CN114738925A - Air conditioner full-state automatic control method based on big data - Google Patents
Air conditioner full-state automatic control method based on big data Download PDFInfo
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- CN114738925A CN114738925A CN202210398905.3A CN202210398905A CN114738925A CN 114738925 A CN114738925 A CN 114738925A CN 202210398905 A CN202210398905 A CN 202210398905A CN 114738925 A CN114738925 A CN 114738925A
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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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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Abstract
The invention discloses an air conditioner full-state automatic control method based on big data, which collects the user full-state use data, compares whether the two data are different in the single operation process of the user through a specific calculation mode, finds out the high-frequency time period for the air conditioner user to adjust the equipment, and carries out modeling analysis on the user use data of the high-frequency time period according to regions and different time periods in a secondary grouping way to obtain the high-frequency adjustment time period automatic control model of the user in different regions in the high-frequency adjustment use time period, and finally further corrects and optimizes the model through the user use data of the model until the model completely conforms to the use habit of the user, outputs the high-frequency adjustment time period automatic control model to form an automatic control function, can detect and learn the full state of the air conditioner equipment, and enables the automatic control model to completely conform to the actual use condition of the user, therefore, the device is further intelligentized, users are liberated, and the use viscosity of the users is improved.
Description
Technical Field
The invention relates to the field of air conditioner control, in particular to an air conditioner full-state automatic control method based on big data.
Background
At present, the air conditioner gradually enters thousands of households as a basic household appliance, and in order to realize further intellectualization of equipment, liberate users and form differentiated selling points, various manufacturers put forward a series of functions for automatically controlling the operation of the air conditioner, such as a full-automatic mode, a one-key refreshing function, a healthy wind function and the like, and the respective dynamic modes mostly take direct conditions such as indoor and outdoor temperature, weather condition and the like as judgment bases, and are concentrated on the automatic judgment of the running mode and the set wind speed in the whole running process of the air conditioner, but the user has obvious difference in equipment adjustment in different time periods, and meets the daily requirements of the air conditioner user, and the air conditioner user needs auxiliary cooperation of other functions such as temperature setting, electric heating function, fresh air function and the like besides the operation mode and the set wind speed, therefore, the study and self-learning of the air conditioning full state of the air conditioning user in the high-frequency adjusting period is very necessary.
At present, the prior art also has a method for judging the automatic operation of the air conditioner, such as patent numbers: the invention discloses a CN202110662831.5 patent name of a method and a device for controlling an air conditioner and the air conditioner, relates to the technical field of intelligent household appliances, and discloses a method for controlling the air conditioner. And obtaining the use habit information of the user in the area where the air conditioner is associated with the user, wherein the use habit information comprises the use time period of each air conditioner and the control scheme of the air conditioner corresponding to each use time period. Meanwhile, the operation schemes of the air conditioners in different set time periods in the big data intelligent analysis area are combined, the target control scheme of the air conditioner in each set time period is determined more accurately through the use habit information of the air conditioners in the learning area, the convenience of the air conditioner control process is improved, and the air conditioners relevant to users are automatically controlled. The disclosed technical scheme is that the air conditioner data used by the user in the whole time period is collected, calculation and memory resources are consumed very much, and the automatic control model is adjusted and optimized through the daily equipment use data of the user instead of the user use data under the automatic control function, so that the automatic control model possibly cannot be completely matched with the actual use condition of the user.
And the patent number: CN202110652731.4, entitled air conditioner control method, air conditioner controller and air conditioning unit, the invention provides an air conditioner control method, an air conditioner controller and an air conditioning unit, so as to at least solve the problem that the air conditioner in the prior art cannot meet both the rapid heat exchange requirement and the user comfort. The method comprises the steps of collecting initial operation parameters and target parameters of a user and various state change values in the operation process, and establishing a user use habit model through a specific calculation model to solve the problem that the rapid heat exchange requirement and the comfort of the user cannot be considered at the same time. The disclosed technical scheme is that an automatic control method related to set temperature, operation mode and set wind speed between an initial state and a set state of a user is adopted, the detection and the learning of the full state of the air conditioning equipment are not realized, the final operation state of the user is not clearly judged, different processing is required to be carried out on the use data of different users, and the calculated amount is overlarge.
Disclosure of Invention
In order to overcome the defects and shortcomings of the prior art, the invention provides an air conditioner full-state automatic control method based on big data.
The technical scheme adopted by the invention is that the method comprises the following steps:
step S1, acquiring the operation data of the air conditioning equipment through the Internet of things communication module to acquire the operation parameter data of the air conditioner;
step S2, finding out the high-frequency time section of the air conditioner user for adjusting the equipment through a specific calculation mode;
step S3, performing secondary grouping on all data according to the reported region field data and the high-frequency adjusting time field data in a box operation manner;
step S4, modeling the use data of the grouped users to obtain the function modes and the setting parameters thereof set by the users at high frequency in different time periods, and screening out each function mode and the setting parameters thereof in combination with different time periods to preliminarily form the automatic control model of the high-frequency adjusting time period of the region;
step S5, the user use data under the model is collected again through the internet of things communication module, the steps are repeated, and the model is further optimized until the model completely accords with the regulation habit of the user;
and step S6, outputting the high-frequency adjusting time interval automatic control model to form an automatic control function.
Further, the operation data of the air conditioner are collected through internet of things communication, various operation parameters of the air conditioner are obtained, the time period of the air conditioner user for adjusting the peak of the equipment is calculated, the equipment data are grouped under a certain condition, functions are solidified in various time periods after the usage proportion of each function exceeds 80%, a high-frequency adjusting time period automatic control model of each group of data in the high-frequency adjusting time period is established, and repeated adjustment and optimization are carried out on each model through the input of the subsequent user usage data.
Furthermore, each operating parameter of the air conditioner comprises equipment SN encoding data, equipment starting data, timestamp data carried by the equipment data during reporting, equipment area data and continuous use data of each function of the equipment.
Further, the calculating of the peak time period of the air conditioner user to the equipment adjustment is to calculate whether different equipment adjusts different functions at different moments, calculate adjustment frequencies of all the equipment to the different functions at the different equipment, and comprehensively analyze the high-frequency adjustment time period of the air conditioner equipment.
Further, the equipment data are grouped primarily according to regions, different data groups are formed, and when the data amount of the local regions is insufficient, the regions with similar geographic positions and similar climatic conditions are divided into one group for processing.
Further, the high-frequency adjustment period automatic control model is used for counting function adjustment data of the equipment in 0-5 minutes, 5-10 minutes, 10-15 minutes, 15-20 minutes, 20-30 minutes and 30-40 minutes, and adding a function command with high adjustment frequency and a change value of each time period into the specific period automatic control model to form the high-frequency adjustment period automatic control model of the user in the peak time period.
Further, the repeated tuning is that users collect tuning data of the automatic control model in the high-frequency tuning period, the collected data are grouped for the second time according to regions and time periods, then statistics is carried out on function tuning conditions in different periods, and when more than 80% of users tune the automatic execution function of the model at a certain moment, the automatic control model in the high-frequency tuning period is tuned to achieve optimization until the control habits of the users in the region are completely met.
The invention provides an air conditioner full-state automatic control method based on big data, which collects the user full-state use data, compares whether the two data are different in the single operation process of the user through a specific calculation mode, finds out the high-frequency time period for the air conditioner user to adjust the equipment, and carries out modeling analysis on the user use data of the high-frequency time period according to the regions and the secondary grouping of different time periods to obtain the high-frequency adjustment time period automatic control model of the user in different regions in the high-frequency adjustment use time period, finally further corrects and optimizes the model through the user use data of the model until the model completely conforms to the use habit of the user, outputs the high-frequency adjustment time period automatic control model to form an automatic control function, can detect and learn the full state of the air conditioner equipment, and enables the automatic control model to completely conform to the actual use condition of the user, therefore, the device is further intelligentized, users are liberated, and the use viscosity of the users is improved.
Drawings
FIG. 1 is a technical diagram of an automatic control method of the present invention;
fig. 2 is a flowchart of an automatic control method of the present invention.
Detailed Description
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict, and the present application will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for automatically controlling the full state of an air conditioner based on big data includes:
step S1, collecting the operation data of the air-conditioning equipment through the communication module of the Internet of things to obtain the operation parameter data of the air conditioner;
step S2, finding out the high-frequency time section of the air conditioner user for adjusting the equipment through a specific calculation mode;
step S3, performing secondary grouping on all data according to the reported region field data and the high-frequency adjusting time field data in a box operation manner;
step S4, modeling the use data of the grouped users, acquiring the function modes and the setting parameters thereof set by the users at high frequency in different time periods, screening out the function modes and the setting parameters thereof in combination with different time periods, and initially forming an automatic control model for the high-frequency adjustment time period of the region;
step S5, the user use data under the model is collected again through the internet of things communication module, the steps are repeated, and the model is further optimized until the model completely accords with the regulation habit of the user;
and step S6, outputting the high-frequency adjusting time interval automatic control model to form an automatic control function.
As shown in fig. 2, the intelligent air conditioning equipment of the communication module of the internet of things can send the running state data of the equipment to the big data platform through the communication module of the internet of things, the big data platform cleans the original data, the cleaning of the original data is to inspect the reported data of the equipment, and only the complete data of the reported field is reserved, and the parameters used by the invention include: the method comprises the following steps that (1) equipment sn encodes data, equipment starting time data, equipment timestamp data, equipment region data and all function use data;
grouping single-use of different equipment for equipment data safety through equipment sn encoding data and equipment opening time data, screening previous 40-minute operation data in the equipment operation data, wherein the main calculation logic is as follows:
Ttime stamp-TTime of opening<40 minutes after
Wherein the T time stamp represents the time stamp data reported by the equipment, and the T opening time represents the opening time data of the equipment
Analyzing the region data uploaded by the equipment according to a certain protocol, and grouping the analyzed data according to regions;
grouping the same group of data again in time periods of 0-5 minutes, 5-10 minutes, 10-15 minutes, 15-20 minutes, 20-30 minutes, 30-40 minutes and the like according to the running time, and counting the utilization rate of each function;
in each time period, if a certain function opening rate exceeds 80%, adding the function opening command into the automatic control command in the time period, and if the certain function closing rate exceeds 80%, adding the function closing command into the automatic control command in the time period, if the set temperature value changes, rounding the change value, and then selecting a mode as the function command, wherein the main calculation logic is as follows:
Numnumber of data pieces for opening the function/NumTotal number of data in the time period>Add the function on command 80%
NumNumber of data pieces for closing the function/NumTotal number of data in the time period>Add the function close command 80% >
NumNumber of set temperature data/NumTotal number of data in the time period>80% adding the set temperature mode command
NumSet temperature data is reduced/NumTotal number of data in the time period>Adding 80% of a down-set temperature mode command
Wherein NumThe function opening stripThe number indicates the number of pieces of the function opening data in the set of data, NumNumber of data pieces for the function closingIndicating the number of pieces of data, Num, of which the function is turned off in the set of dataNumber of set temperature dataIndicates the number of pieces of the set temperature data, Num, of the set dataSet temperature data is reducedIndicates the number of the set temperature data in the group of data, NumTotal number of data in the time periodIndicating the total number of data pieces for that period.
Combining the adding function commands of the users in different areas in each time period to form an automatic control model of the different areas in the previous 40 minutes;
and finally, collecting user adjustment data for opening the automatic control function by the user again in the later stage, repeating the steps, adding the function opening command into the automatic control command if the adjustment opening frequency of a certain function exceeds 80% under automatic control, adding the function closing command into the automatic control command if the closing frequency of the certain function exceeds 80% under automatic control adjustment, repeating the steps until the automatic control function completely accords with the equipment use mode of the regional user, wherein the calculation logic is as follows:
Num1the number of data pieces is adjusted by opening a certain function under the automatic control function/Num1Number of data pieces under automatic opening function>Add the function on command 80%
Num1Under the automatic control function of turning off and adjusting the number of data pieces for a certain function/Num1Number of data pieces under automatic opening function>80%: add the function close command
Num1Set temperature data is increased to be in the automatic control function of opening/Num1Number of data pieces under automatic starting function>Adding 80% of set temperature mode command for increasing
Num1Turning on the automatic control function/Num1Number of data pieces under automatic opening function>Adding 80% of a down-set temperature mode command
Wherein Num1The number of data pieces is adjusted by opening a certain function under the automatic control functionNum1, which indicates the number of pieces of function-on adjustment data in the case where the user uses the automatic control functionUnder the automatic control function of turning off and adjusting the number of data pieces for a certain functionNum1, which indicates the number of pieces of function-off adjustment data under the use of the automatic control function by the userSet temperature data is increased to be in the automatic control function of openingNum1 showing the number of pieces of set temperature data being set high on the premise that the user uses the automatic control functionTurning on the automatic control functionNum1, which indicates the number of pieces for which the set temperature is adjusted down on the premise that the user uses the automatic control functionNumber of data pieces under automatic opening functionThe number of data pieces under the condition that the user starts the automatic function is represented;
and outputting a high-frequency regulation time interval automatic control model to form an automatic control function.
If the cloud computing pressure and the storage pressure are not considered, the self-learning function of the user in the high-frequency adjusting time period can be converted into the self-learning function of the user in the whole time period, and the specific operation mode is the same as the method for researching the self-learning function of the user in the high-frequency adjusting time period.
The invention provides an air conditioner full-state automatic control method based on big data, which collects the user full-state use data, compares whether the two data are different in the single operation process of the user through a specific calculation mode, finds out the high-frequency time period for the air conditioner user to adjust the equipment, and carries out modeling analysis on the user use data of the high-frequency time period according to the regions and the secondary grouping of different time periods to obtain the high-frequency adjustment time period automatic control model of the user in different regions in the high-frequency adjustment use time period, finally further corrects and optimizes the model through the user use data of the model until the model completely conforms to the use habit of the user, outputs the high-frequency adjustment time period automatic control model to form an automatic control function, can detect and learn the full state of the air conditioner equipment, and enables the automatic control model to completely conform to the actual use condition of the user, therefore, further intellectualization of the equipment is realized, users are liberated, and the use viscosity of the users is improved.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "fixed" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made herein without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims (7)
1. A big data-based air conditioner full-state automatic control method is characterized by comprising the following steps:
step S1, acquiring the operation data of the air conditioning equipment through the Internet of things communication module to acquire the operation parameter data of the air conditioner;
step S2, finding out the high-frequency time section of the air conditioner user for adjusting the equipment through a specific calculation mode;
step S3, performing secondary grouping on all data according to the reported region field data and the high-frequency adjusting time field data in a box operation manner;
step S4, modeling the use data of the grouped users, acquiring the function modes and the setting parameters thereof set by the users at high frequency in different time periods, screening out the function modes and the setting parameters thereof in combination with different time periods, and initially forming an automatic control model for the high-frequency adjustment time period of the region;
step S5, the user use data under the model is collected again through the internet of things communication module, the steps are repeated, and the model is further optimized until the model completely accords with the regulation habit of the user;
and step S6, outputting the high-frequency adjusting time interval automatic control model to form an automatic control function.
2. The method as claimed in claim 1, wherein the air conditioner full-state automatic control method based on big data is characterized in that the air conditioner operation data is collected through internet of things communication, operation parameters of the air conditioner are obtained, the peak time period of the air conditioner user for equipment adjustment is calculated, the equipment data is grouped under certain conditions, the functions are solidified in each time period after the usage percentage of each function exceeds 80%, the high-frequency adjustment time period automatic control model of each group of data in the high-frequency adjustment time period is constructed, and each model is repeatedly adjusted and optimized through the input of the subsequent user usage data.
3. The method as claimed in claim 2, wherein the operation parameters of the air conditioner include SN-encoded data of the device, power-on data of the device, timestamp data carried by the device data when reporting the device data, regional data of the device, and continuous use data of the functions of the device.
4. The method as claimed in claim 2, wherein the calculating of the peak time period of the air conditioner user to adjust the devices calculates whether different devices adjust different functions at different times, calculates the adjusting frequency of all devices to different functions at different devices, and comprehensively analyzes the high-frequency adjusting time period of the air conditioner.
5. The method as claimed in claim 2, wherein the equipment data is grouped into the preliminary grouping according to the area, and different data groups are formed, and in case of lack of data amount in the area, the areas with similar geographical position and climate condition are divided into one group for processing.
6. The big-data-based air conditioner full-state automatic control method as claimed in claim 2, wherein the high-frequency regulation period automatic control model is a high-frequency regulation period automatic control model formed by counting function regulation data of equipment in 0-5 minutes, 5-10 minutes, 10-15 minutes, 15-20 minutes, 20-30 minutes and 30-40 minutes, and adding function commands and variation values with higher regulation frequency for each time period into a specific period automatic control model.
7. The big-data-based air conditioner full-state automatic control method as claimed in claim 2, wherein the repeated tuning is that users collect tuning data of the high-frequency tuning period automatic control model, the collected data are grouped twice according to regions and time periods, then statistics is carried out on the function tuning conditions in different time periods, when more than 80% of users tune the automatic execution function of the model at a certain time, the high-frequency tuning period automatic control model is tuned to achieve optimization until the control habits of the users in the region are completely met.
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| CN113375297A (en) * | 2021-06-15 | 2021-09-10 | 青岛海尔空调器有限总公司 | Method and device for controlling air conditioner and air conditioner |
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