CN110162699A - The recommended method and device of air-conditioning target temperature based on region big data - Google Patents
The recommended method and device of air-conditioning target temperature based on region big data Download PDFInfo
- Publication number
- CN110162699A CN110162699A CN201910321736.1A CN201910321736A CN110162699A CN 110162699 A CN110162699 A CN 110162699A CN 201910321736 A CN201910321736 A CN 201910321736A CN 110162699 A CN110162699 A CN 110162699A
- Authority
- CN
- China
- Prior art keywords
- temperature
- air conditioner
- air
- recommended
- big data
- 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.)
- Pending
Links
- 238000004378 air conditioning Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004590 computer program Methods 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 abstract description 2
- 230000002159 abnormal effect Effects 0.000 abstract 1
- 238000012545 processing Methods 0.000 description 5
- 230000006399 behavior Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses the recommended methods and device of the air-conditioning target temperature based on region big data, cloud server end is uploaded to by the temperature value for currently setting each air conditioner in some region, form a region big data, when user needs set temperature, access region big data can be passed through, the big data is according to the geographical location of access originator, it is weighted evaluation and obtains a recommended temperature, user can refer to the recommended temperature and set to air-conditioning, it can be according to the average temperature value of current zone as a recommended temperature, and abnormal temperature value is filtered out by temperature threshold and has been set, the temperature preference of immediate user can be got in a normal range and temperature is set, setting temperature is gone without manually experience, realize intelligentized setting temperature, improve the experience of user.
Description
Technical field
This disclosure relates to air conditioner controlling technology field, and in particular to the recommendation of the air-conditioning target temperature based on region big data
Method and device.
Background technique
As the manufacturing cost of air-conditioning constantly reduces and the development of air-conditioning technical, air-conditioning has been increasingly becoming people must can not
Few temperature control tool, the temperature control parameter of air-conditioning is usually that user is needed voluntarily to control by experience at present, intelligent water
It is flat very low.In current existing technology, air conditioner disclosed in Chinese Patent Application No. CN201710915440.3 and its operation ginseng
Several recommended method, system and big data servers by by the active user's scene and other users of air conditioner to be recommended, its
The application scenarios of his air conditioner are compared to obtain group behavior recommended parameter, and according to the historical operation of air conditioner to be recommended
Record obtains individual behavior recommended parameter, is then finally pushed away according to group behavior recommended parameter and the generation of individual behavior recommended parameter
Parameter is recommended, it, will be compared with thereby, it is possible to combine group's habit and personal preference to be run according to consequently recommended state modulator air conditioner
Air conditioner is recommended for suitable operating parameter；The sleep of big data disclosed in Chinese Patent Application No. CN201710928446.4
The operation shape that curve recommended method, device, server and storage medium upload received various Internet of Things air conditioners
State data are divided into multi-class data, that is, be divided into running state data according to groups of users difference according to classification information difference
Multiclass, and then the sleep curve of such groups of users is generated according to the running state data of different user group, it makes accurate
Curve of sleeping is recommended；Parameter that both methods is got and without screening and processing, so that at part, such as occupy
In the case that firmly environment is different with personal preference, the temperature of recommendation is not appropriate for user, and user experience is bad.
Summary of the invention
To solve the above problems, the disclosure provides the recommended method and device of the air-conditioning target temperature based on region big data
Technical solution, cloud server end is uploaded to by the temperature value for currently setting each air conditioner in some region, formed
One region big data can be by access region big data when user needs set temperature, and the big data is according to access originator
Geographical location, be weighted evaluation and obtain a recommended temperature, user can refer to the recommended temperature and set to air-conditioning.
To achieve the goals above, according to the one side of the disclosure, the air-conditioning target temperature based on region big data is provided
Recommended method, the described method comprises the following steps:
Step 1, the temperature value that each air conditioner in same geographic location is currently set is uploaded to cloud server end；
Step 2, read cloud server end geographical location it is adjacent and be less than temperature threshold N platform air conditioner set temperature
The adjacent temperature sequence of value；
Step 3, the weighted average for calculating adjacent temperature sequence obtains recommended temperature；
Step 4, recommended temperature is set by air-conditioner temperature.
Further, in step 1, the temperature value that each air conditioner in same geographic location is currently set is uploaded to
The method of cloud server end is that each air conditioner of same regional (with city or same province) is divided into same geographic location
Air conditioner, in the operational process of air conditioner, temperature Value Data that each air conditioner in same geographic location is currently set
Cloud server end is uploaded to by Internet of Things fidonetFido or ICP/IP protocol with regional information, cloud server end receives and stores the whole nation
The temperature Value Data that the air conditioner of various regions uploads and the regional information being associated, regional information includes GPS or dipper system
Location information, the air conditioner can carry out telecommunication by Internet of Things or internet with cloud server end.
Further, in step 2, the adjacent and N platform sky less than temperature threshold in the geographical location of cloud server end is read
The method of the adjacent temperature sequence for the temperature value that tune machine is set is reads location information and air conditioner recently and is less than temperature threshold
N platform air conditioner setting temperature value, using this N platform air conditioner setting temperature value as adjacent temperature sequence, N is positive integer,
Value range is 1 to infinity, and the default value of N is 10, and temperature threshold is 26 degrees Celsius, and N and temperature threshold can carry out manually
Adjustment.
Further, in step 3, the method that the weighted average for calculating adjacent temperature sequence obtains recommended temperature is,
Recommended temperature is obtained according to average weighted formula:Wherein, tempiFor geographical location phase
The temperature value of adjacent i-th air conditioner setting, unit are degree Celsius distiFor air conditioner i-th sky adjacent with geographical location
The distance between adjust, unit is rice.
The present invention also provides the recommendation apparatus of the air-conditioning target temperature based on region big data, described device includes: to deposit
Reservoir, processor and storage in the memory and the computer program that can run on the processor, the processing
Device executes the computer program and operates in the unit of following device:
Geographical location temperature collecting cell, the temperature value for currently setting each air conditioner in same geographic location
It is uploaded to cloud server end；
Adjacent temperature sequence reading unit, the geographical location for reading cloud server end is adjacent and is less than temperature threshold
The adjacent temperature sequence of the temperature value of N platform air conditioner setting；
Recommended temperature computing unit, the weighted average for calculating adjacent temperature sequence obtain recommended temperature；
Recommended temperature setting unit, for setting recommended temperature for air-conditioner temperature.
The disclosure have the beneficial effect that the present invention provide the air-conditioning target temperature based on region big data recommended method and
Device, can be according to the average temperature value of current zone as a recommended temperature, and has filtered out exception by temperature threshold
Temperature value setting, the temperature preference of immediate user can be got in a normal range and temperature is set, nothing
Manually experience setting temperature need to be gone, realize intelligentized setting temperature, improve the experience of user.
Detailed description of the invention
By the way that the embodiment in conjunction with shown by attached drawing is described in detail, above-mentioned and other features of the disclosure will
More obvious, identical reference label indicates the same or similar element in disclosure attached drawing, it should be apparent that, it is described below
Attached drawing be only some embodiments of the present disclosure, for those of ordinary skill in the art, do not making the creative labor
Under the premise of, it is also possible to obtain other drawings based on these drawings, in the accompanying drawings:
Fig. 1 show the flow chart of the recommended method of the air-conditioning target temperature based on region big data；
Fig. 2 show the recommendation apparatus figure of the air-conditioning target temperature based on region big data.
Specific embodiment
It is carried out below with reference to technical effect of the embodiment and attached drawing to the design of the disclosure, specific structure and generation clear
Chu, complete description, to be completely understood by the purpose, scheme and effect of the disclosure.It should be noted that the case where not conflicting
Under, the features in the embodiments and the embodiments of the present application can be combined with each other.
As shown in Figure 1 for according to the process of the recommended method of the air-conditioning target temperature based on region big data of the disclosure
Figure, the recommendation of the air-conditioning target temperature based on region big data according to embodiment of the present disclosure is illustrated below with reference to Fig. 1
Method.
The disclosure proposes the recommended method of the air-conditioning target temperature based on region big data, specifically includes the following steps:
Step 1, the temperature value that each air conditioner in same geographic location is currently set is uploaded to cloud server end；
Step 2, read cloud server end geographical location it is adjacent and be less than temperature threshold N platform air conditioner set temperature
The adjacent temperature sequence of value；
Step 3, the weighted average for calculating adjacent temperature sequence obtains recommended temperature；
Step 4, recommended temperature is set by air-conditioner temperature.
Further, in step 1, the temperature value that each air conditioner in same geographic location is currently set is uploaded to
The method of cloud server end is that each air conditioner of same regional (with city or same province) is divided into same geographic location
Air conditioner, in the operational process of air conditioner, temperature Value Data that each air conditioner in same geographic location is currently set
Cloud server end is uploaded to by Internet of Things fidonetFido or ICP/IP protocol with regional information, cloud server end receives and stores the whole nation
The temperature Value Data that the air conditioner of various regions uploads and the regional information being associated, regional information includes GPS or dipper system
Location information, the air conditioner can carry out telecommunication by Internet of Things or internet with cloud server end.
Further, in step 2, the adjacent and N platform sky less than temperature threshold in the geographical location of cloud server end is read
The method for the temperature value that tune machine is set is reads location information and sets recently and less than the N platform air conditioner of temperature threshold with air conditioner
Fixed temperature value, using the temperature value of this N platform air conditioner setting as adjacent temperature sequence, N is positive integer, value range be 1 to
Infinity, the default value of N are 10, and temperature threshold is 26 degrees Celsius, and N can be manually adjusted with temperature threshold.
Further, in step 3, the method that the weighted average for calculating adjacent temperature sequence obtains recommended temperature is,
Recommended temperature is obtained according to average weighted formula:Wherein, tempiFor geographical location phase
The temperature value of adjacent i-th air conditioner setting, distiFor air conditioner i-th air-conditioning the distance between adjacent with geographical location.
The recommendation apparatus for the air-conditioning target temperature based on region big data that embodiment of the disclosure provides, as shown in Figure 2
For the recommendation apparatus figure of the air-conditioning target temperature based on region big data of the disclosure, the embodiment based on region big data
The recommendation apparatus of air-conditioning target temperature include: processor, memory and storage in the memory and can be in the processing
The computer program run on device, the processor realize the above-mentioned sky based on region big data when executing the computer program
Adjust the step in the recommendation apparatus embodiment of target temperature.
Described device includes: memory, processor and storage in the memory and can transport on the processor
Capable computer program, the processor execute the computer program and operate in the unit of following device:
Geographical location temperature collecting cell, the temperature value for currently setting each air conditioner in same geographic location
It is uploaded to cloud server end；
Adjacent temperature sequence reading unit, the geographical location for reading cloud server end is adjacent and is less than temperature threshold
The adjacent temperature sequence of the temperature value of N platform air conditioner setting；
Recommended temperature computing unit, the weighted average for calculating adjacent temperature sequence obtain recommended temperature；
Recommended temperature setting unit, for setting recommended temperature for air-conditioner temperature.
The recommendation apparatus of the air-conditioning target temperature based on region big data can run on desktop PC, notes
Originally, palm PC and cloud server etc. calculate in equipment.The recommendation of the air-conditioning target temperature based on region big data fills
It sets, the device that can be run may include, but be not limited only to, processor, memory.It will be understood by those skilled in the art that the example
Son is only based on the example of the recommendation apparatus of the air-conditioning target temperature of region big data, does not constitute to based on region big data
Air-conditioning target temperature recommendation apparatus restriction, may include component more more or fewer than example, or the certain portions of combination
Part or different components, such as the recommendation apparatus of the air-conditioning target temperature based on region big data can also include defeated
Enter output equipment, network access equipment, bus etc..
Alleged processor can be central processing unit (Central Processing Unit, CPU), can also be it
His general processor, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng, the processor is the control centre of the recommendation apparatus running gear of the air-conditioning target temperature based on region big data,
It can running gear using the recommendation apparatus of the entire air-conditioning target temperature based on region big data of various interfaces and connection
Various pieces.
The memory can be used for storing the computer program and/or module, and the processor is by operation or executes
Computer program in the memory and/or module are stored, and calls the data being stored in memory, described in realization
The various functions of the recommendation apparatus of air-conditioning target temperature based on region big data.The memory can mainly include storage program
Area and storage data area, wherein storing program area can application program needed for storage program area, at least one function (such as
Sound-playing function, image player function etc.) etc.；Storage data area, which can be stored, uses created data (ratio according to mobile phone
Such as audio data, phone directory) etc..In addition, memory may include high-speed random access memory, it can also include non-volatile
Property memory, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card), at least one disk memory, flush memory device or other
Volatile solid-state part.
Although the description of the disclosure is quite detailed and especially several embodiments are described, it is not
Any of these details or embodiment or any specific embodiments are intended to be limited to, but should be considered as is by reference to appended
A possibility that claim provides broad sense in view of the prior art for these claims explanation, to effectively cover the disclosure
Preset range.In addition, the disclosure is described with inventor's foreseeable embodiment above, its purpose is to be provided with
Description, and those equivalent modifications that the disclosure can be still represented to the unsubstantiality change of the disclosure still unforeseen at present.
Claims (7)
1. the recommended method of the air-conditioning target temperature based on region big data, which is characterized in that the described method comprises the following steps:
Step 1, the temperature value that each air conditioner in same geographic location is currently set is uploaded to cloud server end；
Step 2, the geographical location for reading cloud server end is adjacent and be less than the temperature value that the N platform air conditioner of temperature threshold is set
Adjacent temperature sequence；
Step 3, the weighted average for calculating adjacent temperature sequence obtains recommended temperature；
Step 4, recommended temperature is set by air-conditioner temperature.
2. the recommended method of the air-conditioning target temperature according to claim 1 based on region big data, which is characterized in that
In step 1, by the temperature value that each air conditioner in same geographic location is currently set be uploaded to the method for cloud server end as,
Each air conditioner in the same area is divided into the air conditioner of same geographic location.
3. the recommended method of the air-conditioning target temperature according to claim 2 based on region big data, which is characterized in that
In step 1, in the operational process of air conditioner, temperature Value Data that each air conditioner in same geographic location is currently set
Cloud server end is uploaded to by Internet of Things fidonetFido or ICP/IP protocol with regional information, cloud server end receives and stores the whole nation
The temperature Value Data that the air conditioner of various regions uploads and the regional information being associated.
4. the recommended method of the air-conditioning target temperature according to claim 3 based on region big data, which is characterized in that
In step 1, regional information includes the location information of GPS or dipper system, and the air conditioner passes through Internet of Things or internet and cloud
Server end carries out telecommunication.
5. the recommended method of the air-conditioning target temperature according to claim 4 based on region big data, which is characterized in that
In step 2, read cloud server end geographical location it is adjacent and be less than temperature threshold N platform air conditioner set temperature value phase
The method of adjacent temperature sequence is to read location information and air conditioner recently and be less than the temperature that the N platform air conditioner of temperature threshold is set
Angle value, using the temperature value of this N platform air conditioner setting as adjacent temperature sequence.
6. the recommended method of the air-conditioning target temperature according to claim 5 based on region big data, which is characterized in that
In step 3, the method that the weighted average for calculating adjacent temperature sequence obtains recommended temperature is to be obtained according to average weighted formula
To recommended temperature:Wherein, tempiFor the adjacent i-th air conditioner setting in geographical location
Temperature value, distiFor air conditioner i-th air-conditioning the distance between adjacent with geographical location.
7. the recommendation apparatus of the air-conditioning target temperature based on region big data, which is characterized in that described device include: memory,
Processor and storage in the memory and the computer program that can run on the processor, the processor execution
The computer program operates in the unit of following device:
Geographical location temperature collecting cell, the temperature value for currently setting each air conditioner in same geographic location upload
To cloud server end；
Adjacent temperature sequence reading unit, the geographical location for reading cloud server end is adjacent and is less than the N platform of temperature threshold
The adjacent temperature sequence of the temperature value of air conditioner setting；
Recommended temperature computing unit, the weighted average for calculating adjacent temperature sequence obtain recommended temperature；
Recommended temperature setting unit, for setting recommended temperature for air-conditioner temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910321736.1A CN110162699A (en) | 2019-04-22 | 2019-04-22 | The recommended method and device of air-conditioning target temperature based on region big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910321736.1A CN110162699A (en) | 2019-04-22 | 2019-04-22 | The recommended method and device of air-conditioning target temperature based on region big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110162699A true CN110162699A (en) | 2019-08-23 |
Family
ID=67639831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910321736.1A Pending CN110162699A (en) | 2019-04-22 | 2019-04-22 | The recommended method and device of air-conditioning target temperature based on region big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110162699A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110805994A (en) * | 2019-11-27 | 2020-02-18 | 广东美的制冷设备有限公司 | Control method and device of air conditioning equipment and server |
| CN110949428A (en) * | 2019-12-09 | 2020-04-03 | 交控科技股份有限公司 | Train air conditioner parameter adjusting method and system |
| CN112199860A (en) * | 2020-10-27 | 2021-01-08 | 合肥美菱物联科技有限公司 | Refrigerator variable-temperature zone setting optimization method based on big data |
| CN112612547A (en) * | 2020-12-25 | 2021-04-06 | 青岛海尔科技有限公司 | Parameter determination method and device, storage medium and electronic device |
| CN112859623A (en) * | 2020-12-31 | 2021-05-28 | 深圳市九洲电器有限公司 | Digital television receiver, indoor temperature control method and indoor temperature control device |
| CN114544002A (en) * | 2022-02-17 | 2022-05-27 | 深圳市同为数码科技股份有限公司 | Temperature measurement jump processing method and device, computer equipment and medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104279713A (en) * | 2014-10-24 | 2015-01-14 | 珠海格力电器股份有限公司 | Air conditioner control method and system and air conditioner controller |
| CN106871365A (en) * | 2017-03-09 | 2017-06-20 | 美的集团股份有限公司 | The progress control method of air-conditioner, device and air-conditioning system |
-
2019
- 2019-04-22 CN CN201910321736.1A patent/CN110162699A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104279713A (en) * | 2014-10-24 | 2015-01-14 | 珠海格力电器股份有限公司 | Air conditioner control method and system and air conditioner controller |
| CN106871365A (en) * | 2017-03-09 | 2017-06-20 | 美的集团股份有限公司 | The progress control method of air-conditioner, device and air-conditioning system |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110805994A (en) * | 2019-11-27 | 2020-02-18 | 广东美的制冷设备有限公司 | Control method and device of air conditioning equipment and server |
| CN110805994B (en) * | 2019-11-27 | 2021-12-21 | 广东美的制冷设备有限公司 | Control method and device of air conditioning equipment and server |
| CN110949428A (en) * | 2019-12-09 | 2020-04-03 | 交控科技股份有限公司 | Train air conditioner parameter adjusting method and system |
| CN112199860A (en) * | 2020-10-27 | 2021-01-08 | 合肥美菱物联科技有限公司 | Refrigerator variable-temperature zone setting optimization method based on big data |
| CN112199860B (en) * | 2020-10-27 | 2024-05-31 | 合肥美菱物联科技有限公司 | Refrigerator variable temperature zone setting optimization method based on big data |
| CN112612547A (en) * | 2020-12-25 | 2021-04-06 | 青岛海尔科技有限公司 | Parameter determination method and device, storage medium and electronic device |
| CN112859623A (en) * | 2020-12-31 | 2021-05-28 | 深圳市九洲电器有限公司 | Digital television receiver, indoor temperature control method and indoor temperature control device |
| CN114544002A (en) * | 2022-02-17 | 2022-05-27 | 深圳市同为数码科技股份有限公司 | Temperature measurement jump processing method and device, computer equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110162699A (en) | The recommended method and device of air-conditioning target temperature based on region big data | |
| US11965666B2 (en) | Control method for air conditioner, and device for air conditioner and storage medium | |
| Yin et al. | Socialized mobile photography: Learning to photograph with social context via mobile devices | |
| US20070094522A1 (en) | System and method for overclocking a central processing unit | |
| CN109726885A (en) | Electricity consumption anomaly assessment method, apparatus, equipment and computer storage medium | |
| US20180217654A1 (en) | Power-saving processing method, device, mobile terminal and cloud server | |
| CN112944624B (en) | Method for air conditioner control and air conditioner | |
| CN103353881A (en) | Method and device for searching application | |
| CN112255925A (en) | Method and device for controlling intelligent household equipment and computer equipment | |
| CN114565103A (en) | Gradient selection and adaptive learning rate-based weighted K asynchronous federated learning method, system and device | |
| WO2017095413A1 (en) | Incremental automatic update of ranked neighbor lists based on k-th nearest neighbors | |
| CN109409321A (en) | A kind of determination method and device of camera motion mode | |
| CN113590342B (en) | Resource allocation method and system in cloud computing system | |
| CN104978445A (en) | Picture combining method and picture combining device | |
| CN114912033A (en) | Knowledge graph-based recommendation popularity deviation adaptive buffering method | |
| CN108833200A (en) | A kind of adaptive unidirectional transmission method of large data files and device | |
| CN113434286A (en) | Energy efficiency optimization method suitable for mobile application processor | |
| CN112378043A (en) | Cooling water system control method, equipment, device and storage medium | |
| CN110793209A (en) | Configuration parameter determination method and device for water heater | |
| CN112215940B (en) | Construction system and construction method of scene model | |
| CN114448814B (en) | Intelligent laboratory monitoring method and system based on 5G plus | |
| CN116066981A (en) | Air conditioner control method and device, air conditioner and readable storage medium | |
| CN110263010A (en) | A kind of cache file automatic update method and device | |
| CN112944579B (en) | Control method and control device of air conditioner and air conditioning system | |
| CN114071960A (en) | Heat dissipation adjusting method, device, equipment and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190823 |