CN111240984A - Abnormal page identification method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to the field of big data processing, in particular to an abnormal page identification method and device, computer equipment and a storage medium. The method comprises the following steps: receiving an abnormal page identification request sent by a terminal, wherein the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started; acquiring the retention time of a page corresponding to the application program test script; segmenting the screen recorded video according to the page staying time to obtain a plurality of video segments; respectively extracting a plurality of frame key frames from each video segment, and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, wherein the abnormal page identification model is generated based on a normal page and a historical abnormal page; and returning the obtained abnormal page to the terminal. By adopting the method, the identification accuracy of the abnormal page can be improved.
Description
Technical Field
The present application relates to the field of big data processing technologies, and in particular, to a method and an apparatus for identifying an abnormal page, a computer device, and a storage medium.
Background
Generally, the requirement for hardware computing power is constantly changing during the running of the application program, and if the duration of processing a certain frame to display the required resource exceeds the preset standard duration (taking 60 frames per second as an example, the preset standard duration of each frame is 16 milliseconds), an abnormal problem occurs. Therefore, in the process of developing the application program, the display performance of the application program interface needs to be analyzed, and the interface operation is ensured to be maintained above a certain frame rate standard, so that smooth use experience is provided for a user.
Conventionally, during testing, a tester usually performs manual testing, so that during manual testing, the stuck page is easily ignored, and the stuck page is not found in time, so that the stuck page is not identified accurately.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an abnormal page identification method, an abnormal page identification apparatus, a computer device, and a storage medium, which can improve the accuracy of abnormal page identification.
An abnormal page identification method, the method comprising:
receiving an abnormal page identification request sent by a terminal, wherein the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started;
acquiring the retention time of a page corresponding to the application program test script;
segmenting the screen recorded video according to the page staying time to obtain a plurality of video segments;
respectively extracting a plurality of frame key frames from each video segment, and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, wherein the abnormal page identification model is generated based on a normal page and a historical abnormal page;
and returning the obtained abnormal page to the terminal.
In one embodiment, the inputting the keyframe into a pre-trained abnormal page recognition model to obtain an abnormal page includes:
respectively inputting the key frame corresponding to each video segment into an abnormal page identification model so as to judge whether the page corresponding to the video segment is an abnormal page or not;
and if the page corresponding to the video segment is an abnormal page, outputting the abnormal page.
In one embodiment, the respectively inputting the key frame corresponding to each of the video segments into an abnormal page identification model to determine whether the page corresponding to the video segment is an abnormal page includes:
acquiring a first similarity between a color value of a key frame corresponding to each video segment and a first color value of a history abnormal page;
when the first similarity is larger than a first threshold value, judging the key frame as an abnormal frame;
when the first similarity is not larger than a first threshold value, acquiring a second similarity between the color value of the key frame corresponding to each video segment and a second color value of a normal page;
when the second similarity is smaller than a second threshold value, judging the key frame to be an abnormal frame, otherwise, judging the key frame to be a normal frame;
and when the key frames corresponding to the video segments are all normal frames, the page corresponding to the video segments is a normal page, otherwise, the page corresponding to the video segments is an abnormal page.
In one embodiment, the manner of adjusting the first threshold includes:
acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page;
comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value;
and adjusting a first threshold value according to the difference value, wherein the larger the difference value is, the larger the first threshold value is.
In one embodiment, the adjusting manner of the second threshold includes:
acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page;
comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value;
and adjusting a second threshold value according to the difference value, wherein the larger the difference value is, the larger the first threshold value is.
In one embodiment, the generation manner of the abnormal page identification model includes:
acquiring a normal page from a terminal;
acquiring a historical abnormal page from a database, wherein the historical abnormal page is an abnormal page identified after the frame rate detection is started at a terminal;
and training the normal page and the abnormal page to obtain an abnormal page identification model.
In one embodiment, the generation manner of the application test script includes:
setting a terminal as a developer authorization mode;
and receiving an operation instruction of the application program installed in the terminal, and performing script recording through a preset script recording tool to obtain an application program test script.
An anomalous page identification apparatus, said apparatus comprising:
the system comprises a receiving module, a processing module and a display module, wherein the receiving module is used for receiving an abnormal page identification request sent by a terminal, the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started;
the page staying time acquisition module is used for acquiring the page staying time corresponding to the application program test script;
the segmentation module is used for segmenting the screen recorded video according to the page staying time to obtain a plurality of video segments;
the model processing module is used for respectively extracting a plurality of frame key frames from each video segment and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, and the abnormal page identification model is generated based on a normal page and historical abnormal pages;
and the output module is used for returning the obtained abnormal page to the terminal.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
According to the abnormal page identification method, the abnormal page identification device, the computer equipment and the storage medium, when an abnormal page identification request sent by a terminal is received, the screen recorded video is segmented according to the page retention time corresponding to the application program test script to obtain the video segment corresponding to each page, then a plurality of frames of key frames are extracted from each video segment, the key frames are input into the abnormal page identification model obtained through pre-training to obtain the abnormal page, the identified abnormal page is returned to the terminal, and therefore a plurality of key frames are extracted for judgment aiming at each page, and the identification accuracy of the abnormal page is improved.
Drawings
FIG. 1 is a diagram illustrating an exemplary scenario for an exception page identification method;
FIG. 2 is a flowchart illustrating an abnormal page identification method according to an embodiment;
FIG. 3 is a diagram illustrating a frame rate detection page in an embodiment;
FIG. 4 is a block diagram showing the structure of an abnormal page recognizing apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further 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 present application and are not intended to limit the present application.
The abnormal page identification method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 sends an abnormal page identification request to the server 104, wherein the abnormal page identification request may carry a screen recorded video, that is, under the condition that the terminal 102 starts frame rate detection, the terminal records and generates a screen after running different application test scripts, the server 104 obtains the dwell time of the page corresponding to the application test script, then segmenting the screen recorded video according to the residence time of the page to obtain a plurality of video segments, wherein one page corresponds to one video segment, the server 104 obtains a plurality of keywords by sampling the video segments, inputs the key frame corresponding to each page into an abnormal page identification model to obtain an abnormal page, and the abnormal page is returned to the terminal 102, so that a plurality of key frames are extracted for judgment aiming at each page, and the identification accuracy of the abnormal page is improved. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, an abnormal page identification method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
s202: and receiving an abnormal page identification request sent by the terminal, wherein the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that the frame rate detection is started.
Specifically, an exception page refers to a page that shows a delay, such as a stuck page. The method for generating the abnormal page identification request by the terminal comprises the following steps: firstly, setting a terminal to be a developer root authorization mode, for example, if a version number can be continuously clicked for several times, then the corresponding developer mode can be opened; then, setting and starting frame rate detection in the mobile phone, namely clicking in GPU presentation mode analysis in developer options to display a bar graph on a screen, specifically as shown in FIG. 3, wherein FIG. 3 is a page schematic diagram of a frame rate detection page in one embodiment, so that the screen can be displayed as the following graph; and thirdly, controlling the corresponding application program to run through the recorded application program test script and simultaneously carrying out screen recording to obtain a screen recorded video, and finishing recording after the application program test script is executed, so that an abnormal page identification request is generated according to the recorded screen recorded video and is sent to the server.
In one embodiment, the generation mode of the application test script comprises the following steps: setting a terminal as a developer authorization mode; and receiving an operation instruction of the application program installed in the terminal, and performing script recording through a preset script recording tool to obtain an application program test script. Specifically, the terminal is set to be in a developer root authorization mode, then the application program is operated, and the recording script is clicked through the operation of monkey, so that the application program test script is obtained. In addition, when the application program is operated, the operation time of each page may be set, for example, 5 seconds, when the application program test script is generated, it is determined in real time whether the page staying time of the current page is equal to the operation time, for example, 5 seconds, if so, a next instruction for the application program operation is received and page turning is performed, otherwise, even if the next instruction for the application program operation is received, it is also determined in real time whether the page staying time of the current page is equal to the operation time, and until the next instruction for the application program operation is equal to the operation time, the next instruction for the application program operation is responded and page turning is performed.
S204: and acquiring the retention time of the page corresponding to the test script of the application program.
S206: and segmenting the screen recorded video according to the page staying time to obtain a plurality of video segments.
Specifically, the page dwell time corresponds to the application test script, the page dwell time needs to be set each time the application test script is generated, and the application test script, the page dwell time, and the application identifier are stored in association with each other when the application test script is generated. Therefore, when an abnormal page identification request is received, the server can extract the application program identification from the abnormal page identification request, then inquire the application program test script according to the application program identification, inquire the page staying time, and segment the screen recorded video according to the page staying time to obtain a plurality of video segments.
S208: a plurality of frame key frames are respectively extracted from each video segment, and the key frames are input into an abnormal page identification model obtained through pre-training to obtain an abnormal page, wherein the abnormal page identification model is generated based on a normal page and historical abnormal pages.
In practical application, a screen recorded video is segmented to obtain a video segment corresponding to each page, and then the video segments are respectively sampled to obtain a key frame corresponding to each page. For example, when the screen is recorded, the application test script sets the retention time, that is, each time a page is turned, the application test script correspondingly retains a certain time, and the recording is performed. Thus, after receiving a screen recorded video, a server firstly segments the screen recorded video according to the dwell time to obtain different video segments, then samples the video segments to obtain key frames, for example, a segment of video is 20 seconds long, the dwell time of each page is 5 seconds, the video is divided into 5 segments, each segment corresponds to one page, and if the segment is sequentially page a, page B, page C, page D and page E, wherein the page a corresponds to 0 second to 5 seconds of the video, the server extracts key frames of 1 second, 2 seconds, 3 seconds, 4 seconds and 5 seconds from the segment, and then inputs the key frames into an abnormal page identification model to judge the key frames obtained by sampling, namely whether abnormal frames exist in the 5 frames or not, and if the abnormal frames exist, the corresponding pages are abnormal pages.
The abnormal page recognition model is obtained by training according to a normal page and a historical abnormal page in advance, and the training mode comprises the following steps: acquiring a normal page from a terminal; acquiring a historical abnormal page from a database, wherein the historical abnormal page is an abnormal page identified after the frame rate detection is started at the terminal; and training the normal page and the abnormal page to obtain an abnormal page identification model. Specifically, the server firstly acquires a normal page from the development terminal, wherein the normal page is drawn by a developer in the development process; then obtaining a history abnormal page, wherein the history abnormal page is an abnormal page identified after the frame rate detection is started on the mobile phone; and training the normal page and the historical abnormal page to obtain an abnormal page identification model.
S210: and returning the obtained abnormal page to the terminal.
Specifically, the output abnormal page can be used for development to improve optimization, namely, the abnormal page is sent to a corresponding terminal, so that the terminal can position a specific abnormal flow of the application program according to the abnormal page, then the flow is adjusted to optimize the application program, and in addition, the server stores the abnormal page in a folder so as to be convenient for development to check and call.
According to the abnormal page identification method, when an abnormal page identification request sent by a terminal is received, a screen recorded video is segmented according to the page staying time corresponding to an application program test script to obtain a video segment corresponding to each page, then a plurality of frames of key frames are extracted from each video segment, the key frames are input into an abnormal page identification model obtained through pre-training to obtain an abnormal page, the abnormal page obtained through identification is returned to the terminal, and therefore the plurality of key frames are extracted for each page to be judged, and identification accuracy of the abnormal page is improved.
In one embodiment, inputting the keyframes into an abnormal page recognition model obtained by pre-training to obtain abnormal pages includes: respectively inputting the key frame corresponding to each video segment into an abnormal page identification model to judge whether the page corresponding to the video segment is an abnormal page or not; and if the page corresponding to the video segment is an abnormal page, outputting the abnormal page.
In one embodiment, the step of inputting the key frame corresponding to each video segment into the abnormal page identification model to determine whether the page corresponding to the video segment is an abnormal page includes: acquiring a first similarity between a color value of a key frame corresponding to each video segment and a first color value of a history abnormal page; when the first similarity is larger than a first threshold value, judging the key frame as an abnormal frame; when the first similarity is not larger than a first threshold value, acquiring a second similarity between the color value of the key frame corresponding to each video segment and a second color value of the normal page; when the second similarity is smaller than a second threshold value, judging the key frame as an abnormal frame, otherwise, judging the key frame as a normal frame; and when the key frames corresponding to the video segments are all normal frames, the page corresponding to the video segments is a normal page, otherwise, the page corresponding to the video segments is an abnormal page.
Specifically, when the server determines whether a page is an abnormal page, the server may first divide a screen recorded video into a plurality of video segments according to the retention time of the page, where one video segment corresponds to one page, then sample a video frame in each video segment to obtain a key frame, then respectively input the key frame corresponding to each page into the abnormal page identification model to determine whether the page is an abnormal page, and when the page is determined to be one page, continue to obtain a key frame corresponding to a next page, and input the key frame corresponding to the next page into the abnormal page identification model to determine until all pages are determined to be completed.
The method for determining whether a page is an abnormal page may include the following steps: and the server calculates a first similarity between the color value of the extracted key frame and a first color value of the historical abnormal page according to the abnormal page identification model, and when the first similarity is greater than or equal to a first threshold value, the key frame is judged to be an abnormal frame, so that the page corresponding to the key frame is output as the abnormal page. When the first similarity is smaller than or equal to a first threshold value, continuously calculating a second similarity between the color value of the key frame and a second color value of the normal page according to the abnormal page identification model, when the second similarity is smaller than a second threshold value, judging the key frame to be an abnormal frame, and outputting a page corresponding to the key frame to be an abnormal page, otherwise, judging the key frame to be a normal frame, and when all the key frames corresponding to one page are normal frames, judging the page to be a normal page. For example, in the above example, the key frames corresponding to the page a include a 1 second video frame, a 2 second video frame, a 3 second video frame, a 4 second video frame, and a 5 second video frame, when determining, if one of the 1 second video frame, the 2 second video frame, the 3 second video frame, the 4 second video frame, and the 5 second video frame is an abnormal frame, the page is directly output as an abnormal page instead of the next key frame, otherwise, whether the next key frame is an abnormal frame is continuously determined, and if the determination is completed, all the key frames are normal frames, the page is a normal page.
In the above embodiment, the screen recorded video is segmented, so that one page corresponds to one video segment, and one video segment is starved to uniformly sample a plurality of key frames, and whether the page is an abnormal page is determined by judging the key frames.
In one embodiment, the first threshold and/or the second threshold are adjustable, wherein the first threshold is positively correlated with the following difference value, and the second threshold is negatively correlated with the following difference value. In one embodiment, the adjusting manner of the first threshold includes: acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page; comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value; and adjusting the first threshold value according to the difference value and the positive correlation. In one embodiment, the adjusting manner of the second threshold includes: acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page; comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value; and adjusting the second threshold value according to the difference value and the negative correlation relation.
Specifically, the server first obtains a normal page corresponding to the application program to be tested, obtains a color value (e.g., rgb value) of the normal page, and determines a difference between the color value and a standard color value, if the difference value is large, the first threshold value is correspondingly increased, and if the difference value is small, the first threshold value is correspondingly decreased, where the standard color value may be red, which is because after the open frame rate detection, if a large number of red lines are present in the page, it is said that the page is stuck severely. Similarly, for the adjustment of the second threshold, the server first obtains a normal page corresponding to the application program to be tested, and obtains a color value (e.g., rgb value) of the normal page to determine a difference between the color value and the standard color value, if the difference value is large, the second threshold is correspondingly adjusted to be small, if the difference value is small, the first threshold is correspondingly adjusted to be large, the standard color value may be red, which is because after the open frame rate detection, if a large number of red lines are present on the page, it is indicated that the page is stuck severely.
In the above embodiment, the first threshold and the second threshold may be adjusted according to the standard color value to correspond to the colors of the pages of different applications, so as to prevent a determination error caused by a change in the page color of an application.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided an abnormal page identifying apparatus, including: a receiving module 100, a page dwell time acquisition module 200, a segmentation module 300, a model processing module 400, and an output module 500, wherein:
the receiving module 100 receives an abnormal page identification request sent by a terminal, where the abnormal page identification request carries a screen recorded video, and the screen recorded video is generated by recording a terminal screen after the terminal runs different application test scripts under the condition that the frame rate detection is started.
The page staying time obtaining module 200 is configured to obtain a page staying time corresponding to the application test script.
The segmenting module 300 is configured to segment the screen recorded video according to the page staying time to obtain a plurality of video segments.
The model processing module 400 is configured to extract a plurality of frames of key frames from each video segment, and input the key frames into an abnormal page identification model obtained through pre-training to obtain an abnormal page, where the abnormal page identification model is generated based on a normal page and a historical abnormal page.
And an output module 500, configured to return the obtained abnormal page to the terminal.
In one embodiment, the model processing module 400 may include:
and the frame judgment unit is used for respectively inputting the key frames corresponding to each video segment into the abnormal page identification model so as to judge whether the page corresponding to the video segment is an abnormal page.
And the page judgment unit is used for outputting the abnormal page if the page corresponding to the video segment is the abnormal page.
In one embodiment, the frame determining unit may include:
the first similarity obtaining unit is used for obtaining a first similarity between the color value of the key frame corresponding to each video segment and the first color value of the history abnormal page.
And the first judging unit is used for judging the key frame as an abnormal frame when the first similarity is larger than a first threshold value.
And the second similarity acquiring unit is used for acquiring the second similarity between the color value of the key frame corresponding to each video segment and the second color value of the normal page when the first similarity is not larger than the first threshold.
And the second judging unit is used for judging the key frame as an abnormal frame when the second similarity is smaller than a second threshold value, and otherwise, judging the key frame as a normal frame.
And the comprehensive unit is used for determining that the page corresponding to the video segment is a normal page when the key frames corresponding to the video segment are normal frames, and determining that the page corresponding to the video segment is an abnormal page if the key frames corresponding to the video segment are normal frames.
In one embodiment, the above abnormal page identification apparatus further includes:
and the first color value acquisition module is used for acquiring the normal page corresponding to the application program and extracting the normal color value of the normal page.
And the first comparison module is used for comparing the normal color value with the standard color value corresponding to the abnormal page to obtain a difference value.
The first adjusting module is used for adjusting the first threshold value according to the difference value and the positive correlation.
In one embodiment, the above abnormal page identification apparatus further includes:
and the second color value acquisition module is used for acquiring the normal page corresponding to the application program and extracting the normal color value of the normal page.
And the second comparison module is used for comparing the normal color value with the standard color value corresponding to the abnormal page to obtain a difference value.
And the second adjusting module is used for adjusting the second threshold according to the difference value and the negative correlation relation.
In one embodiment, the above abnormal page identification apparatus further includes:
and the normal page acquisition module is used for acquiring the normal page from the terminal.
And the abnormal page acquisition module is used for acquiring a historical abnormal page from the database, wherein the historical abnormal page is an abnormal page identified after the frame rate detection is started at the terminal.
And the training module is used for training the normal page and the abnormal page to obtain an abnormal page identification model.
In one embodiment, the above abnormal page identification apparatus further includes:
and the mode setting module is used for setting the terminal as an authorization mode of the developer.
And the recording module is used for receiving an operation instruction of the application program installed in the terminal and recording the script through a preset script recording tool to obtain the application program test script.
For the specific definition of the abnormal page identification device, reference may be made to the above definition of the abnormal page identification method, which is not described herein again. The modules in the above abnormal page identification device may be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing normal pages, abnormal pages and abnormal page identification models. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an exception page identification method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program: receiving an abnormal page identification request sent by a terminal, wherein the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started; acquiring the retention time of a page corresponding to the test script of the application program; segmenting the screen recorded video according to the retention time of the page to obtain a plurality of video segments; respectively extracting a plurality of frame key frames from each video segment, and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, wherein the abnormal page identification model is generated based on a normal page and a historical abnormal page; and returning the obtained abnormal page to the terminal.
In one embodiment, the inputting of the key frame into the abnormal page recognition model obtained by training in advance to obtain the abnormal page when the processor executes the computer program may include: respectively inputting the key frame corresponding to each video segment into an abnormal page identification model to judge whether the page corresponding to the video segment is an abnormal page or not; and if the page corresponding to the video segment is an abnormal page, outputting the abnormal page.
In one embodiment, the respectively inputting the key frame corresponding to each video segment into the abnormal page identification model when the processor executes the computer program to determine whether the page corresponding to the video segment is an abnormal page may include: acquiring a first similarity between a color value of a key frame corresponding to each video segment and a first color value of a history abnormal page; when the first similarity is larger than a first threshold value, judging the key frame as an abnormal frame; when the first similarity is not larger than a first threshold value, acquiring a second similarity between the color value of the key frame corresponding to each video segment and a second color value of the normal page; when the second similarity is smaller than a second threshold value, judging the key frame as an abnormal frame, otherwise, judging the key frame as a normal frame; and when the key frames corresponding to the video segments are all normal frames, the page corresponding to the video segments is a normal page, otherwise, the page corresponding to the video segments is an abnormal page.
In one embodiment, the manner in which the processor executes the computer program involves adjusting the first threshold includes: acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page; comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value; and adjusting the first threshold value according to the difference value and the positive correlation.
In one embodiment, the manner in which the processor executes the computer program involves adjusting the second threshold includes: acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page; comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value; and adjusting the second threshold value according to the difference value and the negative correlation relation.
In one embodiment, the abnormal page identification model involved in the execution of the computer program by the processor is generated in a manner comprising: acquiring a normal page from a terminal; acquiring a historical abnormal page from a database, wherein the historical abnormal page is an abnormal page identified after the frame rate detection is started at the terminal; and training the normal page and the abnormal page to obtain an abnormal page identification model.
In one embodiment, the application test script involved in the execution of the computer program by the processor is generated in a manner comprising: setting a terminal as a developer authorization mode; and receiving an operation instruction of the application program installed in the terminal, and performing script recording through a preset script recording tool to obtain an application program test script.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: receiving an abnormal page identification request sent by a terminal, wherein the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started; acquiring the retention time of a page corresponding to the test script of the application program; segmenting the screen recorded video according to the retention time of the page to obtain a plurality of video segments; respectively extracting a plurality of frame key frames from each video segment, and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, wherein the abnormal page identification model is generated based on a normal page and a historical abnormal page; and returning the obtained abnormal page to the terminal.
In one embodiment, the inputting of the keyframes into the pre-trained abnormal page recognition model to obtain the abnormal page when the computer program is executed by the processor may include: respectively inputting the key frame corresponding to each video segment into an abnormal page identification model to judge whether the page corresponding to the video segment is an abnormal page or not; and if the page corresponding to the video segment is an abnormal page, outputting the abnormal page.
In one embodiment, the respectively inputting the key frames corresponding to each video segment into the abnormal page identification model when the computer program is executed by the processor to determine whether the page corresponding to the video segment is an abnormal page may include: acquiring a first similarity between a color value of a key frame corresponding to each video segment and a first color value of a history abnormal page; when the first similarity is larger than a first threshold value, judging the key frame as an abnormal frame; when the first similarity is not larger than a first threshold value, acquiring a second similarity between the color value of the key frame corresponding to each video segment and a second color value of the normal page; when the second similarity is smaller than a second threshold value, judging the key frame as an abnormal frame, otherwise, judging the key frame as a normal frame; and when the key frames corresponding to the video segments are all normal frames, the page corresponding to the video segments is a normal page, otherwise, the page corresponding to the video segments is an abnormal page.
In one embodiment, the manner in which the first threshold is adjusted in question when the computer program is executed by the processor comprises: acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page; comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value; and adjusting the first threshold value according to the difference value and the positive correlation.
In one embodiment, the manner in which the second threshold is adjusted in question when the computer program is executed by the processor comprises: acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page; comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value; and adjusting the second threshold value according to the difference value and the negative correlation relation.
In one embodiment, the abnormal page identification model involved in the execution of the computer program by the processor is generated in a manner comprising: acquiring a normal page from a terminal; acquiring a historical abnormal page from a database, wherein the historical abnormal page is an abnormal page identified after the frame rate detection is started at the terminal; and training the normal page and the abnormal page to obtain an abnormal page identification model.
In one embodiment, the manner in which the application test script is generated in relation to when the computer program is executed by the processor includes: setting a terminal as a developer authorization mode; and receiving an operation instruction of the application program installed in the terminal, and performing script recording through a preset script recording tool to obtain an application program test script.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. 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 a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An abnormal page identification method, the method comprising:
receiving an abnormal page identification request sent by a terminal, wherein the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started;
acquiring the retention time of a page corresponding to the application program test script;
segmenting the screen recorded video according to the page staying time to obtain a plurality of video segments;
respectively extracting a plurality of frame key frames from each video segment, and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, wherein the abnormal page identification model is generated based on a normal page and a historical abnormal page;
and returning the obtained abnormal page to the terminal.
2. The method according to claim 1, wherein the inputting the keyframe into a pre-trained abnormal page recognition model to obtain an abnormal page comprises:
respectively inputting the key frame corresponding to each video segment into an abnormal page identification model so as to judge whether the page corresponding to the video segment is an abnormal page or not;
and if the page corresponding to the video segment is an abnormal page, outputting the abnormal page.
3. The method according to claim 2, wherein said inputting the key frame corresponding to each of the video segments into an abnormal page identification model respectively to determine whether the page corresponding to the video segment is an abnormal page comprises:
acquiring a first similarity between a color value of a key frame corresponding to each video segment and a first color value of a history abnormal page;
when the first similarity is larger than a first threshold value, judging the key frame as an abnormal frame;
when the first similarity is not larger than a first threshold value, acquiring a second similarity between the color value of the key frame corresponding to each video segment and a second color value of a normal page;
when the second similarity is smaller than a second threshold value, judging the key frame to be an abnormal frame, otherwise, judging the key frame to be a normal frame;
and when the key frames corresponding to the video segments are all normal frames, the page corresponding to the video segments is a normal page, otherwise, the page corresponding to the video segments is an abnormal page.
4. The method of claim 3, wherein the first threshold is adjusted by:
acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page;
comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value;
and adjusting a first threshold value according to the difference value, wherein the larger the difference value is, the larger the first threshold value is.
5. The method of claim 3, wherein the second threshold is adjusted by:
acquiring a normal page corresponding to an application program, and extracting a normal color value of the normal page;
comparing the normal color value with a standard color value corresponding to the abnormal page to obtain a difference value;
and adjusting a second threshold value according to the difference value, wherein the larger the difference value is, the smaller the second threshold value is.
6. The method according to any one of claims 1 to 5, wherein the abnormal page recognition model is generated in a manner that includes:
acquiring a normal page from a terminal;
acquiring a historical abnormal page from a database, wherein the historical abnormal page is an abnormal page identified after the frame rate detection is started at a terminal;
and training the normal page and the abnormal page to obtain an abnormal page identification model.
7. The method according to any one of claims 1 to 5, wherein the generation of the application test script comprises:
setting a terminal as a developer authorization mode;
and receiving an operation instruction of the application program installed in the terminal, and performing script recording through a preset script recording tool to obtain an application program test script.
8. An abnormal page identification apparatus, comprising:
the system comprises a receiving module, a processing module and a display module, wherein the receiving module is used for receiving an abnormal page identification request sent by a terminal, the abnormal page identification request carries a screen recording video, and the screen recording video is generated by recording a terminal screen after the terminal runs different application program test scripts under the condition that frame rate detection is started;
the page staying time acquisition module is used for acquiring the page staying time corresponding to the application program test script;
the segmentation module is used for segmenting the screen recorded video according to the page staying time to obtain a plurality of video segments;
the model processing module is used for respectively extracting a plurality of frame key frames from each video segment and inputting the key frames into an abnormal page identification model obtained by pre-training to obtain an abnormal page, and the abnormal page identification model is generated based on a normal page and historical abnormal pages;
and the output module is used for returning the obtained abnormal page to the terminal.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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