CN113566376B

CN113566376B - Electrical appliance life prediction method, air conditioner and computer readable storage medium

Info

Publication number: CN113566376B
Application number: CN202110860250.2A
Authority: CN
Inventors: 宋士奇; 汪进; 毛跃辉
Original assignee: Gree Electric Appliances Inc of Zhuhai
Current assignee: Gree Electric Appliances Inc of Zhuhai
Priority date: 2021-07-28
Filing date: 2021-07-28
Publication date: 2022-06-07
Anticipated expiration: 2041-07-28
Also published as: CN113566376A

Abstract

The invention provides an electric appliance service life prediction method, an air conditioner and a computer readable storage medium, wherein the method comprises the following steps: acquiring a component temperature value of at least one component to be detected in an electric appliance mainboard in a current working state; predicting the service life of the element to be detected according to the element temperature value to obtain the predicted service life of the element to be detected; and estimating the service life of the electric appliance according to the predicted service life of the element to be detected. The method for predicting the service life of the electric appliance can utilize the working temperature of the electronic component to realize accurate evaluation on the service life of the equipment.

Description

Electrical appliance life prediction method, air conditioner and computer readable storage medium

Technical Field

The invention relates to the technical field of service life detection of electric appliances, in particular to an electric appliance service life prediction method, an air conditioner applying the electric appliance service life prediction method and a computer readable storage medium applying the electric appliance service life prediction method.

Background

Electronic components are basic components of a main control panel of household appliances such as an air conditioner, and damage of key components can influence normal use of the household appliances such as the air conditioner. Generally, when electronic components are assembled into various electronic devices and applied to the market, the electronic components need to face various external stress reactions, such as physical strain caused by dropping the electronic devices, thermal strain caused by cold and hot temperature differences, electrical strain caused by power supply, and the like, so that the normal service life of the electronic components is generally shorter than the expected service life. Therefore, the service life of the electronic unit is monitored, and the service life of the electronic equipment can be accurately evaluated.

In an existing air conditioner service life monitoring method, grade information currently met by service life influence parameters is determined by acquiring at least one service life influence parameter of a preset component of an air conditioner in real time, wherein the grade information comprises an early warning grade and an alarm grade, if the service life influence parameters of the preset component meet the early warning grade, the early warning information is sent to a server which is in communication connection with the server in advance, and if the service life influence parameters of the preset component meet the alarm grade, alarm information is sent to the server. The at least one lifetime influencing parameter comprises: run time has been run; and/or, number of uses; and/or, factory time. However, in this scheme, the influence of the service life of the electronic components on the service life of the air conditioner is not considered. In another scheme, a fault monitoring device is disclosed, current temperature values of all components in a unit are detected in real time through infrared images, and when the current temperature values of all the components are larger than the corresponding upper temperature limit, alarm processing is carried out. This solution does not disclose using the temperature of the component to predict the useful life of the component.

And along with the increase of the long service life of components and parts, components and parts self can be worn and aged continuously to the components and parts that have lead to having used a period compare with when just beginning to use, thereby the power consumption can increase thereby during operation self temperature can rise, and when certain operating condition, the higher the temperature of components and parts is, then life is shorter, consequently, confirm its life through electronic components's operating temperature, and then confirm that electrical equipment's life is a direction that can study.

Disclosure of Invention

The invention aims to provide an electrical appliance service life prediction method for accurately estimating the service life of equipment by using the working temperature of electronic components.

A second object of the present invention is to provide an air conditioner that uses the operating temperature of electronic components to achieve accurate assessment of the service life of the equipment.

A third object of the present invention is to provide a computer-readable storage medium that utilizes the operating temperature of electronic components to achieve an accurate assessment of the service life of the device.

In order to achieve the first object, the present invention provides a method for predicting an electrical service life, including: acquiring a component temperature value of at least one component to be detected in an electric appliance mainboard in a current working state; predicting the service life of the element to be detected according to the element temperature value to obtain the predicted service life of the element to be detected; and estimating the service life of the electric appliance according to the predicted service life of the element to be detected.

According to the scheme, the service life prediction method of the electric appliance determines the predicted service life of the element to be detected by predicting the service life of the element to be detected in the current working state of the element to be detected, and further determines the service life of the electric appliance according to the predicted service life of the element to be detected, so that the function of accurately evaluating the service life of the electric appliance is achieved.

In a further scheme, the step of obtaining a component temperature value of at least one component to be detected in the electric appliance mainboard in the current working state comprises: the infrared imaging unit is used for acquiring infrared image data of the element to be detected, and the element temperature value is determined according to the infrared image data.

Therefore, when the element temperature value of the element to be detected is obtained, the element temperature value is obtained through the infrared imaging unit, and the temperature of the element can be monitored in a large area.

In a further aspect, the step of determining the component temperature value from the infrared image data comprises: respectively transforming and mapping the infrared image data to an RGB color space, an HSV color space and an LAB color space; respectively extracting characteristic values of RGB components in an RGB color space, an HSV color space and an LAB color space by using a preset model to obtain three RGB color values, wherein each RGB color value corresponds to the RGB color space, the HSV color space and the LAB color space; determining a final RGB color value according to the three RGB color values; and determining the element temperature value of the element to be detected according to the final RGB color value.

Therefore, when infrared image data are processed to obtain an element temperature value, the accuracy of the element temperature value can be improved by respectively processing the RGB color space, the HSV color space and the LAB color space, and therefore the accuracy of component service life prediction is improved.

In a further aspect, the step of determining the final RGB color values from the three RGB color values comprises: the largest of the three RGB color values is selected as the final RGB color value.

Therefore, the service life of the shortest component can be determined by taking the maximum one of the three RGB color values as the final RGB color value.

In a further aspect, the step of determining the final RGB color values from the three RGB color values comprises: comparing the three RGB color values pairwise to obtain three minimum RGB color values; the largest of the three minimum RGB color values is selected as the final RGB color value.

Therefore, after the three RGB color values are compared pairwise, the largest one of the three minimum RGB color values is used as the final RGB color value, the three RGB color values can be comprehensively considered, and the accuracy of prediction is improved.

In a further aspect, the step of determining the final RGB color values from the three RGB color values comprises: selecting a largest one of the three RGB color values as a first predicted RGB color value; comparing the three RGB color values pairwise to obtain three minimum RGB color values, and selecting the largest one of the three minimum RGB color values as a second prediction RGB color value; if the first predicted RGB color value is equal to the second predicted RGB color value, selecting one of the first predicted RGB color value and the second predicted RGB color value as a final RGB color value; and if the first predicted RGB color value is not equal to the second predicted RGB color value, correspondingly adding a value obtained by multiplying each color component in the first predicted RGB color value by a first preset proportion and a value obtained by multiplying each color component in the second predicted RGB color value by a second preset proportion to obtain the final RGB color value, wherein the first preset proportion is larger than the second preset proportion.

Therefore, the second prediction RGB color value is used for compensating the first prediction RGB color value, and the prediction accuracy can be improved.

In a further scheme, the step of predicting the service life of the element to be detected according to the element temperature value to obtain the predicted service life of the element to be detected comprises the following steps: and matching the element temperature value with a preset temperature value corresponding to the current working state to obtain the predicted service life of the element corresponding to the element temperature value.

Therefore, when the predicted service life of the element to be detected is determined, the element temperature value is matched with the preset temperature value, and the predicted service life of the element corresponding to the element to be detected is obtained according to the element temperature value.

In a further aspect, the step of estimating the service life of the electrical appliance according to the predicted service life of the element to be detected comprises: and matching the predicted service life of the element with the service life of a preset electric appliance to obtain the service life of the electric appliance.

Therefore, when the service life of the electric appliance is determined, the predicted service life of the element to be detected is matched with the preset service life of the electric appliance, and therefore the service life of the electric appliance is determined.

In a further scheme, the number of the elements to be detected is multiple; the step of estimating the service life of the electric appliance according to the predicted service life of the element to be detected comprises the following steps: and selecting one of the elements to be detected with the shortest predicted service life to predict the service life of the electric appliance.

Therefore, different components have different influences on the service life of the electric appliance, and when a plurality of detection elements exist, the service life of the electric appliance is predicted through the element with the shortest service life, so that the accuracy of service life prediction of the electric appliance can be improved.

In a further aspect, after estimating the service life of the electrical appliance according to the predicted service life of the component to be detected, the method further includes: and judging whether the service life of the electric appliance reaches a threshold value, and if so, sending alarm information.

In a further scheme, in order to facilitate a user to know the service life of the electric appliance, when the service life of the electric appliance is judged to reach a threshold value, alarm information is sent to inform the user.

In a further aspect, the step of sending the alarm information includes: starting a service reservation service function and/or carrying out voice prompt.

Therefore, when the alarm information is sent, the maintenance reservation service function is started and/or voice prompt is carried out so as to carry out maintenance and remind the user.

In order to achieve the second object of the present invention, the present invention provides an air conditioner comprising a processor and a memory, wherein the memory stores a computer program, and the computer program realizes the steps of the above-mentioned electric appliance life prediction method when being executed by the processor.

In order to achieve the third object of the present invention, the present invention provides a computer readable storage medium, on which a computer program is stored, the computer program, when executed by a controller, implementing the steps of the appliance life prediction method described above.

Drawings

FIG. 1 is a schematic block diagram of an electrical appliance to which the method for predicting the life of an electrical appliance of the present invention is applied.

Fig. 2 is a flowchart of an embodiment of the method for predicting the service life of an electrical appliance according to the present invention.

Fig. 3 is a flowchart of a step of obtaining a component temperature value of at least one component to be detected in the main board of the electrical appliance in the current working state according to the method for predicting the service life of the electrical appliance of the present invention.

The invention is further explained with reference to the drawings and the embodiments.

Detailed Description

The embodiment of the method for predicting the service life of the electric appliance comprises the following steps:

the method for predicting the service life of the electrical appliance is an application program applied to the electrical appliance and used for predicting the service life of the electrical appliance. In this embodiment, as shown in fig. 1, the electrical appliance includes a main controller 1, an infrared imaging unit 2, a speaker 3 and a wireless communication module 4, the infrared imaging unit 2, the speaker 3 and the wireless communication module 4 are all electrically connected to the main controller 1, the infrared imaging unit 2 is used for performing infrared imaging detection on an electrical appliance motherboard, the speaker 3 plays audio, and the wireless communication module 4 is used for performing information interaction with a user terminal 6 through an IOT server 5. The electric appliances comprise air conditioners, refrigerators, televisions, air purifiers and the like.

As shown in fig. 2, in the embodiment, when the method for predicting the service life of an electrical appliance works, step S1 is first executed to obtain an element temperature value of at least one element to be detected in the electrical appliance motherboard in the current working state. In order to detect the service life of the element to be detected in the main board of the electrical appliance, an element temperature value of the element to be detected in the current working state needs to be acquired. Since the temperature of the element to be detected is different in different operating states, for example, the temperature of the element to be detected is higher in high power consumption and lower in low power consumption, the temperature needs to be detected according to different operating states. The element to be detected is a core element of an electrical appliance, for example, a main control chip, a power tube, and the like. The number of elements to be detected can be set as desired.

The element temperature value of the element to be detected can be obtained by arranging a temperature sensor or an infrared imaging unit and the like. In this embodiment, the step of obtaining the component temperature value of the at least one component to be detected in the main board of the electrical appliance in the current working state includes: the infrared imaging unit is used for acquiring infrared image data of the element to be detected, and the element temperature value is determined according to the infrared image data. The infrared imaging unit is used for acquiring infrared image data of the element to be detected, so that the temperature of different elements can be monitored at multiple points conveniently. Certainly, since the element to be detected occupies a certain area on the circuit board, in order to facilitate processing of the infrared image data of the element to be detected, the infrared image data of the area where the element to be detected is located can be averaged, so that the infrared image data of the element to be detected can be obtained.

In order to improve the accuracy of the element temperature value, in this embodiment, referring to fig. 3, when determining the element temperature value according to the infrared image data, step S11 is first executed to transform and map the infrared image data into RGB color space, HSV color space, and LAB color space, respectively. In order to better perform temperature analysis on the infrared image data, the infrared image data is converted and mapped to an RGB color space, an HSV color space and an LAB color space, a plurality of space analyses are performed, and the analysis precision is improved. Spatial transformation of image data is a technique well known to those skilled in the art and will not be described in detail herein.

After the image data is subjected to spatial transformation, step S12 is executed, and feature values of RGB components are extracted in the RGB color space, HSV color space, and LAB color space by using a preset model, respectively, to obtain three RGB color values. Each of the RGB color values respectively corresponds to an RGB color space, an HSV color space, and an LAB color space, and each of the RGB color values includes a red component value, a blue component value, and a green component value, for example, a certain RGB color value includes a red component value R225, a blue component value B110, and a green component value G110. In order to analyze the color of the image data, feature value extraction of RGB components is performed separately in each color space, and RGB color values in each color space are determined. The preset model may be selected from convolutional neural network models such as ResNet50, VGG16, MobileNet, densenert, and the like, and in this embodiment, the preset model is a ResNet50 model, and a ResNet50 model is a technology known by those skilled in the art, and is not described herein again. The preset models of the three color spaces are trained respectively and the parameter weights are saved.

After obtaining the three RGB color values, step S13 is performed to determine a final RGB color value according to the three RGB color values. After acquiring the three RGB color values, the optimal RGB color value needs to be determined in order to calculate the temperature.

In a preferred embodiment, the step of determining the final RGB color values from the three RGB color values comprises: and selecting the largest one of the three RGB color values as a final RGB color value, wherein the RGB color value comprises three component values, so when the largest one of the RGB color values is calculated, the total sum of the three component values in each RGB color value is respectively calculated, the total sum of the three component values of each RGB color value is compared, and the RGB color value with the largest total sum is selected as the final RGB color value.

In another preferred embodiment, the step of determining the final RGB color values from the three RGB color values comprises: comparing the three RGB color values pairwise to obtain three minimum RGB color values; the largest of the three minimum RGB color values is selected as the final RGB color value.

In a further preferred embodiment, the step of determining the final RGB color values from the three RGB color values comprises: selecting one of the three RGB color values with the maximum value as a first predicted RGB color value; comparing the three RGB color values pairwise to obtain three minimum RGB color values, and selecting the largest one of the three minimum RGB color values as a second prediction RGB color value; if the first predicted RGB color value is equal to the second predicted RGB color value, selecting one of the first predicted RGB color value and the second predicted RGB color value as a final RGB color value; and if the first predicted RGB color value is not equal to the second predicted RGB color value, correspondingly adding a value obtained by multiplying each color component in the first predicted RGB color value by a first preset proportion and a value obtained by multiplying each color component in the second predicted RGB color value by a second preset proportion to obtain a final RGB color value. The first predetermined ratio is greater than the second predetermined ratio, and in this embodiment, the first predetermined ratio is 90% and the second predetermined ratio is 10%. For example, if the red component value R of the first predicted RGB color value is 225, the blue component value B is 110, the green component value G is 110, the red component value R of the second predicted RGB color value is 250, the blue component value B is 50, and the green component value G is 50, then the red component value R of the final RGB color value is 225 × 90% +250 × 10% + 227.5, the blue component value B of 110 × 90% +50 × 10% + 104, and the green component value G of 110 × 90% +50 × 10% + 104.

After the final RGB color values are obtained, step S14 is executed to determine the element temperature values of the elements to be detected according to the final RGB color values. The RGB color value and the temperature value are correspondingly arranged, and the current element temperature value of the element to be detected can be determined by carrying out temperature matching on the RGB color value.

After the element temperature value is obtained, step S2 is executed to predict the life of the element to be detected according to the element temperature value, so as to obtain the predicted service life of the element to be detected. After obtaining the element temperature value, the service life of the element to be detected needs to be predicted, in this embodiment, the service life of the element to be detected is predicted according to the element temperature value, and the step of obtaining the predicted service life of the element to be detected includes: and matching the element temperature value with a preset temperature value corresponding to the current working state to obtain the predicted service life of the element corresponding to the element temperature value. The preset temperature value corresponding to the current working state is preset according to the experimental data, so that the service life of the element to be detected corresponds to the temperature in the current working state, when the service life of the element to be detected needs to be obtained, the element temperature value is matched with the preset temperature value, and the service life of the detection element can be obtained.

After the predicted service life of the component to be detected is obtained, step S3 is executed to estimate the service life of the electrical appliance according to the predicted service life of the component to be detected. In electrical equipment, different components have different influences on the service life of the electrical equipment, so the service life of the electrical equipment needs to be estimated according to the predicted service life of the component to be detected. In this embodiment, the step of estimating the service life of the electrical appliance according to the predicted service life of the element to be detected includes: and matching the predicted service life of the element with the service life of a preset electric appliance to obtain the service life of the electric appliance. The service life of the preset electric appliance is preset according to experimental data, so that the predicted service life of the element to be detected is set corresponding to the service life of the electric appliance. The service life of the electric appliance can be obtained by matching the predicted service life of the element with the service life of the preset electric appliance.

In an alternative embodiment, the number of elements to be detected is multiple. The step of estimating the service life of the electric appliance according to the predicted service life of the element to be detected comprises the following steps: and selecting one of the elements to be detected with the shortest predicted service life to predict the service life of the electric appliance. Different components have different influences on the service life of the electric appliance, and when a plurality of components to be detected exist, the shortest one with the shortest service life is selected to predict the service life of the electric appliance, so that the service life of the electric appliance is more accurate.

After the service life of the electrical appliance is obtained, step S4 is executed to determine whether the service life of the electrical appliance reaches a threshold value. And comparing the service life of the electric appliance with the threshold value, and determining that the service life exceeds the service life limit when the service life of the electric appliance exceeds the threshold value.

And if the service life of the electric appliance is confirmed to be not up to the threshold value, returning to the step S1, and continuously monitoring the temperature of the element to be detected. Before returning to step S1, a preset time interval may be set to avoid excessive power consumption due to frequent detection.

If it is confirmed that the service life of the electric appliance reaches the threshold value, step S5 is executed to transmit alarm information. In order to facilitate the user to know the service life of the electric appliance, when the service life of the electric appliance is judged to reach the threshold value, alarm information is sent to inform the user. In this embodiment, the step of sending the alarm information includes: starting a service reservation service function and/or carrying out voice prompt. When the maintenance reservation service is started, reservation service information can be sent to the user terminal 6 through the IOT server 5, so that maintenance personnel can maintain in time. When voice prompt is carried out, fault prompt audio is played through the loudspeaker 3, so that a user can know and carry out corresponding processing.

The embodiment of the air conditioner is as follows:

the air conditioner of the embodiment comprises a controller, and the steps in the embodiment of the electric appliance life prediction method are realized when the controller executes a computer program.

For example, a computer program may be partitioned into one or more modules, which are stored in a memory and executed by a controller to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program in the air conditioner.

The air conditioner may include, but is not limited to, a controller, a memory. Those skilled in the art will appreciate that an air conditioner may include more or fewer components, or combine certain components, or different components, e.g., an air conditioner may also include input output devices, network access devices, buses, etc.

For example, the controller may be a Central Processing Unit (CPU), other general purpose controller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, and so on. The general controller may be a microcontroller or the controller may be any conventional controller or the like. The controller is the control center of the air conditioner, and various interfaces and lines are used for connecting all parts of the whole air conditioner.

The memory may be used to store computer programs and/or modules, and the controller may implement various functions of the air conditioner by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. For example, the memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.

Computer-readable storage medium embodiments:

the air conditioner integrated module of the above embodiment, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment of the method for predicting the lifetime of an electrical appliance may also be implemented by a computer program, which may be stored in a computer-readable storage medium and may implement the steps in the above-mentioned embodiment of the method for predicting the lifetime of an electrical appliance when the computer program is executed by a controller. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The storage medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.

Therefore, the service life prediction method of the electric appliance determines the predicted service life of the element to be detected by predicting the service life of the element to be detected according to the element temperature value of the element to be detected in the current working state, so that the service life of the electric appliance is further determined according to the predicted service life of the element to be detected, and the function of accurately evaluating the service life of the electric appliance is achieved.

It should be noted that the above is only a preferred embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept also fall within the protection scope of the present invention.

Claims

1. A method for predicting the service life of an electric appliance is characterized by comprising the following steps: the method comprises the following steps:

acquiring a component temperature value of at least one component to be detected in an electric appliance mainboard in a current working state;

predicting the service life of the element to be detected according to the element temperature value to obtain the predicted service life of the element to be detected;

estimating the service life of the electrical appliance according to the predicted service life of the element to be detected;

wherein, the step of obtaining the element temperature value of at least one element to be detected in the electric appliance mainboard in the current working state comprises: acquiring infrared image data of the element to be detected through an infrared imaging unit; respectively transforming and mapping the infrared image data to an RGB color space, an HSV color space and an LAB color space; respectively extracting characteristic values of RGB components in the RGB color space, the HSV color space and the LAB color space by using a preset model to obtain three RGB color values, wherein each RGB color value is respectively corresponding to the RGB color space, the HSV color space and the LAB color space; determining a final RGB color value according to the three RGB color values; and determining the element temperature value of the element to be detected according to the final RGB color value.

2. The appliance life prediction method of claim 1, wherein:

the step of determining a final RGB color value from the three RGB color values comprises:

selecting a largest one of the three RGB color values as the final RGB color value.

3. The appliance life prediction method of claim 1, wherein:

comparing the three RGB color values pairwise to obtain three minimum RGB color values;

selecting a largest one of the three minimum RGB color values as the final RGB color value.

4. The appliance life prediction method of claim 1, wherein:

selecting a largest one of the three RGB color values as a first predicted RGB color value;

comparing the three RGB color values pairwise to obtain three minimum RGB color values, and selecting the largest one of the three minimum RGB color values as a second prediction RGB color value;

if the first predicted RGB color value is equal to the second predicted RGB color value, selecting one of the first predicted RGB color value and the second predicted RGB color value as the final RGB color value;

and if the first predicted RGB color value is not equal to the second predicted RGB color value, correspondingly adding a value obtained by multiplying each color component in the first predicted RGB color value by a first preset proportion and a value obtained by multiplying each color component in the second predicted RGB color value by a second preset proportion to obtain the final RGB color value, wherein the first preset proportion is larger than the second preset proportion.

5. The appliance life prediction method according to any one of claims 1 to 4, characterized in that:

the step of predicting the service life of the element to be detected according to the element temperature value to obtain the predicted service life of the element to be detected comprises the following steps:

and matching the element temperature value with a preset temperature value corresponding to the current working state to obtain the predicted service life of the element corresponding to the element temperature value.

6. The appliance life prediction method according to any one of claims 1 to 4, characterized in that:

the step of estimating the service life of the electric appliance according to the predicted service life of the element to be detected comprises the following steps:

and matching the predicted service life of the element with the service life of a preset electric appliance to obtain the service life of the electric appliance.

7. The appliance life prediction method according to any one of claims 1 to 4, characterized in that: the number of the elements to be detected is multiple;

and selecting one of the elements to be detected with the shortest predicted service life to predict the service life of the electric appliance.

8. The appliance life prediction method according to any one of claims 1 to 4, characterized in that: after the service life of the electric appliance is estimated according to the predicted service life of the element to be detected, the method further comprises the following steps:

and judging whether the service life of the electric appliance reaches a threshold value, and if so, sending alarm information.

9. The appliance life prediction method of claim 8, wherein:

the step of transmitting the alarm information includes:

starting a service reservation service function and/or carrying out voice prompt.

10. An air conditioner, includes treater and memory, its characterized in that: the memory stores a computer program which, when executed by the processor, carries out the steps of the appliance life prediction method of any one of claims 1 to 9.

11. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a controller implements the steps of the appliance life prediction method of any one of claims 1 to 9.