Application (APP) player experience analysis method

Abstract

The invention provides an APP player experience analysis method. The APP player experience analysis method comprises the following steps of testing access of a machine; starting a developer mode; an appium environment and an appium inspector are installed in the server; an scrcpy environment is installed, and environment variables are configured; the scrcpy command is executed, element analysis is carried out through appium, a corresponding video playing scene is found, and screen recording is carried out after playing is started; uploading the screen recording file, and uploading the screen recording file to the private cloud storage; analyzing and detecting the screen recording file to obtain a frame jamming rate, a loading rate and a black screen rate; collecting results, and storing analysis and detection results to a cache and a database; and taking out the testing machine. Through screen recording file analysis, comparison of playing experiences of different platforms is realized, and the actual impression of a user can be closer. Through adoption of multi-scene acquisition, operation of a user in a short video application is basically covered, multi-scene and multi-dimensional data definition and acquisition are realized, and support is provided for data analysis.

Description

Technical Field

[0001] The present invention relates to the field of APP player experience analysis, and in particular to an APP player experience analysis method. Background Art

[0002] With the rise of short videos, customer retention rate has become an important indicator of short video platforms, and the video playback experience accounts for a large proportion of it.

[0003] In the prior art, APP player experience analysis is performed by adopting a manual viewing experience comparison method and an ADB technology comparison method.

[0004] However, the manual experience comparison method has the following disadvantages:

[0005] 1) There is no data support and evaluation can only be done through human perception;

[0006] 2) It is highly subjective, and everyone has their own opinions, so it is difficult to form a set of standards;

[0007] 3) Waste of resources, requiring the participation of multiple people in the same environment to minimize variables.

[0008] However, the ADB technical comparison method has the following disadvantages:

[0009] 1) There are errors. Keyword parsing between different players may have errors;

[0010] 2) It is inconsistent with the viewing experience. Log collection is subject to the performance of the mobile phone and may be different from the actual viewing experience.

[0011] In summary, there is currently no suitable analysis method to compare the playback experience of different short video platforms. Summary of the invention

[0012] The present invention aims to solve the following technical problems: how to achieve multi-scenario and multi-dimensional data definition and collection to provide support for data analysis; how to be closer to the actual user experience. An APP player experience analysis method is provided.

[0013] The present invention solves the above technical problems through the following technical solutions:

[0014] The present invention provides an APP player experience analysis method, which comprises the following steps:

[0015] (1) Access to the test machine;

[0016] (2) Enable developer mode and adb debugging on the test machine;

[0017] (3) Install the appium environment and appiuminspector on the server;

[0018] (4) Install the scrcpy environment and configure environment variables;

[0019] (5) Execute the scrcpy command, perform element analysis through appium, find the corresponding video playback scene, start playback and record the screen;

[0020] (6) Upload the screen recording file to private cloud storage;

[0021] (7) Perform screen recording file analysis and detection to obtain frame freeze rate, loading rate, and black screen rate;

[0022] (8) Result collection, saving analysis and detection results to cache and database;

[0023] (9) Take out the test machine.

[0024] Preferably, the test machine in step (2) is one or more of an Android smartphone, an Android tablet, an Android smartwatch, and an Android car-mounted device.

[0025] Preferably, the video playback scenario in step (5) is one of a video fast sliding scenario, a video sliding scenario after normal video playback, and a video sliding scenario after dragging.

[0026] Preferably, the video fast sliding scene is used to simulate the video playback under the continuous fast sliding of the user.

[0027] Preferably, the video sliding scene after normal playback is used to simulate the video playback when the user slides after normal playback.

[0028] Preferably, the video dragging and sliding scene is used to simulate the video playback under the dragging and sliding of the user.

[0029] Preferably, the screen recording file in step (6) is stored in mkv format.

[0030] Preferably, the frame jam rate detection in step (7) adopts the following steps:

[0031] 1. Capture video frames and obtain each frame of the video at a specified frame rate;

[0032] 2. Inter-frame comparison: Use opencv to compare the pixel differences between two frames.

[0033] Preferably, the loading rate in step (7) is determined by comparing pixels of the frame images of the area where the loading icon appears, so as to detect the loading area.

[0034] Preferably, the screen recording file analysis and detection in step (7) also requires a silence detection.

[0035] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0036] The positive and progressive effects of the present invention are:

[0037] The APP player experience analysis method proposed above realizes the comparison of playback experience on different platforms by analyzing screen recording files, and is closer to the actual viewing experience of users. By adopting multi-scene acquisition, it basically covers the user's operations in short video applications, realizes multi-scene and multi-dimensional data definition and acquisition, provides support for data analysis, and further analyzes the inter-frame freeze rate, loading rate and black screen rate to reflect a more realistic playback experience. In summary, the present invention realizes the comparison of the playback experience of different short video applications / platforms by inter-frame comparison in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flowchart of the APP player experience analysis method.

[0039] Figure 2 This is a frame capture detection flow chart of the present invention. DETAILED DESCRIPTION

[0040] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples.

[0041] Embodiment 1

[0042] like Figure 1-2 As shown, the APP player experience analysis method includes the following steps:

[0043] (1) Access to the test machine;

[0044] (2) Enable developer mode and adb debugging on the test machine;

[0045] (3) Install the appium environment and appiuminspector on the server;

[0046] (4) Install the scrcpy environment and configure environment variables;

[0047] (5) Execute the scrcpy command, perform element analysis through appium, find the corresponding video playback scene, start playback and record the screen;

[0048] (6) Upload the screen recording file to private cloud storage;

[0049] (7) Perform screen recording file analysis and detection to obtain frame freeze rate, loading rate, and black screen rate;

[0050] (8) Result collection, saving analysis and detection results to cache and database;

[0051] (9) Take out the test machine.

[0052] The test machine in step (2) is one or more of an Android smartphone, an Android tablet, an Android smartwatch, and an Android car-mounted device.

[0053] The video playback scene in step (5) is a video fast sliding scene.

[0054] The video fast sliding scene is used to simulate the video playback under the continuous fast sliding of the user.

[0055] The screen recording file in step (6) is stored in mkv format.

[0056] The frame jam rate detection in step (7) adopts the following steps:

[0057] 1. Capture video frames and obtain each frame of the video at a specified frame rate;

[0058] 2. Inter-frame comparison: Use opencv to compare the pixel differences between two frames.

[0059] In step (7), the loading rate is determined by comparing the pixels of the frame images in the area where the loading icon appears, so as to detect the loading area.

[0060] The screen recording file analysis and detection in step (7) also requires silent detection.

[0061] The above method is used to simulate the video playback under the continuous and rapid sliding of the user, and the APP player experience analysis in this scenario can be performed.

[0062] Embodiment 2

[0063] like Figure 1-2 As shown, the APP player experience analysis method includes the following steps:

[0064] (1) Access to the test machine;

[0065] (2) Enable developer mode and adb debugging on the test machine;

[0066] (3) Install the appium environment and appiuminspector on the server;

[0067] (4) Install the scrcpy environment and configure environment variables;

[0068] (5) Execute the scrcpy command, perform element analysis through appium, find the corresponding video playback scene, start playback and record the screen;

[0069] (6) Upload the screen recording file to private cloud storage;

[0070] (7) Perform screen recording file analysis and detection to obtain frame freeze rate, loading rate, and black screen rate;

[0071] (8) Result collection, saving analysis and detection results to cache and database;

[0072] (9) Take out the test machine.

[0073] The test machine in step (2) is one or more of an Android smartphone, an Android tablet, an Android smart watch, and an Android car-mounted device.

[0074] The video playback scene in step (5) is a sliding scene after the video is played normally.

[0075] The video sliding after normal playback scenario is used to simulate the video playback when the user slides after normal playback.

[0076] The screen recording file in step (6) is stored in mkv format.

[0077] The frame jam rate detection in step (7) adopts the following steps:

[0078] 1. Capture video frames and obtain each frame of the video at a specified frame rate;

[0079] 2. Inter-frame comparison: Use opencv to compare the pixel differences between two frames.

[0080] In step (7), the loading rate is determined by comparing the pixels of the frame images in the area where the loading icon appears, so as to detect the loading area.

[0081] The screen recording file analysis and detection in step (7) also requires silent detection.

[0082] The above method is used to simulate the video playback when the user slides after normal playback, and the APP player experience analysis in this scenario can be performed.

[0083] Embodiment 3

[0084] like Figure 1-2 As shown, the APP player experience analysis method includes the following steps:

[0085] (1) Access to the test machine;

[0086] (2) Enable developer mode and adb debugging on the test machine;

[0087] (3) Install the appium environment and appiuminspector on the server;

[0088] (4) Install the scrcpy environment and configure environment variables;

[0089] (5) Execute the scrcpy command, perform element analysis through appium, find the corresponding video playback scene, start playback and record the screen;

[0090] (6) Upload the screen recording file to private cloud storage;

[0091] (7) Perform screen recording file analysis and detection to obtain frame freeze rate, loading rate, and black screen rate;

[0092] (8) Result collection, saving analysis and detection results to cache and database;

[0093] (9) Take out the test machine.

[0094] The test machine in step (2) is one or more of an Android smartphone, an Android tablet, an Android smart watch, and an Android car-mounted device.

[0095] The video playback scene in step (5) is a video drag and slide scene.

[0096] The video drag and slide scenario is used to simulate the video playback when the user drags and slides.

[0097] The screen recording file in step (6) is stored in mkv format.

[0098] The frame jam rate detection in step (7) adopts the following steps:

[0099] 1. Capture video frames and obtain each frame of the video at a specified frame rate;

[0100] 2. Inter-frame comparison: Use opencv to compare the pixel differences between two frames.

[0101] In step (7), the loading rate is determined by comparing the pixels of the frame images in the area where the loading icon appears, so as to detect the loading area.

[0102] The screen recording file analysis and detection in step (7) also requires silent detection.

[0103] The above method is used to simulate the video playback when the user drags and slides, and can be used to analyze the APP player experience in this scenario.

[0104] The present invention is not limited to the above-mentioned embodiments. Any changes in shape or structure are within the protection scope of the present invention. The protection scope of the present invention is defined by the attached claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and essence of the present invention, but these changes and modifications are within the protection scope of the present invention.

Claims

1. An APP player experience analysis method, the APP player experience analysis method comprising the following steps: (1) Access to the test machine; (2) Enable developer mode and adb debugging on the test machine; (3) Install the appium environment and appiuminspector on the server; (4) Install the scrcpy environment and configure environment variables; (5) Execute the scrcpy command, perform element analysis through appium, find the corresponding video playback scene, start playback and record the screen; (6) Upload the screen recording file to private cloud storage; (7) Perform screen recording file analysis and detection to obtain frame freeze rate, loading rate, and black screen rate; (8) Result collection, saving analysis and detection results to cache and database; (9) Take out the test machine.

2. The APP player experience analysis method according to claim 1, characterized in that: The test machine in step (2) is one or more of an Android smartphone, an Android tablet, an Android smartwatch, and an Android car-mounted device.

3. The APP player experience analysis method according to claim 1, characterized in that: The video playback scenario in step (5) is one of a video fast sliding scenario, a video sliding scenario after normal video playback, and a video sliding scenario after dragging.

4. The APP player experience analysis method according to claim 3, characterized in that: The video fast sliding scene is used to simulate the video playback under the continuous fast sliding of the user.

5. The APP player experience analysis method according to claim 3, characterized in that: The video sliding after normal playback scenario is used to simulate the video playback when the user slides after normal playback.

6. The APP player experience analysis method according to claim 3, characterized in that: The video drag and slide scenario is used to simulate the video playback when the user drags and slides.

7. The APP player experience analysis method according to claim 1, characterized in that: The screen recording file in step (6) is stored in mkv format.

8. The APP player experience analysis method according to claim 1, characterized in that: The frame jam rate detection in step (7) adopts the following steps:

1. Capture video frames and obtain each frame of the video at a specified frame rate; 2. Inter-frame comparison: Use opencv to compare the pixel differences between two frames.

9. The APP player experience analysis method according to claim 1, characterized in that: In step (7), the loading rate is determined by comparing the pixels of the frame images in the area where the loading icon appears, so as to detect the loading area.

10. The APP player experience analysis method according to claim 1, characterized in that: The screen recording file analysis and detection in step (7) also requires silent detection.

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