Method and device for monitoring processor temperature, storage medium and electronic device
By collecting temperature data from multiple points outside the processor and calculating correlation coefficients, representative temperature data are selected, solving the problem of low measurement accuracy caused by different manufacturers' strategies, and achieving high-accuracy and universality monitoring of processor temperature.
Patent Information
- Application Number
- CN202310116886.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-02-10
- Publication Date
- 2025-12-30
- Estimated Expiration
- 2043-02-10
AI Technical Summary
In the existing technology, different processor manufacturers have different temperature measurement strategies, which means that temperature sensors need to be adapted to different strategies when measuring processor temperature, resulting in low accuracy of measurement results.
By collecting external and core temperature data of the processor at multiple preset points within a preset time period, calculating the temperature correlation coefficient, and selecting the temperature data at the point corresponding to the maximum temperature correlation coefficient as the processor temperature, it is applicable to processors of any manufacturer and model.
It improves the accuracy of processor temperature monitoring and achieves universality for different processors, applicable to processors of any manufacturer and model.
Smart Images
Figure CN116166502B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of computer research and development, in particular to a processor temperature monitoring method and device, a storage medium and an electronic device. BACKGROUND
[0002] Today, personal computers have been popularized, with the progress of the times, computer hardware is still in continuous updating and iteration, with the updating of hardware, the power consumption is also increasing, and the processor temperature is rising accordingly, therefore, how to quickly complete the processor heat dissipation has become an important research direction of computer research and development. Then, the main way to measure the processor heat dissipation capacity is to accurately monitor the temperature of the processor.
[0003] At present, the existing processor temperature monitoring method mainly measures the temperature of the processor by connecting the temperature sensor to the corresponding pin according to the temperature measurement strategy provided by the processor manufacturer. However, since the temperature measurement strategies of different processor manufacturers are different, the temperature sensor needs to be connected to different pins according to the temperature measurement strategy of the corresponding processor manufacturer when measuring the temperature of the processor. In this way, the temperature of the processor can be accurately measured only by adapting the temperature measurement strategy of different processor manufacturers, otherwise the accuracy of the measured processor temperature will be low. SUMMARY
[0004] Therefore, the present application provides a processor temperature monitoring method and device, a storage medium and an electronic device, which mainly aims to improve the technical problem that the temperature measurement strategies of different processor manufacturers are different, and the temperature of the processor can be accurately measured only by adapting the temperature measurement strategy of different processor manufacturers when measuring the temperature of the processor, otherwise the accuracy of the measured processor temperature will be low.
[0005] According to one aspect of the present application, a processor temperature monitoring method is provided, comprising:
[0006] In a preset time period, the external temperature data of the target processor is collected at a plurality of preset points according to a preset time interval, and a processor external temperature data set corresponding to each preset point is generated;
[0007] In the preset time period, the core temperature data of the target processor is collected according to the preset time interval, and a processor core temperature data set of the target processor is generated;
[0008] The temperature correlation coefficients of each preset point are calculated according to each processor external temperature data set and the processor core temperature data set respectively, and the maximum temperature correlation coefficient in the temperature correlation coefficients of each preset point is screened out, and the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor.
[0009] Preferably, the temperature correlation coefficient is used to represent the covariance data of the processor external temperature data set after standardization.
[0010] Preferably, the temperature correlation coefficients of each preset point are calculated according to each processor external temperature data set and the processor core temperature data set respectively, and the maximum temperature correlation coefficient in the temperature correlation coefficients of each preset point is screened out, and the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor.
[0011] The covariance data of each processor external temperature data set is divided by the standard deviation data of the processor external temperature data set and the standard deviation data of the processor core temperature data set in turn to obtain the temperature correlation coefficient of each preset point.
[0012] Preferably, before the temperature correlation coefficients of each preset point are calculated according to each processor external temperature data set and the processor core temperature data set, the method further comprises:
[0013] The standard deviation data of each processor external temperature data set and the standard deviation data of the processor core temperature data set are calculated respectively.
[0014] Preferably, the method further comprises:
[0015] When collecting the external temperature data and the core temperature data of the target processor, the task processing amount of the target processor is randomly increased so that the processor temperature of the target processor changes within the preset time length.
[0016] Preferably, after the maximum temperature correlation coefficient in the temperature correlation coefficients of each preset point is screened out, and the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor, the method further comprises:
[0017] The preset heat dissipation equipment parameter reference table is queried according to the processor temperature data of the target processor to adjust the parameters of the heat dissipation equipment.
[0018] Preferably, the external temperature data of the target processor is collected based on a temperature sensor connected to the target processor;
[0019] The core temperature data of the target processor is obtained based on a preset processor core temperature acquisition strategy.
[0020] According to another aspect of this application, a processor temperature monitoring device is provided, comprising:
[0021] The first acquisition module is used to acquire the external temperature data of the target processor at multiple preset points within a preset time period and at preset time intervals, and generate the processor external temperature dataset corresponding to each preset point.
[0022] The second acquisition module is used to acquire the core temperature data of the target processor at the preset time interval within the preset duration, and generate the processor core temperature dataset of the target processor.
[0023] The determination module is used to calculate the temperature correlation coefficient of each preset point based on each processor external temperature dataset and the processor core temperature dataset, and to filter out the maximum temperature correlation coefficient among the temperature correlation coefficients of each preset point, and to determine the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor.
[0024] Preferably, the temperature correlation coefficient is used to characterize the covariance data of the processor external temperature dataset after standardization.
[0025] Preferably, the determining module is specifically used for:
[0026] The covariance data of each processor external temperature dataset is divided sequentially by the standard deviation data of the processor external temperature dataset and the standard deviation data of the processor core temperature dataset to obtain the temperature correlation coefficient of each preset point.
[0027] Preferably, before the determining module, the device further includes:
[0028] The calculation module is used to calculate the standard deviation data of each of the processor external temperature datasets and the standard deviation data of the processor core temperature dataset.
[0029] Preferably, the device further includes:
[0030] An addition module is used to randomly increase the workload of the target processor when collecting external temperature data and core temperature data of the target processor, so that the processor temperature of the target processor changes within the preset time period.
[0031] Preferably, after the determining module, the device further includes:
[0032] The query module is used to query a preset heat dissipation device parameter comparison table based on the processor temperature data of the target processor, so as to adjust the parameters of the heat dissipation device.
[0033] Preferably, the external temperature data of the target processor is obtained based on the temperature sensor connected to the target processor;
[0034] The core temperature data of the target processor is obtained based on a preset processor core temperature acquisition strategy.
[0035] According to another aspect of this application, a storage medium is provided that stores at least one executable instruction, which causes a processor to perform an operation corresponding to the processor temperature monitoring method described above.
[0036] According to another aspect of this application, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;
[0037] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the processor temperature monitoring method described above.
[0038] By employing the above technical solutions, the technical solutions provided in the embodiments of this application have at least the following advantages:
[0039] This application provides a method, apparatus, storage medium, and electronic device for monitoring processor temperature. First, within a preset time period, external temperature data of a target processor is collected at multiple preset points at preset time intervals, generating processor external temperature datasets corresponding to each preset point. Second, within the preset time period, core temperature data of the target processor is collected at the preset time intervals, generating processor core temperature datasets for the target processor. Finally, based on each processor external temperature dataset and the processor core temperature dataset, temperature correlation coefficients are calculated for each preset point, and the maximum temperature correlation coefficient among the temperature correlation coefficients for each preset point is selected. The processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor. Compared with existing technologies, this application's embodiment collects temperature data for a preset time period at different points outside the processor using temperature sensors, calculates the temperature correlation coefficient between the temperature datasets at each point and the processor core temperature dataset for this period, and further determines the processor external temperature data at the point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor. This improves the accuracy of processor temperature monitoring and is widely applicable to processors of any manufacturer and model, achieving universality.
[0040] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description
[0041] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:
[0042] Figure 1 A flowchart of a processor temperature monitoring method provided in an embodiment of this application is shown;
[0043] Figure 2 A flowchart of another processor temperature monitoring method provided in an embodiment of this application is shown;
[0044] Figure 3 A block diagram of a processor temperature monitoring device provided in an embodiment of this application is shown;
[0045] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation
[0046] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
[0047] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.
[0048] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.
[0049] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.
[0050] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.
[0051] The embodiments of this application can be applied to computer systems / servers that can operate with a wide range of other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations suitable for use with computer systems / servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems, etc.
[0052] Computer systems / servers can be described in the general context of computer system executable instructions (such as program modules) executed by the computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are performed by remote processing devices linked through a communication network. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.
[0053] This application provides a method for monitoring processor temperature, such as...Figure 1 As shown, the method includes:
[0054] 101. Within a preset time period, collect the external temperature data of the target processor at multiple preset locations according to the preset time interval, and generate the processor external temperature dataset corresponding to each preset location.
[0055] The preset points are used to characterize the locations of logical device registers that may represent processor temperature, selected in advance according to the processor datasheet. External temperature data can be collected by temperature sensors arranged around the processor. In this embodiment, the current execution terminal can be used to monitor the operating status of computer hardware. For example, the temperature data at these preset points is read and recorded every 1 second (preset time interval) until 30 seconds (preset duration). Furthermore, a processor external temperature dataset corresponding to each preset point is generated.
[0056] 102. Within a preset duration, collect the core temperature data of the target processor at preset time intervals to generate the processor core temperature dataset of the target processor.
[0057] The core temperature data can be collected based on the processor manufacturer's preset core temperature reading strategy. In this embodiment, for example, the processor's core temperature data is read and recorded every 1 second (preset time interval) until 30 seconds (preset duration) are reached, and then a processor core temperature dataset is generated.
[0058] 103. Calculate the temperature correlation coefficient of each preset point based on the external temperature dataset of each processor and the core temperature dataset of each processor, and select the maximum temperature correlation coefficient among the temperature correlation coefficients of each preset point. Determine the external temperature data of the processor at the preset point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor.
[0059] The temperature correlation coefficient is used to characterize the covariance data of the processor's external temperature dataset after standardization. It reflects the similarity between the processor's external temperature data and the processor's core temperature data per unit change. Since there are differences in the processor's external temperature collected at various preset points, and it is impossible to accurately determine which preset point best represents the processor's temperature, this embodiment selects the temperature correlation coefficient with the highest coefficient from the temperature correlation coefficients of each preset point. The temperature change trend of this preset point is the one most similar to the processor's core temperature change trend, and therefore, the one most representative of the processor's temperature.
[0060] Compared with the prior art, the embodiments of this application collect temperature data for a preset duration at different points outside the processor using temperature sensors, and calculate the temperature correlation coefficient between the temperature dataset at each point and the processor core temperature dataset for this duration. Furthermore, the processor external temperature data at the point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor, which improves the accuracy of processor temperature monitoring and is widely applicable to processors of any manufacturer and model, achieving universality.
[0061] This application provides another method for monitoring processor temperature, such as... Figure 2 As shown, the method includes:
[0062] 201. Within a preset time period, collect the external temperature data of the target processor at multiple preset locations according to the preset time interval, and generate the processor external temperature dataset corresponding to each preset location.
[0063] The external temperature data of the target processor is obtained based on the temperature sensor connected to the target processor.
[0064] In this embodiment, the locations of logic device registers that may represent processor temperature are first selected as preset points according to the processor datasheet. Temperature sensors are then connected to thermistors or thermistors at these preset points via pins to collect temperature data. For example, the temperature data at these preset points is read and recorded every 1 second (preset time interval) until 30 seconds (preset duration). Further, a processor external temperature dataset corresponding to each preset point is generated.
[0065] As a preferred embodiment, in this application embodiment, when collecting the external temperature data and core temperature data of the target processor, the task processing volume of the target processor is randomly increased so that the processor temperature of the target processor changes within a preset time period.
[0066] It should be noted that, generally, if the processor has strong heat dissipation capabilities, the variation in processor core temperature data under low load will be relatively small, resulting in a negligible difference between the core temperature and external temperature data. This can reduce the accuracy of methods for determining processor temperature based on temperature correlation coefficients. Therefore, in this embodiment, determining whether an address represents CPU temperature based on the correlation coefficient value would decrease the accuracy of the result. During the temperature data acquisition phase, by increasing the processor's workload (i.e., increasing the processor's data processing pressure), the processor core temperature is raised, causing a more significant change in the external temperature data collected by the temperature sensor, thereby improving the accuracy of processor temperature determination.
[0067] 202. Within a preset duration, collect the core temperature data of the target processor at preset time intervals to generate the processor core temperature dataset of the target processor.
[0068] The core temperature data of the target processor is collected based on a preset processor core temperature acquisition strategy. In this embodiment, while collecting the external temperature data of the processor in step 201, the processor core temperature data of the same duration is collected at the same time interval.
[0069] 203. Calculate the standard deviation of the external temperature dataset and the standard deviation of the core temperature dataset for each processor.
[0070] In this embodiment, the standard deviation of each processor external temperature dataset generated in step 201 of the embodiment and the standard deviation of the processor core temperature dataset generated in step 202 of the embodiment are calculated. Specifically, the standard deviation formula is: Where X represents temperature data, μ x This represents the average value of the temperature dataset.
[0071] 204. Calculate the temperature correlation coefficients of each preset point based on the external temperature dataset of each processor and the core temperature dataset of each processor.
[0072] The temperature correlation coefficient is used to characterize the covariance data of the processor's external temperature dataset after standardization. Specifically, it can be determined using the formula... Calculate the temperature correlation coefficient for each preset point, where Cov(X,Y) represents the covariance between the processor external temperature dataset and the processor core temperature dataset, and σ X σ represents the standard deviation data of the processor external temperature dataset. Y This represents the standard deviation of the processor core temperature dataset.
[0073] Accordingly, step 204 of the embodiment specifically includes: dividing the covariance data of each processor external temperature dataset by the standard deviation data of the processor external temperature dataset and the standard deviation data of the processor core temperature dataset in turn to obtain the temperature correlation coefficient of each preset point.
[0074] 205. Filter out the maximum temperature correlation coefficient among the temperature correlation coefficients of each preset point, and determine the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor.
[0075] It should be noted that, since there are differences in the external processor temperatures collected at various preset points, and it is impossible to accurately determine which preset point's external processor temperature is most representative of the processor temperature, in this embodiment of the application, the maximum temperature correlation coefficient is selected from the temperature correlation coefficients of various preset points. The temperature change trend of this preset point is the one that is most similar to the processor core temperature change trend, which is the one that is most representative of the processor temperature.
[0076] 206. Based on the processor temperature data of the target processor, query the preset heat dissipation equipment parameter comparison table to adjust the parameters of the heat dissipation equipment.
[0077] The heat dissipation equipment parameter reference table records the mapping relationship between processor temperature data and heat dissipation equipment parameters. For example, when the processor temperature is 50°C, the cooling fan speed needs to reach 300 r / s. In this embodiment, determining the heat dissipation equipment parameters based on the processor temperature can effectively reduce the risk of computer hardware damage due to excessive temperature.
[0078] This application provides a method for monitoring processor temperature. First, within a preset time period, external temperature data of the target processor is collected at multiple preset locations at preset time intervals, generating processor external temperature datasets corresponding to each preset location. Second, within the preset time period, core temperature data of the target processor is collected at the preset time intervals, generating processor core temperature datasets for the target processor. Finally, based on the processor external temperature datasets and the processor core temperature datasets, temperature correlation coefficients are calculated for each preset location, and the maximum temperature correlation coefficient among the temperature correlation coefficients for each preset location is selected. The processor external temperature data at the preset location corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor. Compared with existing technologies, this application's embodiment collects temperature data for a preset time period at different locations outside the processor using temperature sensors, calculates the temperature correlation coefficient between the temperature datasets at each location and the processor core temperature dataset for this period, and further determines the processor external temperature data at the location corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor. This improves the accuracy of processor temperature monitoring and is widely applicable to processors of any manufacturer and model, achieving universality.
[0079] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this application provides a processor temperature monitoring device, such as... Figure 3 As shown, the device includes:
[0080] First acquisition module 31, second acquisition module 32, and determination module 33.
[0081] The first acquisition module 31 is used to acquire the external temperature data of the target processor at multiple preset points within a preset time period and at preset time intervals, and generate the processor external temperature dataset corresponding to each preset point.
[0082] The second acquisition module 32 is used to acquire the core temperature data of the target processor at the preset time interval within the preset duration, and generate the processor core temperature dataset of the target processor.
[0083] The determination module 33 is used to calculate the temperature correlation coefficient of each preset point according to each processor external temperature dataset and the processor core temperature dataset, and to filter out the maximum temperature correlation coefficient among the temperature correlation coefficients of each preset point, and to determine the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor.
[0084] Preferably, the temperature correlation coefficient is used to characterize the covariance data of the processor external temperature dataset after standardization.
[0085] Preferably, the determining module is specifically used for:
[0086] The covariance data of each processor external temperature dataset is divided sequentially by the standard deviation data of the processor external temperature dataset and the standard deviation data of the processor core temperature dataset to obtain the temperature correlation coefficient of each preset point.
[0087] Preferably, before the determining module, the device further includes:
[0088] The calculation module is used to calculate the standard deviation data of each of the processor external temperature datasets and the standard deviation data of the processor core temperature dataset.
[0089] Preferably, the device further includes:
[0090] An addition module is used to randomly increase the workload of the target processor when collecting external temperature data and core temperature data of the target processor, so that the processor temperature of the target processor changes within the preset time period.
[0091] Preferably, after the determining module, the device further includes:
[0092] The query module is used to query a preset heat dissipation device parameter comparison table based on the processor temperature data of the target processor, so as to adjust the parameters of the heat dissipation device.
[0093] Preferably, the external temperature data of the target processor is obtained based on the temperature sensor connected to the target processor;
[0094] The core temperature data of the target processor is obtained based on a preset processor core temperature acquisition strategy.
[0095] This application provides a processor temperature monitoring device. First, within a preset time period, external temperature data of a target processor is collected at multiple preset locations at preset time intervals, generating processor external temperature datasets corresponding to each preset location. Second, within the preset time period, core temperature data of the target processor is collected at the preset time intervals, generating processor core temperature datasets for the target processor. Finally, based on the processor external temperature datasets and the processor core temperature datasets, temperature correlation coefficients are calculated for each preset location, and the maximum temperature correlation coefficient among the temperature correlation coefficients for each preset location is selected. The processor external temperature data at the preset location corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor. Compared with existing technologies, this application embodiment collects temperature data for a preset time period at different locations outside the processor using temperature sensors, calculates the temperature correlation coefficient between the temperature datasets at each location and the processor core temperature dataset for this period, and further determines the processor external temperature data at the location corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor. This improves the accuracy of processor temperature monitoring and is widely applicable to processors of any manufacturer and model, achieving universality.
[0096] According to one embodiment of this application, a storage medium is provided, the storage medium storing at least one executable instruction, the computer-executable instruction being able to execute the processor temperature monitoring method in any of the above method embodiments.
[0097] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.
[0098] Figure 4 The diagram shows a structural schematic of an electronic device according to one embodiment of the present application. The specific embodiments of the present application do not limit the specific implementation of the electronic device.
[0099] like Figure 4 As shown, the electronic device may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.
[0100] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.
[0101] Communication interface 404 is used to communicate with other network elements such as clients or other servers.
[0102] The processor 402 is used to execute program 410, specifically the relevant steps in the above-described embodiment of the processor temperature monitoring method.
[0103] Specifically, program 410 may include program code that includes computer operation instructions.
[0104] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The computer device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.
[0105] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0106] Specifically, program 410 can be used to cause processor 402 to perform the following operations:
[0107] Within a preset time period, external temperature data of the target processor is collected at multiple preset locations at preset time intervals to generate processor external temperature datasets corresponding to each preset location.
[0108] Within the preset duration, core temperature data of the target processor is collected at the preset time interval to generate a processor core temperature dataset of the target processor.
[0109] Calculate the temperature correlation coefficient of each preset point based on the external temperature dataset of each processor and the core temperature dataset of the processor, and select the maximum temperature correlation coefficient among the temperature correlation coefficients of each preset point. Then, determine the external temperature data of the processor at the preset point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor.
[0110] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device for monitoring the processor temperature, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.
[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0112] The methods and systems of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this application are not limited to the order specifically described above, unless otherwise specifically stated. Furthermore, in some embodiments, this application may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this application. Thus, this application also covers recording media storing programs for performing the methods according to this application.
[0113] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0114] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A method of monitoring processor temperature, the method comprising: The method comprises the following steps: In a preset time period, external temperature data of the target processor is collected at a plurality of preset points according to a preset time interval to generate a processor external temperature data set corresponding to each of the preset points; In the preset time period, core temperature data of the target processor is collected according to the preset time interval to generate a processor core temperature data set of the target processor; A temperature correlation coefficient of each of the preset points is calculated according to each of the processor external temperature data sets and the processor core temperature data set, and a maximum temperature correlation coefficient in the temperature correlation coefficients of each of the preset points is screened out, and the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor; The temperature correlation coefficient is used to represent the covariance data of the normalized processor external temperature data set; The temperature correlation coefficient of each of the preset points is calculated according to each of the processor external temperature data sets and the processor core temperature data set, and a maximum temperature correlation coefficient in the temperature correlation coefficients of each of the preset points is screened out, and the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor; The method further comprises: When collecting the external temperature data and the core temperature data of the target processor, the task processing amount of the target processor is randomly increased so that the processor temperature of the target processor changes in the preset time period. After the maximum temperature correlation coefficient in the temperature correlation coefficients of each of the preset points is screened out, and the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient is determined as the processor temperature data of the target processor, the method further comprises: The processor temperature data of the target processor is used to query a preset heat dissipation equipment parameter reference table to adjust the parameters of the heat dissipation equipment.
2. The method of claim 1, wherein, The external temperature data of the target processor is collected based on a temperature sensor connected to the target processor; The core temperature data of the target processor is collected based on a preset processor core temperature acquisition strategy.
3. The method of claim 1, wherein, The method comprises the following steps: A first collection module is configured to collect external temperature data of a target processor at a plurality of preset points according to a preset time interval in a preset time period to generate a processor external temperature data set corresponding to each of the preset points; 4. A processor temperature monitoring apparatus, characterized by comprising: A second collection module is configured to collect core temperature data of the target processor according to the preset time interval in the preset time period to generate a processor core temperature data set of the target processor; A calculation module is configured to calculate standard deviation data of each of the processor external temperature data sets and standard deviation data of the processor core temperature data set. The determining module is configured to calculate a temperature correlation coefficient of each preset point according to each processor external temperature data set and the processor core temperature data set respectively, and to filter out a maximum temperature correlation coefficient from the temperature correlation coefficients of the preset points, and to determine the processor external temperature data at the preset point corresponding to the maximum temperature correlation coefficient as the processor temperature data of the target processor, wherein the temperature correlation coefficient is used to represent the covariance data of the normalized processor external temperature data set; The determining module is specifically configured to divide the covariance data of each processor external temperature data set by the standard deviation data of the processor external temperature data set and the standard deviation data of the processor core temperature data set in sequence to obtain the temperature correlation coefficient of each preset point. The device further comprises: The adding module is configured to randomly increase the task processing amount of the target processor when collecting the external temperature data and the core temperature data of the target processor, so that the processor temperature of the target processor changes within the preset time length.
5. A storage medium having stored therein at least one executable instruction, characterized in that, The executable instructions enable the processor to perform operations corresponding to the processor temperature monitoring method of any one of claims 1-3.
6. An electronic device comprising: The processor, the memory, the communication interface, and the communication bus, wherein the processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is configured to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the processor temperature monitoring method of any one of claims 1-3.
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