A graphics card running condition recognition method and system, and a storage medium

Abstract

This invention provides a method, system, and storage medium for identifying the operating conditions of a graphics card, including: collecting time series of multiple operating parameters during graphics card operation; selecting a corresponding classification model for each operating parameter, inputting the time series of each operating parameter into the corresponding classification model, and obtaining a pre-judgment result for each operating parameter; querying the confidence matrix corresponding to each classification model in a database based on the classification model corresponding to each operating parameter; constructing a BPA function for each classification model based on the confidence matrix, and calculating the fused BPA function value for each operating condition; and determining the operating condition of the graphics card based on the fused BPA function value corresponding to the pre-judgment result for each operating parameter. This invention uses multi-source, multi-modal data, with the pre-judgment result as the final judgment evidence, achieving high data type compatibility, low computing power requirements, and high robustness in identifying graphics card operating conditions and faults.

Description

Technical Field

[0001] This invention relates to the field of graphics card monitoring technology, and more specifically, to a method, system, and storage medium for identifying the operating conditions of a graphics card. Background Technology

[0002] During the operation of a graphics card, its operating conditions may change or even malfunction due to variations in the external environment (such as temperature and humidity) and display stress. Therefore, real-time monitoring of the graphics card's operating conditions and timely identification of faults are crucial.

[0003] During GPU operation, the graphics card's working condition may change abruptly or even malfunction due to various application pressures and changes in the external environment. Therefore, real-time monitoring of the graphics card's operating status and fault conditions is crucial. Existing similar solutions have certain shortcomings, such as relying on a single data source and having high hardware resource requirements. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method, system, and storage medium for identifying the operating conditions of a graphics card, which can overcome the problem that existing solutions have high computing resource requirements and significantly increase the overall cost of the graphics card.

[0005] According to a first aspect of the present invention, a method for monitoring the operating status of a graphics card is provided, comprising:

[0006] Collect time series of multiple operating parameters of the graphics card during runtime;

[0007] Select a corresponding classification model for each working parameter, input the time series of each working parameter into the corresponding classification model, and obtain the pre-judgment working condition result corresponding to each working parameter;

[0008] Based on the classification model corresponding to each working parameter, query the database for the confidence matrix corresponding to each classification model;

[0009] Based on the confidence matrix corresponding to each classification model, construct the BPA function for each classification model;

[0010] Based on the BPA function of each classification model, calculate the fusion BPA function value for each working condition;

[0011] The operating condition of the graphics card is determined based on the fusion BPA function value corresponding to the pre-judgment result of each working parameter.

[0012] According to a second aspect of the present invention, a graphics card operating condition monitoring system is provided, comprising:

[0013] The acquisition module is used to acquire time series data of multiple operating parameters of the graphics card during operation.

[0014] The acquisition module is used to select the corresponding classification model for each working parameter, input the time series of each working parameter into the corresponding classification model, and obtain the pre-judgment working condition result corresponding to each working parameter.

[0015] The query module is used to query the database for the confidence matrix corresponding to each classification model based on each working parameter.

[0016] The building module is used to construct the BPA function for each classification model based on the confidence matrix corresponding to each classification model.

[0017] The calculation module is used to calculate the fused BPA function value for each working condition based on the BPA function of each classification model.

[0018] The determination module is used to determine the operating conditions of the graphics card based on the fusion BPA function value corresponding to the pre-judgment results of each working parameter.

[0019] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is configured to implement a method for monitoring the operating status of a graphics card when executing a computer management program stored in the memory.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer management class program is stored, wherein the computer management class program, when executed by a processor, implements the steps of a method for monitoring the operating status of a graphics card.

[0021] This invention provides a method, system, and storage medium for monitoring the operating conditions of a graphics card. The method involves collecting time series data of multiple operating parameters during graphics card operation; selecting a corresponding classification model for each operating parameter; inputting the time series data of each operating parameter into the corresponding classification model to obtain a pre-judgment result for each operating parameter; querying the database for the confidence matrix corresponding to each classification model based on the classification model for each operating parameter; constructing a BPA function for each classification model based on the confidence matrix and calculating the fused BPA function value for each operating condition; and determining the operating condition of the graphics card based on the fused BPA function value corresponding to the pre-judgment result for each operating parameter. This invention utilizes multi-source, multi-modal data and uses the pre-judgment result as the final judgment evidence, achieving high data type compatibility, low computational power requirements, and high robustness in graphics card operating condition identification and fault identification. Attached Figure Description

[0022] Figure 1 A flowchart of a graphics card operating condition monitoring method provided in one embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram illustrating the principle of a graphics card operating condition identification method according to an embodiment of the present invention.

[0024] Figure 3 This is a structural block diagram of a graphics card operating condition recognition system according to an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0026] Figure 5 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined with each other to form feasible technical solutions. Such combinations are not constrained by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0028] This invention proposes a method for identifying the operating conditions and fault status of a graphics card. It can monitor data such as temperature, power consumption, power supply voltage, and operating frequency through the GPU's built-in sensors to achieve automatic monitoring and identification of the graphics card's operating conditions and fault status.

[0029] Figure 1 A flowchart of a graphics card operating condition identification method according to an embodiment of the present invention is shown, as follows: Figure 1 and Figure 2 As shown, the method includes:

[0030] Step 1: Collect time series of multiple operating parameters of the graphics card during runtime.

[0031] In one embodiment of the present invention, the plurality of operating parameters include at least temperature, power consumption, supply voltage, and operating frequency, and the time series of the plurality of operating parameters collected during graphics card operation includes:

[0032] Within a preset time segment, the time series of the graphics card's temperature, power consumption, power supply voltage, and operating frequency are collected respectively.

[0033] Understandably, during the operation of a graphics card, multiple operating parameters of the graphics card are collected. In this embodiment of the invention, multiple sensors can be built into the graphics card to collect parameters such as temperature, power consumption, power supply voltage, and operating frequency. Specifically, the operating condition of the graphics card can be periodically monitored and identified. Therefore, various operating parameters can be periodically collected over a period of time to obtain a time series of each operating parameter.

[0034] After collecting the time series of each working parameter, the time series of each working parameter are preprocessed, including filtering and normalization.

[0035] First, filtering is performed: Considering the characteristics of sensor data, physical quantities such as temperature and power consumption will not exhibit high-frequency alternation, therefore, low-pass filtering is applied to the time series of each operating parameter: , ,in, These are the low-pass filter parameters. This is the output value of this filter. This is the output value from the previous filter. These are the measured values. The sampling period is The cutoff frequency is used. After filtering, data normalization is further performed: based on... Map the data to Within this range, it facilitates subsequent data processing by the model. It is the minimum value in the parameter sequence. The maximum value in the parameter sequence. These are the normalized parameter values. These are the parameter values ​​before normalization.

[0036] Step 2: Select the corresponding classification model for each working parameter, input the time series of each working parameter into the corresponding classification model, and obtain the pre-judgment working condition result for each working parameter.

[0037] Understandably, a classification model is used to determine the operating condition using the preprocessed time series of each operating parameter as input, resulting in multiple pre-judged operating condition results. Specifically, a corresponding classification model is selected for each operating parameter; different operating parameters can use the same classification model or different models. There are no restrictions on the type of classification model used; models such as CNN, decision tree, support vector machine, and Naive Bayes can be selected based on computing resources. Since multiple sets of data (time series of multiple operating parameters) are used, the time series of each operating parameter is input into the corresponding selected classification model to obtain the pre-judged operating condition results output by the classification model. Multiple pre-judged operating condition results can be obtained as "evidence" for subsequent steps.

[0038] Step 3: Based on the classification model corresponding to each working parameter, query the database for the confidence matrix corresponding to each classification model.

[0039] Understandably, based on the classification model corresponding to each working parameter, the confidence matrix corresponding to the classification model is queried from the database. The confidence matrix represents the accuracy of the classification for each working condition. The confidence matrix is ​​represented as follows:

[0040]

[0041] in, Representing a classification model, the elements in the matrix This indicates the actual working conditions are as follows: The samples were classified by the classification model as The probability of a certain type of working condition.

[0042] Step 4: Construct the BPA function for each classification model based on the confidence matrix corresponding to each classification model.

[0043] Understandably, the basic probability assignment function (BPA) is used to construct the set of working conditions for each working parameter based on the confidence matrix obtained from the lookup table.

[0044] The working condition identification framework is defined as follows: ,in, This represents the set of operating conditions for the nth operating parameter. Includes all possible working conditions. It represents uncertainty.

[0045] Define the BPA function: from the power set arrive mapping ,but Can represent the The degree of accurate trust.

[0046] Based on the credibility matrix corresponding to each classification model, calculate the local credibility. And first global credibility The local confidence level represents the local confidence level of a result under a working condition in the classification model, and the global confidence level represents the global confidence level of the classification model.

[0047] Calculate the second global confidence level based on the local confidence level and the first global confidence level. The second global confidence level represents the global confidence level of a working condition result of the classification model.

[0048] Based on the second global confidence level, construct the BPA function for each classification model:

[0049] ;

[0050] in, They represent The probability of.

[0051] Step 5: Calculate the fusion BPA function value for each working condition based on the BPA function of each classification model.

[0052] Understandably, the fusion BPA function value for each individual operating condition is calculated based on the BPA function of each constructed classification model.

[0053] Taking n operating parameters as an example, the BPA function value for a single operating condition a is:

[0054] in, , This represents the BPA function value for a single operating condition a. Let each of the n working parameters represent the BPA function of the classification model. This represents the set of working conditions for the k-th classification model. This includes a single operating condition a;

[0055] The fusion BPA function value is calculated for each individual operating condition.

[0056] Step 6: Determine the operating condition of the graphics card based on the fusion BPA function value corresponding to the pre-judgment result of each working parameter.

[0057] Understandably, step 5 above calculates the fused BPA function value for each single operating condition, finds the fused BPA function value corresponding to the pre-judgment result of each operating parameter, and finally identifies the operating condition of the graphics card based on the fused BPA function value corresponding to the pre-judgment result of each operating parameter.

[0058] Specifically, based on the fusion BPA function value corresponding to the predicted operating condition result of each working parameter, the operating condition of the graphics card is determined, including:

[0059] Let the predicted working conditions corresponding to multiple working parameters be: , ... and obtain respectively , ... The fusion of BPA function values;

[0060] Get the maximum fusion BPA function value , This indicates the pre-judgment result corresponding to the maximum fusion BPA function value;

[0061] Obtain the BPA function value representing uncertainty. , Represents uncertainty;

[0062] when and At that time, the operating condition of the graphics card is determined to be... Otherwise, the prediction result of the graphics card's operating conditions for this cycle will be invalidated, and the system will wait for the operating conditions to be identified in the next cycle.

[0063] See Figure 3 This invention provides a graphics card operating condition identification system according to one embodiment, the system comprising:

[0064] Acquisition module 301 is used to acquire time series of multiple operating parameters of the graphics card during operation;

[0065] The acquisition module 302 is used to select a corresponding classification model for each working parameter, input the time series of each working parameter into the corresponding classification model, and obtain the pre-judgment working condition result corresponding to each working parameter.

[0066] The query module 303 is used to query the database for the confidence matrix corresponding to each classification model based on each working parameter.

[0067] Module 304 is used to construct the BPA function for each classification model based on the confidence matrix corresponding to each classification model.

[0068] The calculation module 305 is used to calculate the fused BPA function value for each working condition based on the BPA function of each classification model.

[0069] The determination module 306 is used to determine the operating condition of the graphics card based on the fusion BPA function value corresponding to the pre-judgment result of each working parameter.

[0070] It is understood that the graphics card operating condition identification system provided by the present invention corresponds to the graphics card operating condition identification method provided in the foregoing embodiments. The relevant technical features of the graphics card operating condition identification system can be referred to the relevant technical features of the graphics card operating condition identification method, and will not be repeated here.

[0071] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a storage 410, a processor 420, and a computer program 411 stored in the storage 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it implements the steps of the graphics card operating condition identification method.

[0072] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by the present invention. (See diagram below.) Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, it implements the steps of a graphics card operating condition identification method:

[0073] The present invention provides a method, system, and storage medium for identifying the operating conditions of a graphics card, which has the following beneficial effects:

[0074] (1) Using multi-source, multi-modal data can comprehensively reflect the working condition of the graphics card. This patent does not have specific requirements on the types or number of data, only that the data used is related to the working condition of the graphics card.

[0075] (2) During the process of judging the working condition of the graphics card, there is no need to manually determine the parameters, thus avoiding the problem of inaccurate judgment results caused by different operators;

[0076] (2) The results of multi-source prediction are used as the final judgment basis, which does not rely on the prediction accuracy of a single model and has strong robustness.

[0077] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0081] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0082] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0083] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for identifying the operating conditions of a graphics card, characterized in that, include: Collect time series of multiple operating parameters of the graphics card during runtime; Select a corresponding classification model for each working parameter, input the time series of each working parameter into the corresponding classification model, and obtain the pre-judgment working condition result corresponding to each working parameter; Based on the classification model corresponding to each working parameter, query the database for the confidence matrix corresponding to each classification model; Based on the confidence matrix corresponding to each classification model, construct the BPA function for each classification model; Based on the BPA function of each classification model, calculate the fusion BPA function value for each working condition; The operating condition of the graphics card is determined based on the fusion BPA function value corresponding to the pre-judgment result of each working parameter. The confidence matrix characterizes the accuracy of the classification model in classifying each working condition, and the confidence matrix is ​​expressed as: ； in, The elements in the matrix represent the classification model. This indicates the actual working conditions are as follows: The samples were classified by the classification model as The probability of a particular type of working condition; The step of constructing the BPA function for each classification model based on the confidence matrix corresponding to each classification model includes: The working condition identification framework is defined as follows: ,in, This represents the nth set of working conditions. Includes all possible working conditions. Represents uncertainty; Define the BPA function: from the power set arrive mapping ,but Can represent the Accurate level of trust; Based on the credibility matrix corresponding to each classification model, calculate the local credibility. And first global credibility The local confidence level represents the local confidence level of a result under a working condition in the classification model, and the global confidence level represents the global confidence level of the classification model. Calculate the second global confidence level based on the local confidence level and the first global confidence level. The second global confidence level represents the global confidence level of a working condition result of the classification model; Based on the second global confidence level, construct the BPA function for each classification model: ； in, They represent The probability of.

2. The method for identifying the operating conditions of a graphics card according to claim 1, characterized in that, The multiple operating parameters include at least temperature, power consumption, supply voltage, and operating frequency. The time series of the multiple operating parameters collected during graphics card operation includes: Within a preset time segment, the time series of the graphics card's temperature, power consumption, power supply voltage, and operating frequency are collected respectively.

3. The method for identifying the operating conditions of a graphics card according to claim 1, characterized in that, The process involves collecting time series data of multiple operating parameters during graphics card operation, followed by preprocessing of the time series data for each operating parameter. Low-pass filtering is applied to the time series of each operating parameter: ， ； in, These are the low-pass filter coefficients. This is the output value of this filter. This is the output value from the previous filter. These are the measured values. The sampling period is The cutoff frequency; After low-pass filtering, the time series of each operating parameter is normalized according to... Map the data to Within the interval, For the normalized data, The minimum value in the time series. The maximum value in the time series. These are the measured values ​​after this filtering.

4. The method for identifying the operating conditions of a graphics card according to claim 1, characterized in that, The classification models include Convolutional Neural Networks (CNNs), Decision Trees, Support Vector Machines (SVMs), and Naive Bayes models. Selecting the corresponding classification model for each working parameter includes: For each working parameter, select a corresponding classification model based on computing resources.

5. The method for identifying the operating conditions of a graphics card according to claim 1, characterized in that, The calculation of the fused BPA function value for each working condition based on the BPA function of each classification model includes: The BPA function value for single operating condition a is: ； in, , This represents the BPA function value for a single operating condition a. Let each of the n working parameters represent the BPA function of the classification model. This represents the set of working conditions for the k-th classification model. This includes a single operating condition a; The fusion BPA function value is calculated for each individual operating condition.

6. The method for identifying the operating conditions of a graphics card according to claim 1, characterized in that, The step of determining the graphics card's operating condition based on the fusion BPA function value corresponding to the pre-judgment result of each operating parameter includes: Let the predicted working conditions corresponding to multiple working parameters be: , ... and obtain respectively , ... The fusion of BPA function values; Get the maximum fusion BPA function value , This indicates the pre-judgment result corresponding to the maximum fusion BPA function value; Obtain the BPA function value representing uncertainty. , Represents uncertainty; when and At that time, the operating condition of the graphics card is determined to be... Otherwise, the prediction results of the graphics card's motion conditions will be invalid.

7. A graphics card operating condition recognition system, characterized in that, include: The acquisition module is used to acquire time series data of multiple operating parameters of the graphics card during operation. The acquisition module is used to select the corresponding classification model for each working parameter, input the time series of each working parameter into the corresponding classification model, and obtain the pre-judgment working condition result corresponding to each working parameter. The query module is used to query the database for the confidence matrix corresponding to each classification model based on each working parameter. The building module is used to construct the BPA function for each classification model based on the confidence matrix corresponding to each classification model. The calculation module is used to calculate the fused BPA function value for each working condition based on the BPA function of each classification model. The determination module is used to determine the operating conditions of the graphics card based on the fusion BPA function value corresponding to the pre-judgment results of each working parameter. The confidence matrix characterizes the accuracy of the classification model in classifying each working condition, and the confidence matrix is ​​expressed as: ； in, The elements in the matrix represent the classification model. This indicates the actual working conditions are as follows: The samples were classified by the classification model as The probability of a particular type of working condition; The step of constructing the BPA function for each classification model based on the confidence matrix corresponding to each classification model includes: The working condition identification framework is defined as follows: ,in, This represents the nth set of working conditions. Includes all possible working conditions. Represents uncertainty; Define the BPA function: from the power set arrive mapping ,but Can represent the Accurate level of trust; Based on the credibility matrix corresponding to each classification model, calculate the local credibility. And first global credibility The local confidence level represents the local confidence level of a result under a working condition in the classification model, and the global confidence level represents the global confidence level of the classification model. Calculate the second global confidence level based on the local confidence level and the first global confidence level. The second global confidence level represents the global confidence level of a working condition result of the classification model; Based on the second global confidence level, construct the BPA function for each classification model: ； in, They represent The probability of.

8. A computer-readable storage medium, characterized in that, It stores a computer management program, which, when executed by the processor, implements the steps of the graphics card operating condition identification method as described in any one of claims 1-6.

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