CN113157766A

CN113157766A - Application analysis method and device, electronic equipment and computer-readable storage medium

Info

Publication number: CN113157766A
Application number: CN202110271571.9A
Authority: CN
Inventors: 黄儒鸿
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2021-03-12
Filing date: 2021-03-12
Publication date: 2021-07-23

Abstract

The embodiment of the application relates to the technical field of data processing, and discloses an application analysis method and device, electronic equipment and a computer-readable storage medium, wherein the method can be applied to the electronic equipment and comprises the following steps: acquiring historical operating data of the electronic equipment; acquiring application running information associated with a target control event of the electronic equipment within a certain time period according to historical running data of the electronic equipment, wherein the application running information comprises a first application set running during each target control event triggering period; further mining a frequent application set corresponding to the target control event according to the application running information; and then determining a recommended application set corresponding to the target control event according to the frequent application set. By implementing the embodiment of the application, the accuracy of application recommendation can be improved.

Description

Application analysis method and device, electronic equipment and computer-readable storage medium

Technical Field

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

Background

With the rapid development of electronic device technology and application development technology, a large number of applications are generally installed in today's electronic devices to meet various use requirements of users.

In the process of actually using the electronic device, the number of the installed applications is too large, so that when a user wants to open a certain application, the user needs to find the icon of the target application from a large number of application icons and then open the icon, which is tedious, and therefore, how to recommend the corresponding application to the user when the user wants to open the certain application becomes an urgent problem to be solved.

Disclosure of Invention

The embodiment of the application discloses an application analysis method and device, electronic equipment and a computer-readable storage medium, and the accuracy of application recommendation can be improved.

A first aspect of an embodiment of the present application discloses an application analysis method applied to an electronic device, where the method includes:

acquiring historical operating data of the electronic equipment;

acquiring application running information associated with a target control event of the electronic equipment within a certain time period according to the historical running data, wherein the application running information comprises a first application set running during each target control event triggering period;

mining a frequent application set corresponding to the target control event according to the application running information;

and determining a recommended application set corresponding to the target control event according to the frequent application set.

A second aspect of the embodiments of the present application discloses an application analysis apparatus, which is applied to an electronic device, and includes:

the first acquisition unit is used for acquiring historical operating data of the electronic equipment;

the second obtaining unit is used for obtaining application running information related to a target control event of the electronic equipment in a certain time period according to the historical running data, and the application running information comprises a first application set running during each target control event triggering period;

the mining unit is used for mining a frequent application set corresponding to the target control event according to the application running information;

and the generating unit is used for determining a recommended application set corresponding to the target control event according to the frequent application set.

A third aspect of the embodiments of the present application discloses an electronic device, including:

a memory storing executable program code;

a processor coupled with the memory;

the processor calls the executable program code stored in the memory to execute the application analysis method disclosed in the first aspect of the embodiment of the present application.

A fourth aspect of the embodiments of the present application discloses a computer-readable storage medium storing a computer program, where the computer program enables a computer to execute the application analysis method disclosed in the first aspect of the embodiments of the present application.

A fifth aspect of embodiments of the present application discloses a computer program product, which, when run on a computer, causes the computer to perform part or all of the steps of any one of the methods of the first aspect of embodiments of the present application.

A sixth aspect of the present embodiment discloses an application publishing platform, where the application publishing platform is configured to publish a computer program product, where the computer program product, when running on a computer, causes the computer to perform part or all of the steps of any one of the methods in the first aspect of the present embodiment.

Compared with the related art, the embodiment of the application has the following beneficial effects:

by implementing the embodiment of the application, the electronic equipment can acquire historical operating data of the electronic equipment so as to acquire application operating information when the electronic equipment triggers the target control event within a certain time period in the historical operating data of the electronic equipment; the electronic equipment can further mine a frequent application set corresponding to the target control event according to the application running information, further generates a recommended application set corresponding to the target control event according to the frequent application set, and can accurately find out the frequent application set relevant to the target control event by analyzing and mining the application running information of the target control event.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario applicable to an application analysis method disclosed in an embodiment of the present application;

FIG. 2 is a schematic flow chart diagram of an application analysis method disclosed in an embodiment of the present application;

FIG. 3 is a schematic diagram of application running information disclosed in an embodiment of the present application;

FIG. 4 is a schematic flow chart diagram of another method for analyzing an application disclosed in the embodiments of the present application;

FIG. 5A is a schematic diagram of a generation process of a second set of applications disclosed in an embodiment of the present application;

FIG. 5B is a schematic diagram of a frequent pattern tree disclosed in an embodiment of the present application;

FIG. 6 is a schematic flow chart diagram of another analysis method disclosed in the embodiments of the present application;

FIG. 7 is a schematic structural diagram of an application analysis apparatus disclosed in an embodiment of the present application;

fig. 8 is a schematic structural diagram of another application analysis device disclosed in the embodiments of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first", "second", "third" and "fourth", etc. in the description and claims of the present application are used for distinguishing different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and "having," and any variations thereof, of the embodiments of the present application, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The technical solution of the present application will be described in detail with reference to specific examples.

To more clearly illustrate an application analysis method disclosed in the embodiments of the present application, an application scenario suitable for the application analysis method is first introduced. As shown in fig. 1, the application analysis method may be applied to the electronic device 110, for example: a mobile phone, a tablet computer, etc., but not limited thereto. The electronic device 110 may include: memory 1101, processor 1102, etc., and those skilled in the art will appreciate that the electronic device configuration shown in fig. 1 is not intended to constitute a limitation of the electronic device, and that an electronic device may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. The memory 1101 may be used to store a software program or operation data generated during the operation of the electronic device; the processor 1102 may include a Central Processing Unit (CPU), a single chip, and other devices with logic computing capability, and is configured to perform logic operations in the electronic device.

In practice it has been found that a user, after performing a control event for an electronic device, will typically open a music-like application by concomitantly opening some of the applications associated with the control event, for example, after connecting a bluetooth headset to the electronic device. However, since a large number of applications are usually installed in the current electronic device, after the user connects the bluetooth headset to the electronic device, the user needs to find the icon of the music-like application from among a large number of application icons and then open the icon, which is cumbersome, and thus it is not beneficial to improve the efficiency of the user in using the application. In this regard, the processor 1102 may obtain historical operation data of the electronic device from the memory 1101, and obtain application operation information when the electronic device triggers a target control event within a certain time period according to the obtained historical operation data; furthermore, the processor 1102 may mine the application running information through a frequent item mining algorithm (for example, an Apriori association rule algorithm, an FP-Growth frequent pattern mining algorithm, and the like, which are not limited herein) built in the electronic device 110, to obtain a frequent application set corresponding to the target control event, and then generate a recommended application set corresponding to the target control event according to the obtained frequent application set. Further, when the target control event is triggered next time, the processor 1101 may output a recommended application set corresponding to the target control event for reference by the user, so that the user does not need to search applications that the user wants to open from a large number of application icons, user operation is simplified, and use efficiency of the applications is improved. In addition, considering that the electronic equipment runs the most frequent application when the application in the recommended application set is triggered by the historical target control event, the application in the recommended application set is recommended to the user when the target control event is triggered again, so that the application is more likely to be selected by the user, and the accuracy of application recommendation is improved.

Referring to fig. 2, fig. 2 is a schematic flow chart of an application analysis method disclosed in the embodiment of the present application, where the application analysis method can be applied to the electronic device, for example: a mobile phone, a tablet computer, etc., but not limited thereto. The application analysis method may include the steps of:

202. historical operating data of the electronic device is obtained.

In the embodiment of the application, the electronic device may be provided with a memory for storing operation data generated during an operation process of the electronic device, so that a processor of the electronic device may obtain historical operation data of the electronic device from the memory.

204. According to historical operating data of the electronic equipment, acquiring application operating information related to a target control event of the electronic equipment in a certain time period, wherein the application operating information comprises a first application set which is operated during each target control event triggering period.

In this embodiment, the historical operating data of the electronic device may include a plurality of pieces of operating data, and each piece of operating data may include, but is not limited to: the time point of the generation of the operation data, the current location of the electronic device, the operation state of the application program, the name of the application, the trigger state of the target control event, and other information are not limited herein.

The target control event may be an event that a user performs control on the electronic device, such as connecting to bluetooth, connecting to a wireless network, and the like, which is not limited herein.

Considering that the recommended application set generated according to the historical operating data of the electronic device is subsequently recommended to the user for reference when the target control event is triggered, the electronic device may filter and extract the historical operating data, and only extract the application operating information when the electronic device triggers the target control event within a certain time period (for example, one week, one month, and not limited), so that the recommended application set generated according to the application operating information during the triggering of the target control event subsequently has a higher recommended value. For example, if the target control event is a bluetooth headset connection event, the electronic device may extract application running information during connection of the bluetooth headset from the historical running data and reject the application running information during disconnection of the bluetooth headset.

Optionally, the application execution information may include a first application executed during a plurality of target control event triggers. The first application running during each target control event trigger can be divided into a first application set, so that the application running information meets the format requirements of the frequent item set mining algorithm on the input data. As shown in FIG. 3, a first transaction may represent an application that was run by the electronic device during a first target control event trigger, such as: APP1, APP2, APP3, APP 4; while the second transaction may represent an application that the electronic device has run during the second target control event trigger, such as: APP1, APP3, APP5, APP8, which are not limited herein.

206. And mining a frequent application set corresponding to the target control event according to the application running information.

In the embodiment of the application, the electronic device can mine the application running information through a frequent item set mining algorithm so as to obtain a frequent application set corresponding to the target control event. It should be noted that the frequent item set mining algorithm is generally used for mining some item sets that are frequently going together, and then if any item in the item sets appears, other items in the item sets can be output as recommended items. Frequent application sets with strong relevance with the target control events can be mined through a frequent item set mining algorithm, and the frequent applications in the frequent application sets are higher in running probability during the triggering period of the same target control event.

Therefore, in the embodiment of the application, the frequent applications included in the frequent application set mined by the electronic device through the frequent itemset mining algorithm are the applications which are run by the user for a large number of times before the triggering period of the target control event. And the user frequently runs the frequent applications in the frequent application set when the target control event is triggered, and then the user has a high probability to want to run the frequent applications in the frequent application set when the target control event is triggered next time. For example: the music-like application and the social-like application are opened by the user each time the user connects the bluetooth headset to the electronic device before, and the music-like application and the social-like application are likely to be opened by the user the next time the user connects the bluetooth headset to the electronic device. The subsequent electronic device may generate a set of recommended applications from the set of frequent applications to output the set of recommended applications for reference by the user when the target control event is next triggered.

It should be further noted that the frequent item set mining algorithm adopted in the embodiment of the present application may include an Apriori association rule algorithm, an FP-Growth frequent pattern mining algorithm, and the like, which is not limited herein. Since the Apriori association algorithm needs to continuously construct a candidate set and filter the candidate set to dig out a frequent application set corresponding to the target control event, the application running information needs to be scanned for many times. If the data size of the application running information is large, scanning a large amount of data for multiple times will result in low efficiency of constructing the frequent application set. And the FP-Growth frequent pattern mining algorithm can dig out the frequent application set corresponding to the target control event only by scanning the application running information twice. Preferably, in the embodiment of the application, the application running information may be mined through an FP-Growth frequent pattern mining algorithm to obtain a frequent application set corresponding to the target control event, so as to improve mining efficiency of the frequent application set corresponding to the target control event.

208. And determining a recommended application set corresponding to the target control event according to the frequent application set.

As described above, since the applications included in the frequent application set are applications that have been run by the user for a large number of times before the target control event is triggered, the electronic device may directly use the frequent application set as the recommended application set corresponding to the target control event after mining the frequent application set corresponding to the target control event according to the application running information.

In another embodiment, in consideration that there may be a plurality of applications included in the mined frequent application set, and in order to make the applications subsequently recommended to the user have a recommendation value, the electronic device may eliminate the applications in the frequent application set, which are operated less than the threshold number of times, and generate the recommended application set corresponding to the target control event for the remaining applications.

For example, the control event "connect bluetooth headset" corresponds to a frequent application set including: the application is that QQ music is 30 times, WeChat music is 28 times, cool dog music is 25 times, UC reads 10 times, and Xinlang news is 16 times. In order to make the applications subsequently recommended to the user have a recommendation value, the electronic device may reject applications that are executed less than 24 times (i.e., UC reading and news reject. And the remaining QQ music, WeChat and Cool dog music are retained in the recommended application set corresponding to the control event of 'connecting Bluetooth headset'.

By implementing the method, the electronic equipment can remove the applications with less total historical running times in the frequent application set, so that the applications recommended to the user subsequently have higher recommendation value.

By implementing the method disclosed by each embodiment, the electronic device may obtain historical operation data of the electronic device, so as to obtain application operation information when the electronic device triggers a target control event within a certain time period from the historical operation data of the electronic device; and the electronic equipment can further mine a frequent application set corresponding to the target control event according to the application running information, and further generate a recommended application set corresponding to the target control event according to the frequent application set.

Referring to fig. 4, fig. 4 is a schematic flowchart illustrating another application analysis method disclosed in the embodiment of the present application, where the application analysis method can be applied to the electronic device, for example: a mobile phone, a tablet computer, etc., but not limited thereto. The application analysis method may include the steps of:

402. historical operating data of the electronic device is obtained.

404. According to historical operating data of the electronic equipment, acquiring application operating information related to a target control event of the electronic equipment in a certain time period, wherein the application operating information comprises a first application set which is operated during each target control event triggering period.

406. And generating a second application set corresponding to each target control event triggering period according to the first application set running during each target control event triggering period, wherein frequent applications contained in the second application set are frequently applied within a certain time period, and the total running times of each target control event triggering period is greater than a first time threshold value.

In the embodiment of the application, after the electronic device acquires the application running information when the electronic device triggers the target control event within a certain time period from the historical running data, the electronic device can input the application running information into a frequent item set mining algorithm for mining so as to generate a recommended application set corresponding to the target control event. Preferably, the electronic device may input the application running information into the FP-Growth frequent pattern mining algorithm for mining. In the process of executing the FP-Growth frequent pattern mining algorithm, the electronic device may first generate a second application set corresponding to each target control event trigger period according to a first application set running during each target control event trigger period. The total number of operations during each target control event trigger period frequently applied in a certain time period included in the second application set is greater than the first number threshold (for example, 3 times, 4 times, or 6 times, etc., which is not limited herein). To facilitate subsequent electronic devices building the frequent pattern tree from these second set of applications.

In an embodiment, the electronic device may sort the applications included in the application running information in an order from a maximum number of running total times to a minimum number of running total times, and use a result of the sorting as a frequent application list; further, removing the first applications, the total number of times of which is less than the first time threshold, from the first application set corresponding to the first triggering period of the target control event (the first triggering period may be any triggering period of the target control event within the certain time period) to obtain frequent applications corresponding to the first triggering period; the electronic device may then sort the frequent applications according to the ranking order of the frequent application list to generate a second application set corresponding to the first trigger period of the target control event. By analogy, the electronic device may convert all of the corresponding first set of applications during other triggers of the target control event into the second set of applications.

For example, referring to fig. 5A, it is assumed that the application running information includes running information of six applications, i.e., f, c, a, b, m, and p, where f and c are run 4 times respectively, and a, b, m, and p are run 3 times respectively. The electronic device sorts the applications included in the application running information in an order from a maximum number of running times to a minimum number of running times, and the frequent application list generated according to the sorting result may be: l ═ 4, (c: 4), (a: 3), (b: 3), (m: 3), and (p: 3). Assuming that the electronic device runs eight applications { f, c, a, d, g, i, m, p } in total during the first trigger period of the target control event, and assuming that the total number of running times of d, g, and i is less than the first threshold, the electronic device may cull d, g, and i to obtain the corresponding frequent applications during the first trigger period: f. c, a, m, and p, and the electronic device may sort f, c, a, m, and p according to the ranking order of the applications in the frequent application list L, so as to obtain a second application set corresponding to the first trigger period of the target control event: { f, c, a, m, p }.

As further shown in fig. 5A, the electronic device may analogize the first application set corresponding to the other trigger periods of the target control event into the second application set, where the second application set corresponding to the second trigger period is: { f, c, a, b, m }, the corresponding second set of applications during the third trigger period is: { f, b }, which is not limited herein.

By implementing the method, the electronic device can generate the second application set corresponding to each target control event triggering period according to the first application set running during each target control event triggering period, so that a subsequent electronic device can construct the frequent pattern tree according to the second application sets.

408. And constructing a frequent pattern tree according to the plurality of second application sets, and performing recursive mining on the frequent pattern tree to obtain a frequent application set corresponding to the target control event.

In this embodiment of the application, after the electronic device generates the plurality of second application sets, the electronic device may insert frequent applications included in each second application set into a frequent pattern tree FP-tree, where the same prefix path in the frequent pattern tree FP-tree may be shared. As shown in fig. 5B, the corresponding second application set during the first trigger: { f, c, a, m, p }, and a second set of applications corresponding to the second trigger period is: f, c, and a in { f, c, a, b, m } may share one prefix path.

In this embodiment of the application, the electronic device may first create a root node of the frequent pattern tree, and then acquire any one of the plurality of second application sets as a target second application set. And the electronic device may further use the root node as a parent node, determine whether a child node of the parent node exists in the frequent application included in the target second application set, and if a child node of the parent node exists in the frequent application included in the target second application set, may further determine whether the frequent application already exists in the frequent pattern tree. If the child node exists, increasing the support degree of the child node corresponding to the frequent application (the support degree of the child node refers to the number of second application sets containing the frequent application corresponding to the child node) by 1, taking the child node as a new parent node, and then executing the step of judging whether the child node of the parent node exists in the frequent application included in the target second application set until each second application set is traversed; if not, connecting the frequent application as a child node to the parent node, using the child node as a new parent node, and then executing the step of judging whether the child node of the parent node exists in the frequent applications included in the target second application set until each second application set is traversed.

Further optionally, the electronic device may sort the frequent applications included in the second application set in an order from a plurality of running total times, use a sorting result as the item table, and connect the frequent applications in the item table with the nodes in the frequent pattern tree, so that the subsequent electronic device may perform recursive mining on the frequent pattern tree according to the item table.

Referring to fig. 5A and 5B together, for example, the electronic device may first create a root node of a frequent pattern tree (e.g., "{ }" in fig. 5B), and then insert a plurality of second application sets in fig. 5A into the frequent pattern tree according to the method described above to obtain the frequent pattern tree shown in fig. 5B, and connect the item table with the same frequent applications in the frequent pattern tree.

As an optional implementation manner, after the electronic device constructs the frequent pattern tree, the electronic device may sequentially generate the conditional pattern bases corresponding to the frequent applications in the frequent pattern tree according to the ranking order of the frequent applications in the item table (where the conditional pattern bases are paths using the frequent applications as nodes in the frequent pattern, and it should be noted that there may be one or more conditional pattern bases corresponding to one frequent application because there are multiple paths using one frequent application as a node in the frequent pattern, for example, in conjunction with fig. 5B, there are two conditional pattern bases m, which are (f, c, a) and (f, c, a, B), and only one conditional pattern base a is (f, c), which is not limited herein. And the electronic equipment can generate a frequent application set corresponding to the target control event according to the conditional mode base corresponding to the frequent application.

By implementing the method, the electronic device can determine which combinations of applications running during the same target control event triggering period in the application running information by constructing the condition mode base, and then the electronic device can determine the application combination with the largest running frequency during the target control event triggering period by the condition mode base, and then subsequently recommend the application combinations to the user, so that the user can conveniently open the applications associated with the target control event during the target control event triggering period.

As another optional implementation manner, after the electronic device constructs the frequent pattern tree, the electronic device may acquire the last frequent application arranged in the item table as the application to be mined, and generate a conditional pattern base corresponding to the application to be mined according to the frequent pattern tree. For example, with reference to fig. 5B, the electronic device may use "p" arranged at the last in the item table as the application to be mined, and generate a conditional pattern base of "p" according to the frequent pattern tree: (f, c, a, m) and (c, b).

And the electronic device may further determine whether the number of the condition mode bases corresponding to the application to be mined is one, and if the number of the condition mode bases corresponding to the application to be mined is one, the electronic device may generate a frequent application set corresponding to the target control event according to the condition mode base.

And if the number of the condition mode bases corresponding to the application to be mined is more than one, the electronic equipment can acquire the frequent application arranged in the project table before the application to be mined as a new application to be mined, and re-execute the step of generating the condition mode base corresponding to the application to be mined according to the frequent mode tree, and then judge whether the number of the condition mode bases corresponding to the application to be mined is one until the application to be mined with only one condition mode base is found or the frequent application included in the project table is traversed.

For example, referring to fig. 5 again, since the condition pattern base of "p" includes two (f, c, a, m) and (c, b), the electronic device may acquire "m" ranked before "p" as a new application to be mined, and execute the step of generating the condition pattern base corresponding to the application to be mined according to the frequent pattern tree, and then determine whether the number of the condition pattern base corresponding to the application to be mined is one, whereas since the condition pattern base of "m" includes two (f, c, a) and (f, c, a, b), the electronic device may continue to acquire "b" as a new application to be mined, whereas since the condition pattern base of "b" also includes two, the electronic device may continue to acquire "a" as a condition pattern base, and the condition pattern base of "a" includes only one (f, c), so the electronic device may only obtain (f, c) according to the condition pattern base corresponding to "a: (f, c, a), c) And generating a frequent application set corresponding to the target control event.

In an embodiment, if the number of the condition mode bases corresponding to the application to be mined is greater than one, the electronic device may remove the infrequent nodes whose support degrees are less than the support degree threshold (for example, 2, 3, and the like, and specific numerical values may be set by developers according to development needs, and are not limited herein) in each condition mode base, merge the same condition mode bases from the multiple condition mode bases from which the infrequent nodes are removed, and determine again whether the number of the condition mode bases corresponding to the application to be mined is one, and if the number of the condition mode bases corresponding to the application to be mined from which the infrequent nodes are removed is one, the electronic device may generate a frequent application set corresponding to the target control event according to the condition mode base; if the number of the condition mode bases corresponding to the applications to be mined after the infrequent nodes are removed is more than one, the electronic device can acquire the frequent applications arranged in the project table before the applications to be mined as new applications to be mined, and re-execute the step of generating the condition mode bases corresponding to the applications to be mined according to the frequent mode tree, and then judge whether the number of the condition mode bases corresponding to the applications to be mined is one until only one application to be mined is found out or the frequent applications included in the project table are traversed.

By implementing the method, the electronic device can determine which combinations of the applications running in the same target control event triggering period in the application running information through constructing the condition mode base, and further the electronic device can recursively mine the condition mode base corresponding to each frequent application to determine the application combination running for the most times in the target control event triggering period, and further subsequently recommend the application combinations to the user, so that the user can conveniently open the applications associated with the target control event in the target control event triggering period.

410. And determining a recommended application set corresponding to the target control event according to the frequent application set.

In the embodiment of the application, after the electronic device generates the recommended application set corresponding to the target control event, the recommended application set can be recommended to the user when the electronic device triggers the target control event next time.

Further optional. When the next target control event is finished, the electronic device may obtain the historical operation data of the electronic device of the latest version, and extract the latest added target application operation information in the historical operation data. And then, the electronic equipment can directly form the frequent applications operated by the electronic equipment into a target second application set during the latest target control event triggering period according to the target application operation information, insert the applications in the target second application set into the previously constructed frequent pattern tree to update the frequent pattern tree, and further dig out the latest recommended application set according to the updated frequent pattern tree.

By implementing the method, the electronic equipment can update the original frequent pattern tree by adding the newly added application running information after each subsequent target control event is triggered, and then mine the updated frequent pattern tree to obtain the latest recommended application set, so that the recommended application set can keep timeliness to better meet the requirements of users. In addition, each time the frequent pattern tree is updated, only the latest added application running information needs to be scanned, and the previous application running information does not need to be scanned again, so that the calculation amount of the electronic equipment can be reduced, and the generation efficiency of the recommended application set can be improved.

By implementing the method disclosed by each embodiment, the electronic device can mine the frequent application set corresponding to the target control event according to the application running information, and further generate the recommended application set corresponding to the target control event according to the frequent application set, and since the applications in the recommended application set are the applications which are most frequently run during the triggering period of the historical target control event, the applications in the recommended application set are more likely to be selected by the user when being subsequently recommended to the user, so that the application recommendation efficiency can be improved; in addition, which combinations of applications run during the same target control event triggering period in the application running information can be determined by constructing a condition mode base, and then the electronic device can determine the application combination which is run the most frequently during the target control event triggering period through the condition mode base, and then subsequently recommend the application combinations to the user, so that the user can conveniently open the applications associated with the target control event during the target control event triggering period.

Referring to fig. 6, fig. 6 is a schematic flowchart illustrating another application analysis method disclosed in the embodiment of the present application, where the application analysis method can be applied to the electronic device, for example: a mobile phone, a tablet computer, etc., but not limited thereto. The application analysis method may include the steps of:

602. historical operating data of the electronic device is obtained.

In this embodiment of the application, after obtaining the historical operating data of the electronic device, in order to improve the accuracy of processing the data by a subsequent algorithm, the electronic device may first perform preprocessing on the obtained historical operating data, for example, sort the data, merge the same data, and the like, which is not limited herein.

As an optional implementation manner, after the electronic device obtains the historical operating data, the historical operating data may be sorted according to a sequence of generating time points from far to near, it should be noted that, since the generating time point of each piece of operating data in the historical operating data is the system time of the corresponding electronic device, it is inconvenient to sort, and for this electronic device, the generating time point of each piece of operating data in the historical operating data may be converted into a formatting time point by the electronic device, and then the electronic device may sort the historical operating data according to the sequence of generating time points from far to near according to the formatting time point corresponding to each piece of operating data.

Further, after the historical operating data are sorted according to the sequence of the generation time points from far to near, the electronic device may sequentially traverse each operating data according to the sequence of the time points from far to near, so as to eliminate the historical operating data with repeated generation time points and repeated data contents.

For example, there are two pieces of historical operating data 14: 00, the electronic device can remove one of the repeated historical operating data.

By implementing the method, the electronic equipment can sequence the historical operating data according to the sequence of the generation time points from far to near, so that whether the historical operating data with repeated generation time points and repeated data contents exists in the historical operating data or not can be detected according to the sequence of the generation time points from far to near. In addition, the electronic equipment can also remove the historical operating data with repeated generation time points and repeated data content to compress the data volume of the historical operating data, so that the calculation amount of a subsequent algorithm can be reduced, and the accuracy of the algorithm can be improved.

604. According to historical operating data of the electronic equipment, acquiring application operating information related to a target control event of the electronic equipment in a certain time period, wherein the application operating information comprises a first application set which is operated during each target control event triggering period.

In the embodiment of the application, it is considered that a recommended application set generated according to historical operating data of the electronic device is subsequently recommended to a user for reference when the target control event is triggered, so that the electronic device may filter and extract the historical operating data, extract only application operating information when the electronic device triggers the target control event within a certain time period (for example, a week, a month, but not limited thereto), and consider that the electronic device is inevitably subjected to operation errors or abnormalities during operation. Optionally, after the electronic device acquires the historical operating data, the electronic device may extract, from the historical operating data, application operating information when the electronic device triggers the target control event within a certain time period (for example, one week, one month, and the like), remove abnormal data in the application operating information, and further acquire, according to the application operating information from which the abnormal data is removed, a first application set that operates during each target control event triggering period.

It should be noted that the application running information extracted by the electronic device from the historical running data may include a first application running during multiple target control event triggering periods, and in order to facilitate subsequent processing of data and calculation of data through an algorithm, the electronic device may divide the first application running during each target control event triggering period in the application running information into a first application set, so as to distinguish the first applications running during each target control event triggering period, and facilitate subsequent processing of data and calculation of data through an algorithm.

It should be further noted that the exception data may include at least one of application abort data, invalid runtime data, and invalid runtime event data, which is not limited herein.

Here, the application abort data may be data indicating that the same application is closed at a time point and an interval is less than 1 second or 2 seconds to a next open time point, because it is generally impossible for a user to exit the application and enter the application in a short time (1 second or 2 seconds) while the user normally uses the application. The invalid runtime data may be data indicating that an interval from an opening time point to an ending time point of the same application is less than 1 second or 2 seconds, because a user normally opens the application, the time spent on the application is usually greater than 1 second or 2 seconds, and because a certain time is required for loading and running data when the application itself is opened, the data having an interval from an opening time point to an ending time point of the same application is abnormal data. The invalid operational event data may be the event of the pair, data that does not occur in the pair; or only the application open event, but no data of the application end event. Since the user will always turn the application on and off again during normal use.

By implementing the method, the electronic equipment can remove abnormal data in the application running information to compress the data volume of the historical running data, so that the calculation amount of a subsequent algorithm can be reduced, and the accuracy of the algorithm can be improved. In addition, the electronic device can also divide the first application running during each target control event triggering period in the application running information into a first application set, so that the first applications running during each target control event triggering period are distinguished, and data can be conveniently processed and calculated through an algorithm in the follow-up process.

In another embodiment, in order to facilitate subsequent processing and calculation of data through an algorithm, the electronic device may convert the application name in the application running information into an integer type. Optionally, the electronic device may convert the first application in the application running information into any positive integer, sequentially increase the positive integer corresponding to the previous application by a certain value according to the sequence of the applications in the application running information, and copy the increased positive integer to the next application. For example: the electronic device may assign a first application to "11" and then sequentially add 1 to subsequent applications.

606. And mining a frequent application set corresponding to the target control event according to the application running information.

608. And determining a recommended application set corresponding to the target control event according to the frequent application set.

As an optional implementation manner, the electronic device may remove applications in the frequent application set, which are operated for the total number of times less than the second threshold, to generate a recommended application set corresponding to the target control event.

By implementing the method, the electronic equipment can eliminate the applications with the total running times smaller than the time threshold value in the frequent application set, and generate the recommended application set corresponding to the target control event from the rest applications, so that the applications recommended to the user subsequently have a recommendation value.

In this embodiment of the application, if a target control event requires that the electronic device and other electronic devices establish a connection relationship, the electronic device may distinguish application operation information for different other electronic devices in the historical operation data, and generate a recommended application set corresponding to the target control event corresponding to each other electronic device based on the different other electronic devices. And when the subsequent electronic equipment triggers the target control event, the electronic equipment can determine the recommended application set corresponding to other electronic equipment to recommend to the user according to the other connected electronic equipment.

For example, when the electronic device triggers a bluetooth connection, the electronic device may be connected to a bluetooth headset or a projection device, and the electronic device may generate a recommended application set corresponding to the bluetooth headset for an application that has been run when the electronic device is connected to the bluetooth headset (for example, the recommended application set corresponding to the bluetooth headset may include a music application); generating a recommended application set corresponding to the projection device for the application run by the electronic device when the projection device is connected (for example, the recommended application set corresponding to the projection device may include a video application), and then subsequently, if the electronic device is connected to the bluetooth headset, the electronic device may output the recommended application set corresponding to the bluetooth headset; if the electronic device is connected with the projection device, the electronic device may output a recommended application set corresponding to the projection device.

By implementing the method, the electronic equipment can determine the recommended application set corresponding to other electronic equipment according to other electronic equipment connected with the electronic equipment, and the recommended application set is output for the user to refer to, so that the recommended application is more consistent with the current use scene, and the application recommendation efficiency is improved.

610. And outputting a recommended application set corresponding to the target control event when the electronic equipment triggers the target control event next time.

In this embodiment of the present application, the electronic device may generate, by using the application analysis method disclosed in this embodiment of the present application, a recommended application set corresponding to the target control event when the last target control event is ended. And then when the electronic equipment triggers the target control event next time, the generated recommendation application set corresponding to the target control event can be output for the reference of the user.

Optionally, the electronic device may further sort the applications in the recommended application set in order from a few historical running times of the applications, and preferentially recommend the top N applications to the user, where N is a positive integer.

By implementing the method disclosed by each embodiment, the electronic device may first sort the historical operating data according to the sequence from far to near of the generation time point, so as to facilitate the subsequent detection of whether the historical operating data has the repeated generation time points and the repeated data content according to the sequence from far to near of the generation time point. In addition, the electronic equipment can also remove the historical operating data with repeated generation time points and repeated data content to compress the data volume of the historical operating data, so that the calculation amount of a subsequent algorithm can be reduced, and the accuracy of the algorithm can be improved; and abnormal data in the application running information can be removed to compress the data volume of historical running data, so that the calculation amount of a subsequent algorithm can be reduced, and the accuracy of the algorithm can be improved. In addition, the electronic equipment can also divide the first application running during each target control event triggering period in the application running information into a first application set, so that the first application running during each target control event triggering period is distinguished, and the data can be conveniently processed and calculated through an algorithm; and according to other electronic equipment connected with the electronic equipment, determining a recommended application set corresponding to the other electronic equipment to be output for reference of a user, so that the recommended application is more consistent with the current use scene, and the application recommendation efficiency is improved.

Referring to fig. 7, fig. 7 is a schematic structural diagram of an application analysis apparatus disclosed in an embodiment of the present application, where the application analysis apparatus can be applied to the electronic device, for example: a mobile phone, a tablet computer, etc., but not limited thereto. The application analysis apparatus may include: a first acquisition unit 701, a second acquisition unit 702, a mining unit 703 and a generating unit 704, wherein:

a first obtaining unit 701, configured to obtain historical operating data of an electronic device;

a second obtaining unit 702, configured to obtain, according to historical operating data of the electronic device, application operating information associated with a target control event in a certain time period, where the application operating information includes a first application set that operates during each target control event trigger period;

the mining unit 703 is configured to mine a frequent application set corresponding to the target control event according to the application running information;

a generating unit 704, configured to determine, according to the frequent application set, a recommended application set corresponding to the target control event.

By implementing the application analysis device, historical operating data of the electronic equipment can be acquired, so that application operating information of the electronic equipment when a target control event is triggered within a certain time period is acquired from the historical operating data of the electronic equipment; and the electronic equipment can further mine a frequent application set corresponding to the target control event according to the application running information, and further generate a recommended application set corresponding to the target control event according to the frequent application set.

Referring to fig. 8, fig. 8 is a schematic structural diagram of another application analysis apparatus disclosed in the embodiment of the present application, the application analysis apparatus may also be applied to the electronic device, the application analysis apparatus shown in fig. 8 may be obtained by optimizing the application analysis apparatus shown in fig. 7, and compared with the application analysis apparatus shown in fig. 7, a manner that a mining unit 703 included in the application analysis apparatus shown in fig. 8 is used for mining a frequent application set corresponding to a target control event according to application running information may specifically be:

a mining unit 703, configured to generate a second application set corresponding to each target control event triggering period according to a first application set running during each target control event triggering period, where frequent applications included in the second application set are applied within a certain time period, and a total number of running times during each target control event triggering period is greater than a first time threshold; and constructing a frequent pattern tree according to the plurality of second application sets, and performing recursive mining on the frequent pattern tree to obtain a frequent application set corresponding to the target control event.

By implementing the application analysis device, the frequent application set corresponding to the target control event can be obtained by constructing and mining the frequent pattern tree, and the generation efficiency of the frequent application set can be improved as the frequent pattern tree is constructed and the frequent pattern tree is mined, the application running information is only required to be scanned twice.

As an optional implementation manner, the manner in which the mining unit 703 is used to generate the second application set corresponding to each target control event triggering period according to the first application set running during each target control event triggering period may specifically be:

the mining unit 703 is configured to sort the applications included in the application running information in an order from a large number of running total times to a small number of running total times, and use a sorting result as a frequent application list; removing first applications, the total number of running times of which is less than a first time threshold, from a first application set running in a first triggering period of the target control event to obtain frequent applications corresponding to the first triggering period, wherein the first triggering period is any triggering period of the target control event within a certain time period; and sequencing the frequent applications according to the arrangement sequence of the frequent application list to generate a second application set corresponding to the first trigger period.

By implementing the application analysis device, the second application set corresponding to each target control event triggering period can be generated according to the first application set running during each target control event triggering period, so that the subsequent electronic equipment can construct the frequent pattern tree according to the second application sets.

As an optional implementation manner, the mining unit 703 is configured to perform recursive mining on the frequent pattern tree to obtain a frequent application set corresponding to the target control event, and specifically, the manner of the frequent application set may be:

the mining unit 703 is configured to sort the frequent applications included in the second application set according to the order of the total number of running times, and use the sorted result as an item table; and sequentially generating a condition mode base corresponding to the frequent application in the frequent mode tree according to the arrangement sequence of the frequent application in the project table, and generating a frequent application set corresponding to the target control event according to the condition mode base, wherein the condition mode base of the frequent application in the project table is a path which takes the frequent application as a node in the frequent mode tree.

By implementing the application analysis device, which combinations of applications run during the same target control event triggering period in the application running information can be determined by constructing the condition mode base, and then the electronic device can determine the application combination with the largest running frequency during the target control event triggering period by the condition mode base, and then subsequently recommend the application combinations to the user, so that the user can conveniently open the applications associated with the target control event during the target control event triggering period.

As an optional implementation manner, the mining unit 703 is configured to sequentially generate a conditional mode base corresponding to the frequent application in the frequent mode tree according to the ranking order of the frequent applications in the project table, and a manner of generating a frequent application set corresponding to the target control event according to the conditional mode base may specifically be:

the mining unit 703 is configured to obtain the last frequent application arranged in the project table as an application to be mined, and generate a conditional mode base corresponding to the application to be mined according to the frequent mode tree; if the number of the conditional mode bases corresponding to the applications to be mined is one, generating a frequent application set corresponding to the target control event according to the conditional mode bases; and if the number of the condition mode bases corresponding to the applications to be mined is more than one, acquiring the frequent applications arranged in the project table before the applications to be mined as new applications to be mined, and executing the step of generating the condition mode bases corresponding to the applications to be mined according to the frequent mode tree until the frequent applications included in the project table are traversed.

By implementing the application analysis device, which combinations of applications run during the same target control event triggering period in the application running information can be determined by constructing the condition mode base, and then the electronic equipment can recursively mine the condition mode base corresponding to each frequent application to determine the application combination with the largest running frequency during the target control event triggering period, and then subsequently recommend the application combinations to the user, so that the user can conveniently open the applications associated with the target control event during the target control event triggering period.

410. And generating a recommended application set corresponding to the target control event according to the frequent application set.

As an optional implementation manner, the manner of the first obtaining unit 701 for obtaining the historical operation data of the electronic device may specifically be:

a first obtaining unit 701, configured to obtain historical operating data of the electronic device, and sort the historical operating data according to a sequence from far to near of a generation time point; and removing the historical operating data with repeated time points and repeated data contents from the sorted historical operating data.

By implementing the application analysis device, the historical operating data can be sorted according to the sequence of the generation time points from far to near, so that whether the historical operating data with repeated generation time points and repeated data contents exists in the historical operating data or not can be detected according to the sequence of the generation time points from far to near. In addition, the electronic equipment can also remove the historical operating data with repeated generation time points and repeated data content to compress the data volume of the historical operating data, so that the calculation amount of a subsequent algorithm can be reduced, and the accuracy of the algorithm can be improved.

As an optional implementation manner, the manner that the second obtaining unit 702 is configured to obtain, according to historical operation data of the electronic device, the application operation information when the electronic device triggers the target control event within a certain time period may specifically be:

a second obtaining unit 702, configured to extract, from the historical operating data, application operating information when the electronic device triggers a target control event within a certain time period; and eliminating abnormal data in the application running information, and acquiring a first application set running during each target control event triggering period according to the application running information with the abnormal data eliminated.

By implementing the application analysis device, the electronic equipment can conveniently process the data subsequently and calculate the data through an algorithm.

As an alternative embodiment, the exception data includes at least one of application abort data, invalid runtime data, and invalid runtime event data.

By implementing the application analysis device, abnormal data in the application running information can be removed to compress the data volume of the historical running data, so that the calculation amount of a subsequent algorithm can be reduced, and the accuracy of the algorithm can be improved. In addition, the electronic device can also divide the first application running during each target control event triggering period in the application running information into a first application set, so that the first applications running during each target control event triggering period are distinguished, and data can be conveniently processed and calculated through an algorithm in the follow-up process.

As an optional implementation manner, the manner that the generating unit 704 is configured to generate the recommended application set corresponding to the target control event according to the frequent application set may specifically be:

a generating unit 704, configured to remove applications whose total number of times of operation in the frequent application set corresponding to the target control event is smaller than the second threshold, so as to generate a recommended application set corresponding to the target control event.

By implementing the application analysis device, the recommended application set corresponding to other electronic equipment can be determined and output for the user to refer to according to other electronic equipment connected with the electronic equipment, so that the recommended application is more consistent with the current use scene, and the recommendation efficiency of the application is improved.

As an alternative embodiment, the application analysis apparatus shown in fig. 8 may further include an output unit 705, where:

the output unit 705 is configured to output the recommended application set corresponding to the target control event when the electronic device next triggers the target control event after the generating unit 704 generates the recommended application set corresponding to the target control event according to the frequent application set.

By implementing the application analysis device, when the electronic equipment triggers the target control event next time, the generated recommended application set corresponding to the target control event can be output for the reference of the user, so that the user does not need to search applications which the user wants to open from a large number of application icons, and the use efficiency of the applications is improved.

An electronic device disclosed in an embodiment of the present application may include: a memory storing executable program code; a processor coupled to the memory;

the processor calls the executable program code stored in the memory to execute the application analysis method disclosed in the above embodiments.

The embodiment of the application discloses a computer-readable storage medium, which stores a computer program, wherein the computer program enables a computer to execute the application analysis method disclosed in each embodiment.

The embodiment of the present application also discloses an application publishing platform, wherein the application publishing platform is used for publishing a computer program product, and when the computer program product runs on a computer, the computer is caused to execute part or all of the steps of the method in the above method embodiments.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily required for this application.

In various embodiments of the present application, it should be understood that the size of the serial number of each process described above does not mean that the execution sequence is necessarily sequential, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, may be embodied in the form of a software product, stored in a memory, including several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of the embodiments of the present application.

It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by hardware instructions of a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other Memory, such as a magnetic disk, or a combination thereof, A tape memory, or any other medium readable by a computer that can be used to carry or store data.

The application analysis method and apparatus, the electronic device, and the storage medium disclosed in the embodiments of the present application are described in detail above, and specific examples are applied in the present application to explain the principles and implementations of the present application, and the descriptions of the above embodiments are only used to help understand the method and core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. An application analysis method applied to an electronic device, the method comprising:

acquiring historical operating data of the electronic equipment;

2. The method according to claim 1, wherein the mining a frequent application set corresponding to the target control event according to the application running information includes:

generating a second application set corresponding to the target control event triggering period of each time according to the first application set running during the target control event triggering period of each time, wherein the total running times of the target control event triggering periods of each time, frequently applied in the certain time period, contained in the second application set are greater than a first time threshold;

and constructing a frequent pattern tree according to the second application set corresponding to the triggering period of the target control event at each time, and performing recursive mining on the frequent pattern tree to obtain a frequent application set corresponding to the target control event.

3. The method according to claim 2, wherein the generating a second set of applications corresponding to the triggering period of the target control event according to the first set of applications running during the triggering period of the target control event comprises:

sequencing all applications contained in the application running information according to the sequence of running total times from most to least, and taking the sequencing result as a frequent application list;

removing applications, the total number of running times of which is less than a first time threshold, from a first application set corresponding to a first triggering period of the target control event to obtain frequent applications corresponding to the first triggering period, wherein the first triggering period is any triggering period of the target control event within the certain time period;

and sequencing the frequent applications according to the arrangement sequence of the frequent application list to generate a second application set corresponding to the first trigger period.

4. The method of claim 2, wherein the recursively mining the frequent pattern tree to obtain a set of frequent applications corresponding to the target control event comprises:

sequencing the frequent applications contained in the second application set corresponding to the target control event triggering period of each time according to the sequence of the total running times, and taking the sequencing result as an item table;

and sequentially generating a condition mode base corresponding to each frequent application in the frequent mode tree according to the arrangement sequence of each frequent application in the project table, and generating a frequent application set corresponding to the target control event according to the condition mode base, wherein the condition mode base of a first frequent application is a path in the frequent mode tree with the first frequent application as a node, and the first frequent application is any frequent application in the project table.

5. The method according to claim 4, wherein the generating a conditional mode base corresponding to each frequent application in the frequent mode tree in sequence according to the ranking order of the frequent applications in the item table, and generating a frequent application set corresponding to the target control event according to the conditional mode base includes:

acquiring the last frequent application arranged in the project table as an application to be mined, and generating a conditional mode base corresponding to the application to be mined according to the frequent mode tree;

if the number of the conditional mode bases corresponding to the applications to be mined is one, generating a frequent application set corresponding to the target control event according to the conditional mode bases;

if the number of the condition mode bases corresponding to the application to be mined is more than one, acquiring a frequent application arranged in the project table before the application to be mined as a new application to be mined, and executing the step of generating the condition mode base corresponding to the application to be mined according to the frequent mode tree until the application to be mined is a frequent application arranged at the head in the project table.

6. The method of claim 1, wherein the obtaining historical operating data of the electronic device comprises:

acquiring historical operating data of the electronic equipment, and sequencing the historical operating data according to a sequence of generating time points from far to near;

and eliminating the historical operating data with repeated time points and repeated data contents from the sorted historical operating data.

7. The method according to claim 1, wherein the obtaining application running information when the electronic device triggers a target control event within a certain time period according to the historical running data comprises:

extracting historical operating data of the electronic equipment during each target control event trigger within a certain time period from the historical operating data;

and eliminating abnormal data in the extracted historical operating data, and determining a first application set which operates during the triggering period of each target control event according to the historical operating data after the abnormal data is eliminated.

8. The method of claim 7, wherein the exception data comprises at least one of application abort data, invalid runtime data, and invalid runtime event data.

9. The method of claim 1, wherein determining the recommended application set corresponding to the target control event according to the frequent application set comprises:

in the frequent application set, applications with the total running times during the triggering period of each target control event in the certain time period being smaller than a second time threshold value are removed, so that a recommended application set corresponding to the target control event is generated; or

And according to the sequence of the total running times of each target control event triggering period in the certain time period from a few times, taking the applications with the total running times arranged at the top N bits in the frequent application set as recommended applications to obtain a recommended application set corresponding to the target control event, wherein N is a positive integer.

10. The method according to any one of claims 1 to 9, wherein after the generating of the recommended application set corresponding to the target control event according to the frequent application set, the method further comprises:

and outputting the recommended application set when the target control event is triggered next time by the electronic equipment.

11. An application analysis device applied to an electronic apparatus, comprising:

12. An electronic device comprising a memory storing executable program code, and a processor coupled to the memory; wherein the processor calls the executable program code stored in the memory to execute the method of any one of claims 1 to 10.

13. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 10.