditions and linear regression; the input of the pressure control data relation model comprises air supply quantity, and the output of the pressure control data relation model comprises return air quantity;
a mode switching step:
blower frequency f set in operating modeWorker's toolBlower frequency f in on-duty modeValue ofAnd the air supply quantity Q of each clean room in the working modeWorker's toolAir supply amount Q in on-duty modeValue of；
Setting the adjustment step number n of mode switching and the step interval time t0；
Stepping of blower frequency in process of setting working mode to be switched to duty modeThe output value is: f ═ fAt present-(fWorker's tool-fValue of) N, wherein fAt presentThe frequency of the blower in the current state;
the stepping output value of the air supply volume of each clean room in the process of switching the working mode to the duty mode is set as follows: output value of air supply amount of each clean room: qsi＝QCurrent i-(QI worker-QValue i) N; wherein QsiOutput value of air supply amount for ith clean room, QCurrent iIs the air supply volume, Q, of the ith clean room in the current stateI workerIs the air supply amount in the working mode of the ith clean room, QValue iThe air supply amount is the air supply amount under the duty mode of the ith clean room;
in the process of switching the working mode to the duty mode, the stepping output value of the blower frequency is used as the working frequency of a frequency converter and drives the blower to act;
and taking the output value of the air supply quantity of each clean room as the input of the data relation model of the corresponding clean room, and controlling the return air of the corresponding clean room by the return air quantity output by the model.
In one embodiment, the "mode switching step" further includes:
setting the stepping output value of the blower frequency in the process of switching the duty mode to the working mode as follows: f ═ fAt present+(fWorker's tool-fValue of)/n；
The stepping output value of the air supply volume of each clean room in the process of switching the duty mode to the working mode is set as follows: output value of air supply amount of each clean room: qsi＝QCurrent i+(QI worker-QValue i)/n；
Taking the stepping output value of the blower frequency as the working frequency of the frequency converter and driving the blower to act in the process of switching the duty mode to the working mode;
and taking the output value of the air supply quantity of each clean room as the input of the data relation model of the corresponding clean room, and controlling the return air of the corresponding clean room by the output return air quantity.
In one embodiment, after the step of "modeling step", the method further comprises:
obtaining residual error e of each pressure control data relation modelm；
Judging the said emWhether or not-epsilon < e is satisfiedm＜+ε；
And if not, performing nonlinear data model fitting based on the working condition mean parameter sets of all the working conditions, and constructing a new pressure control data relation model.
In one embodiment, the step of constructing the pressure control data relation model corresponding to each clean room based on the set of working condition mean parameter of all the working conditions and using linear regression includes:
establishing a data regression relation between the air supply quantity and the air return quantity corresponding to each clean room in a linear regression mode based on the working condition mean parameter sets of all working conditions corresponding to each clean room;
the data regression relationship of the clean room is as follows: a is0+a1x; in the formula, a0、a1Is a regression coefficient; x represents the amount of air blown and y represents the amount of return air.
In one embodiment, the working condition data further comprises an air supply static pressure and an air return static pressure; the step of constructing the pressure control data relation model corresponding to each clean room based on the working condition mean parameter group of all the working conditions and linear regression comprises the following steps:
establishing a data regression relation among the air supply volume, the air return volume and the air supply static pressure corresponding to each clean room in a linear regression mode based on the working condition mean parameter sets of all working conditions corresponding to each clean room;
the data regression relationship of the clean room is as follows: a is0+a1x1+a2x2(ii) a In the formula, a0、a1、a2Is a regression coefficient; x is the number of1Indicating the amount of air supply, x2The static pressure of the supply air is shown, and the return air volume is shown as y.
A production mode and duty mode switching system based on data driving is used for a clean space formed by a plurality of clean rooms, the clean space is provided with an air conditioning system, the air conditioning system comprises an air feeder and a frequency converter, the air conditioning system is communicated with each clean room through an air supply pipeline and an air return pipeline, an air supply valve is arranged on the air supply pipeline of each clean room, an air return valve is arranged on the air return pipeline of each clean room, a pressure sensor is arranged in each clean room, the air supply valve is an air supply variable air volume valve, and the air return valve is an air return variable air volume valve; the switching system also comprises a storage module, a man-machine interaction module and a controller;
the storage module is used for storing system data, the human-computer interaction module is used for inputting and displaying state parameters of the system, and the controller is used for executing the data-drive-based production mode and duty mode switching method.
In one embodiment, the supply variable air valve and the return variable air valve are provided with air quantity detection sensors.
A production mode and duty mode switching system based on data driving is used for a clean space formed by a plurality of clean rooms, the clean space is provided with an air conditioning system, the air conditioning system comprises an air feeder and a frequency converter, the air conditioning system is communicated with each clean room through an air supply pipeline and an air return pipeline, an air supply valve is arranged on the air supply pipeline of each clean room, an air return valve is arranged on the air return pipeline of each clean room, a pressure sensor is installed in each clean room, a static pressure air supply pressure sensor is arranged on the air supply pipeline of the air conditioning system, the air supply valve is an air supply quick-opening valve, the air return valve is a return air quick-opening valve, and the system further comprises a storage module, a human-computer interaction module and a controller;
the storage module is used for storing system data, the human-computer interaction module is used for inputting and displaying state parameters of the system, and the controller is used for executing the data-drive-based production mode and duty mode switching method.
In one embodiment, a return air static pressure sensor is arranged on a return air pipeline of the air conditioning system.
The invention has the advantages that:
according to the data-driven production mode and duty mode switching method and system, the air return amount is accurately controlled through the air feeder frequency, the stepping output of the air supply amount of each clean room and the pressure control data relation model, the switching between the working mode and the duty mode under the condition of stable pressure gradient of the clean room is realized, the defect that the pollution risk is caused by the disordered pressure gradient of the clean room in the conventional switching method is overcome, and a foundation is laid for the energy-saving operation of the clean factory in the non-working state.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a control scenario of a data-driven production mode and duty mode switching system;
FIG. 2 is a schematic diagram of a control system for a data-driven production mode and duty mode switching system;
FIG. 3 is a schematic diagram of a main process flow of a data-driven production mode and duty mode switching method;
FIG. 4 is a block diagram of a control flow for switching the operation mode to the duty mode;
FIG. 5 is a schematic diagram of another control scenario for a data-driven production mode and shift mode switching system;
FIG. 6 is a schematic diagram of another control principle of a data-driven production mode and duty mode switching system;
fig. 7 is a control flow diagram of another operation mode switched to the duty mode.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.
The invention provides a production mode and duty mode switching method based on data driving, which is used for a clean space formed by a plurality of clean rooms, wherein the clean space is provided with an air conditioning system, the air conditioning system comprises a blower and a frequency converter, and the method mainly comprises a model establishing step and a mode switching step.
The model establishing step comprises:
step S11: and debugging the working conditions of the m groups of air conditioning systems and recording the working condition data in the debugging process. The working condition data at least comprises air supply volume, air return volume, pressure value and time parameter corresponding to each clean room.
Specifically, when the pressure control of the clean room adopts a fixed-delivery fixed-return control mode, m groups of working conditions can be debugged, and m is more than or equal to 3. The frequency of the air feeder and the air supply quantity and the air return quantity of each clean room can be manually adjusted until the requirement of pressure gradient between the clean rooms is met and the clean rooms are stabilized for a period of time. And recording the frequency of the air feeder, the air supply quantity and the air return quantity of each clean room, the pressure value of each clean room and corresponding time parameters in the debugging process according to a certain time interval. The data acquisition period can be 1s or 2s and the like, namely a large amount of working condition data can be acquired to meet the requirement of later use, and the specific sampling period is not limited. It should be noted that, in practical applications, the air supply amount and the air return amount are positively correlated with the opening of the air supply valve and the opening of the air return valve, so that the air supply amount and the air return amount can also be expressed by the opening of the air supply valve and the opening of the air return valve, and such equivalents shall fall within the protection scope of the present invention.
When the pressure control of the clean room adopts a fixed-delivery-return control mode, m groups of working conditions can be debugged, and m is more than or equal to 3. The frequency of the air feeder and the air supply quantity and the air return quantity of each clean room can be manually adjusted until the requirement of pressure gradient between the clean rooms is met and the clean rooms are stabilized for a period of time. And recording the frequency of the air feeder, the air supply quantity and the air return quantity of each clean room, the pressure value of each clean room, the air supply static pressure, the air return static pressure and corresponding time parameters in the debugging process according to a certain time interval. The data acquisition period can meet the data volume used in the later period, and the specific sampling period is not limited. Also, the air supply amount and the air return amount are equivalently replaced by the air supply valve opening degree and the air return valve opening degree.
Step S12: and dividing the collected working condition data into m working condition parameter matrixes according to the corresponding relation between the collected working condition data and m groups of working conditions.
Specifically, the collected working condition data can be arranged horizontally to form a parameter set, and the parameter sets are arranged longitudinally according to time sequence to form a parameter matrix. And dividing the parameter matrix into m working condition parameter matrices according to the corresponding relation between the parameter matrix and m groups of working conditions.
Step S13: and selecting steady-state data in the working condition parameter matrix to form m steady-state working condition parameter matrices.
Specifically, steady-state data in each working condition parameter matrix is screened, and the steady-state data are working condition data between the clean rooms within an operation time period meeting pressure gradient requirements.
Step S14: and comparing the pressure values in the m steady-state working condition parameter matrixes with a preset pressure fluctuation range, and eliminating the pressure values which do not belong to the pressure fluctuation range and other parameters corresponding to the pressure values to form a pressure gradient steady-state data set.
Specifically, the steady state data of each steady state condition parameter matrix is further screened, namely, each clean room pressure value and clean room pressure set value P in the steady state data of each condition parameter matrix are further screenediComparing, wherein the delta P is the maximum allowable value of the pressure fluctuation of the clean room, and eliminating the pressure value of the clean room which is more than or equal to PiB + Δ P and ≦ PiAnd the delta P value and all parameter values (data of the same parameter group) corresponding to the pressure value, so that the clean room pressure value of each working condition in the error allowable range and the corresponding time parameter, blower frequency, air supply quantity, air return quantity, air supply static pressure and air return static pressure are screened out, and a pressure gradient steady-state data set is formed.
Step S15: and removing abnormal points in the pressure gradient steady-state data set, and calculating the mean value of the parameters corresponding to each working condition to form a working condition mean value parameter group corresponding to each working condition.
Specifically, abnormal data in the pressure gradient steady-state data set are removed, then the mean value of parameters corresponding to each working condition is calculated, and a working condition mean value parameter group of each working condition point is formed. Taking the air supply amount as an example, all the air supply amounts corresponding to a certain working condition are added and divided by the number of the air supply amounts to obtain the average value of the air supply amounts corresponding to the working condition, and the average value of other parameters is obtained by analogy. And combining the mean values to obtain a working condition mean value parameter group corresponding to the working condition.
Step S16: and constructing a pressure control data relation model corresponding to each clean room based on the working condition mean parameter sets of all the working conditions and by adopting linear regression.
Specifically, a pressure control data relation model of each clean room is established according to the working condition mean parameter group of all the working conditions and by adopting linear regression.
For example, when the pressure control of the clean space belongs to a control mode of fixed delivery and fixed return, a data regression relation between the air supply quantity and the return air quantity corresponding to each clean room is established by adopting a linear regression mode based on the working condition mean parameter group of all working conditions corresponding to each clean room.
The data regression relationship of the clean room is as follows: a is0+a1x; in the formula, a0、a1Is a regression coefficient; x represents the amount of air blown and y represents the amount of return air. Namely, the input of the model is the air supply quantity, and the output is the return air quantity.
For example, when the pressure control of the clean space belongs to a control mode of changing constant delivery to return, a linear regression mode is adopted to establish a data regression relation among the air supply volume, the air return volume and the air supply static pressure corresponding to each clean room based on the working condition mean parameter group of all working conditions corresponding to each clean room.
The data regression relationship of the clean room is as follows: a is0+a1x1+a2x2(ii) a In the formula, a0、a1、a2Is a regression coefficient; x is the number of1Indicating the amount of air supply, x2The static pressure of the supply air is shown, and the return air volume is shown as y. That is, the input of the model is the air supply volume and the air supply static pressure, and the output of the model is the return air volume.
Step S17: and verifying the reliability of the pressure control data relation model.
In particular, aAnd performing reliability verification on the obtained pressure control data relation model through residual analysis, performing difference on the mean value of the return air amount of each working condition and the fitting value corresponding to the regression equation to obtain a residual, if the residual of each point is within an error range, indicating that the data model is accurate, and if the residual of each point deviates far from the original point, performing nonlinear data model fitting. More specifically, a residual e of each pressure control data relation model is obtainedm(ii) a Judgment emWhether or not-epsilon < e is satisfiedm< + epsilon; and if so, the obtained pressure control data relation model is reliable. And if not, performing nonlinear data model fitting based on the working condition mean parameter sets of all the working conditions, and constructing a new pressure control data relation model. And obtaining a pressure control data relation model corresponding to each clean room.
A mode switching step:
step S21: blower frequency f set in operating modeWorker's toolBlower frequency f in on-duty modeValue ofAnd the amount of air supply Q of each clean room in the operation modeWorker's toolAir supply amount Q in on-duty modeValue of；
Step S22: setting the adjustment step number n of mode switching and the step interval time t0If the total time for mode switching is t ═ nxt0
Step S23: setting the stepping output value of the blower frequency in the process of switching the working mode to the duty mode as follows: f ═ fAt present-(fWorker's tool-fValue of) And/n. Wherein f isAt presentThe frequency of the blower in the current state;
step S24: the stepping output value of the air supply volume of each clean room in the process of switching the working mode to the duty mode is set as follows: qsi＝QCurrent i-(QI worker-QValue i)/n。
Wherein QsiOutput value of air supply amount for ith clean room, QCurrent iIs the air supply volume, Q, of the ith clean room in the current stateI workerIs the air supply amount in the working mode of the ith clean room, QValue iThe air volume in the duty mode of the ith clean room.
Step S25: and in the process of switching the working mode to the duty mode, the stepping output value of the frequency of the air blower is used as the working frequency of the frequency converter and drives the air blower to operate.
Step S26: and taking the output value of the air supply amount of each clean room as the input of the data relation model of the corresponding clean room, and controlling the return air of the corresponding clean room by the return air amount output by the model.
Step S27: setting the stepping output value of the blower frequency in the process of switching the duty mode to the working mode as follows: f ═ fAt present+(fWorker's tool-fValue of)/n；
Step S28: the stepping output value of the air supply volume of each clean room in the process of switching the duty mode to the working mode is set as follows: output value of air supply amount of each clean room: qsi＝QCurrent i+(QI worker-QValue i)/n；
Step S29: in the process of switching the duty mode to the working mode, the stepping output value of the frequency of the air feeder is used as the working frequency of the frequency converter and drives the air feeder to act;
in step S30, the output value of the air output from each clean room is used as the input of the data relation model corresponding to the clean room, and the return air of the corresponding clean room is controlled by the output return air quantity.
It should be noted that although the foregoing embodiments describe each step as being in the above sequential order, those skilled in the art can understand that, in order to achieve the effect of the present embodiment, different steps need not be executed in such an order, and they may be executed simultaneously (in parallel) or in reverse order, and these simple variations are within the scope of the present invention.
In addition, the embodiment of the invention provides a production mode and duty mode switching system based on data driving. A clean space for a plurality of clean rooms are constituteed, clean space is equipped with air conditioning system, air conditioning system includes forced draught blower and converter, air conditioning system passes through supply air duct and return air duct connection every toilet, is equipped with air supply variable air volume valve on the supply air duct of every toilet, is equipped with return air variable air volume valve on the return air duct of every toilet, and air supply variable air volume valve and return air variable air volume valve are all from taking amount of wind detection sensor. Each clean room is provided with a pressure sensor, and the switching system also comprises a storage module, a human-computer interaction module and a controller; the storage module is used for storing system data, the human-computer interaction module is used for inputting and displaying state parameters of the system, and the controller is used for executing the method for switching the production mode and the duty mode based on data driving.
The embodiment of the invention also provides a production mode and duty mode switching system based on data driving, which is used for a clean space formed by a plurality of clean rooms, the clean space is provided with an air conditioning system, the air conditioning system comprises a blower and a frequency converter, the air conditioning system is connected with each clean room through an air supply pipeline and an air return pipeline, the air supply pipeline of each clean room is provided with an air supply quick-opening valve, the air return pipeline of each clean room is provided with an air return quick-opening valve, each clean room is internally provided with a pressure sensor, the air supply pipeline of the air conditioning system is provided with a static pressure air supply pressure sensor, and the air return pipeline of the air conditioning system is provided with a return static pressure sensor. The switching system also comprises a storage module, a man-machine interaction module and a controller; the storage module is used for storing system data, the human-computer interaction module is used for inputting and displaying state parameters of the system, and the controller is used for executing the method for switching the production mode and the duty mode based on data driving.
The technical solution in the embodiments of the present invention is described clearly and completely below by referring to the two specific embodiments in combination with the drawings.
Example 1
Referring to fig. 1 and 2, fig. 1 is a schematic diagram of a control scenario of a data-driven production mode and duty mode switching system. FIG. 2 is a schematic diagram of a control principle of a data-driven production mode and duty mode switching system. The clean space comprises clean rooms (11, 12, 13, 14 and 15), air supply variable air valves (with air supply quantity detection sensors) are arranged on air supply pipelines corresponding to the clean rooms (21, 22, 23, 24 and 25), return air variable air valves (with return air quantity detection sensors) are arranged on return air pipelines (31, 32, 33, 34 and 35), and pressure sensors (41, 42, 43, 44 and 45) are arranged in the clean rooms. 5 clean rooms share one set of air conditioning system. The air conditioning system includes a blower 51 and an inverter 52. And a storage module 61, a human-computer interaction module 62 and a controller 63 are also arranged. The storage module 61 and the human-computer interaction module 62 are both electrically connected with a controller 63, and the controller 63 is also electrically connected with the frequency converter 52, the air supply variable air volume valves (21, 22, 23, 24 and 25) and the return air volume variable air volume valves (31, 32, 33, 34 and 35) of each clean room. Each clean room is a pressure control mode with fixed delivery and fixed return.
Referring to fig. 3, a main process of a data-driven production mode and duty mode switching method is shown:
firstly, debugging the working condition of the clean space and collecting data
And setting the working condition 1 through the man-machine interaction module 62, namely manually adjusting the frequency of the frequency converter 52 to the frequency of the working condition 1, and manually adjusting the air supply variable air volume valve (21, 22, 23, 24, 25) of the clean room (11, 12, 13, 14, 15) to the air supply volume corresponding to the working condition 1. The pressure gradient is stabilized and maintained for a period of time by continuously adjusting the return air variable air valves (31, 32, 33, 34, 35). The above process is repeated to set the working conditions 2 and 3 … … to be not less than 3 groups.
The air supply (Q) of the clean rooms (11, 12, 13, 14, 15) is collected in real time through the air supply variable air valves (21, 22, 23, 24, 25), the return air variable air valves (31, 32, 33, 34, 35) and the pressure sensors (41, 42, 43, 44, 45)s1、Qs2、Qs3、Qs4、Qs5) Air return quantity (Q)h1、Qh2、Qh3、Qh4、Qh5) And pressure value (P)1、P2、P3、P4、P5). And simultaneously records the data collected during the debugging process into the storage module 61.
Second, a pressure gradient steady state data set is constructed
Taking the clean room 12 as an example to explain the data set construction process, firstly, the collected data is divided into m working condition parameter matrixes according to the corresponding relation of m groups of working conditions, namely, parameter groups arranged according to time sequence under each working condition. And secondly, screening steady-state data in each working condition parameter matrix, namely data within the stable running time of the pressure gradient. However, the device is not suitable for use in a kitchenThen, the steady state data of each working condition parameter matrix is further screened, namely the pressure value of the clean room 2 and the pressure set value P of the clean room 2 in the steady state data of each working condition parameter matrix arer2Comparing, wherein the delta P is the maximum allowable value of the pressure fluctuation of the clean room 2, and the pressure value of the clean room 2 is removed and is not less than Pr2B + Δ P and ≦ Pr2The numerical value of the delta P and all the parameter values corresponding to the pressure value, thereby screening the pressure value of the clean room 2 and the corresponding time, the frequency f of the blower and the air delivery Q of each working condition within the error allowable ranges2Air return Qh2And forming a data set construction under m (m is more than or equal to 3) groups of steady-state working conditions of the clean room 2. Pressure gradient steady state datasets for the other 4 cleanrooms were constructed in the same manner.
Thirdly, calculating a set of mean parameters of the working conditions
Taking the clean room 12 as an example to illustrate the working condition point mean value calculation process, all the operation parameters corresponding to each group of working conditions are extracted from the steady state data set corresponding to the clean room 12, including the pressure value P of the clean room2And air delivery quantity Qs2Air return Qh2And removing abnormal points in the parameters, calculating the mean value of the parameters corresponding to each group of working conditions, and forming a steady-state working condition mean value parameter group of the working conditions of the clean room 12. And calculating the mean value of the work state working condition points of other 4 clean rooms in the same way.
Fourthly, establishing a pressure control data relation model of the clean room
Taking the clean room 12 as an example to explain the model building process, the data relationship between the air supply amount and the air return amount of the clean room 12 is built in a linear regression mode according to the steady-state working condition mean parameter set of all the working condition points of the clean room 12 built above. The return air volume is a dependent variable (output parameter) and the regression relation of the clean room 12 is y ═ a0+a1x，a0、a1Is a regression coefficient; x represents the amount of air blown and y represents the amount of return air. The pressure control data relationship model for the other 4 cleanrooms was built in the same manner.
Fifth, the reliability of the pressure control data relation model is analyzed
The pressure control data relation model reliability analysis is explained by taking the clean room 12 as an example, the reliability of the model is verified through residual analysis, and the residual of the clean room 12 is em＝ym-ym’,emIs the mean value y of each operating pointmFitting value y obtained corresponding to regression equationm' the difference, m is the number of operating points, when emSatisfy-epsilon < emWhen the value is less than epsilon, the data model is reliable; when each point residual error emNot less than + epsilon or emWhen the deviation is less than or equal to epsilon, the data model is not reliable, and epsilon is the maximum deviation. And fitting a nonlinear data model to establish a new pressure control data relation model.
Sixthly, step output parameters, namely the frequency of the air blower and the step output value of the air supply volume of each clean room are obtained
Firstly, the fan frequency f of the working mode is given through the man-machine interaction module 62Worker's toolBlower frequency f in on duty modeValue ofAir volume (Q) of each clean room operation modeI1. the、QWorker 2、QI3、QI4. the product、QTool 5) Air volume (Q) of each clean room duty modeValue 1、QValue 2、QValue 3、QValue 4、QValue 5). Setting the step interval time to t0The number of adjustment steps for mode switching is set to n, and the total time for mode switching is t ═ n × t0. The step value of the frequency regulation of the blower is fStep size＝(fWorker's tool-fValue of) The step output value of each fan frequency is f ═ fAt present-(fWorker's tool-fValue of) And/n. The clean room 12 is taken as an example to illustrate the step of air supply adjustment and the step output value of each adjustment of the air supply quantity, and the step length of the adjustment of the air supply quantity of the clean room 12 is QStep size 2＝(QWorker 2-QValue 2) The step output value of each adjustment of the air supply quantity of the clean room 12 is Qs2＝QCurrent 2-(QWorker 2-QValue 2)/n。
Seventh, switching process between working mode and duty mode
Referring to fig. 4, a control flow diagram for switching the operation mode to the duty mode is shown. Feeding the above-mentioned material toThe step output value f of each adjustment of the fan frequency is used as an input value of the inverter 21 to drive the operation of the blower 23. At the same time, the step output value (Q) of each adjustment of the air output of the clean rooms (11, 12, 13, 14, 15)s1、Qs2、Qs3、Qs4、Qs5) Calculating the return air volume (Q) by the pressure control data relation model of each clean room as the input value corresponding to the pressure control data relation modelh1、Qh2、Qh3、Qh4、Qh5) The operation of return air variable air volume valves (31, 32, 33, 34, 35) is driven. The blower 51 operates simultaneously with the blower variable air volume valves (21, 22, 23, 24, 25), and the switching between the operation mode and the duty mode is completed. It should be noted that, the above-mentioned process is only the switching process from the working mode to the duty mode, and in the switching process from the duty mode to the working mode, the step output value of the fan frequency at each time is f ═ fAt present+(fWorker's tool-fValue of) N, and the step output value of each adjustment of the air supply amount of the clean room 2 is Qs2＝QCurrent 2+(QWorker 2-QValue 2) And/n, the rest processes are the same, and are not described again.
Example 2
Referring to fig. 5 and 6, fig. 5 is a schematic diagram illustrating a control scenario of a data-driven production mode and duty mode switching system. FIG. 6 is a schematic diagram of a control principle of a data-driven production mode and duty mode switching system. Different from the embodiment 1, the embodiment is that all the variable air volume valves are replaced by quick-opening valves, the clean spaces comprise clean rooms (11, 12, 13, 14, 15), the air supply quick-opening valves (71, 72, 73, 74, 75) are arranged on air supply pipelines corresponding to the clean rooms, return air quick-opening valves (81, 82, 83, 84, 85) are arranged on return air pipelines, and pressure sensors (41, 42, 43, 44, 45) are arranged in the clean rooms. 5 clean rooms share one set of air conditioning system. The air conditioning system includes a blower 51 and an inverter 52. The air supply pipeline of the air conditioning system is provided with an air supply static pressure sensor 91, and the return air pipeline is provided with a return air static pressure sensor 92. And the supply air static pressure sensor 91 and the return air static pressure sensor 92 are electrically connected to the controller 63. The clean room of the clean space is a pressure control mode for changing the fixed delivery into the return.
Firstly, clean area working condition adjustment and data acquisition
And setting the working condition 1 through the man-machine interaction module 26, namely manually adjusting the frequency of the frequency converter 21 to the frequency of the working condition 1, and manually adjusting the air supply quick-opening valves (71, 72, 73, 74 and 75) of the clean rooms (11, 12, 13, 14 and 15) to the air supply amount corresponding to the working condition 1. The return air quick-opening valves (81, 82, 83, 84, 85) are continuously adjusted until the pressure gradient is stable and continues for a period of time. The above process is repeated to set the working conditions 2 and 3 … … to be not less than 3 groups.
The opening degree (Q) of the air supply valves of the clean rooms (11, 12, 13, 14, 15) is acquired in real time through the air supply quick-opening valves (71, 72, 73, 74, 75), the return air quick-opening valves (81, 82, 83, 84, 85) and the pressure sensors (41, 42, 43, 44, 45)s1、Qs2、Qs3、Qs4、Qs5) Opening degree of air return valve (Q)h1、Qh2、Qh3、Qh4、Qh5) And pressure value (P)1、P2、P3、P4、P5) The real-time air supply static pressure P is collected by an air supply static pressure sensor 24Quiet. And simultaneously records the data collected during the debugging process into the storage module 61.
Second, a pressure gradient steady state data set is constructed
Taking the clean room 12 as an example to explain the data set construction process, firstly, the collected data is divided into m working condition parameter matrixes according to the corresponding relation of m groups of working conditions, namely, parameter groups arranged according to time sequence under each working condition. And secondly, screening steady-state data in each working condition parameter matrix, namely data within the stable running time of the pressure gradient. Then, the steady state data of each working condition parameter matrix is further screened, namely the pressure value of the clean room 12 and the pressure set value P of the clean room 2 in the steady state data of each working condition parameter matrix arer2Comparing, and removing the pressure value of the clean room 12 which is not less than Pr2B + Δ P and ≦ Pr2- Δ P and all the parameter values corresponding to the pressure values, wherein Δ P is the maximum allowable value of the pressure fluctuation of clean room 12, so as to screen out the pressure values of clean room 12 and the corresponding pressure values for each working condition within the allowable error rangeTime, blower frequency f, blower valve opening Qs2Opening degree Q of air return valveh2And forming a pressure gradient steady-state data set construction under m (m is more than or equal to 3) groups of steady-state working conditions of the clean room 2. Pressure gradient steady state datasets for the other 4 cleanrooms were constructed in the same manner.
Thirdly, calculating a set of mean parameters of the working conditions
Taking the clean room 12 as an example to explain the working condition point mean value calculation process, all the operation parameters corresponding to each group of working conditions are extracted from the steady-state data set corresponding to the clean room 12, abnormal points in the parameters are removed, the mean value of the parameters corresponding to each group of working conditions is calculated, and a working condition mean value parameter group of the working condition points of the clean room 12 is formed. The same way is used to calculate the set of working condition mean parameters of other 4 clean rooms.
Fourthly, establishing a pressure control data relation model of the clean room
Taking the clean room 12 as an example to explain the establishment process of the clean room pressure control data relationship model, according to the set working condition mean parameter group of all the working condition points of the clean room 12, the data relationship among the air supply valve opening, the air return valve opening and the air supply static pressure of the clean room 12 is established in a linear regression mode. The return air valve opening degree is a dependent variable (output parameter) and the regression relation of the clean room 12 is that y is a0+a1x1+a2x2，a0、a1、a2Is a regression coefficient; x is the number of1Indicating the opening of the blower valve, x2Representing the static pressure of the supply air and y representing the opening degree of the return air valve. The other 4 cleanroom data models were built in the same way.
Fifth, the reliability of the pressure control data relation model is analyzed
The pressure control data relation model reliability analysis is explained by taking the clean room 12 as an example, and the residual error of the clean room 12 is e through the reliability verification of the residual error analysis verification modelm＝ym-ym’,emIs the mean value y of each operating pointmFitting value y obtained corresponding to regression equationm' the difference, m is the number of operating points, when emSatisfy-epsilon < emWhen the value is less than epsilon, the data model is reliable; when each point residual error emNot less than + epsilon or emWhen the deviation is less than or equal to epsilon, the data model is not reliable, and epsilon is the maximum deviation. And fitting a nonlinear data model to establish a new pressure control data relation model.
Sixthly, step output parameters, namely the frequency of the air blower and the step output value of the air supply volume of each clean room are obtained
Firstly, the fan frequency f of the working mode is given through the man-machine interaction module 62Worker's toolBlower frequency f in on duty modeValue ofOpening degree (Q) of air supply valve in each clean room operation modeI1. the、QWorker 2、QI3、QI4. the product、QTool 5) Air supply opening (Q) of each clean room duty modeValue 1、QValue 2、QValue 3、QValue 4、QValue 5). Setting the step interval time to t0The number of adjustment steps for mode switching is set to n, and the total time for mode switching is t ═ n × t0. The step value of the frequency regulation of the blower is fStep size＝(fWorker's tool-fValue of) The step output value of each fan frequency is f ═ fWorker's tool-(fWorker's tool-fValue of) The ratio of the sum of the absolute values of the/n. The step length of air supply adjustment and the step output value of each adjustment of the air supply quantity are described by taking the clean room 12 as an example, and the step value of the adjustment of the air supply quantity of the clean room 12 is QStep size 2＝(QWorker 2-QValue 2) The step output value of each adjustment of the air supply quantity of the clean room 12 is Qs2＝QWorker 2-(QWorker 2-QValue 2)/n。
Seventh, switching process between working mode and duty mode
Fig. 7 is a block diagram of a control flow for switching the operation mode to the duty mode. The step output value f obtained by adjusting the blower frequency each time is used as an input value of the inverter 52, and the operation of the blower 51 is driven. At the same time, the step output value (Q) of each adjustment of the air output of the clean rooms (11, 12, 13, 14, 15)s1、Qs2、Qs3、Qs4、Qs5) And static pressure P of air supplyQuietAs a relational model of corresponding pressure control dataInput value, calculating to obtain air return quantity (Q) through corresponding pressure control data relation model of each clean roomh1、Qh2、Qh3、Qh4、Qh5) The operation of the return air quick-opening valves (81, 82, 83, 84, 85) is driven. The blower 51 operates simultaneously with the blower quick-opening valves (71, 72, 73, 74, 75), and the switching between the operation mode and the duty mode is completed. It should be noted that, the above-mentioned process is only the switching process from the working mode to the duty mode, and in the switching process from the duty mode to the working mode, the step output value of the fan frequency at each time is f ═ fAt present+(fWorker's tool-fValue of) N, and the step output value of each adjustment of the air supply amount of the clean room 2 is Qs2＝QCurrent 2+(QWorker 2-QValue 2) And/n, the rest processes are the same, and are not described again.
In summary, the data-driven production mode and duty mode switching method and system provided by the invention realize the switching between the working mode and the duty mode under the condition of stable pressure gradient of the clean room through the stepping output of the frequency of the air blower and the air output of each clean room and the accurate control of the air return amount by the pressure control data relation model, overcome the defect that the existing switching method can cause the disturbance of the pressure gradient of the clean room to cause pollution risk, and lay the foundation for the energy-saving operation of the clean factory in the non-working state.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (9)

1. A data-driven switching method between a production mode and an on-duty mode, which is used for a clean space formed by a plurality of clean rooms, wherein the clean space is provided with an air conditioning system, the air conditioning system comprises a blower and a frequency converter, and the method comprises the following steps:
a model establishing step:
debugging m groups of working conditions of the air conditioning system and recording working condition data in the debugging process, wherein the working condition data at least comprise air supply volume, air return volume, pressure value and time parameter corresponding to each clean room;
dividing the collected working condition data into m working condition parameter matrixes according to the corresponding relation between the collected working condition data and m groups of working conditions;
selecting steady-state data in the working condition parameter matrix to form m steady-state working condition parameter matrices, wherein the steady-state data are working condition data in a time period meeting pressure gradient requirements among the clean rooms;
comparing the pressure values in the m steady-state working condition parameter matrixes with a preset pressure fluctuation range, and eliminating the pressure values which do not belong to the pressure fluctuation range and other parameters corresponding to the pressure values to form a pressure gradient steady-state data set;
removing abnormal points in the pressure gradient steady-state data set, and calculating the mean value of parameters corresponding to each working condition to form a working condition mean value parameter set corresponding to each working condition;
constructing a pressure control data relation model corresponding to each clean room based on the working condition mean parameter sets of all working conditions and linear regression; the input of the pressure control data relation model comprises air supply quantity, and the output of the pressure control data relation model comprises return air quantity;
a mode switching step:
blower frequency f set in operating modeWorker's toolBlower frequency f in on-duty modeValue ofAnd the air supply quantity Q of each clean room in the working modeWorker's toolAir supply amount Q in on-duty modeValue of；
Setting the adjustment step number n of mode switching and the step interval time t0；
Setting the stepping output value of the blower frequency in the process of switching the working mode to the duty mode as follows: f ═ fAt present-(fWorker's tool-fValue of) N, wherein fAt presentThe frequency of the blower in the current state;
setting workThe stepping output value of the air supply volume of each clean room in the process of switching the mode to the duty mode is as follows: output value of air supply amount of each clean room: qsi＝QCurrent i-(QI worker-QValue i) N; wherein QsiOutput value of air supply amount for ith clean room, QCurrent iIs the air supply volume, Q, of the ith clean room in the current stateI workerIs the air supply amount in the working mode of the ith clean room, QValue iThe air supply amount is the air supply amount under the duty mode of the ith clean room;
in the process of switching the working mode to the duty mode, the stepping output value of the blower frequency is used as the working frequency of a frequency converter and drives the blower to act;
and taking the output value of the air supply quantity of each clean room as the input of the data relation model of the corresponding clean room, and controlling the return air of the corresponding clean room by the return air quantity output by the model.
2. The method of claim 1, wherein the mode switching step further comprises:
setting the stepping output value of the blower frequency in the process of switching the duty mode to the working mode as follows: f ═ fAt present+(fWorker's tool-fValue of)/n；
The stepping output value of the air supply volume of each clean room in the process of switching the duty mode to the working mode is set as follows: output value of air supply amount of each clean room: qsi＝QCurrent i+(QI worker-QValue i)/n；
Taking the stepping output value of the blower frequency as the working frequency of the frequency converter and driving the blower to act in the process of switching the duty mode to the working mode;
and taking the output value of the air supply quantity of each clean room as the input of the data relation model of the corresponding clean room, and controlling the return air of the corresponding clean room by the output return air quantity.
3. The data-driven-based method for switching between production mode and duty mode as claimed in claim 2, wherein after the step of "modeling step", the method further comprises:
obtaining residual error e of each pressure control data relation modelm；
Judging the said emWhether or not-epsilon < e is satisfiedm＜+ε；
And if not, performing nonlinear data model fitting based on the working condition mean parameter sets of all the working conditions, and constructing a new pressure control data relation model.
4. The method according to claim 2, wherein the step of constructing the pressure control data relation model corresponding to each clean room based on the operating condition mean parameter set of all the operating conditions and linear regression comprises:
establishing a data regression relation between the air supply quantity and the air return quantity corresponding to each clean room in a linear regression mode based on the working condition mean parameter sets of all working conditions corresponding to each clean room;
the data regression relationship of the clean room is as follows: a is0+a1x; in the formula, a0、a1Is a regression coefficient; x represents the amount of air blown and y represents the amount of return air.
5. The data-driven-based method for switching between the production mode and the duty mode as claimed in claim 2, wherein the operating condition data further comprises a static pressure of an air supply and a static pressure of a return air;
the step of constructing the pressure control data relation model corresponding to each clean room based on the working condition mean parameter group of all the working conditions and linear regression comprises the following steps:
establishing a data regression relation among the air supply volume, the air return volume and the air supply static pressure corresponding to each clean room in a linear regression mode based on the working condition mean parameter sets of all working conditions corresponding to each clean room;
the data regression relationship of the clean room is as follows: a is0+a1x1+a2x2(ii) a Formula (II)In (a)0、a1、a2Is a regression coefficient; x is the number of1Indicating the amount of air supply, x2The static pressure of the supply air is shown, and the return air volume is shown as y.
6. A production mode and duty mode switching system based on data driving is used for a clean space formed by a plurality of clean rooms, the clean space is provided with an air conditioning system, the air conditioning system comprises an air feeder and a frequency converter, the air conditioning system is communicated with each clean room through an air supply pipeline and an air return pipeline, an air supply valve is arranged on the air supply pipeline of each clean room, an air return valve is arranged on the air return pipeline of each clean room, and each clean room is internally provided with a pressure sensor; the switching system also comprises a storage module, a man-machine interaction module and a controller;
the storage module is used for storing system data, the human-computer interaction module is used for inputting and displaying state parameters of the system, and the controller is used for executing the method for switching the production mode and the duty mode based on the data driving according to any one of claims 1 to 4.
7. The data-driven based switching system between production mode and duty mode as claimed in claim 6, wherein said variable supply air valve and said variable return air valve are provided with air volume detecting sensors.
8. A production mode and duty mode switching system based on data driving is used for a clean space formed by a plurality of clean rooms, the clean space is provided with an air conditioning system, the air conditioning system comprises an air feeder and a frequency converter, the air conditioning system is communicated with each clean room through an air supply pipeline and an air return pipeline, an air supply valve is arranged on the air supply pipeline of each clean room, an air return valve is arranged on the air return pipeline of each clean room, and a pressure sensor is arranged in each clean room;
the storage module is used for storing system data, the human-computer interaction module is used for inputting and displaying state parameters of the system, and the controller is used for executing the method for switching the production mode and the duty mode based on data driving according to any one of claims 1, 2, 3 or 5.
9. The data-driven switching system between production mode and duty mode as claimed in claim 8, wherein a return air static pressure sensor is provided on a return air duct of said air conditioning system.
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