CN114811840A - Intelligent air conditioner remote operation and fault early warning control method based on Internet of things - Google Patents
Intelligent air conditioner remote operation and fault early warning control method based on Internet of things Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000032683 aging Effects 0.000 claims abstract description 26
- 238000010438 heat treatment Methods 0.000 claims description 12
- 238000005057 refrigeration Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 6
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000036541 health Effects 0.000 abstract description 4
- 238000012821 model calculation Methods 0.000 abstract description 2
- 238000001816 cooling Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
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- 238000009434 installation Methods 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
-
- 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
-
- 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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
-
- 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
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
-
- 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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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention is suitable for the technical field of the Internet of things, and provides various intelligent air conditioner remote operation and fault early warning control methods based on the Internet of things. The method comprises the following steps: acquiring the historical running time and the service life time of the air conditioner to form an aging parameter; establishing a time power consumption operation model of the air conditioner, and calculating expected time and expected power consumption required for reaching a set temperature through the time power consumption operation model; and then correcting according to the number of the people in the room and the door opening and closing state to finally obtain an operation reference value. Therefore, the method can automatically detect the running condition of the air conditioner through model calculation, and give fault early warning, thereby being convenient for knowing the health condition of the air conditioner, being convenient for manufacturers to actively invite users to overhaul the air conditioner, and improving the use friendliness of the air conditioner.
Description
Technical Field
The invention belongs to the technical field of Internet of things, and particularly relates to an intelligent air conditioner remote operation and fault early warning control method based on the Internet of things.
Background
Along with the intelligent development of air conditioners, more and more air conditioners have the internet function. The user can operate the running state of the air conditioner through the mobile phone APP software, and meanwhile, the software can also enable the user to visually know the air conditioner through the running time, the power consumption and the like of the air conditioner. And the air conditioner can also be turned on and off at regular time at present, and the product functions are enriched.
However, even so, there is no solution for manufacturers to actively detect the user air conditioner. The user also can not know whether the current air conditioner is normal, and even if the refrigerating capacity of the outgoing air conditioner is insufficient and the indoor cooling effect is not good, the user can not clearly know whether the air conditioner is normal. Unless obvious faults occur, such as the air conditioner cannot be started, the air conditioner cannot be cooled, and the like, the manufacturer is contacted to overhaul at the moment. However, the method can not enable the user to clearly know the actual condition of the air conditioner, and the manufacturer can not perform fault early warning on the air conditioner of the user, so that the user experience is obviously reduced.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method for controlling intelligent air conditioner remote operation and fault early warning based on the internet of things, and aims to solve the technical problem that the existing air conditioner cannot perform automatic fault detection and reporting.
The invention adopts the following technical scheme:
the intelligent air conditioner remote operation and fault early warning control method based on the Internet of things comprises the following steps:
acquiring the historical running time and the service life time of the air conditioner to form an aging parameter;
establishing a time power consumption operation model of the air conditioner, wherein parameters of the model comprise a refrigeration and heating mode, a set temperature, an outdoor temperature, an indoor temperature, a wind speed, an indoor area and an aging parameter;
according to the set temperature and the wind speed set by the current user, acquiring a current refrigeration and heating mode, an outdoor temperature, an indoor area and an aging parameter, and calculating the expected time and the expected power consumption required for reaching the set temperature through a time power consumption operation model;
pushing the expected time, the expected power consumption, the actual time of the air conditioner operation and the actual power consumption to a user mobile phone end through a home gateway;
calculating an operation reference value according to the number of the rooms, the door opening and closing state, the received expected time, expected power consumption, actual time and actual power consumption selected by the user at the mobile phone end;
and if the operation reference value is larger than the operation threshold value, displaying the fault condition of the air conditioner and pushing the fault condition to a manufacturer management background through a home gateway or a data network.
Further, the historical running time is in hours, the service life time is in months, an aging table is established, and the longer the historical running time and the service life time are, the larger the aging parameter is.
Further, the time power consumption operation model comprises a time sub-model and a power consumption sub-model; wherein the time submodel is
The power consumption submodel is
Wherein S is the indoor area, t 1 For historical run time, t 2 To age time, T 2 Is the outdoor temperature, T 1 Is the indoor temperature, T 0 For setting the temperature, d is the constant wind speed step, X (S) is the area function, Y (t) 1 ,t 2 ) As a function of the aging parameter, F (T) 2 ) As a function of the outdoor temperature, G (d) as a function of the wind speed, K 1 、K 2 The parameters are adjustment parameters of a refrigeration and heating mode, T is expected time, and W is expected power consumption.
Further, if the actual time is T ', the actual power consumption is W', and then the reference value is operated
Wherein alpha is the number of the room, beta is the state of opening and closing the door, R (alpha) is a human number correction function, and M (beta) is an opening and closing door correction function.
Further, the method further comprises:
and in a manufacturer management background, performing data analysis according to the received air conditioner fault condition, pushing a fault item which possibly occurs to a mobile phone end of a user, and reserving to go to the home for overhaul.
The invention has the beneficial effects that: according to the invention, a time power consumption operation model is established, detection is automatically carried out according to the operation condition of the air conditioner, a user can correct at a mobile phone end to finally obtain an operation reference value, the operation reference value is compared with an operation threshold value, if the operation reference value exceeds the operation threshold value, the current air conditioner working condition has a certain problem, the data can be displayed at the mobile phone end of the user and synchronized to a manufacturer management background, background workers can further analyze the data, confirm possible faults and push the faults to the mobile phone of the user, and finally the home visit maintenance can be reserved; therefore, the user can know the health state of the current air conditioner more clearly through the mode, the manufacturer personnel can be overhauled in time, the fault can be prevented from being further expanded, and the trust of the user to the manufacturer can be improved; meanwhile, extra profits can be brought to manufacturers for overhauling the air conditioner by users.
Drawings
Fig. 1 is a structural diagram of an air conditioner early warning system provided in an embodiment of the present invention;
fig. 2 is a flowchart of an intelligent air conditioner remote operation and fault early warning control method based on the internet of things according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 illustrates a structure of an air conditioner early warning system according to an embodiment of the present invention, and only a portion related to the embodiment of the present invention is illustrated for convenience of description.
The method is realized based on the air conditioner early warning system shown in the figure 1, and comprises a manufacturer management background, a home gateway, a user mobile phone and an air conditioner. The air conditioner network is connected to the home gateway, and the mobile phone is connected with the home gateway or directly connected with the manufacturer management background through mobile data. And the home gateway is also connected with a manufacturer management background network.
For the above system, an embodiment of the present invention provides an intelligent air conditioner remote operation and fault early warning control method based on the internet of things, as shown in fig. 2, including the following steps:
and step S1, acquiring the historical running time and the service life time of the air conditioner, and forming an aging parameter.
The healthy operation state of the air conditioner is related to the historical operation time and the service life time of the air conditioner. The historical operating time is the total power-on operating time by the air conditioner, and the longer the operating time, the faster the air conditioner ages. And meanwhile, the air conditioner is related to the service life time, and even if the air conditioner is not electrified to operate, the air conditioner can be naturally aged. Both factors are therefore taken into account. Since the aging of the air conditioner inevitably affects the operation condition of the air conditioner, an aging parameter needs to be acquired, and a time power consumption operation model of the air conditioner is adjusted.
Generally, the energization operating time is in units of hours, and the lifetime time is in units of months. And establishing an aging table, wherein the longer the historical running time and the service life time are, the larger the aging parameter is. The aging table may be represented by an aging parameter function. For example, may be represented as Y (t) 1 ,t 2 ) Wherein t is 1 For historical run time, t 2 The service life time. The corresponding aging tables are specified below (data for t1 greater than 3600 and t2 greater than 84 are not shown):
and step S2, establishing a time power consumption operation model of the air conditioner, wherein parameters of the model comprise a refrigeration and heating mode, a set temperature, an outdoor temperature, an indoor temperature, a wind speed, an indoor area and an aging parameter.
And step S3, according to the set temperature and the wind speed set by the current user, acquiring the current refrigeration and heating mode, the outdoor temperature, the indoor area and the aging parameter, and calculating the expected time and the expected power consumption required for reaching the set temperature through the time power consumption operation model.
The time power consumption operation model is used for calculating expected time and expected power consumption according to the current refrigeration and heating mode, set temperature, outdoor temperature, indoor temperature, wind speed, indoor area and aging parameters of the air conditioner. The cooling and heating mode is a current setting mode of a user, and can be cooling or heating. The set temperature is the current indoor temperature required by a user, the outdoor temperature is the current temperature of the outdoor environment, the indoor temperature is the current indoor temperature, the wind speed is the air conditioner wind speed set by the current user, the indoor area is the area of a room where an air conditioner is located, the indoor area data is data measured and reported by an installer after the installation of the air conditioner installer, and the data is bound with the air conditioner code. The aging parameters are obtained according to the current historical running time and the service life time of the user. For convenience of the following description, S is defined as the indoor area and S, T is defined as the indoor area in this embodiment 2 Is the outdoor temperature, T 1 Is the indoor temperature, T 0 D is the equal wind speed gear for setting the temperature. These parameters are all related to the model and have corresponding functional relationships. These need to be tested and calibrated in the laboratory for each type of air conditioner, and need to pay attention to the influence of the parameters on the model.
Let X (S) be a function of area, Y (t) 1 ,t 2 ) As a function of the aging parameter, F (T) 2 ) G (d) is a function of wind speed for outdoor temperature influence. These functions all have corresponding mapping tables. For example, for 1.5 p air conditioners, the mapping table corresponding to the area function is as follows:
| S | less than 10 | 10-18 | 18-26 | Greater than 26 |
| X | 0.6 | 0.9 | 1.1 | 1.7 |
The outdoor temperature influence function means that the operation conditions of the air conditioner main unit in different outdoor temperature environments are different even under the condition of the same temperature rise or temperature drop. Therefore, the influence degree needs to be calibrated according to the operation conditions of the air conditioner tested at different outdoor temperatures in a laboratory. And the wind speed gear also influences the operation condition of the air conditioner. This all requires a one-to-one calibration. Air conditioners with different models, different powers and different energy efficiency grades have certain differences. The specific calibration process is not described herein.
The time power consumption operation model comprises a time submodel and a power consumption submodel; wherein the time submodel is
The power consumption submodel is
K 1 、K 2 The two parameters are different in the heating mode and the refrigerating mode, and need to be calibrated.
In order to be the expected time of day,
is the expected power consumption.
The air conditioner can automatically collect data of actual operation time and power consumption in the operation process.
And S4, pushing the expected time, the expected power consumption, the actual time of the air conditioner operation and the actual power consumption to the mobile phone end of the user through the home gateway.
The function is that the user sets and opens on the APP software of the mobile phone end, and the default is not opened. When the user wants to detect the running health state of the air conditioner, the starting can be triggered by self. The information pushed by the home gateway can be received at the moment, and the phenomenon that the experience is influenced by frequently pushing the information to the user is avoided. And in the early stage of the air conditioner, the air conditioner normally operates, and the function can be triggered when the user perceives that the air conditioner may have certain problems.
And step S5, calculating an operation reference value according to the number of the users in the room, the door opening and closing state, the received expected time, expected power consumption, actual time and actual power consumption.
The calculation function of the running reference value is realized by mobile phone end software. The number of door openings has an effect on both time and function due to whether and how many people are in the room and whether the door is open or closed. The model is therefore further modified.
Specifically, if the actual time of the air conditioner operation is T 'and the actual power consumption is W', the operation reference value is set
Wherein alpha is the number of the room, beta is the state of opening and closing the door, R (alpha) is a human number correction function, and M (beta) is an opening and closing door correction function. And alpha and beta are selected by the user in the mobile phone software to be classified in a range. Such as 0, 1-2, 3, and more, etc. The door opening and closing state can be that the door is not opened, the door is normally opened, the door is opened in a short time, the door is opened for a long time and the like, and the door opening and closing state has corresponding calibration values.
After considering these influencing factors, the operation reference value is finally obtained.
And step S6, if the operation reference value is larger than the operation threshold value, displaying the fault condition of the air conditioner and pushing the fault condition to a manufacturer management background through a home gateway or a data network.
The operation threshold value is a limit value for judging a fault after an experiment in a laboratory, and can be properly relaxed to avoid misjudgment. And if the calculated operation reference value is larger than the operation threshold value, judging the possibility of the current air conditioner fault, displaying the fault condition, namely the roughly judged fault problem, at the mobile phone end, and displaying all model parameters. And then pushed to the vendor management background. And in a manufacturer management background, performing data analysis according to the received air conditioner fault condition, pushing a fault item which possibly occurs to a mobile phone end of a user, and reserving to go to the home for overhaul. If the running reference value is less than or equal to the running threshold, the push display is normal.
Therefore, the method can automatically detect the running condition of the air conditioner through model calculation, and give fault early warning, thereby being convenient for knowing the health condition of the air conditioner, being convenient for manufacturers to actively invite users to overhaul the air conditioner, and improving the use friendliness of the air conditioner.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. An intelligent air conditioner remote operation and fault early warning control method based on the Internet of things is characterized by comprising the following steps:
acquiring the historical running time and the service life time of the air conditioner to form an aging parameter;
establishing a time power consumption operation model of the air conditioner, wherein parameters of the model comprise a refrigeration and heating mode, a set temperature, an outdoor temperature, an indoor temperature, a wind speed, an indoor area and an aging parameter;
according to the set temperature and the wind speed set by the current user, acquiring a current refrigeration and heating mode, an outdoor temperature, an indoor area and an aging parameter, and calculating the expected time and the expected power consumption required for reaching the set temperature through a time power consumption operation model;
pushing the expected time, the expected power consumption, the actual time of the air conditioner operation and the actual power consumption to a user mobile phone end through a home gateway;
calculating an operation reference value according to the number of the rooms selected by the user at the mobile phone end, the door opening and closing state, the received expected time, expected power consumption, actual time and actual power consumption;
and if the operation reference value is larger than the operation threshold value, displaying the fault condition of the air conditioner and pushing the fault condition to a manufacturer management background through a home gateway or a data network.
2. The intelligent air conditioner remote operation and fault pre-warning control method based on the internet of things as claimed in claim 1, wherein the historical operation time is in hours, the service life time is in months, an aging table is established, and the longer the historical operation time and the service life time are, the larger the aging parameter is.
3. The method of claim 2The intelligent air conditioner remote operation and fault early warning control method based on the Internet of things is characterized in that the time power consumption operation model comprises a time sub-model and a power consumption sub-model; wherein the time submodel is
The power consumption submodel is
Wherein S is the indoor area, t 1 For historical run time, t 2 To age time, T 2 Is the outdoor temperature, T 1 Is the indoor temperature, T 0 For setting the temperature, d is the constant wind speed step, X (S) is the area function, Y (t) 1 ,t 2 ) As a function of the aging parameter, F (T) 2 ) As a function of the outdoor temperature, G (d) as a function of the wind speed, K 1 、K 2 All are the adjusting parameters of the refrigeration and heating mode,
in order to be the expected time of day,
is the expected power consumption.
4. The intelligent air conditioner remote operation and fault early warning control method based on the internet of things as claimed in claim 3, wherein if the actual time is T ', the actual power consumption is W', and the reference value is operated
Wherein alpha is the number of the room, beta is the state of opening and closing the door, R (alpha) is a human number correction function, and M (beta) is an opening and closing door correction function.
5. The intelligent air conditioner remote operation and fault early warning control method based on the internet of things of claim 4, wherein the method further comprises the following steps:
and in a manufacturer management background, performing data analysis according to the received air conditioner fault condition, pushing a fault item which possibly occurs to a mobile phone end of a user, and reserving to go to the home for overhaul.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107044710A (en) * | 2016-12-26 | 2017-08-15 | 深圳达实智能股份有限公司 | Energy-saving control method for central air conditioner and system based on joint intelligent algorithm |
| CN110779168A (en) * | 2019-11-18 | 2020-02-11 | 珠海格力电器股份有限公司 | Air conditioner regular maintenance reminding method |
| US20210034024A1 (en) * | 2016-06-30 | 2021-02-04 | Johnson Controls Technology Company | Building hvac system with multi-level model predictive control |
| CN113566376A (en) * | 2021-07-28 | 2021-10-29 | 珠海格力电器股份有限公司 | Electrical appliance life prediction method, air conditioner and computer readable storage medium |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210034024A1 (en) * | 2016-06-30 | 2021-02-04 | Johnson Controls Technology Company | Building hvac system with multi-level model predictive control |
| CN107044710A (en) * | 2016-12-26 | 2017-08-15 | 深圳达实智能股份有限公司 | Energy-saving control method for central air conditioner and system based on joint intelligent algorithm |
| CN110779168A (en) * | 2019-11-18 | 2020-02-11 | 珠海格力电器股份有限公司 | Air conditioner regular maintenance reminding method |
| CN113566376A (en) * | 2021-07-28 | 2021-10-29 | 珠海格力电器股份有限公司 | Electrical appliance life prediction method, air conditioner and computer readable storage medium |
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