CN113446713A - Digital twin-based intelligent data optimal control and energy saving method and system - Google Patents
Digital twin-based intelligent data optimal control and energy saving method and system Download PDFInfo
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- CN113446713A CN113446713A CN202110696717.4A CN202110696717A CN113446713A CN 113446713 A CN113446713 A CN 113446713A CN 202110696717 A CN202110696717 A CN 202110696717A CN 113446713 A CN113446713 A CN 113446713A
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000005457 optimization Methods 0.000 claims abstract description 57
- 230000007613 environmental effect Effects 0.000 claims abstract description 31
- 238000001816 cooling Methods 0.000 claims abstract description 20
- 230000001105 regulatory effect Effects 0.000 claims abstract description 17
- 238000001514 detection method Methods 0.000 claims abstract description 11
- 230000001276 controlling effect Effects 0.000 claims abstract description 9
- 238000012549 training Methods 0.000 claims description 26
- 239000012530 fluid Substances 0.000 claims description 16
- 239000000178 monomer Substances 0.000 claims description 15
- 238000004378 air conditioning Methods 0.000 claims description 10
- 238000005057 refrigeration Methods 0.000 claims description 5
- 238000005265 energy consumption Methods 0.000 abstract description 7
- 230000008569 process Effects 0.000 description 7
- 238000004891 communication Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
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- 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
- F24F2110/30—Velocity
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/40—Pressure, e.g. wind pressure
Abstract
The invention provides a digital twin-based intelligent data optimal control and energy saving method and system, wherein the method comprises the steps of acquiring environmental data sensed by a field detection unit in real time; acquiring the air conditioner cooling capacity requirement regulated and controlled by a user; determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cold quantity requirement; and controlling the input and output power of the central air conditioner according to the operation parameters. The data intelligent optimal control and energy saving method based on the digital twin can provide comfortable environment for users and simultaneously reduce the energy consumption of the air conditioner.
Description
Technical Field
The disclosure relates to the technical field of air conditioners, in particular to a digital twin-based intelligent data optimal control and energy saving method and system.
Background
The air conditioner can refrigerate in summer and heat in winter, can adjust the indoor temperature to be warm in winter and cool in summer, and provides a comfortable environment for users. While air conditioning provides comfort to the user, it is accompanied by a contradiction to high energy consumption. Energy consumption not only increases economic burden of users, but also is opposite to the trend of energy conservation and environmental protection. Therefore, how to reduce the energy consumption of the air conditioner while providing a comfortable environment for users by using the air conditioner is a problem that manufacturers of the air conditioner are trying to solve at present.
Disclosure of Invention
The embodiment of the disclosure provides a data intelligent optimal control and energy saving method and system based on digital twins, which can provide a comfortable environment for users and reduce the energy consumption of an air conditioner.
In a first aspect of the embodiments of the present disclosure, a digital twin-based data intelligent optimization and energy saving method is provided, where the method includes:
acquiring environmental data sensed by a field detection unit in real time;
acquiring the air conditioner cooling capacity requirement regulated and controlled by a user;
determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cold quantity requirement;
and controlling the input and output power of the central air conditioner according to the operation parameters.
In an alternative embodiment, the environmental data includes at least one of air temperature, air flow, barometric pressure, and air humidity.
In an alternative embodiment, the air conditioning cooling requirement comprises at least one of a target temperature, a target humidity and a target wind speed regulated by a user.
In an alternative embodiment, the data optimization model includes at least one of a monomer plant digital twin model, a system plant digital twin model, and a fluid mechanics, thermal model.
In an optional embodiment, before determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the cooling capacity requirement of the air conditioner, the method further comprises the following steps:
training the data optimization model, wherein the method of training the data optimization model comprises:
respectively training at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model in the data optimization model based on a plurality of training data,
the trained data are used for optimizing the operation parameters output by the model, so that the central air conditioner can be operated in the lowest power state while meeting the operation requirement.
In a second aspect of the embodiments of the present disclosure, a digital twin-based data intelligent optimization and energy saving system is provided, where the system includes:
the system comprises a first unit, a second unit and a control unit, wherein the first unit is used for acquiring environmental data sensed by a field detection unit in real time, and the environmental data comprises at least one of air temperature, air flow, atmospheric pressure and air humidity;
the second unit is used for acquiring the air conditioning refrigeration requirement regulated and controlled by a user, wherein the refrigeration requirement comprises at least one of target temperature, target humidity and target wind speed;
the third unit is used for determining the operating parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cooling capacity requirement, wherein the data optimization model comprises at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model;
and the fourth unit is used for controlling the input and output power of the central air conditioner according to the operation parameters.
In an alternative embodiment, the environmental data includes at least one of air temperature, air flow, barometric pressure, and air humidity.
In an alternative embodiment, the air conditioning cooling requirement comprises at least one of a target temperature, a target humidity and a target wind speed regulated by a user.
In an alternative embodiment, the data optimization model includes at least one of a monomer plant digital twin model, a system plant digital twin model, and a fluid mechanics, thermal model.
In an alternative embodiment, the system further comprises a fifth unit for:
training the data optimization model, wherein the method of training the data optimization model comprises:
respectively training at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model in the data optimization model based on a plurality of training data,
the trained data are used for optimizing the operation parameters output by the model, so that the central air conditioner can be operated in the lowest power state while meeting the operation requirement.
The intelligent data optimal control and energy saving method based on the digital twin comprises the steps of obtaining environmental data sensed by a field detection unit in real time; acquiring the air conditioner cooling capacity requirement regulated and controlled by a user; determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cold quantity requirement; and controlling the input and output power of the central air conditioner according to the operation parameters.
The central air conditioner can run in the lowest power state while meeting the running requirement by acquiring the field data and the air conditioner cooling capacity requirement regulated and controlled by the user, so that the energy consumption of the air conditioner is reduced while a comfortable environment is provided for the user.
Drawings
FIG. 1 is a schematic flow chart of a digital twin-based intelligent data optimization and energy saving method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a data intelligent optimization and energy saving system based on a digital twin according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present disclosure, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in the present disclosure, "including" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present disclosure, "plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in this disclosure, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present disclosure is explained in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 schematically illustrates a flow chart of a digital twin-based data intelligent optimization and energy saving method according to an embodiment of the present disclosure, where as shown in fig. 1, the method includes:
s101, acquiring environmental data sensed by a field detection unit in real time;
in an embodiment of the present disclosure, the environmental data includes at least one of air temperature, air flow, atmospheric pressure, and air humidity.
In practical application, the field detection system can acquire one or more data of air temperature, air flow, atmospheric pressure and air humidity, and upload the data to the data intelligent optimal control and energy-saving system, and the data intelligent optimal control and energy-saving system uploads the data to the data optimization model in real time, so that the data optimization model analyzes the optimal operation parameters.
Step S102, acquiring the air conditioning cold quantity requirement regulated and controlled by a user;
in the embodiment of the disclosure, the air conditioning cooling capacity requirement includes at least one of a target temperature, a target humidity and a target wind speed regulated and controlled by a user.
In practical application, a user can regulate and control the cold quantity requirement of the air conditioner according to the requirement of the user, and specifically, the user can regulate and control at least one of the target temperature, the target humidity and the target wind speed of the air conditioner through the control end. In the embodiment of the disclosure, after the user regulates and controls the air conditioner cooling capacity requirement, the data can be uploaded to the data intelligent optimal control and energy saving system, and the data intelligent optimal control and energy saving system uploads the data to the data optimization model in real time, so that the data optimization model analyzes the optimal operation parameters.
Step S103, determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cold quantity requirement;
in an embodiment of the disclosure, the data optimization model includes at least one of a monomer equipment digital twin model, a system equipment digital twin model, and a fluid mechanics and thermal model.
In practical application, the data optimization model can be constructed based on a neural network and used for continuously learning an optimal energy-saving strategy, so that the air conditioner can meet the requirement of cold quantity and can operate in the lowest power state.
In an optional embodiment, before determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the cooling capacity requirement of the air conditioner, the method further comprises the following steps:
training the data optimization model, wherein the method of training the data optimization model comprises:
respectively training at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model in the data optimization model based on a plurality of training data,
the trained data are used for optimizing the operation parameters output by the model, so that the central air conditioner can be operated in the lowest power state while meeting the operation requirement.
The operation parameters output by the trained data optimization model are enabled to meet the operation requirements of the central air conditioner and operate in the lowest power state at the same time through the training of the data optimization model. Wherein the training method includes adjusting model parameters of the data optimization model, including but not limited to adjusting model parameters of a monomer plant digital twin model, a system plant digital twin model, and a fluid mechanics, thermal model.
And step S104, controlling the input and output power of the central air conditioner according to the operation parameters.
The data intelligent optimal control and energy saving system can receive the real-time operation state of the air conditioner operation unit, issues the optimal control strategy to the corresponding operation unit, and controls the input and output power of the central air conditioner through the operation parameters, so that the central air conditioner can operate in the lowest power state while meeting the operation requirement.
The intelligent data optimal control and energy saving method based on the digital twin comprises the steps of obtaining environmental data sensed by a field detection unit in real time; acquiring the air conditioner cooling capacity requirement regulated and controlled by a user; determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cold quantity requirement; and controlling the input and output power of the central air conditioner according to the operation parameters.
The central air conditioner can run in the lowest power state while meeting the running requirement by acquiring the field data and the air conditioner cooling capacity requirement regulated and controlled by the user, so that the energy consumption of the air conditioner is reduced while a comfortable environment is provided for the user.
Fig. 2 is a schematic structural diagram schematically illustrating a digital twin-based data intelligent optimization and energy saving system according to an embodiment of the present disclosure, where as shown in fig. 2, the system includes:
the first unit 21 is configured to acquire environmental data sensed by the field detection unit in real time, where the environmental data includes at least one of air temperature, air flow, atmospheric pressure, and air humidity;
the second unit 22 is configured to acquire a cooling requirement of an air conditioner regulated by a user, where the cooling requirement includes at least one of a target temperature, a target humidity, and a target wind speed;
a third unit 23, configured to determine an operating parameter of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cooling requirement, where the data optimization model includes at least one of a single device digital twin model, a system device digital twin model, and a fluid mechanics and thermal model;
and a fourth unit 24 for controlling input and output power of the central air conditioner according to the operation parameters.
In an alternative embodiment, the environmental data includes at least one of air temperature, air flow, barometric pressure, and air humidity.
In an alternative embodiment, the air conditioning cooling requirement comprises at least one of a target temperature, a target humidity and a target wind speed regulated by a user.
In an alternative embodiment, the data optimization model includes at least one of a monomer plant digital twin model, a system plant digital twin model, and a fluid mechanics, thermal model.
In an alternative embodiment, the system further comprises a fifth unit for:
training the data optimization model, wherein the method of training the data optimization model comprises:
respectively training at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model in the data optimization model based on a plurality of training data,
the trained data are used for optimizing the operation parameters output by the model, so that the central air conditioner can be operated in the lowest power state while meeting the operation requirement.
It should be noted that the beneficial effects of the digital twin-based data intelligent optimization and energy saving system according to the embodiments of the present disclosure can refer to the beneficial effects of the digital twin-based data intelligent optimization and energy saving method, and the embodiments of the present disclosure are not described herein again.
The present disclosure also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present disclosure may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (10)
1. A data intelligent optimization and energy-saving method based on digital twin is characterized by comprising the following steps:
acquiring environmental data sensed by a field detection unit in real time;
acquiring the air conditioner cooling capacity requirement regulated and controlled by a user;
determining the operation parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cold quantity requirement;
and controlling the input and output power of the central air conditioner according to the operation parameters.
2. The method of claim 1, wherein the environmental data includes at least one of air temperature, air flow, barometric pressure, and air humidity.
3. The method of claim 1, wherein the air conditioning capacity requirement comprises at least one of a target temperature, a target humidity, and a target wind speed regulated by a user.
4. The method of claim 1, wherein the data optimization model comprises at least one of a monomer plant digital twin model, a system plant digital twin model, and a fluid mechanics, thermal model.
5. The method of claim 1, wherein before determining the operating parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the cooling capacity requirement of the air conditioner, the method further comprises:
training the data optimization model, wherein the method of training the data optimization model comprises:
respectively training at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model in the data optimization model based on a plurality of training data,
the trained data are used for optimizing the operation parameters output by the model, so that the central air conditioner can be operated in the lowest power state while meeting the operation requirement.
6. A data intelligent optimization and energy-saving system based on digital twin is characterized by comprising:
the system comprises a first unit, a second unit and a control unit, wherein the first unit is used for acquiring environmental data sensed by a field detection unit in real time, and the environmental data comprises at least one of air temperature, air flow, atmospheric pressure and air humidity;
the second unit is used for acquiring the air conditioning refrigeration requirement regulated and controlled by a user, wherein the refrigeration requirement comprises at least one of target temperature, target humidity and target wind speed;
the third unit is used for determining the operating parameters of the central air conditioner through a pre-trained data optimization model according to the environmental data and the air conditioner cooling capacity requirement, wherein the data optimization model comprises at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model;
and the fourth unit is used for controlling the input and output power of the central air conditioner according to the operation parameters.
7. The system of claim 6, wherein the environmental data includes at least one of air temperature, air flow, barometric pressure, and air humidity.
8. The system of claim 6, wherein the air conditioning refrigeration requirement comprises at least one of a target temperature, a target humidity, and a target wind speed regulated by a user.
9. The system of claim 6, wherein the data optimization model comprises at least one of a monomer plant digital twin model, a system plant digital twin model, and a fluid mechanics, thermal model.
10. The system according to claim 6, characterized in that the system further comprises a fifth unit for:
training the data optimization model, wherein the method of training the data optimization model comprises:
respectively training at least one of a monomer equipment digital twin model, a system equipment digital twin model and a fluid mechanics and thermal model in the data optimization model based on a plurality of training data,
the trained data are used for optimizing the operation parameters output by the model, so that the central air conditioner can be operated in the lowest power state while meeting the operation requirement.
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| CN114322199A (en) * | 2021-11-26 | 2022-04-12 | 嘉兴英集动力科技有限公司 | Ventilation system autonomous optimization operation regulation and control platform and method based on digital twins |
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