CN114328154A

CN114328154A - Attribution method, device, equipment and storage medium of page loading performance

Info

Publication number: CN114328154A
Application number: CN202111625411.6A
Authority: CN
Inventors: 王凤娇; 汤金华; 顾轶灵
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-12-28
Filing date: 2021-12-28
Publication date: 2022-04-12

Abstract

The disclosure provides a method, a device, equipment and a storage medium for attributing page loading performance, and relates to the technical field of computers, in particular to the field of monitoring of webpage performance. The specific implementation scheme is as follows: determining a comparison result between the loading performance statistical index of the target page in the ith time period and the loading performance statistical index of the target page in the (i + k) th time period; and determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical index, wherein i and k are positive integers. According to the technology disclosed by the invention, the loading performance of the target page is analyzed.

Description

Attribution method, device, equipment and storage medium of page loading performance

Technical Field

The disclosure relates to the technical field of computers, in particular to the technical field of webpage performance monitoring.

Background

The web page performance directly affects the aspects of user retention, page conversion, user experience and the like for the business. The page loading performance is a core factor in a page performance system and is a precondition for whether subsequent interaction is possible and whether the interaction is smooth. Therefore, it is particularly important to measure and improve page loading performance.

Disclosure of Invention

The disclosure provides a page loading performance attribution method, a page loading performance attribution device, a page loading performance attribution equipment and a storage medium.

According to an aspect of the present disclosure, there is provided an attribution method of page loading performance, including:

determining a comparison result between the loading performance statistical index of the target page in the ith time period and the loading performance statistical index of the target page in the (i + k) th time period;

and determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical index, wherein i and k are positive integers.

According to another aspect of the present disclosure, there is provided a device for attributing page loading performance, including:

the determining module is used for determining a comparison result between the loading performance statistical index of the target page in the ith time period and the loading performance statistical index of the target page in the (i + k) th time period;

and the attribution module is used for determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical index, wherein i and k are positive integers.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the methods of attributing page load performance in the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform any one of the methods for attributing page load performance in the embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements any one of the methods of attributing page load performance in embodiments of the present disclosure.

One embodiment in the above application has the following advantages or benefits:

the loading performance of the target page in the (i + k) th time period is determined based on the comparison result of the loading performance statistical index in the (i + k) th time period and the loading performance statistical index in the (i + k) th time period, and then the influence factor of the loading performance of the target page in the (i + k) th time period is determined according to the comparison result and the type of the loading performance statistical index, so that the loading performance of the target page is accurately analyzed, and meanwhile, the corresponding influence factor is determined, so that developers can optimize the target page according to the influence factor, and further, the experience of the users in browsing websites is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow diagram of attribution of page load performance according to an embodiment of the present disclosure;

FIG. 2 is a flow diagram of an attribution method of page load performance according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a get script load time parameter, according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of obtaining data interface parameters according to an embodiment of the present disclosure;

FIG. 5 is a block diagram of an attribution device of page load performance according to an embodiment of the present disclosure;

FIG. 6 is a block diagram of an attribution device of page load performance according to an embodiment of the present disclosure;

FIG. 7 is a block diagram of an electronic device for implementing the attribution method of page load performance of embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

FIG. 1 is a flow diagram of an attribution method of page load performance according to an embodiment of the present disclosure. As shown in fig. 1, the method may include:

s101, determining a comparison result between the loading performance statistical index of the target page in the ith time period and the loading performance statistical index of the target page in the (i + k) th time period;

s102, determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical index, wherein i and k are positive integers.

In step S101, the target page is typically a web page for loading text, images, animations, videos, and the like, as an example. The time length of each time period is the same, and the time length can be set according to actual needs, for example, 3 hours, 6 hours or one day.

The loading performance statistical index is used for reflecting the loading performance of the target page in the ith time period, and the loading performance statistical index may be one value or a plurality of values, for example, the loading performance of the target page in the ith time period is reflected by an average number and a mode, or the loading performance of the target page in the ith time period is reflected by a trend graph formed by a plurality of values. Correspondingly, the comparison result between the loading performance statistical indicator of the target page in the ith time period and the loading performance statistical indicator of the target page in the (i + k) th time period may be one value or a plurality of values.

In step S102, each of the loading performance statistics indexes corresponds to one type, and each type corresponds to an influencing factor of the loading performance of at least one target page. The type can be set according to the attribute of the loading performance statistical index, and can also be defined according to the actual requirement.

According to the technical scheme, the loading performance of the target page in the (i + k) th time period is determined based on the comparison result of the loading performance statistical index in the (i + k) th time period and the loading performance statistical index in the (i) th time period, and the influence factors of the loading performance of the target page in the (i + k) th time period are determined according to the comparison result and the type of the loading performance statistical index, so that the loading performance of the target page is accurately analyzed, the corresponding influence factors are determined, developers can optimize the target page according to the influence factors, and the experience of the users in browsing websites is improved.

In one embodiment, the determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical indicator includes:

and under the condition that the comparison result meets a preset condition, determining the influence factors of the loading performance of the target page in the (i + k) th time period according to the type of the loading performance statistical index and a preset mapping relation.

Illustratively, the preset condition is a condition indicating that the loading performance of the target page in the (i + k) th time period is lower than that of the target page in the (i) th time period. Then, the preset condition may be that if the loading performance statistical index in the (i + k) th time period is different from the loading performance statistical index in the (i) th time period, the obtained comparison result is greater than 0; the loading performance statistical index in the (i + k) th time period can be larger than that in the (i) th time period; the ratio of the loading performance statistical index in the (i + k) th time period to the loading performance statistical index in the (i) th time period may be greater than 1.

Illustratively, loading the performance statistics index may include: a first byte time statistical index, a script loading time-consuming statistical index and a data interface statistical index. The preset mapping relationship may pre-store the corresponding relationship between the loading performance statistical index and the influence factor for each type.

In this embodiment, the preset mapping relationship may include: the influence factor corresponding to the first byte time statistic index is that the number of pre-jump times is large or the performance of the server is poor, wherein the pre-jump can be the redirection caused by the reasons of network page authentication and the like. The preset mapping relationship may further include: the influence factors corresponding to the script loading time-consuming statistical indexes are large script size, multiple loading files or script dependent data interfaces. Specifically, the statistical index of time consumed by script loading includes: the statistical method comprises a first statistical index corresponding to script request time before first-time data interface access and a second statistical index corresponding to script request time before last-time data interface access. Further, the preset mapping relationship may further include: the influence factor corresponding to the first statistical index is that the script size is large or the number of loaded files is large, and the influence factor corresponding to the second statistical index is that the number of loaded files is large or the script depends on the data interface, and it should be noted that since the script request occurring after the data interface is accessed for the first time may depend on the data interface accessed for the first time, the time consumed by the scripts is counted in the second statistical index, which may result in a larger second statistical index. The reason why the second statistical indicator does not satisfy the preset condition may be that the script depends on the data interface. The influence factors corresponding to the data interface statistical indexes are large number of data interfaces, poor interface service performance or large data volume. The influence of the loading performance of the target page is considered from multiple aspects of the server, the data interface and the resource, so that the relation between the loading performance statistical index and multiple influence factors is determined, and the corresponding influence factors can be determined according to different loading performance statistical indexes.

In this embodiment, different optimization schemes may be determined according to different influence factors. For example, the script dependent data interface can consider to perform first access or script parallel in advance, the data interface quantity is large, the data interface quantity can consider to be parallel to a plurality of data interfaces, the loading file quantity is large, the loading file quantity can consider to be parallel to a plurality of loading files, the script size is large, the script can be divided in advance, therefore, the target page optimization scheme can be determined from multiple aspects through influencing factors, and the loading performance of the target page is improved.

In one embodiment, as shown in fig. 2, the manner of obtaining the loading performance statistic index of the ith time period includes:

s201, acquiring a loading performance parameter of each resource loading in the ith time period;

s202, determining a loading performance statistical index of the ith time period based on the loading performance parameter of each resource loading in the ith time period.

Exemplarily, the process of loading the target page includes: taking a single-page application (SPA) as an example, a main file (index. html) of a website first page is loaded, resources (such as pictures, styles, script (JavaScript, JS) frames and scripting languages (JS)) in the main file are loaded, the script frames match routes after the analysis is completed, and access (XML Http Request, XHR) interfaces, scripting languages, pictures and styles of corresponding routes are loaded.

For example, if the resource is loaded multiple times in the ith time period, and multiple loading performance parameters exist, the multiple loading performance parameters may be used as the loading performance statistical index, or a target value obtained by performing preset calculation (such as taking an average or mode) on the multiple loading performance parameters may be used as the loading performance statistical index. Under the condition that a plurality of loading performance parameters are taken as the loading performance statistical indexes, the plurality of loading performance parameters in the loading performance statistical indexes in the (i + k) th time period can be compared with the plurality of loading performance parameters in the loading performance statistical indexes in the ith time period in a one-to-one correspondence manner, and a plurality of obtained results are taken as comparison results; the multiple loading performance parameters in the loading performance statistical index in the (i + k) th time period and the multiple loading performance parameters in the loading performance statistical index in the ith time period are compared in a one-to-one correspondence manner, then the multiple results are subjected to proportion calculation according to the weight, and one result obtained by the proportion calculation is used as a comparison result. In the case of taking the target value as the loading performance statistical index, the target value in the loading performance statistical index in the (i + k) th time period may be compared with the target value in the loading performance statistical index in the (i) th time period to obtain a comparison result.

Illustratively, loading performance parameters may include: and loading time-consuming parameters, data interface parameters, initial time parameters and the like by the script.

Illustratively, the time-to-first-byte parameter is the sum of the time from the issuance of the page request to the receipt of the first byte of the response data, including: the method includes the steps of analyzing time of a Domain Name Server (DNS), connecting time of a Transmission Control Protocol (TCP), sending a hypertext Transfer Protocol (HTTP) request time and obtaining time of a first byte of a response message, wherein a first byte time parameter is the basis of loading of all subsequent resources and can reflect the response speed of a website Server. For example, a Navigation Timing Interface (Navigation Timing Application Programming Interface) under a Web specification is adopted as a data Interface of the target page, and since the Navigation Timing Interface can help a website developer to detect real user data (RUM), a first-byte time parameter can be collected through the Navigation Timing Interface under the Web front-end specification.

Illustratively, the script loading time-consuming parameter is the total span of script loading, which includes the sum of time consumption of each stage in the script loading process, and it should be noted that overlapping parts are not repeatedly counted in actual calculation, and the script break time is not counted.

In this embodiment, resources may be loaded multiple times in the ith time period, and each time the resources are loaded, the corresponding script loading time-consuming parameter, data interface parameter, and initial time parameter may be obtained, so that there are multiple script loading time-consuming parameters, multiple data interface parameters, and multiple initial time parameters in the ith time period. And respectively obtaining corresponding script loading time-consuming statistical indexes, data interface statistical indexes and first time interval statistical indexes according to the plurality of script loading time-consuming parameters, the plurality of data interface parameters and the plurality of first time interval parameters. The average of the plurality of parameters or the mode of the plurality of parameters may be used as the statistical index, or a trend graph formed by the plurality of parameters may be used as the statistical index. Therefore, the loading performance of the target page in the ith time period can be reflected more accurately according to the loading performance statistical index determined by the loading performance of each time of loading the resource in the ith time period.

In one embodiment, loading the performance parameter includes script loading time-consuming parameter; acquiring the loading performance parameters of each time of loading the resources in the ith time period, wherein the loading performance parameters comprise:

determining script request time before the first data interface of the mth loading resource is accessed according to the data interface of the mth loading resource in the ith time period;

determining script request time before the last data interface of the mth loading resource is accessed according to the last accessed data interface of the mth loading resource;

obtaining script loading time-consuming parameters of the mth loading resource according to the script request time before the first data interface of the mth loading resource is accessed and the script request time before the last data interface of the mth loading resource is accessed; wherein m is a positive integer.

In this embodiment, as shown in fig. 3, a data interface a accessed first for the mth loading resource and a data interface B accessed last for the mth loading resource are determined, and since the data interfaces are based on navigation timing interfaces under Web specifications, a first interface request time corresponding to the first accessed data interface can be determined according to the first accessed data interface, and then a script request time T before the first accessed data interface is determined according to the first interface request time₁(ii) a Determining last interface request time corresponding to the last accessed data interface based on the last accessed data interface, and determining script request time T before the last accessed data interface according to the last interface request time₂. Note that overlapping portions are not counted repeatedly, and the intermittent time is not counted. Adding the m timesAnd determining script loading time-consuming parameters from two aspects of the script request time before the first data interface access of the loading resource and the script request time before the last data interface access of the mth loading resource, thereby more comprehensively determining the loading performance of the target page.

The script loading time-consuming parameter of each time of loading the resource in the ith time period is obtained, a first statistical index corresponding to the script request time before the first data interface access of each time of loading the resource is determined according to the script request time before the first data interface access, a second statistical index corresponding to the script request time before the last data interface access of each time of loading the resource is determined according to the script request time before the last data interface access, and therefore the script loading time-consuming statistical index is determined according to the first statistical index and the second statistical index.

In one embodiment, loading the performance parameters includes data interface parameters; acquiring the loading performance parameters of each time of loading the resources in the ith time period, wherein the loading performance parameters comprise:

acquiring at least one event triggered by the nth resource loading in the ith time period and the corresponding number of data interfaces;

determining response time corresponding to at least one event triggered by the nth resource loading based on at least one event triggered by the nth resource loading;

obtaining data interface parameters of the nth loading resource in the ith time period based on the response time and the number of the data interfaces; wherein n is a positive integer.

For example, loading the resource n times may trigger multiple events, may be that the user selects to trigger multiple events in succession by performing multiple operations on the page, or may be that the user performs only one operation, but the one operation needs to trigger multiple events.

In this embodiment, as shown in fig. 4, since the data interface is based on the navigation timing interface under the Web specification, the corresponding data interface for each event can be detected, and thus, the data interface can be detectedThe number of data interfaces for each event can be calculated, for example, event C corresponds to 4 data interfaces, event D corresponds to 6 data interfaces, and event E corresponds to 3 data interfaces. Sequencing each data interface according to the triggering sequence of each event in a plurality of events, for example, a waterfall flow analysis table can be adopted, so that the access time corresponding to each data interface can be intuitively determined, and the longest access time of the data interface in each event is taken as the response time T of the event₃、T₄、T₅And adding the obtained plurality of response times to obtain the total time of the data interfaces, adding the number of the corresponding data interfaces of each event to obtain the total number of the data interfaces, and taking the total time of the data interfaces and the total number of the data interfaces as data interface parameters, so that the problem of page loading performance can be determined according to the interface parameters, and the experience of a user in browsing the page is further improved.

In one embodiment, the method further includes:

and processing the resources to be loaded of the target page after the (i + k) th time period according to the influence factors of the loading performance of the target page in the (i + k) th time period.

For example, if the influence factor of the loading performance of the target page in the (i + k) th time period is that the size of the script is large, the script can be divided in advance when facing the resource to be loaded after the (i + k) th time period; or if the influence factor of the loading performance of the target page in the (i + k) th time period is that a plurality of files are loaded, the files in the resources to be loaded after the (i + k) th time period can be directly paralleled, so that the aim that developers can optimize the target page according to the influence factor is fulfilled.

FIG. 5 is a block diagram of an attribution device of page load performance according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus may include:

a determining module 501, configured to determine a comparison result between a loading performance statistical index of a target page in an ith time period and a loading performance statistical index of the target page in an (i + k) th time period;

and an attribution module 502, configured to determine, based on the comparison result and the type of the loading performance statistical indicator, an influence factor of the loading performance of the target page in the (i + k) th time period, where i and k are positive integers.

In one embodiment, wherein attribution module 502 is further configured to:

FIG. 6 is a block diagram of an attribution device of page load performance according to another embodiment of the present disclosure. As shown in fig. 6, the apparatus may include:

in an embodiment, the above apparatus further includes:

an obtaining module 601, configured to obtain a loading performance parameter of each resource loading in an ith time period;

a first processing module 602, configured to determine a loading performance statistical indicator of an ith time period based on a loading performance parameter of each time resource is loaded in the ith time period;

a determining module 603, configured to determine a comparison result between the loading performance statistical indicator of the target page in the ith time period and the loading performance statistical indicator of the target page in the (i + k) th time period;

and an attribution module 604, configured to determine, based on the comparison result and the type of the loading performance statistical indicator, an influence factor of the loading performance of the target page in the (i + k) th time period, where i and k are positive integers.

In one embodiment, as shown in FIG. 6, loading performance parameters includes script load time consumption parameters; the obtaining module 601 is further configured to:

In one embodiment, as shown in FIG. 6, loading performance parameters includes data interface parameters; the obtaining module 601 is further configured to:

In one embodiment, as shown in fig. 6, the apparatus further includes:

the second processing module 605 is configured to process the resource to be loaded after the (i + k) th time period of the target page according to the influence factor of the loading performance of the target page in the (i + k) th time period.

Therefore, the device of the embodiment of the disclosure determines the loading performance of the target page in the (i + k) th time period based on the comparison result of the loading performance statistical index in the (i + k) th time period and the loading performance statistical index in the (i + k) th time period, and determines the influence factor of the loading performance of the target page in the (i + k) th time period according to the comparison result and the type of the loading performance statistical index, so that the loading performance of the target page is accurately analyzed, and the corresponding influence factor is determined at the same time, so that a developer can optimize the target page according to the influence factor, and further, the experience of a user when the user browses a website is improved.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 7 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the device 700 comprises a computing unit 701, which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM)702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, or the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 701 may be a variety of general purpose and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 701 performs the various methods and processes described above, such as the attribution method of page load performance. For example, in some embodiments, the attribution of page load performance may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into RAM703 and executed by the computing unit 701, one or more steps of the above-described attribution method of page load performance may be performed. Alternatively, in other embodiments, the compute unit 701 may be configured in any other suitable manner (e.g., by way of firmware) to perform the attribution method of page load performance.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. An attribution method of page loading performance, comprising:

determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical index, wherein i and k are positive integers.

2. The method of claim 1, wherein the determining the influence factors of the loading performance of the target page in the (i + k) th time period based on the comparison result and the type of the loading performance statistical indicator comprises:

and determining the influence factors of the loading performance of the target page in the (i + k) th time period according to the type of the loading performance statistical index and a preset mapping relation under the condition that the comparison result meets a preset condition.

3. The method of claim 1, wherein obtaining the loading performance statistic for the ith time period comprises:

acquiring the loading performance parameter of each time of loading the resource in the ith time period;

and determining the loading performance statistical index of the ith time period based on the loading performance parameters of each time of loading the resources in the ith time period.

4. The method of claim 3, wherein the load performance parameter comprises a script load time parameter; the obtaining of the loading performance parameter of each time of loading the resource in the ith time period includes:

5. The method of claim 3 or 4, the load performance parameters comprising data interface parameters; the obtaining of the loading performance parameter of each time of loading the resource in the ith time period includes:

determining a response time corresponding to at least one event triggered by the nth resource loading based on at least one event triggered by the nth resource loading;

obtaining data interface parameters of the nth resource loading in the ith time period based on the response time and the number of the data interfaces; wherein n is a positive integer.

6. The method of any of claims 1-5, further comprising:

7. An apparatus for attributing page loading performance, comprising:

8. The apparatus of claim 7, wherein the attribution module is further to:

9. The apparatus of claim 7, further comprising:

an obtaining module, configured to obtain a loading performance parameter of each resource loading in the ith time period;

and the first processing module is used for determining the loading performance statistical index of the ith time period based on the loading performance parameter of each time of loading the resource in the ith time period.

10. The apparatus of claim 9, wherein the load performance parameter comprises a script load time parameter; the acquisition module is further configured to:

11. The apparatus of claim 9 or 10, the load performance parameters comprising data interface parameters; the acquisition module is further configured to:

12. The apparatus of any of claims 7-11, further comprising:

and the second processing module is used for processing the resources to be loaded of the target page after the (i + k) th time period according to the influence factors of the loading performance of the target page in the (i + k) th time period.

13. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.