CN111126737B

CN111126737B - Cross-scene cross analysis method and device, electronic equipment and storage medium

Info

Publication number: CN111126737B
Application number: CN201811297413.5A
Authority: CN
Inventors: 尹畅文; 叶峻; 高春旭
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2018-11-01
Filing date: 2018-11-01
Publication date: 2022-09-13
Anticipated expiration: 2038-11-01
Also published as: CN111126737A

Abstract

The application provides a cross-scene cross analysis method and device, electronic equipment and a storage medium, and belongs to the technical field of computer application. Wherein, the method comprises the following steps: acquiring a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events; acquiring a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events; generating a first analysis index of a first scene according to the first meta-events and the corresponding first attributes, and generating a second analysis index of a second scene according to the second meta-events and the corresponding second attributes; and performing cross-analysis according to the first analysis index and the second analysis index. Therefore, through the cross-scene cross analysis method, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and through customizing the analysis indexes of the scenes according to the analysis requirements, the cross-scene multi-dimensional cross analysis is realized.

Description

Cross-scene cross analysis method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of computer application technologies, and in particular, to a cross-scene cross analysis method and apparatus, an electronic device, and a storage medium.

Background

In the operation process of internet service products such as websites and mobile terminal applications, developers/operators generally need to know the operation condition of the products to improve and optimize the products, so that statistics and analysis need to be performed on access data (such as click quantity of the products, browsing quantity of key pages, user regions and the like) of the products to evaluate the operation condition of the products.

In the related art, the method for counting and analyzing the use conditions of products such as websites and APPs can predefine the dimension of a scene needing counting and the index of an evaluation scene by understanding a specific application scene. However, when a new scene is added, the analysis method not only needs to predefine a new dimension and is complex to operate, but also cannot generate a new evaluation index as required, and is poor in flexibility and incapable of realizing multi-dimension cross analysis across scenes.

Disclosure of Invention

The cross-scene cross analysis method, the cross-scene cross analysis device, the electronic equipment and the storage medium are used for solving the problems that in the related technology, when a new scene is added, new dimensionality needs to be predefined, operation is complex, new evaluation indexes cannot be generated according to requirements, flexibility is poor, and cross-scene multi-dimensional cross analysis cannot be achieved.

An embodiment of an aspect of the present application provides a cross-scene cross analysis method, including: acquiring a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events; acquiring a plurality of second elements of a second scene and a plurality of second attributes corresponding to the second elements; generating a first analysis indicator of the first scene from the plurality of first meta-events and the corresponding plurality of first attributes, and generating a second analysis indicator of the second scene from the plurality of second meta-events and the corresponding plurality of second attributes; and performing cross-analysis according to the first analysis index and the second analysis index.

The cross-scene cross analysis device provided by another embodiment of the application includes: the device comprises a first acquisition module, a second acquisition module and a processing module, wherein the first acquisition module is used for acquiring a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events; the second obtaining module is used for obtaining a plurality of second element events of a second scene and a plurality of second attributes corresponding to the second element events; a generating module, configured to generate a first analysis indicator of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, and generate a second analysis indicator of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes; and the analysis module is used for carrying out cross analysis according to the first analysis index and the second analysis index.

An embodiment of another aspect of the present application provides an electronic device, which includes: memory, processor and computer program stored on the memory and executable on the processor, wherein the processor implements the cross-scene analysis method as described above when executing the program.

In another aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the cross-scene analysis method as described above.

In another aspect of the present application, a computer program is provided, which is executed by a processor to implement the cross-scene cross analysis method described in the embodiment of the present application.

The cross-scene cross-analysis method, the cross-scene cross-analysis device, the electronic device, the computer-readable storage medium, and the computer program provided in the embodiments of the present application may obtain a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events, obtain a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events, generate a first analysis indicator of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, generate a second analysis indicator of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, and further perform cross-analysis according to the first analysis indicator and the second analysis indicator. Therefore, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and then the meta-events and the corresponding attributes of the scenes can be utilized to generate analysis indexes of the scenes according to analysis requirements, so that the cross-scene multi-dimensional cross analysis is realized by customizing the analysis indexes of the scenes according to the analysis requirements.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flowchart of a cross-scene cross analysis method provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of another cross-scene cross analysis method provided in the embodiment of the present application;

fig. 3 is a schematic structural diagram of a cross-scene cross analysis apparatus provided in an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present application and should not be construed as limiting the present application.

The embodiment of the application aims at the problems that in the related art, when a new scene is added, new dimensionality needs to be predefined, the operation is complex, new evaluation indexes cannot be generated as required, the flexibility is poor, and multi-dimensional cross analysis of a cross scene cannot be realized by the existing method for counting and analyzing the service conditions of products such as websites and APPs, and the cross scene cross analysis method is provided.

The cross-scene cross-analysis method provided by the embodiment of the application can acquire a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events, acquire a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events, generate a first analysis index of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, generate a second analysis index of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, and perform cross-analysis according to the first analysis index and the second analysis index. Therefore, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and then the meta-events and the corresponding attributes of the scenes can be used for generating analysis indexes of the scenes according to analysis requirements, so that multi-dimensional cross analysis of the cross-scenes is realized by customizing the analysis indexes of the scenes according to the analysis requirements.

The cross-scene cross-analysis method, apparatus, electronic device, storage medium, and computer program provided by the present application are described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic flowchart of a cross-scene cross analysis method according to an embodiment of the present application.

As shown in fig. 1, the cross-scene cross analysis method includes the following steps:

step 101, acquiring a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events.

In the embodiment of the application, the data of the scene can be modeled through meta-events and event attributes. The meta-event may represent any event type, the meta-event of a scene may customize the meta-event corresponding to the scene according to the characteristics and the analysis requirements of the scene, and each scene may correspond to multiple meta-events. The attributes corresponding to the meta-event can be used to describe the characteristics of the meta-event, and the types and numbers of the attributes corresponding to different meta-events can be different. For example, the attribute corresponding to the meta-event may be information such as a main body of the meta-event, the number of times the meta-event occurs, and the time when the meta-event occurs.

In a possible implementation form of the embodiment of the present application, when modeling data of a scene through meta-events and event attributes, a relationship between the meta-events and the event attributes may be as shown in table 1. In this model, one column of the table is used to represent M meta-events of a scene, and the remaining columns are used to represent N attributes corresponding to all meta-events. The attributes corresponding to all events form a set of all attributes, that is, the attribute corresponding to each meta-event is a subset of all attributes, for example, there are three meta-events: event 1, event 2, event 3, four attributes: attribute 1, attribute 2, attribute 3, and attribute 4, then the corresponding attribute of event 1 may be: attribute 1, attribute 2, attribute 3; the attributes corresponding to event 2 are: attribute 2, attribute 3; the corresponding attributes of event 3 are: attribute 2, attribute 3, attribute 4.

TABLE 1

Meta-event	Attribute 1	Attribute 2	…	Attribute N
					Event 1	Value of	Value of	Value of	Value of
Event 2	Value of	Value of	Value of	Value of
					…	…	…	…	…
Event M	Value of	Value of	Value of	Value of

It should be noted that, according to the characteristics and the analysis requirements of the first scenario, a plurality of first meta-events of the first scenario may be customized, a first attribute corresponding to each first meta-event is defined, and data of the first scenario is modeled to perform statistics on data generated in the first scenario.

For example, assuming that one of the first meta-events in the first scene is event 1, and the attribute corresponding to event 1 has "the subject initiating event 1, the time when event 1 occurs", the event 1 and the attribute corresponding to the acquired first scene may be "event 1, the subject initiating event 1: a user A; time of occurrence of event 1: 10 month 25 d 2018.

Step 102, a plurality of second elements of a second scene and a plurality of second attributes corresponding to the second elements are obtained.

It should be noted that the cross-scene analysis method provided in the embodiment of the present application can analyze data of multiple scenes simultaneously, so that different meta-events and attributes corresponding to the meta-events can be customized for different scenes according to the scenes that need to be applied, and new meta-events and attributes corresponding to the meta-events can be added at any time when the applied scenes are increased, which is highly flexible.

In a possible implementation form of the embodiment of the application, a plurality of second element events of a second scene can be customized according to characteristics and analysis requirements of the second scene, a second attribute corresponding to each second element event is defined, and data of the second scene is modeled to perform statistics on data generated in the second scene.

It should be noted that, in the embodiment of the present application, frequencies of obtaining a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events may also be preset, and frequencies of obtaining a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events may also be preset, where the frequencies of the two may be the same or different. In actual use, the setting can be preset according to actual needs, and the embodiment of the application does not limit the setting. For example, the predetermined frequency may be 1 time/week, 1 time/month, etc.

Step 103, generating a first analysis index of the first scene according to the multiple first meta-events and the corresponding multiple first attributes, and generating a second analysis index of the second scene according to the multiple second meta-events and the corresponding multiple second attributes.

The first analysis index is an index for evaluating characteristics such as a first scene state, a use condition, or performance. The first analysis indexes are obtained by screening a plurality of first meta-events according to analysis requirements and combining the screened first meta-events and the corresponding first attributes thereof, and different first analysis indexes can be generated by different combinations of the first meta-events and the attributes thereof, so that the first scene is analyzed from multiple dimensions.

Accordingly, the second analysis index is an index for evaluating characteristics such as the second scene state, the use condition, or the performance. The second analysis indexes are obtained by screening a plurality of second elements according to analysis requirements and combining the screened second elements and the corresponding second attributes thereof, and different second elements and the combination of the attributes thereof can generate different second analysis indexes, so that the second scene is analyzed from multiple dimensions.

Furthermore, in order to facilitate a user to customize analysis indexes according to analysis requirements on a scene, a visual statistical table can be generated according to the acquired meta-events and the corresponding specific attribute values, and then the analysis indexes are customized according to the statistical table and the analysis requirements. That is, in a possible implementation form of the embodiment of the present application, the step 103 may include:

generating a statistical table according to the plurality of first meta-events and the corresponding plurality of first attributes, and the plurality of second meta-events and the corresponding plurality of second attributes;

and generating the first analysis index and the second analysis index according to the statistical table.

For example, assuming that one of the second events in the second scenario is the second event 1, the second attributes corresponding to the second event 1 are "occurrence time and occurrence location", respectively, and the obtained second event 1 and the second attribute corresponding to the second event 1 may form a statistical table as shown in table 2. If the current analysis requirement is to count the occurrence frequency of the second meta-event 1, the second analysis index "the occurrence frequency of the second meta-event 1" may be obtained by a screening condition "meta-event is the second meta-event 1"; further, the second meta-event 1 may be statistically analyzed from different dimensions, for example, the second analysis index "the number of times the second meta-event 1 occurs" is analyzed from the "place of occurrence" dimension, and the second meta-event 1 may be sorted according to the "place of occurrence" by screening the condition.

It should be noted that the foregoing examples are merely exemplary, and the table does not list the first meta-event corresponding to the first scenario and other second meta-events corresponding to the second scenario, so as to further understand the embodiments of the present application, and the embodiments of the present application are not considered to be limited to the present application.

TABLE 2

Meta-event	Time of occurrence	Place of occurrence
			Second meta-event 1	2018.10.20	Site 1
Second element event 1	2018.10.23	Site 1
			Second element event 1	2018.10.23	Site 2

And 104, performing cross analysis according to the first analysis index and the second analysis index.

In the embodiment of the application, after a first analysis index corresponding to a first scene and a second analysis index corresponding to a second scene are generated, statistics is performed on the obtained data according to the first analysis index and the second analysis index, statistics results corresponding to the first analysis index and the second analysis index respectively are obtained, and then the first scene and the second scene are measured and analyzed according to the statistics results corresponding to the first analysis index and the second analysis index respectively.

For example, the second meta-event 1 and the corresponding attributes thereof shown in table 2 can be analyzed from the "place of occurrence" dimension by using the filtering condition "meta-event is the second meta-event 1", so as to obtain the analysis results shown in table 3.

TABLE 3

The cross-scene cross-analysis method provided by the embodiment of the application can acquire a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events, acquire a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events, generate a first analysis index of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, generate a second analysis index of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, and perform cross-analysis according to the first analysis index and the second analysis index. Therefore, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and then the meta-events and the corresponding attributes of the scenes can be utilized to generate analysis indexes of the scenes according to analysis requirements, so that the cross-scene multi-dimensional cross analysis is realized by customizing the analysis indexes of the scenes according to the analysis requirements.

In a possible implementation form of the present application, the first scenario may refer to a website, the second scenario may refer to an APP, and the cross-scenario cross analysis method provided in the embodiment of the present application is further described below through two specific application scenarios, namely, the website and the APP.

The cross-scene cross-analysis method provided in the embodiment of the present application is further described below with reference to fig. 2.

Fig. 2 is a schematic flowchart of another cross-scene cross analysis method provided in the embodiment of the present application.

As shown in fig. 2, the cross-scene cross-analysis method includes the following steps:

step 201, a plurality of first meta-events of a website scene and a plurality of first attributes corresponding to the first meta-events are obtained.

It should be noted that, in a website scene, a plurality of first meta-events of the website scene may be customized according to characteristics and analysis requirements of the website scene, a first attribute corresponding to each first meta-event is defined, and data of the website scene is modeled to perform statistics on data generated in the website scene.

In a possible implementation form of the embodiment of the application, in a website scene, the click rate of a website page, the click rate of a page element, and the like may be counted and analyzed to determine the use habit of a user on the website, and the like. Therefore, in a website scenario, the first meta-event may include page browsing and page element clicking, where the attributes corresponding to page browsing may include: user ID, time, IP address, region, title; the clicking of the corresponding attribute by the page element may include: user ID, time, IP address, region, element name. Thus, the first attribute may include a user ID, time, IP address, region, title, and element name.

It should be noted that, in the embodiment of the present application, the first meta-event and the first attribute include, but are not limited to, the above-mentioned contents. In actual use, the first meta-event and the first attribute corresponding to the first meta-event may be customized according to actual needs, which is not limited in the embodiment of the present application.

Step 202, a plurality of second meta-events of the APP scene and a plurality of second attributes corresponding to the second meta-events are obtained.

It should be noted that, in the APP scenario, a plurality of second element events of the APP scenario may be customized according to the characteristics and the analysis requirements of the APP scenario, a second attribute corresponding to each second element event is defined, and data of the APP scenario is modeled to perform statistics on data generated in the APP scenario.

In a possible implementation form of the embodiment of the application, in an APP scene, statistics and analysis can be performed on the number of users of the APP, the click rate of APP page elements, and the like, and the use habits and the like of the users on the APP are determined so as to improve and optimize the APP. Therefore, in the APP scenario, the second meta-event includes APP start and APP element click, where the attributes corresponding to APP start may include: user ID, time, IP address, region, first start identification; clicking on the corresponding attribute by the APP element may include: user ID, time, IP address, region, element name. Thus, the second attribute may include a user ID, time, IP address, region, first-time-launch identification, and element name.

It should be noted that, in the embodiment of the present application, the second meta-event and the second attribute include, but are not limited to, the contents listed above. In actual use, the second element event and the second attribute corresponding to the second element event may be customized according to actual needs, which is not limited in the embodiment of the present application.

It should be noted that, in this embodiment of the application, frequencies of obtaining a plurality of first meta-events of a website scene and a plurality of first attributes corresponding to the first meta-events may also be preset, frequencies of obtaining a plurality of second meta-events of an APP scene and a plurality of second attributes corresponding to the second meta-events may also be preset, and a plurality of first meta-events of the website scene and a plurality of first attributes corresponding to the first meta-events, and a plurality of second meta-events of the APP scene and a plurality of second attributes corresponding to the second meta-events may also be obtained according to the preset frequencies. The frequencies of the two may be the same or different. In actual use, the setting can be preset according to actual needs, and the embodiment of the application does not limit the setting. For example, the preset frequency may be 1 time/week, 1 time/month, etc.

In a possible implementation form of the embodiment of the present application, the first meta-event and the corresponding first attribute thereof, and the second meta-event and the corresponding second attribute thereof may be represented by table 4.

TABLE 4

Meta-event	Associated attributes
		Page browsing	User ID, time, IP, region, title
Page element clicking	User ID, time, IP, region, element name
		App startup	User ID, time, IP, region, first-time activation identification
App element clicking	User ID, time, IP, region, element name

Step 203, generating a first analysis index of the first scene according to the multiple first meta-events and the corresponding multiple first attributes, and generating a second analysis index of the second scene according to the multiple second meta-events and the corresponding multiple second attributes.

The first analysis index is an index for evaluating characteristics such as a website scene state, a use condition, or performance. The first analysis indexes are obtained by screening a plurality of first meta-events according to analysis requirements and combining the screened first meta-events and the corresponding first attributes thereof, and different first analysis indexes can be generated by different combinations of the first meta-events and the attributes thereof, so that the website scene is analyzed from multiple dimensions. In one possible implementation form of the embodiment of the present application, the first analysis index may include a page browsing amount and a number of elements clicked by a page element.

Correspondingly, the second analysis index is an index for evaluating characteristics such as APP scene state, use condition or performance. The second analysis indexes are obtained by screening the plurality of second events according to analysis requirements and combining the screened second events and the corresponding second attributes thereof, and different second analysis indexes can be generated by different combinations of the second events and the attributes thereof, so that the APP scene is analyzed from a plurality of dimensions. In a possible implementation form of the embodiment of the application, the second analysis index may include the number of APP starting users and the number of times per capita of clicking of APP elements.

It should be noted that the first analysis index and the second analysis index may include, but are not limited to, the above-mentioned list. During actual use, the analysis index can be customized according to actual needs, and the embodiment of the application does not limit the analysis index.

Furthermore, in order to facilitate a user to customize analysis indexes according to analysis requirements on a scene, a visual statistical table can be generated according to the acquired meta-events and the corresponding specific attribute values, and then the analysis indexes are customized according to the statistical table and the analysis requirements. As shown in table 5, a statistical table is constructed according to the acquired first meta-event and the first attribute corresponding to the first meta-event, and the acquired second meta-event and the second attribute corresponding to the second meta-event.

TABLE 5

In a possible implementation form of the embodiment of the application, after a first meta-event of a website scene and a corresponding first attribute thereof, and a second meta-event of an APP scene and a corresponding second attribute thereof are obtained and a statistical table is formed, the meta-event can be screened and four arithmetic operations can be performed on the attribute corresponding to the meta-event according to an analysis requirement on the scene, so as to define a first analysis index corresponding to the website scene and a second analysis index corresponding to the APP scene by user, and perform statistics and analysis on the analysis indexes corresponding to the scene from different dimensions. As shown in table 6, the first analysis index and the second analysis index are generated by grouping according to the "region" dimension. The generation mode refers to the screening conditions of the first meta-event and the second meta-event and the algorithm of the first attribute and the second attribute, different screening conditions adopted by the first meta-event and the second meta-event and different algorithms adopted by the first attribute and the second attribute, and different first analysis indexes and second analysis indexes can be generated so as to realize the self-defining analysis indexes according to the actual analysis requirements.

TABLE 6

And 204, performing cross analysis according to the first analysis index and the second analysis index.

In a possible implementation form of the embodiment of the application, after the first analysis index corresponding to the website scene and the second analysis index corresponding to the APP scene are determined, statistics and cross analysis can be performed on each analysis index according to the statistical table and the selected dimension shown in table 5. As shown in table 7, the results of the statistics and analysis of each analysis index in the "region" dimension are shown. The user can analyze and summarize the use data and the operation condition of the website and the APP according to the statistical result of each analysis index.

TABLE 7

It should be noted that, in the embodiment of the present application, after the analysis index corresponding to the scene is determined, the grouping condition may be changed according to actual needs, that is, the analysis index is statistically analyzed from different dimensions, and multiple dimensions may be simultaneously selected as the grouping condition, so as to implement multi-dimensional cross analysis on the scene, and comprehensively know the operation and use conditions of the scene.

In order to implement the above embodiments, the present application further provides a cross-scene cross analysis apparatus.

Fig. 3 is a schematic structural diagram of a cross-scene cross analysis apparatus according to an embodiment of the present application.

As shown in fig. 3, the cross-scene cross analysis apparatus 30 includes:

a first obtaining module 31, configured to obtain a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events;

a second obtaining module 32, configured to obtain a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events;

a generating module 33, configured to generate a first analysis indicator of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, and generate a second analysis indicator of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes;

and the analysis module 34 is configured to perform cross analysis according to the first analysis index and the second analysis index.

In practical use, the cross-scene cross analysis apparatus provided in the embodiment of the present application may be configured in any electronic device to execute the cross-scene cross analysis method.

The cross-scene cross-analysis device provided by the embodiment of the application can acquire a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events, acquire a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events, generate a first analysis index of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, generate a second analysis index of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, and perform cross-analysis according to the first analysis index and the second analysis index. Therefore, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and then the meta-events and the corresponding attributes of the scenes can be used for generating analysis indexes of the scenes according to analysis requirements, so that multi-dimensional cross analysis of the cross-scenes is realized by customizing the analysis indexes of the scenes according to the analysis requirements.

In a possible implementation form of the present application, the first scenario is a website, and the second scenario is an APP.

Further, in another possible implementation form of the present application, the first analysis index includes a page browsing amount and a number of elements clicked by a page element, and the second analysis index includes a number of APP starting users and a number of times per capita clicked by an APP element.

Further, in another possible implementation form of the present application, the first meta-event includes page browsing and page element clicking, the first attribute includes user ID, time, IP address, region, title, and element name, the second meta-event includes APP start and APP element clicking, and the second attribute includes user ID, time, IP address, region, first start identifier, and element name.

In a possible implementation form of the present application, the generating module 33 is specifically configured to:

It should be noted that the foregoing explanation on the cross-scene cross analysis method embodiment shown in fig. 1 and fig. 2 is also applicable to the cross-scene cross analysis device 30 of this embodiment, and is not repeated here.

The cross-scene cross-analysis device provided by the embodiment of the application can acquire a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events, acquire a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events, generate a first analysis index of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, generate a second analysis index of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, and perform cross-analysis according to the first analysis index and the second analysis index. Therefore, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and then the meta-events and the corresponding attributes of the scenes can be utilized to generate analysis indexes of the scenes according to analysis requirements, so that the cross-scene multi-dimensional cross analysis is realized by customizing the analysis indexes of the scenes according to the analysis requirements.

In order to implement the above embodiments, the present application further provides an electronic device.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

As shown in fig. 4, the electronic device 200 includes:

a memory 210 and a processor 220, and a bus 230 connecting different components (including the memory 210 and the processor 220), wherein the memory 210 stores a computer program, and when the processor 220 executes the program, the cross-scenario cross-analysis method according to the embodiment of the present application is implemented.

Bus 230 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 200 typically includes a variety of electronic device readable media. Such media may be any available media that is accessible by electronic device 200 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 210 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)240 and/or cache memory 250. The electronic device 200 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 260 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 by one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 280 having a set (at least one) of program modules 270, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment, may be stored in, for example, the memory 210. The program modules 270 generally perform the functions and/or methodologies of the described embodiments of the invention.

Electronic device 200 may also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), with one or more devices that enable a user to interact with electronic device 200, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 200 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 292. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 293. As shown, the network adapter 293 communicates with the other modules of the electronic device 200 via the bus 230. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processor 220 executes various functional applications and data processing by executing programs stored in the memory 210.

It should be noted that, for the implementation process and the technical principle of the electronic device of the embodiment, reference is made to the foregoing explanation of the cross-scene cross analysis method in the embodiment of the present application, and details are not described here again.

The electronic device provided by the embodiment of the application may execute the cross-scene cross analysis method as described above, acquire a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events, acquire a plurality of second meta-events of a second scene and a plurality of second attributes corresponding to the second meta-events, generate a first analysis index of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, generate a second analysis index of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, and perform cross analysis according to the first analysis index and the second analysis index. Therefore, different meta-events and attributes corresponding to the meta-events are customized for different scenes respectively according to the characteristics of the scenes, and then the meta-events and the corresponding attributes of the scenes can be used for generating analysis indexes of the scenes according to analysis requirements, so that multi-dimensional cross analysis of the cross-scenes is realized by customizing the analysis indexes of the scenes according to the analysis requirements.

In order to implement the above embodiments, the present application also proposes a computer-readable storage medium.

The computer readable storage medium stores thereon a computer program, and the computer program is executed by a processor to implement the cross-scene cross analysis method according to the embodiment of the present application.

In order to implement the foregoing embodiments, an embodiment of a further aspect of the present application provides a computer program, which when executed by a processor, implements the cross-scene cross analysis method described in the embodiments of the present application.

In an alternative implementation, the embodiments may be implemented in any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., through the internet using an internet service provider).

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A cross-scene cross-analysis method is characterized by comprising the following steps:

acquiring a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events;

acquiring a plurality of second elements of a second scene and a plurality of second attributes corresponding to the second elements;

generating a first analysis indicator of the first scene from the plurality of first meta-events and the corresponding plurality of first attributes, and generating a second analysis indicator of the second scene from the plurality of second meta-events and the corresponding plurality of second attributes; and

performing cross-analysis according to the first analysis index and the second analysis index;

the generating a first analysis indicator of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, and generating a second analysis indicator of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes, comprising:

generating the first analysis index and the second analysis index according to the statistical table;

the performing cross-analysis according to the first analysis index and the second analysis index comprises:

and selecting one or more dimensions from the first analysis index and the second analysis index to perform cross analysis on the first scene and the second scene.

2. The cross-scenario cross-analysis method of claim 1, wherein the first scenario is a website and the second scenario is an APP.

3. The cross-scenario cross-analysis method of claim 2, wherein the first analysis index comprises a page browsing amount and an element number of page element clicks, and the second analysis index comprises an APP starting user number and an APP element click per capita number.

4. The cross-scenario cross-analysis method of claim 2, wherein the first meta-event comprises page browsing and page element clicking, the first attribute comprises user ID, time, IP address, region, title and element name, the second meta-event comprises APP start and APP element clicking, and the second attribute comprises user ID, time, IP address, region, first start identifier and element name.

5. A cross-scene cross-analysis apparatus, comprising:

the device comprises a first acquisition module, a second acquisition module and a processing module, wherein the first acquisition module is used for acquiring a plurality of first meta-events of a first scene and a plurality of first attributes corresponding to the first meta-events;

the second obtaining module is used for obtaining a plurality of second element events of a second scene and a plurality of second attributes corresponding to the second element events;

a generating module, configured to generate a first analysis indicator of the first scene according to the plurality of first meta-events and the corresponding plurality of first attributes, and generate a second analysis indicator of the second scene according to the plurality of second meta-events and the corresponding plurality of second attributes; and

the analysis module is used for carrying out cross analysis according to the first analysis index and the second analysis index;

the generation module comprises:

a first generating unit, configured to generate a statistical table according to the plurality of first meta-events and the corresponding plurality of first attributes, and the plurality of second meta-events and the corresponding plurality of second attributes;

a second generation unit configured to generate the first analysis index and the second analysis index according to the statistical table;

the analysis module is further configured to select one or more dimensions from the first analysis index and the second analysis index to perform cross analysis on the first scene and the second scene.

6. The cross-scene cross-analysis apparatus of claim 5, wherein the first scene is a website and the second scene is an APP.

7. An electronic device, comprising: a memory, a processor, and a program stored on the memory and executable on the processor, the processor implementing the cross-scenario analysis method of any of claims 1-4 when executing the program.

8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a cross-scene analysis method according to any one of claims 1 to 4.