CN112594937B - Control method and device of water heater, electronic equipment and storage medium - Google Patents
Control method and device of water heater, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112594937B CN112594937B CN202011488944.XA CN202011488944A CN112594937B CN 112594937 B CN112594937 B CN 112594937B CN 202011488944 A CN202011488944 A CN 202011488944A CN 112594937 B CN112594937 B CN 112594937B
- Authority
- CN
- China
- Prior art keywords
- time period
- water heater
- time
- target water
- periods
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 116
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000010438 heat treatment Methods 0.000 claims abstract description 32
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 16
- 238000012544 monitoring process Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 230000001174 ascending effect Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H9/00—Details
- F24H9/20—Arrangement or mounting of control or safety devices
- F24H9/2007—Arrangement or mounting of control or safety devices for water heaters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Thermal Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- Heat-Pump Type And Storage Water Heaters (AREA)
Abstract
The method comprises the steps of dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of a target water heater in each time interval of a plurality of days; determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods; and adjusting the automatic heating time of the target water heater according to the common time period. The method self-adaptively shortens the instant heating function of the water heater to a short time range according to the use habit of the current user through a simple self-learning method, is more accurate, improves the utilization rate of an intelligent mode, saves electric quantity, is more intelligent, and also improves user experience.
Description
Technical Field
The present application relates to the field of household appliance technologies, and in particular, to a method and an apparatus for controlling a water heater, an electronic device, and a storage medium.
Background
The existing water heater realizes That a shower head is opened and heated immediately after cold water is discharged from a water pipe, but the triggering condition needs to be set by a user or adopts an If This Then (IFTTT) mode, and the user can not be provided with more intelligent experience. Even if the user can turn on the heating switch of the water heater in advance through the terminal equipment, if the user forgets to turn on in advance, intelligent experience cannot be achieved. If the solar energy is started and heated all day long, the electric quantity is greatly consumed.
At present, for the prediction of water consumption behaviors of users, only regular water consumption time periods, such as morning or evening, can be predicted, and specific water consumption time points cannot be predicted, so that the prediction is not accurate enough, and the water heater is still heated when the users do not want to use the water heater, and unnecessary power consumption is caused.
Disclosure of Invention
In order to solve the problems, the application provides a control method and device of a water heater, an electronic device and a storage medium, and solves the technical problems of electric quantity loss and inconvenience in use of a user caused by inaccurate prediction of the water heater in the prior art.
In a first aspect, the present application provides a method of controlling a water heater, the method comprising:
dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of the target water heater in each time interval of a plurality of days;
determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods;
and adjusting the automatic heating time of the target water heater according to the common time period.
According to an embodiment of the application, preferably, in the above control method of the water heater, the determining the common time period of the target water heater by the K-nearest neighbor algorithm according to all the usage time periods includes the following steps:
calculating the Mahalanobis distance between each use time period and a preset time period, sequencing all the use time periods in an ascending order according to the calculation result of the Mahalanobis distance between the use time periods and the preset time period, and selecting K use time periods with the minimum Mahalanobis distance from the use time periods to the preset time period; wherein K is an integer greater than 1;
and determining the common time period of the target water heater according to the selected K service time periods.
According to an embodiment of the present application, preferably, in the above method for controlling a water heater, determining a common time period of the target water heater according to the selected K usage time periods includes the following steps:
determining the selected overlapping time period of the K using time periods;
and taking the overlapping time period as a common time period of the target water heater.
According to an embodiment of the application, preferably, in the control method of the water heater, the preset time period is an automatic heating time of the target water heater within the several days.
According to an embodiment of the application, preferably, in the control method of the water heater, the usage time period includes an earliest starting usage time and a latest ending usage time of the target water heater in a corresponding time interval of a corresponding day.
According to an embodiment of the application, preferably, in the control method of the water heater, a mahalanobis distance between each of the use time periods and a preset time period is calculated by the following calculation formula:
wherein x isiFor the ith use period, mu is the preset time period, S is a covariance matrix of all the use periods, DiThe mahalanobis distance between the ith use time period and the preset time period.
According to an embodiment of the application, preferably, in the above control method of the water heater, before the step of dividing 24 hours a day into a plurality of time intervals, the method further includes:
and receiving an intelligent mode starting instruction, and starting the intelligent mode of the target water heater according to the intelligent mode starting instruction.
In a second aspect, the present application provides a control device for a water heater, the device comprising:
the monitoring module is used for dividing 24 hours a day into a plurality of time intervals and monitoring the use time period of the target water heater in each time interval of a plurality of days;
the determining module is used for determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods;
and the adjusting module is used for adjusting the automatic heating time of the target water heater according to the common time period.
In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to execute the control method of the water heater according to any one of the first aspect.
In a fourth aspect, the present application provides a storage medium storing a computer program which, when executed by one or more processors, implements the control method of the water heater according to any one of the first aspect.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
the method comprises the steps of dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of a target water heater in each time interval of a plurality of days; determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods; and adjusting the automatic heating time of the target water heater according to the common time period. The method self-adaptively shortens the instant heating function of the water heater to a short time range according to the use habit of the current user through a simple self-learning method, is more accurate, improves the utilization rate of an intelligent mode, saves electric quantity, is more intelligent, and also improves user experience.
Drawings
The present application will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings:
fig. 1 is a schematic flow chart of a control method of a water heater according to an embodiment of the present disclosure;
fig. 2 is another schematic flow chart of a control method of a water heater according to an embodiment of the present disclosure;
fig. 3 is another schematic flow chart of a control method of a water heater according to an embodiment of the present disclosure;
fig. 4 is another schematic flow chart of a control method of a water heater according to an embodiment of the present disclosure;
fig. 5 is a connection block diagram of a control device of a water heater according to an embodiment of the present disclosure;
in the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The following detailed description will be provided with reference to the accompanying drawings and embodiments, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and various features in the embodiments of the present application can be combined with each other without conflict, and the formed technical solutions are all within the scope of protection of the present application.
Example one
Referring to fig. 1, the present embodiment provides a control method of a water heater, including:
step S101: and dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of the target water heater in each time interval of the plurality of days.
Illustratively, the 24 hours a day is divided into 12 intervals, for example, an interval from any even-numbered time to the next even-numbered time is 18: 00-20: 00, 20: 00-22: 00.
Beginning with step S101, i.e. entering the intelligent control mode, before step S101, the method further includes:
as shown in fig. 2, an intelligent mode starting instruction is received, and an intelligent mode of the target water heater is started according to the intelligent mode starting instruction.
That is to say, the user can select to turn on the intelligent mode or turn off the intelligent mode as required, and turn off the intelligent mode, namely the target water heater is in the ordinary mode, and under the ordinary mode, when the user triggers the switch, heating is carried out again.
Intelligent mode switch button in the user's accessible APP opens intelligent mode, and the interactive mode is simple, convenient operation.
Wherein one usage time period sample indicates that a target water heater is a time period within a time interval of a certain day, and the usage time period includes (but is not limited to) the earliest start usage time and the latest end usage time of the target water heater within the corresponding time interval of a corresponding day. It can be understood that the usage time of the target water heater in a certain time interval of a certain day is intermittent, for example, the usage time of the target water heater is 20: 00-20: 20, 21: 00-21: 15, 21: 30-21: 50 in a certain day, and then the usage time period samples in the certain time interval of the day include (20: 00-20: 20, 21: 00-21: 15, 21: 30-21: 50) these time data. And if the use time of the user is only 20: 30-21: 30 within 20: 00-22: 00 of a certain day, the use time period sample in the time interval of the day is (20: 30-21: 30). And the target water heater reports the collected use time period samples through the wifi module phase server.
In the intelligent mode, a default automatic heating time period, that is, a preset time period, for example, 20:00 to 22:00 at night, may be set by a user according to a requirement when the preset time period starts, or may be set by a manufacturer, and of course, if the user does not use the water heater in the first day of 20:00 to 22:00, the server on the second day optimizes the preset time period (automatic heating time period) on the basis of the use time on the first day, for example, according to the use time on the first day, the optimized preset time period is set to an interval where the use time on the first day is located, or may be set to 1 hour before and after the use time on the first day, that is, (the start time on the first day-1 h, the end time on the first day-1 h) or (the start time on the first day +1h, the end time of the first day +1h), and so on, until the end of data collection.
The number of days may be set by the user as desired, such as 7 days, 10 days, 14 days, etc.
Step S102: and determining the common time period of the target water heater by a K-nearest neighbor algorithm according to all the use time periods.
Among them, the K-Nearest Neighbor algorithm (KNN) may perform an ordering of the similarity between samples (using time slots).
Referring to fig. 3, step S102 specifically includes the following steps:
s102 a: calculating the Mahalanobis distance between each use time period and a preset time period, sequencing all the use time periods in an ascending order according to the calculation result of the Mahalanobis distance between the use time periods and the preset time period, and selecting K use time periods with the minimum Mahalanobis distance from the use time periods to the preset time period; wherein K is an integer greater than 1;
s102 b: and determining the common time period of the target water heater according to the selected K service time periods.
Wherein the mahalanobis distance between each of the use time periods and the preset time period is calculated by the following calculation formula:
wherein x isiFor the ith use period, mu is the preset time period, S is a covariance matrix of all the use periods, DiThe mahalanobis distance between the ith use time period and the preset time period.
The K using time periods selected in the steps are a plurality of time periods with higher similarity.
It should be noted that the preset time period may be a fixed time period, or may be the optimized time period.
Preferably, step S102b specifically includes the following steps:
(a) determining the selected overlapping time period of the K using time periods;
(b) and taking the overlapping time period as a common time period of the target water heater.
It can be understood that, among the K usage time periods selected above, there is an overlapping time period (i.e. the closest start time and end time of the K usage time periods), and this overlapping time period is the time period in which the user is most likely to use the water heater, and in order to ensure that the user is on-demand and hot, this overlapping time period is taken as the common time period of the target water heater, in order to reduce the power consumption to the maximum extent. Since the usage time periods may be discontinuous, the corresponding overlapping time periods (common time periods) are also multiple, that is, the target water heater has multiple self-heating common time periods (including multiple on-times and multiple off-times) in one day.
The K value may be the number of the usage periods when the data size is small, and may be set as required when the data size is large.
Step S103: and adjusting the automatic heating time of the target water heater according to the common time period.
The obtained common time is the time period with the maximum use probability of the target water heater, so that the automatic heating time of the target water heater is adjusted according to the common time period, the automatic heating (namely heating when starting) time is shortened to a short time, and the user experience is improved while the utilization rate of the intelligent function of the target water heater is improved specifically at the moment.
It can be understood that the common time period is updated in real time, and the above steps are repeatedly executed according to the actual use data of several days before the current time, so as to obtain a new common time period, thereby adjusting the automatic heating time of the target water heater in real time. After the data acquisition and the first adjustment in the first stage, the automatic heating time in the data acquisition process in each stage is the common time period calculated in the previous stage. For example, the auto-warm-up time may be set to be updated every 7 days, every 10 days, or every 14 days.
The embodiment can determine the automatic heating (i.e. heating on) time of the target water heater through simple self-learning.
The automatic heating time period is displayed in the display interface, the user can freely drag the automatic heating time (namely heating when starting), the interaction mode is diversified, and the use is convenient.
In this embodiment, it can be understood that, because the algorithm has a large calculation amount and needs a large amount of memory, when the calculated common time period is more than 5, the common time period is stored, the previous calculation process is deleted, only the time for the user to use the water heater and the calculation result are reserved, and the storage space is saved.
Referring to fig. 4, another flow chart of a method for controlling a water heater is also provided in this embodiment.
The embodiment provides a control method of a water heater, which comprises the steps of dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of a target water heater in each time interval of a plurality of days; determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods; and adjusting the automatic heating time of the target water heater according to the common time period. The method self-adaptively shortens the instant heating function of the water heater to a short time range according to the use habit of the current user through a simple self-learning method, is more accurate, improves the utilization rate of an intelligent mode, saves electric quantity, is more intelligent, and also improves user experience.
Example two
Referring to fig. 5, the present embodiment provides a control device for a water heater, including: a monitoring module 101, a determination module 102 and an adjustment module 103.
The monitoring module 101 is configured to divide 24 hours a day into a plurality of time intervals, and monitor a usage time period of the target water heater in each time interval of the plurality of days;
a determining module 102, configured to determine, according to all the usage time periods, a common time period of the target water heater through a K-nearest neighbor algorithm;
and the adjusting module 103 is used for adjusting the automatic heating time of the target water heater according to the common time period.
The monitoring module 101 divides 24 hours a day into a plurality of time intervals, and monitors the use time period of the target water heater in each time interval of a plurality of days; the determining module 102 determines the common time period of the target water heater through a K-nearest neighbor algorithm according to all the use time periods; the adjusting module 103 adjusts the automatic heating time of the target water heater according to the common time period.
The specific embodiment process of the above method steps can be referred to as embodiment one, and the details of this embodiment are not repeated herein.
EXAMPLE III
The embodiment provides an electronic device, which may be a mobile phone, a computer, a tablet computer, or the like, and includes a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, implements the control method of the water heater as described in the first embodiment. It is to be appreciated that the electronic device can also include input/output (I/O) interfaces, as well as communication components.
The processor is used for executing all or part of the steps in the control method of the water heater in the first embodiment. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to execute the method for controlling the water heater in the first embodiment.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
Example four
The present embodiments provide a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., having stored thereon a computer program which, when executed by a processor, may implement the method steps of:
step S101: dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of the target water heater in each time interval of a plurality of days;
step S102: determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods;
step S103: and adjusting the automatic heating time of the target water heater according to the common time period.
The specific embodiment process of the above method steps can be referred to as embodiment one, and the details of this embodiment are not repeated herein.
In summary, the method, the device, the electronic device and the storage medium for controlling the water heater provided by the present application include dividing 24 hours a day into a plurality of time intervals, and monitoring the usage time period of the target water heater in each time interval of the plurality of days; determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods; and adjusting the automatic heating time of the target water heater according to the common time period. The method self-adaptively shortens the instant heating function of the water heater to a short time range according to the use habit of the current user through a simple self-learning method, is more accurate, improves the utilization rate of an intelligent mode, saves electric quantity, is more intelligent, and also improves user experience.
In the embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. The above-described method embodiments are merely illustrative.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments disclosed in the present application are described above, the descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (8)
1. A method of controlling a water heater, the method comprising:
dividing 24 hours a day into a plurality of time intervals, and monitoring the use time period of the target water heater in each time interval of a plurality of days;
determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods;
adjusting the automatic heating time of the target water heater according to the common time period;
determining the common time period of the target water heater by a K nearest neighbor algorithm according to all the use time periods, wherein the method comprises the following steps:
calculating the Mahalanobis distance between each use time period and a preset time period, sequencing all the use time periods in an ascending order according to the calculation result of the Mahalanobis distance between the use time periods and the preset time period, and selecting K use time periods with the minimum Mahalanobis distance from the use time periods to the preset time period; wherein K is an integer greater than 1;
determining the common time period of the target water heater according to the selected K service time periods;
the method comprises the following steps of determining the common time period of the target water heater according to the selected K using time periods, wherein the method comprises the following steps:
determining the selected overlapping time period of the K using time periods;
and taking the overlapping time period as a common time period of the target water heater.
2. The method of claim 1, wherein the predetermined time period is an auto-heat time of the target water heater over the number of days.
3. The method of claim 1, wherein the usage time period comprises an earliest starting usage time and a latest ending usage time of the target water heater within a corresponding time interval of a corresponding day.
4. A method as claimed in claim 3, wherein the mahalanobis distance for each said period of use from the preset period is calculated by:
wherein x isiFor the ith use period, mu is the preset time period, S is a covariance matrix of all the use periods, DiThe mahalanobis distance between the ith use time period and the preset time period.
5. The method of claim 1, wherein prior to the step of dividing 24 hours a day into time intervals, the method further comprises:
and receiving an intelligent mode starting instruction, and starting the intelligent mode of the target water heater according to the intelligent mode starting instruction.
6. A control device for a water heater, comprising:
the monitoring module is used for dividing 24 hours a day into a plurality of time intervals and monitoring the use time period of the target water heater in each time interval of a plurality of days;
the determining module is used for determining the common time period of the target water heater through a K nearest neighbor algorithm according to all the use time periods;
the adjusting module is used for adjusting the automatic heating time of the target water heater according to the common time period;
the determining module is further configured to calculate mahalanobis distances between the use time periods and a preset time period, sort all the use time periods in an ascending order according to calculation results of the mahalanobis distances between the use time periods and the preset time period, and select K use time periods with the lowest mahalanobis distance from the preset time period; wherein K is an integer greater than 1; determining the common time period of the target water heater according to the selected K service time periods;
the determining module is further configured to: determining the selected overlapping time period of the K using time periods; and taking the overlapping time period as a common time period of the target water heater.
7. An electronic device, characterized by comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the method of controlling a water heater according to any one of claims 1 to 5.
8. A storage medium characterized in that it stores a computer program which, when executed by one or more processors, implements the control method of a water heater according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011488944.XA CN112594937B (en) | 2020-12-16 | 2020-12-16 | Control method and device of water heater, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011488944.XA CN112594937B (en) | 2020-12-16 | 2020-12-16 | Control method and device of water heater, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112594937A CN112594937A (en) | 2021-04-02 |
| CN112594937B true CN112594937B (en) | 2022-03-18 |
Family
ID=75196450
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011488944.XA Active CN112594937B (en) | 2020-12-16 | 2020-12-16 | Control method and device of water heater, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112594937B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109425117B (en) * | 2017-07-21 | 2022-01-18 | 青岛经济技术开发区海尔热水器有限公司 | Intelligent operation-free control method of water heater and water heater |
| CN114484882B (en) * | 2021-12-03 | 2023-05-09 | 珠海格力电器股份有限公司 | Method and device for regulating and controlling zero cold water function, storage medium and electronic equipment |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104110878A (en) * | 2013-12-23 | 2014-10-22 | 芜湖美的厨卫电器制造有限公司 | Electric water heater with ECO (ecology, conservation and optimization) function mode and controlling method thereof |
| CN105608639A (en) * | 2016-01-07 | 2016-05-25 | 国网智能电网研究院 | Residential electricity consumption behavior analysis method based on user electricity consumption information |
| CN105785776A (en) * | 2016-03-03 | 2016-07-20 | 武汉博虎科技有限公司 | Household electrical appliance control system based on resident behavior model |
| CN106123305A (en) * | 2016-07-29 | 2016-11-16 | 芜湖美的厨卫电器制造有限公司 | The method for heating and controlling of electric heater and electric heater |
| CN107886130A (en) * | 2017-11-21 | 2018-04-06 | 华北电力大学（保定） | A kind of kNN rapid classification methods based on cluster and Similarity-Weighted |
| CN108286795A (en) * | 2017-01-09 | 2018-07-17 | 芜湖美的厨卫电器制造有限公司 | Water heater and its control method |
| CN108595884A (en) * | 2018-05-09 | 2018-09-28 | 清华大学 | Power system transient stability appraisal procedure and device |
| CN108954850A (en) * | 2018-09-25 | 2018-12-07 | 佛山市云米电器科技有限公司 | The method, apparatus and electric heater that electric heater is reserved automatically |
| CN109458703A (en) * | 2018-12-21 | 2019-03-12 | 杭州睿兴栋宇建筑科技有限公司 | A kind of wisdom building air conditioning control system based on KNN algorithm and BIM technology |
| CN109489171A (en) * | 2017-12-01 | 2019-03-19 | 北京新风到家科技有限公司 | A kind of control method of intelligent fresh air system, system, device and storage medium |
| CN109931709A (en) * | 2019-04-02 | 2019-06-25 | 深圳市佳运通电子有限公司 | Oil field heating furnace energy conservation adjusting method and system |
| CN110906571A (en) * | 2019-11-30 | 2020-03-24 | 华南理工大学 | Solar heat pump hot water system control strategy optimization method based on machine learning |
| CN111954287A (en) * | 2020-08-13 | 2020-11-17 | 上海传英信息技术有限公司 | Power saving control method based on user behavior, mobile terminal and storage medium |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101625957B1 (en) * | 2015-03-09 | 2016-05-31 | 경북대학교 산학협력단 | A system and method to process moving range k-nearest neighbor queries in road networks |
| CN105005204B (en) * | 2015-07-31 | 2018-02-23 | 深圳广田智能科技有限公司 | The intelligent engine system and method for smart home and wisdom scene of life can be triggered automatically |
| EP3142069B1 (en) * | 2015-09-10 | 2020-05-13 | Agfa HealthCare | Method, apparatus and system for analyzing medical images of blood vessels |
| CN106022800A (en) * | 2016-05-16 | 2016-10-12 | 北京百分点信息科技有限公司 | User feature data processing method and device |
| CN106846163A (en) * | 2016-07-05 | 2017-06-13 | 国网内蒙古东部电力有限公司 | A kind of electric power payment channel overall analysis system |
| CN108595231B (en) * | 2018-05-15 | 2021-06-15 | Oppo广东移动通信有限公司 | Application program preloading method and device, storage medium and intelligent terminal |
| CN111798260A (en) * | 2019-04-09 | 2020-10-20 | Oppo广东移动通信有限公司 | User behavior prediction model construction method and device, storage medium and electronic equipment |
| CN110298487B (en) * | 2019-05-30 | 2023-05-16 | 同济大学 | Indoor temperature prediction method for meeting personalized demands of users |
| CN111178587B (en) * | 2019-12-06 | 2022-11-22 | 广东工业大学 | Spark framework-based short-term power load rapid prediction method |
| CN111520883B (en) * | 2020-04-29 | 2022-03-04 | 宁波溪棠信息科技有限公司 | Air conditioner control method and system based on target room load prediction |
-
2020
- 2020-12-16 CN CN202011488944.XA patent/CN112594937B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104110878A (en) * | 2013-12-23 | 2014-10-22 | 芜湖美的厨卫电器制造有限公司 | Electric water heater with ECO (ecology, conservation and optimization) function mode and controlling method thereof |
| CN105608639A (en) * | 2016-01-07 | 2016-05-25 | 国网智能电网研究院 | Residential electricity consumption behavior analysis method based on user electricity consumption information |
| CN105785776A (en) * | 2016-03-03 | 2016-07-20 | 武汉博虎科技有限公司 | Household electrical appliance control system based on resident behavior model |
| CN106123305A (en) * | 2016-07-29 | 2016-11-16 | 芜湖美的厨卫电器制造有限公司 | The method for heating and controlling of electric heater and electric heater |
| CN108286795A (en) * | 2017-01-09 | 2018-07-17 | 芜湖美的厨卫电器制造有限公司 | Water heater and its control method |
| CN107886130A (en) * | 2017-11-21 | 2018-04-06 | 华北电力大学（保定） | A kind of kNN rapid classification methods based on cluster and Similarity-Weighted |
| CN109489171A (en) * | 2017-12-01 | 2019-03-19 | 北京新风到家科技有限公司 | A kind of control method of intelligent fresh air system, system, device and storage medium |
| CN108595884A (en) * | 2018-05-09 | 2018-09-28 | 清华大学 | Power system transient stability appraisal procedure and device |
| CN108954850A (en) * | 2018-09-25 | 2018-12-07 | 佛山市云米电器科技有限公司 | The method, apparatus and electric heater that electric heater is reserved automatically |
| CN109458703A (en) * | 2018-12-21 | 2019-03-12 | 杭州睿兴栋宇建筑科技有限公司 | A kind of wisdom building air conditioning control system based on KNN algorithm and BIM technology |
| CN109931709A (en) * | 2019-04-02 | 2019-06-25 | 深圳市佳运通电子有限公司 | Oil field heating furnace energy conservation adjusting method and system |
| CN110906571A (en) * | 2019-11-30 | 2020-03-24 | 华南理工大学 | Solar heat pump hot water system control strategy optimization method based on machine learning |
| CN111954287A (en) * | 2020-08-13 | 2020-11-17 | 上海传英信息技术有限公司 | Power saving control method based on user behavior, mobile terminal and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 基于最大相关-最小冗余的动作识别算法;龚静等;《西南大学学报(自然科学版)》;20180620(第06期);158-167页 * |
| 基于距离度量学习的棉花品级分类方法研究;张婷等;《中国棉花》;20180815(第08期);12-19页 * |
| 数据挖掘算法在葡萄酒信息数据分析系统中的研究;郝艳妮等;《计算机科学》;20170615;491-494页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112594937A (en) | 2021-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112594937B (en) | Control method and device of water heater, electronic equipment and storage medium | |
| CN108873730B (en) | Control method and device for household equipment | |
| CN110398069B (en) | Water heater, control method thereof and computer-readable storage medium | |
| CN110934502B (en) | Water dispenser control method and device, storage medium and water dispenser | |
| CN110553405A (en) | Control method, device and equipment of water heater and storage medium | |
| JP5631292B2 (en) | Electric power demand adjustment system and electric power demand adjustment device | |
| CN109287055B (en) | Method and device for controlling intelligent lamp | |
| CN112997134B (en) | Application icon display method and device, storage medium and electronic equipment | |
| CN109298640A (en) | A kind of management method and device of smart machine | |
| JP2015103241A (en) | Power consumption prediction device, method, and non-temporary computer readable storage medium | |
| CN102279572A (en) | Demand response method and demand response system | |
| CN107577387B (en) | Method and device for switching watch dial plates | |
| CN114218036A (en) | Resource allocation method and device | |
| CN112363473A (en) | User behavior prediction method, device control method and device and electronic device | |
| GB2508755A (en) | Renewable Energy Supply and Demand Management System | |
| CN106411671A (en) | Peak valley power utilization oriented intelligent household electrical appliance control method and gateway | |
| CN117433058A (en) | Thermal energy management method, device, equipment and medium for heat supply equipment | |
| JP5615403B1 (en) | Controller, control method of electric device, program, and device control system | |
| CN111857477A (en) | Display control method and device, mobile terminal and storage medium | |
| CN114271678B (en) | Heating method, heating device, computer equipment and storage medium | |
| JP2012178915A (en) | Apparatus controller, apparatus control method, and apparatus control program | |
| CN115164340A (en) | Air conditioner sterilization control method and device, air conditioner and computer readable storage medium | |
| CN115143638B (en) | Water heater control method and electronic equipment | |
| CN114963552A (en) | Water heater starting control method and device, electronic equipment and storage medium | |
| CN115076880A (en) | Method and device for controlling air conditioner to repel mosquitoes and air conditioner |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |