Display problem positioning method and electronic equipment

Abstract

The invention provides a display problem positioning method. The method comprises the steps of obtaining a to-be-output display image generated by a display device based on display logic scheduling system resources at a first moment; determining target image feature data of the to-be-output display image; judging whether the target image feature data meets a preset condition or not, wherein the condition that the target image feature data meets the preset condition shows that the to-be-output display image meets a predefined abnormal display problem; when the target image feature data is responded to meet a preset condition, a snapshot operation is triggered to obtain system snapshot information, and the system snapshot information comprises system snapshot information of one or more discrete moments from a second moment when the to-be-output display image is started to be generated to the first moment, and the system snapshot information comprises the system snapshot information of one or more discrete moments from the second moment to the first moment when the to-be-output display image is generated. The second moment is earlier than the first moment, and the system snapshot information is used for positioning display logic and / or system resources causing the abnormal display problem.

Description

Technical Field

[0001] The present disclosure relates to the field of electronic technologies, and more particularly, to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for locating display problems. Background Art

[0002] In the user usage scenario, display problems usually occur with a low frequency and a short manifestation time, making it difficult to reproduce and locate the root cause of the problems, which seriously affects the user experience. In the related art, due to the difficulty in dealing with problems that are difficult to reproduce, it mostly relies on manual troubleshooting, with low troubleshooting efficiency and poor accuracy. Therefore, there is an urgent need for a more efficient method for locating display problems. Summary of the Invention

[0003] In view of this, the present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for locating display problems.

[0004] One aspect of the present disclosure provides a method for locating display problems, including: obtaining, at a first moment, a to-be-output display image generated by a display device based on a display logic for scheduling system resources; determining target image feature data of the to-be-output display image; determining whether the target image feature data meets a preset condition, where meeting the preset condition indicates that the to-be-output display image meets a predefined abnormal display problem; and in response to the target image feature data meeting the preset condition, triggering a snapshot operation to obtain system snapshot information, where the system snapshot information includes system snapshot information at one or more discrete moments between a second moment when generating the to-be-output display image starts and the first moment, the second moment being earlier than the first moment, and the system snapshot information is used to locate the display logic and / or system resources that cause the abnormal display problem.

[0005] In some embodiments, determining whether the target image feature data meets the preset condition includes:

[0006] inputting the target image feature data into a scene recognition model to output a recognition result, where the scene recognition model is pre-trained based on abnormal display problem scene images; and

[0007] when the recognition result is an abnormal display problem scene, determining that the target image feature data meets the preset condition.

[0008] In some embodiments, pre-training the scene recognition model based on abnormal display problem scene images includes: using image data with display problems and normal display image data as a training data set, and preprocessing the training data set; extracting feature values of the images based on an artificial intelligence algorithm; and inputting the extracted feature values into a classifier for training to obtain the scene recognition model.

[0009] In some embodiments, the method further includes: when it is detected that the target image feature data meets a preset condition, automatically starting a display problem analysis process, where the display problem analysis process includes an automatic analysis process based on system snapshot information and a semi-automatic analysis process based on user interaction; outputting to the user the display logic and the abnormal points of system resources related to the abnormal display problem; and receiving an analysis instruction input by the user based on the abnormal points to locate the cause of the abnormal display problem.

[0010] In some embodiments, the automatic analysis process based on system snapshot information includes: performing a timing analysis on the parameters related to the display logic at multiple moments to determine the evolution process of the display logic; and locating the abnormal points of the display logic based on the evolution process.

[0011] In some embodiments, the automatic analysis process based on system snapshot information further includes: analyzing the change trend of the system resource usage data; determining the bottleneck moment of the system resources according to the change trend; and associating the bottleneck moment with the abnormal display problem to locate the abnormal points of the system resources.

[0012] In some embodiments, the automatic analysis process based on system snapshot information further includes: inputting the system snapshot information into a pre-trained AI analysis model; and automatically identifying, by the AI analysis model, the display logic problems and system resource problems that cause the abnormal display problem, where the AI analysis model is trained based on the system snapshot information in historical display problem cases and the corresponding display logic and system resource problems that cause the abnormal display problem.

[0013] In some embodiments, the training process of the AI analysis model includes: collecting historical system snapshot information including various display problems; marking the display logic problems and system resource problems that cause the display problems in each historical system snapshot information; and using the marked historical system snapshot information to train an initial AI model to obtain the AI analysis model.

[0014] In some embodiments, the system snapshot information includes parameters related to the display logic and system resource usage data at multiple moments, where the parameters related to the display logic include at least one of image generation parameters, image processing parameters, image composition parameters, and image output parameters, and the system resource usage data includes at least one of CPU usage rate, memory occupancy, GPU resource allocation, and storage device I / O.

[0015] Another aspect of the present disclosure provides a display problem positioning device, including:

[0016] An image processing module, configured to obtain, at a first moment, a to-be-output display image generated by a display device based on a display logic for scheduling system resources, and determine target image feature data of the to-be-output display image; a judgment module, configured to judge whether the target image feature data meets a preset condition, and meeting the preset condition indicates that the to-be-output display image meets a predefined abnormal display problem; and a system snapshot acquisition component, configured to trigger a snapshot operation to acquire system snapshot information in response to the target image feature data meeting the preset condition, where the system snapshot information includes system snapshot information at one or more discrete moments between a second moment when generating the to-be-output display image starts and the first moment, the second moment being earlier than the first moment, and the system snapshot information is used to locate the display logic and / or system resources that cause the abnormal display problem.

[0017] In some embodiments, the apparatus further includes:

[0018] A display logic analysis module, configured to perform a timing analysis on display logic related parameters at multiple moments in the system snapshot information, determine an evolution process of the display logic, and locate an abnormal point of the display logic based on the evolution process;

[0019] A system resource analysis module, configured to analyze a change trend of system resource usage data at multiple moments in the system snapshot information, determine a bottleneck moment of the system resources according to the change trend, and associate the bottleneck moment with the abnormal display problem to locate an abnormal point of the system resources.

[0020] Another aspect of the present disclosure provides an electronic device, including one or more processors and one or more memories, where the memories are configured to store executable instructions, and when the executable instructions are executed by the processors, the above method is implemented.

[0021] Another aspect of the present disclosure provides a computer-readable storage medium, storing computer-executable instructions, and the instructions are used to implement the above method when executed.

[0022] Another aspect of the present disclosure provides a computer program product, including a computer program, where the computer program includes computer-executable instructions, and the instructions are used to implement the above method when executed.

[0023] According to some embodiments of the present disclosure, by obtaining, at a first moment, a to-be-output display image generated by a display device based on a display logic for scheduling system resources, the image state generated by the display device can be captured in a timely manner. When it is determined that the target image feature data meets a preset condition, a snapshot operation is triggered to obtain system snapshot information, which is used to locate the display logic and / or system resources that cause abnormal display problems, avoiding the error of manual supervisor judgment, reducing the problem troubleshooting time, and improving the location efficiency. Since the system snapshot information covers the system states at one or more discrete moments between a second moment when the generation of the to-be-output display image starts and the first moment, it provides an information basis for analyzing display problems and helps to locate the system environment and the execution of display logic when the problem occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above and other objects, features, and advantages of the present disclosure will become more apparent. In the drawings:

[0025] Figure 1 Schematically shows a flowchart of a method for locating a display problem according to an embodiment of the present disclosure;

[0026] Figure 2 Schematically shows a flowchart of a method for training a scene recognition model according to an embodiment of the present disclosure;

[0027] Figure 3 Schematically shows a flowchart of a method for locating a display problem according to another embodiment of the present disclosure;

[0028] Figure 4 Schematically shows a structural block diagram of a display problem location device according to an embodiment of the present disclosure; and

[0029] Figure 5 Schematically shows a block diagram of an electronic device suitable for implementing the method for locating a display problem according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0031] In the technical solution of the present disclosure, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations, necessary confidentiality measures are taken, and it does not violate public order and good customs. In the technical solution of the present disclosure, the processing of data such as acquisition, collection, storage, use, processing, transmission, provision, disclosure, and application all comply with the provisions of relevant laws and regulations, necessary confidentiality measures are taken, and it does not violate public order and good customs.

[0032] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0033] In the case of using expressions such as "at least one of A, B, or C", generally, it should be interpreted according to the meaning that those skilled in the art usually understand this expression (for example, "a system having at least one of A, B, or C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more features.

[0034] Based on the above technical problems, an embodiment of the present disclosure provides a display problem localization method, which is applied to a display device and includes: obtaining a to-be-output display image generated by the display device based on a display logic scheduling system resource at a first moment; determining target image feature data of the to-be-output display image; determining whether the target image feature data meets a preset condition, and meeting the preset condition indicates that the to-be-output display image meets a predefined abnormal display problem; and when the target image feature data meets the preset condition, triggering a snapshot operation to obtain system snapshot information, where the system snapshot information includes system snapshot information of one or more discrete moments between a second moment when starting to generate the to-be-output display image and the first moment, the second moment is earlier than the first moment, and the system snapshot information is used to locate the display logic and / or system resource that causes the abnormal display problem.

[0035] By Figures 1 to 3 The display problem localization method of the embodiment of the present disclosure is described in detail.

[0036] Figure 1 The flowchart of the display problem localization method according to an embodiment of the present disclosure is schematically shown.

[0037] As Figure 1As shown, the display problem localization method of this embodiment includes operations S210 to S240.

[0038] In operation S210, at a first moment, obtain a to-be-output display image generated by the display device based on the display logic for scheduling system resources.

[0039] In operation S220, determine the target image feature data of the to-be-output display image.

[0040] The display problem localization method of the embodiments of the present disclosure is applied to a display device. Taking a computer usage scenario as an example, when a user opens a large 3D model file, the system continuously monitors the display output. When the model is rendered to a complex scene, there may be problems of abnormal display at this time. For example, there is abnormal pure black display in some areas of the screen, or the position of the display image is incorrect. In the related art, when encountering problems of abnormal display, the problem is usually located by manual reproduction, and the efficiency is low. In the embodiments of the present disclosure, the background system obtains the to-be-output display image generated by the display device based on the display logic for scheduling system resources at the first moment, and extracts the image feature data of the to-be-output display image. The image feature data can be, for example, color value and color gamut related data, such as RGB color value distribution, gray value distribution, etc.; for example, it can also be the image update frequency, including the number of image frames; for example, it can also be the geometric features and content features of the image.

[0041] In operation S230, determine whether the target image feature data meets a preset condition.

[0042] According to the embodiments of the present disclosure, meeting the preset condition means that the to-be-output display image meets the predefined abnormal display problem.

[0043] In one example, judge the target image feature data obtained in operation S220 to determine whether the target image feature data meets a preset condition. Meeting the preset condition means that the to-be-output display image meets the predefined abnormal display problem. Specifically, compare the target image feature data with the preset feature data. For example, it can be a similarity comparison. The preset feature data is extracted from the display problem image and pre-stored in the system memory.

[0044] According to the embodiments of the present disclosure, determining whether the target image feature data meets a preset condition includes: inputting the target image feature data into a scene recognition model to output a recognition result, where the scene recognition model is pre-trained based on abnormal display problem scene images; and when the recognition result is an abnormal display problem scene, determining that the target image feature data meets the preset condition.

[0045] In one example, after the system obtains the target image feature data, it inputs the data into the scene recognition model. The model is pre-trained based on a large number of abnormal display problem scene images and can accurately identify various abnormal display scenes. Suppose the model determines that the feature data of the current image conforms to the abnormal scene feature of "pure black in the center area of the screen under a complex scene" and outputs an abnormal recognition result. Based on this, the system determines that the target image feature data meets the preset conditions, and then triggers the subsequent snapshot acquisition and problem location processes.

[0046] In operation S240, in response to the target image feature data meeting the preset conditions, a snapshot operation is triggered to obtain system snapshot information.

[0047] According to an embodiment of the present disclosure, the system snapshot information includes system snapshot information at one or more discrete moments between the second moment when the generation of the display image to be output starts from startup and the first moment. The second moment is earlier than the first moment. The system snapshot information is used to locate the display logic and / or system resources that cause the abnormal display problem.

[0048] In one example, when it is determined that the target image feature data meets the preset conditions, a snapshot operation is triggered to obtain system snapshot information at one or more discrete moments between the software startup moment (the second moment) and the current moment (the first moment). The system snapshot information covers the parameters of each stage of the display logic and the system resource usage data. The system snapshot information can be used to locate the display logic and system resource problems. For the specific location process, refer to Figure 3 The operations S410 to S430 shown, which will not be elaborated here.

[0049] According to an embodiment of the present disclosure, by obtaining the display image to be output generated by the display device based on the display logic scheduling system resources at the first moment, the image state generated by the display device can be captured in time. When it is determined that the target image feature data meets the preset conditions, a snapshot operation is triggered to obtain system snapshot information. The system snapshot information is used to locate the display logic and / or system resources that cause the abnormal display problem, avoiding the error of manual subjective judgment, reducing the problem troubleshooting time, and improving the location efficiency. Since the system snapshot information covers the system states at one or more discrete moments between the second moment when the generation of the display image to be output starts from startup and the first moment, it provides an information basis for analyzing the display problem and helps to locate the system environment and the execution situation of the display logic when the problem occurs.

[0050] Figure 2 Schematically shows a flowchart of a method for training a scene recognition model according to an embodiment of the present disclosure.

[0051] As Figure 2 shown, it includes operations S310 to S330.

[0052] In operation S310, the image data with display problems and the normal display image data are used as the training data set, and the training data set is preprocessed.

[0053] In operation S320, the feature values of the image are extracted based on the artificial intelligence algorithm.

[0054] In operation S330, the extracted feature values are input into a classifier for training to obtain a scene recognition model.

[0055] In one example, before training the scene recognition model, a large number of image data with display problems and normal display image data are collected to form the training data set. The image data is preprocessed, including operations such as adjusting the resolution and normalizing the pixel values. Then, deep learning algorithms are used to extract the key feature values in the image, such as color distribution features and shape features. Finally, the extracted feature values are input into the classifier for training to obtain a scene recognition model that can distinguish between normal display scenes and abnormal display scenes, which is used for the subsequent target image feature data judgment process. By preprocessing to optimize the data quality, using deep learning algorithms to extract image features, and combining classifier training to obtain a reliable scene recognition model, the recognition performance of the model for abnormal scenes is improved.

[0056] Figure 3 The flowchart of the display problem localization method according to another embodiment of the present disclosure is schematically shown.

[0057] As Figure 3 shown, it includes operation S410 to operation S430.

[0058] In operation S410, when it is detected that the target image feature data meets the preset conditions, the display problem analysis process is automatically started.

[0059] According to the embodiment of the present disclosure, the display problem analysis process includes an automatic analysis process based on system snapshot information and a semi-automatic analysis process based on user interaction.

[0060] In operation S420, the abnormal points of the display logic and system resources related to the abnormal display problem are output to the user.

[0061] In operation S430, the analysis instruction input by the user based on the abnormal point is received to locate the cause of the abnormal display problem.

[0062] In one example, when the system detects that the target image feature data meets the preset conditions, the display problem analysis process is automatically started. The display problem analysis process includes an automatic analysis process based on system snapshot information and a semi-automatic analysis process based on user interaction. On the one hand, automated analysis is performed based on the acquired system snapshot information to analyze the display logic problems and system resource problems that cause abnormal display problems. On the other hand, the abnormal points of the display logic and system resources related to the abnormal display problem are output to the user. For example, it is prompted that "the pure black in the center area of the screen may be caused by the incorrect rendering order of the 3D model or insufficient GPU resources". The user inputs an analysis instruction according to the prompt, such as selecting to view the detailed GPU resource usage or adjusting the model rendering parameters. The system further locates the specific cause of the abnormality based on this, such as determining that the incorrect rendering order of the 3D model causes problems with the display logic. By automatically starting the analysis process, the problem handling efficiency is improved. By combining automated analysis with user interaction, the intelligent advantages of the system and the manual experience judgment are fully utilized to improve the accuracy and efficiency of locating complex display problems.

[0063] According to an embodiment of the present disclosure, the system snapshot information includes display logic-related parameters and system resource usage data at multiple moments. Among them, the display logic-related parameters include at least one of image generation parameters, image processing parameters, image synthesis parameters, and image output parameters, and the system resource usage data includes at least one of CPU usage rate, memory occupancy, GPU resource allocation, and storage device I / O.

[0064] According to an embodiment of the present disclosure, the automatic analysis process based on system snapshot information includes: performing a timing analysis on the display logic-related parameters at multiple moments to determine the evolution process of the display logic; and locating the abnormal points of the display logic based on the evolution process.

[0065] In one example, when analyzing the system snapshot information, the system focuses on the display logic-related parameters at multiple moments, such as the drawing order of the 3D model and the texture loading timing. Through timing analysis, the system tracks the evolution process of these parameters from software startup to the occurrence of the abnormality. For example, it is observed that during the rendering of a complex scene, the drawing order of a key model is abnormally adjusted, resulting in it covering other contents in the center area of the screen. Thus, the specific abnormal point in the display logic is located as the incorrect model drawing order. By showing the dynamic changes of the display logic parameters through timing analysis, the abnormal link of the display logic is determined, and thus the abnormal points of the display logic are located.

[0066] According to an embodiment of the present disclosure, the automatic analysis process based on system snapshot information further includes: analyzing the change trend of the system resource usage data; determining the bottleneck moment of the system resources according to the change trend; and associating the bottleneck moment with the abnormal display problem to locate the abnormal points of the system resources.

[0067] In one example, the system deeply analyzes the changing trends of system resource usage data in the snapshot, such as the changing trends of CPU and memory occupancy rates over time. If it is found that the CPU usage rate rises sharply when rendering complex scenes and finally reaches a level close to 100% at the abnormal moment, it can be determined that the CPU resource bottleneck is an important factor leading to the display problem. This moment is determined as the bottleneck moment and associated with the abnormal display problem of the pure black in the center area of the screen, so as to accurately locate the abnormal point of system resources - insufficient CPU resources, which affects the normal rendering process of the 3D model.

[0068] According to an embodiment of the present disclosure, the automatic analysis process based on system snapshot information further includes: inputting the system snapshot information into a pre-trained AI analysis model; automatically identifying display logic problems and system resource problems that cause abnormal display problems through the AI analysis model, where the AI analysis model is trained based on the system snapshot information in historical display problem cases and the corresponding display logic and system resource problems that cause abnormal display problems.

[0069] According to an embodiment of the present disclosure, the training process of the AI analysis model includes: collecting historical system snapshot information containing various display problems; marking the display logic problems and system resource problems that cause display problems in each historical system snapshot information; using the marked historical system snapshot information to train an initial AI model to obtain the AI analysis model.

[0070] In one example, when training the AI analysis model, historical system snapshot information of various display problems is collected, covering various types of problems from simple display errors to complex resource conflicts. Each snapshot is carefully marked to clearly label the abnormal display logic and system resource abnormalities that cause the problems. Then, these marked data are used to train an AI model such as a neural network for multiple rounds, continuously optimizing the model parameters to obtain the final target AI analysis model. The system snapshot information obtained by triggering the snapshot operation is input into the target AI analysis model. Since this model is trained based on the system snapshot information in a large number of historical display problem cases, covering various display logics and system resource problems, the model can automatically identify the abnormal patterns in the current snapshot information, determine specific problems such as incorrect rendering order of the 3D model or insufficient GPU resources, and then output to the user the abnormal points of display logic and system resources related to the abnormal display problem.

[0071] Based on the above display problem localization method, the present disclosure also provides a display problem localization device. The following will be combined with Figure 4 to describe the device in detail.

[0072] Figure 4 Schematically shows a structural block diagram of a display problem localization device according to an embodiment of the present disclosure.

[0073] As shown Figure 4 in the figure, the display problem location device 400 of this embodiment includes an image processing module A1, a judgment module A2, a system snapshot acquisition component A3, a display logic analysis module A4, and a system resource analysis module A5.

[0074] The image processing module A1 is configured to obtain a to-be-output display image generated by the display device based on the display logic for scheduling system resources at a first moment, and determine target image feature data of the to-be-output display image; in an embodiment, the image processing module A1 may be configured to perform the operations S210 and S220 described above, which will not be elaborated herein.

[0075] The judgment module A2 is configured to judge whether the target image feature data meets a preset condition, and meeting the preset condition indicates that the to-be-output display image meets a predefined abnormal display problem; in an embodiment, the judgment module A2 may be configured to perform the operation S230 described above, which will not be elaborated herein.

[0076] The system snapshot acquisition component A3 is configured to trigger a snapshot operation to obtain system snapshot information in response to the target image feature data meeting the preset condition, where the system snapshot information includes system snapshot information of one or more discrete moments between a second moment when the to-be-output display image starts to be generated and the first moment, the second moment is earlier than the first moment, and the system snapshot information is used to locate the display logic and / or system resources that cause the abnormal display problem. In an embodiment, the system snapshot acquisition component A3 may be configured to perform the operation S240 described above, which will not be elaborated herein.

[0077] According to an embodiment of the present disclosure, the display problem location device further includes:

[0078] The display logic analysis module A4 is configured to perform a timing analysis on display logic related parameters at multiple moments in the system snapshot information, determine the evolution process of the display logic, and locate the abnormal point of the display logic based on the evolution process. In an embodiment, the display logic analysis module A4 may be configured to perform the operation S410 described above, which will not be elaborated herein.

[0079] The system resource analysis module A5 is configured to analyze the change trend of system resource usage data at multiple moments in the system snapshot information, determine the bottleneck moment of the system resources according to the change trend, and associate the bottleneck moment with the abnormal display problem to locate the abnormal point of the system resources. In an embodiment, the system resource analysis module A5 may be configured to perform the operation S410 described above, which will not be elaborated herein.

[0080] According to an embodiment of the present disclosure, any multiple modules among the image processing module A1, the judgment module A2, the system snapshot acquisition component A3, the display logic analysis module A4, and the system resource analysis module A5 can be combined and implemented in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the image processing module A1, the judgment module A2, the system snapshot acquisition component A3, the display logic analysis module A4, and the system resource analysis module A5 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable means such as hardware or firmware through circuit integration or packaging, or can be implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the image processing module A1, the judgment module A2, the system snapshot acquisition component A3, the display logic analysis module A4, and the system resource analysis module A5 can be at least partially implemented as a computer program module, and when the computer program module is run, corresponding functions can be executed.

[0081] Figure 5 A block diagram of an electronic device suitable for implementing the display problem location method according to an embodiment of the present disclosure is schematically shown.

[0082] As Figure 5 shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage section 508 into a random access memory (RAM) 503. The processor 501 can include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 501 can also include on-board memory for caching purposes. The processor 501 can include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0083] In the RAM 503, various programs and data required for the operation of the electronic device 500 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 502 and / or the RAM 503. It should be noted that the program can also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 can also perform various operations of the method flow according to the embodiments of the present disclosure by executing programs stored in one or more memories.

[0084] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, and the input / output (I / O) interface 505 is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the input / output (I / O) interface 505: an input part 506 including a keyboard, a mouse, etc.; an output part 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 508 including a hard disk, etc.; and a communication part 509 including a network interface card such as a LAN card, a modem, etc. The communication part 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read from it can be installed into the storage part 508 as needed.

[0085] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0086] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 502 and / or RAM 503 and / or one or more memories other than ROM 502 and RAM 503.

[0087] An embodiment of the present disclosure further includes a computer program product, which includes a computer program, and the computer program contains program codes for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program codes are used to enable the computer system to implement the display problem location method provided by the embodiments of the present disclosure.

[0088] When the computer program is executed by the processor 501, it executes the above functions defined in the system / apparatus of the embodiments of the present disclosure. According to an embodiment of the present disclosure, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0089] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and is downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program codes included in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0090] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, it executes the above functions defined in the system of the embodiments of the present disclosure. According to an embodiment of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0091] In accordance with embodiments of the present disclosure, program code for executing the computer programs provided by the embodiments of the present disclosure may be written in any combination of one or more programming languages. Specifically, these computing programs may be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or alternatively, may be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0093] Those skilled in the art can understand that the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0094] The above describes the embodiments of the present disclosure. However, these embodiments are merely for illustrative purposes and not for limiting the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. A display problem location method, applied to a display device, wherein, The method includes: Obtaining, at a first moment, a to-be-output display image generated by a display device based on a display logic scheduling system resource; Determining target image feature data of the to-be-output display image; Judging whether the target image feature data meets a preset condition, where meeting the preset condition indicates that the to-be-output display image meets a predefined abnormal display problem; and When the target image feature data meets the preset condition, triggering a snapshot operation to obtain system snapshot information, where the system snapshot information includes system snapshot information at one or more discrete moments between a second moment when generating the to-be-output display image starts and the first moment, the second moment being earlier than the first moment, and the system snapshot information is used to locate the display logic and / or system resource that causes the abnormal display problem.

2. The method according to claim 1, wherein The judging whether the target image feature data meets the preset condition includes: Inputting the target image feature data into a scene recognition model to output a recognition result, where the scene recognition model is pre-trained based on abnormal display problem scene images; and When the recognition result is an abnormal display problem scene, determining that the target image feature data meets the preset condition.

3. The method according to claim 2, wherein, Pre-training a scene recognition model based on display problem scene images includes: Using image data with display problems and normal display image data as a training data set, and preprocessing the training data set; Extracting feature values of images based on an artificial intelligence algorithm; and Inputting the extracted feature values into a classifier for training to obtain a scene recognition model.

4. The method according to any one of claims 1 to 3, the method further includes: When it is detected that the target image feature data meets the preset condition, automatically starting a display problem analysis process, where the display problem analysis process includes an automatic analysis process based on the system snapshot information and a semi-automatic analysis process based on user interaction; Outputting to the user abnormal points of the display logic and system resource related to the abnormal display problem; And Receiving an analysis instruction input by the user based on the abnormal points to locate the cause of the abnormal display problem.

5. The method according to claim 4, wherein The automatic analysis process based on the system snapshot information includes: Performing a timing analysis on display logic related parameters at multiple moments to determine the evolution process of the display logic; Locating abnormal points of the display logic based on the evolution process.

6. The method according to claim 4, wherein, The automatic analysis process based on the system snapshot information further includes: Analyzing the change trend of system resource usage data; Determining a bottleneck moment of the system resource according to the change trend; Associating the bottleneck moment with the abnormal display problem to locate abnormal points of the system resource.

7. The method according to claim 4, wherein The automatic analysis process based on the system snapshot information further includes: Inputting the system snapshot information into a pre-trained AI analysis model; Automatically identifying display logic problems and system resource problems that cause the abnormal display problem through the AI analysis model, where the AI analysis model is trained based on system snapshot information in historical display problem cases and corresponding display logic and system resource problems that cause abnormal display problems.

8. The method according to claim 7, wherein, The training process of the AI analysis model includes: Collecting historical system snapshot information containing various display problems; Marking the display logic problems and system resource problems that cause display problems in each piece of historical system snapshot information; Using the marked historical system snapshot information to train the initial AI model to obtain the AI analysis model.

9. The method according to claim 1, wherein the system snapshot information includes display logic related parameters and system resource usage data at multiple moments, where The display logic related parameters include at least one of image generation parameters, image processing parameters, image composition parameters, and image output parameters, and the system resource usage data includes at least one of CPU usage rate, memory occupancy, GPU resource allocation, and storage device I / O.

10. An electronic device, characterized in that, It includes: One or more processors; One or more memories for storing executable instructions, which when executed by the processor, implement the method according to any one of claims 1 to 9.

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