A full-process automatic detection method and device for a computing device

Abstract

This application relates to the field of automated testing of computer systems, and discloses a method and apparatus for fully automated testing of computing devices throughout the entire process. The method includes: S1, a power control module performing power-on, power-off, or restart control on the computing device under test, and generating a testing task containing testing stage identifiers, wherein the testing stages include at least a power-on stage, a firmware startup stage, an operating system startup stage, and an operating system running stage; S2, a computing terminal agent module deployed on the computing device under test collecting running information data. This invention achieves continuous coverage of the testing process by automating the entire process of powering on, firmware startup, operating system startup, and running of the computing device, avoiding the problem of stage fragmentation in manual testing; and by integrating running information data, visual representation data, and physical quantity data, it performs multi-dimensional judgment of the computing device's operating status, improving the accuracy of anomaly detection.

Description

Technical Field

[0001] This application relates to the field of automated testing of computer systems, and for example to a method and apparatus for fully automated testing of computing devices. Background Technology

[0002] With the widespread application of computer equipment in servers, embedded systems and various information technology scenarios, stability testing during power-on startup, firmware initialization, operating system startup and operation has become an important part of product development and quality verification.

[0003] Existing methods for testing computing devices typically rely on manual operation or semi-automated testing techniques, such as manually powering on the device, observing the display output, and analyzing system logs to locate problems. These methods are not only inefficient, but also struggle to perform continuous and consistent testing and assessment across multiple stages, including the power-on phase, firmware startup phase, and operating system startup phase.

[0004] Furthermore, existing detection solutions often focus on a single data source, such as relying solely on runtime logs or image display results, making it difficult to comprehensively depict the operational status of computing devices. When abnormal situations such as black screens or startup freezes occur, it is often impossible to accurately distinguish whether the problem is due to a display link malfunction, a system freeze, or a power supply failure. Moreover, key evidence data at the time of the anomaly is difficult to preserve completely, posing challenges for subsequent problem analysis and reproduction.

[0005] Therefore, there is an urgent need for a detection solution that can cover the entire operation process of computing devices, integrate multiple data sources, and achieve automated judgment in order to improve detection efficiency and the accuracy of detection results. Summary of the Invention

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0007] This disclosure provides a fully automated testing method for computing devices, including: S1. The power control module performs power-on, power-off, or restart control on the computing device under test and generates a detection task containing a detection stage identifier. The detection stage includes at least the power-on stage, firmware startup stage, operating system startup stage, and operating system running stage. S2. The computing agent module deployed on the computing device under test collects the operation information data and reports the operation information data to the intelligent detection module. The operation information data includes at least one of operation status data and log data. S3. The visual acquisition module acquires the visual representation data of the computing device under test at each detection stage and sends it to the intelligent detection module. S4. The physical quantity acquisition module collects physical quantity data related to the operation of the computing device under test and sends it to the intelligent detection module. The physical quantity data includes at least one of power consumption data, current data and voltage data. S5. The intelligent detection module performs fusion analysis and comparison based on the operation information data, the visual representation data, and the physical quantity data, outputs the judgment results of each detection stage, and forms a test record. S6. After the detection task is completed, a test report is generated based on the test record. The test report includes at least the stage in which the anomaly occurred and the differences from the preset normal baseline.

[0008] Preferably, a time reference for determining the stage boundary is based on the power control event, and the visual representation data is time-aligned with the operation information data and physical quantity data.

[0009] Preferably, the preset normal benchmark includes at least a reference visual benchmark and a state baseline benchmark, and establishes an association index with the hardware model, firmware version, or operating system version; the intelligent detection module selects the matching preset normal benchmark according to the association index before analysis and comparison.

[0010] Preferably, the analysis and comparison of the visual representation data includes: Perform normalization processing on the visual representation data; Perform Region of Interest (ROI) extraction on visual representation data; It performs similarity or difference calculations based on the normalized visual representation data and the reference visual benchmark to output visual difference items.

[0011] Preferably, when an anomaly is determined to exist, an anomaly evidence solidification process is triggered. The anomaly evidence solidification process includes at least saving visual representation data and physical quantity data within the time window before and after the anomaly occurs, and triggering the computing agent module to collect and save anomaly-related log data.

[0012] Preferably, the intelligent detection module performs a joint determination on black screen anomalies. The joint determination is based at least on the brightness or synchronization characteristics of visual representation data and the power consumption or current characteristics of physical quantity data to distinguish between display link anomalies and system freeze anomalies, and writes the anomaly type into the test record.

[0013] In some embodiments, a fully automated testing device for computing devices is provided, comprising: The power control module is used to control the power-on, power-off, or restart of the computing device under test. The computing agent module is used to collect and report operational information data, wherein the operational information data includes at least one of operational status data and log data; The visual acquisition module is used to acquire and send visual representation data at each detection stage; A physical quantity acquisition module is used to acquire and transmit physical quantity data, wherein the physical quantity data includes at least one of power consumption data, current data, and voltage data. The intelligent detection module is used to fuse, analyze, and compare operational information data, visual representation data, and physical quantity data to output stage judgment results and form test records, and generate test reports after the detection is completed.

[0014] In some embodiments, a fully automated inspection device for computing devices is provided, including a processor and a memory storing program instructions, the processor being configured to execute a fully automated inspection method for computing devices when the program instructions are executed.

[0015] In some embodiments, a computer-readable storage medium is provided storing program instructions, characterized in that the program instructions, when executed, perform a fully automated detection method for computing devices.

[0016] In some embodiments, a computer program product is provided that, when run by the processor of a computer device, executes a fully automated detection method for the computing device.

[0017] The fully automated testing method for computing devices provided in this disclosure can achieve the following technical effects: (1) By automating the entire process of powering on the computing device, firmware startup, operating system startup and operation, continuous coverage of the detection process is achieved, avoiding the problem of stage fragmentation in manual detection; (2) By integrating operational information data, visual representation data and physical quantity data, the operating status of computing equipment is determined in multiple dimensions, which improves the accuracy of anomaly detection; (3) When an anomaly occurs, the evidence solidification process can be automatically triggered to save key data before and after the anomaly, providing a reliable basis for subsequent problem analysis and reproduction; (4) It can make joint judgments on abnormal situations such as black screen, which helps to distinguish between display link abnormalities and system freeze abnormalities, and improves the efficiency of fault location.

[0018] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0019] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein: Figure 1 This is a schematic diagram of the composition structure of a fully automated testing device for computing devices provided in an embodiment of this disclosure; Figure 2 This is a flowchart illustrating a fully automated testing method for computing devices provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the process interaction of the power control module in an embodiment of this disclosure; Figure 4 This is a schematic diagram of the process interaction of the computing-side agent module in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the fusion judgment process of the intelligent detection module in this embodiment of the disclosure; Figure 6 This is a schematic diagram of visual representation data processing in an embodiment of this disclosure; Figure 7 This is a schematic diagram of the acquisition link of the physical quantity acquisition module in this embodiment of the present disclosure; Figure 8 This is a flowchart illustrating the combined determination of abnormal evidence preservation and black screen in this embodiment of the present disclosure; Figure 9 This is a schematic diagram of a computing device in an embodiment of this disclosure.

[0020] 100: Power control module; 200: Computing agent module; 300: Visual acquisition module; 400: Physical quantity acquisition module; 500: Intelligent detection module; 901: Memory; 902: Communication interface; 903: Bus; 904: Processor. Detailed Implementation

[0021] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0022] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0023] Unless otherwise stated, the term "multiple" means two or more.

[0024] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0025] The term "or" describes an association between objects, indicating that there can be three relationships. For example, A or B means: A or B, or, A and B.

[0026] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0027] Combination Figure 2 As shown, this disclosure provides a fully automated inspection method for computing devices, which is executed by a fully automated inspection device. The method includes the following steps: S1. The power control module performs power-on, power-off, or restart control on the computing device under test and generates a detection task corresponding to the power control operation. The detection task includes at least several stages, such as the power-on stage, firmware startup stage, operating system startup stage, and operating system running stage.

[0028] S2. In each detection stage, the computing agent module deployed on the computing device under test collects operation information data and sends the operation information data to the intelligent detection module, wherein the operation information data includes at least one of operation status data and log data.

[0029] S3. The visual acquisition module acquires the display output of the computing device under test in each detection stage, generates visual representation data corresponding to the display output, and sends the visual representation data to the intelligent detection module.

[0030] S4. The physical quantity acquisition module collects physical quantity data related to the operation of the computing device under test during the detection process, and sends the physical quantity data to the intelligent detection module. The physical quantity data includes at least one of power consumption data, current data and voltage data.

[0031] S5. The intelligent detection module performs time-series alignment and fusion analysis on the operation information data, visual representation data, and physical quantity data, determines the operation status of each detection stage, and generates corresponding test records.

[0032] S6. After the testing task is completed, a test report is generated based on the test record. The test report is used to characterize the operating status and abnormal situations of the computing device under test in each testing stage.

[0033] In one embodiment, in order to ensure the repeatability of the detection process and the comparability of the detection results between different devices, the division of the detection stages is uniformly managed through stage identifiers.

[0034] Specifically, when the power control module performs power-on, power-off, or restart control operations, it synchronously generates stage identifier information corresponding to the control operation and injects this stage identifier information as a time reference into the detection task. The stage identifier is used to characterize the start and end times of different operating stages during the detection process.

[0035] In one embodiment, the stage identifier includes at least a stage type identifier and timestamp information, wherein the stage type identifier is used to indicate whether the current detection stage belongs to the power-on stage, firmware startup stage, operating system startup stage, or operating system running stage, and the timestamp information is used to identify the time node of the stage switch.

[0036] The computational proxy module, visual acquisition module, and physical quantity acquisition module all associate the current stage identifier with the acquired data during data collection to form data records with stage attributes. In this way, data from different sources can be uniformly aligned based on the stage identifier during subsequent analysis, thereby avoiding misjudgments caused by inconsistent collection times.

[0037] By introducing a stage identification mechanism, the testing process can be managed in a refined manner without relying on manual intervention, thereby improving the stability and consistency of test results.

[0038] In one implementation, when the intelligent detection module determines that there is an anomaly in a certain detection stage during the fusion analysis process, it triggers the abnormal evidence solidification process.

[0039] Specifically, when an anomaly occurs, the intelligent detection module saves the operation information data, visual representation data, and physical quantity data within a preset time window before and after the anomaly occurs, and stores the data in association with the corresponding detection stage identifier.

[0040] In one implementation, to facilitate subsequent anomaly reproduction and problem analysis, the data saved in the anomaly evidence solidification process can be stored in a structured manner.

[0041] Specifically, after triggering the abnormal evidence solidification process, the intelligent detection module generates a corresponding abnormal identifier for each abnormal event and associates the abnormal identifier with the detection stage identifier. Visual representation data, physical quantity data, and operational information data before and after the abnormality occur are stored around the abnormal identifier.

[0042] In one embodiment, the visual representation data can be numbered and stored in chronological order, the physical quantity data can be stored in time series form, and the operation information data can be stored in the order in which the logs are generated.

[0043] By adopting a structured storage method, all evidence data related to a certain anomaly can be quickly located during subsequent analysis, thereby reducing the cost of manual screening and improving the efficiency of anomaly analysis.

[0044] In one embodiment, when the visual representation data is detected to be in a black screen state, the intelligent detection module combines the physical quantity data in the corresponding detection stage to make a joint judgment to distinguish between display link abnormality and system freeze abnormality, and records the judgment result in the test record.

[0045] In one embodiment, when the intelligent detection module determines an anomaly, it does not only output a binary result indicating whether an anomaly exists, but further determines the anomaly type in a hierarchical manner based on the stage of an anomaly occurrence, the duration of the anomaly, and the form of the anomaly.

[0046] Specifically, when an anomaly occurs during the power-on or firmware startup phase, and no effective display output of visual representation data is observed during the corresponding phase, while the physical quantity data exhibits a low-power stable state, the intelligent detection module can determine the anomaly as a startup link anomaly.

[0047] When an anomaly occurs during the operating system startup phase, and the visual representation data shows that the screen is frozen or the startup interface is stagnant for a long time, while the physical quantity data shows periodic fluctuation characteristics, the intelligent detection module can determine that the anomaly is a system initialization anomaly.

[0048] When an anomaly occurs during the operating system's runtime phase, and the visual representation data shows a black screen while the physical quantity data remains at a high power consumption level, the intelligent detection module can determine the anomaly as a system freeze anomaly.

[0049] By classifying and determining the anomaly type, more detailed reference information can be provided for subsequent fault location and problem analysis, thereby enhancing the engineering practical value of the detection results.

[0050] In one embodiment, the detection task can be executed in multiple rounds according to a preset scheduling strategy to improve the detection coverage.

[0051] Specifically, after receiving a testing task, the intelligent testing module can determine the number of times the testing task will be executed and the time interval between each round of testing based on the equipment type, testing requirements, or historical testing results. During the multi-round testing process, each round of testing follows the same phase division and data acquisition process.

[0052] In one implementation, if no anomaly is detected in a certain round of detection, the intelligent detection module can decide whether to terminate the subsequent detection rounds in advance according to preset rules; if an anomaly is detected in a certain round of detection, the intelligent detection module can trigger the supplementary execution of the subsequent detection rounds to further confirm the stability of the anomaly.

[0053] By introducing a detection task scheduling and multi-round detection mechanism, the impact of occasional anomalies on detection results can be effectively reduced, and the reliability of detection conclusions can be improved.

[0054] In one embodiment, when making anomaly determinations, the intelligent detection module can analyze the changing trends of physical quantity data over time, in addition to analyzing data based on single-point physical quantity data.

[0055] Specifically, the physical quantity acquisition module continuously collects power consumption data, current data, or voltage data during the detection process and forms time series data. Based on the time series data, the intelligent detection module calculates the magnitude or rate of change of the physical quantity within adjacent time windows.

[0056] When an abnormal fluctuation in physical quantity data is detected within a short period of time, and the abnormal fluctuation is related to the operating stage corresponding to the stage identifier, the intelligent detection module can use the abnormal fluctuation as an auxiliary judgment basis.

[0057] By analyzing the trends in physical quantity changes, potential faults can be identified in advance, even before obvious anomalies appear in visual representation data or operational information data, thereby improving the sensitivity of anomaly detection.

[0058] In one implementation, the functional modules work together through a preset data interaction mechanism.

[0059] In one embodiment, after completing multiple rounds of detection tasks, the intelligent detection module can also perform consistency verification on the detection results of each round.

[0060] Specifically, the intelligent detection module compares and analyzes the test records generated in multiple rounds of detection. When multiple rounds of detection output consistent judgment results in the same detection stage, the judgment result can be marked as a high-confidence result.

[0061] When the judgment results output by multiple rounds of detection in the same detection stage are different, the intelligent detection module can further analyze the visual representation data and physical quantity data in the corresponding rounds to determine the cause of the difference.

[0062] By introducing a consistency verification mechanism for test results, the impact of occasional anomalies or environmental disturbances on the final test conclusions can be reduced, thereby improving the reliability of the test report.

[0063] The computing agent module, visual acquisition module, and physical quantity acquisition module are each responsible for collecting different types of data, and send the collected data to the intelligent detection module through a unified data interface. The intelligent detection module caches the received data and stores it according to stage identifiers.

[0064] In one embodiment, the intelligent detection module can dynamically adjust the data processing priority during the detection process. For example, when an anomaly candidate is detected, data related to the anomaly is processed first to improve the response speed.

[0065] Through module collaboration and data flow mechanisms, the detection process can maintain good timing consistency even when multiple modules are working in parallel.

[0066] In one embodiment, the fully automated testing device can be deployed in a server testing environment to test the stability of the server during power-on startup and operation.

[0067] In another embodiment, the fully automated detection device can be deployed in an embedded system testing environment to detect abnormalities in embedded devices during firmware startup and system operation.

[0068] In a further embodiment, the fully automated testing device can also be deployed as an independent testing unit in a production line or laboratory environment for batch testing of the computing devices under test.

[0069] By supporting deployment methods in different application scenarios, the adaptability and versatility of the embodiments disclosed herein can be improved in practical applications.

[0070] Combination Figure 1 Based on the above-described fully automated testing method for computing devices, this disclosure provides a fully automated testing device for computing devices, comprising: The power control module 100 is used to control the power-on, power-off, or restart of the computing device under test. The computing agent module 200 is used to collect operational information data and send it to the intelligent detection module; The visual acquisition module 300 is used to acquire the display output of the computing device under test and generate visual representation data. The physical quantity acquisition module 400 is used to acquire physical quantity data related to the operation of the computing device under test. The intelligent detection module 500 is used to perform fusion analysis on the above data and generate test records and test reports.

[0071] Given that the solutions and methods for solving the problem are similar, the specific implementation of the device in the embodiments of this specification can be referred to the foregoing method embodiments, and will not be repeated here.

[0072] Combination Figure 9 As shown, this disclosure provides a computing device including a processor 904 and a memory 901. Optionally, the device may further include a communication interface 902 and a bus 903, wherein the processor 904, communication interface 902, and memory 901 can communicate with each other via the bus 903. The communication interface 902 can be used for information transmission and data interaction, such as transmitting operational information data, visual characterization data, physical quantity data, test records, or test reports during the testing process. The processor 904 can call logical instructions in the memory 901 to execute the fully automated testing method for computing devices described in the embodiments of this specification, thereby realizing functions such as stage division, data acquisition, timing alignment, fusion analysis and comparison, anomaly determination, and report generation for the computing device under test.

[0073] Furthermore, the logic instructions in the aforementioned memory 901 can be implemented in the form of software functional units, and when sold or used as an independent product, they can be stored in a computer-readable storage medium.

[0074] The memory 901, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of this disclosure. The processor 904 executes data processing and control logic by running the program instructions or modules stored in the memory 901, thereby realizing the fully automated detection method for computing devices in the above embodiments.

[0075] The memory 901 may include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function. The data storage area may store runtime information data, visual representation data, physical quantity data, stage judgment results, test records, and test reports generated during the execution of the detection task. Furthermore, the memory 901 may include high-speed random access memory and may also include non-volatile memory to meet the storage and retrieval requirements under different implementation methods.

[0076] This disclosure provides a computer program product, which is a software product that mainly implements the methods described in this specification through a computer program.

[0077] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method.

[0078] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0079] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code. It can also be a transient storage medium.

[0080] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “or,” as used in this application, means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the terms “comprise” and its variations “comprises” or “comprising” refer to the presence of stated features, integrals, steps, operations, elements, or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, or groups thereof. Unless otherwise specified, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on its differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, then the relevant parts can be referred to the description of the method section.

[0081] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] The methods and products disclosed in the embodiments herein (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks, and the operations or steps corresponding to different blocks in the flowchart and block diagram descriptions, may occur in a different order than indicated in the drawings or descriptions. For example, two consecutive blocks or operation steps may actually be executed substantially in parallel, or sometimes in reverse order, depending on the functions involved. Sometimes there is no specific order between different operations or steps. Each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A fully automated testing method for computing devices, characterized in that, include: S1. The power control module performs power-on, power-off, or restart control on the computing device under test and generates a detection task containing a detection stage identifier. The detection stage includes at least the power-on stage, firmware startup stage, operating system startup stage, and operating system running stage. S2. The computing agent module deployed on the computing device under test collects the operation information data and reports the operation information data to the intelligent detection module. The operation information data includes at least one of operation status data and log data. S3. The visual acquisition module acquires the visual representation data of the computing device under test at each detection stage and sends it to the intelligent detection module. S4. The physical quantity acquisition module collects physical quantity data related to the operation of the computing device under test and sends it to the intelligent detection module. The physical quantity data includes at least one of power consumption data, current data and voltage data. S5. The intelligent detection module performs fusion analysis and comparison based on the operation information data, the visual representation data, and the physical quantity data, outputs the judgment results of each detection stage, and forms a test record. S6. After the detection task is completed, a test report is generated based on the test record. The test report includes at least the stage in which the anomaly occurred and the differences from the preset normal baseline.

2. The method according to claim 1, characterized in that, The time reference for determining the stage boundary is based on the power control event, and the visual representation data is time-aligned with the operation information data and physical quantity data.

3. The method according to claim 1, characterized in that, The preset normal benchmark includes at least a reference visual benchmark and a state baseline benchmark, and establishes an association index with the hardware model, firmware version or operating system version; Before analysis and comparison, the intelligent detection module selects a matching preset normal benchmark based on the associated index.

4. The method according to claim 1, characterized in that, The analysis and comparison of the visual representation data includes: Perform normalization processing on the visual representation data; Perform Region of Interest (ROI) extraction on visual representation data; It performs similarity or difference calculations based on the normalized visual representation data and the reference visual benchmark to output visual difference items.

5. The method according to claim 1, characterized in that, When an anomaly is detected, an anomaly evidence solidification process is triggered. The anomaly evidence solidification process includes at least saving visual representation data and physical quantity data within the time window before and after the anomaly occurs, and triggering the computing agent module to collect and save anomaly-related log data.

6. The method according to claim 1, characterized in that, The intelligent detection module performs a joint judgment on black screen anomalies. The joint judgment is based at least on the brightness or synchronization characteristics of visual representation data and the power consumption or current characteristics of physical quantity data to distinguish between display link anomalies and system freeze anomalies, and writes the anomaly type into the test record.

7. A fully automated testing device for computing devices, characterized in that, include: The power control module is used to control the power-on, power-off, or restart of the computing device under test. The computing agent module is used to collect and report operational information data, wherein the operational information data includes at least one of operational status data and log data; The visual acquisition module is used to acquire and send visual representation data at each detection stage; A physical quantity acquisition module is used to acquire and transmit physical quantity data, wherein the physical quantity data includes at least one of power consumption data, current data, and voltage data. The intelligent detection module is used to fuse, analyze, and compare operational information data, visual representation data, and physical quantity data to output stage judgment results and form test records, and generate test reports after the detection is completed.

8. A fully automated testing device for computing devices, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running program instructions, the fully automated detection method for computing devices as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the fully automated detection method for computing devices as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, When the computer program product is run by the processor of the computer device, it executes the fully automated detection method for computing devices according to any one of claims 1 to 6.

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