Page performance analysis optimization method and device, equipment and storage medium

Abstract

The invention relates to a page performance analysis optimization method and device, equipment and a storage medium. According to the main technical scheme, the method comprises the steps that the page view of a current access item is obtained, the current access item comprises a plurality of access pages, and each access page corresponds to the page view; obtaining core page data according to the page view of each access page; obtaining page data to be optimized according to the second opening rate of the core page data; performing scene division on the to-be-optimized page data to obtain to-be-optimized scene page data; and performing optimization processing on the to-be-optimized scene page data to obtain optimized page data. According to the method, the access page of the current access item is analyzed and processed, the performance problem of the current access item is analyzed in combination with the access service condition, the bottleneck of the problem is found from the global perspective, and then the to-be-optimized page data is obtained, so that the effect of improving the page optimization efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of performance optimization, and in particular to a page performance analysis and optimization method, device, equipment and storage medium. Background Art

[0002] As we all know, Google has launched a set of indicators for website page performance, namely LCP (Largest Contentful Paint, the time required to display the largest content element), FID (First Input Delay, the first input delay time) and CLS (Cumulative Layout Shift, cumulative layout shift), which are used to measure the website's loading efficiency, interactivity and page stability. These three indicators are all related to website speed and have long been very important to search engines and users.

[0003] In the traditional implementation, LCP can calculate how long it takes to load the largest content element within the visible range of a web page. The meaning of this indicator is: how long it takes for the main content on a web page to be seen by the user, which is equivalent to the first impression of the web page. FID can calculate the delay time when the user interacts with the web page for the first time (such as clicking a link or button, opening a drop-down menu, entering text in a text dialog box, etc.). This indicator represents the interactivity and responsiveness of the web page: whether the web page can respond immediately when the user tries to interact with the web page. CLS can calculate whether the images that have appeared when the web page is loaded will be squeezed down due to the sudden loading of a certain element, that is, it is calculated by: "Affected range (percentage of visible range) * moving distance = component shift score". This indicator represents the stability of the page.

[0004] However, the above indicators are all technical indicators with a relatively large range and too many result indicators. As the technical indicators themselves have great versatility, the effect of their use is not obvious when extended to business indicators. There is a lack of process observation of specific business conditions, and specific problem points cannot be found. Moreover, only the performance of a single page can be analyzed. The performance overview, business characteristics and global performance points of the overall access project cannot be analyzed, and effective optimization strategies for the business characteristics of different access projects cannot be provided. Summary of the invention

[0005] Based on this, the present application provides a page performance analysis and optimization method, device, equipment and storage medium, which analyzes and processes the access page of the current access project, analyzes the performance problems of the current access project in combination with the access business situation, finds the problem bottleneck from a global perspective, and then obtains the page data to be optimized, so as to achieve the effect of improving the efficiency of page optimization.

[0006] In a first aspect, a page performance analysis and optimization method is provided, the method comprising:

[0007] Obtain the page views of the current accessed item, wherein the current accessed item includes multiple accessed pages, each of which has a corresponding page view;

[0008] According to the page views of each visited page, the core page data is obtained;

[0009] According to the opening rate of core page data in seconds, the page data to be optimized is obtained;

[0010] Divide the page data to be optimized into scenes to obtain scene page data to be optimized;

[0011] The scene page data to be optimized is optimized to obtain optimized page data.

[0012] According to an achievable method in an embodiment of the present application, obtaining the page views of the currently accessed project includes:

[0013] Monitor the address bar changes of each visited page in real time;

[0014] According to the change information of each address bar, the access log record data is obtained;

[0015] According to the access log record data, the page views of each access page of the current access project are obtained.

[0016] According to an achievable method in an embodiment of the present application, core page data is obtained according to the page views of each access page, including:

[0017] According to the page views of each visited page, determine the visit proportion of each visited page;

[0018] According to the proportion of each visit volume, the page rank of each visited page is obtained;

[0019] According to the level of each page, the core page data is obtained.

[0020] According to an achievable method in an embodiment of the present application, the core page data includes multiple core pages, and the page data to be optimized includes multiple pages to be optimized; according to the second opening rate of the core page data, the page data to be optimized is obtained, including:

[0021] Get the opening rate of each core page in seconds;

[0022] Compare each second opening rate with the preset second opening rate;

[0023] The core pages whose opening rate in seconds is less than the preset opening rate in seconds are regarded as pages to be optimized, and the data of pages to be optimized are formed.

[0024] According to an achievable method in an embodiment of the present application, the scene page data to be optimized includes a plurality of scene pages to be optimized; the scene page data to be optimized is divided into scenes to obtain the scene page data to be optimized, including:

[0025] Obtain the page ID of each page to be optimized;

[0026] According to each page identifier, a plurality of corresponding scene pages to be optimized are obtained.

[0027] According to an achievable method in an embodiment of the present application, optimizing the scene page data to be optimized to obtain optimized page data includes:

[0028] Obtain the execution link of each scene page to be optimized;

[0029] According to each execution link, each scene page to be optimized is divided into stages to obtain page stage data of each scene page to be optimized;

[0030] Optimize the data at each page stage to obtain optimized page data.

[0031] According to an achievable method in an embodiment of the present application, the page stage data includes multiple stage information, and the optimized page data includes multiple optimized page information; each page stage data is optimized to obtain optimized page data, including:

[0032] Obtain the loading time information of each stage of each page stage data, sort the loading time information according to the size, and obtain the stage with the largest loading time information corresponding to each page stage data;

[0033] The stages are optimized to obtain optimized page information corresponding to the stage data of each page to form optimized page data.

[0034] In a second aspect, a device for analyzing and optimizing page performance is provided, the device comprising:

[0035] The page acquisition module is used to acquire the page views of the current access project, wherein the current access project includes multiple access pages, and each access page has a corresponding page view;

[0036] The core page module is used to obtain core page data according to the page views of each visited page;

[0037] The page to be optimized module is used to obtain the page data to be optimized based on the second opening rate of the core page data;

[0038] A scene division module is used to divide the page data to be optimized into scenes to obtain scene page data to be optimized;

[0039] The optimization processing module is used to optimize the scene page data to be optimized to obtain optimized page data.

[0040] In a third aspect, a computer device is provided, comprising:

[0041] at least one processor; and

[0042] a memory communicatively connected to at least one processor; wherein,

[0043] The memory stores computer instructions that can be executed by at least one processor, and the computer instructions are executed by at least one processor to enable the at least one processor to execute the method involved in the first aspect above.

[0044] In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored, characterized in that the computer instructions are used to enable a computer to execute the method involved in the above-mentioned first aspect.

[0045] According to the technical content provided in the embodiments of the present application, the page views of the current access project are obtained, wherein the current access project includes multiple access pages, and each access page has a corresponding page view; according to the page views of each access page, the core page data is obtained; according to the second opening rate of the core page data, the page data to be optimized is obtained; the page data to be optimized is divided into scenes to obtain scene page data to be optimized; the scene page data to be optimized is optimized to obtain optimized page data. The above operations analyze and process the access pages of the current access project, analyze the performance problems of the current access project in combination with the access business situation, find the problem bottleneck from a global perspective, and then obtain the page data to be optimized, so as to achieve the effect of improving the efficiency of page optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 An application environment diagram of a page performance analysis and optimization method in one embodiment;

[0047] Figure 2 A schematic diagram of a process of a page performance analysis and optimization method in one embodiment;

[0048] Figure 3 A schematic diagram of a preferred process of a page performance analysis and optimization method in one embodiment;

[0049] Figure 4 It is a structural block diagram of a device for analyzing and optimizing page performance in one embodiment;

[0050] Figure 5 FIG. 4 is a schematic structural diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The page performance analysis and optimization method provided in this application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. Specifically, the server 104 obtains the page views of the terminal of the current access project, wherein the current access project includes multiple access pages, and each access page corresponds to a page view; according to the page views of each access page, the core page data is obtained; according to the core page data, the page data to be optimized is obtained; the page data to be optimized is divided into links to obtain the page data of the link to be optimized; the page data of the link to be optimized is optimized to obtain the optimized page data. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones and tablets, etc., and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0053] In one embodiment, Figure 2 As shown, a page performance analysis and optimization method is provided, which is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0054] Step S201: Obtain the page views of the currently accessed item.

[0055] The current access item includes multiple access pages, and each access page has a corresponding page visit volume.

[0056] Here, the currently visited item is the item to be visited currently, which may be different websites or terminal APPs, etc. The currently visited item may include multiple visited pages, each of which may be clicked by the user, and thus the page views of each visited page may be obtained.

[0057] Step S203: obtaining core page data according to the page views of each visited page.

[0058] The core page data includes multiple core pages.

[0059] Here, based on the page views of each visited page, we can get the pages with relatively high click-through rates, that is, the core pages. Since the click-through rates are relatively high, it means that the current pages are still playing a relatively large role. Therefore, in the later stage, only this type of pages can be optimized. As for the pages with relatively low click-through rates, that is, other pages except the core pages, it means that basically no one pays attention to them, so there is no need to optimize them.

[0060] Step S205: obtaining the page data to be optimized according to the second opening rate of the core page data.

[0061] The core page data includes multiple core pages, each of which has its corresponding second opening rate; the to-be-optimized page data includes multiple to-be-optimized pages.

[0062] Here, the second opening rate is an important indicator to measure the performance of the application of the web page, i.e. the page accessed. It can reflect the proportion of users with the best performance and is an important dimension for evaluating user experience. Specifically, it refers to the ratio of pages that can be opened within one second. The specific calculation method is: within a fixed time period, the number of visits with a page first screen time of less than 1 second divided by the total number of visits within the time period. The specific expression is as follows:

[0063] Seconds opening rate = (number of visits with page first screen time < 1 second within a fixed time period) / (total number of visits within a fixed time period)

[0064] By determining the opening rate of the core pages above, we can understand the user experience of the current core pages and obtain the data of the pages to be optimized.

[0065] Step S207: Divide the page data to be optimized into scenes to obtain scene page data to be optimized.

[0066] The scene page data to be optimized includes multiple scene pages to be optimized.

[0067] Here, since different scene pages to be optimized have corresponding performance indicators that are different, the scenes to be optimized can be divided into scenes, and then the scene page data to be optimized can be obtained to facilitate subsequent optimization operations.

[0068] Step S209: Optimize the scene page data to be optimized to obtain optimized page data.

[0069] Here, after obtaining the scene page to be optimized, the scene page to be optimized can be divided into stages. Since different stages may take different time, the time-consuming stage can be optimized to obtain the corresponding optimized page data.

[0070] It can be seen that the embodiment of the present application obtains the page views of the current access project, wherein the current access project includes multiple access pages, and each access page has a corresponding page view; obtains the core page data according to the page views of each access page; obtains the page data to be optimized according to the second opening rate of the core page data; divides the page data to be optimized into scenarios to obtain the scene page data to be optimized; and optimizes the scene page data to be optimized to obtain the optimized page data. The above operations analyze and process the access pages of the current access project, analyze the performance problems of the current access project in combination with the access business situation, find the problem bottleneck from a global perspective, and then obtain the page data to be optimized, so as to achieve the effect of improving the efficiency of page optimization.

[0071] The following describes each step in the above method flow in detail. First, the above step 201, namely "obtaining the page views of the currently accessed item", is described in detail in conjunction with the embodiment.

[0072] Monitor the address bar change information of each accessed page in real time; obtain access log record data based on the address bar change information; and obtain the page views of each accessed page of the current accessed project based on the access log record data.

[0073] Here, the address bar change information of each accessed page is monitored in real time. Whenever the address bar information changes, the preset statistical script, i.e., the API (Application Program Interface) related to the SDK (Software Development Kit) can be called to record it, so as to obtain the access log record information corresponding to each accessed page and form access log record data. The server summarizes and analyzes the access log record data to obtain the page views of each accessed page of the current accessed project. The page views of each accessed page are returned to the human-computer interaction interface so that the operator can see it. It should be noted that the preset statistical script is pre-established by the R&D personnel based on the performance of the current accessed project. It can automatically collect page performance information and capture errors and other information, and can also manually report information such as PV (Page View, page statistics), rather than third-party statistical tools such as Google Analytics or Baidu Statistics.

[0074] The above method, through a preset statistical script, realizes the statistics of the page views of each access page of the current access project, which is convenient for the later staff to optimize each access page based on the page views.

[0075] Next, the above step 203, ie, "obtaining core page data according to the page views of each visited page", is described in detail in conjunction with the embodiment.

[0076] According to the page views of each visited page, the visit volume proportion of each visited page is determined; according to each visit volume proportion, the page rank of each visited page is obtained; according to each page rank, the core page data is obtained.

[0077] Here, according to the page views of each access page, the page view proportion of each access page can be determined. Specifically, the page views of all access pages of the current access project are summarized to obtain the aggregate page views. Assuming that PV 总 Indicates the page views of each visited page, assuming PV 各 Indicates that the page views of each visited page are compared with the total page views to obtain the visit ratio of each visited page. Assuming that PV 占比 The specific expression is as follows:

[0078] PV 占比 =PV 各 / PV 总

[0079] According to the percentage of visits, the page level of each visited page is obtained. Here, the page level is divided into S, A, B, and C. If the page level is greater than or equal to 1%, that is, PV 占比 >=1%, then the page level of the currently visited page is determined to be S level; if the page volume ratio is greater than or equal to 0.5% and less than 1%, that is, 0.5%= <PV 占比 <1%, then the page grade of the currently visited page is determined to be A; if the page volume ratio is greater than or equal to 0.1% and less than 0.5%, that is, 0.1% = <PV 占比 <0.5%, then the page level of the currently visited page is determined to be B level; if the page volume ratio is less than 0.1%, that is, PV 占比 <0.1%, it is determined that the page grade of the currently visited page is C.

[0080] According to each page level, the core page data is obtained. As can be seen from the above, there are four levels of page levels: S, A, B, and C. Among them, the access pages corresponding to the S level and the A level are the core page data.

[0081] The above operation determines the page level of each accessed page according to the page views of each accessed page, and then obtains the core page data. That is, the higher the proportion of page views, the greater the impact on the performance of the current accessed project. Therefore, in the later stage, only the core page data can be optimized, so as to effectively improve the performance indicators of the current accessed project and bring a faster loading experience to the frequently accessed pages.

[0082] Next, the above step S205, ie, "the page data to be optimized includes a plurality of pages to be optimized, and the page data to be optimized is obtained according to the second opening rate of the core page data" is described in detail in conjunction with the embodiment.

[0083] The opening rate of each core page in seconds is obtained; each opening rate in seconds is compared with a preset opening rate in seconds; the core pages whose opening rate in seconds is less than the preset opening rate in seconds are regarded as pages to be optimized, thereby forming data of pages to be optimized.

[0084] The preset opening rate is determined based on the user's current needs and is not specifically limited here.

[0085] Here, from the above, we can know that each core page has its own corresponding second opening rate, and the expression of the second opening rate is as follows:

[0086] Seconds opening rate = (number of visits with page first screen time < 1 second within a fixed time period) / (total number of visits within a fixed time period)

[0087] Among them, the page first screen time is the homepage loading time.

[0088] Specifically, the opening rate of each core page in seconds is counted by using DOM (Document Object Model, Chinese name is Document Object Model) monitoring, that is, real-time monitoring of DOM changes, when there is no new DOM change or no change within 3 seconds or interaction occurs, for example, clicking on the core page; at this time, the access time to the core page is reported, and the time is counted to see whether it is less than 1 second, and then the opening rate of each core page in seconds is obtained. Each opening rate in seconds is compared with the preset opening rate in seconds, and the core pages with opening rates in seconds less than the preset opening rate in seconds are taken as pages to be optimized, forming the data of pages to be optimized. For example, a core page is visited 1,000 times in one day, and the first screen loading time of 800 visits is less than 1 second, then the opening rate of the core page in seconds is 80%. If the preset opening rate in seconds is 50%, then 80% is greater than 50%, indicating that the core page can provide a good user experience, and therefore, does not need to be optimized for the time being; but if a core page is visited 1,000 times in one day, and the first screen loading time of 200 visits is less than 1 second, then the opening rate in seconds of the core page is 20%, and 20% is less than 50%, indicating that the core page needs to be optimized, that is, it is the page to be optimized.

[0089] The above operation determines the pages to be optimized based on the opening rate of each core page in seconds, that is, determines the focus of optimization, reduces the time for pages that do not currently need to be optimized, and achieves the effect of improving the optimization efficiency of the pages to be optimized.

[0090] The above step S207, ie, "dividing the to-be-optimized page data into scenes to obtain the to-be-optimized scene page data", is described in detail below in conjunction with an embodiment.

[0091] Obtain page identifiers of each page to be optimized; and obtain corresponding multiple scene pages to be optimized based on each page identifier.

[0092] Here, since the scene page data to be optimized includes multiple scene pages to be optimized, the scene pages to be optimized can be divided into off-end scene pages and on-end scene pages. Off-end scene pages include but are not limited to newly opened pages and refreshed pages, that is, the current overall page will be refreshed, and you can see that there is a white screen for a moment on the current overall page; on-end scene pages include but are not limited to single-page SPA jumps, that is, the current page is only partially refreshed, and the overall framework will not be refreshed. For example, in the case of header navigation, left menu, right content, etc., only some areas change, and the rest of the areas do not change. The key to distinguishing off-end scene pages from on-end scene pages is that the page identifiers are different. Therefore, the page identifiers of each page to be optimized can be obtained. For off-end scene pages, the identifier can be firstPV: true; for on-end scene pages, the identifier can be firstPV: false. According to the identifiers, statistics are distinguished and obtained to obtain the corresponding multiple scene pages to be optimized.

[0093] The above operation obtains a plurality of corresponding scene pages to be optimized based on the page identifiers of the pages to be optimized. Since the performance of each scene page to be optimized may be different, targeted optimization operations can be performed based on different scene pages to be optimized later.

[0094] Finally, the above step S209, namely "optimizing the scene page data to be optimized to obtain optimized page data", is described in detail in conjunction with the embodiment.

[0095] Obtain the execution link of each scene page to be optimized; divide each scene page to be optimized into stages according to each execution link to obtain page stage data of each scene page to be optimized; optimize each page stage data to obtain optimized page data.

[0096] The page stage data includes multiple stage information, and the optimized page data includes multiple optimized page information.

[0097] Here, different links correspond to different scene pages to be optimized. The link of the off-end scene page is the longest, covering the link of the on-end scene page. Therefore, the execution link of each scene page to be optimized can be obtained; according to each execution link, each scene page to be optimized is divided into stages to obtain the page stage data of each scene page to be optimized. Assuming that the current scene page to be optimized is an off-end scene page, the link process that can be executed in one page access can be: main application business code loading, code execution, routing execution, layout component life cycle execution, micro-front-end sub-application resource loading, resource code execution, sub-application routing execution, sub-application layout component life cycle, page component life cycle, interface request and page area stability, etc. Therefore, the scene page to be optimized can be divided into the aforementioned 10 stages to obtain 10 stage information; and if the current scene page to be optimized is an on-end scene page, the link process that can be executed in one page access can be: micro-front-end sub-application resource loading, resource code execution, sub-application routing execution, sub-application layout component life cycle, page component life cycle, interface request and page area stability, etc. Therefore, the scene page to be optimized can be divided into the aforementioned 6 stages to obtain 6 stage information.

[0098] Optimize the data of each page stage to obtain optimized page data. In one achievable method,

[0099] Obtain the loading time information of each stage of each page stage data, sort each loading time information by size, and obtain the stage with the largest loading time information corresponding to each page stage data; optimize the stages to obtain the optimized page information corresponding to each page stage data to form optimized page data.

[0100] Here, since each scene page to be optimized is divided into multiple stages, it is possible to obtain the loading time information of each stage of each page stage data, sort the loading time information corresponding to each page stage data by size, obtain the stage with the largest loading time information corresponding to each page stage data, optimize the stage with the largest loading time information corresponding to each page stage data, obtain the optimized page information corresponding to each page stage data, and multiple optimized page information form optimized page data. That is, by determining the scene page to be optimized, that is, the key optimized scene page, and optimizing the time consumption of the core link stage for each scene page to be optimized, the optimization efficiency of each long-term page to be optimized is improved.

[0101] Specifically, the optimization process here can be: optimize the application package size of the stage with the longest loading time-consuming information in each page stage data. The application package can include multiple sub-page information, and it can be determined whether the multiple sub-page information contains invalid sub-pages. Among them, an invalid sub-page means that the page has been offline and is not maintained, that is, it has been offline from the product level, but has not been deleted at the code level. The specific judgment process can be: since each sub-page is associated with a set of permission menu systems, that is, only sub-pages registered in the permission menu system are valid, and sub-pages that are not registered in the system are invalid sub-pages. Therefore, sub-pages that are not in the permission menu system can be cleared; or the routing address information of each sub-page is scanned. If the route has been commented, the relevant code is considered invalid, that is, it is determined to be an invalid sub-page, and it can be cleared to achieve the effect of effectively reducing the size of the application package and improving execution efficiency. Of course, there can be many specific optimization methods, which will not be repeated here.

[0102] In the above operation, since the loading and execution link of the entire scene page to be optimized is very long, it is impossible to determine where the main bottleneck problem is. Therefore, the page to be optimized can be divided into stages, that is, the time-consuming segmentation of the main application and sub-application can be performed to determine the main execution bottleneck time and the key time-consuming cycle, and then optimize it to achieve the effect of improving the optimization efficiency of the scene page to be optimized.

[0103] In combination with the implementation method in the above embodiment, Figure 3 An example of a preferred method flow provided in the embodiment of the present application is described. Figure 3 As shown, the method may include the following steps:

[0104] Step S301, monitoring the address bar change information of each access page of the current access project in real time.

[0105] Step S302, obtaining access log record data according to the change information of each address bar.

[0106] Step S303: obtaining the page views of each access page of the current access project according to the access log record data.

[0107] Step S304: determining the page views ratio of each page according to the page views of each page.

[0108] Step S305, obtaining the page rank of each visited page according to the percentage of each visit volume.

[0109] Step S306, obtaining core page data according to each page level; wherein the core page data includes a plurality of core pages.

[0110] Step S307, obtaining the opening rate of each core page in seconds.

[0111] Step S308, comparing each second opening rate with a preset second opening rate.

[0112] Step S309: core pages whose opening rate in seconds is less than a preset opening rate in seconds are taken as pages to be optimized, and data of pages to be optimized are formed.

[0113] Step S310, obtaining the page identifier of each page to be optimized.

[0114] Step S311, according to each page identifier, a plurality of corresponding scene pages to be optimized are obtained to form scene page data to be optimized.

[0115] Step S312, obtaining the execution link of each scene page to be optimized.

[0116] Step S313, according to each execution link, each scene page to be optimized is divided into stages to obtain page stage data of each scene page to be optimized; wherein the page stage data includes multiple stage information.

[0117] Step S314, obtaining the loading time consumption information of each stage of each page stage data, sorting each loading time consumption information according to size, and obtaining the stage with the largest loading time consumption information corresponding to each page stage data.

[0118] Step S315, optimizing the stages to obtain optimized page information corresponding to the stage data of each page to form optimized page data.

[0119] It should be understood that although Figure 2-Figure 3 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in the application, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-Figure 3 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0120] Figure 4 A schematic diagram of a page performance analysis and optimization device provided in an embodiment of the present application, which can be arranged in Figure 1 The server in the application environment shown is used to execute Figure 2 , Figure 3 The method flow shown in Figure 4As shown, the device may include: a page acquisition module 401, a core page module 403, a page to be optimized module 405, a scene division module 407, and an optimization processing module 409. The main functions of each component module are as follows:

[0121] The page acquisition module 401 is used to acquire the page views of the current accessed item, wherein the current accessed item includes multiple accessed pages, each of which has a corresponding page view;

[0122] The core page module 403 is used to obtain core page data according to the page views of each access page;

[0123] The page to be optimized module 405 is used to obtain the page data to be optimized according to the second opening rate of the core page data;

[0124] The scene division module 407 is used to divide the page data to be optimized into scenes to obtain scene page data to be optimized;

[0125] The optimization processing module 409 is used to optimize the scene page data to be optimized to obtain optimized page data.

[0126] In one embodiment, the page acquisition module 401 is further used to:

[0127] Monitor the address bar changes of each visited page in real time;

[0128] According to the change information of each address bar, the access log record data is obtained;

[0129] According to the access log record data, the page views of each access page of the current access project are obtained.

[0130] In one embodiment, the core page module 403 is further used to:

[0131] According to the page views of each visited page, determine the visit proportion of each visited page;

[0132] According to the proportion of each visit volume, the page rank of each visited page is obtained;

[0133] According to the level of each page, the core page data is obtained.

[0134] In one embodiment, the core page data includes a plurality of core pages; the page to be optimized module 405 is further used for:

[0135] Get the opening rate of each core page in seconds;

[0136] Compare each second opening rate with the preset second opening rate;

[0137] The core pages whose opening rate in seconds is less than the preset opening rate in seconds are regarded as pages to be optimized, and the data of pages to be optimized are formed.

[0138] In one embodiment, the scene page data to be optimized includes a plurality of scene pages to be optimized; the scene division module 407 is further used to:

[0139] Obtain the page ID of each page to be optimized;

[0140] According to each page identifier, a plurality of corresponding scene pages to be optimized are obtained.

[0141] In one embodiment, the optimization processing module 409 is further used to:

[0142] Obtain the execution link of each scene page to be optimized;

[0143] According to each execution link, each scene page to be optimized is divided into stages to obtain page stage data of each scene page to be optimized;

[0144] Optimize the data at each page stage to obtain optimized page data.

[0145] In one embodiment, the page stage data includes multiple stage information, and the optimized page data includes multiple optimized page information; the optimization processing module 409 is further used to:

[0146] The loading time information of each stage of each page stage data is obtained, and each loading time information is sorted according to size to obtain the stage with the largest loading time information corresponding to each page stage data.

[0147] The stages are optimized to obtain optimized page information corresponding to the stage data of each page to form optimized page data.

[0148] According to the specific embodiments provided in this application, the technical solution provided in this application can have the following advantages:

[0149] 1) Analyze the bottlenecks in a targeted manner according to the specific status of the current access project.

[0150] 2) Conduct a comprehensive and global analysis of the performance of the currently accessed project to facilitate more targeted optimization.

[0151] 3) By analyzing and observing the current status of the access project from multiple dimensions, the specific time-consuming stages can be located to achieve better optimization.

[0152] It is understandable that the implementation of any method or product of the present application does not necessarily require all of the above advantages to be achieved at the same time.

[0153] The same or similar parts between the above embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0154] It should be noted that the embodiments of the present application may involve the use of user data. In actual applications, user-specific personal data can be used in the scheme described in this article within the scope permitted by applicable laws and regulations, provided that the applicable laws and regulations of the country are met (for example, the user's explicit consent, effective notification to the user, and the user's explicit authorization, etc.).

[0155] According to an embodiment of the present application, the present application also provides a computer device and a computer-readable storage medium.

[0156] like Figure 5 , is a block diagram of a computer device according to an embodiment of the present application. The computer device is intended to represent various forms of digital computers or mobile devices. The digital computer may include a desktop computer, a portable computer, a workbench, a personal digital assistant, a server, a mainframe computer, and other suitable computers. The mobile device may include a tablet computer, a smart phone, a wearable device, etc.

[0157] like Figure 5 As shown, the device 500 includes a computing unit 501, a ROM 502, a RAM 503, a bus 504, and an input / output (I / O) interface 505, and the computing unit 501, ROM 502 and RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0158] The computing unit 501 can perform various processes in the method embodiment of the present application according to the computer instructions stored in the read-only memory (ROM) 502 or the computer instructions loaded from the storage unit 508 to the random access memory (RAM) 503. The computing unit 501 can be various general and / or special processing components with processing and computing capabilities. The computing unit 501 may include, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. In some embodiments, the method provided in the embodiment of the present application can be implemented as a computer software program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508.

[0159] RAM 503 may also store various programs and data required for the operation of device 500. Part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509.

[0160] The input unit 506, output unit 507, storage unit 508 and communication unit 509 in the device 500 can be connected to the I / O interface 505. The input unit 506 can be, for example, a keyboard, a mouse, a touch screen, a microphone, etc. The output unit 507 can be, for example, a display, a speaker, an indicator light, etc. The device 500 can exchange information, data, etc. with other devices through the communication unit 509.

[0161] It should be noted that the device may also include other components necessary for normal operation, or may only include components necessary for implementing the solution of the present application, rather than all the components shown in the figure.

[0162] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof.

[0163] The computer instructions for implementing the method of the present application can be written in any combination of one or more programming languages. These computer instructions can be provided to the computing unit 501, so that when the computer instructions are executed by the computing unit 501 such as a processor, the steps involved in the method embodiment of the present application are executed.

[0164] The computer-readable storage medium provided in the present application may be a tangible medium that may contain or store computer instructions for executing the steps involved in the method embodiments of the present application. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, and other forms of storage media.

[0165] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A page performance analysis and optimization method, characterized in that: The method comprises: Obtaining the page views of the current accessed item, wherein the current accessed item includes multiple accessed pages, each accessed page has a corresponding page view; According to the page views of each of the access pages, core page data is obtained; According to the opening rate of the core page data in seconds, the page data to be optimized is obtained; Dividing the to-be-optimized page data into scenes to obtain to-be-optimized scene page data; The scene page data to be optimized is optimized to obtain optimized page data.

2. The method according to claim 1, characterized in that The method of obtaining the page views of the currently accessed project includes: Monitor the address bar changes of each visited page in real time; Obtain access log record data according to each of the address bar change information; The page views of each access page of the current access project are obtained according to the access log record data.

3. The method according to claim 2, characterized in that The core page data is obtained according to the page views of each of the access pages, including: Determine the page views of each of the visited pages based on the page views of each of the visited pages; According to the visit volume ratios, the page rank of each visit page is obtained; According to each of the page levels, core page data is obtained.

4. The method according to claim 1, characterized in that The core page data includes a plurality of core pages, and the page data to be optimized includes a plurality of pages to be optimized; the page data to be optimized is obtained according to the second opening rate of the core page data, including: Obtaining the opening rate of each core page in seconds; Comparing each of the second opening rates with a preset second opening rate; The core pages whose opening rate in seconds is less than the preset opening rate in seconds are taken as pages to be optimized, and the data of pages to be optimized are formed.

5. The method according to claim 4, characterized in that The scene page data to be optimized includes a plurality of scene pages to be optimized; and the scene page data to be optimized is divided into scenes to obtain the scene page data to be optimized, including: Obtaining page identifiers of the pages to be optimized; According to each of the page identifiers, a plurality of corresponding scene pages to be optimized are obtained.

6. The method according to claim 5, characterized in that: The optimizing process of the scene page data to be optimized to obtain optimized page data includes: Obtaining the execution link of each of the scene pages to be optimized; According to each of the execution links, each of the scene pages to be optimized is divided into stages to obtain page stage data of each of the scene pages to be optimized; The page stage data are optimized to obtain optimized page data.

7. The method according to claim 6, characterized in that: The page stage data includes a plurality of stage information, and the optimized page data includes a plurality of optimized page information; the optimizing process is performed on each of the page stage data to obtain the optimized page data, including: Obtaining the loading time consumption information of each stage of each page stage data, sorting each loading time consumption information according to size, and obtaining the stage with the largest loading time consumption information corresponding to each page stage data; The stages are optimized to obtain optimized page information corresponding to each of the page stage data to form the optimized page data.

8. A page performance analysis and optimization device, characterized in that: The device comprises: A page acquisition module is used to acquire the page views of the current accessed project, wherein the current accessed project includes multiple accessed pages, each of which has a corresponding page view count; A core page module, used to obtain core page data according to the page views of each of the access pages; A page module to be optimized, used to obtain page data to be optimized according to the second opening rate of the core page data; A scene division module, used for dividing the page data to be optimized into scenes to obtain scene page data to be optimized; The optimization processing module is used to optimize the scene page data to be optimized to obtain optimized page data.

9. A computer device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores computer instructions that can be executed by the at least one processor, and the computer instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: The computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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