CN110704772A - Page abnormity monitoring method, system, device, electronic equipment and computer readable medium - Google Patents
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Abstract
The disclosure relates to a method, a system, a device, an electronic device and a computer readable medium for monitoring page abnormity. Relates to the field of computer information processing, and the method comprises the following steps: performing screenshot processing on a current webpage to generate a current image; comparing the current image with a reference image to obtain a first similarity; comparing the similarity of the current image to the previous page screenshot to obtain a second similarity; and determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity. The page abnormity monitoring method, the system, the device, the electronic equipment and the computer readable medium can automatically detect the abnormity condition of the webpage so as to repair the webpage defects in time.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a method, a system, an apparatus, an electronic device, and a computer-readable medium for monitoring page anomalies.
Background
In the website service application, a user completes information interaction with a website service system through auxiliary functions provided by different pages, and further the final purpose of accessing a website by the user is achieved. Taking shopping-like websites as an example, a user may browse a large number of different pages from selecting a good to making a final payment, which may be: shop front pages, activity pages, item detail pages, shopping cart pages, and payment pages, among others.
In general, if a data or rendering of a certain page has an error, a developer cannot know the error at the first time when the error occurs, and in general, the developer needs to wait until a business department finds the error and then notify the developer of the error. The time period between the time of the business department informing the research and development department of the page abnormity and the occurrence time of the page abnormity can be long or short. How to enable a research and development worker of a page to timely and quickly know the abnormal condition of the page so as to quickly repair the page is a difficult problem in the prior art.
Therefore, a new method, system, apparatus, electronic device and computer readable medium for monitoring page anomalies is needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present disclosure provides a method, a system, an apparatus, an electronic device and a computer readable medium for monitoring page abnormality, which can automatically detect an abnormal condition of a web page so as to timely repair a web page defect.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to one aspect of the disclosure, a method for monitoring page exception is provided, which includes: performing screenshot processing on a current webpage to generate a current image; comparing the current image with a reference image to obtain a first similarity; carrying out similarity on the current image to a previous page screenshot to obtain a second similarity; and determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
In an exemplary embodiment of the present disclosure, screenshot processing is performed on a current webpage, and generating a current image includes: screenshot processing is carried out on a current webpage to generate a first image; and performing ashing treatment on the first image to generate the current image.
In an exemplary embodiment of the present disclosure, the screenshot processing is performed on the current webpage, and the generating the current image further includes: and carrying out segmentation processing on the current image by taking a pixel as a unit to obtain a preset segmentation image unit.
In an exemplary embodiment of the present disclosure, comparing the similarity between the current image and the reference image, and acquiring the first similarity includes: and respectively carrying out similarity comparison on the segmentation image units in the current image and the segmentation image units in the reference image to obtain a first similarity.
In an exemplary embodiment of the present disclosure, the performing similarity comparison between each sliced image unit and the sliced image unit in the reference image respectively, and obtaining the first similarity includes: recording the number of similar units of the segmentation image unit in the current image and the segmentation image unit in the reference image as M; recording the number of dissimilar units of the segmentation image unit in the current image and the segmentation image unit in the reference image as N; and determining the first similarity of the current image and the reference image through a similarity formula.
In an exemplary embodiment of the present disclosure, the similarity formula includes:
S＝M/N*100％；
wherein S is similarity, and M is the number of similar units; n is the number of dissimilar units.
In an exemplary embodiment of the present disclosure, determining whether an abnormal condition exists in the current webpage page through the first similarity and the second similarity includes: generating a first similarity curve and a second similarity curve according to the first similarity and the second similarity; and determining whether the current webpage has an abnormal condition or not through the first similarity curve and the second similarity curve.
In an exemplary embodiment of the present disclosure, determining whether an abnormal condition exists in the current webpage through the first similarity curve and the second similarity curve includes: when the first similarity curve and the second similarity curve meet the first characteristic, determining that the current webpage is in a normal state; and when the first similarity curve and the second similarity curve meet the second characteristic, determining that the current webpage is in a normal state.
In an exemplary embodiment of the present disclosure, determining whether an abnormal condition exists in the current webpage through the first similarity curve and the second similarity curve includes: when the first similarity curve and the second similarity curve meet the third characteristic, calculating to obtain a similarity variance; when the similarity variance is smaller than a preset threshold value, determining that the current webpage is in a normal state; and when the similarity variance is larger than a preset threshold value, determining that the current webpage has an abnormal condition.
In an exemplary embodiment of the present disclosure, user behavior is analyzed to generate page exception information; and performing screenshot processing on the current webpage according to the page abnormal information to generate a current image.
According to an aspect of the present disclosure, a page anomaly monitoring system is provided, which includes: the screenshot server is used for carrying out screenshot processing on the current webpage to generate a current image; the crawler server is used for periodically acquiring the current image, and comparing the current image with a reference image to acquire a first similarity; carrying out similarity on the current image to a previous page screenshot to obtain a second similarity; and the network server is used for determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
According to an aspect of the present disclosure, a page anomaly monitoring apparatus is provided, the apparatus including: the screenshot module is used for carrying out screenshot processing on the current webpage to generate a current image; the first comparison module is used for comparing the similarity of the current image with a reference image to obtain a first similarity; the second comparison module is used for carrying out similarity on the current image to the previous page screenshot so as to obtain a second similarity; and the judging module is used for determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the page abnormity monitoring method, the system, the device, the electronic equipment and the computer readable medium, the abnormity condition of the webpage can be automatically detected so as to repair the webpage defects in time.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a method and an apparatus for monitoring page exceptions according to an exemplary embodiment.
FIG. 2 is a flowchart illustrating a method of page anomaly monitoring in accordance with an exemplary embodiment.
FIG. 3 is a flowchart illustrating a method of page anomaly monitoring according to another exemplary embodiment.
FIG. 4 is a schematic illustration of a statistical curve in a method for monitoring page faults according to another exemplary embodiment.
FIG. 5 is a schematic illustration of a statistical curve in a method for monitoring page faults according to another exemplary embodiment.
FIG. 6 is a schematic illustration of a statistical curve in a method for monitoring page faults according to another exemplary embodiment.
FIG. 7 is a schematic illustration of a statistical curve in a method for monitoring page faults according to another exemplary embodiment.
FIG. 8 is a schematic diagram illustrating a page anomaly monitoring system in accordance with an exemplary embodiment.
FIG. 9 is a block diagram illustrating a page anomaly monitoring system in accordance with an exemplary embodiment.
Fig. 10 is a block diagram illustrating a page anomaly monitoring apparatus according to another exemplary embodiment.
FIG. 11 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 12 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
Fig. 1 is a system block diagram illustrating a method and an apparatus for monitoring page exceptions according to an exemplary embodiment.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that provides page support for shopping websites browsed by users using the terminal devices 101, 102, 103. The background management server can analyze and process the received data such as the product information inquiry request and feed back the processing result to the terminal equipment.
The server 105 may be a server of one entity, and may be composed of a plurality of servers, for example, and the server 105 may be composed of a screenshot server, a crawler server, and a web server, for example.
The screenshot server can be used for carrying out screenshot processing on the current webpage to generate a current image; the crawler server can be used for periodically acquiring the current image, and comparing the current image with a reference image to acquire a first similarity; carrying out similarity on the current image to a previous page screenshot to obtain a second similarity; and the network server can be used for determining whether the current webpage has abnormal conditions or not through the first similarity and the second similarity.
It should be noted that the method for monitoring page anomalies provided by the embodiment of the present disclosure may be executed by the server 105, and accordingly, the device for monitoring page anomalies may be disposed in the server 105. The web page end for browsing the goods and the monitoring end for performing the screenshot of the web page provided for the user are generally located in the terminal devices 101, 102, and 103.
FIG. 2 is a flowchart illustrating a method of page anomaly monitoring in accordance with an exemplary embodiment. The page anomaly monitoring method 20 includes at least steps S202 to S208.
As shown in fig. 2, in S202, screenshot processing is performed on the current web page, and a current image is generated. For example, screenshot processing is carried out on a current webpage to generate a first image; and performing ashing treatment on the first image to generate the current image.
The ashing is to convert R, G, B three-channel values of a pixel in a picture into a brightness value for representation. Because the coordinate and the size of each screenshot of the Web page can be completely consistent easily, the displacement is not considered when the similarity of the two screenshots is calculated. And the screenshot of the Web page is different from a real photo and is not influenced by the illumination condition, so that after the picture is ashed, the similarity is calculated by independently utilizing the gray value of the pixel point in the screenshot, and the scene described by the invention can be met.
In one embodiment, the screenshot processing is performed on the current webpage, and the generating the current image further includes: and carrying out segmentation processing on the current image by taking a pixel as a unit to obtain a preset segmentation image unit. The current page is screenshot and stored as a 1. Ashing treatment is carried out on A1, and then the picture is divided according to the unit of X pixels in the transverse direction and Y pixels in the longitudinal direction. For example, a picture with a resolution PX PY is sliced into N cells with a minimum cell of 10(PX) × 10(PX), each cell having 100 pixels.
In S204, the current image is compared with the reference image for similarity to obtain a first similarity. For example, similarity comparison may be performed between the segmented image units in the current image and the segmented image units in the reference image, respectively, to obtain a first similarity.
In one embodiment, two screenshots may be taken, for example, of the same page at different times and taken as A1 and A2. Where a1 represents the current image and a2 represents the reference image. Ashing treatment is carried out on A1 and A2, then the pictures are respectively segmented according to the unit of X horizontal pixels and Y vertical pixels, and then the segmented units are compared in sequence. A picture with the resolution of PX PY is cut into N units with the minimum unit of 10(PX) × 10(PX), and each unit comprises 100 pixels.
The reference image may be, for example, a current web page image captured at any time, or may also be, for example, an image of a current web page captured at a fixed time period every day, which is not limited in this application.
In one embodiment, the comparing the similarity of each sliced image unit with the sliced image unit in the reference image, and the obtaining the first similarity includes: recording the number of similar units of the segmentation image unit in the current image and the segmentation image unit in the reference image as M; recording the number of dissimilar units of the segmentation image unit in the current image and the segmentation image unit in the reference image as N; and determining the first similarity of the current image and the reference image through a similarity formula.
For example, if pixels with different gray values in a certain unit U1 in the a1 image and a corresponding unit U2 in the a2 image exceed a preset threshold, the pixels are marked as U1 and U2 are dissimilar, the number of all similar units is M, the number of dissimilar units is marked as N, and the page similarity between a1 and a2 is:
S＝M/N*100％。
in S206, the current image is compared with the page screenshot at the previous time for similarity, and a second similarity is obtained. For example, similarity comparison may be performed between the segmented image units in the current image and the segmented image units in the page screenshot at the previous time, so as to obtain a second similarity.
In one embodiment, screenshots may be taken, for example, for the same page for two consecutive time periods, respectively, and stored as A1 and A3. Where A1 represents the current image and A3 represents the previous time page shot. Ashing treatment is carried out on A1 and A3, then the pictures are respectively segmented according to the unit of X horizontal pixels and Y vertical pixels, and then the segmented units are compared in sequence. A picture with the resolution of PX PY is cut into N units with the minimum unit of 10(PX) × 10(PX), and each unit comprises 100 pixels.
For example, if pixels with different gray values in a certain unit U1 in the a1 image and a corresponding unit U3 in the A3 image exceed a preset threshold, the pixels are marked as U1 and U3 are dissimilar, the number of all similar units is M, the number of dissimilar units is marked as N, and the page similarity between a1 and A3 is:
S＝M/N*100％。
in S208, it is determined whether an abnormal condition exists in the current webpage according to the first similarity and the second similarity. The first similarity curve and the second similarity curve may be generated, for example, by the first similarity and the second similarity; and determining whether the current webpage has an abnormal condition or not through the first similarity curve and the second similarity curve.
In one embodiment, when the first similarity curve and the second similarity curve meet the first characteristic, the current webpage is determined to be in a normal state.
In one embodiment, when the first similarity curve and the second similarity curve meet the second characteristic, the current webpage is determined to be in a normal state.
In one embodiment, when the first similarity curve and the second similarity curve satisfy the third feature, calculating and acquiring a similarity variance; and when the similarity variance is smaller than a preset threshold value, determining that the current webpage is in a normal state.
In one embodiment, when the first similarity curve and the second similarity curve satisfy the third feature, calculating and acquiring a similarity variance; and when the similarity variance is larger than a preset threshold value, determining that the current webpage has an abnormal condition.
According to the page abnormity monitoring method, the mode of judging whether the current page has the abnormal condition or not is achieved by comparing the similarity data of the page screenshots at different moments, so that the abnormal condition of the webpage can be automatically detected, and the webpage defects can be repaired in time.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Based on the similarity calculation formula introduced above, the idea of establishing similarity statistical data for a fixed page is as follows: intercepting a screenshot of a tracked page (marked as P) as a baseline picture (B0), then carrying out screenshot on the P according to a fixed frequency, calculating the similarity (marked as screenshot) between the intercepted picture and B0, recording the values of all screenings, calculating the fluctuation range of the page P according to the recorded screenings (as shown in figure 3), and if the screenings of the next screenshot exceed the range, preliminarily considering that the page P is abnormal.
However, limited to the prior art and the reason that pages contain dynamic content, unless the pages are completely static (i.e., the portions of the pages that do not dynamically change), the similarity is difficult to achieve 100% accuracy. There is generally a fluctuation in the page similarity, which is minimized by ashing the page in this embodiment. However, the similarity curve of pages in a real environment may present more complicated situations, and different situations are classified and separately illustrated.
The following parameter definitions are made: the similarity between the current screenshot of the page and the reference picture is recorded as S1; the similarity between the current screenshot of the page and the previous screenshot is recorded as S2.
In one embodiment, the pages do not change substantially over time (static pages, or only a small portion of a page may change dynamically). Such as a "user personal information" page. The balance of the user, the number of messages and the number of shopping carts are dynamically changed, and the rest of the balance of the user, the number of messages and the number of shopping carts are basically unchanged along with time. The statistical curve of the similarity of such pages is shown in fig. 4: since the changed part does not show periodic changes over time, such as the user balance, it is also related to the user's purchasing behavior. The area can be excluded during screenshot by using a technical means, so that the similarity curves S1 and S2 are simplified into straight-like lines.
In one embodiment, the page fixes are periodically changed (search results page, page with BI function.)
For example, a result page obtained by inputting an "apple" search on a home page of a shopping website, the content of the recommendation of goods on one side of the page may be different every time, but the specific content of the search result is fixed for a period of time. The S1 and S2 similarity curves for this type of page are shown in FIG. 5: the similarity curve of S1 in fig. 5 changes the search result due to the change of the relevance value of the background item, and the similarity is reduced from about 80% to about 30%, however, since the change of the search result is fixed in a plane range, after the first change, the similarity will stabilize at about 30% even if the search result changes again. If rendering abnormality occurs to the page in the period of time, and the abnormal content is located in the plane range of the search result variation, the similarity statistics are not perceived through S1.
With the introduction of the S2 similarity curve, it can be seen from fig. 5 that the similarity change of the S2 curve only occurs in the first screenshot calculation after the search result change, and since S2 is the comparison between the current screenshot and the previous screenshot, the similarity level is subsequently restored to the previous similarity level. The page can fix the similarity fluctuation range in two intervals of [30,31] and [80,81] based on the S2 curve, even eliminate the similarity value when the change occurs, and only reserve the interval of [80,81 ].
The two types of pages mentioned above can detect the abnormal condition of the page by establishing a similarity statistical curve, and eliminate the interference elements by means of technical means, thereby achieving the purpose of simplifying the curve. However, this method is only applicable to static pages or pages with regular changes, and a determination scenario when the similarity curve does not conform to the similarity curves in the above two embodiments is described below.
In one embodiment, the non-fixed portion of the page changes non-periodically (merchant first page, channel first page). For example, the 'XX shopping' home page, most of the content is dynamic content, and the change frequency of different parts of the page is also different (the change frequency of the carousel large image is lower than that of the bottom shopping), which results in different similarity change amplitudes of each screenshot of the whole page. And the change frequency of the dynamic part is unpredictable, which also results in the irregular following of the similarity change frequency of the whole page (as shown in fig. 6). In general, such pages cannot establish statistical regularity and exploration through the S1 and S2 curves.
Although page anomalies cannot be detected through the similarity curve, the fluctuation range of the similarity still has reference value, because generally, the more frequently-changed pages have higher error probability, and the important attention is needed for the pages. The concept of similarity variance is introduced in this application to describe this variation, namely:
variance of similarity
Wherein D (S) is a variance value, SkFor the current similarity, SaveFor average similarity, n is the number of screenshots.
In conjunction with the three types of page similarity curves mentioned above, if D is calculated (S2), the page of type three will be significantly more diverse than the pages of type one and two. This enables the amplitude of the variation of the page to be quantified by the variance. And when the variance is larger than a preset threshold value, determining that the current webpage is abnormal.
With the help of the above description of different situations, the method for determining page fault is described in detail below.
FIG. 7 is a flowchart illustrating a method of page anomaly monitoring according to another exemplary embodiment. The page anomaly monitoring method 70 is a specific description of the step S208 "determining whether there is an anomaly in the current web page according to the first similarity and the second similarity" in the page anomaly monitoring method 20.
As shown in fig. 7, in S702, the first similarity and the second similarity are acquired.
In S704, a first similarity curve and a second similarity curve are generated according to the first similarity and the second similarity.
In S706, when the first similarity curve and the second similarity curve satisfy the first feature, it is determined that the current webpage is in a normal state. The first feature may for example be a curve feature as described in 4.
In S708, when the first similarity curve and the second similarity curve satisfy the second characteristic, it is determined that the current webpage is in a normal state. The second feature may for example be a curve feature as described in 5.
In S710, when the first similarity curve and the second similarity curve satisfy the third feature, a similarity variance is calculated. The third feature may be, for example, a curve feature as described in fig. 6.
In S712, when the similarity variance is smaller than a predetermined threshold, it is determined that the current web page is in a normal state.
In S714, when the similarity variance is greater than a predetermined threshold, it is determined that an abnormal condition exists in the current webpage.
According to the page abnormity monitoring method disclosed by the invention, not only can abnormal conditions be monitored for static pages and pages with relatively regular page changes, but also the webpage with more dynamic contents can be judged in a variance judgment mode. The page abnormity is actively detected through similarity statistics, and the time from occurrence of the page abnormity to discovery of the page abnormity can be greatly shortened.
FIG. 8 is a schematic diagram illustrating a page anomaly monitoring system in accordance with an exemplary embodiment. In one embodiment, page exception information may also be generated, for example, through user behavior analysis; and performing screenshot processing on the current webpage according to the page abnormal information to generate a current image. For example, after the current web page is judged to be abnormal according to the similarity curve, the abnormal condition of the web page can be further confirmed by monitoring the specified operation of the user. The present application is not limited thereto.
The user behavior analysis refers to that a script is injected into a monitored page, when a user browses the page and completes certain specific operations (such as clicking and refreshing), a specific request is triggered to be sent to a server, and then the server completes corresponding data analysis according to different requests.
As shown in fig. 8, the abnormality monitoring system 800 may be constituted by, for example, a similarity analysis system 802 and a user behavior analysis system 804.
As shown in fig. 8, the similarity analysis system 802 includes: screenshot server 8022, crawler server 8024, and web server 8026. And performing screenshot processing on the current webpage to generate a current image.
The crawler server 8022 is configured to periodically obtain the current image, and compare the current image with a reference image to obtain a first similarity; and performing similarity on the current image to the previous page screenshot to obtain a second similarity.
The web server 8024 is configured to determine whether an abnormal condition exists in the current web page according to the first similarity and the second similarity.
As shown in fig. 8, the similarity analysis system 804 includes: a proxy server 8041, a load balancing server 8043, an analytics server 8045, a data structure server 8047, and a distributed database 8049.
The proxy server 8041 is configured to monitor a user specified operation, and generate a first request, where the specified operation includes a page refresh operation; the first request is filtered to eliminate invalid requests;
the load balancing server 8043 is configured to analyze the first request to obtain user information and access information; the load balancing server 6043 includes a plurality of logical aggregation units 60431 therein for processing and computing of the first request.
The analysis server 8045 is configured to determine, according to the user information and the access information, a time interval between the current designated operation and the last designated operation of the user; and generating page exception information when the time interval is less than a predetermined threshold.
The data structure server 8047 is used for storing the user information and the access information;
distributed database 8049 is used to store page exception information.
May also include, for example: message queue server 8040 for serving as an intermediate server for storing filtered valid requests.
In the similarity analysis system 802, a Crawler Server 8022(Crawler Server) is responsible for periodically crawling the screenshots of the monitored pages, and calculating and generating a similarity value. The Web Server 8024(Web Server) is responsible for visualization of similarity statistics, management of similarity data, and management of a crawling policy, and provides a similarity data interface for the analysis Server 8045(Analyze worker) to call, so as to generate anomaly monitoring data.
The cycle period of the analysis server 8045(Analyze worker) in the user analysis system 804 may be, for example, 1 minute, so when a page abnormality occurs, a problem page may be detected in time by a user refresh behavior. Of course, if the page is abnormal but no user accesses the page, the user behavior analysis is not triggered naturally, and in this case, the detection time of the page abnormality is prolonged to the cycle time of the similarity statistics. If the frequency of the similarity statistics is one day, the page abnormality can be found only when the similarity statistics is obtained next time. In practice this is rare because pages that are accessed so frequently by the user are not of monitoring value.
According to the page abnormity monitoring method, the certainty problem of page abnormity is converted into the probability problem, the probability of abnormity is quantified through similarity statistics and user behavior analysis, and data are provided for final page abnormity judgment.
The page abnormity monitoring method solves a certainty problem through probability: the page abnormity is actively detected through similarity statistics and user behavior analysis, and the data of the two aspects are integrated to provide a basis for finally judging the page abnormity. The time from occurrence of the page exception to discovery is greatly shortened.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
FIG. 9 is a block diagram illustrating a page anomaly monitoring system in accordance with an exemplary embodiment. The page anomaly monitoring system 90 includes: screenshot server 902, crawler server 904, and web server 906.
The screenshot server 902 is configured to perform screenshot processing on a current webpage to generate a current image.
The crawler server 904 is configured to periodically obtain the current image, and compare the current image with a reference image to obtain a first similarity; and performing similarity on the current image to the previous page screenshot to obtain a second similarity.
The web server 906 is configured to determine whether an abnormal condition exists in the current web page according to the first similarity and the second similarity.
According to the page abnormity monitoring system, the mode of judging whether the current page has the abnormal condition or not is achieved by comparing the similarity data of the page screenshots at different moments, so that the abnormal condition of the page can be automatically detected, and the page defects can be timely repaired.
Fig. 10 is a block diagram illustrating a page anomaly monitoring apparatus according to another exemplary embodiment. The page abnormality monitoring apparatus 1000 includes: screenshot module 1002, first comparison module 1004, second comparison module 1006, and decision module 1008.
The screenshot module 1002 is configured to perform screenshot processing on a current webpage to generate a current image;
the first comparison module 1004 is configured to perform similarity comparison between the current image and a reference image to obtain a first similarity;
the second comparing module 1006 is configured to perform similarity on the current image to a previous-time page screenshot, for example, to obtain a second similarity;
the judging module 1008 is configured to determine whether an abnormal condition exists in the current webpage according to the first similarity and the second similarity.
According to the page abnormity monitoring device disclosed by the invention, the mode of judging whether the current page has the abnormal condition or not is further judged by comparing the similarity data of the page screenshots at different moments, so that the abnormal condition of the webpage can be automatically detected, and the webpage defects can be timely repaired.
FIG. 11 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 200 according to this embodiment of the present disclosure is described below with reference to fig. 11. The electronic device 200 shown in fig. 11 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 11, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 2, fig. 7.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present disclosure.
Fig. 12 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure.
Referring to fig. 12, a program product 400 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: performing screenshot processing on a current webpage to generate a current image; comparing the current image with a reference image to obtain a first similarity; carrying out similarity on the current image to a previous page screenshot to obtain a second similarity; and determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
In addition, the structures, the proportions, the sizes, and the like shown in the drawings of the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used for limiting the limit conditions which the present disclosure can implement, so that the present disclosure has no technical essence, and any modification of the structures, the change of the proportion relation, or the adjustment of the sizes, should still fall within the scope which the technical contents disclosed in the present disclosure can cover without affecting the technical effects which the present disclosure can produce and the purposes which can be achieved. In addition, the terms "above", "first", "second" and "a" as used in the present specification are for the sake of clarity only, and are not intended to limit the scope of the present disclosure, and changes or modifications of the relative relationship may be made without substantial changes in the technical content.
Claims (14)
1. A method for monitoring page abnormity is characterized by comprising the following steps:
performing screenshot processing on a current webpage to generate a current image;
comparing the current image with a reference image to obtain a first similarity;
comparing the similarity of the current image to the previous page screenshot to obtain a second similarity; and
and determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
2. The method of claim 1, wherein performing a screenshot process on a current web page, generating a current image comprises:
screenshot processing is carried out on a current webpage to generate a first image; and
and performing ashing treatment on the first image to generate the current image.
3. The method of claim 1, wherein performing a screenshot process on a current web page, generating a current image further comprises:
and carrying out segmentation processing on the current image by taking a pixel as a unit to obtain a preset segmentation image unit.
4. The method of claim 3, wherein comparing the similarity of the current image with the reference image to obtain a first similarity comprises:
and respectively carrying out similarity comparison on the segmentation image units in the current image and the segmentation image units in the reference image to obtain a first similarity.
5. The method of claim 4, wherein comparing each sliced image element with the respective sliced image element in the reference image to obtain a first similarity comprises:
recording the number of similar units of the segmentation image unit in the current image and the segmentation image unit in the reference image as M;
recording the number of dissimilar units of the segmentation image unit in the current image and the segmentation image unit in the reference image as N; and
determining the first similarity of the current image and the reference image through a similarity formula.
6. The method of claim 5, wherein the similarity formula comprises:
S＝M/N*100％；
wherein S is similarity, and M is the number of similar units; n is the number of dissimilar units.
7. The method of claim 1, wherein determining whether the current webpage page has the abnormal condition through the first similarity and the second similarity comprises:
generating a first similarity curve and a second similarity curve according to the first similarity and the second similarity; and
and determining whether the current webpage has abnormal conditions or not through the first similarity curve and the second similarity curve.
8. The method of claim 1, wherein determining whether the current web page has the abnormal condition through the first similarity curve and the second similarity curve comprises:
when the first similarity curve and the second similarity curve meet the first characteristic, determining that the current webpage is in a normal state; and
and when the first similarity curve and the second similarity curve meet the second characteristic, determining that the current webpage is in a normal state.
9. The method of claim 1, wherein determining whether the current web page has the abnormal condition through the first similarity curve and the second similarity curve comprises:
when the first similarity curve and the second similarity curve meet the third characteristic, calculating to obtain a similarity variance;
when the similarity variance is smaller than a preset threshold value, determining that the current webpage is in a normal state; and
and when the similarity variance is larger than a preset threshold value, determining that the current webpage has an abnormal condition.
10. The method of claim 1, further comprising:
analyzing the user behavior to generate page abnormal information;
and performing screenshot processing on the current webpage according to the page abnormal information to generate a current image.
11. A system for monitoring page exceptions, comprising:
the screenshot server is used for carrying out screenshot processing on the current webpage to generate a current image;
the crawler server is used for periodically acquiring the current image, and comparing the current image with a reference image to acquire a first similarity; carrying out similarity on the current image to a previous page screenshot to obtain a second similarity; and
and the network server is used for determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
12. A page anomaly monitoring device, comprising:
the screenshot module is used for carrying out screenshot processing on the current webpage to generate a current image;
the first comparison module is used for comparing the similarity of the current image with a reference image to obtain a first similarity;
the second comparison module is used for carrying out similarity on the current image to the previous page screenshot so as to obtain a second similarity; and
and the judging module is used for determining whether the current webpage has abnormal conditions or not according to the first similarity and the second similarity.
13. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.
14. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.
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