CN113742881B - Method, device and storage medium for predicting working life of liquid cooling heat dissipation system - Google Patents
Method, device and storage medium for predicting working life of liquid cooling heat dissipation system Download PDFInfo
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Abstract
The disclosure relates to a method, a device and a storage medium for predicting the service life of a liquid cooling heat dissipation system. The method for predicting the service life of the liquid cooling heat dissipation system comprises the following steps: acquiring an application type and application use duration of the terminal running in a specified time period to obtain a first temperature, wherein the first temperature is a temperature detected by a temperature sensor in the terminal in real time; determining a second temperature based on the application type, the application use duration and the first temperature, wherein the second temperature is the operating temperature of the liquid cooling heat dissipation system; and calling a service life distribution curve representing the corresponding relation of the temperature, the service life duration and the probability distribution density, and determining the service life duration probability distribution of the liquid cooling heat dissipation system based on the second temperature and the service life distribution curve. The service life duration probability distribution of the liquid cooling heat dissipation system can be accurately predicted through the method and the device.
Description
Technical Field
The disclosure relates to the field of heat dissipation, and in particular relates to a method and a device for predicting the service life of a liquid cooling heat dissipation system and a storage medium.
Background
With the development of 5G communication technology, the overall power consumption of terminals such as electronic consumer products is greatly improved, and liquid cooling heat dissipation systems such as Vapor Chamber (VC) and heat pipes have excellent heat dissipation capability, so that more and more applications are being used in electronic consumer products.
In the related art, due to the increase of the application scenes of users using terminals, the reliability of the liquid cooling heat dissipation system is reduced, so that the service life of the liquid cooling heat dissipation system is shortened. In the daily application scene of the terminal, the service life of the liquid cooling heat dissipation system is accurately estimated, and the service life becomes a key problem.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a method, an apparatus and a storage medium for predicting the service life of a liquid cooling heat dissipation system.
According to a first aspect of an embodiment of the present disclosure, there is provided a method for predicting a service life of a liquid cooling heat dissipation system, which is applied to a terminal, including:
Acquiring an application type, an application use duration and a first temperature of the terminal running in a specified time period, wherein the first temperature is a temperature detected by a temperature sensor in the terminal in real time; determining a second temperature based on the application type, the application use duration and the first temperature, wherein the second temperature is the operating temperature of the liquid cooling heat dissipation system; and calling a service life distribution curve representing the corresponding relation of the temperature, the service life duration and the probability distribution density, and determining the service life duration probability distribution of the liquid cooling heat dissipation system based on the second temperature and the service life distribution curve.
In one embodiment, the determining the second temperature based on the application type, the application usage duration, and the first temperature includes:
Training a behavior analysis model based on historical data, wherein the input of the behavior analysis model is an application type, an application use duration and a first temperature, and the output of the behavior analysis model is a second temperature; and inputting the application type, the application use duration and the first temperature into the behavior analysis model to obtain a second temperature.
In one embodiment, the lifetime profile is obtained by the following method:
Determining a first functional relation curve representing the corresponding relation between the temperature and the service life duration; determining probability distribution density of service life time of the liquid cooling heat dissipation system at different temperatures; and determining a life distribution curve of the corresponding relation among the temperature, the life duration and the probability distribution density based on the first functional relation curve and the probability distribution density.
In one embodiment, determining a first functional relationship curve characterizing a temperature versus a length of life correspondence, includes:
under the experimental condition, performing high-temperature aging on the liquid cooling heat dissipation system to obtain multiple groups of experimental data of temperature and service life of the liquid cooling heat dissipation system; fitting the plurality of sets of experimental data to the first functional relationship, thereby obtaining a first functional relationship curve.
In one embodiment, the first functional form is as follows:
wherein T is the second temperature, alpha is the coefficient to be determined, delta is a constant, beta is the coefficient to be determined, and tau is the life duration.
In one embodiment, determining probability distribution density of lifetime of the liquid cooling heat dissipation system at different temperatures includes:
And carrying out probability statistics on the plurality of groups of experimental data to obtain probability distribution densities of the service life time durations at different temperatures.
According to a second aspect of the embodiments of the present disclosure, there is provided a device for predicting a service life of a liquid cooling heat dissipation system, which is applied to a terminal, including:
The terminal comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the type of an application, the application use duration and a first temperature of the terminal, which are operated in a specified time period, and the first temperature is the temperature detected by a temperature sensor in the terminal in real time; the determining module is used for determining a second temperature based on the application type, the application using time length and the first temperature, wherein the second temperature is the operation temperature of the liquid cooling heat dissipation system; and the calling module is used for calling a service life distribution curve representing the corresponding relation among the temperature, the service life duration and the probability distribution density, and determining the service life duration probability distribution of the liquid cooling heat dissipation system based on the second temperature and the service life distribution curve.
In one embodiment, the determining module is configured to:
Training a behavior analysis model based on historical data, wherein the input of the behavior analysis model is an application type, an application use duration and a first temperature, and the output of the behavior analysis model is a second temperature; and inputting the application type, the application use duration and the first temperature into the behavior analysis model to obtain a second temperature.
In one embodiment, the obtaining module is further configured to:
Determining a first functional relation curve representing the corresponding relation between the temperature and the service life duration; determining probability distribution density of service life time of the liquid cooling heat dissipation system at different temperatures; and determining a life distribution curve of the corresponding relation among the temperature, the life duration and the probability distribution density based on the first functional relation curve and the probability distribution density.
In one embodiment, the determining module is configured to:
under the experimental condition, performing high-temperature aging on the liquid cooling heat dissipation system to obtain multiple groups of experimental data of temperature and service life of the liquid cooling heat dissipation system; fitting the plurality of sets of experimental data to the first functional relationship, thereby obtaining a first functional relationship curve.
In one embodiment, the first functional form is as follows:
wherein T is the second temperature, alpha is the coefficient to be determined, delta is a constant, beta is the coefficient to be determined, and tau is the life duration.
In one embodiment, the determining module is configured to:
And carrying out probability statistics on the plurality of groups of experimental data to obtain probability distribution densities of the service life time durations at different temperatures.
According to a third aspect of the embodiments of the present disclosure, there is provided a device for predicting an operating life of a liquid-cooled heat dissipation system, including:
A processor; a memory for storing processor-executable instructions; wherein the processor is configured to: the method for predicting the service life of the liquid cooling heat dissipation system according to the first aspect or any one of the embodiments of the first aspect is performed.
According to a fourth aspect of the disclosed embodiments, there is provided a non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, enables the mobile terminal to perform the above-described first aspect or the method for predicting the service life of the liquid cooling heat dissipation system according to any one of the embodiments of the first aspect.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: the second temperature used for predicting the service life of the liquid cooling heat dissipation system is determined through the application type and the application use duration of the terminal running in the appointed time period and the first temperature of the liquid cooling heat dissipation system detected by the temperature sensor in real time, and the service life duration probability distribution of the liquid cooling heat dissipation system can be determined based on the second temperature and the service life distribution curve, so that data support is provided for the liquid cooling heat dissipation system arranged on the subsequent terminal.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating a method of predicting the operating life of a liquid-cooled heat dissipation system, according to an exemplary embodiment.
Fig. 2 is a schematic diagram showing a temperature sensor setting position of a method for predicting the service life of a liquid cooling heat dissipation system according to an exemplary embodiment.
FIG. 3 is a schematic diagram illustrating a lifetime profile of a method for predicting operating lifetime of a liquid-cooled heat sink system, according to an exemplary embodiment.
Fig. 4 is a schematic diagram illustrating a behavior analysis of a method for predicting an operating life of a liquid-cooled heat dissipation system according to an exemplary embodiment.
Fig. 5 is a flow chart illustrating yet another method of predicting the operating life of a liquid-cooled heat dissipation system, in accordance with an exemplary embodiment.
Fig. 6 is a schematic diagram showing a functional relationship between a second temperature and a lifetime of a liquid cooling heat dissipation system in a method for predicting an operating lifetime of the liquid cooling heat dissipation system according to an exemplary embodiment.
Fig. 7 is a schematic diagram of probability distribution density curves of a lifetime of a liquid cooling heat sink system according to a method for predicting an operating lifetime of the liquid cooling heat sink system according to an exemplary embodiment.
Fig. 8 is a flowchart illustrating yet another method of predicting the operating life of a liquid-cooled heat dissipation system, in accordance with an exemplary embodiment.
Fig. 9 is a block diagram illustrating an apparatus for predicting an operating life of a liquid-cooled heat dissipation system, according to an exemplary embodiment.
Fig. 10 is a block diagram of an apparatus according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
The invention provides a method for predicting the service life of a liquid cooling heat dissipation system, which can accurately predict the service life of the liquid cooling heat dissipation system arranged at a terminal. The service life of the liquid cooling system arranged on the terminal in the process of using the terminal by a user is estimated, and data support is provided for the research and development of the subsequent liquid cooling heat dissipation time.
The following examples illustrate a method for predicting the operating life of a liquid-cooled heat dissipation system.
Fig. 1 is a flowchart illustrating a method for predicting the operating life of a liquid-cooled heat dissipation system, according to an exemplary embodiment, as shown in fig. 1, including the following steps.
In step S11, an application type, an application use duration, and a first temperature of the terminal running in a specified period of time are acquired.
For convenience of description, the temperature detected in real time by the temperature sensor inside the terminal is referred to as a first temperature. The temperature detected by the temperature sensor in the terminal in real time is the temperature of the liquid cooling heat dissipation system detected by the temperature sensor in real time.
In the embodiment of the disclosure, a temperature sensor for detecting the temperature of the liquid cooling heat dissipation system is installed on a terminal provided with the liquid cooling heat dissipation system. Fig. 2 is a schematic diagram showing a temperature sensor setting position of a method for predicting the service life of a liquid cooling heat dissipation system according to an exemplary embodiment. As shown in fig. 2, the temperature sensing element is a temperature sensor disposed on the liquid cooling heat dissipation system, and the power consumption element on the VC/HP liquid cooling heat dissipation system generates temperature when working, wherein the VC/HP liquid cooling heat dissipation system dissipates the temperature generated by the power consumption element. The temperature sensing element is used for detecting the real-time temperature of the VC/HP liquid cooling heat dissipation system in real time.
In the embodiment of the disclosure, the terminal is provided with the application, different application types and the application use time length can have an influence on the temperature generated by heat dissipation of the power consumption element of the terminal. The liquid cooling heat dissipation system needs to work under the condition that the temperature generated by the terminal power consumption element exceeds a certain temperature threshold value, and in the embodiment of the disclosure, in order to enable the liquid cooling heat dissipation system to work, the application type, the application using time length and the first temperature of the liquid cooling heat dissipation system of the terminal running in a specified time period are obtained.
In step S12, a temperature for predicting an operating life of the liquid-cooled heat dissipation system is determined based on the application type, the application use period, and the first temperature of the liquid-cooled heat dissipation system.
In one implementation manner, the embodiment of the disclosure may screen the first temperature of the liquid cooling heat dissipation system detected by the temperature sensor based on the temperature threshold of the operation of the liquid cooling heat dissipation system, so as to obtain the first temperature greater than or equal to the temperature threshold of the operation of the liquid cooling heat dissipation system. And carrying out weighted average on the obtained first temperature to obtain the operating temperature of the liquid cooling heat dissipation system. For convenience of description, the operating temperature of the liquid cooling heat dissipation system is referred to as the second temperature in the embodiments of the present disclosure.
In an embodiment of the disclosure, the second temperature may be a temperature interval.
In step S13, a lifetime distribution curve representing a correspondence between a temperature, lifetime duration, and probability distribution density is called, and lifetime duration probability distribution of the liquid cooling heat dissipation system is determined based on the second temperature and lifetime distribution curve.
In the embodiment of the disclosure, a lifetime distribution curve of the correspondence relationship between the temperature, the lifetime length and the probability distribution density is represented, as shown in fig. 3. Referring to fig. 3, a lifetime distribution curve representing the correspondence between temperature, lifetime duration, and probability distribution density is a correspondence curve established in a three-dimensional coordinate system. Wherein, three coordinate axes of the three-dimensional coordinate system are temperature, service life duration and probability distribution density respectively. And determining the service life time length corresponding to the liquid cooling heat dissipation system and probability distribution corresponding to the service life time length according to the position of the determined second temperature value on the temperature coordinate axis of the service life distribution curve.
In the method for predicting the service life of the liquid cooling heat dissipation system, the service life distribution curve is called by acquiring the application type, the application service time length and the first temperature of the liquid cooling heat dissipation system of the terminal running in the appointed time period, so that the service life time length of the liquid cooling heat dissipation system arranged on the terminal is predicted.
The method for predicting the service life of the liquid cooling heat dissipation system according to the embodiment will be described below in connection with practical application.
In one implementation manner, historical data of a plurality of terminals are acquired, and the embodiment of the disclosure can be trained in advance to obtain a behavior analysis model based on the application type, the application use duration and the first temperature of the liquid cooling heat dissipation system in the acquired historical data, and then the second temperature is determined based on the behavior model. In the embodiment of the disclosure, the input of the behavior analysis model is the application type, the application use duration and the first temperature of the liquid cooling heat dissipation system, and the output is the second temperature.
The following describes embodiments of training behavioral analysis models.
According to the embodiment of the disclosure, training of the behavior analysis model can be performed based on the application type, the application use duration, the first temperature and the second temperature of the terminal running in the historical time period. For example, the application type, the application use duration and the first temperature of the terminal running in the historical time are obtained for large data capture, and the application type and the application use duration used by the user are analyzed in a behavior mode. For example, fig. 4 is a schematic diagram illustrating a behavior analysis of a method for predicting an operating life of a liquid-cooled heat dissipation system according to an exemplary embodiment of the present disclosure. Classifying the captured big data, for example, the application type is a game, and the corresponding using time of the game is t1; the application type is video playing, and the corresponding using time of the video playing is t2; the application type is a browsed webpage, and the corresponding use time of the browsed webpage is t3; the application type is photographing, and the photographing time is t4; the application type is charging, and the charging corresponds to the using time period of t5; the application type is WeChat video, and the corresponding use time of WeChat video is t6. And acquiring corresponding temperatures of each application type in a using time length manner, wherein the temperatures are respectively T1, T2, T3, T4, T5 and T6.
And screening the temperatures T1, T2, T3, T4, T5 and T6 acquired by the temperature sensor in real time according to the temperature threshold value of the operation of the liquid cooling heat radiation system and the behavior analysis result of the user, and then carrying out weighted average to obtain a second temperature. And obtaining a behavior analysis model which is input into the application type, the application use duration and the first temperature by utilizing the application type, the application use duration, the big data of the first temperature and the second temperature training of the terminal running in the historical time period, and outputting the behavior analysis model which is the second temperature.
In the embodiment of the disclosure, after the behavior analysis model is obtained through training, when the second temperature is determined, the application type, the application use duration and the first temperature of the liquid cooling heat dissipation system of the terminal in the specified time period can be input into the behavior analysis model obtained through training to obtain the second temperature.
In the embodiment of the disclosure, a lifetime distribution curve characterizing the correspondence relationship between the second temperature, the lifetime length, and the probability distribution density may be predetermined. The process of determining the lifetime distribution curve characterizing the correspondence relationship of the second temperature, the lifetime length, and the probability distribution density will be described below.
FIG. 5 is a flowchart illustrating a method of determining a lifetime profile characterizing a correspondence between a second temperature, a lifetime duration, and a probability distribution density, according to an exemplary embodiment, a lifetime profile acquisition method as illustrated in FIG. 5, including steps S21-S23.
In step S21, a first functional relationship curve representing the correspondence between the temperature and the lifetime duration is determined.
In the embodiment of the disclosure, a functional relation curve representing the corresponding relation between the temperature and the service life duration can be obtained by analyzing the relation between the temperature of the liquid cooling heat dissipation system used by the terminal in historical time and the service life duration. For convenience of description in the embodiments of the present disclosure, a functional relationship curve representing a correspondence between a temperature and a lifetime is referred to as a first functional relationship curve. As shown in fig. 6, a first functional curve characterizing temperature versus duration of life is established in a two-dimensional coordinate system. In fig. 6, coordinate axes in the two-dimensional coordinate system are temperature and lifetime respectively, and a curve in the two-dimensional coordinate system represents a corresponding relationship between the temperature and lifetime of the liquid cooling heat dissipation system.
In step S22, probability distribution densities of lifetime durations of the liquid cooling heat dissipation system at different temperatures are determined.
In the embodiment of the disclosure, probability distribution density of service life of the liquid cooling heat dissipation system at different temperatures can be shown in fig. 7. In fig. 7, probability distribution density curves of service life time of the liquid cooling heat dissipation system at the second temperature are set in a two-dimensional coordinate system, coordinate axes in the two-dimensional coordinate system are probability density and service life time respectively, and the curves in the two-dimensional coordinate system represent the relationship between the service life time and corresponding probability distribution density at the second temperature.
In step S23, a lifetime distribution curve of the correspondence relationship of the temperature, lifetime length, and probability distribution density is determined based on the first functional relationship curve and the probability distribution density.
In the embodiment of the disclosure, a functional relation curve of the temperature and the service life duration of the liquid cooling heat dissipation system determined in the two-dimensional coordinate system in the embodiment is integrated with a probability distribution density curve of the service life duration of the liquid cooling heat dissipation system, so as to obtain a service life distribution curve representing the corresponding relation of the temperature, the service life duration and the probability distribution density in the three-dimensional coordinate system.
Fig. 8 is a flowchart illustrating a method for predicting the service life of a liquid cooling heat dissipation system according to an exemplary embodiment, and as shown in fig. 8, a first functional relationship curve representing a correspondence relationship between a temperature and a service life duration is determined, including step S31 and step S32.
In step S31, the liquid cooling heat dissipation system is aged at high temperature under the experimental condition, so as to obtain multiple groups of experimental data of temperature and service life of the liquid cooling heat dissipation system.
In step S32, a plurality of sets of experimental data are fitted with the first functional relationship, thereby obtaining a first functional relationship curve.
In the embodiment of the disclosure, the liquid cooling heat dissipation system can be aged at a high temperature by adopting a high Wen Jiasu under experimental conditions, so that a plurality of groups of experimental data of the temperature and the service life of the liquid cooling heat dissipation system are obtained, and further, the plurality of groups of experimental data of the temperature and the service life of the liquid cooling heat dissipation system are subjected to fitting analysis by adopting an accelerated life model in a first functional relation, so as to obtain a first functional relation curve.
In the embodiment of the disclosure, the accelerated lifetime model related to the above embodiment is to perform an accelerated lifetime test on a liquid cooling heat dissipation system, and is used for the lifetime of the liquid cooling heat dissipation system. The accelerated life test is that the life duration of the liquid cooling heat dissipation system which is shown by the liquid cooling heat dissipation system under the action of short time and high stress is consistent with the life duration of the liquid cooling heat dissipation system which is shown by the liquid cooling heat dissipation system under the action of long time and low stress. Therefore, an accelerated life model is adopted, so that the time for determining the functional relation curve of the temperature and the life time of the liquid cooling heat dissipation system is shortened, the analysis efficiency is improved, and the time cost is saved.
In the disclosed embodiment, the first functional form is as follows:
wherein T is the second temperature, alpha is the coefficient to be determined, delta is a constant, beta is the coefficient to be determined, and tau is the life duration.
In the embodiment of the disclosure, it should be understood that in the above embodiment, the fitting analysis is performed on multiple sets of experimental data of the temperature and the lifetime of the liquid cooling heat dissipation system by using an accelerated lifetime model according to a first functional relationship, and the undetermined coefficient α and the undetermined coefficient β are actually adjusted. And obtaining an appropriate undetermined coefficient alpha and undetermined coefficient beta for determining the first functional relation curve. Further, substituting the obtained undetermined coefficient alpha and undetermined coefficient beta into a first function, and drawing a first function relation curve in a two-dimensional coordinate system by utilizing the corresponding relation between the temperature and the service life of the liquid cooling heat dissipation system under test conditions. The obtained first functional relation curve is more accurate.
Embodiments of the present disclosure are described below with respect to determining probability distribution density for a lifetime of a liquid-cooled heat dissipation system at different temperatures.
In the embodiment of the disclosure, the service life of the liquid cooling heat dissipation system arranged on each terminal can be obtained while the liquid cooling heat dissipation system is subjected to high-temperature aging treatment. And carrying out statistical analysis on the service life of the liquid cooling heat dissipation system to obtain failure distribution of the service life of the liquid cooling heat dissipation system at a plurality of different temperatures.
And determining probability distribution density of the corresponding service life of the liquid cooling heat dissipation system in the service life failure distribution of the liquid cooling heat dissipation system at different temperatures based on the service life failure distribution of the liquid cooling heat dissipation system at different temperatures and the temperature.
In the method for predicting the service life of the liquid cooling heat dissipation system, the probability distribution density for determining the service life duration of the liquid cooling heat dissipation system at the second temperature is obtained by adopting the service life acceleration model, the service life prediction of the liquid cooling heat dissipation system is closer to the result of the actual use environment, and the accuracy is high.
Based on the same conception, the embodiment of the disclosure also provides a device for predicting the service life of the liquid cooling heat dissipation system.
It can be understood that, in order to implement the above functions, the device for predicting the service life of the liquid cooling heat dissipation system provided in the embodiments of the present disclosure includes a hardware structure and/or a software module that perform each function. The disclosed embodiments may be implemented in hardware or a combination of hardware and computer software, in combination with the various example elements and algorithm steps disclosed in the embodiments of the disclosure. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not to be considered as beyond the scope of the embodiments of the present disclosure.
Fig. 9 is a block diagram illustrating a liquid-cooled heat dissipation system operating life prediction device, according to an example embodiment. Referring to fig. 9, the apparatus includes an acquisition module 901, a determination module 902, and a call module 903.
The acquiring module 901 is configured to acquire an application type, an application usage duration, and a first temperature of the terminal, where the first temperature is a temperature detected by a temperature sensor inside the terminal in real time, where the application type, the application usage duration, and the first temperature are run in a specified time period. The determining module 902 is configured to determine a second temperature based on the application type, the application usage duration, and the first temperature, where the second temperature is an operating temperature of the liquid cooling heat dissipation system. The calling module 903 is configured to call a lifetime distribution curve representing a correspondence between a temperature, a lifetime duration, and a probability distribution density, and determine a lifetime duration probability distribution of the liquid cooling heat dissipation system based on the second temperature and the lifetime distribution curve.
In the embodiment of the present disclosure, the determining module 902 is configured to train a behavior analysis model based on the historical data, where the input of the behavior analysis model is an application type, an application usage period, and a first temperature, and the output is a second temperature. And inputting the application type, the application use duration and the first temperature into a behavior analysis model to obtain a second temperature.
In the embodiment of the present disclosure, the obtaining module 901 is configured to determine a first functional relationship curve that characterizes a correspondence between a temperature and a lifetime duration. And determining probability distribution density of service life time of the liquid cooling heat dissipation system at different temperatures. And determining a life distribution curve of the corresponding relation among the temperature, the life duration and the probability distribution density based on the first functional relation curve and the probability distribution density.
In the embodiment of the present disclosure, the determining module 902 is configured to perform high-temperature aging on the liquid cooling heat dissipation system under an experimental condition, so as to obtain multiple sets of experimental data of temperature and service life of the liquid cooling heat dissipation system. A plurality of sets of experimental data are fitted with the first functional relationship, thereby obtaining a first functional relationship curve.
In an embodiment of the present disclosure, the first functional form is as follows:
wherein T is the second temperature, alpha is the coefficient to be determined, delta is a constant, beta is the coefficient to be determined, and tau is the life duration.
In the embodiment of the present disclosure, the determining module 902 is configured to perform probability statistics on multiple sets of experimental data to obtain probability distribution densities of life durations at different temperatures.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 10 is a block diagram illustrating an apparatus 1000 for liquid cooled heat dissipation system operational life prediction, according to an example embodiment. For example, apparatus 1000 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 10, the apparatus 1000 may include one or more of the following components: a processing component 1002, a memory 1004, a power component 1006, a multimedia component 1008, an audio component 1010, an input/output (I/O) interface 1012, a sensor component 1014, and a communication component 1016.
The processing component 1002 generally controls overall operation of the apparatus 1000, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 1002 can include one or more processors 1020 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 1002 can include one or more modules that facilitate interaction between the processing component 1002 and other components. For example, the processing component 1002 can include a multimedia module to facilitate interaction between the multimedia component 1008 and the processing component 1002.
The memory 1004 is configured to store various types of data to support operations at the device 1000. Examples of such data include instructions for any application or method operating on the device 1000, contact data, phonebook data, messages, pictures, videos, and the like. The memory 1004 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power component 1006 provides power to the various components of the device 1000. Power component 1006 can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for device 1000.
The multimedia component 1008 includes a screen between the device 1000 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia assembly 1008 includes a front-facing camera and/or a rear-facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 1000 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 1010 is configured to output and/or input audio signals. For example, the audio component 1010 includes a Microphone (MIC) configured to receive external audio signals when the device 1000 is in an operational mode, such as a call mode, a recording mode, and a speech recognition mode. The received audio signals may be further stored in memory 1004 or transmitted via communication component 1016. In some embodiments, the audio component 1010 further comprises a speaker for outputting audio signals.
The I/O interface 1012 provides an interface between the processing assembly 1002 and peripheral interface modules, which may be a keyboard, click wheel, buttons, and the like. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 1014 includes one or more sensors for providing status assessment of various aspects of the device 1000. For example, the sensor assembly 1014 may detect an on/off state of the device 1000, a relative positioning of the components, such as a display and keypad of the apparatus 1000, the sensor assembly 1014 may also detect a change in position of the apparatus 1000 or a component of the apparatus 1000, the presence or absence of user contact with the apparatus 1000, an orientation or acceleration/deceleration of the apparatus 1000, and a change in temperature of the apparatus 1000. The sensor assembly 1014 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 1014 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1014 can also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 1016 is configured to facilitate communication between the apparatus 1000 and other devices, either wired or wireless. The device 1000 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 1016 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1016 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 1000 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 1004, including instructions executable by processor 1020 of apparatus 1000 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It is further understood that the term "plurality" in this disclosure means two or more, and other adjectives are similar thereto. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It is further understood that the terms "first," "second," and the like are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the expressions "first", "second", etc. may be used entirely interchangeably. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.
It will be further understood that although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (14)
1. The method for predicting the service life of the liquid cooling heat dissipation system is applied to a terminal and is characterized by comprising the following steps of:
acquiring an application type, an application use duration and a first temperature of the terminal running in a specified time period;
Determining a second temperature based on the application type, the application using time length and the first temperature, wherein the second temperature is the operation temperature of the liquid cooling heat dissipation system and is obtained by carrying out weighted average on a first temperature which is greater than or equal to a temperature threshold value of the operation of the liquid cooling heat dissipation system, and the first temperature is the temperature of the liquid cooling heat dissipation system detected by a temperature sensor inside the terminal in real time;
And calling a life distribution curve representing the corresponding relation among the temperature, the life duration and the probability distribution density, and determining the life duration probability distribution of the liquid cooling heat dissipation system based on the second temperature and the life distribution curve, wherein the life distribution curve is a corresponding relation curve which is established in a three-dimensional coordinate system and is based on the temperature, the life duration and the probability distribution density, and the life duration probability distribution is determined according to the position of the determined second temperature value on a temperature coordinate axis of the life distribution curve.
2. The method of claim 1, wherein determining the second temperature based on the application type, the application usage period, and the first temperature comprises:
training a behavior analysis model based on historical data, wherein the input of the behavior analysis model is an application type, an application use duration and a first temperature, and the output of the behavior analysis model is a second temperature;
and inputting the application type, the application use duration and the first temperature into the behavior analysis model to obtain a second temperature.
3. The method for predicting the service life of a liquid-cooled heat dissipation system according to claim 1, wherein the service life distribution curve is obtained by:
Determining a first functional relation curve representing the corresponding relation between the temperature and the service life duration;
determining probability distribution density of service life time of the liquid cooling heat dissipation system at different temperatures;
and determining a life distribution curve of the corresponding relation among the temperature, the life duration and the probability distribution density based on the first functional relation curve and the probability distribution density.
4. The method for predicting the service life of a liquid-cooled heat dissipation system according to claim 3, wherein determining a first functional relationship curve representing a correspondence between temperature and a service life duration comprises:
Under the experimental condition, performing high-temperature aging on the liquid cooling heat dissipation system to obtain multiple groups of experimental data of temperature and service life of the liquid cooling heat dissipation system;
fitting the plurality of sets of experimental data to the first functional relationship, thereby obtaining a first functional relationship curve.
5. The method of claim 4, wherein the first function is as follows:
Wherein T is the second temperature, alpha is the coefficient to be determined, delta is a constant, beta is the coefficient to be determined, tau is the life time, and the coefficient to be determined is obtained by adjusting the plurality of groups of experimental data fitted to the first functional relation.
6. The method for predicting the service life of a liquid-cooled heat sink system according to claim 4, wherein determining the probability distribution density of the service life of the liquid-cooled heat sink system at different temperatures comprises:
And carrying out probability statistics on the plurality of groups of experimental data to obtain probability distribution densities of the service life time durations at different temperatures.
7. The utility model provides a liquid cooling heat dissipation system life prediction device, is applied to the terminal, its characterized in that includes:
The acquisition module is used for acquiring the application type, the application use duration and the first temperature of the terminal running in the appointed time period;
The determining module is used for determining a second temperature based on the application type, the application using time length and the first temperature, wherein the second temperature is the operation temperature of the liquid cooling heat dissipation system and is obtained by carrying out weighted average on a first temperature which is greater than or equal to a temperature threshold value of the operation of the liquid cooling heat dissipation system, and the first temperature is the temperature of the liquid cooling heat dissipation system detected by a temperature sensor in the terminal in real time;
The calling module is used for calling a life distribution curve representing the corresponding relation of temperature, life duration and probability distribution density, and determining the life duration probability distribution of the liquid cooling heat dissipation system based on the second temperature and the life distribution curve, wherein the life distribution curve is a corresponding relation curve which is established in a three-dimensional coordinate system and is based on the temperature, the life duration and the probability distribution density, and the life duration probability distribution is determined according to the position of the determined second temperature value on a temperature coordinate axis of the life distribution curve.
8. The liquid-cooled heat dissipation system operating life prediction apparatus of claim 7, wherein the determination module is configured to:
training a behavior analysis model based on historical data, wherein the input of the behavior analysis model is an application type, an application use duration and a first temperature, and the output of the behavior analysis model is a second temperature;
and inputting the application type, the application use duration and the first temperature into the behavior analysis model to obtain a second temperature.
9. The liquid-cooled heat dissipation system operating life prediction device of claim 7, wherein the acquisition module is further configured to:
Determining a first functional relation curve representing the corresponding relation between the temperature and the service life duration;
determining probability distribution density of service life time of the liquid cooling heat dissipation system at different temperatures;
and determining a life distribution curve of the corresponding relation among the temperature, the life duration and the probability distribution density based on the first functional relation curve and the probability distribution density.
10. The apparatus of claim 9, wherein the determining module is configured to:
Under the experimental condition, performing high-temperature aging on the liquid cooling heat dissipation system to obtain multiple groups of experimental data of temperature and service life of the liquid cooling heat dissipation system;
fitting the plurality of sets of experimental data to the first functional relationship, thereby obtaining a first functional relationship curve.
11. The liquid-cooled heat dissipation system operating life prediction device of claim 10, wherein the first functional form is as follows:
Wherein T is the second temperature, alpha is the coefficient to be determined, delta is a constant, beta is the coefficient to be determined, tau is the life time, and the coefficient to be determined is obtained by adjusting the plurality of groups of experimental data fitted to the first functional relation.
12. The apparatus of claim 10, wherein the determining module is configured to:
And carrying out probability statistics on the plurality of groups of experimental data to obtain probability distribution densities of the service life time durations at different temperatures.
13. The utility model provides a liquid cooling system life prediction device which characterized in that includes:
A processor;
A memory for storing processor-executable instructions;
Wherein the processor is configured to: a method of predicting the operating life of a liquid-cooled heat dissipation system as recited in any one of claims 1 to 6.
14. A non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the liquid-cooled heat dissipation system operational life prediction method of any one of claims 1 to 6.
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