CN106341248B - Fault processing method and device based on cloud platform - Google Patents
Fault processing method and device based on cloud platform Download PDFInfo
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- CN106341248B CN106341248B CN201510401576.3A CN201510401576A CN106341248B CN 106341248 B CN106341248 B CN 106341248B CN 201510401576 A CN201510401576 A CN 201510401576A CN 106341248 B CN106341248 B CN 106341248B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The embodiment of the application provides a fault processing method and device based on a cloud platform, wherein the method comprises the following steps: when a fault processing request of a target object is received, acquiring first target data of a terminal where the target object is located and/or second target data of a server where the target object is located; matching the first target data and/or the second target data with a preset fault model, wherein the fault model is associated with one or more fault solving modes; when the matching is successful, selecting a target failure solution from the one or more failure solutions; and outputting the target failure solution. The embodiment of the application improves the coverage rate of detection, avoids the problem of direct description of a user, improves the detection efficiency, and meanwhile, the fault detection operation of the fault model is simple, so that the frequency of manual participation is greatly reduced, and the energy consumption of the user is reduced.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a cloud platform-based fault handling method and a cloud platform-based fault handling apparatus.
Background
With the rapid development of science and technology, various products based on a cloud platform, such as virtual hosts, cloud storage and the like, widely enter the fields of life, learning, work and the like of people.
The current cloud platform uses a standard question-answering recording system to support the fault processing service of products.
Specifically, the user abstracts and describes the problems in the use of the product in the modes of characters, pictures, sounds and the like, and the customer service checks the faults according to the abstracted descriptions.
However, such descriptions are relatively easy to implement on visible physical products, whereas cloud platform products exist in a form that is not visible to the user.
The cloud platform products are characterized in that the products are served at a remote end, and connection with a client is needed to use the products. The difference between a client system and the environment is large, the description is very difficult, the customer service is difficult to understand, and particularly, the description is difficult to be clear for users with weak technical skills, so that the fault processing efficiency is low.
Moreover, if the problem is clearly described, knowledge in the field needs to be accumulated, the technical threshold is high, and the problem is difficult to be solved independently for users or customer service with weak technical success, so that the fault processing cost is high.
Disclosure of Invention
In view of the above problems, embodiments of the present application are provided to provide a cloud platform-based fault handling method and a corresponding cloud platform-based fault handling apparatus, which overcome or at least partially solve the above problems.
In order to solve the above problem, an embodiment of the present application discloses a fault handling method based on a cloud platform, including:
when a fault processing request of a target object is received, acquiring first target data of a terminal where the target object is located and/or second target data of a server where the target object is located;
matching the first target data and/or the second target data with a preset fault model, wherein the fault model is associated with one or more fault solving modes;
when the matching is successful, selecting a target failure solution from the one or more failure solutions;
and outputting the target failure solution.
Preferably, the first target data comprises terminal environment data and/or target object test data;
the step of acquiring first target data of a terminal where the target object is located and/or second target data of a server side where the target object is located comprises the following steps:
inquiring the type information of the target object;
searching a collector corresponding to the type information;
sending the collector to a terminal where the target object is located;
and receiving terminal environment data returned by the collector and obtained by detecting the terminal, and/or detecting the target object to obtain target object test data.
Preferably, the second target data includes target object state data and/or server monitoring data;
the step of acquiring first target data of a terminal where the target object is located and/or second target data of a server side where the target object is located comprises the following steps:
querying instance information of the target object;
searching user information corresponding to the example information;
inquiring target object state data corresponding to the user information;
and/or the presence of a gas in the gas,
and extracting server monitoring data obtained by monitoring the server where the target object is located.
Preferably, the fault model includes one or more reference detection data, and one or more reference combinatorial relationships;
the step of matching the first target data and/or the second target data with a preset fault model comprises:
judging whether the first target data and/or the second target data are matched with the reference detection data;
judging whether the combination relation of the first target data and/or the second target data matched with the reference detection data is matched with the one or more reference combination relations; and if so, judging that the first target data and/or the second target data are matched with a preset fault model.
Preferably, the first target data includes a terminal detection item and a first numerical value, the second target data includes a service detection item and a second numerical value, and the reference detection data includes a reference detection item and a reference numerical value range;
the step of determining whether the first target data and/or the second target data match the reference detection data comprises:
searching a reference detection item matched with the terminal detection item and/or the service detection item;
determining whether the first value or the second value is within the reference value range;
if so, judging that the first target data and/or the second target data are matched with the reference detection data;
if not, judging that the first target data and/or the second target data are not matched with the reference detection data.
Preferably, the method further comprises the following steps:
training a fault model by adopting the first target data and/or the second target data;
and performing effectiveness screening on one or more failure solutions associated with the failure model.
Preferably, the step of training the fault model using the first target data and/or the second target data comprises:
searching first target data and/or second target data matched with the reference detection item;
filtering noise data from the matched first target data and/or second target data;
and adjusting the reference value range of the reference detection item by using the first target data and/or the second target data with the noise data filtered.
Preferably, the step of filtering noise data from the matched first target data and/or second target data comprises:
calculating an average value of the matched first target data and/or second target data;
calculating a first variance of the first target data and/or the second target data using the mean;
first and/or second target data differing by more than the first variance from the mean are filtered out.
Preferably, the step of adjusting the reference value range of the reference detection item using the first target data and/or the second target data filtered out of the noise data includes:
calculating a second variance of the first target data and/or the second target data filtered from the noise data;
and adjusting the reference value range of the reference detection item according to the second variance.
Preferably, the step of screening the effectiveness of one or more failure solutions associated with the failure model comprises:
acquiring the application times and/or user feedback information of one or more fault solving modes related to the fault model;
and screening out an effective fault solution by adopting the application times and/or the user feedback information.
The embodiment of the application further discloses a fault handling device based on the cloud platform, which includes:
the data acquisition module is used for acquiring first target data of a terminal where a target object is located and/or second target data of a server side where the target object is located when a fault processing request of the target object is received;
the fault model matching module is used for matching the first target data and/or the second target data with a preset fault model, and the fault model is associated with one or more fault solving modes;
the target fault solution selection module is used for selecting a target fault solution from the one or more fault solutions when the matching is successful;
and the target failure solution output module is used for outputting the target failure solution.
Preferably, the first target data comprises terminal environment data and/or target object test data;
the data acquisition module comprises:
the type information query submodule is used for querying the type information of the target object;
the collector searching submodule is used for searching a collector corresponding to the type information;
the collector sending submodule is used for sending the collector to a terminal where the target object is located;
and the data receiving submodule is used for receiving terminal environment data returned by the collector and obtained by detecting the terminal and/or target object test data obtained by detecting the target object.
Preferably, the second target data includes target object state data and/or server monitoring data;
the data acquisition module comprises:
the example information query submodule is used for querying example information of the target object;
the user information searching submodule is used for searching the user information corresponding to the example information;
the state data query submodule is used for querying the state data of the target object corresponding to the user information;
and/or the presence of a gas in the gas,
and the server side monitoring data extraction submodule is used for extracting server side monitoring data obtained by monitoring the server side where the target object is located.
Preferably, the fault model includes one or more reference detection data, and one or more reference combinatorial relationships;
the fault model matching module includes:
a reference detection data matching sub-module, configured to determine whether the first target data and/or the second target data match the reference detection data;
a combination relation matching sub-module, configured to determine whether a combination relation of the first target data and/or the second target data that is matched with the reference detection data matches with the one or more reference combination relations; if yes, calling a matching judgment sub-module;
and the matching judgment sub-module is used for judging that the first target data and/or the second target data are matched with a preset fault model.
Preferably, the first target data includes a terminal detection item and a first numerical value, the second target data includes a service detection item and a second numerical value, and the reference detection data includes a reference detection item and a reference numerical value range;
the reference detection data matching sub-module includes:
the reference detection item searching unit is used for searching a reference detection item matched with the terminal detection item and/or the service detection item;
a reference numerical value range judgment unit that judges whether the first numerical value or the second numerical value is within the reference numerical value range; if yes, calling a first judging unit, and if not, calling a second judging unit;
a first judgment unit configured to judge that the first target data and/or the second target data match the reference detection data;
a second determination unit configured to determine that the first target data and/or the second target data do not match the reference detection data.
Preferably, the method further comprises the following steps:
the fault model training module is used for training a fault model by adopting the first target data and/or the second target data;
and the effectiveness screening module is used for carrying out effectiveness screening on one or more failure solutions associated with the failure model.
Preferably, the fault model training module comprises:
the matching data searching submodule is used for searching the first target data and/or the second target data matched with the reference detection item;
the noise data filtering sub-module is used for filtering the noise data from the matched first target data and/or second target data;
and the reference value range adjusting sub-module is used for adjusting the reference value range of the reference detection item by adopting the first target data and/or the second target data with the noise data filtered.
Preferably, the noise data filtering sub-module includes:
the average value calculating unit is used for calculating the average value of the matched first target data and/or second target data;
a first variance calculating unit configured to calculate a first variance of the first target data and/or the second target data using the average value;
and the data filtering unit is used for filtering the first target data and/or the second target data with the difference value larger than the first variance from the average value.
Preferably, the reference value range adjustment submodule includes:
a second variance calculating unit for calculating a second variance of the first target data and/or the second target data from which the noise data is filtered;
and the variance adjusting unit is used for adjusting the reference value range of the reference detection item according to the second variance.
Preferably, the effectiveness screening module comprises:
the obtaining submodule is used for obtaining the application times and/or user feedback information of one or more fault solving modes related to the fault model;
and the screening submodule is used for screening out an effective fault solution by adopting the application times and/or the user feedback information.
The embodiment of the application has the following advantages:
according to the fault detection method and the fault detection device, the first target data of the terminal where the target object is located and/or the second target data of the server where the target object is located are matched with the preset fault model, a target fault solution mode associated with the fault model is output, the detection coverage rate is improved, the problem of direct description of a user is avoided, the detection efficiency is improved, meanwhile, the fault detection operation using the fault model is simple, the frequency of manual participation is greatly reduced, the energy consumption of the user is reduced, meanwhile, the fault is processed by using knowledge points in the fault model formed by massive worksheet data, the technical threshold is greatly reduced, the problem can be independently solved by the user with weak technical skill or a customer service, the fault processing efficiency is greatly improved, and the fault processing cost is greatly reduced.
The embodiment of the application further improves the accuracy of the fault model and the fault solution mode by training the fault model and screening the effectiveness of the fault solution mode, thereby further improving the efficiency of fault processing.
Drawings
FIG. 1 is a flowchart illustrating steps of an embodiment of a cloud platform based fault handling method of the present application;
fig. 2 is a block diagram of an embodiment of a cloud platform-based fault handling apparatus according to the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
One of the core ideas of the embodiment of the application is to provide a digital fault processing scheme, describe the information of a product as a digital index, and rapidly identify a fault by matching the digital information with a fault model, thereby providing a corresponding solution.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a cloud platform-based fault handling method according to the present application is shown, which may specifically include the following steps:
a cloud platform (cloud platforms) is a computer cluster applying cloud computing (cloud computing), such as a distributed system, providing cloud computing services, such as an ECS (electronic computer Service) virtual machine, an RDS (Relational Database Service) Database, an OSS (Open storage Service) storage, and the like.
In the cloud platform, a user can put a written program in the cloud platform to run, can also use the service provided by the cloud platform, and can also put the written program in the cloud platform to run and use the service provided by the cloud platform.
The cloud platform automatically divides a huge computing processing program into a plurality of small subprograms through a network, and then sends the small subprograms to a huge system consisting of a plurality of servers to send processing results back to a user after searching, computing and analyzing.
Taking OSS as an example, OSS is a cloud storage service that a certain cloud platform provides to the outside and is massive, safe, low-cost, and highly reliable.
A user can upload and download data through a simple REST (Representational State Transfer) interface, and can also manage the data by using a WEB page.
Based on the OSS, the user can build various multimedia sharing websites, network disks, personal enterprise data backup and other large-scale data-based services.
When a certain object (such as a product or a service) bearing sub-computing services fails, a user can fill in a work order through a client (such as a browser), send a failure processing request and request the cloud platform to perform failure processing on the object, and the object can be called a target object.
If the cloud platform receives a fault processing request of a target object, two data states can be collected: the target object comprises first target data of a terminal where the target object is located, namely user private data, and second target data of a server where the target object is located, namely cloud platform private data.
Digital fault handling can improve the existing fault handling service system from two aspects, namely question and answer.
The problem description (detection item) of a target object (such as a product or a service) is converted into a digital index (key).
For example:
whether the ECS server state is normal: ecs _ server _ status;
whether the ECS server remote connection is normal: ecs _ server _ remote _ status;
ECS pressure case: ecs _ server _ load _ status.
The reply conversion bit of the user is automatically detected, and the reply of the question is converted into a numerical value (value), which needs to be noted that the numerical value can be a digital numerical value or a logic numerical value.
For example:
the service normally runs: ecs _ server _ status: 1;
the server cannot be normally remote: ecs _ server _ remote _ status: 0;
the server is slow: ecs _ server _ load _ status: 60.
In a preferred embodiment of the present application, the first target data may include terminal environment data and/or target object test data, and in this embodiment of the present application, the step 101 may include the following sub-steps:
a substep S11 of inquiring type information of the target object;
substep S12, searching a collector corresponding to the type information;
substep S13, sending the collector to a terminal where the target object is located;
and a substep S14, receiving terminal environment data obtained by detecting the terminal and/or target object test data obtained by detecting the target object, which are returned by the collector.
In the embodiment of the application, after the user submits the work order to send the fault detection request, the work order system can be used for judging the type information (such as the product type or the service type) of the target object, and the collector corresponding to the target object (such as the product or the service) is sent to the user for downloading.
The collector can be a client program developed by languages such as Java and the like, and is used for collecting environmental data of a terminal where the target object is located and detecting the target object.
Because the client program of the Java can cross the platform, the use difficulty of the user can be reduced.
In practical application, the collector obtains a corresponding first numerical value by detecting a preset terminal detection item to form first target data. The terminal detection item may correspond to the type information of the target object, and may be different according to the type information:
firstly, terminal environment data;
the terminal environment data may be information representing an environment of a terminal (e.g., a mobile phone, a tablet computer, etc.) where the target object is located.
Examples of terminal detection items are as follows:
operating system version, development environment version, network information, machine configuration information, load conditions, and the like.
Secondly, testing data of the target object;
the cloud platform can obtain target object test data according to a function test and a performance test of a service provided by a target object (such as a product or a service).
Examples of terminal test items for functional testing are as follows:
and (3) ECS: a console API (Application Programming Interface) calls a test, a remote connection test, ECS information and cloud monitoring ECS monitoring information;
OSS, uploading, downloading, multi-concurrent uploading and deleting test;
RDS, console API test and RDS database operation test;
examples of the terminal test items for the performance test are as follows:
method call response time;
a method call response state;
ID of each call.
It should be noted that, in order to ensure the privacy and the right of awareness of the user, an authorization prompt message may be generated for the first target data, such as "do first target data upload? If the user selects to confirm the uploading, it is confirmed that the user authorizes the acquisition of the first target data, the terminal may continue to execute the uploading process of the first target data, if the user selects to refuse the uploading, it is confirmed that the user does not authorize the acquisition of the first target data, and the terminal terminates the execution of the uploading process of the first target data.
In another preferred embodiment of the present application, the first target data includes terminal environment data and/or target object test data. Further, the second target data may be composed by detecting a preset service detection item to obtain a corresponding second value.
Then, in the embodiment of the present application, step 101 may include the following sub-steps:
a substep S21 of querying instance information of the target object;
substep S22, searching user information corresponding to the instance information;
substep S23, querying the target object state data corresponding to the user information;
in a specific implementation, in a work order, a user may enter instance information, such as an instance ID, of a target object (such as a product or a service), and the user information, such as the user ID, a company name, and the like, may be queried through the instance ID.
And querying state data of the target object corresponding to the background, such as state data of penalty, finance, safety and the like, through the user information.
Taking financial status data as an example, a user's account balance is low, which may cause a target object (e.g., a product or service) to be locked.
Taking the security status data as an example, the website (target object) of the user may not be opened while the website is punished due to records, green web (the green web is used to check illegal information in the user website), and the like.
And/or the presence of a gas in the gas,
and a substep S24, extracting server monitoring data obtained by monitoring the server where the target object is located.
In specific implementation, the operation and maintenance layer such as a machine room and a network of a basic operation and maintenance layer can be monitored for a server where a target object (such as a product or a service) is located, and server monitoring data can be obtained.
Such as room temperature, rack power status, disk space, CPU usage, memory usage, etc.
The problem of the background physical device causes the target object (such as a product or a service) to malfunction, for example, if the CPU usage rate and the memory usage rate are too high, the target object (such as a product or a service) may be accessed slowly.
It should be noted that the cloud platform may merge and serially connect the user private data and the cloud platform private data through the ordering relationship and the operation and maintenance basic information relationship (that is, combine the user private data belonging to the same user and the cloud platform private data into a data set) to determine a fault.
For example, user → user information (contact address, work order ID) → terminal environment data → target object test data → target object status data → server monitor data, where "→" represents concatenation.
the fault model may be for the purpose of simplifying the fault and describing the main features of the fault with appropriate representations or rules.
After the first target data and the second target data which can be identified by the computer are obtained, the work of matching the fault model and selecting a target fault solution can be carried out, and the fault processing of the cloud platform product of the digital diagnosis system is realized.
In a preferred embodiment of the present application, the fault model may include one or more reference detection data and one or more reference combination relations, and in an embodiment of the present application, the step 102 may include the following sub-steps:
a substep S31 of determining whether the first target data and/or the second target data match the reference detection data;
a substep S32, determining whether the combination relationship of the first target data and/or the second target data matched with the reference detection data matches with the one or more reference combination relationships; if so, then the process proceeds to substep S33,
and a substep S33, determining that the first target data and/or the second target data match a preset fault model.
In the embodiment of the present application, the reference detection data may be data describing a certain attribute of a certain fault, and the reference detection data characterizes the fault by referring to a combination relationship (such as and, or, etc.).
For example, the reference detection data in the fault model is as follows:
case1 ═ ecs _ server _ status 1; case1 characterizes the service as functioning properly;
case2 ═ ecs _ server _ remote _ status 0; case2 characterizes remote inability to login;
case3 ═ ecs _ server _ load _ status > 10; case3 indicates that the server is stressed;
if the reference combination relations of Case1& Case2, Case1& Case2& Case3, Case1& Case2| | Case3 are satisfied, the fault model can characterize the remote service fault.
And if the combination relation of the first target data and the second target data matched with the reference detection data is matched with the reference combination relation, the detection of the fault represented by the fault model can be confirmed.
It should be noted that some faults are complex and have close performance, for example, network congestion and database load may be slow to access the database, and therefore, when matching the fault model, multiple fault models may be matched.
In a preferred example of the embodiment of the present application, the first target data may include a terminal detection item and a first numerical value, the second target data may include a service detection item and a second numerical value, and the reference detection data includes a reference detection item and a reference numerical value range, and in this example, the sub-step S31 may include the following sub-steps:
substep S311, searching a reference detection item matched with the terminal detection item and/or the service detection item;
substep S312, determining whether the first value and/or the second value is within the reference value range; if yes, go to substep S313, if no, go to substep S314;
substep S313, determining that the first target data and/or the second target data match the reference detection data;
and a substep S314 of determining that the first target data and/or the second target data do not match the reference detection data.
In defining the fault model, a reference test item (key) required by the fault model, and a reference value range (limit) thereof may be selected.
If the first value (value) of the terminal detection item (key) corresponding to the reference detection item (key) and the second value (value) of the service detection item (key) are within the reference value range (limit), the first target data and the second target data can be considered to be matched with the reference detection data, otherwise, the first target data and the second target data are considered to be not matched.
For example, the reference test item (key) is ping, and the reference value range (limit) is less than 10, i.e., ping < 10. If the service check item (key) is ping, and the second value (value) is 5, within the reference value range (limit), the second target data is considered to match the reference check data.
103, when the matching is successful, selecting a target failure solution from the one or more failure solutions;
in particular implementations, the fault model may be associated with one or more fault resolution methods that describe how to resolve the fault characterized by the fault model.
For example, if a certain fault model satisfies the following reference combination relationship:
Case1＝ecs_server_status 1&&Case2＝ecs_server_remote_status 0
the fault model characterizes the remote service fault and the fault resolution may be to check the ECS remote service.
For another example, a certain fault model satisfies the following reference combination relationship:
OssDel:function_RunTime>5&&ping<10&&OssLog＝netWorkTimeOut,Rpcretry&&net_tcp_error>0
the fault model represents the network fault of the cloud platform, the fault solving method can be that the feedback network worker checks the uplink exchange, namely, the data monitored by the rear-end switch is selected to judge whether the switch has the fault, the data network worker can see the data simultaneously, and if the judgment is that the switch has the fault, the detected fault and the work order are converted into the network worker to be confirmed.
In practical application, a suitable solution can be selected by a person skilled in the art as a target failure solution according to actual conditions.
Of course, other selection rules, such as random selection, may also be adopted in the embodiment of the present application to select a target failure solution, which is not limited in the embodiment of the present application.
And 104, outputting the target failure solution.
The cloud platform sends the selected target fault solution to the client and feeds the target fault solution back to the user, and the user can process the fault according to the target fault solution.
The number of applications can be recorded once every time the fault solution is selected as the target fault solution, or every time the fault solution is output.
If the current fault processing is finished, the user can score the fault processing process through the client, for example, the score is 1-100, the higher the score is, the better the quality of fault processing is, the more satisfied the fault processing is, user feedback information is generated and fed back to the cloud platform, so as to further generate a fault processing report.
According to the fault detection method and the fault detection device, the first target data of the terminal where the target object is located and/or the second target data of the server where the target object is located are matched with the preset fault model, a target fault solution mode associated with the fault model is output, the detection coverage rate is improved, the problem of direct description of a user is avoided, the detection efficiency is improved, meanwhile, the fault detection operation using the fault model is simple, the frequency of manual participation is greatly reduced, the energy consumption of the user is reduced, meanwhile, the fault is processed by using knowledge points in the fault model formed by massive worksheet data, the technical threshold is greatly reduced, the problem can be independently solved by the user with weak technical skill or a customer service, the fault processing efficiency is greatly improved, and the fault processing cost is greatly reduced.
In a preferred embodiment of the present application, the method may further comprise the steps of:
105, training a fault model by adopting the first target data and/or the second target data;
in a specific implementation, the first target data and/or the second target data in the fault processing inspection report may be set as a sample vector of the fault model, and the fault model may be trained to further improve the accuracy of the fault model.
It should be noted that the first target data and the second target data may be currently acquired first target data and second target data, or may be historical first target data and second target data, that is, after step 104, the fault model may be retrained by using the currently acquired first target data and second target data, or before step 101, the fault model may be trained by using the historical first target data and second target data.
In a preferred embodiment of the present application, step 105 may comprise the following sub-steps:
a substep S41 of finding first target data and/or second target data matching the reference detection item;
a substep S42 of filtering noise data from the matched first target data and/or second target data;
the noise data can refer to random errors of the measured first target data or the second target data, the noise data is filtered, the rationality of the first target data and the second target data is improved, and therefore the accuracy of the training fault model is improved.
In a preferred example of the embodiment of the present application, the sub-step S42 may include the following sub-steps:
substep S421, calculating an average value of the matched first target data and/or second target data;
substep S422, calculating a first variance of the first target data and/or the second target data using the average value;
the first variance may refer to an expected value of the square of the difference between the actual value and the expected value, i.e., the average of the squares of the differences between the respective data (the first target data and/or the second target data) and the average.
And a substep S423 of filtering out the first target data and/or the second target data having a difference greater than the first variance from the mean.
If the difference from the average is greater than the first variance, it may indicate that the data (the first target data and/or the second target data) does not match the expectation, and may be filtered out.
And a substep S43 of adjusting the reference value range of the reference detection item using the first target data and/or the second target data filtered from the noise data.
In a specific implementation, a second variance of the first target data and/or the second target data filtered from the noise data may be calculated, and the reference range of values for the reference detection term may be adjusted according to the second variance, e.g., by adding the second variance to an upper limit of the reference range and subtracting the second variance from a lower limit of the reference range.
And 106, carrying out effectiveness screening on one or more failure solutions associated with the failure model.
In specific implementation, the failure solutions can be sorted according to effectiveness, N (N is a positive integer, e.g., 20) failure solutions with the lowest effectiveness can be placed in a garbage warehouse, and if the failure solutions are not selected within a certain time (e.g., within half a year), the failure solutions can be eliminated and the failure solutions are not checked.
In a preferred embodiment of the present application, step 106 may comprise the following sub-steps:
substep S51, obtaining application times and/or user feedback information of one or more failure solutions associated with the failure model;
and a substep S52 of screening out an effective failure solution by using the application times and/or the user feedback information.
In the embodiment of the application, linear regression can be performed by adopting the application times and the user feedback information, and the effectiveness of the fault solution is scored according to the intersection of the application times and the user feedback information.
Generally, the effectiveness score is higher when a fault solution with a higher application frequency and a higher user evaluation (e.g., higher score) is selected.
The embodiment of the application further improves the accuracy of the fault model and the fault solution mode by training the fault model and screening the effectiveness of the fault solution mode, thereby further improving the efficiency of fault processing.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
Referring to fig. 2, a block diagram of a cloud platform-based fault handling apparatus according to an embodiment of the present disclosure is shown, which may specifically include the following modules:
the data acquisition module 201 is configured to acquire, when a fault processing request of a target object is received, first target data of a terminal where the target object is located and/or second target data of a server where the target object is located;
a fault model matching module 202, configured to match a preset fault model with the first target data and/or the second target data, where the fault model is associated with one or more fault solutions;
a target failure solution selecting module 203, configured to select a target failure solution from the one or more failure solutions when matching is successful;
and a target failure solution output module 204, configured to output the target failure solution.
In a preferred embodiment of the present application, the first target data may include terminal environment data and/or target object test data;
the data acquisition module 201 may include the following sub-modules:
the type information query submodule is used for querying the type information of the target object;
the collector searching submodule is used for searching a collector corresponding to the type information;
the collector sending submodule is used for sending the collector to a terminal where the target object is located;
and the data receiving submodule is used for receiving terminal environment data returned by the collector and obtained by detecting the terminal and/or target object test data obtained by detecting the target object.
In another preferred embodiment of the present application, the second target data may include target object status data and/or server monitoring data;
the data acquisition module 201 may include the following sub-modules:
the example information query submodule is used for querying example information of the target object;
the user information searching submodule is used for searching the user information corresponding to the example information;
the state data query submodule is used for querying the state data of the target object corresponding to the user information;
and/or the presence of a gas in the gas,
and the server side monitoring data extraction submodule is used for extracting server side monitoring data obtained by monitoring the server side where the target object is located.
In another preferred embodiment of the present application, the fault model may include one or more reference detection data, and one or more reference combinatorial relationships;
the fault model matching module 202 may include the following sub-modules:
a reference detection data matching sub-module, configured to determine whether the first target data and/or the second target data match the reference detection data;
a combination relation matching sub-module, configured to determine whether a combination relation of the first target data and/or the second target data that is matched with the reference detection data matches with the one or more reference combination relations; if yes, calling a matching judgment sub-module;
and the matching judgment sub-module is used for judging that the first target data and/or the second target data are matched with a preset fault model.
In a preferred embodiment of the present application, the first target data may include a terminal detection item and a first numerical value, the second target data may include a service detection item and a second numerical value, and the reference detection data may include a reference detection item and a reference numerical value range;
the reference detection data matching sub-module may include the following units:
the reference detection item searching unit is used for searching a reference detection item matched with the terminal detection item and/or the service detection item;
a reference numerical value range judgment unit that judges whether the first numerical value or the second numerical value is within the reference numerical value range; if yes, calling a first judging unit, and if not, calling a second judging unit;
a first judgment unit configured to judge that the first target data and/or the second target data match the reference detection data;
a second determination unit configured to determine that the first target data and/or the second target data do not match the reference detection data.
In a preferred embodiment of the present application, the apparatus may further include the following modules:
the fault model training module is used for training a fault model by adopting the first target data and/or the second target data;
and the effectiveness screening module is used for carrying out effectiveness screening on one or more failure solutions associated with the failure model.
In a preferred embodiment of the present application, the fault model training module may include the following sub-modules:
the matching data searching submodule is used for searching the first target data and/or the second target data matched with the reference detection item;
the noise data filtering sub-module is used for filtering the noise data from the matched first target data and/or second target data;
and the reference value range adjusting sub-module is used for adjusting the reference value range of the reference detection item by adopting the first target data and/or the second target data with the noise data filtered.
In a preferred example of the embodiment of the present application, the noise data filtering sub-module may include the following units:
the average value calculating unit is used for calculating the average value of the matched first target data and/or second target data;
a first variance calculating unit configured to calculate a first variance of the first target data and/or the second target data using the average value;
and the data filtering unit is used for filtering the first target data and/or the second target data with the difference value larger than the first variance from the average value.
In a preferred example of the embodiment of the present application, the reference value range adjustment submodule may include the following units:
a second variance calculating unit for calculating a second variance of the first target data and/or the second target data from which the noise data is filtered;
and the variance adjusting unit is used for adjusting the reference value range of the reference detection item according to the second variance.
In a preferred embodiment of the present application, the validity screening module may include the following sub-modules:
the obtaining submodule is used for obtaining the application times and/or user feedback information of one or more fault solving modes related to the fault model;
and the screening submodule is used for screening out an effective fault solution by adopting the application times and/or the user feedback information.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In a typical configuration, the computer device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (fransitory media), such as modulated data signals and carrier waves.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The cloud platform-based fault processing method and the cloud platform-based fault processing device provided by the application are introduced in detail, specific examples are applied in the description to explain the principle and the implementation mode of the application, and the description of the above embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (16)
1. A fault handling method based on a cloud platform is characterized by comprising the following steps:
when a fault processing request of a target object is received, acquiring first target data of a terminal where the target object is located and/or second target data of a server where the target object is located;
matching the first target data and/or the second target data with a preset fault model, wherein the fault model is associated with one or more fault solving modes; the fault model comprises one or more reference detection data and one or more reference combination relations;
when the matching is successful, selecting a target failure solution from the one or more failure solutions;
outputting the target failure solution;
training the fault model using the first target data and/or the second target data;
performing effectiveness screening on one or more failure solutions associated with the failure model; the fault solution with the lowest effectiveness is put into a garbage warehouse, and if the fault solution with the lowest effectiveness is not selected any more within preset time, the fault solution with the lowest effectiveness is deleted;
the step of matching the first target data and/or the second target data with a preset fault model comprises:
judging whether the first target data and/or the second target data are matched with the reference detection data;
judging whether the combination relation of the first target data and/or the second target data matched with the reference detection data is matched with the one or more reference combination relations; and if so, judging that the first target data and/or the second target data are matched with a preset fault model.
2. The method of claim 1, wherein the first target data comprises terminal environment data and/or target object test data;
the step of acquiring first target data of a terminal where the target object is located and/or second target data of a server side where the target object is located comprises the following steps:
inquiring the type information of the target object;
searching a collector corresponding to the type information;
sending the collector to a terminal where the target object is located;
and receiving terminal environment data returned by the collector and obtained by detecting the terminal, and/or detecting the target object to obtain target object test data.
3. The method according to claim 1, wherein the second target data comprises target object status data and/or server monitoring data;
the step of acquiring first target data of a terminal where the target object is located and/or second target data of a server side where the target object is located comprises the following steps:
querying instance information of the target object;
searching user information corresponding to the example information;
inquiring target object state data corresponding to the user information;
and/or the presence of a gas in the gas,
and extracting server monitoring data obtained by monitoring the server where the target object is located.
4. The method of claim 1, wherein the first target data comprises a terminal detection item and a first numerical value, the second target data comprises a service detection item and a second numerical value, and the reference detection data comprises a reference detection item and a reference numerical value range;
the step of determining whether the first target data and/or the second target data match the reference detection data comprises:
searching a reference detection item matched with the terminal detection item and/or the service detection item;
determining whether the first value or the second value is within the reference value range;
if so, judging that the first target data and/or the second target data are matched with the reference detection data;
if not, judging that the first target data and/or the second target data are not matched with the reference detection data.
5. The method of claim 1, wherein the step of training a fault model using the first target data and/or the second target data comprises:
searching first target data and/or second target data matched with the reference detection item;
filtering noise data from the matched first target data and/or second target data;
and adjusting the reference value range of the reference detection item by using the first target data and/or the second target data with the noise data filtered.
6. The method of claim 5, wherein the step of filtering noise data from the matched first target data and/or second target data comprises:
calculating an average value of the matched first target data and/or second target data;
calculating a first variance of the first target data and/or the second target data using the mean;
first and/or second target data differing by more than the first variance from the mean are filtered out.
7. The method of claim 5, wherein the step of adjusting the reference value range of the reference detection term using the first target data filtered from the noise data and/or the second target data comprises:
calculating a second variance of the first target data and/or the second target data filtered from the noise data;
and adjusting the reference value range of the reference detection item according to the second variance.
8. The method of claim 1, wherein the step of screening for effectiveness of the one or more fault resolution methods associated with the fault model comprises:
acquiring the application times and/or user feedback information of one or more fault solving modes related to the fault model;
and screening out an effective fault solution by adopting the application times and/or the user feedback information.
9. A fault handling device based on a cloud platform is characterized by comprising:
the data acquisition module is used for acquiring first target data of a terminal where a target object is located and/or second target data of a server side where the target object is located when a fault processing request of the target object is received;
the fault model matching module is used for matching the first target data and/or the second target data with a preset fault model, and the fault model is associated with one or more fault solving modes; the fault model comprises one or more reference detection data and one or more reference combination relations;
the target fault solution selection module is used for selecting a target fault solution from the one or more fault solutions when the matching is successful;
a target failure solution output module for outputting the target failure solution;
the fault model training module is used for training a fault model by adopting the first target data and/or the second target data;
the effectiveness screening module is used for carrying out effectiveness screening on one or more failure solutions associated with the failure model; the fault solution with the lowest effectiveness is put into a garbage warehouse, and if the fault solution with the lowest effectiveness is not selected any more within preset time, the fault solution with the lowest effectiveness is deleted;
the fault model matching module includes:
a reference detection data matching sub-module, configured to determine whether the first target data and/or the second target data match the reference detection data;
a combination relation matching sub-module, configured to determine whether a combination relation of the first target data and/or the second target data that is matched with the reference detection data matches with the one or more reference combination relations; if yes, calling a matching judgment sub-module;
and the matching judgment sub-module is used for judging that the first target data and/or the second target data are matched with a preset fault model.
10. The apparatus of claim 9, wherein the first target data comprises terminal environment data and/or target object test data;
the data acquisition module comprises:
the type information query submodule is used for querying the type information of the target object;
the collector searching submodule is used for searching a collector corresponding to the type information;
the collector sending submodule is used for sending the collector to a terminal where the target object is located;
and the data receiving submodule is used for receiving terminal environment data returned by the collector and obtained by detecting the terminal and/or target object test data obtained by detecting the target object.
11. The apparatus of claim 9, wherein the second target data comprises target object status data and/or server monitoring data;
the data acquisition module comprises:
the example information query submodule is used for querying example information of the target object;
the user information searching submodule is used for searching the user information corresponding to the example information;
the state data query submodule is used for querying the state data of the target object corresponding to the user information;
and/or the presence of a gas in the gas,
and the server side monitoring data extraction submodule is used for extracting server side monitoring data obtained by monitoring the server side where the target object is located.
12. The apparatus of claim 9, wherein the first target data comprises a terminal detection item and a first value, the second target data comprises a service detection item and a second value, and the reference detection data comprises a reference detection item and a reference value range;
the reference detection data matching sub-module includes:
the reference detection item searching unit is used for searching a reference detection item matched with the terminal detection item and/or the service detection item;
a reference numerical value range judgment unit that judges whether the first numerical value or the second numerical value is within the reference numerical value range; if yes, calling a first judging unit, and if not, calling a second judging unit;
a first judgment unit configured to judge that the first target data and/or the second target data match the reference detection data;
a second determination unit configured to determine that the first target data and/or the second target data do not match the reference detection data.
13. The apparatus of claim 9, wherein the fault model training module comprises:
the matching data searching submodule is used for searching the first target data and/or the second target data matched with the reference detection item;
the noise data filtering sub-module is used for filtering the noise data from the matched first target data and/or second target data;
and the reference value range adjusting sub-module is used for adjusting the reference value range of the reference detection item by adopting the first target data and/or the second target data with the noise data filtered.
14. The apparatus of claim 13, wherein the noise data filtering sub-module comprises:
the average value calculating unit is used for calculating the average value of the matched first target data and/or second target data;
a first variance calculating unit configured to calculate a first variance of the first target data and/or the second target data using the average value;
and the data filtering unit is used for filtering the first target data and/or the second target data with the difference value larger than the first variance from the average value.
15. The apparatus of claim 13, wherein the reference value range adjustment submodule comprises:
a second variance calculating unit for calculating a second variance of the first target data and/or the second target data from which the noise data is filtered;
and the variance adjusting unit is used for adjusting the reference value range of the reference detection item according to the second variance.
16. The apparatus of claim 9, wherein the effectiveness screening module comprises:
the obtaining submodule is used for obtaining the application times and/or user feedback information of one or more fault solving modes related to the fault model;
and the screening submodule is used for screening out an effective fault solution by adopting the application times and/or the user feedback information.
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| PCT/CN2016/087463 WO2017005117A1 (en) | 2015-07-09 | 2016-06-28 | Cloud platform-based fault handling method and device |
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| CN108763039B (en) * | 2018-04-02 | 2021-09-21 | 创新先进技术有限公司 | Service fault simulation method, device and equipment |
| CN108769047A (en) * | 2018-06-06 | 2018-11-06 | 厦门华厦学院 | A kind of big data risk monitoring system |
| CN108874968B (en) * | 2018-06-07 | 2023-04-18 | 平安科技（深圳）有限公司 | Risk management data processing method and device, computer equipment and storage medium |
| CN108965049B (en) * | 2018-06-28 | 2021-04-09 | 深信服科技股份有限公司 | Method, device, system and storage medium for providing cluster exception solution |
| CN109284200A (en) * | 2018-09-04 | 2019-01-29 | 深圳市宝德计算机系统有限公司 | Server exception processing method, equipment and processor |
| CN111859047A (en) * | 2019-04-23 | 2020-10-30 | 华为技术有限公司 | Fault solving method and device |
| CN109976318B (en) * | 2019-04-28 | 2021-07-02 | 郑州万特电气股份有限公司 | Internet-based electric energy metering fault diagnosis and troubleshooting expert system |
| CN110704225B (en) * | 2019-09-18 | 2024-08-23 | 平安科技（深圳）有限公司 | Monitoring method, monitoring device, electronic equipment and computer readable storage medium |
| CN111431733B (en) * | 2020-02-20 | 2021-06-22 | 拉扎斯网络科技（上海）有限公司 | Service alarm coverage information evaluation method and device |
| CN112383435B (en) * | 2020-11-17 | 2022-03-29 | 珠海大横琴科技发展有限公司 | Fault processing method and device |
| CN113411204B (en) * | 2021-05-17 | 2023-05-02 | 吴志伟 | Method and device for detecting facility fault of telecommunication access network and computer storage medium |
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