A refrigerator intelligent noise reduction method

Abstract

The application discloses a refrigerator intelligent noise reduction method and relates to the technical field of refrigerator noise reduction.The data analysis end work flow of the application is as follows: collecting refrigerator door opening time point data of a user for two weeks; according to the refrigerator door opening time point data of the user for two weeks, the door opening time point is counted according to 24 hours a day; all time point clusters are found out; for each point cluster, a time period in which 90% of points are located is found out and is respectively recorded as a time period a1, a time period a2 and a time period an; the refrigerator door opening time point data of the user on non-working days is analyzed according to steps S2-S4 to obtain a time period b1, a time period b2 and a time period bn; and the door opening time period data is sent to the refrigerator end for use.The application uses cloud data to analyze the refrigerator use habit of a user, reduces the rotating speed of a refrigerator compressor and a fan motor in a time period in which the user uses the refrigerator more frequently, so that the running noise of the refrigerator is reduced, and the comfort is increased.

Description

Technical Field

[0001] This invention belongs to the field of refrigerator noise reduction technology, and in particular relates to an intelligent noise reduction method for refrigerators. Background Technology

[0002] With the continuous advancement of the national smart city strategy, the number of internet-connected home appliances is increasing. A key feature of these appliances is their ability to send operational status data, such as that of refrigerators, to the cloud. Currently, the amount of operational data collected in the cloud is substantial; how to utilize this data and maximize its value is a challenge facing the entire industry.

[0003] With continuous technological advancements, modern refrigerators generally use inverter compressors as the core component for refrigeration. Inverter compressors adjust their speed according to the refrigerator's cooling needs; when the cooling demand is high, the compressor runs faster, resulting in faster cooling, but also higher noise levels. Conversely, when the cooling demand is low, the compressor runs slower, resulting in slower cooling and lower noise levels. The control method for the inverter fan used in the refrigerator is the same as that for the inverter compressor.

[0004] To address the high speed and noise levels of inverter compressors and fans, some existing noise reduction technologies incorporate human infrared sensors and noise sensors. The human infrared sensor detects the presence of people near the refrigerator, while the noise sensor detects background noise. When someone is present and the background noise is low, the compressor and fan motor speeds are reduced to decrease noise and increase comfort.

[0005] However, the noise reduction methods mentioned above require the addition of human infrared sensors and noise sensors, which significantly increases the cost of the refrigerator and is detrimental to the market sales of the product. Summary of the Invention

[0006] The purpose of this invention is to provide a smart noise reduction method for refrigerators. Without increasing costs, this method utilizes refrigerator operating data stored in the cloud to reduce the perceived noise level of the refrigerator, thus solving the problems of high operating noise and low user comfort in existing refrigerators.

[0007] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0008] This invention relates to a smart noise reduction method for refrigerators. The smart noise reduction system for refrigerators involved in this method includes a refrigerator end and a data analysis end deployed in the cloud.

[0009] The workflow of the data analysis terminal is as follows:

[0010] Step S1: Collect data on the time the user opens the refrigerator door during the first two weeks;

[0011] Step S2: Statistically analyze the refrigerator door opening times of the user on weekdays within two weeks, categorized by 24 hours per day;

[0012] Step S3: Find the point clusters at all time points;

[0013] Step S4: For each point cluster, find the time period in which 90% of the points are located, and denot them as time period a1, time period a2, and time period an respectively;

[0014] Step S5: Analyze the refrigerator door opening time data of users on non-working days according to the steps S2 to S4 to obtain time period b1, time period b2 and time period bn;

[0015] Step S6: If the time interval between two adjacent time periods is less than 90 minutes, then merge the two time periods into one time period;

[0016] Step S7: Send the door opening time period data to the refrigerator for use;

[0017] The refrigerator acquires data on the time period during which the door is opened, and reduces the operating speed of the compressor and the operating speed of the fan based on this time period.

[0018] As a preferred technical solution, in step S1, the refrigerator door opening time data are statistically analyzed according to two categories: weekdays and non-weekdays.

[0019] As a preferred technical solution, in step S2, the data for two weeks of working days is recorded in 24-hour format and accurate to the second.

[0020] As a preferred technical solution, in step S3, the time point clusters are several densely distributed time periods of all time points.

[0021] As a preferred technical solution, in step S4, each point cluster finds that 90% of the points are in no more than eight time periods; if there are more than eight point clusters, the eight point clusters containing the most data are analyzed.

[0022] As a preferred technical solution, after the data analysis terminal analyzes two weeks of data, it obtains the data of the user using the refrigerator on a new day, while discarding the data of the latest day as a new dataset, and derives new time periods a1, a2, an, b1, b2, and bn, and sends them to the refrigerator terminal for use.

[0023] As a preferred technical solution, the refrigerator acquires time periods a1, a2, an, b1, b2, and bn, and controls the compressor and fan speed to be reduced during these time periods a1, a2, an, b1, b2, and bn through the refrigerator's controller.

[0024] The present invention has the following beneficial effects:

[0025] This invention utilizes cloud data analysis to understand users' refrigerator usage habits and reduces the speed of the refrigerator compressor and fan motor during periods when users are more likely to use the refrigerator, thereby reducing refrigerator operating noise and increasing comfort.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the data analysis terminal of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see Figure 1 As shown, the present invention is a method for intelligent noise reduction of a refrigerator. The intelligent noise reduction system of the refrigerator involved in the method includes a refrigerator end and a data analysis end deployed in the cloud.

[0031] The workflow for data analysis is as follows:

[0032] Step S1: Collect refrigerator door opening time data for the first two weeks; perform statistical analysis on the refrigerator door opening time data according to two categories: weekdays and non-weekdays;

[0033] Step S2: Statistically record the refrigerator door opening times for the user on weekdays within two weeks, based on a 24-hour day. The data for the two weeks of weekdays should be recorded in 24-hour format, accurate to the second, such as the recorded time format "2022-10-08 15:59:38".

[0034] Step S3: Find the point clusters at all time points; the point clusters at all time points are several densely distributed time periods at all time points;

[0035] Step S4: For each point cluster, find the time period in which 90% of the points are located, and denote them as time period a1, time period a2, and time period an respectively; the time periods in which 90% of the points are located in each point cluster shall not exceed eight; if there are more than eight point clusters, then analyze the eight point clusters containing the most data.

[0036] Step S5: Analyze the refrigerator door opening time data of users on non-working days according to the steps S2 to S4 to obtain time period b1, time period b2 and time period bn;

[0037] Step S6: If the time interval between two adjacent time periods is less than 90 minutes, the two time periods are merged into one time period; After the data analysis terminal analyzes the data of two weeks, it obtains the data of the user using the refrigerator on the new day, while discarding the data of the latest day as a new dataset, and obtains new time periods a1, a2, an, b1, b2, and bn, and sends them to the refrigerator terminal for use;

[0038] Step S7: Send the door opening time period data to the refrigerator for use;

[0039] The refrigerator acquires data on the time periods during which the door is opened and reduces the compressor and fan speeds accordingly to lower operating noise and increase comfort. The refrigerator acquires time periods a1, a2, an, b1, b2, and bn, and its controller adjusts these time periods to reduce compressor and fan speeds.

[0040] Example 1

[0041] The refrigerator is equipped with a wireless communication module, a controller, a door opening sensor, and refrigerator cooling components. The refrigerator connects to a data analysis terminal deployed in the cloud via the wireless communication module. Every time the user opens or closes the door, the door opening sensor sends information to the controller, which records the time of the refrigerator door opening or closing, down to the second, such as the recording format "2022-10-08 15:59:38 to 2022-10-08 15:59:55". This time is marked as a set of points and plotted on a 24-hour time axis. The time point set of all door opening time periods within a day is also recorded. For each time axis set of points, the time period in which 90% of the points are located is identified and denoted as time period a1, time period a2, and time period an, respectively. If the user opens the door for less than 90 minutes in a day, the two door opening time periods are directly merged into one time period.

[0042] Example 2

[0043] The refrigerator acquires and stores a set of time points for one day, and then stores two sets of time points for two weeks before sending them to the data analysis end. After the data analysis end has analyzed the data for the two weeks, it acquires the data for the user's use of the refrigerator on the new day, while discarding the data for the latest day as a new dataset, and derives new time periods a1, a2, an, b1, b2, and bn, which are then sent to the refrigerator for use.

[0044] The refrigerator acquires new datasets, which are then monitored by the controller. When the current time period falls within the time period when the user frequently opens the refrigerator door, the controller reduces the operating efficiency of the refrigerator's cooling components, such as the compressor speed and fan speed. This effectively reduces the refrigerator's operating noise during the time when the user frequently opens the refrigerator door. When the user does not open the refrigerator door, indicating that the user is not at home or not in the living room where the refrigerator is located, the refrigerator returns to its normal operating efficiency. This achieves the goal of reducing refrigerator operating noise, reducing power consumption to some extent, and increasing comfort.

[0045] It is worth noting that the various units included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0046] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0047] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent noise reduction in a refrigerator, the method involving an intelligent noise reduction system for a refrigerator comprising a refrigerator-side component and a data analysis component deployed in the cloud, characterized in that: The workflow of the data analysis terminal is as follows: Step S1: Collect data on the time the user opens the refrigerator door during the first two weeks; Step S2: Statistically analyze the refrigerator door opening times of the user on weekdays within two weeks, categorized by 24 hours per day; Step S3: Find the point clusters at all time points; Step S4: For each point cluster, find the time period in which 90% of the points are located, and denot them as time period a1, time period a2, and time period an respectively; Step S5: Analyze the refrigerator door opening time data of users on non-working days according to the steps S2 to S4 to obtain time period b1, time period b2 and time period bn; Step S6: If the time interval between two adjacent time periods is less than 90 minutes, then merge the two time periods into one time period; Step S7: Send the door opening time period data to the refrigerator for use; The refrigerator acquires data on the time period during which the door is opened, and reduces the operating speed of the compressor and the operating speed of the fan based on this time period.

2. The intelligent noise reduction method for a refrigerator according to claim 1, characterized in that, In step S1, the refrigerator door opening time data are statistically analyzed according to two categories: weekdays and non-weekdays.

3. The intelligent noise reduction method for a refrigerator according to claim 1, characterized in that, In step S2, the data for two weeks of workdays is recorded in 24-hour format and accurate to the second.

4. The intelligent noise reduction method for a refrigerator according to claim 1, characterized in that, In step S3, the time point clusters are several densely distributed time periods across all time points.

5. The intelligent noise reduction method for a refrigerator according to claim 1, characterized in that, In step S4, for each point cluster, 90% of the points are found to be in no more than eight time periods; if there are more than eight point clusters, the eight point clusters with the most data are analyzed.

6. The intelligent noise reduction method for a refrigerator according to claim 1, characterized in that, After analyzing two weeks of data, the data analysis terminal obtains the data of the user's use of the refrigerator on the newest day, while discarding the data of the latest day as a new dataset, and derives new time periods a1, a2, an, b1, b2, and bn, which are then sent to the refrigerator for use.

7. The intelligent noise reduction method for a refrigerator according to claim 1, characterized in that, The refrigerator acquires time periods a1, a2, an, b1, b2, and bn, and controls the compressor and fan speeds to be reduced during these time periods a1, a2, an, b1, b2, and bn through its controller.

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