CN110765189A - Exception management method and system for Internet products - Google Patents
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Abstract
The application relates to an abnormality management method and system for Internet products, wherein the method comprises the following steps: collecting abnormal data uploaded by a client of an internet product; processing the collected abnormal data in real time, and outputting structured data according to a preset rule; generating an abnormal analysis report of the internet product according to the structured data; and when a query request is received, calling and returning a corresponding abnormal analysis report according to the query condition of the query request. According to the scheme, the abnormal data of the client can be monitored, and the abnormal data of the client of the internet product can be obtained in real time; by analyzing the details of the abnormal data in real time, the problems of the internet products in the using process are sensed in time, and a basis is provided for the upgrading and maintenance work of a research and development team; the abnormal conditions of the products can be conveniently and timely processed by a research and development team, and large-area product experience accidents are avoided.
Description
Technical Field
The application relates to the technical field of internet product monitoring, in particular to an abnormality management method and system for internet products.
Background
When a user uses an internet product, the user inevitably encounters some anomalies or bugs, such as App card pause and crash, or failure in adding a shopping cart, failure in playing video, failure in payment, and the like, which cause the product use experience of the user to be extremely poor, and if a large-scale user encounters similar anomalies, the user experience of the product needs to be improved urgently. In the development and test stage of the product, research personnel can ensure that the product does not generate abnormity or bug through multiple rounds of tests, but the scenes of users are extremely diversified, and the research personnel cannot predict all conditions in advance. After the product is released, the operation conditions of the product on all user sides can not be sensed, the product use experience of a user is good or bad, and the user can not obtain the product use experience in time, so that the user experience can be comprehensively mastered, and the product experience can be accurately improved.
Disclosure of Invention
To overcome, at least to some extent, the problems in the related art, the present application provides a method and system for managing an abnormality of an internet product.
According to a first aspect of embodiments of the present application, there is provided an abnormality management method for internet products, including:
collecting abnormal data uploaded by a client of an internet product;
processing the collected abnormal data in real time, and outputting structured data according to a preset rule;
generating an abnormal analysis report of the internet product according to the structured data;
and when a query request is received, calling and returning a corresponding abnormal analysis report according to the query condition of the query request.
Further, the method further comprises:
dividing the collected abnormal data into system performance abnormal data and service state abnormal data for storage;
wherein the system performance abnormal data is abnormal information caused by system technical reasons; the abnormal data of the service state is abnormal information caused by a problem in service.
Further, the processing the collected abnormal data in real time includes:
and the collected abnormal data is arranged into four kinds of structured data, namely user information, behavior information, abnormal information and equipment information.
Further, the generating an anomaly analysis report of the internet product according to the structured data includes:
actively initiating a processing process of the structured data to generate an abnormal analysis report form of the internet product; alternatively, the first and second electrodes may be,
when receiving a query request, initiating a processing process of the structured data to generate an abnormal analysis report of the internet product;
wherein the actively initiating a process to the structured data further comprises:
monitoring user experience indexes of all dimensions of the internet product;
and when the change of the user experience value of a certain dimensionality exceeds a preset threshold value, sending out the corresponding alarm information in a preset alarm mode.
Further, the information in the anomaly analysis report includes: product, product dimension, index; wherein, a product has a plurality of product dimensions, and a product dimension is quantified by a plurality of indexes;
the product dimensions include: product line, page, exception type, user; the indicators include: abnormal grade, abnormal occurrence frequency, abnormal influence number of people and abnormal influence number of users.
According to a second aspect of embodiments of the present application, there is provided an abnormality management system for internet products, including:
the cloud server is used for collecting abnormal data uploaded by a client of an Internet product;
the big data computing platform is used for processing the collected abnormal data in real time and outputting the structured data according to a preset rule;
the management module is used for generating an abnormal analysis report form of the internet product according to the structured data;
and the query module is used for calling and returning a corresponding abnormal analysis report according to the query condition of the query request when receiving the query request.
Further, the cloud server is further configured to:
dividing the collected abnormal data into system performance abnormal data and service state abnormal data for storage;
wherein the system performance abnormal data is abnormal information caused by system technical reasons; the abnormal data of the service state is abnormal information caused by a problem in service.
Further, the big data computing platform processes the collected abnormal data in real time, and specifically includes:
and the collected abnormal data is arranged into four kinds of structured data, namely user information, behavior information, abnormal information and equipment information.
Further, the management module generates an abnormal analysis report of the internet product according to the structured data, and specifically includes:
actively initiating a processing process of the structured data to generate an abnormal analysis report form of the internet product; alternatively, the first and second electrodes may be,
when receiving a query request, initiating a processing process of the structured data to generate an abnormal analysis report of the internet product;
wherein, the management module actively initiates a process for the structured data, further comprising:
monitoring user experience indexes of all dimensions of the internet product;
and when the change of the user experience value of a certain dimensionality exceeds a preset threshold value, sending out the corresponding alarm information in a preset alarm mode.
Further, the system further comprises: the resource management database is used for storing the abnormal analysis report;
the information in the anomaly analysis report includes: product, product dimension, index; wherein, a product has a plurality of product dimensions, and a product dimension is quantified by a plurality of indexes;
the product dimensions include: product line, page, exception type, user; the indicators include: abnormal grade, abnormal occurrence frequency, abnormal influence number of people and abnormal influence number of users.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
according to the scheme, the abnormal data of the client can be monitored, and the abnormal data of the client of the internet product can be obtained in real time; by analyzing the details of the abnormal data in real time, the problems of the internet products in the using process are sensed in time, and a basis is provided for the upgrading and maintenance work of a research and development team; the abnormal conditions of the products can be conveniently and timely processed by a research and development team, and large-area product experience accidents are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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.
FIG. 1 is a schematic diagram of the service architecture of the present invention.
FIG. 2 is a schematic flow diagram of the process of the present invention.
FIG. 3 is a schematic diagram of the system architecture of the present invention
FIG. 4 is a diagram of an exception data structure of the present invention.
FIG. 5 is a schematic diagram of the structure of the structured data of the present invention.
Fig. 6 is a schematic diagram of the structure of the management module of the present invention.
FIG. 7 is a diagram of the structure of the resource management database of the present invention.
FIG. 8 is a schematic diagram of the query module architecture of the present invention.
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 embodiment of the invention provides an abnormality management method of an internet product, and the flow of the method can be realized in a system as shown in fig. 1 through a computer program. The system comprises: an exception acquisition system, an analysis server and a user terminal.
The anomaly acquisition system is composed of an end-side anomaly data acquisition system covering all terminals and all users, wherein an anomaly acquisition and reporting functional module is embedded in the product research and development process, and an anomaly set related to the requirements of the anomaly acquisition and reporting functional module is combed out and is uniformly integrated in a product application package.
And the analysis server is mainly used for storing, calculating and managing the acquired end-side abnormal data. The abnormal data are reported to the analysis server by the abnormal acquisition system, the abnormal data are subjected to cloud storage by a storage module of the analysis server, the structured abnormal data are output by a cleaning of the calculation module, and the structured abnormal data are generated into an abnormal analysis report by the management module.
The user terminal is a device for acquiring the abnormal analysis report, and the report can be acquired in various manners such as a PC Web (using a browser or a client), a mobile terminal (using an App), and the like. And receiving an abnormal analysis report form returned by the analysis server by sending a query request to the analysis server.
According to the scheme, the abnormal data encountered by the user are monitored and acquired on the end side of the internet product, so that the user experience data of the product are quantized, and the user experience of the product is improved through data driving.
Fig. 2 is a flowchart illustrating an anomaly management method for an internet product according to an exemplary embodiment. The method can be applied to the abnormity monitoring of the Internet products, and comprises the following steps:
step S1: collecting abnormal data uploaded by a client of an internet product;
step S2: processing the collected abnormal data in real time, and outputting structured data according to a preset rule;
step S3: generating an abnormal analysis report of the internet product according to the structured data;
step S4: and when a query request is received, calling and returning a corresponding abnormal analysis report according to the query condition of the query request.
According to the scheme, the abnormal data of the client can be monitored, and the abnormal data of the client of the internet product can be obtained in real time; by analyzing the details of the abnormal data in real time, the problems of the internet products in the using process are sensed in time, and a basis is provided for the upgrading and maintenance work of a research and development team; the abnormal conditions of the products can be conveniently and timely processed by a research and development team, and large-area product experience accidents are avoided.
The following describes the scheme of the present application in an expanded manner with reference to a specific application scenario.
As shown in fig. 2 and 3, the method includes:
and S0, acquiring the end-side abnormal data generated in the Internet product through an abnormal acquisition module embedded in the Internet product.
The method is characterized in that a function module for monitoring the abnormity is embedded in a code packet of each terminal of the Internet product, so that all possible abnormity of a user can be monitored, wherein the abnormity mainly comprises the abnormity of a system performance level and a service state level.
The current types of the terminal are mainly classified into three types according to different acquisition modes used by different terminals: a mobile end, a browser end and an applet end. The mobile terminal mainly comprises an Android App, an iOS App and the like, and reports the abnormal data of the user in a log mode by embedding an SDK (software development kit) so as to achieve the purpose of acquisition. The browser end mainly comprises a PCweb, a WAP, an App embedded H5 and the like, the applet end mainly comprises a WeChat applet, a Paobao applet, a Baidu applet, a today's first-line applet and the like, and the two terminals report abnormal data of a user in a log mode by introducing an abnormal JS probe so as to achieve the purpose of collection.
And S1, when the user on the end side encounters an exception, namely the abnormal data on the end side meets the reporting policy, triggering the exception to report to the cloud server. And the subsequent big data computing platform processes the abnormal data in real time and outputs the structured data according to a certain rule.
The reporting strategies that can be used are different for different products and different terminals. Currently, there are three reporting strategies: one is that the background sets a threshold value of the abnormal quantity, and when the abnormal quantity is accumulated to the set threshold value, the report is triggered; and the other is to report periodically according to a fixed interval (e.g., 5 minutes, 15 minutes, etc.). In order to avoid the interference of reporting abnormal data on the normal use of the user as much as possible, the network environment for reporting can be set as follows: reporting only WiFi environment, reporting 4G and WiFi environment, reporting all environments and the like; the other method is quasi-real-time reporting, and reporting to the cloud server after an exception occurs, or reporting with very low delay (millisecond, second).
In some embodiments, the method further comprises:
dividing the collected abnormal data into system performance abnormal data and service state abnormal data for storage;
wherein the system performance abnormal data is abnormal information caused by system technical reasons; the abnormal data of the service state is abnormal information caused by a problem in service.
Specifically, after the abnormal data is reported to the cloud server, the abnormal data is mainly divided into two types of data to be stored in the cloud server, as shown in fig. 4, which includes: system performance exception data and service state exception data. The system performance abnormality refers to abnormal information which causes that the system cannot operate or cannot stably operate, performance, throughput, network and the like, and the abnormality is caused by system technical reasons, such as: an HTTP state exception, an HTTP latency exception, an Ajax state exception, an Ajax latency exception, a slow Ajax exception, an ANR exception, a page stuck-at exception, a JS exception, a slow page exception, and the like. The abnormal business state mainly refers to an abnormality caused by a problem (shortage, wrong calculation of business index, and the like) in business when a system runs stably and each system index is healthy, for example: no goods, no sales at a time, failure to join a shopping cart, failure to submit an order, failure to pay, no results from a search, failure to interface call, no data from a page refresh, failure to save, etc.
And S2, processing the abnormal data in real time by the big data computing platform, and outputting the structured abnormal data.
In some embodiments, the processing the collected abnormal data in real time includes:
and the collected abnormal data is arranged into four kinds of structured data, namely user information, behavior information, abnormal information and equipment information.
As shown in fig. 5, the processed data is mainly divided into four kinds of structured data, including: user information, behavior information, anomaly information, device information. The user information includes a member account number, a member level, a member tag, an operator, a network, a geographical location, and the like; the behavior information comprises a page name, a page entering time, a page leaving time, a behavior name, a behavior occurrence time, a behavior object, other behavior information and the like; the exception information comprises exception codes, exception names, exception grades, exception types, exception categories, exception files, exception details, a research and development center, products, product lines, pages, service time, service domain names, service request codes, original URLs, sources, link IDs, occurrence scenes, occurrence time and the like; the device information includes a terminal type, a device model, a device OS, a device ID, a device manufacturer, a browser type, a browser version, an App channel, an IP address, and the like.
And S3, the management module generates an abnormal analysis report according to the structured abnormal data and stores the abnormal analysis report in the resource management database.
In some embodiments, the generating an anomaly analysis report of the internet product according to the structured data includes:
actively initiating a processing process of the structured data to generate an abnormal analysis report form of the internet product; alternatively, the first and second electrodes may be,
and when receiving a query request, initiating a processing process of the structured data, and generating an abnormal analysis report of the Internet product.
Wherein the actively initiating a process to the structured data further comprises:
monitoring user experience indexes of all dimensions of the internet product;
and when the change of the user experience value of a certain dimensionality exceeds a preset threshold value, sending out the corresponding alarm information in a preset alarm mode.
As shown in fig. 6, the management module mainly includes six kinds of functional modules, which are: the method comprises the steps of real-time data calculation (a user sends a query request through a query module, a system calculates in real time and returns a report), offline data calculation (aiming at a report of a part of core scenes, the system calculates and stores a corresponding report according to a set timing task in time, the process does not need to be sent by the user), multidimensional fusion calculation (aiming at four kinds of structured data of user information, behavior information, abnormal information and equipment information, data are correlated through using an identification field), a situation perception engine (the system calculates periodic data, and the situation trend of a data curve is judged by comparing the data of the current period with the data of the previous period, wherein the situation trend comprises a 'surge' situation), an intelligent alarm engine (aiming at the 'surge' to send an alarm message to a related user through acquiring the situation of the data), a user experience index evaluation system (aiming at abnormal quantity, the situation of the data, And (4) deducing the value of user experience of index data such as abnormal grade and the number of users affected). The management module generates an abnormal analysis report form in two ways: one is active generation, which is actively initiated by the system according to set timing tasks and configuration, and comprises reports such as daily reports, monthly reports and the like generated by off-line data calculation and alarm messages triggered by a situation awareness engine and an intelligent alarm engine; one is passive generation, which is generated only when a user terminal initiates a request through a query module, and comprises the steps of acquiring corresponding data according to query conditions through real-time data calculation and associating various structured data through identification fields through multi-dimensional fusion calculation. The common point of the two modes is that the abnormal analysis report can be generated only by adopting a user experience index evaluation system.
Specifically, the system actively generates an anomaly analysis report for monitoring user experience indexes of each dimension of the product. An intelligent alarm engine is introduced into a big data computing platform, and situation perception of multi-dimensional user experience of products can be achieved. When the user experience value change of a certain dimensionality exceeds a threshold value, the situation awareness engine outputs data to the intelligent alarm engine, relevant alarm information is informed to relevant personnel in various modes (short messages, mails, telephones, IM communication apps and the like), and the situation awareness engine is manually intervened earlier to avoid larger accidents.
In some embodiments, the information in the anomaly analysis report includes: product, product dimension, index; wherein, a product has a plurality of product dimensions, and a product dimension is quantified by a plurality of indexes;
the product dimensions include: product line, page, exception type, user; the indicators include: abnormal grade, abnormal occurrence frequency, abnormal influence number of people and abnormal influence number of users.
As shown in fig. 7, the resource management database is used for storing an anomaly analysis report of the product. Take Product A as an example, characterized by a plurality of Product dimensions of separating: dimension A1, Dimension A2, Dimension A3 … Dimension AN. For each product dimension, it is quantified by a number of indicators: index 1, index 2, index 3 …, index N. Specifically, the product dimension may be a product line, a page, an abnormal type, a user, and the like, and the index may be an abnormal level, an abnormal occurrence frequency, an abnormal influence number of people, an abnormal influence number of users, an abnormal influence XX member number, and the like.
And S4, feeding back the abnormal analysis report forms of various dimensions to the user terminal through the query module and the resource management database.
When a product research and development team member sends a query request to the resource management database through a PC (personal computer) end browser or a mobile end App (application program) by using a query module, the query module returns a corresponding product abnormity analysis report to the terminal according to a product query condition.
Through the product anomaly analysis report, members of a research and development team can obtain user experience values of all dimensions of a product and know which anomalies which affect the user experience most seriously at present, so that the head anomalies are solved by concentrated resources; and the user can also know which users have the worst experience currently, so that care measures are taken to avoid the loss of core users.
Wherein, the query module is specifically reflected on the user terminal, and comprises: product, product line, page, member account, member rating, member tag, operator, network, geographical location, exception code, exception name, exception rating, exception type, exception category, exception pattern, terminal type, device model, device OS, device ID, browser type, browser version, App channel, IP address, time period, and the like. The user can obtain the report form through various devices and modes, such as a PC Web (a mode of using a browser or a client) and a mobile terminal (a mode of using App).
According to the method, a research and development team of the product can acquire the abnormal data of the user side of the product in real time, the details of the abnormal data of the user can be analyzed in real time, the experience condition of the user using the product and the overall user experience state of the product can be sensed, the influence value of the user experience of each dimension of the product is calculated and analyzed, the user experience of the product is accurately improved by the research and development team driven by the data used by the user, and the development of the product is supported.
In addition, the real-time situation perception of multi-dimensional user experience can be carried out on the product, so that the possibly occurring product abnormity is informed to the corresponding product research and development team members at the first time, and large-area product experience accidents are avoided through intervention and intervention in the germination stage.
The present application further provides the following embodiments:
an anomaly management system for internet products, comprising:
the cloud server is used for collecting abnormal data uploaded by a client of an Internet product;
the big data computing platform is used for processing the collected abnormal data in real time and outputting the structured data according to a preset rule;
the management module is used for generating an abnormal analysis report form of the internet product according to the structured data;
and the query module is used for calling and returning a corresponding abnormal analysis report according to the query condition of the query request when receiving the query request.
In some embodiments, the cloud server is further configured to:
dividing the collected abnormal data into system performance abnormal data and service state abnormal data for storage;
wherein the system performance abnormal data is abnormal information caused by system technical reasons; the abnormal data of the service state is abnormal information caused by a problem in service.
In some embodiments, the real-time processing of the collected abnormal data by the big data computing platform specifically includes:
and the collected abnormal data is arranged into four kinds of structured data, namely user information, behavior information, abnormal information and equipment information.
In some embodiments, the generating, by the management module, an anomaly analysis report of the internet product according to the structured data specifically includes:
actively initiating a processing process of the structured data to generate an abnormal analysis report form of the internet product; alternatively, the first and second electrodes may be,
when receiving a query request, initiating a processing process of the structured data to generate an abnormal analysis report of the internet product;
wherein, the management module actively initiates a process for the structured data, further comprising:
monitoring user experience indexes of all dimensions of the internet product;
and when the change of the user experience value of a certain dimensionality exceeds a preset threshold value, sending out the corresponding alarm information in a preset alarm mode.
In some embodiments, the system further comprises: the resource management database is used for storing the abnormal analysis report;
the information in the anomaly analysis report includes: product, product dimension, index; wherein, a product has a plurality of product dimensions, and a product dimension is quantified by a plurality of indexes;
the product dimensions include: product line, page, exception type, user; the indicators include: abnormal grade, abnormal occurrence frequency, abnormal influence number of people and abnormal influence number of users.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
The method and the system for managing the abnormity of the Internet product can be used for:
1. a research and development team (development member) of the product obtains user experience values of all dimensions of the product, and knows which links and which exceptions affect the user experience at present, so that the direction and the target of product construction are determined;
2. by acquiring user experience values of all dimensions of a product, a research and development team (operation member) of the product knows which users experience the worst currently, and can take care measures in time to avoid core user loss;
3. a research and development team (user experience member) of the product learns about a short board of the product on the user experience level by acquiring user experience values of all dimensions of the product, so that the product experience is improved pertinently;
4. a research and development team (management layer member) of the product learns the construction capacity of the product research and development team, corrects construction ideas and reasonably arranges research and development resources by acquiring user experience values of all dimensions of the product.
5. The research and development team of the product realizes the situation perception of the user experience numerical values of all dimensions of the product through the intelligent alarm engine, and can know and intervene in the dimension of the fluctuation of the user experience numerical values at the first time.
Aiming at the above means of product experience quantification and improvement, the user experience numerical value can be obtained only by investigating users in a small range in the past, the time consumption is long (taking 'day' as a unit), the cost is high (the investigation cost of each person is more than one hundred yuan), the final conclusion accuracy is poor, the user experience numerical value of the product can be checked in real time by using the method, the time consumption is low (second level), the cost is low (only early research and development investment is applied), and the accuracy is high (all users and all scenes are covered).
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. An anomaly management method for an internet product, comprising:
collecting abnormal data uploaded by a client of an internet product;
processing the collected abnormal data in real time, and outputting structured data according to a preset rule;
generating an abnormal analysis report of the internet product according to the structured data;
and when a query request is received, calling and returning a corresponding abnormal analysis report according to the query condition of the query request.
2. The method of claim 1, further comprising:
dividing the collected abnormal data into system performance abnormal data and service state abnormal data for storage;
wherein the system performance abnormal data is abnormal information caused by system technical reasons; the abnormal data of the service state is abnormal information caused by a problem in service.
3. The method of claim 1, wherein the processing the collected anomaly data in real-time comprises:
and the collected abnormal data is arranged into four kinds of structured data, namely user information, behavior information, abnormal information and equipment information.
4. The method of claim 1, wherein generating an anomaly analysis report for an internet product from the structured data comprises:
actively initiating a processing process of the structured data to generate an abnormal analysis report form of the internet product; alternatively, the first and second electrodes may be,
when receiving a query request, initiating a processing process of the structured data to generate an abnormal analysis report of the internet product;
preferably, the actively initiating the processing procedure of the structured data further includes:
monitoring user experience indexes of all dimensions of the internet product;
and when the change of the user experience value of a certain dimensionality exceeds a preset threshold value, sending out the corresponding alarm information in a preset alarm mode.
5. The method of claim 1, wherein the information in the anomaly analysis report comprises: product, product dimension, index; wherein, a product has a plurality of product dimensions, and a product dimension is quantified by a plurality of indexes;
preferably, the product dimensions include: product line, page, exception type, user; the indicators include: abnormal grade, abnormal occurrence frequency, abnormal influence number of people and abnormal influence number of users.
6. An anomaly management system for internet products, comprising:
the cloud server is used for collecting abnormal data uploaded by a client of an Internet product;
the big data computing platform is used for processing the collected abnormal data in real time and outputting the structured data according to a preset rule;
the management module is used for generating an abnormal analysis report form of the internet product according to the structured data;
and the query module is used for calling and returning a corresponding abnormal analysis report according to the query condition of the query request when receiving the query request.
7. The system of claim 6, wherein the cloud server is further configured to:
dividing the collected abnormal data into system performance abnormal data and service state abnormal data for storage;
wherein the system performance abnormal data is abnormal information caused by system technical reasons; the abnormal data of the service state is abnormal information caused by a problem in service.
8. The system of claim 6, wherein the big data computing platform processes the collected abnormal data in real time, specifically comprising:
and the collected abnormal data is arranged into four kinds of structured data, namely user information, behavior information, abnormal information and equipment information.
9. The system according to claim 6, wherein the management module generates an anomaly analysis report of the internet product according to the structured data, specifically comprising:
actively initiating a processing process of the structured data to generate an abnormal analysis report form of the internet product; alternatively, the first and second electrodes may be,
when receiving a query request, initiating a processing process of the structured data to generate an abnormal analysis report of the internet product;
preferably, the management module actively initiates a process for processing the structured data, further comprising:
monitoring user experience indexes of all dimensions of the internet product;
and when the change of the user experience value of a certain dimensionality exceeds a preset threshold value, sending out the corresponding alarm information in a preset alarm mode.
10. The system of claim 6, further comprising: the resource management database is used for storing the abnormal analysis report;
the information in the anomaly analysis report includes: product, product dimension, index; wherein, a product has a plurality of product dimensions, and a product dimension is quantified by a plurality of indexes;
preferably, the product dimensions include: product line, page, exception type, user; the indicators include: abnormal grade, abnormal occurrence frequency, abnormal influence number of people and abnormal influence number of users.
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