CN114064757A

CN114064757A - Application program optimization method, device, equipment and medium

Info

Publication number: CN114064757A
Application number: CN202111357961.4A
Authority: CN
Inventors: 李露
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2021-11-16
Filing date: 2021-11-16
Publication date: 2022-02-18

Abstract

The method comprises the steps of acquiring a data processing flow of service units in the application program, wherein the data processing flow is used for representing information of process nodes through which user data passes when the user data is processed, so that each service unit can be respectively managed, and simultaneously, the association among the process nodes can be better reflected; counting the processing result of the user data at the corresponding process node according to the data processing process to obtain a statistical result, and acquiring the processing condition of each process node on the user data through the statistical result; performing data analysis on the statistical result to obtain abnormal data so as to quickly locate the abnormal position, thereby facilitating subsequent optimization analysis; and confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type, thereby improving the efficiency and accuracy of optimization.

Description

Application program optimization method, device, equipment and medium

Technical Field

The present application relates to the technical field of terminal application software, and in particular, to a method, an apparatus, a device, and a medium for optimizing an application program.

Background

With the development of the internet market, a number of internet products, which are a set of programs designed to solve a certain kind of problems using computers, are derived for users to use. However, the mobility of users is increased due to the increasing number of internet products, and in order to improve the use effect of users and increase the viscosity of users, the internet products need to be optimized continuously.

Therefore, how to optimize internet products and improve the use effect of users is an urgent problem to be solved.

Disclosure of Invention

In order to solve the above technical problem, embodiments of the present application provide an optimization method, apparatus, device, and medium for an application program, so as to perform accurate optimization on the application program.

In a first aspect, the present application provides an application optimization method, including: acquiring a data processing flow of a service unit in an application program, wherein the data processing flow is used for representing information of a flow node through which user data passes when the service unit processes the user data; counting the processing result of the user data at the process node according to the data processing process to obtain a statistical result; performing data analysis on the statistical result to obtain abnormal data; and confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type.

According to the preferred embodiment of the present invention, the statistical processing of the user data at the corresponding process node according to the data processing process to obtain the statistical result includes: acquiring a process node of a data processing process; acquiring a processing record table corresponding to the process node, wherein the processing record table is used for recording user data processed by the process node within a preset time and a processing result of the user data; and classifying and counting the user data according to the processing result of the user data to obtain the statistical result.

According to the preferred embodiment of the present invention, the data analysis of the statistical result to obtain abnormal data includes: acquiring historical data corresponding to the service unit; and analyzing the statistical result according to the historical data to obtain the abnormal data.

According to a preferred embodiment of the present invention, the anomaly data comprises user anomaly data; analyzing the statistical result according to the historical data to obtain the abnormal data, wherein the analyzing comprises: acquiring first active user data meeting the user activity condition according to the historical data, and acquiring second active user data meeting the user activity condition according to the statistical result; obtaining user abnormal data according to the first active user data and the second active user data; wherein the user activity condition comprises at least one of the user on-line time, the user use times and the user use frequency.

According to a preferred embodiment of the present invention, the anomaly data includes performance anomaly data; analyzing the statistical result according to the historical data to obtain the abnormal data, wherein the analyzing comprises: acquiring first processing failure data meeting the user data processing failure conditions according to the historical data, and acquiring second processing failure data meeting the user data processing failure conditions according to the statistical result; obtaining performance abnormal data according to the first processing failure data and the second processing failure data; wherein, the user data processing failure condition comprises at least one of data processing overtime, data loss and processing result error.

According to the preferred embodiment of the invention, the method for confirming the optimization type of the application program according to the abnormal data comprises the following steps: if the abnormal data comprises the user abnormal data, confirming that the optimization type comprises content optimization; if the exception data includes performance exception data, then the validation optimization type includes performance optimization.

According to the preferred embodiment of the invention, the application program is optimized according to the optimization type, and the optimization method comprises the following steps: if the optimization type comprises content optimization, acquiring at least one of user information, browsing records, preference tag data and questionnaire survey data of a user corresponding to the user data to generate optimization information; if the optimization type comprises performance optimization, acquiring abnormal data associated with the user data to generate optimization information; and optimizing the application program according to the optimization information.

In a second aspect, the present application provides an apparatus for optimizing an application, including: the system comprises a process acquisition module, a data processing module and a processing module, wherein the process acquisition module is used for acquiring a data processing process of a service unit in an application program, and the data processing process is used for representing information of a process node through which user data passes when the user data is processed; the statistical module is used for counting the processing results of the user data at the corresponding process nodes according to the data processing process to obtain statistical results; the analysis module is used for carrying out data analysis on the statistical result to obtain abnormal data; and the optimization module is used for confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type.

In a third aspect, the present application provides a computer device comprising a memory and a processor; the memory for storing a computer program; the processor is used for executing the computer program and realizing the steps of the optimization method of the application program when the computer program is executed.

In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the steps of the optimization method of the application program.

According to the method, the device, the equipment and the medium for optimizing the application program, the data processing flow of the service units in the application program is obtained, the data processing flow is used for representing the information of the flow nodes through which the user data pass when the user data are processed, each service unit can be respectively managed, and meanwhile, the association among the flow nodes can be better reflected; counting the processing result of the user data at the corresponding process node according to the data processing process to obtain a statistical result, and acquiring the processing condition of each process node on the user data through the statistical result; performing data analysis on the statistical result to obtain abnormal data so as to quickly locate the abnormal position, thereby facilitating subsequent optimization analysis; and confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type, thereby improving the efficiency and accuracy of optimization.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 is a schematic application environment diagram of an optimization method for an application program according to an embodiment of the present disclosure;

FIG. 2 is a flowchart of an application optimization method provided by an embodiment of the present application;

FIG. 3 is a schematic block diagram of an optimization apparatus for an application provided by an embodiment of the present application;

fig. 4 is a schematic block diagram of a computer device provided by an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It should also be noted that: reference to "a plurality" in this application means two or more. "and/or" describe the association relationship of the associated objects, meaning that there may be three relationships, e.g., A and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

Fig. 1 is a system architecture diagram illustrating an operating environment of an exemplary embodiment of the present application, and referring to fig. 1, the system may include a user terminal 110, a network, and a server 120. The user terminal 110 and the server 120 are communicatively coupled via a network, which may include various types of connections, such as wired, wireless communication links, or fiber optic cables, among others. An execution subject of the method for optimizing an application program provided in the embodiment of the present application may be the user terminal 110 or the server 120, which is not limited herein in the embodiment of the present application.

The user terminal 110 may be hardware or software. When the user terminal 110 is hardware, it may be various electronic devices including, but not limited to, a smart phone, a tablet computer, a smart band, a desktop computer, and the like. When the user terminal 110 is software, it can be installed in the electronic devices listed above.

The server 120 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, middleware service, a domain name service, a security service, a CDN, a big data and artificial intelligence platform, and the like.

It should be understood that the numbers of the terminal devices and the servers in fig. 1 are merely illustrative and are only used for understanding the embodiments of the present application, and the numbers of the specific terminal devices and the servers should be flexibly determined according to practical situations.

Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.

Referring to fig. 2, fig. 2 is a flowchart illustrating an optimization method for an application according to an embodiment of the present disclosure. As shown in fig. 2, the method includes steps S210 to S240.

Step S210, obtaining a data processing flow of a service unit in an application program, where the data processing flow is used to represent information of a flow node through which user data passes when the service unit processes the user data.

A business unit refers to a collection of program elements such as data descriptions, executable statements, etc. that are intended to perform a certain function. An application may include multiple service units, with different service functions being implemented between different service units. Business units such as applications include, but are not limited to: the system comprises a login unit, an information browsing unit, an information publishing unit, an online payment unit and the like. The business units can be combined to obtain new business units, for example, the order submitting unit and the online payment unit can be combined to obtain the transaction unit, and the specific division mode of the business units is not limited in the application.

Each service unit can correspond to different data processing flows, and the corresponding data processing flows are matched for the user data according to the received user data of different service units. The data processing flow is used for representing information of a flow node through which the user data passes when the service unit processes the user data, namely, a corresponding data transmission path when the user data is processed. The data processing flow is composed of a plurality of flow nodes, and it can be understood that each flow node has a precedence relationship for processing user data.

For example, when the service unit is a content auditing module, after receiving the user data of the module, the matched process nodes included in the data processing process are machine auditing, manual auditing and data application respectively. Therefore, according to the precedence relationship of processing user data among the process nodes, the user data is transmitted to the machine auditing process, and the corresponding processing result is obtained after the machine auditing. And when the processing result of the machine audit is passed, transmitting the user data to a manual audit process, and obtaining a corresponding processing result after the manual audit. And when the processing result of the manual audit is passed, transmitting the user data to the data application process, and executing corresponding operations, such as data storage, deletion, modification, display and the like, on the user data through the data application process.

The same service unit may correspond to multiple data processing flows, and a specific data processing flow may be flexibly determined according to an actual situation, which is not limited herein.

For example, for user data received by the same service template, a corresponding data processing flow may be matched according to attribute information of the user data, for example, when the user data is user login information, the corresponding data processing flow may be data format check and database query; when the user data is the user website accessed, the corresponding data processing flow can be sensitive word auditing, attack filtering and website query. Or matching the corresponding data processing flow according to the processing result of the user data at each flow node, namely matching the next flow node of the user data. For example, the current process node is machine audit, and when the processing result of the user data at the current process node is pass, the corresponding next process node is manual audit; and when the processing result of the user data at the node is failed, the corresponding next process node is still checked by the machine.

The process nodes included in the data processing process may be all nodes through which the user data passes when the service unit processes the user data, or may be some nodes through which the user data passes when the service unit processes the user data, and the selection of the process nodes may be flexibly determined in combination with the actual situation, which is not limited herein.

The corresponding data processing flows are respectively obtained based on the difference of the service units so as to obtain the flow node information of the user data, and then each flow node is conveniently subjected to subsequent analysis, the processing condition of the user data can be clearly obtained according to the relation between the flow nodes, and the abnormal data can be conveniently positioned.

Step S220, counting the processing result of the user data at the process node according to the data processing process to obtain a counting result.

When the user data passes through the corresponding process node, the processing result corresponding to the process node is obtained, and the processing result is recorded to obtain the corresponding processing record so as to record the processing condition corresponding to the user data. And correspondingly analyzing the execution condition of each process node by counting the processing records of the user data of each process node in the data processing process.

The processing record may be stored in the flow node or in the user data, and the storage method of the processing record is not specifically limited in this application.

In some embodiments, the counting, according to the data processing procedure, a processing result of the user data at a corresponding procedure node to obtain a statistical result includes: acquiring a process node of the data processing process; acquiring a processing record table corresponding to the process node, wherein the processing record table is used for recording user data processed by the process node within a preset time and a processing result of the user data; and classifying and counting the user data according to the processing result of the user data to obtain the statistical result.

Illustratively, the process node is provided with a corresponding processing record table for recording the user data processed by the corresponding process node within the preset time and the processing result corresponding to the user data. The processing record table may be a record corresponding to a single process node, or may be a record corresponding to a plurality of process nodes. And respectively acquiring a processing record table of each process node in the data processing process, and classifying and counting the user data according to the processing result corresponding to the user data to obtain the statistical result of each process node.

For example, all the user data and the processing results of the user data processed by the flow node a in one hour are obtained according to the processing record table, and the same processing results are classified into one class, so that the specific amount of the user data included in each class of processing results is obtained. If the total number of user data processed by the flow node a in one hour is 100, each piece of user data corresponds to one processing result, the processing results are classified and counted, and the obtained statistical results include: the processing result is 80 pieces of user data that pass, 10 pieces of user data that fail, and 10 pieces of user data that overtime.

In some embodiments, a processing record table may be further provided for each user data received by the service unit, and is used to record each process node through which the user data passes and a processing result corresponding to the process node. Counting the processing result of the user data at the corresponding process node according to the data processing process to obtain a statistical result, wherein the statistical result comprises the following steps: acquiring user data processed by a service unit within preset time; acquiring a processing record table corresponding to the user data, wherein the processing record table is used for recording process nodes passed by the user data and processing results corresponding to the process nodes; and classifying and counting the user data according to the processing result of the user data corresponding to the process node to obtain the statistical result.

Illustratively, the user data is provided with a corresponding processing record table for recording process nodes through which the user data passes and processing results corresponding to the process nodes. And acquiring the user data processed by the service unit within the preset time to acquire a processing record table corresponding to the user data. And acquiring the process nodes through which the user data passes and the processing results corresponding to the process nodes through the processing records, and further executing classification and statistical operation to respectively obtain the statistical results of the process nodes.

For example, the processing result of the user data is divided according to the difference of the process nodes in the processing record table to obtain the processing result sets of the plurality of process nodes. Then, the processing results in each processing result set are classified, the same processing results are classified into one type, and the specific quantity of the user data of the processing results contained in each type is obtained. For example, a total of 4000 user data processed by a service unit within one hour, each piece of user data corresponds to a processing record table, and for example, the processing record table of the user data a includes: the process node a is verified successfully; and b, the flow node fails in verification. Dividing the processing result of each user data according to the difference of the process nodes to obtain a processing result set, wherein the processing result set divided by the process node a comprises the following steps: user data A, verification is successful; user data B, check failed. Then, the processing result set of each process node is classified and counted, for example, the statistical result obtained by the process node a includes: the processing result is 2000 pieces of user data which are successfully verified, the verification success rate is 50 percent, the processing result is 1000 pieces of user data which are failed to be verified, the verification failure rate is 25 percent, the processing result is 1000 pieces of user data which are overtime verified, and the verification overtime rate is 25 percent.

By counting the user data processed by each process node and the processing result of the user data, the user data processing condition of each process node can be clearly reflected, and the execution condition of the application program can be conveniently and specifically analyzed subsequently.

And step S230, performing data analysis on the statistical result to obtain abnormal data.

By analyzing the statistical result, the execution condition of each process node and between each process node and the use condition of the application program used by the user can be obtained, and after the abnormal data is found, the abnormal data can be processed in time to correspondingly optimize the application program.

In some embodiments, the performing data analysis on the statistical result to obtain abnormal data includes: acquiring historical data corresponding to the service unit; and analyzing the statistical result according to the historical data to obtain the abnormal data.

The historical data of the service unit may refer to user data processed by each process node of the service unit before the current time node and a processing result of the user data. For example, the service unit periodically acquires the user data and the processing result of the user data processed by each process node within a preset time, and stores the acquired user data and the processing result of the user data as historical data. If the user data and the processing results of each process node are acquired once every day within a specific time period, the data acquired in the last week is saved as historical data. Whether large fluctuation exists between the data can be analyzed by comparing the data before the current time node with the data before the current time node, namely whether a large difference exists between the data of the current time node and the data before the current time node. And when the large fluctuation exists, the current service unit is indicated to be abnormal, and then abnormal data and corresponding process nodes are obtained and analyzed.

The historical data of the service unit may also be a configuration parameter preset by the service unit, for example, a threshold parameter is configured for each process node included in the data processing flow of the service unit, for example, the processing result of the process node a is that the threshold for processing timeout is 20 percent, and the processing result is that the threshold for processing failure is 20 percent. And determining whether the business unit is abnormal or not by judging the relation between the statistical result of the current time node and the threshold parameter, and further acquiring and analyzing abnormal data and the corresponding process node when the business unit is abnormal.

Illustratively, in the history data, the verification success rate of the flow node a in the first time period is 65 percent, the verification success rate of the flow node a in the second time period is 75 percent, and the verification success rate of the flow node a in the current time period in the statistical result is 50 percent. The change value of the verification success rate of the process node a in the current time period relative to the verification success rate of the process node a in the first time period is calculated to be-15 percent, and the change value of the verification success rate of the process node a in the second time period relative to-25 percent, and then abnormal data is obtained to be-20 percent according to the average value of the two values. Comparing the data before the current time node with the data of the current time node to obtain a corresponding fluctuation value of 20 and a corresponding fluctuation value threshold of 5. The verification success rate of the flow node a in the current time period is obtained through comparison to have large fluctuation, and the corresponding abnormal data can be the user data of which the processing result is verification failure in the user data processed by the flow node a in the current time period.

And counting the processing condition of the user data of each process node through real-time monitoring so as to carry out exception analysis according to the processing condition and find out exception data in time. Whether the user data of the service unit has larger fluctuation or exceeds the corresponding threshold parameter can be known through the abnormal data, the corresponding abnormal condition is discovered in time, the process node generated by the abnormal data is positioned, and the abnormal reason is accurately positioned.

Taking the example that the historical data includes the user data processed by each flow node of the service unit before the current time node and the processing result of the user data, the obtaining of the abnormal data is explained in detail.

In some embodiments, the anomaly data comprises user anomaly data; analyzing the statistical result according to the historical data to obtain the abnormal data, wherein the analyzing comprises: acquiring first active user data meeting user activity conditions according to the historical data, and acquiring second active user data meeting the user activity conditions according to the statistical result; obtaining the user abnormal data according to the first active user data and the second active user data; wherein the user activity condition comprises at least one of the user on-line time, the user use times and the user use frequency.

The first active user data and the second active user data refer to a specific number of active users.

The user data comprises active users and inactive users, and whether the corresponding users belong to the active users can be judged through the user online time, the user use times, the user use frequency and other data. The usage duration is used for determining the time of the user using the application program, and specifically, the average duration of using the application program in a certain time period may be obtained. The number of times of use is used to determine the number of times of using the application by the user, and specifically, the average number of times of using the application in the time period may be obtained. The frequency of use determines the frequency with which the user uses the application, where the frequency may be the number of uses per week, day, or hour, etc.

Illustratively, user data in the historical data is analyzed, and data such as user online time, user usage times, user usage frequency and the like of a user corresponding to the user data are obtained to judge whether the user belongs to an active user. For example, the user data carries a user identification code, the user identification code is used to query the user online time length, the user usage times, the user usage frequency and other data of the user, and whether at least one of the user online time length, the user usage times, the user usage frequency and other data exceeds a data threshold is determined, if at least one of the user online time length, the user usage times, the user usage frequency and other data exceeds the data threshold, the user belongs to an active user.

The number of the active users can reflect the degree of the user groups to like the application program, so that the change of the degree of the users to like the application program can be obtained by calculating the first active user data and the second active user data. The user abnormal data is used for representing user information associated with at least one of the first active user data and the second active user data when the number of active users of the application program fluctuates negatively, namely when the users lose, and the user information includes but is not limited to user attributes, user behavior data and the like.

For example, if the parameter threshold of the user churn is 50, the first active user data is 5000 within the preset time period, and the second active user data is 4500 within the preset time period, the number of user churns is 500. Therefore, the application program suffers from user churn, and the number of churn users is greater than the threshold value, which indicates that the user's preference for the application program is reduced, and the application program needs to be optimized correspondingly to enhance the user's viscosity. At this time, the corresponding user abnormal data may be corresponding user information in the first active user data and the second active user data, and subsequent optimization analysis is performed according to the user information.

In some embodiments, the confirming of the user anomaly data further comprises: acquiring behavior data of a user corresponding to the user data, and acquiring user abnormal data according to the user behavior data, wherein the behavior data of the user comprises at least one of the following items: page browsing duration and page clicking frequency.

The behavior data of the user includes, but is not limited to, page browsing duration, page clicking frequency, and the like. The page browsing duration is used for representing the time of the user staying at the corresponding page, and the page clicking frequency is used for representing the number of times that the user clicks the corresponding page within the preset time.

When the user displays the type A delivery information and the type B delivery information to the user through the application program, and when the page browsing duration and the page click frequency of the type A delivery information by the user are higher than the page browsing duration and the page click frequency of the type B delivery information by the user, the preference information of the user is the type A delivery information.

And in the behavior data of the user, when the data such as the page browsing duration, the page clicking frequency and the like are smaller than the data threshold, indicating that the current content of the application program is different from the preference of the user. For example, class A delivery information and class B delivery information are presented to the user through the application. The data such as the page browsing duration, the page clicking frequency and the like of the analogical information A are larger than the data threshold value, and the data such as the page browsing duration, the page clicking frequency and the like of the analogical information B are smaller than the data threshold value. And the type B sending information is abnormal and is used as the user abnormal data corresponding to the user so as to perform corresponding optimization on the application program.

Through the detection to user's abnormal data, can real-time supervision application receive the degree that the user liked to and the condition that the degree that discovery application was liked by the user appears descending in time, and then carry out corresponding optimization to application to promote user's use and experience the sense, prevent that the user from running off.

In some embodiments, the anomaly data includes performance anomaly data; analyzing the statistical result according to the historical data to obtain the abnormal data, wherein the analyzing comprises: acquiring first processing failure data meeting user data processing failure conditions according to the historical data, and acquiring second processing failure data meeting the user data processing failure conditions according to the statistical result; obtaining the performance abnormal data according to the first processing failure data and the second processing failure data; wherein, the user data processing failure condition comprises at least one of data processing overtime, data loss and processing result error.

When the flow node processes the user data, a processing failure may occur, and when a large amount of user data with processing failure occurs, it indicates that the application program may have a performance problem. The user data processing failure condition includes at least one of: data processing timeout, data loss, processing result error.

The data processing timeout refers to that when user data is processed at a process node, a corresponding processing result is still not obtained after a preset time is exceeded, for example, if the user data currently required to be processed by the process node is too much, the user data cannot be processed in time, so that the user data waiting timeout is caused. The data loss means that the user data does not reach the designated process node when being transmitted between the process nodes, and for example, the user data is sent to the wrong process node because of a logic judgment error during data transmission, so that the designated process node cannot receive the user data. The processing result error means that an erroneous processing result is output when the flow node processes the user data.

The first processing failure data can be obtained by counting the number of user data in the history data, which have failed in processing due to data processing timeout, data loss, processing result error, and the like. And counting the number of the user data with processing failure caused by data processing overtime, data loss, processing result error and the like in the statistical result to obtain second processing failure data. And then obtaining performance abnormal data according to the difference value between the second processing failure data and the first processing failure data.

Performance anomaly data may refer to data corresponding to user data or process nodes associated with the second processing failure data that characterize possible anomalies in the performance of the application. For example, the parameter threshold of the data processing timeout rate is 5, the first processing failure data acquired according to the history data includes the data processing timeout rate of 10 percent, the second processing failure data acquired according to the statistical result includes the data processing timeout rate of 50 percent, the corresponding data processing timeout rate is 40 percent, and the corresponding data processing timeout rate is greater than the parameter threshold of the data processing timeout rate. Therefore, the application performance is abnormal, and the corresponding abnormal data can be user data associated with the second processing failure data, and subsequent optimization analysis is performed according to the user data.

And S240, confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type.

And analyzing the abnormal data to confirm the optimization type of the abnormal business unit needed to be optimized by the application program according to the abnormal data. The optimization type is used to indicate the application to optimize, and in particular from which aspect the application is optimized.

In some embodiments, said identifying the type of optimization of the application based on said exception data comprises: confirming that the optimization type comprises content optimization if the abnormal data comprises user abnormal data; confirming that the optimization type comprises performance optimization if the exception data comprises performance exception data.

For example, the optimization type can include a content optimization type and/or a performance optimization type, and the optimization type of the application can be determined from the exception data. According to the optimization type of the application program, indicating a technician to determine the optimization mode of the application program, for example, checking the performance of the application program, positioning the reason of the abnormality, solving the problem of the abnormal performance and realizing the performance optimization; or changing the information display content of the application program, or changing the use steps of the application program, and the like, thereby realizing content optimization.

In some embodiments, said optimizing said application according to said optimization type comprises: if the optimization type comprises content optimization, acquiring at least one of user information, browsing records, preference tag data and questionnaire survey data of a user corresponding to the user data to generate optimization information; if the optimization type comprises performance optimization, obtaining abnormal data associated with the user data to generate optimization information; and optimizing the application program according to the optimization information.

The optimization information may include at least one of optimization cause location, optimization content, and optimization strategy.

If the optimization type comprises content optimization, the defects of the content such as the display content and the use step of the current application program are indicated. Optimization information may be generated to optimize the application based on analyzing at least one of user information, browsing history, preference tag data, and questionnaire survey data of the user corresponding to the user data.

The user information may be attribute information of the user, such as gender, age, marital status, etc. of the user. The user's browsing history includes, but is not limited to, data related to the user's access to the page, such as the user's time spent on the page, the number of times a page button was clicked, the page's topic opened, the time logged in to view the advertisement, and so on.

The questionnaire survey data is used for directly obtaining the evaluation of the user on the application program, for example, a questionnaire survey page may be issued to the client, the client displays the questionnaire survey page, and the user inputs answer data for questions on the questionnaire survey page. The questionnaire survey can set questions according to actual conditions, and calculate corresponding scores according to input data of the user. For example, the questionnaire survey page comprises application scoring questions, and the number input by the user is directly used as the score. Illustratively, the method includes the steps of acquiring questionnaire survey data completed by a user, calculating an average score corresponding to each question in the questionnaire survey data, and selecting question items with scores lower than a threshold value, wherein the question items with scores lower than the threshold value include: "whether the page operation steps are simple", "whether the page design is beautiful", etc.

The user's preference tag data is used to characterize the direction in which the user is preferring to use the application. For example, data such as user information and browsing records of the user are input into a preset preference identification model, and preference tag data is extracted to obtain the preference tag data of the user. For example, the preference tag data extracted from data such as user information and browsing history of a user includes "science and technology news", "fun video", "travel strategy", and the like.

The data such as user information, browsing records, preference tag data and the like corresponding to the lost user can be obtained, for example, the active user is stored and recorded to obtain an active user list, the lost user is obtained by comparing the active user at the current time with the user in the active user list, and then the data such as the user information, the browsing records, the preference tag data and the like of the part of users are obtained. And the data such as user information, browsing records, preference tag data and the like of the active user at the current time can be directly acquired. Specific acquisition rules of data such as user information, browsing records, preference tag data and the like can be flexibly selected according to actual conditions, and the application is not limited herein.

The optimization information is generated by analyzing the user information, the browsing records, the preference tag data, the questionnaire survey data and the like, so that the application program content is optimized according to the optimization information, such as optimizing the page operation logic, optimizing the page design and the like, and formulating personalized information recommendation and the like for different users.

If the optimization type comprises performance optimization, the defects of the running parameters, the function codes and the like of the current application program are indicated. Optimization information may be generated from analyzing the anomalous data associated with the user data to optimize the application.

When user data processing fails due to application performance, exception data corresponding to the user data is acquired. The abnormal data may include information of the corresponding process node, such as parameter information, function information, data transmission link, and the like of the process node.

For example, the flow node with the exception may be tested to further determine the cause of the exception. For example, copying the corresponding function code to a simulation operation environment for performance testing, and monitoring the test result in real time according to the performance index to be tested, wherein the performance index to be tested includes but is not limited to performance data such as data processing efficiency, data processing accuracy, memory occupancy rate, CPU utilization rate and the like of the process node. When a plurality of performance indexes to be tested are abnormal, different performance indexes to be tested correspond to different types of performance problems. For example, the problem type of the performance problem determined in this step may include at least one of a plurality of types, such as memory occupancy being higher than an occupancy threshold, data processing efficiency being lower than an efficiency threshold, CPU usage being higher than a usage threshold, and data processing accuracy being lower than an accuracy threshold.

By monitoring the test result in real time, the running state of the flow node to be tested can be judged, so that the performance problem can be found and recorded in time.

In some embodiments, the application monitoring log may be generated according to information such as statistical results, abnormal data, optimization types, and the like, so that a technician may know the operation condition and effect of the application according to the monitoring log, and perform corresponding optimization and improvement on the application by analyzing the problem shown by the log.

In some embodiments, data such as statistical results and abnormal data of each service unit may be mapped to a specific view, and different display manners may be adopted for data of different service units in the same application program. The display mode includes but is not limited to the following one or more modes in combination: displaying the display data in a list form; displaying the display data in a histogram form; displaying the display data in the form of a circular data analysis graph; displaying the display data in the form of a data comparison analysis graph; the presentation data is shown in the form of a data trend analysis graph.

For displaying the data of the evolution process, the state of each time period can be shown in an animation mode, and the state can also be shown by adopting a function curve with time as a horizontal axis. Through view display, technicians can visually acquire the operation effect of the application program according to the view, and each service unit of the application program is favorably and respectively monitored.

By acquiring a data processing flow of service units in an application program, wherein the data processing flow is used for representing information of flow nodes through which user data pass when the user data are processed, each service unit can be managed respectively, and meanwhile, the association among the flow nodes can be better reflected; counting the processing result of the user data at the corresponding process node according to the data processing process to obtain a statistical result, and acquiring the processing condition of each process node on the user data through the statistical result; performing data analysis on the statistical result to obtain abnormal data so as to quickly locate the abnormal position, thereby facilitating subsequent optimization analysis; and confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type, thereby improving the efficiency and accuracy of optimization.

Referring to fig. 3, fig. 3 is a schematic block diagram of an application optimization apparatus according to an embodiment of the present application, which can be configured in a server or a computer device for executing the foregoing application optimization method.

As shown in fig. 3, the apparatus 300 includes: a flow acquisition module 310, a statistics module 320, an analysis module 330, and an optimization module 340.

A process obtaining module 310, configured to obtain a data processing process of a service unit in an application, where the data processing process is used to represent information of a process node through which user data passes when the user data is processed;

the statistical module 320 is configured to count a processing result of the user data at a corresponding process node according to the data processing process to obtain a statistical result;

the analysis module 330 is configured to perform data analysis on the statistical result to obtain abnormal data;

and the optimization module 340 is configured to confirm the optimization type of the application according to the abnormal data, so as to optimize the application according to the optimization type.

It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus, the modules and the units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The methods, apparatus, and devices of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

The above-described methods and apparatuses may be implemented, for example, in the form of a computer program that can be run on a computer device as shown in fig. 4.

Referring to fig. 4, fig. 4 is a schematic diagram of a computer device according to an embodiment of the present disclosure. The computer device may be a server or a terminal.

As shown in fig. 4, the computer device 400 includes a processor 410, a memory 430, and a network interface 440 connected by a system bus 420, wherein the memory 430 may include a non-volatile storage medium and an internal memory.

Non-volatile storage media may store operating system 450 and computer programs 460. The computer program 460 includes program instructions that, when executed, cause the processor 410 to perform any of the methods for optimizing an application.

The processor 410 is used to provide computing and control capabilities that support the operation of the overall computer device 400.

The internal memory 430 provides an environment for the execution of a computer program 460 in a non-volatile storage medium, which computer program 460, when executed by the processor 410, causes the processor 410 to perform any of the methods for optimization of an application.

The network interface 440 is used for network communication such as sending assigned tasks and the like. Those skilled in the art will appreciate that the configuration of the computer device 400 is merely a block diagram of a portion of the configuration associated with the present solution and does not constitute a limitation of the computer device 400 to which the present solution applies, and in particular that the computer device 400 may include more or less components than those shown, or combine certain components, or have a different arrangement of components.

It should be understood that Processor 410 may be a Central Processing Unit (CPU), and that Processor 410 may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. The general purpose processor 410 may be a microprocessor, or the processor 410 may be any conventional processor, etc.

Wherein, in some embodiments, the processor 410 is configured to run a computer program 460 stored in the memory to implement the steps of:

acquiring a data processing flow of a service unit in an application program, wherein the data processing flow is used for representing information of a flow node through which user data passes when the service unit processes the user data;

counting the processing result of the user data at the process node according to the data processing process to obtain a statistical result;

performing data analysis on the statistical result to obtain abnormal data;

and confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type.

In some embodiments, counting a processing result of the user data at a corresponding process node according to the data processing process to obtain a statistical result, including:

acquiring a process node of a data processing process;

acquiring a processing record table corresponding to the process node, wherein the processing record table is used for recording user data processed by the process node within a preset time and a processing result of the user data;

and classifying and counting the user data according to the processing result of the user data to obtain a statistical result.

In some embodiments, performing data analysis on the statistical result to obtain abnormal data includes:

acquiring historical data corresponding to the service unit;

and analyzing the statistical result according to the historical data to obtain abnormal data.

In some embodiments, the anomaly data includes user anomaly data; analyzing the statistical result according to the historical data to obtain abnormal data, wherein the abnormal data comprises the following steps:

acquiring first active user data meeting the user activity condition according to the historical data, and acquiring second active user data meeting the user activity condition according to the statistical result;

obtaining user abnormal data according to the first active user data and the second active user data;

wherein the user activity condition comprises at least one of the user on-line time, the user use times and the user use frequency.

In some embodiments, the anomaly data includes performance anomaly data; analyzing the statistical result according to the historical data to obtain abnormal data, wherein the abnormal data comprises the following steps:

acquiring first processing failure data meeting the user data processing failure conditions according to the historical data, and acquiring second processing failure data meeting the user data processing failure conditions according to the statistical result;

obtaining performance abnormal data according to the first processing failure data and the second processing failure data;

wherein, the user data processing failure condition comprises at least one of data processing overtime, data loss and processing result error.

In some embodiments, validating the optimization type of the application from the exception data includes:

if the abnormal data comprises the user abnormal data, confirming that the optimization type comprises content optimization;

if the exception data includes performance exception data, then the validation optimization type includes performance optimization.

In some embodiments, optimizing the application according to the optimization type includes:

if the optimization type comprises content optimization, acquiring at least one of user information, browsing records, preference tag data and questionnaire survey data of a user corresponding to the user data to generate optimization information;

if the optimization type comprises performance optimization, acquiring abnormal data associated with the user data to generate optimization information;

and optimizing the application program according to the optimization information.

The embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and the program instructions, when executed, implement any one of the methods for optimizing an application program provided in the embodiments of the present application.

The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk provided on the computer device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like.

While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for optimizing an application, the method comprising:

performing data analysis on the statistical result to obtain abnormal data;

2. The method according to claim 1, wherein the counting the processing result of the user data at the process node according to the data processing process to obtain a statistical result comprises:

acquiring a process node of the data processing process;

and classifying and counting the user data according to the processing result of the user data to obtain the statistical result.

3. The method of claim 1, wherein the performing data analysis on the statistical result to obtain abnormal data comprises:

acquiring historical data corresponding to the service unit;

and analyzing the statistical result according to the historical data to obtain the abnormal data.

4. The method of claim 3, wherein the anomaly data comprises user anomaly data; analyzing the statistical result according to the historical data to obtain the abnormal data, wherein the analyzing comprises:

acquiring first active user data meeting user activity conditions according to the historical data, and acquiring second active user data meeting the user activity conditions according to the statistical result;

obtaining the user abnormal data according to the first active user data and the second active user data;

5. The method of claim 3, wherein the anomaly data comprises performance anomaly data; analyzing the statistical result according to the historical data to obtain the abnormal data, wherein the analyzing comprises:

acquiring first processing failure data meeting user data processing failure conditions according to the historical data, and acquiring second processing failure data meeting the user data processing failure conditions according to the statistical result;

obtaining the performance abnormal data according to the first processing failure data and the second processing failure data;

6. The method of any of claims 1 to 5, wherein said determining a type of optimization for an application based on said exception data comprises:

if the abnormal data comprises user abnormal data, confirming that the optimization type comprises content optimization;

and if the abnormal data comprises performance abnormal data, confirming that the optimization type comprises performance optimization.

7. The method of claim 6, wherein optimizing the application according to the optimization type comprises:

8. An apparatus for optimizing an application, comprising:

the system comprises a process acquisition module, a data processing module and a processing module, wherein the process acquisition module is used for acquiring a data processing process of a service unit in an application program, and the data processing process is used for representing information of a process node through which user data passes when the service unit processes the user data;

the statistical module is used for counting the processing result of the user data at the process node according to the data processing process to obtain a statistical result;

the analysis module is used for carrying out data analysis on the statistical result to obtain abnormal data;

and the optimization module is used for confirming the optimization type of the application program according to the abnormal data so as to optimize the application program according to the optimization type.

9. A computer device, wherein the computer device comprises a memory and a processor;

the memory for storing a computer program;

the processor is configured to execute the computer program and to implement the method for optimizing an application program according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to implement the method of optimization of an application according to any one of claims 1 to 7.