CN111368157A - Refrigerator operation method based on user behavior analysis - Google Patents
Refrigerator operation method based on user behavior analysis Download PDFInfo
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- CN111368157A CN111368157A CN202010162047.3A CN202010162047A CN111368157A CN 111368157 A CN111368157 A CN 111368157A CN 202010162047 A CN202010162047 A CN 202010162047A CN 111368157 A CN111368157 A CN 111368157A
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- G—PHYSICS
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9035—Filtering based on additional data, e.g. user or group profiles
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D29/00—Arrangement or mounting of control or safety devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D2600/00—Control issues
- F25D2600/02—Timing
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D2600/00—Control issues
- F25D2600/06—Controlling according to a predetermined profile
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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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Abstract
The invention discloses a refrigerator operation method based on user behavior analysis, and relates to the technical field of refrigerator operation methods. The method comprises an algorithm for calculating the maximum idle time reference value of the refrigerator in the refrigerator waiting stabilization period, an algorithm for learning the habit of a refrigerator user and an algorithm for the intelligent operation period of the refrigerator. The invention collects and records the time when the user uses the refrigerator by taking the door switch signal and the temperature gear change signal as the basis, finds the time attribute of the operation of opening and closing the refrigerator door and the temperature gear change signal, thereby analyzing and calculating the idle time period and the busy time period when the user uses the refrigerator every 24 hours, and dividing the operation period between the power-on time and the power-off time of the refrigerator into three stages: the method comprises the steps of calculating a reference value algorithm for the maximum idle time of the refrigerator, a habit learning period algorithm for the refrigerator user and an intelligent operation period algorithm for the refrigerator according to the refrigerator to-be-stabilized period, the habit learning period of the refrigerator user and the intelligent operation period algorithm of the refrigerator, realizing intelligent control on busy and idle periods of the refrigerator, saving energy and improving the utilization rate of cold energy of the refrigerator.
Description
Technical Field
The invention belongs to the technical field of refrigerator operation methods, and particularly relates to a refrigerator operation method based on user behavior analysis.
Background
With the development of science and technology, household appliances become more and more intelligent. However, in the prior art, the refrigerator does not have a function of intelligently controlling the refrigeration parameters according to the requirements of users. So-called intelligence stays only on networking interactions.
The refrigerator is a household appliance with uninterrupted power supply, but the refrigeration appeal of a user to the refrigerator is not maintained on the same level in 24 hours all day, in the use process of different refrigerators, idle use periods and busy use periods exist, and different users have the conditions that the busy and idle periods are distributed differently and the refrigeration requirements of different periods on the refrigerator are different. In busy hours, a user usually wants to refrigerate as soon as possible when using the refrigerator, however, in idle hours, the time for the user to use the refrigerator is short, the temperature in the refrigerator does not change much, and the requirement of the user on the refrigerating speed is not high, so that it is very necessary to design an algorithm to collect the habits of the user and analyze and utilize the habits through signal sources commonly possessed by the refrigerator, so as to intelligently control the refrigerating parameters of the refrigerator, namely, on the basis of not increasing the cost, an intelligent algorithm capable of aiming at different users is designed, and the busy hours and the idle hours when the user uses the refrigerator are calculated.
The invention provides a refrigerator operation method based on user behavior analysis, which is used for solving the problems.
Disclosure of Invention
The invention aims to provide a refrigerator operation method based on user behavior analysis, which collects and records the time when a user uses a refrigerator by taking a door switch signal and a temperature gear change signal as a basis, finds the time attributes of door opening and closing operation and the temperature gear change signal, so as to analyze and calculate the idle time period and the busy time period when the user uses the refrigerator every 24 hours, and divides the operation period from the power-on time to the power-off time of the refrigerator into three stages: the method comprises the steps of calculating a reference value algorithm for the maximum idle time of the refrigerator, a habit learning period algorithm for the refrigerator user and an intelligent operation period algorithm for the refrigerator in the refrigerator waiting stabilization period, the habit learning period algorithm for the refrigerator user and the intelligent operation period algorithm for the refrigerator, and achieving intelligent control over busy and idle periods of the refrigerator.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a refrigerator operation method based on user behavior analysis, which comprises the following steps: calculating a reference value algorithm for the maximum idle time of the refrigerator, a habit learning period algorithm of a refrigerator user and an intelligent operation period algorithm of the refrigerator in a refrigerator waiting stabilization period;
the algorithm for calculating the maximum idle time reference value of the refrigerator in the period of waiting for stabilization of the refrigerator comprises the following steps:
a00: judging whether the unit time period is an idle time period or not; if yes, A01 is executed; if not, executing A02;
the first 48 hours after the refrigerator is powered on are evenly divided into N parts to form 0, 1, 2. The idle time period is no door opening action or no refrigerator refrigeration gear change in the current unit time period;
a01: tidle1[ i ] ═ STATE0, where STATE0 indicates that the busy-idle attribute is idle;
a02: tidle1[ i ] ═ STATE1, where STATE1 indicates the busy-free attribute is busy;
a03: judging i + + > N; if yes, A04 is executed; if not, executing A00;
a04: calculating the continuous longest idle period within the first 48 hours as Tmax ideal 1;
a05: calculating the continuous longest idle time period within the second 48 hours after the refrigerator is powered on as the steps of A00-A04, wherein the continuous longest idle time period is Tmax ideal 2;
a06: calculating the continuous longest idle time period within the third 48 hours after the refrigerator is powered on as the steps of A00-A04, wherein the continuous longest idle time period is Tmax ideal 3;
a07: calculating the average value of the Tmax ideal 1, the Tmax ideal 2 and the Tmax ideal 3, namely the maximum idle time reference value Trefmaxidle;
the refrigerator user habit learning period algorithm is as follows:
b00: calculating the continuous longest idle time period Tj in the jth learning time period;
wherein j is a natural number; the initial value of j is 0; the learning period is one 24 hours; the continuous longest idle time period is continuous longest without a door opening signal and without a gear setting change signal within 24 hours;
b01: judging whether Tj is located in S ═ Trefmaxidle-delta T, Trefmaxidle + delta T; if yes, execute B02; if not, executing B03;
wherein, the Trefmaxidle is a reference value for calculating the maximum idle time of the refrigerator based on the period to be stabilized; (ii) S ═ Trefmaxidle- Δ T, Trefmaxidle + Δ T ] is the effective maximum idle period range; Δ T is a floating value;
b02: record Tj ═ Nstartj, Nendj ];
b03: judging j + + > N; if so, finishing the recording of the effective value; if not, executing B00;
the intelligent operation period algorithm of the refrigerator comprises the following steps:
after the longest idle segment value of N analysis time units and the starting point label value of the longest idle segment are obtained, the refrigerator enters an intelligent operation period, timing is started from the starting point of the Nth analysis time unit, when the refrigerator operates to the starting point label value of the longest idle segment, the refrigerator operates according to the idle segment optimization parameters until the intelligent operation time reaches the longest idle segment value; after the refrigerator enters the intelligent operation period, the rolling analysis of N analysis time units is still carried out, and the longest idle segment value and the longest idle segment starting point label value are corrected according to the analysis result.
Preferably, the refrigerator user habit learning period algorithm further comprises the following steps:
after N effective time periods Tn are obtained through calculation, N starting point labels are analyzed, and the absolute value of the difference is obtained by sequentially solving Nstart2 … …, Nstart and Nstart1 to obtain N-1 absolute values; labeled Δ Nstart1, Δ Nstart2 … … Δ Nstart, respectively; setting effective range of difference values S1, and if any difference value delta Nstarti is within the effective range S1, considering the difference value to be effective; if any difference value Δ Nstarti is not outside the valid range S1, the difference value is considered invalid; the corresponding reference value Nstartj is invalid; if the Ninvalid difference values are not in the valid range S1, considering that Nstart1 is invalid; acquiring the N24-hour data of the next round again; if the (N-Ninvalid) difference values delta Nstarti are all in the valid difference value range S1, the N starting point index values are all determined to be valid, the maximum value is found, and the starting point index value of the longest idle segment is obtained.
The invention has the following beneficial effects:
the invention collects and records the time when the user uses the refrigerator by taking the door switch signal and the temperature gear change signal as the basis, finds the time attribute of the operation of opening and closing the refrigerator door and the temperature gear change signal, thereby analyzing and calculating the idle time period and the busy time period when the user uses the refrigerator every 24 hours, and dividing the operation period between the power-on time and the power-off time of the refrigerator into three stages: the method comprises the steps of calculating a reference value algorithm for the maximum idle time of the refrigerator, a habit learning period algorithm for the refrigerator user and an intelligent operation period algorithm for the refrigerator according to the refrigerator to-be-stabilized period, the habit learning period of the refrigerator user and the intelligent operation period algorithm of the refrigerator, realizing intelligent control on busy and idle periods of the refrigerator, saving energy and improving the utilization rate of cold energy of the refrigerator.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of an algorithm for calculating a reference value of a refrigerator in a maximum idle time during a refrigerator waiting stabilization period according to the present invention;
FIG. 2 is a flowchart of a learning period algorithm for refrigerator user habits according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a refrigerator operation method based on user behavior analysis, including: calculating a reference value algorithm for the maximum idle time of the refrigerator, a habit learning period algorithm of a refrigerator user and an intelligent operation period algorithm of the refrigerator in a refrigerator waiting stabilization period;
the algorithm for calculating the maximum idle time reference value of the refrigerator in the period of waiting for stabilization of the refrigerator comprises the following steps:
a00: judging whether the unit time period is an idle time period or not; if yes, A01 is executed; if not, executing A02;
the first 48 hours after the refrigerator is powered on are evenly divided into N parts to form 0, 1, 2. The idle time period is no door opening action or no refrigerator refrigeration gear change in the current unit time period;
in the actual use process, after the refrigerator is powered on, setting Tcell as the minimum time unit for collecting a door switch signal and a temperature gear change signal, namely, 48 hours are divided into 48 × 60/Tcell minimum time units, namely 48 × 60/Tcell time units; in each time unit timing period, collecting door switch signals or refrigerator refrigeration gear change signals in units of seconds;
if the time unit has door opening action or refrigerator refrigerating gear changing operation within each Tcell minute, marking the busy and idle attribute of the time unit as STATE 0; if no door opening action exists in Tcell minutes and no refrigerator refrigerating gear changing operation exists, marking the busy and idle attribute of the time unit as STATE 1;
a01: tidle1[ i ] ═ STATE0, where STATE0 indicates that the busy-idle attribute is idle;
a02: tidle1[ i ] ═ STATE1, where STATE1 indicates the busy-free attribute is busy;
a03: judging i + + > N; if yes, A04 is executed; if not, executing A00;
a04: calculating the continuous longest idle period within the first 48 hours as Tmax ideal 1;
a05: calculating the continuous longest idle time period within the second 48 hours after the refrigerator is powered on as the steps of A00-A04, wherein the continuous longest idle time period is Tmax ideal 2;
a06: calculating the continuous longest idle time period within the third 48 hours after the refrigerator is powered on as the steps of A00-A04, wherein the continuous longest idle time period is Tmax ideal 3;
a07: calculating the average value of the Tmax ideal 1, the Tmax ideal 2 and the Tmax ideal 3, namely the maximum idle time reference value Trefmaxidle;
referring to fig. 2, the habit learning period algorithm for the refrigerator user is as follows:
b00: calculating the continuous longest idle time period Tj in the jth learning time period;
wherein j is a natural number; the initial value of j is 0; the learning period is one 24 hours; the continuous longest idle time period is continuous longest without a door opening signal and without a gear setting change signal within 24 hours;
b01: judging whether Tj is located in S ═ Trefmaxidle-delta T, Trefmaxidle + delta T; if yes, execute B02; if not, executing B03;
wherein, the Trefmaxidle is a reference value for calculating the maximum idle time of the refrigerator based on the period to be stabilized; (ii) S ═ Trefmaxidle- Δ T, Trefmaxidle + Δ T ] is the effective maximum idle period range; Δ T is a floating value;
b02: record Tj ═ Nstartj, Nendj ];
b03: judging j + + > N; if so, finishing the recording of the effective value; if not, executing B00;
the intelligent operation period algorithm of the refrigerator comprises the following steps:
after the longest idle segment value of N analysis time units and the starting point label value of the longest idle segment are obtained, the refrigerator enters an intelligent operation period, timing is started from the starting point of the Nth analysis time unit, when the refrigerator operates to the starting point label value of the longest idle segment, the refrigerator operates according to the idle segment optimization parameters until the intelligent operation time reaches the longest idle segment value; after the refrigerator enters the intelligent operation period, the rolling analysis of N analysis time units is still carried out, and the longest idle segment value and the longest idle segment starting point label value are corrected according to the analysis result.
The refrigerator operating method based on user behavior analysis according to claim 1, wherein the refrigerator user habit learning period algorithm further comprises the following:
after N effective time periods Tn are obtained through calculation, N starting point labels are analyzed, and the absolute value of the difference is obtained by sequentially solving Nstart2 … …, Nstart and Nstart1 to obtain N-1 absolute values; labeled Δ Nstart1, Δ Nstart2 … … Δ Nstart, respectively; setting effective range of difference values S1, and if any difference value delta Nstarti is within the effective range S1, considering the difference value to be effective; if any difference value Δ Nstarti is not outside the valid range S1, the difference value is considered invalid; the corresponding reference value Nstartj is invalid; if the Ninvalid difference values are not in the valid range S1, considering that Nstart1 is invalid; acquiring the N24-hour data of the next round again; if the (N-Ninvalid) difference values delta Nstarti are all in the valid difference value range S1, the N starting point index values are all determined to be valid, the maximum value is found, and the starting point index value of the longest idle segment is obtained.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (2)
1. The refrigerator operation method based on user behavior analysis is characterized by comprising the following steps: calculating a reference value algorithm for the maximum idle time of the refrigerator, a habit learning period algorithm of a refrigerator user and an intelligent operation period algorithm of the refrigerator in a refrigerator waiting stabilization period;
the algorithm for calculating the maximum idle time reference value of the refrigerator in the period of waiting for stabilization of the refrigerator comprises the following steps:
a00: judging whether the unit time period is an idle time period or not; if yes, A01 is executed; if not, executing A02;
the first 48 hours after the refrigerator is powered on are evenly divided into N parts to form 0, 1, 2. The idle time period is no door opening action or no refrigerator refrigeration gear change in the current unit time period;
a01: tidle1[ i ] ═ STATE0, where STATE0 indicates that the busy-idle attribute is idle;
a02: tidle1[ i ] ═ STATE1, where STATE1 indicates the busy-free attribute is busy;
a03: judging i + + > N; if yes, A04 is executed; if not, executing A00;
a04: calculating the continuous longest idle period within the first 48 hours as Tmax ideal 1;
a05: calculating the continuous longest idle time period within the second 48 hours after the refrigerator is powered on as the steps of A00-A04, wherein the continuous longest idle time period is Tmax ideal 2;
a06: calculating the continuous longest idle time period within the third 48 hours after the refrigerator is powered on as the steps of A00-A04, wherein the continuous longest idle time period is Tmax ideal 3;
a07: calculating the average value of the Tmax ideal 1, the Tmax ideal 2 and the Tmax ideal 3, namely the maximum idle time reference value Trefmaxidle;
the refrigerator user habit learning period algorithm is as follows:
b00: calculating the continuous longest idle time period Tj in the jth learning time period;
wherein j is a natural number; the initial value of j is 0; the learning period is one 24 hours; the continuous longest idle time period is continuous longest without a door opening signal and without a gear setting change signal within 24 hours;
b01: judging whether Tj is located in S ═ Trefmaxidle-delta T, Trefmaxidle + delta T; if yes, execute B02; if not, executing B03;
wherein, the Trefmaxidle is a reference value for calculating the maximum idle time of the refrigerator based on the period to be stabilized; (ii) S ═ Trefmaxidle- Δ T, Trefmaxidle + Δ T ] is the effective maximum idle period range; Δ T is a floating value;
b02: record Tj ═ Nstartj, Nendj ];
b03: judging j + + > N; if so, finishing the recording of the effective value; if not, executing B00;
the intelligent operation period algorithm of the refrigerator comprises the following steps:
after the longest idle segment value of N analysis time units and the starting point label value of the longest idle segment are obtained, the refrigerator enters an intelligent operation period, timing is started from the starting point of the Nth analysis time unit, when the refrigerator operates to the starting point label value of the longest idle segment, the refrigerator operates according to the idle segment optimization parameters until the intelligent operation time reaches the longest idle segment value; after the refrigerator enters the intelligent operation period, the rolling analysis of N analysis time units is still carried out, and the longest idle segment value and the longest idle segment starting point label value are corrected according to the analysis result.
2. The refrigerator operating method based on user behavior analysis according to claim 1, wherein the refrigerator user habit learning period algorithm further comprises the following:
after N effective time periods Tn are obtained through calculation, N starting point labels are analyzed, and the absolute value of the difference is obtained by sequentially solving Nstart2 … …, Nstart and Nstart1 to obtain N-1 absolute values; labeled Δ Nstart1, Δ Nstart2 … … Δ Nstart, respectively; setting effective range of difference values S1, and if any difference value delta Nstarti is within the effective range S1, considering the difference value to be effective; if any difference value Δ Nstarti is not outside the valid range S1, the difference value is considered invalid; the corresponding reference value Nstartj is invalid; if the Ninvalid difference values are not in the valid range S1, considering that Nstart1 is invalid; acquiring the N24-hour data of the next round again; if the (N-Ninvalid) difference values delta Nstarti are all in the valid difference value range S1, the N starting point index values are all determined to be valid, the maximum value is found, and the starting point index value of the longest idle segment is obtained.
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| CN112082322B (en) * | 2020-09-17 | 2022-07-05 | 长虹美菱股份有限公司 | Refrigerator operation method for optimizing refrigeration by time interval based on busy or idle attribute |
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