Terminal system and resolution ratio compatible automatic test method and equipment based on AI

Abstract

The invention discloses an AI-based terminal system and resolution ratio compatible automatic testing method and device, and relates to the technical field of terminal application testing, and the method comprises the steps: receiving a test task issued by a test control end, and intelligently distributing the task to a preset single terminal device for testing and / or a virtual frame for testing through the AI; according to single-device dynamic simulation, AI is used for modifying system parameters, multiple versions and resolutions are dynamically simulated on one physical television device, and the compatibility of the resolutions is verified through the visual recognition technology; the virtual framework simulation uses AI to construct a hardware characteristic model, and simulates multiple hardware characteristics to cover multiple scenes in a virtual environment; two test paths of dynamic simulation and virtual framework simulation of a single terminal device work cooperatively through dual-path cooperation and cross validation, and a final report containing all test results and cross validation conclusions is generated. Multi-scene compatibility testing is achieved through single-device dynamic simulation and virtual frame simulation, the testing efficiency is high, and the credibility is high.

Description

Technical Field

[0001] This invention relates to the field of terminal application testing technology, and in particular to an AI-based automated testing method, apparatus, terminal device, and storage medium for terminal systems and resolution compatibility. Background Technology

[0002] Current smart TV compatibility testing primarily relies on two methods: physical device cluster testing and traditional simulator testing. Physical device cluster testing requires purchasing multiple TVs with different Android versions (Android 9-14) and resolutions (720P-4K) for manual or simple script testing, resulting in high hardware costs (over 100,000 RMB per batch) and complex maintenance; testing efficiency is low, with full-scale testing taking 3-5 days, making it difficult to adapt to rapid development cycles; and it heavily relies on manual judgment, leading to a low detection rate of hidden problems (such as momentary stuttering). Traditional simulator testing (based on Android TV simulators), while lower in cost, cannot reproduce real hardware characteristics (such as chip decoding and interface latency), resulting in significant differences between simulators and physical devices and insufficient test reliability. Furthermore, regardless of whether it's physical devices or simulators, when a new Android version or resolution appears, it requires waiting for device procurement or simulator updates, causing testing delays.

[0003] Therefore, existing technologies suffer from problems such as strong hardware dependence, low efficiency, and insufficient reliability; existing technologies still need to be improved and developed. Summary of the Invention

[0004] To address the technical problems of strong hardware dependence, low efficiency, and insufficient reliability in existing technologies, this invention provides an AI-based automated testing method, device, terminal equipment, and storage medium for terminal systems and resolution compatibility. This invention achieves multi-scenario compatibility testing through single-device dynamic simulation and virtual framework simulation, resulting in high testing efficiency and strong reliability.

[0005] The technical solution of this application is as follows: An AI-based automated testing method for terminal system resolution compatibility, comprising: Receive test tasks from the test control terminal, and intelligently allocate the tasks to preset single terminal device tests and / or virtual framework tests through AI based on the task requirements and risk assessment of the test tasks. When a task is assigned to a single terminal device for testing, AI dynamically modifies system parameters to control the dynamic simulation of multiple different system versions and resolutions on that single terminal device. During the simulation, preset test cases are executed, and test data is collected for automatic analysis to verify resolution compatibility. When the assigned task combination test is completed, a single terminal device test report is generated. When a task is assigned to virtual framework testing, the virtual framework is deployed on the server cluster based on the hardware characteristic model using AI. The virtual framework loads the system image and virtual hardware abstraction layer, simulates various hardware characteristics in the virtual environment, covers multiple scenarios, and performs cross-validation with physical devices to generate a virtual test report. Through a dual-path collaboration and cross-validation mechanism, the two test paths of dynamic simulation of a single terminal device and virtual framework simulation work together, and integrate the test reports of a single terminal device and the virtual test reports; for the risk issues found in either path, cross-validation is performed, and a final report containing all test results and cross-validation conclusions is generated.

[0006] The AI-based automated testing method for terminal systems and resolution compatibility includes, prior to the step of receiving test tasks from the test control terminal and intelligently allocating tasks to preset single terminal device tests and / or virtual framework tests via AI based on the task requirements and risk assessment of the test tasks: The test pre-configures a single-terminal device test for testing certain test tasks where hardware performance requirements exceed a first predetermined requirement, and a virtual framework test for testing regular test tasks.

[0007] The AI-based automated testing method for terminal systems and resolution compatibility includes the following steps: receiving test tasks from the test control terminal and intelligently allocating tasks to preset single terminal device tests and / or virtual framework tests using AI based on the task requirements and risk assessment of the test tasks. Obtain test tasks to perform multi-version and multi-resolution compatibility testing on newly developed terminal applications; The test tasks are intelligently allocated using AI. Test tasks with hardware performance requirements exceeding the first predetermined requirement are assigned to a single terminal device for testing in a real hardware environment. Meanwhile, other routine compatibility test tasks are assigned to a virtual framework for testing.

[0008] The AI-based automated testing method for terminal system and resolution compatibility includes the following steps: when a task is assigned to a single terminal device for testing, system parameters are dynamically modified using AI to control the dynamic simulation of multiple different system versions and resolutions on the single terminal device; during the simulation, preset test cases are executed, and test data is collected for automatic analysis to verify resolution compatibility; when the assigned task combination test is completed, a test report for the single terminal device is generated, including: When a task is assigned to a single terminal device for testing, the system parameters of the single terminal device are dynamically modified through AI to control the simulation of running different Android versions and resolutions on the single terminal device. During the simulation, preset test cases are executed and user interface, logs, and performance data are collected. It also automatically analyzes the collected data through a preset AI visual recognition module and performance monitoring module to determine whether there are display, functional or performance problems, and automatically determines resolution compatibility issues; Once the assigned task combination test is completed, a test report for a single terminal device is generated.

[0009] The AI-based automated testing method for terminal systems and resolution compatibility includes the following steps: when a task is assigned to a virtual framework for testing, a virtual framework is deployed on a server cluster using AI based on a hardware characteristic model. The virtual framework loads a system image and a virtual hardware abstraction layer, simulating various hardware characteristics in a virtual environment, covering multiple scenarios, and performing cross-validation with physical devices to generate a virtual test report. When a task is assigned to a virtual framework test, a preset virtual framework is deployed on a server cluster using AI based on a hardware characteristic model. The virtual framework loads the Android image and the virtual hardware abstraction layer to restore the characteristics of real hardware. Parallel test tasks are allocated through the virtual framework to simulate user operations and collect data. Detect and classify anomalies using AI; For the detected issues, physical TV verification is performed, and a virtual test report is generated.

[0010] The AI-based automated testing method for terminal systems and resolution compatibility includes the following steps: First, through a dual-path collaboration and cross-validation mechanism, two testing paths—dynamic simulation of a single terminal device and virtual framework simulation—are worked collaboratively, and the single terminal device test report and virtual test report are integrated. Second, for risks discovered in either path, cross-validation is performed, and a final report containing all test results and cross-validation conclusions is generated. Through a pre-set dual-path collaboration and cross-validation mechanism, the system controls the collaborative operation of two test paths: dynamic simulation of a single terminal device and virtual framework simulation, and integrates the test reports of a single terminal device and the virtual test reports. For risks discovered in any path, cross-validation is performed; when potential problems are discovered in a virtual environment, they are sent to a single terminal device for reproduction and confirmation, or vice versa; testing efficiency and accuracy are balanced through collaborative work. Finally, a final report containing all test results and cross-validation conclusions is generated.

[0011] The AI-based automated testing method for terminal system and resolution compatibility further includes the step of deploying a virtual framework on a server cluster using AI based on a hardware characteristic model: Obtain performance logs from a specified number of real terminal devices; The performance logs of the specified number of real terminal devices are used as training data and input into the hardware characteristic model constructed by AI for training to obtain the trained virtual framework. The virtual framework is deployed on a server cluster.

[0012] An AI-based automated testing device for terminal systems and resolution compatibility, wherein the device includes: The test task receiving and allocation module is used to receive test tasks issued by the test control terminal, and intelligently allocate the tasks to preset single terminal device tests and / or virtual framework tests through AI based on the task requirements and risk assessment of the test tasks. The single-device dynamic simulation testing module is used to dynamically modify system parameters through AI when a task is assigned to a single terminal device for testing. It controls the dynamic simulation of running multiple different system versions and resolutions on a single terminal device. During the simulation, it controls the execution of preset test cases and collects test data for automatic analysis to verify resolution compatibility. When the assigned task combination test is completed, a single terminal device test report is generated. The virtual framework simulation test module is used to deploy a virtual framework on a server cluster based on a hardware characteristic model using AI when a task is assigned to virtual framework testing. The virtual framework loads a system image and a virtual hardware abstraction layer to simulate various hardware characteristics in a virtual environment, covering multiple scenarios, and cross-validates with physical devices to generate a virtual test report. The dual-path collaboration and cross-validation module is used to coordinate the two test paths of dynamic simulation of a single terminal device and virtual framework simulation through a dual-path collaboration and cross-validation mechanism, and integrate the test reports of a single terminal device and the virtual test reports; cross-validate the risk issues found in either path, and generate a final report containing all test results and cross-validation conclusions.

[0013] A terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs including steps for performing any of the methods described herein.

[0014] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it enables an electronic device to perform the steps of any of the methods described.

[0015] As can be seen from the above, the present application provides an AI-based automated testing method, apparatus, terminal device, and storage medium for terminal system and resolution compatibility. This invention can quickly and comprehensively complete compatibility testing of television applications, while reducing hardware costs, improving testing efficiency, and increasing scene coverage and accuracy. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the AI-based automated testing method for terminal system and resolution compatibility according to Embodiment 1 of the present invention.

[0018] Figure 2 This is a schematic diagram of the overall framework of the AI-based terminal system and resolution compatibility automated testing method of Embodiment 2 of the present invention.

[0019] Figure 3 This is a schematic diagram of the single-device dynamic simulation process of the AI-based terminal system and resolution compatibility automated testing method of Embodiment 2 of the present invention.

[0020] Figure 4 This is a schematic diagram of the virtual framework simulation process of the AI-based terminal system and resolution compatibility automated testing method according to Embodiment 2 of the present invention.

[0021] Figure 5 The present invention provides a schematic diagram of an embodiment of an AI-based terminal system and a resolution-compatible automated testing device.

[0022] Figure 6 This is a block diagram illustrating the internal structure of the terminal device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0024] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0025] Existing smart TV compatibility testing relies on two types of solutions: one is physical device clusters, which involves purchasing multiple TVs with different Android versions (Android 9-14) and resolutions (720P-4K) and conducting manual or simple script tests; the other is traditional emulators, which simulate the environment based on Android TV emulators, but cannot reproduce the real hardware characteristics (such as chip decoding and interface latency).

[0026] The existing technology has the following drawbacks: 1) High hardware cost and complex maintenance; 2) Low testing efficiency and difficulty in adapting to rapid iteration; 3) Reliance on manual judgment, resulting in a low detection rate of hidden problems (such as momentary lag); 4) Significant differences between simulators and physical devices, leading to insufficient test reliability; 5) Lagging support for new versions / resolutions (requiring waiting for equipment procurement or simulator updates).

[0027] To address the aforementioned technical issues, this invention provides an AI-based automated testing method for terminal system and resolution compatibility, as detailed in the following embodiments.

[0028] Example 1 like Figure 1 As shown in the figure, an AI-based automated testing method for terminal system and resolution compatibility according to an embodiment of the present invention includes the following steps: Step S100: Receive the test task issued by the test control terminal, and intelligently allocate the task to the preset single terminal device test and / or virtual framework test through AI according to the task requirements and risk assessment of the test task. In this embodiment, the test control terminal refers to the terminal device, such as a computer or dedicated control host, that uniformly initiates, manages, and monitors test tasks. It is the core scheduling entry point for the test process and can realize functions such as task distribution, progress viewing, and result summarization. The risk assessment in this embodiment refers to the process of quantitatively evaluating potential risks of a test task, such as high incidence of compatibility issues or excessively long test times, based on the complexity of the test task (e.g., the number of system versions / resolutions involved), the frequency of historical test issues, and business priorities, using AI algorithms.

[0029] The single-terminal device test in this embodiment refers to the test conducted based on a single real physical television terminal, which is different from the traditional multi-device cluster test. It completes multi-scenario simulation using only a single device.

[0030] The virtual framework test in this embodiment refers to a test method that uses a virtual test environment built on a server cluster to simulate hardware characteristics and system environment through software. This method differs from traditional Android TV emulators and has stronger hardware simulation capabilities.

[0031] In the specific implementation of this step, the test task issued by the test control terminal is received first. In this invention, the AI ​​(Artificial Intelligence) module first analyzes the task requirements, such as the Android version to be covered, resolution range, test case type, etc., and combines the risk assessment results. If a certain version and resolution combination has a lot of historical issues, it is judged as high risk. The intelligent decision is made on the task allocation method, which can be assigned to a single terminal device for testing, assigned to a virtual framework for testing, or both paths can be executed simultaneously to achieve optimal matching of test resources. For example, when a test task is received, if the analysis shows that the application to be tested may have some functions with high hardware performance requirements, this invention uses AI to decide to assign some test tasks to a single terminal device for verification in a real hardware environment, while assigning most of the routine compatibility test tasks to the virtual framework for testing to improve efficiency. As can be seen, this invention uses AI to allocate tasks on demand, eliminating the need to simultaneously deploy all tasks to dual-path testing, thus improving resource utilization of individual terminals and server clusters. Furthermore, this invention prioritizes high-risk tasks by assigning them to suitable test paths through risk assessment; for example, tasks with high hardware dependencies are assigned to single terminals, improving problem detection efficiency. This invention also supports single-path or dual-path collaborative testing, dynamically adjusting based on test urgency and task complexity to adapt to rapid development cycles. Moreover, this invention automatically completes task parsing and allocation using AI, replacing traditional manual test planning, reducing human error, and improving test initiation efficiency.

[0032] Step S200: When a task is assigned to a single terminal device for testing, the system parameters are dynamically modified using AI to control the dynamic simulation of multiple different system versions and resolutions on the single terminal device; during the simulation, preset test cases are executed and test data is collected for automatic analysis to verify resolution compatibility; when the assigned task combination test is completed, a single terminal device test report is generated. The dynamic modification of system parameters in this embodiment refers to a technical means of adjusting the system configuration of the terminal device in real time through AI algorithms, enabling switching between different Android versions and resolution parameters without manual flashing or device replacement. The test cases in this embodiment refer to standardized test scenarios and operation procedures preset to verify TV compatibility, such as video playback, interface switching, and API calls, and are the core basis for test execution.

[0033] In this embodiment, test data acquisition refers to automatically capturing key indicators during the test process, such as frame rate, number of stutters, resolution display accuracy, and interface response time, without the need for manual recording.

[0034] The single-terminal device test report in this embodiment refers to a standardized document that summarizes the results of multi-scenario testing of a single terminal, including compatibility performance, problem details, data indicators, etc. under various system versions / resolutions.

[0035] In the specific implementation of step S200, when a task is assigned to a single terminal device for testing, this invention automatically and dynamically rewrites the system parameters of the terminal device using AI technology. Different Android versions (e.g., 9-14) and resolutions (e.g., 720P-4K) are simulated sequentially on the same physical TV, eliminating the need to purchase multiple devices. During the simulation, the system of this embodiment automatically executes preset test cases, synchronously collects test data such as frame rate, display effect, and functional integrity, and completes automatic analysis, focusing on verifying compatibility issues such as interface adaptation and functional normality under different resolutions. After all assigned version and resolution combinations have been tested, a single terminal device test report containing detailed results is automatically generated.

[0036] This embodiment of the steps significantly reduces hardware costs because the present invention uses a single terminal to simulate multiple scenarios, replacing the traditional multi-device cluster approach, avoiding the high costs of bulk equipment purchases, and reducing equipment maintenance complexity. Furthermore, the present invention improves testing efficiency; no manual device switching or parameter configuration is required, and full-scale combined testing can be completed in just a few hours, far exceeding the traditional 3-5 day cycle, adapting to rapid iteration needs. Moreover, the present invention automatically collects data on hidden issues such as momentary stuttering and frame rate fluctuations, replacing manual judgment, avoiding the omission of hidden problems, and improving the problem detection rate. Furthermore, the present invention eliminates the need for manual firmware flashing or parameter debugging; AI-automated control throughout the process lowers the operational threshold for testers and reduces labor costs.

[0037] Step S300: When the task is assigned to the virtual framework test, the virtual framework is deployed on the server cluster by AI according to the hardware characteristic model. The system image and virtual hardware abstraction layer are loaded through the virtual framework. Various hardware characteristics are simulated in the virtual environment, covering multiple scenarios, and cross-validated with physical devices to generate a virtual test report. The hardware characteristic model in this embodiment refers to an AI model trained based on massive amounts of physical TV hardware parameters, such as chip model, decoding capability, interface type, and latency characteristics, which can accurately replicate the operating logic of real hardware. The server cluster in this embodiment refers to a computing cluster composed of multiple servers, which has powerful parallel processing and virtual simulation capabilities and can deploy multiple virtual test environments simultaneously.

[0038] The virtual framework in this embodiment refers to a software framework running on a server cluster. It can load system images and virtual hardware abstraction layers, serving as the core carrier for simulating hardware characteristics.

[0039] In this embodiment, the system image refers to a packaged installation file for different Android TV versions, used to quickly build the corresponding system in a virtual environment. The virtual hardware abstraction layer described in this embodiment refers to a software layer that simulates physical hardware interfaces and functions, replicating hardware characteristics such as chip decoding and interface latency, thus bridging the gap between traditional emulators and physical devices.

[0040] The cross-validation in this embodiment is a cross-validation of virtual and physical data. Specifically, it refers to the process of comparing the test results of the virtual framework with the test results of a single terminal device to verify the credibility of the virtual test data.

[0041] In the specific implementation of step S300, when a task is assigned to virtual framework testing, AI deploys a dedicated virtual framework on a server cluster based on a pre-trained hardware characteristic model. This virtual framework loads the corresponding Android version's system image and virtual hardware abstraction layer, accurately simulating the core hardware characteristics of a physical TV in a virtual environment, such as chip decoding, interface latency, and hardware drivers. This covers compatibility testing across multiple versions, resolutions, and scenarios. After testing, the virtual test results are cross-validated with test data from a single terminal device to correct deviations in the virtual environment, ultimately generating an accurate virtual test report.

[0042] This embodiment overcomes the limitations of traditional simulators by replicating real hardware characteristics through a virtual hardware abstraction layer, significantly reducing the testing discrepancies between virtual and physical devices and solving the problem of insufficient reliability in traditional simulators. Furthermore, this invention eliminates the need to purchase excessive physical equipment, relying on server clusters to achieve parallel testing across multiple scenarios, balancing low cost and high coverage. This invention can also expand testing scenarios, flexibly simulating the hardware characteristics of different brands and models of televisions, covering more edge scenarios and avoiding scenario omissions due to insufficient physical devices. Moreover, this invention can quickly adapt to new features; when a new Android version or resolution appears, only the system image and hardware feature model need to be updated, without waiting for physical device procurement or traditional simulator updates, thus solving the problem of testing lag.

[0043] Step S400: Through the dual-path collaboration and cross-validation mechanism, the two test paths of dynamic simulation of a single terminal device and virtual framework simulation are made to work together, and the test reports of a single terminal device and virtual test reports are integrated; for the risk issues found in any path, cross-validation is performed, and a final report containing all test results and cross-validation conclusions is generated.

[0044] The dual-path collaboration and cross-validation mechanism in this embodiment is a standardized process that allows dynamic simulation of a single terminal (physical path) and virtual framework simulation (virtual path) to work synchronously and verify each other, ensuring accurate test results. Cross-validation of risk issues in this embodiment refers to reproducing and testing compatibility issues (such as functional abnormalities or lag) found in one path in another path to verify the authenticity of the problem and locate its root cause (whether it is hardware-related or system-related).

[0045] The final report generated in this embodiment is a comprehensive report that integrates dual-path test data, problem details, and cross-validation conclusions, providing a complete basis for product compatibility optimization.

[0046] In the specific implementation of step S400, a pre-set dual-path collaboration mechanism is used to achieve parallel operation and data interoperability between single-terminal device testing and virtual framework testing. First, the core data from both test reports, such as compatibility results for various scenarios, problem details, and key indicators, are integrated. Then, for high-risk issues discovered in either path, such as decoding anomalies detected in the virtual path or resolution adaptation errors detected in the physical path, targeted reproduction tests are conducted in the other path to verify whether the problem truly exists and whether it is affected by the test path. Finally, a final report is generated, containing all test results, root cause analysis of the problem, and cross-validation conclusions, providing comprehensive support for R&D optimization.

[0047] This invention employs dual-path cross-validation to eliminate testing biases from single paths, such as simulation errors in virtual paths and individual device differences in physical paths, ensuring accurate problem localization and enhancing the authority of test results. Furthermore, this invention enables closed-loop testing because it integrates dual-path data, avoiding scenario omissions in single-path testing and achieving full-scenario coverage through physical coverage and virtual completion, solving the problem of incomplete traditional testing scenarios. This invention improves problem-solving efficiency by quickly locating the root cause of problems through cross-validation, clarifying optimization directions, and reducing troubleshooting time. Moreover, the final report integrates all data and conclusions, eliminating the need for R&D personnel to compare two reports manually, reducing communication costs and facilitating rapid decision-making on product optimization solutions.

[0048] The present invention will be further illustrated by a specific example below: For example, a smart TV manufacturer needs to conduct compatibility testing on its newly developed TV app to ensure that the app can function properly on TVs with different Android versions (such as Android 9, 10, 11, 12) and different resolutions (such as 720P, 1080P, 4K).

[0049] Using traditional methods with existing technology would require purchasing at least 12 physical TVs with different configurations (4 Android versions x 3 resolutions) or using a traditional emulator. Purchasing physical equipment is costly, the testing cycle is long, and manual judgment is inefficient. While traditional emulators are cheaper, they cannot accurately simulate the chip decoding and interface latency of real TVs, leading to unreliable test results.

[0050] The following steps are performed using the method of the present invention: 1) Test task issuance: The control system issues test tasks through the test control terminal, requiring multi-version and multi-resolution compatibility testing of the newly developed TV application.

[0051] 2) AI Decision Scheduling: Tasks are received through the AI ​​decision scheduling module. Considering that the application may have some functions with high hardware performance requirements, this embodiment uses AI to decide to allocate some test tasks to a single terminal device for verification in a real hardware environment, while allocating most routine compatibility test tasks to the virtual framework to improve efficiency.

[0052] 3) Single-device dynamic simulation steps: AI automatically sends commands to control a single 4K+Android11+ baseline TV. By modifying system parameters, such as ro.build.version.sdk, it dynamically simulates a TV running Android 9, Android 10, and Android 12, and simulates 720P, 1080P, and 4K resolutions respectively.

[0053] On this TV, the system automatically installs and runs the TV application, executing preset test cases, such as opening the application, playing videos, switching channels, and adjusting settings.

[0054] During testing, AI visual recognition modules, such as YOLOv5 (the fifth-generation object detection algorithm), monitor the screen display in real time, automatically identifying resolution compatibility issues such as UI misalignment, blurry text, and screen tearing. Simultaneously, a performance monitoring module records data such as CPU usage, memory usage, and frame rate to determine if performance issues such as stuttering or slow response exist.

[0055] For example, when simulating Android 9 and 720P resolution, AI can detect that a button in the application is not fully displayed and immediately mark it as a problem.

[0056] 4) Virtual framework simulation steps: Deploy a virtual framework on the server cluster through AI automatic control, load multiple Android versions of images and virtual hardware abstraction layer (HAL) to accurately simulate the decoding characteristics of different TV chips and HDMI interface latency.

[0057] The virtual framework runs multiple test instances in parallel, each simulating a different Android version and resolution combination, simultaneously running the TV app and executing test cases. AI is used to detect anomalies in the virtual environment; for example, when simulating a TV with a specific chip, if a decoding error is detected when the app plays 4K video, it is marked as a high-risk issue.

[0058] 5) Dual-path collaboration and cross-validation: In this embodiment, the AI ​​decision scheduling module collects single-device test reports and virtual test reports.

[0059] If a 4K video playback decoding error is detected in the virtual framework, the AI ​​is controlled to mark it as a high-risk issue and instructs the system to re-verify the problem on a real 4K TV using a single terminal device. Verification on the real TV confirms the existence of the decoding error.

[0060] If a UI misalignment issue is discovered during single-device testing, this invention will also attempt to reproduce it in a virtual framework using AI control to confirm whether the issue is widespread.

[0061] Finally, the system generates a detailed report, pointing out which compatibility issues exist for the TV app on which Android versions and resolutions, such as UI misalignment, video decoding errors, and performance lag, and provides detailed logs and screenshots for developers to fix.

[0062] As can be seen from the above, this invention enables manufacturers to quickly and comprehensively complete compatibility testing of TV applications with lower hardware costs (requiring only one benchmark TV and server cluster), higher efficiency (reducing the testing cycle from 3-5 days to 8 hours), higher scene coverage (95%), and higher accuracy (95%).

[0063] In a further embodiment of the present invention, the AI-based automated testing method for terminal system and resolution compatibility includes, prior to step S100: S01. Pre-set up single-terminal device testing for testing certain test tasks where hardware performance requirements exceed a first predetermined requirement, and set up virtual framework testing for testing regular test tasks.

[0064] In this embodiment of the invention, two types of test paths are pre-defined and assigned tasks. One type is single terminal device testing, which is specifically used to undertake test tasks with high hardware performance requirements that exceed the first predetermined standard. The other type is virtual framework testing, which is specifically used to handle regular test tasks with lower hardware performance requirements. The appropriate scenarios for each type are clearly defined, which lays the foundation for subsequent task allocation and dual-path collaboration.

[0065] In this invention, the first predetermined standard (hardware performance requirements) is used as an example. In the context of smart TV compatibility testing, the first predetermined standard can be quantified into specific hardware performance indicators. Tasks exceeding these indicators require testing with a single terminal device. The following are examples relevant to this scenario: Chip decoding performance: It needs to support 4K 120fps / H.265 high bitrate video decoding, or Dolby Vision, HDR10+ dynamic decoding. These tasks, which require high computing power from the actual chip, exceed the predetermined standard of ordinary 4K 60fps decoding.

[0066] Interface real-time performance: The HDMI 2.1 interface was tested for low latency in games (≤5ms) and the USB 3.2 interface for high-speed transmission stability, exceeding the predetermined standards for basic connectivity of ordinary interfaces.

[0067] Hardware driver adaptation: This involves compatibility testing of TV-specific hardware modules, such as backlight zone control, image quality chip calibration, and Bluetooth 5.3 wireless transmission, exceeding the predetermined standards of basic system functions.

[0068] Extreme performance scenario: It is necessary to simulate the hardware load under multiple concurrent tasks (video playback + game running + screen mirroring synchronization), which exceeds the predetermined standard of normal single-task operation.

[0069] These scenarios are highly dependent on real hardware characteristics, and virtual frameworks cannot accurately replicate them. Therefore, they are classified as tasks that exceed the first predetermined standard and require testing on a single terminal device to ensure reliability.

[0070] In a further embodiment of the present invention, the AI-based automated testing method for terminal system and resolution compatibility includes step S100 specifically comprising: S101. Obtain a test task to perform multi-version and multi-resolution compatibility testing on newly developed terminal applications; S102. The test tasks are intelligently allocated using AI. Test tasks whose hardware performance requirements exceed the first predetermined requirements are allocated to a single terminal device for testing in a real hardware environment. Meanwhile, other routine compatibility test tasks are allocated to a virtual framework for testing.

[0071] In this embodiment of the invention, testers can issue test tasks through the test control terminal, requesting multi-version and multi-resolution compatibility testing of newly developed TV applications. The AI ​​decision-making and scheduling module then receives the tasks. Considering that the application to be tested may have some functions with high hardware performance requirements, the AI ​​decides to allocate some test tasks for these functions to a single terminal device for verification in a real hardware environment, while allocating most routine compatibility test tasks to a virtual framework to improve efficiency.

[0072] For example, testers can issue test tasks via the test control terminal, requesting multi-version and multi-resolution compatibility testing of a newly developed TV application. After receiving the task, the AI ​​decision-making and scheduling module, considering that the application may have some hardware-intensive functions (such as 4K HDR video playback), decides to assign these hardware-sensitive test tasks to a "single terminal device" for verification in a real hardware environment to ensure accuracy. Simultaneously, most routine UI display and functional logic compatibility test tasks are assigned to a "virtual framework" to improve testing efficiency, as these tasks have relatively low dependence on hardware characteristics.

[0073] This invention utilizes AI-powered intelligent decision-making to select the most suitable test path based on task characteristics (such as hardware dependency and risk level), thereby balancing testing efficiency and accuracy. For example, routine compatibility tests with low hardware requirements are prioritized for more efficient virtual frameworks; high-risk or hardware-sensitive issues are assigned to real-world verification on a single device. This makes the entire testing process more intelligent and efficient.

[0074] In a further embodiment of the present invention, the AI-based automated testing method for terminal system and resolution compatibility includes step S200 specifically comprising: S201. When a task is assigned to a single terminal device for testing, the system parameters of the single terminal device are dynamically modified through AI to control the simulation of different Android versions and resolutions on the single terminal device. In this embodiment of the invention, when a task is assigned to a single terminal device for testing, the AI ​​modifies the system parameters of the single physical TV (such as `ro.build.version.sdk`) to simulate running different Android versions and resolutions. For example, a physical TV factory-configured with Android 11 and 4K resolution, when testing the compatibility of an application in an Android 9 environment, controls the AI ​​to modify the TV's `ro.build.version.sdk` parameter (a read-only system attribute parameter of the Android system) to set its virtual Android version number to the SDK version corresponding to Android 9. Simultaneously, the AI ​​control can also modify display-related system parameters to dynamically adjust the screen resolution to 720P or 1080P, thereby simulating multiple different test environments on the same physical device.

[0075] Thus, this step provides an efficient and low-cost method for simulating multiple versions / resolutions on a single device. By modifying system parameters, it avoids the high cost and complex maintenance of purchasing multiple physical devices, greatly improving testing efficiency and flexibility.

[0076] S202. During the simulation, control the execution of preset test cases and collect user interface, log and performance data; In the simulation process of this invention, the system automatically installs and runs the TV application and executes preset test cases, such as opening the application, playing videos, switching channels, and adjusting settings.

[0077] S203, and through the preset AI visual recognition module and performance monitoring module, automatically analyzes the collected data to determine whether there are display, functional or performance problems, and automatically determines resolution compatibility issues; In this embodiment, during the dynamic simulation of a single device, an AI visual recognition module, such as YOLOv5 (the fifth-generation object detection algorithm) UI recognition, is used to automatically analyze the collected UI interface data, determine whether there are display, functional or performance problems, and automatically determine resolution compatibility issues.

[0078] For example, when an AI instructs a single 4K+ Android 11+ benchmark TV to modify system parameters to simulate running Android 9 at 720P resolution, the system automatically installs and runs TV applications. At this time, the AI ​​visual recognition module (such as YOLOv5) monitors the screen display in real time, automatically identifying resolution compatibility issues such as UI misalignment, blurry text, and screen tearing. For instance, if the AI ​​detects that a button in an application is not fully displayed at 720P resolution, it immediately marks it as a problem without requiring manual comparison.

[0079] As can be seen, this embodiment utilizes AI visual recognition technology to automate and intelligently detect UI display issues, avoiding the subjectivity and inefficiency of traditional manual judgment, and improving the detection rate and testing efficiency of resolution compatibility problems. This step specifies the particular AI visual recognition tool (YOLOv5), making its implementation more concrete and operable.

[0080] S204. Once the assigned task combination test is completed, a test report for a single terminal device is generated.

[0081] In a further embodiment of the present invention, the AI-based automated testing method for terminal system and resolution compatibility includes step S300 specifically comprising: S301. When a task is assigned to a virtual framework test, a preset virtual framework is deployed on the server cluster using AI based on the hardware characteristic model. The step of deploying a virtual framework on a server cluster using AI based on a hardware characteristic model further includes: obtaining performance logs of a specified number of real terminal devices; using the performance logs of the specified number of real terminal devices as training data and inputting them into the hardware characteristic model constructed by AI for training to obtain the trained virtual framework; and deploying the virtual framework on the server cluster.

[0082] In this embodiment, a server cluster is used to deploy the virtual framework, meaning the virtual framework simulation is deployed on the server cluster. For example, when large-scale compatibility testing of a TV application is required, AI is used to deploy the virtual framework on a cluster composed of multiple high-performance servers. This server cluster can simultaneously launch dozens or even hundreds of independent virtual instances, each simulating a different Android version and resolution combination. For example, one instance might simulate Android 9, 720P, while another simulates Android 12, 4K. They can run test cases in parallel, significantly reducing the time required for full-scale combination testing.

[0083] Thus, this invention provides powerful computing and parallel processing capabilities, enabling the virtual framework to run multiple test instances simultaneously, significantly improving testing efficiency and scenario coverage. Through the elastic scaling capabilities of the server cluster, resources can be dynamically adjusted according to testing needs, achieving efficient and large-scale virtual simulation testing.

[0084] S302. Load the Android image and virtual hardware abstraction layer through the virtual framework to restore the real hardware characteristics; In this embodiment of the invention, the virtual framework loads an Android image and a virtual HAL layer (virtual hardware abstraction layer) in the virtual framework simulation to highly replicate the characteristics of real hardware. For example, when the virtual framework needs to simulate a TV equipped with a specific video decoding chip, it not only loads the system image of the corresponding Android version but also a virtual HAL layer specifically designed for that decoding chip. This virtual HAL layer simulates the interaction between the real hardware abstraction layer and the underlying hardware (such as the video decoder), including the sending of decoding commands, the processing of data streams, and possible latency or error modes. Thus, when the TV application plays video in the virtual environment, it can call the virtual decoder through the virtual HAL layer, just like on a real device, thereby accurately simulating the chip's decoding performance and potential compatibility issues.

[0085] This invention introduces a Virtual Hardware Abstraction Layer (HAL), enabling the virtual environment to simulate the behavior of real hardware at a deeper level, including low-level characteristics such as chip decoding and interface latency. This is closer to a real hardware environment than simply loading an Android image, thereby improving the reliability and accuracy of virtual testing.

[0086] S303. Parallel test tasks are allocated through the virtual framework to simulate user operations and collect data; S304. Detect anomalies and classify them using AI; S305. For the detected problems, perform physical TV verification and generate a virtual test report.

[0087] In this embodiment of the invention, a virtual framework is deployed on a server cluster based on a hardware characteristic model (trained from performance logs of 100+ TVs), loading an Android image and a virtual HAL layer to highly replicate real hardware characteristics (with an error of less than 5%). The virtual framework allocates parallel test tasks to simulate user operations and collect data. AI detects and classifies anomalies. For critical issues detected, physical TV verification is performed to ensure the accuracy of the virtual environment. Finally, a virtual test report is generated.

[0088] In a further embodiment of the present invention, the AI-based automated testing method for terminal system and resolution compatibility includes step S400, which specifically includes: The S401 uses a pre-defined dual-path collaboration and cross-validation mechanism to control the collaborative operation of two test paths: dynamic simulation of a single terminal device and virtual framework simulation, and integrates the test report of a single terminal device and the virtual test report. S402. For any risk issues discovered in any path, cross-validation is controlled; when potential issues are discovered in a virtual environment, they are sent to a single terminal device for reproduction and confirmation, or vice versa; test efficiency and accuracy are balanced through collaborative work. S403. Finally, generate a final report containing all test results and cross-validation conclusions.

[0089] In this embodiment, a dual-path collaboration and cross-validation mechanism is employed to integrate single-device test results and virtual test results. For high-risk issues discovered in either path, the system performs cross-validation. For example, a potential problem discovered in a virtual environment may be reproduced and confirmed on a single device, or vice versa. This collaborative approach balances testing efficiency and accuracy, ensures the comprehensiveness and reliability of the tests, and ultimately generates a final report containing all test results and cross-validation conclusions.

[0090] The present invention will be further described in detail below through specific application examples: This specific application embodiment presents an AI-based automated testing method for terminal systems and resolution compatibility. Figure 2 The system framework shown, and the AI-based automated testing method for terminal system and resolution compatibility described in this specific application embodiment, include the following steps: S10: The test control terminal obtains the test task and then proceeds to S11; S11, AI Decision Scheduling: The AI ​​system is responsible for the decision-making and scheduling of test tasks, coordinating the work of single terminal device testing and virtual framework testing, and entering S20, S30 and / or S40 respectively. S20, Single terminal device test, then proceed to S29; S29. Single device test results, and then feed the single device test results back to AI decision-making and scheduling; S30, virtual framework test, then proceed to S39; S39. The virtual test results are then fed back to the AI ​​decision-making and scheduling system. S40, cross-validate high-risk issues, then proceed to S15; S41. Generate the final report; In this embodiment of the invention, by using dual-path collaboration and cross-validation, the two test paths of single-device dynamic simulation and virtual framework simulation work together to cross-validate high-risk issues, thereby balancing test efficiency and accuracy.

[0091] Among them, Path 1: Single device testing process is as follows Figure 3 As shown, the single-TV simulation test process includes the following steps: S21, Deploy the agent and data collection tools; and proceed to S22; S22, Configure the test task; and proceed to S23; S23, AI generates version parameter script, and proceeds to S24; S24. Switch Android version and resolution, then enter S25; S25. Execute the test case, collect UI / logs / performance data, and then proceed to S26; S26, AI identifies display / function / performance issues and proceeds to S27; S27. Determine whether all combinations have completed the test. If not, return to S24; if yes, proceed to S28. S28. Generate equipment report.

[0092] Among them, path 2: virtual framework simulation test process is as follows Figure 4 As shown, it includes the following steps: S31. Deploy the hardware model and image repository, then proceed to step S32; S32. Assign parallel test tasks, then proceed to S33; S33, Load Android image + virtual HAL layer, then proceed to S34; S34. Simulate operation to collect data, then proceed to S35; S35. AI detects anomalies and classifies them, then proceeds to S36; S36. Determine if there is a fatal system problem. If yes, proceed to step S37; otherwise, proceed to step S38. S37, verify the physical TV, then proceed to S38; S38. Generate a virtual framework test report.

[0093] As can be seen, the present invention Figure 2 , Figure 3 and Figure 4 In a specific embodiment, a dual-path collaborative architecture combined with AI technology is used to address the pain points of existing compatibility testing.

[0094] First, the AI ​​decision-making and scheduling module receives the test tasks issued by the test control terminal, and intelligently allocates the tasks to single terminal device testing and / or virtual framework testing based on task requirements and risk assessment.

[0095] When a task is assigned to a single terminal device for testing, the AI ​​dynamically modifies the system parameters of that physical TV, such as `ro.build.version.sdk`, to simulate running different Android versions and resolutions. During the simulation, the system executes preset test cases and collects UI interface, logs, and performance data. Subsequently, AI visual recognition modules, such as the YOLOv5 UI recognition and performance monitoring modules, automatically analyze the collected data to determine if there are any display, functional, or performance issues, and automatically identify resolution compatibility problems. Once all combined tests are completed, a device test report is generated.

[0096] When a task is assigned to a virtual framework for testing, the AI ​​deploys the virtual framework on a server cluster based on the hardware characteristic model, loading the Android image and a virtual HAL layer to highly replicate real hardware characteristics. The virtual framework assigns parallel test tasks, simulating user operations and collecting data. The AI ​​detects and categorizes anomalies. For critical issues detected, physical television verification is performed to ensure the accuracy of the virtual environment. Finally, a virtual test report is generated.

[0097] Finally, the dual-path collaboration and cross-validation mechanism integrates single-device test results and virtual test results. For high-risk issues discovered in either path, the system performs cross-validation; for example, a potential problem discovered in a virtual environment may be reproduced and confirmed on a single device, or vice versa. This collaborative approach balances testing efficiency and accuracy, ensures the comprehensiveness and reliability of testing, and ultimately generates a final report containing all test results and cross-validation conclusions.

[0098] Exemplary device like Figure 5As shown, this embodiment of the invention provides an AI-based automated testing device for terminal systems and resolution compatibility, the device comprising: The test task receiving and allocation module 310 is used to receive test tasks issued by the test control terminal, and intelligently allocate the tasks to preset single terminal device tests and / or virtual framework tests through AI according to the task requirements and risk assessment of the test tasks. The single-device dynamic simulation test module 320 is used to dynamically modify system parameters through AI when a task is assigned to a single terminal device for testing. It controls the dynamic simulation of running multiple different system versions and resolutions on a single terminal device. During the simulation, it controls the execution of preset test cases and collects test data for automatic analysis to verify resolution compatibility. When the assigned task combination test is completed, it generates a single terminal device test report. The virtual framework simulation test module 330 is used to deploy a virtual framework on a server cluster based on a hardware characteristic model using AI when a task is assigned to virtual framework testing. The virtual framework loads a system image and a virtual hardware abstraction layer, simulates various hardware characteristics in a virtual environment, covers multiple scenarios, performs cross-validation with physical devices, and generates a virtual test report. The dual-path collaboration and cross-validation module 340 is used to coordinate the two test paths of dynamic simulation of a single terminal device and virtual framework simulation through a dual-path collaboration and cross-validation mechanism, and integrate the test report of a single terminal device and the virtual test report; cross-validate the risk issues found in either path, and generate a final report containing all test results and cross-validation conclusions, as described above.

[0099] Based on the above embodiments, the present invention also provides a terminal device, which can be a smart TV, and its principle block diagram can be as follows. Figure 6 As shown. The terminal device includes a processor, memory, network interface, display screen, and database connected via a system bus.

[0100] The memory stores one or more programs configured to be executed by a processor to implement the AI-based automated testing method for resolution compatibility of terminal systems described in the above embodiments.

[0101] In this context, "terminal device" refers to an intelligent computer or similar device with data processing capabilities. The memory can be internal memory, flash memory, hard disk, or cloud storage, used to store program code and various data such as pre-set test tasks. The processor can be a central processing unit (CPU), used to execute the algorithmic logic within the program. The program includes an AI-based terminal system and a resolution-compatible automated testing method.

[0102] In a further embodiment, a terminal device of this embodiment includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations: Receive test tasks from the test control terminal, and intelligently allocate the tasks to preset single terminal device tests and / or virtual framework tests through AI based on the task requirements and risk assessment of the test tasks. When a task is assigned to a single terminal device for testing, AI dynamically modifies system parameters to control the dynamic simulation of multiple different system versions and resolutions on that single terminal device. During the simulation, preset test cases are executed, and test data is collected for automatic analysis to verify resolution compatibility. When the assigned task combination test is completed, a single terminal device test report is generated. When a task is assigned to virtual framework testing, the virtual framework is deployed on the server cluster based on the hardware characteristic model using AI. The virtual framework loads the system image and virtual hardware abstraction layer, simulates various hardware characteristics in the virtual environment, covers multiple scenarios, and performs cross-validation with physical devices to generate a virtual test report. Through a dual-path collaboration and cross-validation mechanism, the two test paths of dynamic simulation of a single terminal device and virtual framework simulation work together, and integrate the test reports of a single terminal device and the virtual test reports. For the risk issues found in either path, cross-validation is performed, and a final report containing all test results and cross-validation conclusions is generated, as described above.

[0103] The step of receiving test tasks from the test control terminal and intelligently allocating the tasks to preset single terminal device tests and / or virtual framework tests via AI based on the task requirements and risk assessment of the test tasks includes the following steps before: The test pre-configures a single-terminal device test for testing certain test tasks where hardware performance requirements exceed a first predetermined requirement, and a virtual framework test for testing regular test tasks.

[0104] The step of receiving test tasks from the test control terminal and intelligently allocating the tasks to preset single-terminal device tests and / or virtual framework tests using AI based on the task requirements and risk assessment of the test tasks includes: Obtain test tasks to perform multi-version and multi-resolution compatibility testing on newly developed terminal applications; The test tasks are intelligently allocated using AI. Test tasks with hardware performance requirements exceeding the first predetermined requirement are assigned to a single terminal device for testing in a real hardware environment. Meanwhile, other routine compatibility test tasks are assigned to a virtual framework for testing.

[0105] The steps involved in assigning a task to a single terminal device for testing, dynamically modifying system parameters via AI, and controlling the dynamic simulation of multiple different system versions and resolutions on the single terminal device; during the simulation, executing preset test cases and collecting test data for automatic analysis to verify resolution compatibility; and generating a single terminal device test report after the assigned task combination test is completed include: When a task is assigned to a single terminal device for testing, the system parameters of the single terminal device are dynamically modified through AI to control the simulation of running different Android versions and resolutions on the single terminal device. During the simulation, preset test cases are executed and user interface, logs, and performance data are collected. It also automatically analyzes the collected data through a preset AI visual recognition module and performance monitoring module to determine whether there are display, functional or performance problems, and automatically determines resolution compatibility issues; Once the assigned task combination test is completed, a test report for a single terminal device is generated.

[0106] The steps involved in assigning a task to a virtual framework for testing, deploying a virtual framework on a server cluster using AI based on a hardware characteristic model, loading a system image and a virtual hardware abstraction layer through the virtual framework, simulating various hardware characteristics in a virtual environment, covering multiple scenarios, and cross-validating with physical devices to generate a virtual test report include: When a task is assigned to a virtual framework test, a preset virtual framework is deployed on a server cluster using AI based on a hardware characteristic model. The virtual framework loads the Android image and the virtual hardware abstraction layer to restore the characteristics of real hardware. Parallel test tasks are allocated through the virtual framework to simulate user operations and collect data. Detect and classify anomalies using AI; For the detected issues, physical TV verification is performed, and a virtual test report is generated.

[0107] The steps involved in using a dual-path collaboration and cross-validation mechanism to coordinate the two test paths—dynamic simulation of a single terminal device and virtual framework simulation—and integrate the single terminal device test report and the virtual test report; cross-validating any risks discovered in either path; and generating a final report containing all test results and cross-validation conclusions include: Through a pre-set dual-path collaboration and cross-validation mechanism, the system controls the collaborative operation of two test paths: dynamic simulation of a single terminal device and virtual framework simulation, and integrates the test reports of a single terminal device and the virtual test reports. For risks discovered in any path, cross-validation is performed; when potential problems are discovered in a virtual environment, they are sent to a single terminal device for reproduction and confirmation, or vice versa; testing efficiency and accuracy are balanced through collaborative work. Finally, a final report containing all test results and cross-validation conclusions is generated.

[0108] The step of deploying a virtual framework on a server cluster using AI based on a hardware characteristic model further includes: Obtain performance logs from a specified number of real terminal devices; The performance logs of the specified number of real terminal devices are used as training data and input into the hardware characteristic model constructed by AI for training to obtain the trained virtual framework. The virtual framework is deployed on a server cluster, as described above.

[0109] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, enables an electronic device to perform the steps of any of the methods described above, specifically as described above.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0111] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An AI-based automated testing method for terminal system resolution compatibility, characterized in that, include: Receive test tasks from the test control terminal, and intelligently allocate the tasks to preset single terminal device tests and / or virtual framework tests through AI based on the task requirements and risk assessment of the test tasks. When a task is assigned to a single terminal device for testing, AI dynamically modifies system parameters to control the dynamic simulation of multiple different system versions and resolutions on that single terminal device. During the simulation, preset test cases are executed, and test data is collected for automatic analysis to verify resolution compatibility. When the assigned task combination test is completed, a single terminal device test report is generated. When a task is assigned to virtual framework testing, the virtual framework is deployed on the server cluster based on the hardware characteristic model using AI. The virtual framework loads the system image and virtual hardware abstraction layer, simulates various hardware characteristics in the virtual environment, covers multiple scenarios, and performs cross-validation with physical devices to generate a virtual test report. Through a dual-path collaboration and cross-validation mechanism, the two test paths of dynamic simulation of a single terminal device and virtual framework simulation are combined to work together, and the test reports of a single terminal device and the virtual test reports are integrated. For any risk issues discovered in any path, cross-validation is performed, and a final report containing all test results and cross-validation conclusions is generated.

2. The AI-based automated testing method for terminal system and resolution compatibility according to claim 1, characterized in that, Before the step of receiving the test task issued by the test control terminal and intelligently assigning the task to a preset single terminal device test and / or virtual framework test using AI based on the task requirements and risk assessment of the test task, the following steps are included: The test pre-configures a single-terminal device test for testing certain test tasks where hardware performance requirements exceed a first predetermined requirement, and a virtual framework test for testing regular test tasks.

3. The automated testing method for resolution compatibility of AI-based terminal systems according to claim 1, characterized in that, The steps of receiving test tasks from the test control terminal and intelligently allocating the tasks to preset single terminal device tests and / or virtual framework tests using AI based on the task requirements and risk assessment of the test tasks include: Obtain test tasks to perform multi-version and multi-resolution compatibility testing on newly developed terminal applications; The test tasks are intelligently allocated using AI. Test tasks with hardware performance requirements exceeding the first predetermined requirement are assigned to a single terminal device for testing in a real hardware environment. Meanwhile, other routine compatibility test tasks are assigned to a virtual framework for testing.

4. The automated testing method for resolution compatibility of AI-based terminal systems according to claim 1, characterized in that, When a task is assigned to a single terminal device for testing, system parameters are dynamically modified using AI to control the dynamic simulation of multiple different system versions and resolutions on the single terminal device. During the simulation, preset test cases are executed, and test data is collected for automatic analysis to verify resolution compatibility. Once the assigned task combination test is completed, the steps for generating a test report for a single terminal device include: When a task is assigned to a single terminal device for testing, the system parameters of the single terminal device are dynamically modified through AI to control the simulation of running different Android versions and resolutions on the single terminal device. During the simulation, preset test cases are executed and user interface, logs, and performance data are collected. It also automatically analyzes the collected data through a preset AI visual recognition module and performance monitoring module to determine whether there are display, functional or performance problems, and automatically determines resolution compatibility issues; Once the assigned task combination test is completed, a test report for a single terminal device is generated.

5. The AI-based automated testing method for terminal system and resolution compatibility according to claim 1, characterized in that, The steps of assigning a task to a virtual framework test, deploying a virtual framework on a server cluster using AI based on a hardware characteristic model, loading a system image and a virtual hardware abstraction layer through the virtual framework, simulating various hardware characteristics in a virtual environment, covering multiple scenarios, and cross-validating with physical devices to generate a virtual test report include: When a task is assigned to a virtual framework test, a preset virtual framework is deployed on a server cluster using AI based on a hardware characteristic model. The virtual framework loads the Android image and the virtual hardware abstraction layer to restore the characteristics of real hardware. Parallel test tasks are allocated through the virtual framework to simulate user operations and collect data. Detect and classify anomalies using AI; For the detected issues, physical TV verification is performed, and a virtual test report is generated.

6. The automated testing method for resolution compatibility of AI-based terminal systems according to claim 1, characterized in that, The method employs a dual-path collaboration and cross-validation mechanism to coordinate the two test paths of dynamic simulation of a single terminal device and virtual framework simulation, and integrates the test reports of a single terminal device and the virtual test reports. The steps for cross-validating risks identified in any path and generating a final report containing all test results and cross-validation conclusions include: Through a pre-set dual-path collaboration and cross-validation mechanism, the system controls the collaborative operation of two test paths: dynamic simulation of a single terminal device and virtual framework simulation, and integrates the test reports of a single terminal device and the virtual test reports. For risks discovered in any path, cross-validation is performed; when potential problems are discovered in a virtual environment, they are sent to a single terminal device for reproduction and confirmation, or vice versa; testing efficiency and accuracy are balanced through collaborative work. Finally, a final report containing all test results and cross-validation conclusions is generated.

7. The automated testing method for resolution compatibility of AI-based terminal systems according to claim 4, characterized in that, The step of deploying a virtual framework on a server cluster using AI based on a hardware characteristic model also includes: Obtain performance logs from a specified number of real terminal devices; The performance logs of the specified number of real terminal devices are used as training data and input into the hardware characteristic model constructed by AI for training to obtain the trained virtual framework. The virtual framework is deployed on a server cluster.

8. An AI-based terminal system and resolution-compatible automated testing device, characterized in that, The device includes: The test task receiving and allocation module is used to receive test tasks issued by the test control terminal, and intelligently allocate the tasks to preset single terminal device tests and / or virtual framework tests through AI based on the task requirements and risk assessment of the test tasks. The single-device dynamic simulation testing module is used to dynamically modify system parameters through AI when a task is assigned to a single terminal device for testing. It controls the dynamic simulation of running multiple different system versions and resolutions on a single terminal device. During the simulation, it controls the execution of preset test cases and collects test data for automatic analysis to verify resolution compatibility. When the assigned task combination test is completed, a single terminal device test report is generated. The virtual framework simulation test module is used to deploy a virtual framework on a server cluster based on a hardware characteristic model using AI when a task is assigned to virtual framework testing. The virtual framework loads a system image and a virtual hardware abstraction layer to simulate various hardware characteristics in a virtual environment, covering multiple scenarios, and cross-validates with physical devices to generate a virtual test report. The dual-path collaboration and cross-validation module is used to coordinate the two test paths of dynamic simulation of a single terminal device and virtual framework simulation through a dual-path collaboration and cross-validation mechanism, and integrate the test reports of a single terminal device and the virtual test reports; cross-validate the risk issues found in either path, and generate a final report containing all test results and cross-validation conclusions.

9. A terminal device, characterized in that, It includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors, wherein the one or more programs include steps for performing the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it enables the electronic device to perform the steps of the method as described in any one of claims 1-7.

Images