CN114811840B - 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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- CN114811840B CN114811840B CN202210527102.3A CN202210527102A CN114811840B CN 114811840 B CN114811840 B CN 114811840B CN 202210527102 A CN202210527102 A CN 202210527102A CN 114811840 B CN114811840 B CN 114811840B
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- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000032683 aging Effects 0.000 claims abstract description 26
- 238000010438 heat treatment Methods 0.000 claims description 13
- 238000012423 maintenance Methods 0.000 claims description 5
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000005057 refrigeration Methods 0.000 claims description 2
- 230000036541 health Effects 0.000 abstract description 4
- 238000012821 model calculation Methods 0.000 abstract description 2
- 238000001816 cooling Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 238000013507 mapping Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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
- F24F11/30—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
-
- 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
-
- 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
-
- 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
Landscapes
- 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 historical operation time and service life time of an air conditioner to form aging parameters; establishing a time power consumption operation model of the air conditioner, and calculating the expected time and the expected power consumption required by reaching the 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, and finally obtaining the operation reference value. Therefore, the method can autonomously detect the running condition of the air conditioner through model calculation, and make fault early warning, is convenient for knowing the health condition of the air conditioner, is convenient for manufacturers to actively invite users to overhaul the air conditioner, and improves the use friendliness of the air conditioner.
Description
Technical Field
The invention belongs to the technical field of the 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
With the intelligent development of air conditioners, more and more air conditioners have internet functions. 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 intuitively know the air conditioner through the running time, the power consumption and the like of the air conditioner. Moreover, the current air conditioner can be started and shut down at regular time, and the product functions are enriched.
However, there is no solution for manufacturers to actively detect the user's air conditioner. The user also has no clear whether the current air conditioner operates normally, and even if the refrigerating capacity of the outlet air conditioner is insufficient, the indoor cooling effect is poor, but the user cannot clearly know whether the air conditioner operates normally. 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 for maintenance. However, the actual condition of the air conditioner cannot be clearly known by the user in the mode, and the manufacturer cannot 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 invention aims to provide a method for controlling intelligent air conditioner remote operation and fault early warning based on the Internet of things, which aims to solve the technical problem that the existing air conditioner cannot automatically detect and report faults.
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 historical operation time and service life time of an air conditioner to form aging parameters;
establishing a time power consumption operation model of the air conditioner, wherein parameters of the model comprise a refrigerating and heating mode, a set temperature, an outdoor temperature, an indoor temperature, a wind speed, an indoor area and aging parameters;
according to the set temperature and wind speed set by the current user, acquiring the current refrigeration and heating mode, the outdoor temperature, the indoor area and the aging parameters, and calculating the expected time and the expected power consumption required by reaching the set temperature through a time power consumption operation model;
pushing the expected time and the expected power consumption, the actual time and the actual power consumption of the operation of the air conditioner to a mobile phone end of a user through a home gateway;
calculating an operation reference value according to the number of people in a room, the door opening and closing state, the received expected time, expected power consumption, actual time and actual power consumption selected by a user at a mobile phone end;
and if the operation reference value is larger than the operation threshold value, displaying the air conditioner fault condition and pushing the air conditioner fault condition to a manufacturer management background through a home gateway or a data network.
Further, the historical operation time is in an hour unit, the service life time is in a month unit, an aging table is built, and the aging parameters are larger as the historical operation time and the service life time are longer.
Further, the time power consumption operation model comprises a time sub-model and a power consumption sub-model; wherein the time submodel isThe power consumption submodel isWherein S is the indoor area, t 1 For historical run time, t 2 For life time, T 2 Is the outdoor temperature, T 1 Is the indoor temperature, T 0 For the set temperature, d is equal wind speed gear, X (S) is area function, Y (t) 1 ,t 2 ) As a function of the ageing parameters, F (T 2 ) G (d) is wind speed function, K 1 、K 2 The temperature control parameters are the adjustment parameters of a refrigerating and heating mode, T is expected time, and W is expected power consumption.
Further, if the actual time is T 'and the actual power consumption is W', the reference value is operatedWherein alpha is the number of people in a room, beta is the state of opening and closing a door, R (alpha) is a person number correction function, M (beta) is the function of opening and closing a door。
Further, the method further comprises:
and in a manufacturer management background, carrying out data analysis according to the received air conditioner fault condition, pushing possible fault items to a mobile phone end of a user, and reserving to go to the door for maintenance.
The beneficial effects of the invention are as follows: according to the invention, by establishing a time power consumption operation model, detection is automatically carried out according to the operation condition of an air conditioner, a user can correct the operation model at a mobile phone end, an operation reference value is finally obtained, the operation reference value is compared with an operation threshold value, if the operation reference value exceeds the operation threshold value, a certain problem exists in the current air conditioner operation condition, the data can be displayed at the mobile phone end of the user and synchronized to a manufacturer management background, background staff can further analyze the data, confirm possible faults and push the faults to the mobile phone of the user, and finally the user can reserve to go to the door for maintenance; therefore, the health state of the current air conditioner can be known more clearly by the user in the mode, manufacturer personnel can be overhauled in time, further expansion of faults can be avoided, and the trust degree of the user to the manufacturer can be improved; meanwhile, extra profits can be brought to manufacturers for overhauling the air conditioner for the user.
Drawings
FIG. 1 is a block diagram of an air conditioner early warning system provided by an embodiment of the present invention;
fig. 2 is a flowchart of a method for intelligent air conditioner remote operation and fault early warning control based on the internet of things according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
Fig. 1 shows a structure of an air conditioner early warning system provided in an embodiment of the present invention, and only a portion related to the embodiment of the present invention is shown for convenience of explanation.
The method is realized based on the air conditioner early warning system shown in fig. 1, and comprises a manufacturer management background, a home gateway, a mobile phone of a user and an air conditioner. The air conditioning 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 to the vendor management background network.
For the above system, the embodiment of the invention provides a method for controlling intelligent air conditioner remote operation and fault early warning based on the internet of things, as shown in fig. 2, comprising the following steps:
and S1, acquiring historical operation time and service life time of the air conditioner to form ageing parameters.
The healthy operating state of an air conditioner is related to the historical operating time and the service life of the air conditioner. The historical operation time is the total power-on operation time of the air conditioner, and the longer the operation time is, the faster the air conditioner ages. And meanwhile, the service life is also related to the service life, and even if the air conditioner is not powered on to run, the air conditioner can naturally age. Both factors are thus taken into account. The aging of the air conditioner inevitably has a certain influence on the running condition of the air conditioner, and the aging parameters are required to be acquired to adjust the time power consumption running model of the air conditioner.
In general, the power-on operation time is in hours and the service life time is in months. The aging table is established, and the longer the historical operation time and the service life time are, the larger the aging parameters are. The aging table may be represented by an aging parameter function. For example, can be expressed as Y (t 1 ,t 2 ) Wherein t is 1 For historical run time, t 2 Is the service life. The corresponding aging table is specifically as follows (data with t1 greater than 3600 and t2 greater than 84 not shown):
and S2, establishing a time power consumption operation model of the air conditioner, wherein parameters of the model comprise a refrigerating and heating mode, a set temperature, an outdoor temperature, an indoor temperature, a wind speed, an indoor area and aging parameters.
And step S3, according to the set temperature and the wind speed set by the current user, acquiring the current refrigerating and heating mode, the outdoor temperature, the indoor area and the aging parameters, and calculating the expected time and the expected power consumption required by reaching the set temperature through a time power consumption operation model.
The time power consumption operation model calculates expected time and expected power consumption according to the current refrigerating 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 temperature of the current outdoor environment, the indoor temperature is the temperature of the current indoor environment, the wind speed is the wind speed of the air conditioner set by the current user, the indoor area is the area of the room where the air conditioner is located, the indoor area data is the data reported by the installer after the installer is installed, and the data are bound with the air conditioner codes. The aging parameters are derived from the current historical operating time and age time of the user. For convenience of the following description, the present embodiment defines S as an indoor area and S, T 2 Is the outdoor temperature, T 1 Is the indoor temperature, T 0 D is equal wind speed gear for setting temperature. These parameters are all related to the model and necessarily have corresponding functional relationships. These require test calibration in a laboratory for each model of air conditioner, and attention is paid to the magnitude of the influence of the parameters on the model.
Let X (S) be the area function, Y (t) 1 ,t 2 ) As a function of the ageing parameters, F (T 2 ) G (d) is a wind speed function as an outdoor temperature influence function. These functions all have a corresponding mapping table. For example, for a 1.5-piece air conditioner, 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 host under different outdoor temperature environments are different even under the condition of the same temperature rise or temperature drop. It is therefore also necessary to calibrate the degree of influence according to the operating conditions of the air conditioner at different outdoor temperatures in the laboratory. The wind speed gear also affects the running condition of the air conditioner. This requires a one-to-one calibration. Different models, different powers and different energy efficiency grades of air conditioners have certain differences. The specific calibration process is not described here in detail.
The time power consumption operation model comprises a time sub-model and a power consumption sub-model; wherein the time submodel isThe power consumption submodel isK 1 、K 2 The parameters are the adjustment parameters of the refrigerating and heating modes, and the two parameters are different in the refrigerating mode and the heating mode, and the calibration is also needed. />For the expected time，/>Is the expected power consumption.
The air conditioner can automatically collect data of actual running time and power consumption in the running process.
And S4, pushing the expected time and the expected power consumption, the actual time and the actual power consumption of the operation of the air conditioner to a mobile phone end of the user through the home gateway.
The function is that a user sets and opens the mobile phone terminal APP software, and the mobile phone terminal APP software is not opened by default. When the user wants to detect the running health state of the air conditioner, the user can trigger the start automatically. The information pushed by the home gateway can be received at the moment, so that the frequent message pushing to the user is avoided, and the experience is prevented from being influenced. In addition, the air conditioner normally operates in the initial stage of use, and the function can be triggered when a certain problem exists in the air conditioner.
And S5, calculating an operation reference value according to the number of people in the room, the door opening and closing state selected by the user at the mobile phone end, and the received expected time, expected power consumption, actual time and actual power consumption.
The calculation function of the running reference value is realized in mobile phone software. The number of times the door is opened and closed has an influence on time and functions due to whether and how many people are in the room and whether the door is opened and closed. The model is further modified accordingly.
Specifically, assuming that the actual time of the air conditioner is T 'and the actual power consumption is W', the reference value is operatedWherein alpha is the number of people in a room, beta is the door opening and closing state, R (alpha) is a person number correction function, and M (beta) is a door opening and closing correction function. Alpha and beta are selected by the user at the mobile phone end software to be classified in a range. Such as 0, 1-2, 3, 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 all have corresponding calibration values.
Taking these influencing factors into consideration, the operation reference value is finally obtained.
And 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 is a limit value for judging faults after experiments in a laboratory, and the operation threshold can be properly relaxed to avoid misjudgment. If the calculated operation reference value is larger than the operation threshold value, judging the possibility of the current air conditioner to be faulty, 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, carrying out data analysis according to the received air conditioner fault condition, pushing possible fault items to a mobile phone end of a user, and reserving to go to the door for maintenance. If the operation reference value is smaller than or equal to the operation threshold value, the push display is normal.
Therefore, the method can autonomously detect the running condition of the air conditioner through model calculation, and make fault early warning, is convenient for knowing the health condition of the air conditioner, is convenient for manufacturers to actively invite users to overhaul the air conditioner, and improves the use friendliness of the air conditioner.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (3)
1. The intelligent air conditioner remote operation and fault early warning control method based on the Internet of things is characterized by comprising the following steps of:
acquiring historical operation time and service life time of an air conditioner to form aging parameters;
establishing a time power consumption operation model of the air conditioner, wherein parameters of the model comprise a refrigerating and heating mode, a set temperature, an outdoor temperature, an indoor temperature, a wind speed, an indoor area and aging parameters;
according to the set temperature and wind speed set by the current user, acquiring the current refrigeration and heating mode, the outdoor temperature, the indoor area and the aging parameters, and calculating the expected time and the expected power consumption required by reaching the set temperature through a time power consumption operation model;
pushing the expected time and the expected power consumption, and the actual time and the actual power consumption of the operation of the air conditioner to a mobile phone end of a user through a home gateway;
calculating an operation reference value according to the number of people in a room, the door opening and closing state, the received expected time, expected power consumption, actual time and actual power consumption selected by a user at a mobile phone end;
if the operation reference value is larger than the operation threshold value, displaying the air conditioner fault condition and pushing the air conditioner fault condition to a manufacturer management background through a home gateway or a data network;
wherein the time power consumption operation model comprises a time sub-model and a power consumption sub-model; wherein the time submodel isThe power consumption submodel isWherein S is the indoor area, t 1 For historical run time, t 2 For life time, T 2 Is the outdoor temperature, T 1 Is the indoor temperature, T 0 For the set temperature, d is equal wind speed gear, +.>As a function of area->For ageing parameter function, +.>As an outdoor temperature influence function->As a function of wind speed>、/>Are all the adjusting parameters of the refrigerating and heating modes, and are +.>For the expected time->Is the expected power consumption;
the actual time isThe actual power consumption is +.>Then run the reference value +.>Wherein->Is the number of people in the room>In order to open or close the door>Correcting the function for the number of people, ">The function is modified for opening and closing the door.
2. The intelligent air conditioner remote operation and fault early warning control method based on the internet of things according to claim 1, wherein the historical operation time is in units of hours, the service life time is in units of months, an aging table is built, and aging parameters are larger as the historical operation time and the service life time are longer.
3. The intelligent air conditioner remote operation and fault early warning control method based on the internet of things according to claim 2, wherein the method further comprises:
and in a manufacturer management background, carrying out data analysis according to the received air conditioner fault condition, pushing possible fault items to a mobile phone end of a user, and reserving to go to the door for maintenance.
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Citations (3)
| 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 |
| 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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| US11789415B2 (en) * | 2016-06-30 | 2023-10-17 | Johnson Controls Tyco IP Holdings LLP | Building HVAC system with multi-level model predictive control |
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Patent Citations (3)
| 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 |
| 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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