CN112948224A - Data processing method, device, terminal and storage medium - Google Patents
Data processing method, device, terminal and storage medium Download PDFInfo
- Publication number
- CN112948224A CN112948224A CN201911338738.8A CN201911338738A CN112948224A CN 112948224 A CN112948224 A CN 112948224A CN 201911338738 A CN201911338738 A CN 201911338738A CN 112948224 A CN112948224 A CN 112948224A
- Authority
- CN
- China
- Prior art keywords
- application
- data
- information
- analysis
- data processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3089—Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents
- G06F11/3093—Configuration details thereof, e.g. installation, enabling, spatial arrangement of the probes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3438—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment monitoring of user actions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3447—Performance evaluation by modeling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/865—Monitoring of software
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Mathematical Physics (AREA)
- Debugging And Monitoring (AREA)
Abstract
The application is applicable to the technical field of data processing, and provides a data processing method, a device, a terminal and a storage medium, wherein the data processing method comprises the following steps: when detecting that a user starts an application, acquiring application data information acquired through detection of a preset probe; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data; matching an analysis model according to the acquired application data information to analyze the application data; and generating a monitoring result according to the data analysis result. The method and the device can solve the problem that the existing data in the aspects of independently monitoring user behaviors, application performance or application abnormity and the like cause various data information to be in an isolated island state, so that abnormity can not be quickly and accurately positioned.
Description
Technical Field
The present application belongs to the field of data processing technologies, and in particular, to a data processing method, an apparatus, a terminal, and a storage medium.
Background
In order to stably operate the application product, data processing in the operation process of the application product becomes more important, the data processing is used as a means for timely and effectively feeding back data abnormity, whether the data is abnormal or not is observed through monitoring the data, and then the data is analyzed, so that the real condition of the application product is obtained. For example, in order to know the situation that the user uses the application product, the operator of the application product may pay attention to the behavior of the user, such as the number of times and frequency that the user accesses the application, and analyze the collected user behavior data to know information such as the popularity of the application or the user experience from the analysis result.
However, in some cases, the operator of the application product observes that the system access volume (PV) is decreased from the user behavior analysis end, and the actual situation is that the page response speed is slow, that is, the application performance data is abnormal, which results in the user experience being deteriorated, thereby causing the PV to decrease, but the content of the application product is not welcomed by the user, so that if only the user behavior data is concerned, the obtained analysis result is deviated from the actual result, and the true reason cannot be known. The deeper reason for this situation is that monitoring of user behavior data, application performance data or application abnormal data is performed independently, so that when one or more data items are abnormal due to various data information being in an isolated island state, abnormal positioning cannot be performed quickly and accurately.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device, a terminal and a storage medium, which are used for solving the problems that data information is isolated in the existing data processing, and when one or more items of data are abnormal, abnormal positioning cannot be rapidly and accurately carried out.
In a first aspect, an embodiment of the present application provides a data processing method, where the method includes:
when detecting that a user starts an application, acquiring application data information acquired through detection of a preset probe; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data;
matching an analysis model according to the acquired application data information to analyze the application data;
and generating a monitoring result according to the data analysis result.
In a second aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes:
the acquisition module is used for acquiring application data information acquired through the detection of a preset probe when detecting that a user starts an application; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data;
the analysis module is used for matching an analysis model according to the acquired application data information to analyze the application data;
and the result generation module is used for generating a monitoring result according to the data analysis result.
In a third aspect, an embodiment of the present application provides a data processing terminal, including: comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including: the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method.
In a fifth aspect, embodiments of the present application provide a computer program product, which, when run on a data processing terminal, causes the data processing terminal to perform the method of any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that: according to the method, the mode of integrating the probes is adopted, data in aspects of user behaviors, application performance, application abnormity and the like are collected simultaneously, and the problem that the abnormal positioning cannot be performed quickly and accurately due to the fact that various data information is in an isolated island state due to the fact that the data in aspects of user behaviors, application performance, application abnormity and the like are monitored independently in the prior art is solved.
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 or the prior art descriptions 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 based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating that a data processing method according to an embodiment of the present application is applied to a user login scenario;
fig. 3 is a schematic flowchart of a data processing method applied to verify a login status in a user login scenario according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a data processing terminal according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance. Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The data processing method provided by the embodiment of the application can be applied to terminal devices such as a mobile phone, a tablet personal computer, a desktop computer, a wearable device, a vehicle-mounted device, an Augmented Reality (AR)/Virtual Reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, and a Personal Digital Assistant (PDA), and the embodiment of the application does not limit the specific types of the terminal devices at all.
For example, the terminal device may be a Station (ST) in a WLAN, which may be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with Wireless communication capability, a computing device or other processing device connected to a Wireless modem, a vehicle-mounted device, a vehicle-mounted networking terminal, a computer, a laptop, a handheld communication device, a handheld computing device, a satellite Wireless device, a Wireless modem card, a television set-top box (STB), a Customer Premises Equipment (CPE), and/or other devices for communicating over a Wireless system and a next generation communication system, such as a Mobile terminal in a 5G Network or a Public Land Mobile Network (future evolved, PLMN) mobile terminals in the network, etc.
In order to facilitate understanding of the present application, a brief description of the embodiments of the present application will be provided herein. In order to better operate/operate and maintain the published application program product, multiple parties generally participate, for example, product operators pay attention to user behavior data information and analyze behavior data to obtain the operation condition of the application program product; and the developer pays attention to the data message of the application performance and the application abnormity in a core mode, and performs related data analysis to obtain the running condition of the application program product. Generally, the user behavior data concerned by a product operator is separated from the application performance and application abnormal data concerned by a developer, so that an intersection does not exist, the data of the user behavior data concerned by the product operator is only concerned during data analysis, and data isolated islands are mostly present between the user behavior data and the application abnormal data, so that the product operator observes the reduction of the access volume (PV) from a user behavior analysis platform, and the PV is reduced due to the fact that the page response speed is reduced and the user experience is poor, and the PV is reduced, and the real reason cannot be known if only the user behavior data is concerned; or the research and development personnel receive the alarm of the abnormal system, if an API request is fed back once and an error occurs, the error is found to be the lost part of the parameters of the API caller when the system is checked, then the caller is informed to check the related codes, the parameters are determined to be complete when being transmitted, and the problem of troubleshooting is trapped in impasse. In practical situations, data is emptied due to a certain operation of a user, so that an abnormality is caused, and if only abnormal data is concerned, the reason for the problem cannot be quickly located.
Therefore, the data processing method provided by the application is used for solving the problem that the existing method for independently monitoring and acquiring user behavior, application performance or application abnormal data enables various data information to be in an isolated island state, so that abnormal positioning cannot be rapidly and accurately performed.
The following describes an exemplary data processing method provided by the present application with specific embodiments.
Referring to fig. 1, a schematic flowchart of a data processing method provided in an embodiment of the present application is shown, by way of example and not limitation, an execution subject of the data processing method in the present embodiment may be a terminal device. The method comprises the following steps:
s101: when detecting that a user starts an application, acquiring application data information acquired through detection of a preset probe; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data.
In this embodiment, in order to more comprehensively acquire application data information, when a user starts an application, the preset probe detects and acquires the application data information in real time according to state information of application start. The application has only one preset probe and is used for simultaneously detecting and acquiring any one or more data including user behavior data, application performance data and application abnormal data, and the data are acquired through the same preset probe, so that the data are acquired in a certain relevance manner, acquired time points or data storage, and when data analysis and problem location are carried out, serial analysis can be carried out based on the relevant points, and the problem can be quickly located. The predetermined probe is introduced to the probe address of the application by a code, such as "< script >! (function (b) { variable ═ b. createlement ("script"); variable ═ b. getelementsbytagname ("script") [0 ]; n.async ═ 1; n.src ═ current application probe address "; (tag.parttnde.insertbeform (n, tag);) (document); </script > ".
For example, referring to fig. 2, after the user starts the application, the user inputs a user name and a password according to a prompt of the application, and clicks login, and the preset probe records a user login behavior, that is, user behavior data; and when the login state is successfully verified by the application, the user enters the application, and the preset probe records the action of the user entering the application.
Referring to fig. 3, after the user clicks a login button on a page of the application, the application verifies the login status of the user. When the user name and the password are not in accordance with the application rules, the preset probe records abnormal information of user login failure, namely the abnormal information is used as application abnormal data; and after the application rule is verified to be met, the application sends a login request to the server and waits for a returned login state, and the preset probe records the time consumed by the login request, namely the time is used as application performance data.
S102: and matching an analysis model according to the acquired application data information to analyze the application data.
In this embodiment, a user behavior analysis model, an application performance analysis model, and an application anomaly analysis module are respectively provided as default analysis models for user behavior data, application performance data, and application anomaly data. In addition, according to different scene requirements, a custom analysis model is selected for analysis, which can be specific to partial data or all data, such as: counting the visit amount, and analyzing only according to the page event data acquired by the preset probe; counting the number of active users, and analyzing the data acquired according to all preset probes; or when the positioning application is abnormal, the comprehensive analysis can be carried out by combining the user behavior and the application performance data.
S103: and generating a monitoring result according to the data analysis result.
In this embodiment, the monitoring result may be displayed in a form of a text report in a summary manner, so that an operator or a research and development worker can quickly analyze and locate the application condition.
Optionally, for step S101, after acquiring the application data information acquired by the preset probe, the method further includes: acquiring a preset identifier; and according to the preset identifier matched with the application data information, marking the application data information based on the preset identifier.
Wherein the preset identification comprises: the behavior, performance and abnormal data are respectively in one-to-one correspondence with user behavior data, application performance data and application abnormal data, and the application data information is identified through the preset identification, so that the data acquired by the preset probe can be shunted according to different analysis models, and the analysis operation is facilitated.
Further, for step S102, analysis request information is further acquired, an analysis model is matched according to a preset identifier of the application data information, and application data information meeting the analysis requirement is acquired according to the analysis request information, the preset identifier and the analysis model to perform application data analysis.
For example, to acquire user behavior data and application performance data, and to perform independent analysis on the two types of data, that is, to perform user behavior data analysis and application performance data analysis, corresponding data acquired by the preset probe may be acquired through two identifiers, namely behavior and performance, so as to perform data analysis according to a corresponding analysis model.
In one embodiment, after a monitoring result is generated according to a data analysis result, whether the application data starts an alarm rule or not is judged according to the monitoring result; and if the alarm rule is set, acquiring data information associated with the application data of the alarm and generating alarm notification information. The alarm notification information may include an access exception alarm, an application response speed exception alarm, an application crash rate exception alarm, and the like.
The alarm rule can be customized according to different conditions, namely monitoring definition information of a user is obtained, and the alarm rule is defined according to the monitoring definition information. Specifically, the alarms can be customized according to the check interval, the trigger condition, the trigger notification threshold, the notification interval, the notification content, the alarm upgrade and the like, and the influence level can also be customized according to the influence range. The analysis of the influence range can be obtained by aiming at the actual data evaluation.
In an embodiment, in order to facilitate accurate learning and concatenation of all events related to a user, when an application started by the user is detected, the method further includes: generating a session identification of the user; the session identifier is recorded in log record information of N activity events related to the user after the application is started, wherein N is more than or equal to 1 and is a positive number. Through the session identifier, operators and research personnel can clearly see all operation tracks of the user and the processing process of the application program when analyzing problems, so that abnormal points and reasons of the abnormal points are found. The session identification comprises one or more of user terminal equipment ID, user name and session start time.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 4 shows a block diagram of a data processing apparatus provided in an embodiment of the present application, which corresponds to the method described in the above embodiment, and only shows a part related to the embodiment of the present application for convenience of description.
Referring to fig. 4, the apparatus includes: the system comprises an acquisition module 100, an analysis module 200 and a result generation module 300.
The acquisition module is used for acquiring application data information acquired through detection of a preset probe when detecting that a user starts application; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data;
the analysis module is used for matching an analysis model according to the acquired application data information to analyze the application data;
and the result generation module is used for generating a monitoring result according to the data analysis result.
It should be noted that, because the contents of information interaction, execution process, and the like between the modules are based on the same concept as that of the embodiment of the method of the present application, specific functions and technical effects thereof may be specifically referred to a part of the embodiment of the method, and details are not described here.
Fig. 5 is a schematic structural diagram of a data processing terminal according to an embodiment of the present application. As shown in fig. 5, the data processing terminal 5 of this embodiment includes: at least one processor 50 (only one processor is shown in fig. 5), a memory 51, and a computer program 52 stored in the memory 51 and executable on the at least one processor 50, wherein the steps in any of the various data processing method embodiments described above are implemented when the computer program 52 is executed by the processor 50.
The data processing terminal 5 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or other computing devices. The data processing terminal may include, but is not limited to, a processor 50, a memory 51. It will be understood by those skilled in the art that fig. 5 is only an example of the data processing terminal 5, and does not constitute a limitation of the data processing terminal 5, and may include more or less components than those shown, or combine some components, or different components, such as input output devices, network access devices, etc.
The Processor 50 may be a Central Processing Unit (CPU), and the Processor 50 may also 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. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may in some embodiments be an internal storage unit of the data processing terminal 5, such as a hard disk or a memory of the data processing terminal 5. The memory 51 may also be an external storage device of the data processing terminal 5 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the data processing terminal 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the data processing terminal 5. The memory 51 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 51 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a data processing terminal, enables the data processing terminal to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a data processing terminal, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
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.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A data processing method, comprising:
when detecting that a user starts an application, acquiring application data information acquired through detection of a preset probe; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data;
matching an analysis model according to the acquired application data information to analyze the application data;
and generating a monitoring result according to the data analysis result.
2. The data processing method of claim 1, wherein the acquiring, after acquiring the application data information through the preset probe, comprises:
acquiring a preset identifier;
and according to the preset identifier matched with the application data information, marking the application data information based on the preset identifier.
3. The data processing method of claim 2, wherein the matching an analysis model based on the collected application data information to perform application data analysis comprises:
acquiring analysis request information;
matching an analysis model according to a preset identifier of the application data information;
and acquiring application data information meeting the analysis requirement according to the analysis request information, the preset identifier and the analysis model to analyze the application data.
4. The data processing method of claim 1, wherein after generating the monitoring result according to the data analysis result, further comprising:
judging whether the application data triggers an alarm rule or not according to the monitoring result;
and if the alarm rule is triggered, acquiring data information associated with the application data of the alarm and generating alarm notification information.
5. The data processing method of claim 4, wherein the method further comprises:
acquiring monitoring definition information of a user;
and configuring the alarm rule according to the monitoring definition information.
6. The data processing method of claim 1, wherein when it is detected that a user starts an application, further comprising:
generating a session identification of the user; the session identifier is recorded in log record information of N activity events related to the user after the application is started, wherein N is more than or equal to 1 and is a positive number.
7. The data processing method of claim 1, wherein the analytical model includes one or more of a user behavior analytical model, an application performance analytical model, an application anomaly analytical model, and a custom analytical model.
8. A data processing apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring application data information acquired through the detection of a preset probe when detecting that a user starts an application; wherein the application data information comprises: one or more of user behavior data, application performance data, and application anomaly data;
the analysis module is used for matching an analysis model according to the acquired application data information to analyze the application data;
and the result generation module is used for generating a monitoring result according to the data analysis result.
9. A data processing terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911338738.8A CN112948224B (en) | 2019-12-23 | 2019-12-23 | Data processing method, device, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911338738.8A CN112948224B (en) | 2019-12-23 | 2019-12-23 | Data processing method, device, terminal and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112948224A true CN112948224A (en) | 2021-06-11 |
| CN112948224B CN112948224B (en) | 2023-10-10 |
Family
ID=76234361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911338738.8A Active CN112948224B (en) | 2019-12-23 | 2019-12-23 | Data processing method, device, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112948224B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113779932A (en) * | 2021-08-24 | 2021-12-10 | 深圳市明源云科技有限公司 | Digital formatting method, device, terminal equipment and storage medium |
| CN114064757A (en) * | 2021-11-16 | 2022-02-18 | 中国平安人寿保险股份有限公司 | Application program optimization method, device, equipment and medium |
| CN114139853A (en) * | 2021-10-21 | 2022-03-04 | 杭萧钢构股份有限公司 | Steel structure product list processing method and device based on big data |
| CN114323143A (en) * | 2021-12-30 | 2022-04-12 | 上海商汤临港智能科技有限公司 | Vehicle data detection method and device, computer equipment and storage medium |
| CN115638833A (en) * | 2022-12-23 | 2023-01-24 | 保定网城软件股份有限公司 | Monitoring data processing method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150213109A1 (en) * | 2013-01-15 | 2015-07-30 | Datorama Technologies, Ltd. | System and method for providing big data analytics on dynamically-changing data models |
| CN107870988A (en) * | 2017-10-17 | 2018-04-03 | 厦门市美亚柏科信息股份有限公司 | A kind of information verification method, terminal device and storage medium |
| CN108491320A (en) * | 2018-03-05 | 2018-09-04 | 平安普惠企业管理有限公司 | Exception analysis method, device, computer equipment and the storage medium of application program |
| CN108536573A (en) * | 2018-04-17 | 2018-09-14 | 中山市华南理工大学现代产业技术研究院 | A kind of VR application performances and the method for user behavior monitoring |
| CN109343987A (en) * | 2018-08-20 | 2019-02-15 | 科大国创软件股份有限公司 | IT system fault diagnosis and restorative procedure, device, equipment, storage medium |
| CN109582701A (en) * | 2018-11-30 | 2019-04-05 | 广州净松软件科技有限公司 | Supervise warning information acquisition methods, device, equipment and the storage medium of data |
-
2019
- 2019-12-23 CN CN201911338738.8A patent/CN112948224B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150213109A1 (en) * | 2013-01-15 | 2015-07-30 | Datorama Technologies, Ltd. | System and method for providing big data analytics on dynamically-changing data models |
| CN107870988A (en) * | 2017-10-17 | 2018-04-03 | 厦门市美亚柏科信息股份有限公司 | A kind of information verification method, terminal device and storage medium |
| CN108491320A (en) * | 2018-03-05 | 2018-09-04 | 平安普惠企业管理有限公司 | Exception analysis method, device, computer equipment and the storage medium of application program |
| CN108536573A (en) * | 2018-04-17 | 2018-09-14 | 中山市华南理工大学现代产业技术研究院 | A kind of VR application performances and the method for user behavior monitoring |
| CN109343987A (en) * | 2018-08-20 | 2019-02-15 | 科大国创软件股份有限公司 | IT system fault diagnosis and restorative procedure, device, equipment, storage medium |
| CN109582701A (en) * | 2018-11-30 | 2019-04-05 | 广州净松软件科技有限公司 | Supervise warning information acquisition methods, device, equipment and the storage medium of data |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113779932A (en) * | 2021-08-24 | 2021-12-10 | 深圳市明源云科技有限公司 | Digital formatting method, device, terminal equipment and storage medium |
| CN113779932B (en) * | 2021-08-24 | 2024-07-19 | 深圳市明源云科技有限公司 | Digital formatting method, device, terminal equipment and storage medium |
| CN114139853A (en) * | 2021-10-21 | 2022-03-04 | 杭萧钢构股份有限公司 | Steel structure product list processing method and device based on big data |
| CN114064757A (en) * | 2021-11-16 | 2022-02-18 | 中国平安人寿保险股份有限公司 | Application program optimization method, device, equipment and medium |
| CN114323143A (en) * | 2021-12-30 | 2022-04-12 | 上海商汤临港智能科技有限公司 | Vehicle data detection method and device, computer equipment and storage medium |
| CN115638833A (en) * | 2022-12-23 | 2023-01-24 | 保定网城软件股份有限公司 | Monitoring data processing method and system |
| CN115638833B (en) * | 2022-12-23 | 2023-03-31 | 保定网城软件股份有限公司 | Monitoring data processing method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112948224B (en) | 2023-10-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112948224A (en) | Data processing method, device, terminal and storage medium | |
| CN108038130B (en) | Automatic false user cleaning method, device, equipment and storage medium | |
| CN112311617A (en) | Configured data monitoring and alarming method and system | |
| CN110609937A (en) | Crawler identification method and device | |
| CN110362455B (en) | Data processing method and data processing device | |
| CN111552633A (en) | Interface abnormal call testing method and device, computer equipment and storage medium | |
| CN107066536B (en) | Comment determining method and device | |
| CN111176202A (en) | Safety management method, device, terminal equipment and medium for industrial control network | |
| CN104579830A (en) | Service monitoring method and device | |
| CN112953753A (en) | Data acquisition method and device, terminal equipment and storage medium | |
| CN108197002B (en) | Mobile device non-buried point data statistical method, system, terminal and medium | |
| CN108650123B (en) | Fault information recording method, device, equipment and storage medium | |
| CN112671878B (en) | Block chain information subscription method, device, server and storage medium | |
| CN112948225B (en) | Link monitoring method and device, monitoring equipment and storage medium | |
| CN115208695B (en) | Black box safety scanning method, device and system and electronic equipment | |
| CN109542743B (en) | Log checking method and device, electronic equipment and computer readable storage medium | |
| CN115190008B (en) | Fault processing method, fault processing device, electronic equipment and storage medium | |
| CN113452533B (en) | Charging self-inspection and self-healing method and device, computer equipment and storage medium | |
| CN114513334B (en) | Risk management method and risk management device | |
| CN103326892B (en) | The operating method and device of web interface | |
| CN111737129B (en) | Service control method, device, computer readable medium and electronic equipment | |
| CN114531340A (en) | Log acquisition method and device, electronic equipment, chip and storage medium | |
| CN114968696A (en) | Index monitoring method, electronic equipment and chip system | |
| CN113760315A (en) | Method and device for testing system | |
| CN114996106B (en) | Test method of business system, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |