A shell temperature fitting method, device, equipment and storage medium

Abstract

The shell temperature fitting method, device, equipment and storage medium disclosed by the embodiments of the present application comprise the following steps: determining the optimal time delay of each heat source device in consideration of the time delay characteristic of heat from the heat source device to the shell; configuring the corresponding temperature preprocessing model by using the optimal time delay of each heat source device; and preprocessing the real-time temperature (i.e., the first temperature) of the heat source device collected by the temperature sensor by using the temperature preprocessing model. In this way, the problem that the shell temperature fitting result is inaccurate due to the sudden change of the temperature of the heat source device can be solved, and the time delay characteristic makes the peak value of the temperature of the heat source device and the peak value of the temperature of the shell more consistent, so that the rising and falling trend of the temperature of the shell can be accurately determined. Overall, the fitting accuracy of the temperature of the shell is improved, and the temperature control effect of the subsequent electronic device is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to temperature detection technology, and in particular to a shell temperature fitting method and device, equipment and a storage medium. BACKGROUND

[0002] Electronic devices are usually only allowed to dissipate heat naturally through the surface of the shell due to the limitations of size and shape. As the functions of electronic devices gradually increase and the computing power gradually improves, the heat generated by electronic devices also increases, resulting in high shell temperature, affecting the normal operation of electronic devices, and even exceeding the safety standard to cause user burns. To avoid these problems, on the one hand, the heat dissipation capacity of electronic devices needs to be improved, and on the other hand, the performance of electronic devices needs to be limited when the temperature of electronic devices is too high, so as to reduce the generation of heat and reduce the shell temperature.

[0003] From the aspects of performance and temperature, too early performance control will cause problems such as lag and slow operation, affecting user experience; too late performance control will also cause high shell temperature, affecting user experience and even burning users. Therefore, how to efficiently, accurately and timely obtain shell temperature information to provide accurate data support for electronic device temperature control strategy will become the key to optimizing user experience.

[0004] Currently, some electronic devices cannot directly arrange temperature sensing devices on the surface of the shell, such as mobile phones, tablet computers, smart watches and other electronic devices with high design requirements for the shell. The temperature sensing devices of these electronic devices are arranged at the position of internal heat source devices to collect the real-time temperature of the heat source devices, and the real-time temperature is used for fitting calculation to obtain the shell temperature. When the temperature of the heat source device fluctuates greatly, the fitted shell temperature obtained by using this method has a large deviation from the actual shell temperature, thereby affecting the temperature control effect of the electronic device. SUMMARY

[0005] To solve the above technical problems, the embodiments of the present application aim to provide a shell temperature fitting method, device, equipment and storage medium.

[0006] The technical solution of the present application is implemented as follows:

[0007] In a first aspect, a shell temperature fitting method is provided, comprising:

[0008] obtaining a temperature preprocessing model corresponding to a heat source device in an electronic device, wherein the temperature preprocessing model is determined based on the optimal time delay of heat conduction between the heat source device and the shell;

[0009] obtain a second temperature of the heat source device after preprocessing based on the temperature preprocessing model, wherein the first temperature is a real-time temperature of the heat source device collected by a temperature sensor;

[0010] fit a shell temperature based on the second temperature of the heat source device after preprocessing.

[0011] In a second aspect, a shell temperature fitting device is provided, comprising:

[0012] an obtaining unit configured to obtain a temperature preprocessing model corresponding to a heat source device in an electronic device, wherein the temperature preprocessing model is determined based on an optimal time delay of heat conduction between the heat source device and a shell;

[0013] a preprocessing unit configured to preprocess a first temperature of the heat source device based on the temperature preprocessing model to obtain a second temperature of the heat source device after preprocessing, wherein the first temperature is a real-time temperature of the heat source device collected by a temperature sensor;

[0014] a temperature fitting unit configured to fit a shell temperature based on the second temperature of the heat source device after preprocessing.

[0015] In a third aspect, an electronic device is provided, comprising a processor and a memory configured to store a computer program capable of running on the processor,

[0016] wherein the processor is configured to execute the steps of the foregoing method when the computer program is running.

[0017] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the foregoing method.

[0018] With the above technical solution, the optimal time delay of each heat source device is determined considering the time delay characteristics of heat from the heat source device to the shell, a corresponding temperature preprocessing model is configured using the optimal time delay of each heat source device, and the real-time temperature (i.e. the first temperature) of the heat source device collected by the temperature sensor is preprocessed using the temperature preprocessing model, which can solve the problem of inaccurate shell temperature fitting results caused by sudden changes in the temperature of the heat source device, and considering the time delay characteristics makes the heat source device temperature peak and the shell temperature peak more consistent, and accurately judges the rising and falling trend of the shell temperature. Overall, the fitting accuracy of the shell temperature is improved, which is beneficial to improve the temperature control effect of the subsequent electronic device. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 FIG. 1 is a first flowchart of a shell temperature fitting method in the embodiments of the present application;

[0020] Figure 2 This is a schematic diagram of the distribution structure of heat source devices in a mobile phone in an embodiment of this application;

[0021] Figure 3 This is a waveform diagram of temperature monitoring data in an embodiment of this application;

[0022] Figure 4 This is a schematic diagram illustrating the correspondence between time delay and thermal conductivity in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram of temperature rise curves corresponding to different values ​​in the embodiments of this application;

[0024] Figure 6 This is a schematic diagram illustrating the breakdown of the temperature change process into multiple steady-state processes in the embodiments of this application;

[0025] Figure 7 This is a schematic diagram illustrating the effect of temperature pretreatment in the embodiments of this application;

[0026] Figure 8 This is a schematic diagram of the second process of the shell temperature fitting method in the embodiments of this application;

[0027] Figure 9 This is a schematic diagram of the composition of the shell temperature fitting device in the embodiments of this application;

[0028] Figure 10 This is a schematic diagram of the composition structure of the electronic device in the embodiments of this application. Detailed Implementation

[0029] In order to gain a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration only and are not intended to limit the embodiments of this application.

[0030] Figure 1 This is a schematic diagram of the first process of the shell temperature fitting method in the embodiments of this application, as shown below. Figure 1 As shown, the method may specifically include:

[0031] Step 101: Obtain the temperature preprocessing model corresponding to the heat source device inside the electronic device;

[0032] The temperature preprocessing model is determined based on the optimal time delay of heat conduction between the corresponding heat source device and the housing. In this embodiment, the heat source device refers to any one of one or more heat source devices in the electronic device, and the temperature preprocessing model refers to the temperature preprocessing model corresponding to each heat source device. That is, the establishment and preprocessing process of the temperature preprocessing model for each heat source device are the same.

[0033] A heat source device refers to a component in an electronic device that generates heat, such as a chip, battery, screen, camera, or circuitry. One or more temperature sensors can be installed on each heat source device to monitor its real-time temperature (i.e., the initial temperature). For example, the temperature sensor can be a thermistor with a negative temperature coefficient (NTC) or a thermistor with a positive temperature coefficient (PTC).

[0034] Considering the time delay characteristic of heat transfer from the heat source device to the casing, a temperature preprocessing model is used to preprocess the real-time temperature (i.e., the first temperature) of the heat source device collected by the temperature sensor. This solves the problem of inaccurate casing temperature fitting results caused by sudden temperature changes in the heat source device. Furthermore, considering the time delay characteristic, the peak temperature of the heat source device and the peak temperature of the casing are more closely matched, accurately determining the rising and falling trends of the casing temperature.

[0035] Taking mobile phones as an example, electronic devices Figure 2 This is a schematic diagram of the distribution structure of heat source devices in a mobile phone in an embodiment of this application, as shown below. Figure 2 As shown, including a front view, left view, and rear view of the mobile phone, the heat source components inside the mobile phone include at least a camera, battery, circuitry, and various integrated circuit chips (ICs). Each heat source component is equipped with an NTC thermistor (hereinafter referred to as "NTC") for real-time monitoring of the real-time temperature of the heat source component. In the embodiments of this application, the NTC temperature represents the temperature of the heat source component.

[0036] In practical applications, mobile phones can also include other heat source devices, such as the mobile phone screen. Since the mobile phone screen has a large heat-generating area, NTCs can be installed in multiple locations on the mobile phone screen to detect multiple real-time temperatures of the screen.

[0037] The following provides further examples illustrating the method for establishing a temperature preprocessing model.

[0038] For example, in some embodiments, obtaining the temperature preprocessing model corresponding to the heat source device inside the electronic device includes: obtaining the time delay range of heat conduction delay between the heat source device and the casing; determining the optimal time delay corresponding to the heat source device from the time delay range of the heat source device based on a preset optimization algorithm; and determining the temperature preprocessing model based on the optimal time delay corresponding to the heat source device.

[0039] Here, the time delay range includes the minimum and maximum time delay of heat conduction between the heat source device and the housing. The time delay range can be represented by the time difference between the peak temperature of the housing and the peak temperature of the heat source device.

[0040] For example, in some embodiments, obtaining the time delay range of heat conduction delay between the heat source device and the housing includes:

[0041] Within a preset time period, first monitoring data of the shell temperature and second monitoring data of the heat source device temperature are acquired. The first monitoring data includes real-time temperatures of the shell at multiple moments, and the second monitoring data includes real-time temperatures of the heat source device at multiple moments. The heat source device is one of several heat source devices within the shell. Based on the first monitoring data, the times of multiple shell temperature peaks are determined. Based on the second monitoring data, the times of multiple heat source device temperature peaks are determined. Based on the times of the multiple shell temperature peaks and the times of the multiple heat source device temperature peaks, the maximum and minimum time differences between the peak values ​​are determined. Based on the maximum and minimum time differences, the range of the heat conduction delay between the heat source device and the shell is determined. The conduction delay is related to the fluctuation range of the heat generated by the heat source, the duration of heat generation, and the accumulation of heat along the propagation path, thus causing fluctuations in the conduction delay.

[0042] Specifically, Figure 3 This is a waveform diagram of temperature monitoring data in an embodiment of this application, such as... Figure 3 As shown, over a period of time, the temperatures of the heat source device and the casing are simultaneously collected. It is very obvious that the fluctuation amplitude of the heat source device temperature is significantly greater than that of the casing temperature. There is a time difference between the peak times of the heat source device temperature and the peak times of the casing temperature, for example, t1 and t2, t3 and t4. Similarly, there is a time difference at the valley positions, and the value of the time difference also varies. To determine the peak times of the heat source device temperature and the subsequent peak times of the casing temperature, the difference between the two peak times is calculated, for example, Δt = t2 - t1, Δt = t4 - t3. The peak time differences are statistically analyzed to determine the maximum and minimum time differences. These maximum and minimum time differences are then used to define the time delay range.

[0043] For example, the method for determining the optimal time delay includes: determining an initial time delay from the time delay range of the heat source device; constructing a temperature preprocessing model based on the initial time delay; preprocessing a first temperature of the heat source device based on the temperature preprocessing model to obtain a preprocessed second temperature of the heat source device; fitting the shell temperature based on the preprocessed second temperature of the heat source device to obtain the shell temperature; iteratively updating the initial time delay based on the shell temperature until the iterative convergence condition is met to obtain the optimal time delay corresponding to the heat source device.

[0044] Specifically, the error between the fitted shell temperature and the actual shell temperature is calculated. Based on the error, it is determined whether the iterative convergence condition is met. If the convergence condition is met, the current time delay is taken as the optimal time delay. If the convergence condition is not met, a new time delay is obtained from the time delay range, and a new error is calculated until the iterative convergence condition is met.

[0045] For example, the iterative convergence conditions include the shell temperature error being less than a preset error threshold and the number of iterations reaching an upper limit.

[0046] When an electronic device contains multiple heat source devices, the optimization algorithm for multi-objective search includes one of the following: simulated annealing algorithm, ant colony algorithm, particle swarm optimization algorithm, and genetic algorithm.

[0047] For example, when an electronic device includes a heat source device, the time delay corresponding to the minimum temperature error of the housing is directly selected as the optimal time delay.

[0048] It should be noted that in the temperature preprocessing model establishment and shell temperature fitting calculation process in this application embodiment, the first temperature is preprocessed to reduce the impact of short-term heat generation mutation, improve the preprocessing accuracy of the temperature preprocessing model, and enhance the stability of the fitted shell temperature.

[0049] For example, in some embodiments, determining the temperature preprocessing model based on the optimal time delay corresponding to the heat source device includes: determining the thermal conductivity coefficient of the heat source device based on the optimal time delay corresponding to the heat source device and a preset relative error rate; configuring the thermal conductivity coefficient of the standard temperature preprocessing model based on the thermal conductivity coefficient of the heat source device to obtain the temperature preprocessing model; wherein the standard temperature preprocessing model is constructed based on the heat transfer rate relationship.

[0050] Here, the relative error rate is used to represent the error rate between the pre-processed second temperature and the steady-state temperature, where the steady-state temperature is the stable temperature reached after the heat generated by the heat source device changes. The relative error rate serves as the criterion for determining whether the NTC temperature pre-processing has reached the steady-state temperature. In some embodiments, a larger relative error rate indicates higher processing accuracy of the temperature pre-processing model, and a pre-processed temperature closer to the steady-state temperature.

[0051] The standard temperature preprocessing model can be understood as a temperature preprocessing model with the thermal conductivity coefficient as the default parameter or the parameter to be configured. After determining the thermal conductivity coefficient based on the optimal time delay and the preset relative error rate, the standard temperature preprocessing model is configured using the determined thermal conductivity coefficient to obtain the temperature preprocessing model specific to the heat source device.

[0052] Specifically, according to the heat transfer rate relationship:

[0053]

[0054] Where ΔQ represents the heat transferred, Δt represents the unit time, k represents the thermal conductivity, s represents the cross-sectional area, d represents the thickness, and ΔT represents the temperature difference. Since the internal heat conduction structure of the mobile phone is stable, let the constant a = -ks / d. Then...

[0055]

[0056] The greater the temperature difference, the faster the heat conduction rate, thus yielding the temperature preprocessing model:

[0057] T NTC_i =a(T i -T NTC_i-1 )+T NTC_i-1 (3)

[0058] Wherein: T NTC_i-1 T NTC_i They represent t respectively i-1 t i The temperature after NTC temperature pretreatment (i.e., the second temperature), t i-1 t i Difference of 1 sampling interval, T i Show t i Real-time NTC temperature (i.e., first temperature).

[0059] Furthermore, combining the peak time difference t Q To calculate the value of a, the formula for calculating a can be derived from formula (3).

[0060] Assume the state before time 0 is steady, and the real-time temperature of the heat source device acquired by the NTC is T0. Starting at time 0, the heat generation of the heat source device changes, and the real-time temperature acquired by the NTC becomes T1, then remains at T1. Then, the difference between the real-time temperature T1 acquired by the NTC and the preprocessed NTC temperature T obtained at delay time i is... NTC_i Temperature difference ΔT between i The derivation process is as follows:

[0061] ΔT i =T1-T NTC_i =T1-T NTC_i-1 -a(T1-T NTC_i-1 )=ΔT i-1 -aΔT i-1 =(1-a)ΔT i-1 =(1-a) 2 ΔT i-2 …=(1-a) i ΔT0=(1-a) i (T1-T0)

[0062] If the delay is tQ After a certain period of time, if the relative error rate reaches η, then...

[0063]

[0064] Where: η represents the relative error rate, which is the error rate between the pre-processed second temperature and the steady-state temperature. It serves as the criterion for the NTC temperature pre-processing to reach the steady-state temperature. The larger η is, the higher the processing accuracy of the temperature pre-processing model, and the closer the pre-processed temperature is to the steady-state temperature.

[0065] For example, according to formula (4), the relative error rate can be expressed as The relative error rate is the delay t Q The temperature obtained after pretreatment The ratio of the difference between the steady-state temperature T0 before the change in the heat output of the heat source device and the actual difference between the steady-state temperatures T1 and T0 after the change in the heat output of the heat source device, the larger the value of η, the greater the difference between the steady-state temperatures T1 and T0. The closer to the steady-state temperature T1, the better. η can be a preset empirical value; taking η as 95% as an example, the values ​​of a and t can be obtained. Q The corresponding curve is as follows Figure 4 As shown, the value of a changes with t Q It decreases as it increases.

[0066] Figure 5 For different t in the embodiments of this application Q A diagram showing the temperature rise curve corresponding to the value, as shown below. Figure 5 As shown, the horizontal axis represents time, and the vertical axis represents the ratio of the second temperature after pretreatment of the heat source device to the first temperature. The closer the ratio is to 1, the more stable the heat source temperature tends to be. Q The values ​​are taken as 5s, 10s, and 15s respectively. It can be seen that t... Q The smaller the value, the faster the heat source temperature tends to stabilize. Q The larger the value, the slower the heat source temperature stabilizes. Therefore, it is necessary to select the optimal t. Q The value is used to accurately represent the thermal conduction delay characteristics of the heat source device.

[0067] Furthermore, considering that the heat generated by actual heat source devices is constantly changing—for example, the amount of data processed by the chip varies at different times, resulting in different power consumption and thus different heat generation—NTC temperature preprocessing can be viewed as a superposition of multiple steady-state transformation processes. That is, the transformed temperature preprocessing model is:

[0068]

[0069] Where: N represents N temperature acquisition times, from T1 to T... N T represents the real-time temperature of the heat source device at N time points. NTC_NT represents the temperature at the Nth time out of N time points after preprocessing. NTC_N-1 This represents the pre-processed temperature at the (N-1)th time out of N time points. Formula (5) can be understood as transforming the change process from the 1st time point to the Nth time point into the superposition of multiple steady-state processes, which can improve the temperature pre-processing effect.

[0070] Figure 6 This is a schematic diagram illustrating how the temperature change process in this embodiment is broken down into multiple steady-state processes, as shown below. Figure 6 As shown, time 1 is the starting time. From time 1, curve 1 represents the actual temperature change process. At time 2, temperature variable 1 is introduced, and curve 2 is the temperature change curve considering only temperature variable 1. Curve 2 is used as the borrowing term 1 of curve 1. At time 5, temperature variable 2 is introduced, and curve 3 is the temperature change curve considering only temperature variable 2. Curve 3 is used as the borrowing term 2 of curve 1. The preprocessed temperature (dashed line) of curves 2 and 3 is more stable than the actual detected temperature (solid line). If there are only these two temperature variables, then the sum of borrowing term 1 and borrowing term 2 is the actual temperature change curve. That is, curve 4 is the difference between the sum of the decomposed terms and the actual temperature, which tends to 0.

[0071] In other words, multiple temperature variables are introduced during the actual temperature change of a heat source, affecting the temperature change process. By decomposing the transient temperature change into a superposition of multiple steady states, the steady-state temperature of each heat source device can be obtained, thereby improving the fitting accuracy of the casing temperature during the temperature change of the heat source. For example, the temperature variable can be the computational load of the chip. The computational load and power consumption of the chip are different at different times, resulting in different heat generation.

[0072] It should be noted that the temperature preprocessing model can be determined by electronic devices, or it can be predetermined by other devices and sent to electronic devices.

[0073] Step 102: Based on the temperature preprocessing model, preprocess the first temperature of the heat source device to obtain the preprocessed second temperature of the heat source device, wherein the first temperature is the real-time temperature of the heat source device collected by the temperature sensor.

[0074] For example, the step of preprocessing the first temperature of the heat source device based on the temperature preprocessing model to obtain the preprocessed second temperature of the heat source device includes: obtaining the first temperature of the heat source device at N times and the preprocessed second temperature at the (N-1)th time, where N is an integer greater than or equal to 1, and the heat source device is one of the heat source devices; inputting the first temperature at the N times and the preprocessed second temperature at the (N-1)th time into the temperature preprocessing model corresponding to the heat source device to obtain the preprocessed second temperature of the heat source device at the Nth time.

[0075] In other words, the first temperature at N time points and the pre-processed second temperature at the (N-1)th time point are input into the temperature preprocessing model to obtain the pre-processed second temperature at the Nth time point.

[0076] For example, in some embodiments, when N is an integer greater than 1, the preprocessing process of the temperature preprocessing model includes: determining the first temperature difference at N-1 intervals based on the first temperature at the N times; performing a weighted summation operation on the first temperature difference at the N-1 intervals based on the thermal conductivity coefficient of the temperature preprocessing model corresponding to the heat source device to obtain a first intermediate temperature value; and determining the second temperature of the heat source device at the Nth time after preprocessing based on the first intermediate temperature value and the second temperature after preprocessing at the N-1th time. Here, when N is an integer greater than 1, the transient process is decomposed into multiple steady-state processes based on formula (5) to construct the temperature preprocessing model.

[0077] For example, in some embodiments, when N equals 1, the preprocessing process of the temperature preprocessing model includes: calculating the temperature difference between the first temperature of the heat source device at the Nth time and the second temperature after preprocessing at the (N-1)th time to obtain a second temperature difference value; multiplying the thermal conductivity coefficient of the temperature preprocessing model corresponding to the heat source device with the second temperature difference value to obtain a second intermediate temperature value; and obtaining the second temperature of the heat source device after preprocessing at the Nth time based on the second intermediate temperature value and the second temperature after preprocessing at the (N-1)th time. Here, when N equals 1, the temperature preprocessing model is constructed based on formula (3).

[0078] For example, in some embodiments, the preprocessing process of the temperature preprocessing model includes: obtaining the first temperature of the heat source device at the first time of the N time points; and using the first temperature at the first time point as the second temperature of the heat source device after preprocessing at the first time point.

[0079] In other embodiments, the first or second temperature at a time before the first time step is set to 0, and the first temperature at the first time step is preprocessed using a temperature preprocessing model to obtain the second temperature at the first time step after preprocessing.

[0080] Here, by pre-processing the first temperature of the heat source device, the impact of short-term heat generation changes of the heat source device can be reduced, thereby improving the stability of the fitted shell temperature.

[0081] Figure 7 This is a schematic diagram illustrating the effect of temperature pretreatment in an embodiment of this application, as shown below. Figure 7As shown, the temperature fluctuation after preprocessing is significantly reduced compared to the actual temperature, avoiding fluctuations in the shell temperature fitting results caused by actual temperature fluctuations. Furthermore, the temperature peaks and valleys after preprocessing more closely match the shell temperature, improving the accuracy during temperature rises and falls. These two factors combined result in an overall improvement in shell temperature accuracy.

[0082] Step 103: Fit the shell temperature based on the second temperature after preprocessing of the heat source device to obtain the shell temperature.

[0083] For example, in some embodiments, the housing temperature is obtained by fitting the second temperature of the heat source device after preprocessing based on a temperature fitting model.

[0084] For example, the temperature fitting model can be

[0085] T = k1T NTC1 +k2T NTC2 +k3T NTC3 +…+k n T NTCn +C (6)

[0086] Where: T is the monitored shell temperature, obtained from actual measurement by temperature monitoring equipment. NTCn The NTC temperature (i.e., the second temperature) after preprocessing the first temperature (nth temperature) is given by the coefficients k1, k2, k3…kn, C, which are calculated using the least squares method. Once these coefficients are determined, a temperature fitting model can be constructed based on the preprocessed NTC temperature T. NTCn Fitting the casing temperature T. In practical applications, electronic devices may include one or more heat source devices. A corresponding temperature preprocessing model is established for each heat source device. The casing temperature is obtained by fitting the casing temperature using one or more preprocessed second temperatures.

[0087] For example, the temperature fitting model can also be a neural network model, where the pre-processed NTC temperature is input into a pre-trained neural network model to obtain the shell temperature.

[0088] To better illustrate the purpose of this application, based on the above embodiments, further examples are provided to illustrate the shell temperature fitting, such as... Figure 8 As shown, the method specifically includes:

[0089] Step 801: Within a preset time period, acquire the first monitoring data of the shell temperature and the second monitoring data of the heat source device temperature;

[0090] Step 802: Calculate the time delay range between the peak temperature of the casing and the peak temperature of the heat source device based on the monitoring data;

[0091] Here, the time delay range is represented as [t1, t2]. The conduction time delay is related to the fluctuation range of the heat source's heat output, the duration of heat generation, and the accumulation of heat along the propagation path, which causes the conduction time delay to fluctuate.

[0092] Assuming an ideal state, the chip's heat output stabilizes at Q1, and both the NTC temperature and the casing temperature reach a steady state. At time 1, the chip's heat output instantaneously rises from Q1 to Q2 and remains constant. The NTC temperature reaches a new stable value at time 2, and the casing temperature reaches a new stable value at time 3. Then, the time required for heat conduction between the NTC and casing temperatures is t. Q , that is, the time difference between time 3 and time 2, whose magnitude lies between [t1, t2]. Considering the complexity of the internal structure of electronic devices, t Q It cannot be obtained through calculation, but it can be obtained through optimization calculation based on the shell temperature error.

[0093] For example, for t Q Perform the calculation. For a simple first-order calculation, simply substitute the values ​​in [t1, t2], calculate the corresponding a values ​​according to formula (4), and then substitute them into formula (3) or formula (5) to obtain the NTC temperature curves of multiple heat source devices after preprocessing. Calculate the corresponding error according to the shell temperature fitting model, and select the time difference that minimizes the final error as t. Q That's all.

[0094] For an nth-order fitting function, that is, to determine n optimal time delays t from n time delay ranges. Q1 , t Q2 , t Q3 , ..., t Qn The combinable ways are the product of the selectable delay quantities for n delay ranges, which obviously cannot be calculated by exhaustive enumeration. Therefore, an optimization algorithm is used to calculate t. Q1 , t Q2 , t Q3 , ..., t Qn The calculation and solution are as follows.

[0095] Step 803: Construct a standard temperature preprocessing model based on the heat transfer rate relationship;

[0096] For example, a standard temperature preprocessing model can be constructed based on formula (3) or formula (5).

[0097] Step 804: Obtain the optimal time delay for each heat source device based on the simulated annealing algorithm;

[0098] For example, the simulated annealing algorithm is selected for t Q1 , t Q2 , t Q3 , ..., t QnThe calculation and solution are as follows. Specific methods include:

[0099] Step 1: Take the median value of each time delay range [t1, t2] as the initial solution, preprocess each NTC temperature, and then perform shell temperature fitting calculation to obtain the error.

[0100] Step 2: Set the initial temperature (a parameter in the simulated annealing algorithm, which is independent of the NTC temperature / shell temperature) to a sufficiently large φ, and the number of iterations for each φ value is L.

[0101] Step 3: Randomly generate new solutions within the time difference range [t1, t2] of each NTC temperature peak, and calculate the error using the same method as in Step 1. Calculate the increment

[0102] Step 4: If the new solution is accepted as the optimal solution, then the solution is accepted based on probability. Accept the new solution as the optimal solution.

[0103] Step 5: Repeat steps 3 and 4 L times, then decrease the value of φ.

[0104] Step 6: Repeat steps 3 through 6 until φ becomes too small, or until several consecutive new solutions are not accepted, at which point the algorithm terminates. The current solution is the optimal solution.

[0105] Step 805: Determine the thermal conductivity coefficient of the temperature preprocessing model based on the optimal time delay;

[0106] Step 806: The temperature preprocessing model preprocesses the first temperature to obtain the second temperature;

[0107] Step 807: Use the pre-processed second temperature to fit the shell temperature to obtain the shell temperature.

[0108] like Figure 7 As shown, the temperature fluctuation after preprocessing is significantly reduced compared to the actual temperature, avoiding fluctuations in the shell temperature fitting results caused by actual temperature fluctuations. Furthermore, the temperature peaks and valleys after preprocessing are more closely aligned with the shell temperature, improving the accuracy during temperature rises and falls. These two factors combined improve the overall accuracy of the shell temperature fitting calculation. The accuracy of shell temperature fitting calculation for smart terminal devices is optimized. When the electronic device temperature is high, a more precise temperature control strategy is triggered, improving the temperature control effect of the electronic device, achieving a balance between performance and temperature, and ensuring the normal operation of the electronic device.

[0109] To implement the method of the embodiments of this application, based on the same inventive concept, the embodiments of this application also provide a shell temperature fitting device, such as... Figure 9 As shown, the housing temperature fitting device 90 includes:

[0110] The acquisition unit 901 is used to acquire the temperature preprocessing model corresponding to the heat source device inside the electronic device, wherein the temperature preprocessing model is determined based on the optimal time delay of heat conduction between the heat source device and the casing;

[0111] The preprocessing unit 902 is used to preprocess the first temperature of the heat source device based on the temperature preprocessing model to obtain the second temperature of the heat source device after preprocessing, wherein the first temperature is the real-time temperature of the heat source device collected by the temperature sensor.

[0112] The temperature fitting unit 903 is used to fit the shell temperature based on the second temperature after preprocessing of the heat source device to obtain the shell temperature.

[0113] For example, in some embodiments, the preprocessing unit 902 is used to obtain the first temperature of the heat source device at N times and the preprocessed second temperature at the (N-1)th time, wherein N is an integer greater than or equal to 1, and the heat source device is one of the heat source devices; the first temperature at N times and the preprocessed second temperature at the (N-1)th time are input into the temperature preprocessing model corresponding to the heat source device to obtain the preprocessed second temperature of the heat source device at the Nth time.

[0114] For example, in some embodiments, when N is an integer greater than 1, the preprocessing process of the temperature preprocessing model includes: determining a first temperature difference at N-1 intervals based on the first temperature at the N times; performing a weighted summation operation on the first temperature difference at the N-1 intervals based on the thermal conductivity coefficient of the temperature preprocessing model corresponding to the heat source device to obtain a first intermediate temperature value; and determining the second temperature of the heat source device at the Nth time after preprocessing based on the first intermediate temperature value and the second temperature after preprocessing at the N-1th time.

[0115] For example, in some embodiments, when N equals 1, the preprocessing process of the temperature preprocessing model includes: calculating the temperature difference between the first temperature of the heat source device at the Nth time and the second temperature after preprocessing at the (N-1)th time to obtain a second temperature difference value; multiplying the thermal conductivity coefficient of the temperature preprocessing model corresponding to the heat source device with the second temperature difference value to obtain a second intermediate temperature value; and obtaining the second temperature of the heat source device after preprocessing at the Nth time based on the second intermediate temperature value and the second temperature after preprocessing at the (N-1)th time.

[0116] For example, in some embodiments, the preprocessing process of the temperature preprocessing model further includes: obtaining the first temperature of the heat source device at the first time of the N time points; and using the first temperature at the first time point as the second temperature of the heat source device after preprocessing at the first time point.

[0117] For example, in some embodiments, the acquisition unit 901 is used to acquire the time delay range of heat conduction delay between the heat source device and the housing; determine the optimal time delay corresponding to the heat source device from the time delay range of the heat source device based on a preset optimization algorithm; and determine a temperature preprocessing model based on the optimal time delay corresponding to the heat source device.

[0118] For example, in some embodiments, the acquisition unit 901 is used to acquire first monitoring data of the shell temperature and second monitoring data of the heat source device temperature within a preset time period; wherein, the first monitoring data includes the real-time temperature of the shell at multiple moments, and the second monitoring data includes the real-time temperature of the heat source device at multiple moments, wherein the heat source device is one of the heat source devices; based on the first monitoring data, the times of multiple temperature peaks of the shell are determined; based on the second monitoring data, the times of multiple temperature peaks of the heat source device are determined; based on the times of the multiple shell temperature peaks and the times of the multiple heat source device temperature peaks, the maximum time difference and the minimum time difference of the peaks are determined; based on the maximum time difference and the minimum time difference, the time delay range of heat conduction delay between the heat source device and the shell is determined.

[0119] For example, in some embodiments, the acquisition unit 901 is used to determine the thermal conductivity coefficient of the heat source device based on the optimal time delay corresponding to the heat source device and a preset relative error rate; configure the thermal conductivity coefficient of the standard temperature preprocessing model based on the thermal conductivity coefficient of the heat source device to obtain the temperature preprocessing model; wherein, the standard temperature preprocessing model is constructed based on the heat transfer rate relationship.

[0120] Here, the relative error rate is used to represent the error rate between the pre-processed second temperature and the steady-state temperature, where the steady-state temperature is the stable temperature reached after the heat generated by the heat source device changes. The relative error rate serves as the criterion for determining whether the NTC temperature pre-processing has reached the steady-state temperature. In some embodiments, a larger relative error rate indicates higher processing accuracy of the temperature pre-processing model, and a pre-processed temperature closer to the steady-state temperature.

[0121] The standard temperature preprocessing model can be understood as a temperature preprocessing model with the thermal conductivity coefficient as the default parameter or the parameter to be configured. After determining the thermal conductivity coefficient based on the optimal time delay and the preset relative error rate, the standard temperature preprocessing model is configured using the determined thermal conductivity coefficient to obtain a temperature preprocessing model specific to the heat source device.

[0122] Based on the hardware implementation of each unit in the aforementioned shell temperature fitting device, this application embodiment also provides an electronic device, such as... Figure 10 As shown, the electronic device 100 includes: a processor 1001 and a memory 1002 configured to store computer programs capable of running on the processor;

[0123] When the processor 1001 is configured to run a computer program, it executes the method steps described in the foregoing embodiments.

[0124] Of course, in practical applications, such as Figure 10 As shown, the various components in the electronic device 100 are coupled together via a bus system 1003. It is understood that the bus system 1003 is used to enable communication between these components. In addition to a data bus, the bus system 1003 also includes a power bus, a control bus, and a status signal bus. However, for clarity, all buses are labeled as bus system 1003 in the figure.

[0125] In practical applications, the aforementioned processor can be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field-Programmable Gate Array (FPGA), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of the aforementioned processor can also be other types, and the embodiments of this application do not specifically limit this.

[0126] The aforementioned memory can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provides instructions and data to the processor.

[0127] In practical applications, the aforementioned casing temperature fitting device can be an electronic device or a chip applied to an electronic device. In this application, the device can implement the functions of multiple units through software, hardware, or a combination of both, enabling the device to execute the casing temperature fitting method provided in any of the above embodiments. Furthermore, the technical effects of each technical solution of this device can be referenced to the technical effects of the corresponding technical solutions in the casing temperature fitting method, and will not be elaborated upon further in this application.

[0128] In an exemplary embodiment, this application also provides a computer-readable storage medium, such as a memory including a computer program, which can be executed by a processor of an electronic device to perform the steps of the aforementioned method.

[0129] This application also provides a computer program product, including computer program instructions.

[0130] Optionally, the computer program product can be applied to the electronic device in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the electronic device in the various methods of the embodiments of this application. For the sake of brevity, they will not be described in detail here.

[0131] This application also provides a computer program.

[0132] Optionally, the computer program can be applied to the electronic device in the embodiments of this application. When the computer program is run on a computer, it causes the computer to execute the corresponding processes implemented by the electronic device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0133] It should be understood that the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items. The expressions “having,” “may have,” “comprising,” and “including,” or “may include” and “may contain” used herein may be used to indicate the presence of a corresponding feature (e.g., an element such as a number, function, operation, or component), but do not exclude the presence of additional features.

[0134] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and are not necessarily used to describe a specific order or sequence. For example, without departing from the scope of this invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information.

[0135] The technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatus, and devices can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0137] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for fitting shell temperature, characterized in that, The method includes: Obtain a temperature preprocessing model corresponding to the heat source device inside the electronic device, wherein the temperature preprocessing model is determined based on the optimal time delay of heat conduction between the heat source device and the casing; The first temperature of the heat source device is preprocessed based on the temperature preprocessing model to obtain the second temperature of the heat source device after preprocessing, wherein the first temperature is the real-time temperature of the heat source device collected by the temperature sensor. The shell temperature is obtained by fitting the shell temperature based on the second temperature after preprocessing of the heat source device. The step of preprocessing the first temperature of the heat source device based on the temperature preprocessing model to obtain the preprocessed second temperature of the heat source device includes: The first temperature of the heat source device at N time points and the second temperature after preprocessing at the (N-1)th time point are obtained, where N is an integer greater than or equal to 1; The first temperature at the N time points and the pre-processed second temperature at the (N-1)th time point are input into the temperature pre-processing model corresponding to the heat source device to obtain the pre-processed second temperature of the heat source device at the Nth time point.

2. The method according to claim 1, characterized in that, When N is an integer greater than 1, the preprocessing process of the temperature preprocessing model includes: Based on the first temperature at the N times, determine the first temperature difference at N-1 intervals; Based on the thermal conductivity coefficient of the temperature preprocessing model corresponding to the heat source device, the first temperature difference at the N-1 time intervals is weighted and summed to obtain the first intermediate temperature value. Based on the first intermediate temperature value and the second temperature after preprocessing at the (N-1)th time, the second temperature of the heat source device after preprocessing at the Nth time is determined.

3. The method according to claim 1, characterized in that, When N equals 1, the preprocessing process of the temperature preprocessing model includes: Calculate the temperature difference between the first temperature of the heat source device at the Nth time and the second temperature after preprocessing at the (N-1)th time to obtain the second temperature difference value; The thermal conductivity coefficient of the temperature preprocessing model corresponding to the heat source device is multiplied by the second temperature difference to obtain the second intermediate temperature value. Based on the second intermediate temperature and the second temperature after preprocessing at the (N-1)th time, the second temperature of the heat source device after preprocessing at the Nth time is obtained.

4. The method according to claim 2 or 3, characterized in that, The preprocessing process of the temperature preprocessing model also includes: Obtain the first temperature of the heat source device at the first of the N time points; The first temperature at the first moment is taken as the second temperature of the heat source device after preprocessing at the first moment.

5. The method according to claim 1, characterized in that, The temperature preprocessing model for obtaining the heat source device inside the electronic device includes: Obtain the time delay range of heat conduction delay between the heat source device and the housing; Based on a preset optimization algorithm, the optimal time delay corresponding to the heat source device is determined from the time delay range of the heat source device; Based on the optimal time delay corresponding to the heat source device, a temperature preprocessing model is determined.

6. The method according to claim 5, characterized in that, The step of obtaining the time delay range of heat conduction delay between the heat source device and the housing includes: Within a preset time period, first monitoring data of the housing temperature and second monitoring data of the heat source device temperature are acquired; wherein, the first monitoring data includes the real-time temperature of the housing at multiple times, and the second monitoring data includes the real-time temperature of the heat source device at multiple times; Based on the first monitoring data, the times of temperature peaks in the multiple shells are determined; Based on the second monitoring data, the time of the temperature peak of the plurality of heat source devices is determined; Based on the times of the multiple shell temperature peaks and the times of the multiple heat source device temperature peaks, determine the maximum and minimum time differences of the peak values; Based on the maximum time difference and the minimum time difference, the time delay range of heat conduction delay between the heat source device and the housing is determined.

7. The method according to claim 5, characterized in that, The step of determining the temperature preprocessing model based on the optimal time delay corresponding to the heat source device includes: Based on the optimal time delay corresponding to the heat source device and the preset relative error rate, the thermal conductivity coefficient of the heat source device is determined; Based on the thermal conductivity coefficient of the heat source device, the thermal conductivity coefficient of the standard temperature preprocessing model is configured to obtain the temperature preprocessing model; The standard temperature preprocessing model is constructed based on the heat transfer rate relationship.

8. A shell temperature fitting device, characterized in that, The device includes: The acquisition unit is used to acquire the temperature preprocessing model corresponding to the heat source device inside the electronic device, wherein the temperature preprocessing model is determined based on the optimal time delay of heat conduction between the heat source device and the casing; A preprocessing unit is used to preprocess the first temperature of the heat source device based on the temperature preprocessing model to obtain the second temperature of the heat source device after preprocessing, wherein the first temperature is the real-time temperature of the heat source device collected by the temperature sensor. A temperature fitting unit is used to fit the shell temperature based on the second temperature after preprocessing of the heat source device, so as to obtain the shell temperature. The preprocessing unit is further configured to obtain the first temperature of the heat source device at N time points and the preprocessed second temperature at the (N-1)th time point, wherein N is an integer greater than or equal to 1; input the first temperature at N time points and the preprocessed second temperature at the (N-1)th time point into the temperature preprocessing model corresponding to the heat source device to obtain the preprocessed second temperature of the heat source device at the Nth time point.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory configured to store computer programs capable of running on the processor. Wherein, when the processor is configured to run the computer program, it performs the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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