A method, apparatus and device for determining a temperature sensor arrangement

Abstract

The method for determining a temperature sensor arrangement scheme provided in this disclosure includes: for each candidate temperature sensor arrangement scheme corresponding to a terminal's structural model, obtaining a housing temperature prediction model corresponding to that temperature sensor arrangement scheme; wherein, the housing temperature prediction model is used to predict the terminal's housing temperature based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme; and based on the prediction accuracy of each housing temperature prediction model, selecting a temperature sensor arrangement scheme that matches the structural model from the temperature sensor arrangement schemes corresponding to each housing temperature prediction model. The method provided in this disclosure can find the optimal arrangement scheme from multiple candidate temperature sensor arrangement schemes, thereby improving the accuracy of the temperature sensing data reported by the temperature sensors, and thus improving the accuracy of housing temperature prediction and the precision of temperature control.

Description

Technical Field

[0001] This disclosure relates to the field of terminal technology, and in particular to a method, apparatus and device for determining a temperature sensor arrangement scheme. Background Technology

[0002] During use, the terminal's temperature will rise. The higher the terminal's temperature, the greater the radiation. Therefore, it is necessary to control the terminal's temperature.

[0003] In practice, the terminal's temperature needs to be controlled based on the terminal's casing temperature. Therefore, the accuracy of casing temperature prediction determines the effectiveness of the temperature control system. The accuracy of casing temperature prediction depends on temperature data reported by temperature sensors located throughout the terminal. Therefore, the accuracy of the temperature data reported by these sensors affects the effectiveness of the temperature control system. Summary of the Invention

[0004] This disclosure provides a method, apparatus, and device for determining a temperature sensor arrangement scheme to address the shortcomings of related technologies.

[0005] According to a first aspect of the present disclosure, a method for determining a temperature sensor arrangement scheme is provided, the method comprising:

[0006] For each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, a housing temperature prediction model corresponding to the temperature sensor arrangement scheme is obtained; wherein, the housing temperature prediction model is used to predict the housing temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme.

[0007] Based on the prediction accuracy of each of the shell temperature prediction models, a temperature sensor arrangement scheme that matches the structural model is selected from the temperature sensor arrangement schemes corresponding to each of the shell temperature prediction models.

[0008] According to a second aspect of the present disclosure, an apparatus for determining a temperature sensor arrangement scheme is provided, the apparatus comprising an acquisition module and a processing module, wherein,

[0009] The acquisition module is used to acquire a housing temperature prediction model corresponding to each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal for each candidate temperature sensor arrangement scheme; wherein, the housing temperature prediction model is used to predict the housing temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme.

[0010] The processing module is used to select a temperature sensor arrangement scheme that matches the structural model from the temperature sensor arrangement schemes corresponding to each of the shell temperature prediction models, based on the prediction accuracy of each shell temperature prediction model.

[0011] According to a third aspect of the present disclosure, a computer device is provided, comprising:

[0012] processor;

[0013] Memory used to store processor-executable instructions;

[0014] The processor is configured to implement any of the methods described in the first aspect of this disclosure.

[0015] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described in any of the first aspects of the present disclosure.

[0016] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0017] As can be seen from the above embodiments, the method, apparatus, and device provided in this disclosure for determining a temperature sensor arrangement scheme, for each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, obtains a shell temperature prediction model corresponding to the temperature sensor arrangement scheme, and then selects a temperature sensor arrangement scheme that matches the structural model from the temperature sensor arrangement schemes corresponding to each shell temperature prediction model based on the prediction accuracy of each shell temperature prediction model. The shell temperature prediction model is used to predict the shell temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme. In this way, the optimal arrangement scheme can be found from multiple candidate temperature sensor arrangement schemes, which can improve the accuracy of the temperature sensing data reported by the temperature sensors, thereby improving the prediction accuracy of the shell temperature and the precision of temperature control.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0020] Figure 1 A flowchart of an embodiment of the method for determining a temperature sensor arrangement provided in this disclosure;

[0021] Figure 2 This is a schematic diagram of a PCB board in a structural model of a terminal according to an exemplary embodiment of the present disclosure;

[0022] Figure 3This is a schematic diagram illustrating a candidate sensor location according to an exemplary embodiment of the present disclosure;

[0023] Figure 4 This is a schematic diagram illustrating a candidate temperature sensor arrangement scheme according to an exemplary embodiment of the present disclosure;

[0024] Figure 5 This is a schematic diagram illustrating a target temperature sensor arrangement scheme as an exemplary embodiment of the present disclosure;

[0025] Figure 6 A flowchart of Embodiment 2 of the method for determining the arrangement scheme of temperature sensors provided in this disclosure;

[0026] Figure 7 This is a schematic diagram of the structure of a first embodiment of the device for determining the arrangement scheme of temperature sensors provided in this disclosure;

[0027] Figure 8 This is a schematic diagram of the structure of a device for determining a temperature sensor arrangement scheme according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0029] The method and apparatus for determining a temperature sensor arrangement scheme provided in this disclosure can be applied to devices used to determine such arrangements. For example, the device can be a computer, PDA, mobile phone, etc.

[0030] The method, apparatus, and device disclosed herein for determining a temperature sensor arrangement scheme involve, for each candidate temperature sensor arrangement scheme corresponding to a terminal's structural model, acquiring a housing temperature prediction model corresponding to that scheme, and then, based on the prediction accuracy of each housing temperature prediction model, selecting a temperature sensor arrangement scheme that matches the structural model from among the temperature sensor arrangement schemes corresponding to each housing temperature prediction model. The housing temperature prediction model is used to predict the terminal's housing temperature based on the temperature sensing data of each temperature sensor under the given temperature sensor arrangement scheme. This allows for finding the optimal arrangement scheme from multiple candidate schemes, improving the accuracy of the temperature sensing data reported by the temperature sensors, thereby enhancing the accuracy of housing temperature prediction and improving the precision of temperature control.

[0031] Figure 1 This is a flowchart of an embodiment of the method for determining the temperature sensor arrangement provided in this disclosure. Please refer to... Figure 1 The method provided in this embodiment may include:

[0032] S101. For each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, obtain the housing temperature prediction model corresponding to the temperature sensor arrangement scheme; wherein, the housing temperature prediction model is used to predict the housing temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme.

[0033] It should be noted that the casing temperature prediction model is used to predict the casing temperature of the terminal based on the temperature data of each temperature sensor under a given temperature sensor arrangement scheme. For example, after inputting the temperature data of each temperature sensor into this casing temperature prediction model, the corresponding casing temperature can be output.

[0034] Specifically, the implementation process of this step may include:

[0035] (1) For each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, determine the temperature data of the terminal under the candidate temperature sensor arrangement scheme.

[0036] Specifically, the temperature data includes temperature sensing data and shell temperature data of the terminal under different power consumption scenarios.

[0037] The structural model of a terminal refers to the three-dimensional stacked model of the terminal structure designed during the terminal development process. For details on the specific implementation process and principles of obtaining the terminal's structural model, please refer to the descriptions in relevant technical documents; they will not be elaborated upon here.

[0038] Figure 2 This is a schematic diagram of a PCB board in a structural model of a terminal according to an exemplary embodiment of this disclosure. Please refer to... Figure 2 ,exist Figure 2 In the example shown, the terminal includes five heat-generating devices, namely a CPU chip, a 5GPA chip, an IC chip, a 4GPA chip, and a WIFI chip.

[0039] It should be noted that, in order to achieve the purpose of temperature measurement, a temperature sensor is arranged around any of the heat-generating devices in the terminal, and the temperature sensor can be arranged at any of the candidate sensor positions corresponding to the heat-generating device.

[0040] Furthermore, the candidate sensor location for a given heat-generating device is set according to actual needs. For example, Figure 3 This is a schematic diagram illustrating a candidate sensor location according to an exemplary embodiment of this disclosure. (Refer to...) Figure 3 ,exist Figure 3 In the examples shown, for instance, in a Wi-Fi chip, the candidate sensor locations corresponding to a heat-generating device could be four locations arranged in a cross shape around the heat-generating device. As another example, in a CPU chip, the candidate sensor locations corresponding to a heat-generating device could also be eight locations arranged in a star shape around the heat-generating device.

[0041] Furthermore, the candidate temperature sensor arrangement scheme corresponding to the structural model is set according to actual needs. It is related to the heat-generating devices included in the terminal and the corresponding candidate sensor positions for each heat-generating device. For example, if a terminal includes 5 heat-generating devices, and each heat-generating device has 4 candidate sensor positions (for example, these 4 positions can be four positions around the heat-generating device arranged in a cross shape), then the structural model corresponds to 20 candidate temperature sensor arrangement schemes. For example, Figure 4 This is a schematic diagram illustrating a candidate temperature sensor arrangement according to an exemplary embodiment of this disclosure. (Refer to...) Figure 4 ,exist Figure 4 In the example shown, the temperature sensors around any heating device are arranged above the heating device.

[0042] It should be noted that the temperature sensing data represents the temperature data reported by the temperature sensors. Furthermore, the temperature sensing data of the terminal under a given power consumption scenario includes the temperature data from all temperature sensors included in the terminal. Referring to the example above, when the terminal contains 5 heat-generating devices, with a temperature sensor arranged around each heat-generating device, the temperature sensing data of the terminal under a given power consumption scenario can be represented as: (T1, T2, T3, T4, T5). Correspondingly, assuming the terminal contains j heat-generating devices and there are N different power consumption scenarios, the temperature data of the terminal under a given temperature sensor arrangement can be represented as:

[0043] "Power consumption scenario 1: T11, T12, T13, ..., T1j; Case temperature data: T10;"

[0044] Power consumption scenario 2: T21, T22, T23, ..., T2j; Case temperature data: T20;

[0045] ...

[0046] Power consumption scenarios N: TN1, TN2, T33, ..., TNj; Case temperature data: TN0;

[0047] Where TNj represents the temperature data of the temperature sensor corresponding to the j-th heat-generating device in the N-th power consumption scenario.

[0048] Specific embodiments will be given below to illustrate the specific methods for determining temperature data, which will not be repeated here.

[0049] (2) Based on the temperature data of the terminal under the candidate temperature sensor arrangement scheme and the pre-built correspondence between temperature sensing data and shell temperature, determine the coefficients in the correspondence to obtain the shell temperature prediction model corresponding to the temperature sensor arrangement scheme.

[0050] It should be noted that the pre-constructed correspondence between temperature sensing data and shell temperature represents the correlation between the temperature sensing data and shell temperature (the coefficients in this correspondence are unknown). For example, in one embodiment, the pre-constructed correspondence between temperature data and shell temperature is a linear function. In another embodiment, the pre-constructed correspondence between temperature data and shell temperature is a quadratic function. The specific implementation principles and processes for constructing the correspondence between temperature sensing data and shell temperature can be found in related technical descriptions, and will not be repeated here. For example, the correspondence between temperature sensing data and shell temperature can be constructed based on the heat dissipation characteristics of the terminal under this structural model.

[0051] In practice, the coefficients in the correspondence can be obtained using temperature data from the terminal under the candidate temperature sensor arrangement scheme, thus obtaining the shell temperature prediction model. For example, in one embodiment, the correspondence can be represented by a pre-built neural network model. In this case, the neural network model can be trained using temperature data to obtain the coefficients of the neural network model, ultimately resulting in a trained shell temperature prediction model.

[0052] Continuing with the example above, for instance, the terminal has 20 candidate temperature sensor arrangement schemes. For any given scheme, the terminal's temperature data includes temperature sensing data and casing temperature data under N different power consumption scenarios. In this step, for each candidate temperature sensor arrangement scheme, the coefficients in the pre-built correspondence can be calculated using the N sets of data (the terminal's temperature data under that scheme) to obtain a casing temperature prediction model. Ultimately, this results in 20 casing temperature prediction models, each corresponding one-to-one with one of the 20 candidate sensor arrangement schemes.

[0053] S102. Based on the prediction accuracy of each of the shell temperature prediction models, select a temperature sensor arrangement scheme that matches the structural model from the temperature sensor arrangement schemes corresponding to each of the shell temperature prediction models.

[0054] In practice, the target temperature sensor arrangement scheme corresponding to the shell temperature prediction model with the highest prediction accuracy can be determined as the temperature sensor arrangement scheme that matches the structural model.

[0055] It should be noted that once the target temperature sensor arrangement scheme corresponding to the shell temperature prediction model with the highest prediction accuracy is determined as the temperature sensor arrangement scheme that matches the structural model, the temperature sensors can then be arranged according to this scheme. For example, Figure 5 This is a schematic diagram illustrating a target temperature sensor arrangement scheme as an exemplary embodiment of this disclosure. Please refer to... Figure 5 When the temperature sensor follows Figure 5 When arranged in the indicated positions, the interference between the temperature sensors will be minimized, the temperature data reported by the temperature sensors will be most accurate, and the shell temperature predicted based on the temperature data will be most accurate.

[0056] The method provided in this embodiment, for each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, obtains the shell temperature prediction model corresponding to that arrangement scheme, and then selects the temperature sensor arrangement scheme that matches the structural model from among the temperature sensor arrangement schemes corresponding to each shell temperature prediction model, based on the prediction accuracy of each model. The shell temperature prediction model is used to predict the terminal's shell temperature based on the temperature sensing data of each temperature sensor under the given arrangement scheme. In this way, the optimal arrangement scheme can be found from multiple candidate schemes, improving the accuracy of the temperature sensing data reported by the temperature sensors, thereby increasing the accuracy of shell temperature prediction and improving the precision of temperature control.

[0057] Figure 6 A flowchart of Embodiment 2 of the method for determining the arrangement scheme of temperature sensors provided in this disclosure.

[0058] Please refer to Figure 6 The method provided in this embodiment may include:

[0059] S601. After obtaining the structural model of the terminal, determine the power consumption allocation data of the terminal according to the power consumption range of each heat-generating device included in the terminal; wherein, the power consumption allocation data includes power consumption allocation information of the terminal in at least one power consumption scenario.

[0060] It should be noted that the power allocation information under a power consumption scenario includes the power consumption value corresponding to each heat-generating device under that scenario. For example, in one embodiment, assuming the terminal contains j heat-generating elements, the power allocation information of the terminal under a power consumption scenario can be represented as: P1, P1, P1, ..., Pj (Pj is the power consumption value corresponding to the j-th heat-generating device). Correspondingly, assuming the terminal corresponds to N power consumption scenarios, the power allocation data of the terminal can be represented as:

[0061] "Power consumption scenario 1: P11, P12, P13, ..., P1j;

[0062] Power consumption scenario 2: P21, P22, P23, ..., P2j;

[0063] ...

[0064] Power consumption scenarios N: PN1, PN2, PN33, ..., PNj; where PNj represents the power consumption value of the j-th heat-generating device in the Nth power consumption scenario.

[0065] Specifically, the implementation process of this step may include:

[0066] (1) For each heat-generating device, determine at least one power consumption value corresponding to the heat-generating device based on the power consumption range of the heat-generating device and the power consumption value rule corresponding to the heat-generating device.

[0067] The power consumption range of each heat-generating component is set according to actual needs. For example, the power consumption range of a CPU chip is 0-8W, that of a WIFI chip is 0-2W, that of a 4GPA chip is 0-2W, that of a 5GPA chip is 0-2W, and that of an IC chip is 0-3W.

[0068] Furthermore, the power consumption rules for each heat-generating device are set according to actual needs. This embodiment does not limit their specific content. For example, the power consumption rules for different heat-generating devices can be the same or different; this embodiment does not impose any limitations on them.

[0069] For example, in one embodiment, the power consumption value for each heating device follows the same rule: within the power consumption value range corresponding to the heating device, a power consumption value is taken at every specified interval. The specified value is set according to actual needs. For example, in one embodiment, the specified value is 0.2.

[0070] Based on the examples above, the CPU chip has 40 power consumption values, the WIFI chip has 10 power consumption values, the 4PGA chip has 10 power consumption values, the 5PGA chip has 10 power consumption values, and the IC chip has 15 power consumption values.

[0071] (2) The power consumption allocation data of the terminal is obtained by combining the power consumption values ​​of each heat-generating device.

[0072] Specifically, by utilizing the power consumption values ​​corresponding to each heat-generating device, and based on the permutation and combination method, the power consumption allocation information of the terminal under different power consumption scenarios can be obtained, and finally the power consumption allocation data of the terminal can be obtained.

[0073] In practice, for each heat-generating device, a value can be taken from the power consumption value corresponding to that heat-generating device as the power consumption value corresponding to that heat-generating device, so as to obtain the power consumption allocation information of the terminal in a power consumption scenario. Finally, the power consumption allocation information of the terminal in all power consumption scenarios is combined to obtain the power consumption allocation data of the terminal.

[0074] For example, combining the above examples, based on the power consumption values ​​of each heat-generating component, 6000 power consumption scenarios can be combined (6000 = 40 * 10 * 10 * 10 * 15). That is, the terminal's power consumption allocation data includes 6000 power consumption scenarios. For example, one power consumption scenario is: Power Consumption Scenario 5: CPU - 2W, 4PGA - 1.8W, 5PGA - 1.6W, WIFI - 0.2W, IC - 0.4W.

[0075] S602. For each candidate temperature sensor arrangement scheme corresponding to the structural model, determine the temperature data of the terminal under the temperature sensor arrangement scheme based on the power consumption allocation data and the temperature sensor distribution information under the temperature sensor arrangement scheme.

[0076] Specifically, the implementation process of this step may include:

[0077] (1) For each power consumption scenario in the power consumption allocation data, predict the temperature sensing data of the terminal in the power consumption scenario based on the power consumption allocation information and the temperature sensor distribution information.

[0078] Specifically, the temperature data of the terminal under this power consumption scenario can be predicted based on a temperature simulation model.

[0079] It should be noted that the temperature simulation model is used to predict the temperature data of the terminal under the power consumption scenario based on the power consumption allocation information and the temperature sensor distribution information (when the power consumption allocation information and the temperature sensor distribution information are input into the temperature simulation model, the temperature simulation model will output the temperature data).

[0080] For details on the specific methods for obtaining the temperature simulation model and its working principle, please refer to the descriptions in related technologies; they will not be repeated here. For example, in one possible implementation, the temperature simulation model can be pre-trained.

[0081] It should be noted that the temperature sensor distribution information refers to the location of each temperature sensor in the terminal. It should also be noted that when the power consumption allocation information is the same, but the temperature sensor distribution information is different, the temperature data output by the temperature simulation model will be different. That is, the temperature data is affected not only by the power consumption allocation information but also by the temperature sensor arrangement scheme (the location of the temperature sensors differs under different arrangement schemes).

[0082] Based on the above example, for example, for power consumption scenario 1: P11, P12, P13, ..., P1j, assuming the temperature sensor distribution information under this candidate temperature sensor arrangement scheme is as follows: Figure 4 As shown, at this time, the temperature sensing data of the terminal under this power consumption scenario can be represented as power consumption scenario 1: T11, T12, T13, ..., T1j.

[0083] (2) The maximum temperature value in the temperature sensing data of the terminal under the power consumption scenario is determined as the shell temperature data of the terminal under the power consumption scenario.

[0084] Combining the above example, for instance, in the temperature sensing data (T11, T12, T13, ..., T1j) corresponding to power consumption scenario 1, T13 is the maximum temperature value. In this case, the casing temperature data of the terminal under power consumption scenario 1 is determined to be T13.

[0085] (3) Use the temperature sensing data and shell temperature data of the terminal under different power consumption scenarios to construct the temperature data of the terminal under the temperature sensor arrangement scheme.

[0086] In practice, the temperature data from the terminal under different power consumption scenarios and the shell temperature data are combined to obtain the temperature data of the terminal under this temperature sensor arrangement scheme, specifically represented as follows:

[0087] Power consumption scenario 1: T11, T12, T13, ..., T1j; Case temperature data: T10 (where T10 is the maximum value among T11, T12, T13, ..., T1j);

[0088] Power consumption scenario 2: T21, T22, T23, ..., T2j; Case temperature data: T20;

[0089] ...

[0090] Power consumption scenarios N: TN1, TN2, T33, ..., TNj; Case temperature data: TN0;

[0091] Where TNj represents the temperature sensing data of the j-th heat-generating device under the N-th power consumption scenario.

[0092] S603. For each candidate temperature sensor arrangement scheme, based on the temperature sensing data of the terminal under the temperature sensor arrangement scheme and the pre-built correspondence between the temperature sensing data and the shell temperature, determine the coefficients in the correspondence to obtain the shell temperature prediction model corresponding to the temperature sensor arrangement scheme.

[0093] The specific implementation principle and process of step S603 can be found in the description in the previous embodiments, and will not be repeated here.

[0094] S604. Evaluate the prediction accuracy of each shell temperature prediction model, and determine the target temperature sensor arrangement scheme corresponding to the target shell temperature prediction model with the highest prediction accuracy as the temperature sensor arrangement scheme that matches the structural model.

[0095] Specifically, in one embodiment, the prediction accuracy of each shell temperature prediction model can be evaluated using the temperature data corresponding to that shell temperature prediction model.

[0096] It should be noted that the temperature data corresponding to the shell temperature prediction model is the same temperature data used when determining the shell temperature prediction model.

[0097] In addition, in a specific implementation, the goodness of fit and average error of the shell temperature prediction model can be calculated using the temperature data corresponding to the shell temperature prediction model, and then the prediction accuracy of the shell temperature prediction model can be evaluated based on the goodness of fit and the average error.

[0098] The following is a brief description of the specific calculation method for the goodness of fit and average error of a shell temperature prediction model. This method may include:

[0099] (1) For the shell temperature prediction model, each set of temperature sensing data in the temperature data corresponding to the shell temperature prediction model is input into the shell temperature prediction model to obtain the shell temperature prediction value corresponding to each set of temperature sensing data.

[0100] (2) Calculate the goodness of fit and average error based on the predicted shell temperature value corresponding to each set of temperature sensing data and the shell temperature data corresponding to each set of temperature sensing data recorded in the temperature data.

[0101] Specifically, the goodness of fit R can be calculated according to Formula 1. 2 :

[0102] Formula 1: R 2 =1-S1 / S2

[0103] in, Where yi is the shell temperature data recorded in the temperature data; fi is the predicted shell temperature value corresponding to the temperature sensing data; y0 is the average value of all shell temperature data in the temperature sensing dataset; and n is the number of power consumption scenarios included in the temperature data.

[0104] Calculate the average error w according to Formula 2:

[0105] Formula 2:

[0106] Furthermore, when evaluating the prediction accuracy of the shell temperature prediction model based on goodness of fit and average error, candidate shell temperature prediction models with average error less than a specified value can be selected from all shell temperature prediction models. Then, the shell temperature prediction model with the highest goodness of fit among the candidate shell temperature prediction models can be determined as the shell temperature prediction model with the highest prediction accuracy.

[0107] The method provided in this embodiment, based on an exhaustive power consumption approach, can pre-determine the power consumption allocation data of the terminal. Then, for each candidate temperature sensor arrangement scheme, based on the terminal's power consumption allocation data and the temperature sensor distribution information under that candidate arrangement scheme, the temperature data of the terminal under that scheme is determined. Using this temperature data, and a pre-built correspondence between the temperature data and the casing temperature, a casing temperature prediction model corresponding to that arrangement scheme is determined. Thus, the target temperature sensor arrangement scheme corresponding to the casing temperature prediction model with the highest prediction accuracy among multiple models is determined as the temperature sensor arrangement scheme matching the structural model. In this way, the optimal arrangement scheme can be found from multiple candidate schemes. Since the placement of the temperature sensors directly affects the accuracy of the reported temperature data, the accuracy of the reported temperature data can be improved, thereby increasing the accuracy of casing temperature prediction and improving the precision of temperature control.

[0108] Corresponding to the embodiments of the method for determining the arrangement of temperature sensors described above, this disclosure also provides embodiments of an apparatus for determining the arrangement of temperature sensors.

[0109] Figure 7 This is a schematic diagram of an embodiment of the device for determining the location of temperature sensors provided in this disclosure. Please refer to... Figure 7 The apparatus provided in this embodiment may include an acquisition module 710 and a processing module 720, wherein...

[0110] The acquisition module 710 is used to acquire a housing temperature prediction model corresponding to each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal for each candidate temperature sensor arrangement scheme; wherein, the housing temperature prediction model is used to predict the housing temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme.

[0111] The processing module 720 is used to select a temperature sensor arrangement scheme that matches the structural model from the temperature sensor arrangement schemes corresponding to each of the shell temperature prediction models based on the prediction accuracy of each shell temperature prediction model.

[0112] The apparatus of this embodiment can be used to perform... Figure 1The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0113] Furthermore, the acquisition module 710 is also used to determine the temperature data of the terminal under each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, and to determine the coefficients in the correspondence relationship based on the temperature data of the terminal under the candidate temperature sensor arrangement scheme and the pre-built correspondence relationship between temperature sensing data and shell temperature, so as to obtain the shell temperature prediction model corresponding to the temperature sensor arrangement scheme.

[0114] Furthermore, the acquisition module is also used to determine the power consumption allocation data of the terminal based on the power consumption range of each heat-generating device included in the terminal before determining the temperature data of the terminal under the temperature sensor arrangement scheme; wherein, the power consumption allocation data includes power consumption allocation information of the terminal under at least one power consumption scenario;

[0115] The acquisition module 710 is further configured to determine the temperature data of the terminal under the temperature sensor arrangement scheme based on the power consumption allocation data and the temperature sensor distribution information under the temperature sensor arrangement scheme.

[0116] Furthermore, the acquisition module 710 is also specifically used to determine at least one power consumption value corresponding to each heat-generating device based on the power consumption range of the heat-generating device and the power consumption value rule corresponding to the heat-generating device, and to combine the power consumption values ​​corresponding to each heat-generating device to generate the power consumption allocation data of the terminal.

[0117] Furthermore, the acquisition module 710 is also specifically used for:

[0118] For each power consumption scenario in the power consumption allocation data, the temperature sensing data of the terminal under that power consumption scenario is predicted based on the power consumption allocation information and the temperature sensor distribution information.

[0119] The maximum temperature value in the temperature sensing data of the terminal under this power consumption scenario is determined as the shell temperature data of the terminal under this power consumption scenario.

[0120] Temperature data of the terminal under this temperature sensor arrangement scheme is constructed by using the temperature sensing data and shell temperature data of the terminal in different scenarios.

[0121] Furthermore, the prediction accuracy of each of the aforementioned shell temperature prediction models is calculated using the following method:

[0122] For each shell temperature prediction model, the prediction accuracy of the shell temperature prediction model is evaluated using the temperature data corresponding to that model.

[0123] Furthermore, the processing module 720 is also used to calculate the goodness of fit and average error of the shell temperature prediction model using temperature data corresponding to the shell temperature prediction model, and to evaluate the prediction accuracy of the shell temperature prediction model based on the goodness of fit and the average error.

[0124] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments of the relevant methods, and will not be elaborated upon here.

[0125] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0126] Embodiments of this disclosure also provide an apparatus for determining a temperature sensor arrangement scheme, comprising:

[0127] processor;

[0128] Memory used to store processor-executable instructions;

[0129] The processor is configured to implement the method described in any of the above embodiments.

[0130] Embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any of the above embodiments.

[0131] Figure 8 This is a schematic diagram of a device for determining a temperature sensor arrangement scheme according to an exemplary embodiment of this disclosure. Please refer to... Figure 8 This device can be a computer, tablet, personal digital assistant, etc. The following explanation uses a computer as an example.

[0132] Reference Figure 8The computer device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0133] Processing component 802 typically controls the overall operation of computer device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0134] Memory 804 is configured to store various types of data to support the operation of computer device 800. Examples of such data include instructions for any application or method operating on computer device 800, contact data, phone book data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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 storage, flash memory, magnetic disk, or optical disk.

[0135] Power supply component 806 provides power to various components of computer device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to computer device 800.

[0136] Multimedia component 808 includes a screen that provides an output interface between the computer device 800 and the user. 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 touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the computer device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0137] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when computer device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0138] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0139] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of computer device 800. For example, sensor assembly 814 may detect the on / off state of computer device 800, the relative positioning of components such as the display and keypad of computer device 800, changes in position of computer device 800 or a component of computer device 800, the presence or absence of user contact with computer device 800, the orientation or acceleration / deceleration of computer device 800, and temperature changes of computer device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0140] Communication component 816 is configured to facilitate wired or wireless communication between computer device 800 and other devices. Computer device 800 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G LTE, 5G NR, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. 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.

[0141] In an exemplary embodiment, the computer device 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 components to perform the methods described in any of the above embodiments.

[0142] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory *04 including instructions, which can be executed by a processor *20 of the device *00 to perform the above method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0143] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0144] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining a temperature sensor arrangement scheme, characterized in that, The method includes: For each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, a housing temperature prediction model corresponding to the temperature sensor arrangement scheme is obtained; wherein, the housing temperature prediction model is used to predict the housing temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme. Based on the prediction accuracy of each of the shell temperature prediction models, a temperature sensor arrangement scheme that matches the structural model is selected from the temperature sensor arrangement schemes corresponding to each of the shell temperature prediction models. The step of obtaining the housing temperature prediction model corresponding to the temperature sensor arrangement scheme includes: For each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, determine the temperature data of the terminal under that candidate temperature sensor arrangement scheme. Based on the temperature data of the terminal under the candidate temperature sensor arrangement scheme, and the pre-built correspondence between temperature sensing data and shell temperature, the coefficients in the correspondence are determined to obtain the shell temperature prediction model corresponding to the temperature sensor arrangement scheme, wherein the correspondence includes a pre-built neural network model.

2. The method according to claim 1, characterized in that, Before determining the temperature data of the terminal under the temperature sensor arrangement scheme, the method further includes: Based on the power consumption range of each heat-generating device included in the terminal, the power consumption allocation data of the terminal is determined; wherein, the power consumption allocation data includes power consumption allocation information of the terminal in at least one power consumption scenario; Determining the temperature data of the terminal under this temperature sensor arrangement includes: Based on the power consumption allocation data and the temperature sensor distribution information under the temperature sensor arrangement scheme, the temperature data of the terminal under the temperature sensor arrangement scheme is determined.

3. The method according to claim 2, characterized in that, The step of determining the power allocation data of the terminal based on the power consumption range of each heat-generating device included in the terminal includes: For each heat-generating device, based on the power consumption range of the heat-generating device and the power consumption value rules corresponding to the heat-generating device, at least one power consumption value corresponding to the heat-generating device is determined. The power consumption allocation data of the terminal is obtained by combining the power consumption values ​​corresponding to each heat-generating device.

4. The method according to claim 2, characterized in that, The step of determining the temperature data of the terminal under the temperature sensor arrangement scheme based on the power consumption allocation data and the temperature sensor distribution information under the temperature sensor arrangement scheme includes: For each power consumption scenario in the power consumption allocation data, the temperature sensing data of the terminal under that power consumption scenario is predicted based on the power consumption allocation information and the temperature sensor distribution information. The maximum temperature value in the temperature sensing data of the terminal under this power consumption scenario is determined as the shell temperature data of the terminal under this power consumption scenario. Temperature data of the terminal under this temperature sensor arrangement scheme is constructed by using the temperature sensing data and shell temperature data of the terminal in different scenarios.

5. The method according to claim 1, characterized in that, The prediction accuracy of each of the aforementioned shell temperature prediction models was calculated using the following method: For each shell temperature prediction model, the prediction accuracy of the shell temperature prediction model is evaluated using the temperature data corresponding to that model.

6. The method according to claim 5, wherein evaluating the prediction accuracy of the shell temperature prediction model using the target temperature data corresponding to the shell temperature prediction model includes: Using the temperature data corresponding to the shell temperature prediction model, calculate the goodness of fit and average error of the shell temperature prediction model; The prediction accuracy of the shell temperature prediction model is evaluated based on the goodness of fit and the average error.

7. An apparatus for determining a temperature sensor arrangement scheme, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to acquire a housing temperature prediction model corresponding to each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal for each candidate temperature sensor arrangement scheme; wherein, the housing temperature prediction model is used to predict the housing temperature of the terminal based on the temperature sensing data of each temperature sensor under the temperature sensor arrangement scheme. The processing module is used to select a temperature sensor arrangement scheme that matches the structural model from the temperature sensor arrangement schemes corresponding to each of the shell temperature prediction models, based on the prediction accuracy of each shell temperature prediction model. The acquisition module is also used for: For each candidate temperature sensor arrangement scheme corresponding to the structural model of the terminal, determine the temperature data of the terminal under that candidate temperature sensor arrangement scheme. Based on the temperature data of the terminal under the candidate temperature sensor arrangement scheme, and the pre-built correspondence between temperature sensing data and shell temperature, the coefficients in the correspondence are determined to obtain the shell temperature prediction model corresponding to the temperature sensor arrangement scheme, wherein the correspondence includes a pre-built neural network model.

8. A device for determining a temperature sensor arrangement scheme, characterized in that, The device includes: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of any one of claims 1 to 6.

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