CN109408320A - Abnormality eliminating method, device, computer equipment and storage medium are developed in front end - Google Patents
Abnormality eliminating method, device, computer equipment and storage medium are developed in front end Download PDFInfo
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Abstract
This application involves the neural network in artificial intelligence field, a kind of front end exploitation abnormality eliminating method, device, computer equipment and storage medium are provided.The described method includes: judging whether front end development status is just in development status, when front end development status is just in development status, corresponding goal task mark and target developing person mark are obtained, corresponding front end development task is identified to goal task according to goal task mark and target developing person mark and is monitored；When front end development task occurs abnormal, obtain exception information, in the exception information processing model that exception information input has been trained, obtain output vector, it is to obtain abnormal solution according to output vector using what neural network algorithm was trained according to history exception information and corresponding solution that exception information, which handles model, and returning to target developing for abnormal solution, person identifies corresponding exploitation terminal.Front end development efficiency can be improved using this method.
Description
Technical field
This application involves field of computer technology, develop abnormality eliminating method, device, calculating more particularly to a kind of front end
Machine equipment and storage medium.
Background technique
Front end, that is, website front end part operates in the end PC, and the webpage of user's browsing is presented on the browsers such as mobile terminal.?
In the development process of front end, when developing when something goes wrong, then developer positions abnormal problem, finds abnormal cause, finds solution
Certainly scheme carries out abnormality processing, then can just continue to develop, these require developer and devote a tremendous amount of time energy,
So that developer can not be normally carried out front end exploitation, the efficiency for causing front end to be developed is substantially reduced.
Summary of the invention
Based on this, it is necessary in view of the above technical problems, provide a kind of front end exploitation that can be improved front end development efficiency
Abnormality eliminating method, device, computer equipment and storage medium.
A kind of front end exploitation abnormality eliminating method, which comprises
The goal task mark and target developing person mark, front end development task for obtaining front end development task are in and are opening
Hair-like state identifies corresponding front end development task to goal task according to goal task mark and target developing person mark and supervises
Control；
When determining that front end development task deposits when abnormal, exception information is obtained, exception information is inputted into the exception trained
In information processing model, output vector is obtained, exception information processing model makes according to history exception information and corresponding solution
It is trained to obtain with neural network algorithm；
Abnormal solution is obtained according to output vector, returning to target developing for abnormal solution, person's mark is corresponding
Develop terminal.
It is identified in one of the embodiments, in the goal task mark and target developing person for obtaining front end development task,
Front end development task is in just in development status, according to goal task mark and target developing person mark to goal task mark pair
Before the front end development task answered is monitored, further includes:
The task creation instruction that exploitation terminal is sent is received, task creation instruction carries task identification, developer's mark
With front end development status, wherein developer's mark is corresponding with task identification, and task identification is corresponding with front end development status；
Task identification, developer's mark are associated with preservation with front end development status according to task creation instruction.
In one of the embodiments, when existing exception is developed in front end, exception information is obtained, by exception information input
In trained exception information processing model, output vector is obtained, comprising:
When existing exception is developed in front end, exception information is obtained, judges exception information type；
According to exception information type obtain it is corresponding trained exception information handle model, exception information is inputted corresponding
It has trained in exception information processing model, has obtained output vector.
Exception information type includes that Web page picture type is abnormal in one of the embodiments,；It is obtained according to exception information type
Take it is corresponding trained exception information handle model, by exception information input it is corresponding trained exception information handle model in,
Obtain output vector, comprising:
When exception information type is Web page picture type exception, obtained corresponding having trained volume extremely according to Web page picture type
Product neural network model, convolutional neural networks are abnormal according to history web pages picture type and corresponding solution uses convolutional Neural
Network algorithm is trained to obtain；
By in exception information input training convolutional neural networks model, output vector is obtained.
Exception information type includes that code type is abnormal in one of the embodiments,；According to the acquisition pair of exception information type
The exception information processing model trained answered, the exception information that exception information input has been trained is handled in model, is obtained defeated
Outgoing vector, comprising:
When exception information type be code type exception when, according to code type obtain extremely it is corresponding trained multilayer feedforward mind
Through network model, multilayer feedforward neural network model is abnormal according to history codes type and corresponding solution uses multilayer feedforward
Neural network algorithm is trained to obtain；
By in exception information input Training Multilayer Feedforward Neural Networks model, output vector is obtained.
In one of the embodiments, the method also includes:
Exception information database is established, exception information database includes identification code field, exception information field and solution party
Case field；
After then obtaining exception information, further includes:
Judge the corresponding exception information type of exception information, corresponding target identifying code is obtained according to exception information type；
In exception information database lookup target identifying code, when finding consistent target identifying code, obtains target and know
The corresponding solution of other code, returns to exploitation terminal for solution.
The corresponding solution of target identifying code is obtained in one of the embodiments, and solution is returned into exploitation
After terminal, further includes:
The targeted solution selection instruction that exploitation terminal is sent is received, targeted solution pair is recorded according to selection instruction
The selection number answered；
According to the priority for selecting number setting solution；
Solution is then returned into exploitation terminal, comprising:
The priority for obtaining solution, returns to exploitation terminal for solution according to priority.
A kind of front end exploitation exception handling device, described device include:
Monitoring module, the goal task for obtaining front end development task identifies and target developing person mark, front end exploitation
Task is in just in development status, identifies corresponding front end to goal task according to goal task mark and target developing person mark
Development task is monitored；
Vector obtains module, for depositing when abnormal when determining front end development task, exception information is obtained, by exception information
It inputs in the exception information processing model trained, obtains output vector, exception information handles model according to history exception information
It is trained to obtain using neural network algorithm with corresponding solution；
Abnormal solution is returned to mesh for obtaining abnormal solution according to output vector by abnormal solution module
It marks developer and identifies corresponding exploitation terminal.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
Device performs the steps of when executing the computer program
The goal task mark and target developing person mark, front end development task for obtaining front end development task are in and are opening
Hair-like state identifies corresponding front end development task to goal task according to goal task mark and target developing person mark and supervises
Control；
When determining that front end development task deposits when abnormal, exception information is obtained, exception information is inputted into the exception trained
In information processing model, output vector is obtained, exception information processing model makes according to history exception information and corresponding solution
It is trained to obtain with neural network algorithm；
Abnormal solution is obtained according to output vector, returning to target developing for abnormal solution, person's mark is corresponding
Develop terminal.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
It is performed the steps of when row
The goal task mark and target developing person mark, front end development task for obtaining front end development task are in and are opening
Hair-like state identifies corresponding front end development task to goal task according to goal task mark and target developing person mark and supervises
Control；
When determining that front end development task deposits when abnormal, exception information is obtained, exception information is inputted into the exception trained
In information processing model, output vector is obtained, exception information processing model makes according to history exception information and corresponding solution
It is trained to obtain with neural network algorithm；
Abnormal solution is obtained according to output vector, returning to target developing for abnormal solution, person's mark is corresponding
Develop terminal.
Above-mentioned front end exploitation abnormality eliminating method, device, computer equipment and storage medium, are appointed by obtaining front end exploitation
The goal task mark and target developing person mark of business, according to goal task mark and target developing person mark to goal task mark
Know corresponding front end development task to be monitored；When determine front end development task deposit when abnormal, obtain exception information, will be abnormal
In the exception information processing model that information input has been trained, output vector is obtained, exception information handles model according to history exception
Information and corresponding solution are trained to obtain using neural network algorithm, obtain abnormal solution according to output vector,
Returning to target developing for abnormal solution, person identifies corresponding exploitation terminal, and when front end, development task is deposited when abnormal, energy
It is enough that abnormal solution is supplied to developer, it allows a developer to quickly solve exception, thus continue front end exploitation,
Improve front end development efficiency.
Detailed description of the invention
Fig. 1 is the application scenario diagram that abnormality eliminating method is developed in front end in one embodiment；
Fig. 2 is the flow diagram that abnormality eliminating method is developed in front end in one embodiment；
Fig. 3 is the flow diagram that incidence relation is established in one embodiment；
Fig. 4 is to obtain the flow diagram of output vector in one embodiment；
Fig. 5 is to obtain the flow diagram of output vector in further embodiment；
Fig. 6 is to obtain the flow diagram of output vector in another embodiment；
Fig. 7 is the structural block diagram that exception handling device is developed in front end in one embodiment；
Fig. 8 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
Abnormality eliminating method is developed in front end provided by the present application, can be applied in application environment as shown in Figure 1.Its
In, exploitation terminal 102 is communicated with server 104 by network by network.Server 104 obtains front end development task
Goal task mark and target developing person mark, front end development task are in just in development status, according to goal task mark and
Target developing person mark identifies corresponding front end development task to goal task and is monitored.When determine front end development task exist
When abnormal, exception information is obtained, in the exception information processing model that exception information input has been trained, obtains output vector, it is different
Normal information processing model is trained to obtain according to history exception information and corresponding solution using neural network algorithm.Service
Device 104 obtains abnormal solution according to output vector, and returning to target developing for abnormal solution, person identifies corresponding open
Send out terminal 102.Wherein, it is various personal computers that exploitation terminal 102, which can be, but not limited to, laptop, smart phone, is put down
Plate computer and portable wearable device, server 104 can use the clothes of the either multiple server compositions of independent server
Device cluster be engaged in realize.
In one embodiment, it as shown in Fig. 2, providing a kind of front end exploitation abnormality eliminating method, applies in this way
It is illustrated for server in Fig. 1, comprising the following steps:
S202, obtains corresponding goal task mark and target developing person mark, front end development task are in and are developing
State identifies corresponding front end development task to goal task according to goal task mark and target developing person mark and supervises
Control.
Wherein, front end development status is used to describe the state that developer carries out front end exploitation comprising is developing shape
State, halted state and exploitation completion status.Task identification pre-establishes front end development task for identifying front end development task
Corresponding relationship between task identification, developer's mark refer to the corresponding mark of developer.
Specifically, server is obtained in just in the corresponding goal task mark of the front end development task of development status and mesh
Developer's mark is marked, corresponding front end development task is identified to goal task according to goal task mark and target developing person mark
It is monitored, wherein identifying the determination front end development task to be monitored according to goal task, is identified and determined according to target developing person
The corresponding terminal of the developer to be monitored.
S204 obtains exception information, exception information input has been trained when determining that front end development task deposits when abnormal
Exception information is handled in model, obtains output vector, and exception information handles model according to history exception information and corresponding solution party
Case is trained to obtain using neural network algorithm.
Wherein, front end development task refers to the problem in the development process of front end in the presence of abnormal.Such as front end page
Data load is abnormal, page response exception and program start a leak etc..
Specifically, when determining that front end development task deposits when abnormal, server captures exception information, gets different
Obtained exception information, is input to the exception information processing model trained by normal information, and exception information handles model according to defeated
The exception information entered carries out that output vector is calculated.In one embodiment, available to multiple front end development tasks
Exception information is first filtered obtained multiple exception informations, identical Method for Filtering Abnormal Information is fallen, then again will be abnormal
Information is inputted respectively in the exception information processing model trained, and obtains output vector.
S206 obtains abnormal solution according to output vector, and returning to target developing for abnormal solution, person identifies
Corresponding exploitation terminal.
Wherein, the corresponding relationship of output vector and abnormal solution is pre-set.
Specifically, server finds the corresponding abnormal solution of output vector according to the corresponding relationship pre-set,
Then returning to target developing for obtained abnormal solution, person identifies corresponding exploitation terminal, and exploitation terminal receives exception
It is shown when solution, so that developer solves according to abnormal solution to abnormal.For example, if what is obtained is defeated
Outgoing vector is [01,15,23], then the number of solution is pre-set, according to the volume for the solution that output vector obtains
Number be 01,15 and 23, No. 01 solution, No. 15 solutions, No. 23 solutions are then returned into exploitation terminal.It can also
No. 88 solutions are then returned to exploitation terminal for [88] by the output vector to obtain.
In above-mentioned front end exploitation abnormality eliminating method, by above-mentioned technical characteristic, when front end, development task is abnormal,
Abnormal solution can be supplied to developer, allow a developer to quickly solve exception, be opened to continue front end
Hair, improves front end development efficiency.
In one embodiment, as shown in figure 3, before step 202, that is, the goal task mark of front end development task is obtained
Know and target developing person mark, front end development task are in just in development status, according to goal task mark and target developing person
Before mark is monitored the corresponding front end development task of goal task mark, further comprise the steps of:
S302, receives the task creation instruction that exploitation terminal is sent, and task creation instruction carries task identification, developer
Mark and front end development status, wherein developer's mark is corresponding with task identification, and task identification is corresponding with front end development status.
Task identification, developer's mark are associated with preservation with front end development status according to task creation instruction by S304.
Specifically, server receives exploitation terminal and develops the task creation instruction that monitoring analysis platform is sent by front end,
Task creation instruction carries task identification, developer's mark and front end development status, wherein front end development status includes just
In development status, halted state and exploitation completion status, then the front end development status that task creation instruction carries is to develop
State.Server instructs according to task creation and is associated with and is saved in data task identification, developer's mark with front end development status
In library, wherein developer's mark is corresponding with task identification, and task identification is corresponding with front end development status, i.e., each developer's mark
Know a corresponding current task identification, the corresponding current front end development status of each task identification will be closed according to corresponding
Three's association is saved in database by system.Front end development task can be pre-established, enables the server to open to front end
The development status of hair task.Such as: developer logs on to front end exploitation monitoring using developer's mark 01 by exploitation terminal
Analysis platform, in front end, exploitation monitoring analysis platform establishes incoming task mark 12 and front end development status in task interface and is positive
It in development status and clicks and establishes button, exploitation terminal receives task creation instruction, obtains developer and identifies 01, task identification
12 and front end development status be then to send task creation instruction to server, which carries exploitation just in development status
Person's mark 01, task identification 12 and front end development status are after server receives task creation instruction, to incite somebody to action just in development status
Developer's mark 01, task identification 12 are saved in database with being just associated in development status.In one embodiment, exploit person
Member can develop monitoring analysis platform by front end and be updated to front end development status.
In one embodiment, as shown in figure 4, step S204, that is, determine that exploitation is deposited when abnormal when front end, acquisition is abnormal
The exception information that exception information input has been trained is handled in model, obtains output vector by information, comprising steps of
S402 obtains exception information, judges exception information type when existing exception is developed in front end.
S404, according to exception information type obtain it is corresponding trained exception information handle model, exception information is inputted
It is corresponding to have trained in exception information processing model, obtain output vector.
Wherein, exception information type is used to reflect that the type of exception information, every one kind exception information to have corresponding instructed
Practice the exception information completed and handle model, exception information type includes at least Web page picture type exception and code type is abnormal.
Specifically, when existing exception is developed in front end, server obtains exception information, which can be server
From exploitation capture terminal, it is also possible to exploitation terminal and submits to server.Server receives exception information, first determines whether
Exception information type judges that exception information type is that Web page picture type exception or code type are abnormal, can be according to exception information
Format judge exception information type, for example text formatting is code word, and picture format is Web page picture class.Server root
According to exception information type obtain it is corresponding trained exception information handle model, by exception information input it is corresponding train extremely
In information processing model, output vector is obtained.
In the above-described embodiments, by obtaining exception information, judging exception information class when existing exception is developed in front end
Type, according to exception information type obtain it is corresponding trained exception information handle model, exception information is inputted into corresponding instructed
Practice in exception information processing model, obtains output vector, the accuracy for the scheme of being resolved can be improved.
In one embodiment, as shown in figure 5, step S404, i.e., obtain corresponding trained according to exception information type
Exception information handle model, by exception information input it is corresponding trained exception information processing model in, obtain output vector, packet
Include step:
S502, when exception information type be Web page picture type exception when, according to Web page picture type obtain extremely it is corresponding
Training convolutional neural networks model, convolutional neural networks are abnormal according to history web pages picture type and corresponding solution uses volume
Product neural network algorithm is trained to obtain.
Wherein, convolutional neural networks are a kind of feedforward neural networks, and artificial neuron can respond surrounding cells, can be with
Carry out large-scale image procossing.Convolutional neural networks include convolutional layer and pond layer.
Specifically, it when exception information type is Web page picture type exception, is obtained extremely according to Web page picture type corresponding
Training convolutional neural networks model, convolutional neural networks are made according to Web page picture class exception information and corresponding solution
It is trained with convolutional neural networks algorithm, wherein the excitation function that convolutional neural networks model is used in training
For ReLU function.
S504 obtains output vector in exception information input training convolutional neural networks model.
Specifically, obtained Web page picture type is input to extremely in the convolutional neural networks model trained, obtains mould
The output vector of type can more be accurately obtained the extremely corresponding solution of Web page picture type.
In one embodiment, as shown in fig. 6, step S404, i.e., obtain corresponding trained according to exception information type
Exception information handle model, the exception information trained of exception information input is handled in model, output vector is obtained, including
Step:
S602 obtains corresponding having trained multilayer according to code type extremely when exception information type is code type exception
BP network model, multilayer feedforward neural network model are used according to code word exception information and corresponding solution
Multilayer feedforward neural network algorithm is trained.
Wherein, multilayer feedforward neural network refers to that BP neural network, BP neural network are that one kind is inversely propagated according to error
The multilayer feedforward neural network of algorithm training.
Specifically, when server judges exception information type for code type exception, server obtains pair according to code word
The BP neural network model that should have been trained, the BP neural network model are according to history codes type exception and corresponding solution
It is trained using BP neural network algorithm.Wherein, the activation primitive that BP neural network model is used in training is S
Type function.
S604 obtains output vector in exception information input Training Multilayer Feedforward Neural Networks model.
Specifically, code characteristic value, such as key code sentence, word are extracted from obtained code type exception
Deng.Code characteristic value is input in the multilayer feedforward neural network model trained, i.e. input BP neural network model
In, output vector is obtained, can be improved to obtain the accuracy of code word exception information solution.
In one embodiment, exploitation abnormality eliminating method in front end further comprises the steps of:
Exception information database is established, exception information database includes identification code field, exception information field and solution party
Case field.
Wherein, identification code is for identifying exception information type.
Specifically, exception information database is established in the server, which is used for recording exceptional information, exception information
Corresponding identification code and the corresponding solution of identification code.
After then obtaining exception information, further comprise the steps of:
Judge the corresponding exception information type of exception information, corresponding target identifying code is obtained according to exception information type.
Specifically, server judges the corresponding exception information type of exception information, according to pre- after getting exception information
Corresponding relationship between the exception information type first set and identification code obtains the corresponding target identifying code of exception information type.
In exception information database lookup target identifying code, when finding consistent target identifying code, obtains target and know
The corresponding solution of other code, returns to exploitation terminal for solution.
Specifically, server obtains target identifying code and searches the target identification in the exception information database pre-established
Code, when that can find the target identifying code, specification exception information database is stored with corresponding solution, then server
The corresponding solution of target identifying code is got, solution is returned into exploitation terminal.When not finding consistent mesh
When marking identification code, at this point, keyword is extracted from exception information for server or picture scans in a search engine, it will
Search result returns to exploitation terminal.
It in the above-described embodiments, can be straight when getting exception information by pre-establishing exception information database
It connects and searches corresponding solution in exception information database, solution is supplied to developer, can be improved exploitation
Personnel front end development efficiency.
In one embodiment, the corresponding solution of target identifying code is being obtained, solution is returned into exploitation eventually
After end, further comprise the steps of:
The targeted solution selection instruction that exploitation terminal is sent is received, targeted solution pair is recorded according to selection instruction
The selection number answered；According to the priority for selecting number setting solution.
Specifically, exception information can have corresponding multiple solutions, and solution is being returned to exploitation by server
After terminal, server, which can be, returns a variety of solutions, is also possible to a solution.Developer can select at this time
A kind of targeted solution, server receive the targeted solution selection instruction that exploitation terminal is sent, just record the target solution
Certainly the corresponding selection number of scheme adds one.Then server is according to the priority for selecting number setting solution.Developer
Often, then the priority of the solution is just high for the solution of selection.
Solution is then returned into exploitation terminal, comprising steps of
The priority for obtaining solution, returns to exploitation terminal for solution according to priority.
Specifically, when server needs to return to solution exploitation terminal, the solution party to be returned is got first
Then the high solution of priority is preferentially returned to exploitation terminal by the priority of case, can also return to priority and arrange first three
Solution, remaining scheme do not return.
In the above-described embodiments, the targeted solution selection instruction that exploitation terminal is sent is received, is remembered according to selection instruction
Record the corresponding selection number of targeted solution；According to the priority for selecting number setting solution, solution party is then obtained
Solution is returned to exploitation terminal according to priority, the accuracy of the solution of return can be improved by the priority of case.
It should be understood that although each step in the flow chart of Fig. 2-6 is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, these steps can execute in other order.Moreover, at least one in Fig. 2-6
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, the execution sequence in these sub-steps or stage is also not necessarily successively
It carries out, but can be at least part of the sub-step or stage of other steps or other steps in turn or alternately
It executes.
In one embodiment, as shown in fig. 7, providing a kind of front end exploitation exception handling device 700, comprising: monitoring
Module 702, vector obtain module 704 and abnormal solution module 706, in which:
Monitoring module 702, the goal task for obtaining front end development task identifies and target developing person mark, and front end is opened
Hair task is in just in development status, according to goal task mark and target developing person mark it is corresponding to goal task mark before
End development task is monitored.
Vector obtains module 704, for determining that acquisition exception information will be abnormal when front end development task is deposited when abnormal
In the exception information processing model that information input has been trained, output vector is obtained, exception information handles model according to history exception
Information and corresponding solution are trained to obtain using neural network algorithm.
The abnormal module 706 that solves returns to abnormal solution for obtaining abnormal solution according to output vector
Target developing person identifies corresponding exploitation terminal.
Exception handling device 700 is developed in above-mentioned front end, is monitored by monitoring module 702 to front end development task, leads to
Cross vector obtain module 704 exception information is input to trained exception information processing model in, obtain output vector, finally
Abnormal solution is obtained according to output vector by abnormal solution module 706, abnormal solution is returned into target developing
Person identifies corresponding exploitation terminal, can be abnormal when front end development task, abnormal solution can be supplied to exploitation
Person allows a developer to quickly solve exception, to continue front end exploitation, improves front end development efficiency.
In one embodiment, exception handling device 700 is developed in front end, further includes:
Command reception module, the task creation instruction sent for receiving exploitation terminal, task creation instruction, which carries, appoints
Business mark, developer's mark and front end development status, wherein developer identifies, task identification and front end corresponding with task identification
Development status is corresponding；
It is associated with preserving module, for instructing according to task creation by task identification, developer's mark and front end development status
Association saves.
In one embodiment, vector obtains module 704, comprising:
Judgment module, for obtaining exception information, judging exception information type when existing exception is developed in front end；
Model obtains module, for according to exception information type obtain it is corresponding trained exception information handle model, will
Exception information input is corresponding have been trained in exception information processing model, and output vector is obtained.
In one embodiment, model obtains module, comprising:
Convolution model obtains module, is used for when exception information type is Web page picture type exception, according to Web page picture type
Exception obtains corresponding training convolutional neural networks model, and convolutional neural networks are abnormal and corresponding according to history web pages picture type
Solution be trained to obtain using convolutional neural networks algorithm；
Convolution model output module, for being exported in exception information input training convolutional neural networks model
Vector.
In one embodiment, model obtains module, comprising:
Feed forward models obtain module, for being obtained extremely according to code type when exception information type is code type exception
Corresponding Training Multilayer Feedforward Neural Networks model, multilayer feedforward neural network model are abnormal and corresponding according to history codes type
Solution be trained to obtain using multilayer feedforward neural network algorithm；
Feed forward models output module, for obtaining in exception information input Training Multilayer Feedforward Neural Networks model
Output vector.
In one embodiment, exception handling device 700 is developed in front end, further includes:
Database module, for establishing exception information database, exception information database includes identification code field, different
Normal information field and solution Scheme field；
Then exception handling device 700 is developed in front end, further includes:
Target identifying code obtains module, for judging the corresponding exception information type of exception information, according to exception information class
Type obtains corresponding target identifying code；
Searching module, in exception information database lookup target identifying code, when finding consistent target identifying code
When, the corresponding solution of target identifying code is obtained, solution is returned into exploitation terminal.
In one embodiment, exception handling device 700 is developed in front end, further includes:
Number logging modle, the targeted solution selection instruction sent for receiving exploitation terminal, according to selection instruction
Record the corresponding selection number of targeted solution；
Priority setup module, for according to the priority for selecting number setting solution；
Then searching module, comprising:
Priority obtains module and is returned to out solution according to priority for obtaining the priority of solution
Send out terminal.
Specific restriction about front end exploitation exception handling device may refer to develop abnormality processing above for front end
The restriction of method, details are not described herein.Modules in above-mentioned front end exploitation exception handling device can be fully or partially through
Software, hardware and combinations thereof are realized.Above-mentioned each module can be embedded in the form of hardware or independently of the place in computer equipment
It manages in device, can also be stored in a software form in the memory in computer equipment, in order to which processor calls execution or more
The corresponding operation of modules.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junction
Composition can be as shown in Figure 8.The computer equipment include by system bus connect processor, memory, network interface and
Database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipment
Include non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and data
Library.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The database of machine equipment is for storing abnormal solution data.The network interface of the computer equipment is used for and external terminal
It is communicated by network connection.To realize a kind of front end exploitation abnormality eliminating method when the computer program is executed by processor.
It will be understood by those skilled in the art that structure shown in Fig. 8, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specifically computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment, including memory and processor are provided, which is stored with
Computer program, the processor perform the steps of the goal task mark for obtaining front end development task when executing computer program
Know and target developing person mark, front end development task are in just in development status, according to goal task mark and target developing person
Mark identifies corresponding front end development task to goal task and is monitored；When determining that front end development task deposits when abnormal, obtain
Exception information is taken, in the exception information processing model that exception information input has been trained, obtains output vector, exception information processing
Model is trained to obtain according to history exception information and corresponding solution using neural network algorithm；It is obtained according to output vector
To abnormal solution, returning to target developing for abnormal solution, person identifies corresponding exploitation terminal.
In one embodiment, reception exploitation terminal is also performed the steps of when processor executes computer program to send
Task creation instruction, task creation instruction carries task identification, developer mark and front end development status, wherein exploitation
Person's mark is corresponding with task identification, and task identification is corresponding with front end development status；It instructed according to task creation by task identification, opened
Originator mark is associated with preservation with front end development status.
In one embodiment, processor execute computer program when also perform the steps of when front end develop it is now different
Chang Shi obtains exception information, judges exception information type；Corresponding trained at exception information is obtained according to exception information type
Manage model, by exception information input it is corresponding trained exception information handle model in, obtain output vector.
In one embodiment, it also performs the steps of when processor executes computer program when exception information type is
When Web page picture type exception, corresponding training convolutional neural networks model, convolutional Neural are obtained according to Web page picture type extremely
Network is trained to obtain with corresponding solution according to history web pages picture type is abnormal using convolutional neural networks algorithm；It will
Exception information input in training convolutional neural networks model, obtains output vector.
In one embodiment, it also performs the steps of when processor executes computer program when exception information type is
When code type exception, corresponding Training Multilayer Feedforward Neural Networks model, multilayer feedforward nerve are obtained according to code type extremely
Network model is trained with corresponding solution using multilayer feedforward neural network algorithm according to history codes type is abnormal
It obtains；By in exception information input Training Multilayer Feedforward Neural Networks model, output vector is obtained.
In one embodiment, it is also performed the steps of when processor executes computer program and establishes exception information data
Library, exception information database include identification code field, exception information field and solve Scheme field；Then processor executes computer
It is also performed the steps of when program and judges the corresponding exception information type of exception information, corresponded to according to exception information type
Target identifying code；In exception information database lookup target identifying code, when finding consistent target identifying code, mesh is obtained
The corresponding solution of identification code is marked, solution is returned into exploitation terminal.
In one embodiment, reception exploitation terminal is also performed the steps of when processor executes computer program to send
Targeted solution selection instruction, the corresponding selection number of targeted solution is recorded according to selection instruction；It is secondary according to selecting
The priority of number setting solution；It is also performed the steps of when then processor executes computer program and obtains solution
Solution is returned to exploitation terminal according to priority by priority.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of the goal task mark and target developing person mark for obtaining front end development task when being executed by processor
Know, front end development task is in just in development status, according to goal task mark and target developing person mark to goal task mark
Know corresponding front end development task to be monitored；When determine front end development task deposit when abnormal, obtain exception information, will be abnormal
In the exception information processing model that information input has been trained, output vector is obtained, exception information handles model according to history exception
Information and corresponding solution are trained to obtain using neural network algorithm；Abnormal solution is obtained according to output vector,
Returning to target developing for abnormal solution, person identifies corresponding exploitation terminal.
In one embodiment, reception exploitation terminal hair is also performed the steps of when computer program is executed by processor
The task creation instruction sent, task creation instruction carry task identification, developer's mark and front end development status, wherein open
Originator mark is corresponding with task identification, and task identification is corresponding with front end development status；According to task creation instruction by task identification,
Developer's mark is associated with preservation with front end development status.
In one embodiment, it is also performed the steps of when computer program is executed by processor when front end is developed now
When abnormal, exception information is obtained, judges exception information type；It is obtained corresponding having trained exception information according to exception information type
Handle model, by exception information input it is corresponding trained exception information handle model in, obtain output vector.
In one embodiment, it also performs the steps of when computer program is executed by processor when exception information type
When for Web page picture type exception, corresponding training convolutional neural networks model, convolution mind are obtained according to Web page picture type extremely
It is trained to obtain using convolutional neural networks algorithm with corresponding solution according to history web pages picture type is abnormal through network；
By in exception information input training convolutional neural networks model, output vector is obtained.
In one embodiment, it also performs the steps of when computer program is executed by processor when exception information type
When for code type exception, corresponding Training Multilayer Feedforward Neural Networks model, multilayer feedforward nerve net are obtained according to code word
Network model is trained with corresponding solution using multilayer feedforward neural network algorithm according to code type is abnormal；
By in exception information input Training Multilayer Feedforward Neural Networks model, output vector is obtained.
In one embodiment, it is also performed the steps of when computer program is executed by processor and establishes exception information number
According to library, exception information database includes identification code field, exception information field and solves Scheme field；Then computer program is located
Reason device also performs the steps of when executing judges the corresponding exception information type of exception information, is obtained according to exception information type
Corresponding target identifying code；It is obtained in exception information database lookup target identifying code when finding consistent target identifying code
The corresponding solution of target identifying code is taken, solution is returned into exploitation terminal.
In one embodiment, reception exploitation terminal hair is also performed the steps of when computer program is executed by processor
The targeted solution selection instruction sent records the corresponding selection number of targeted solution according to selection instruction；According to selection
The priority of number setting solution；It is also performed the steps of when then computer program is executed by processor and obtains solution party
Solution is returned to exploitation terminal according to priority by the priority of case.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. abnormality eliminating method is developed in a kind of front end, which comprises
The goal task mark and target developing person mark, the front end development task for obtaining front end development task are in and are opening
Hair-like state identifies the corresponding front end to the goal task according to goal task mark and target developing person mark and opens
Hair task is monitored；
When determining that the front end development task deposits when abnormal, exception information is obtained, has been instructed exception information input is described
In experienced exception information processing model, output vector is obtained, the exception information processing model is according to history exception information and right
Solution is answered to be trained to obtain using neural network algorithm；
Abnormal solution is obtained according to the output vector, the abnormal solution is returned into the target developing person and is marked
Know corresponding exploitation terminal.
2. the method according to claim 1, wherein obtaining the goal task mark and target of front end development task
Developer's mark, the front end development task are in just in development status, according to goal task mark and target developing person
Before mark is monitored the corresponding front end development task of goal task mark, further includes:
The task creation instruction that exploitation terminal is sent is received, the task creation instruction carries task identification, developer's mark
With front end development status, wherein developer's mark is corresponding with the task identification, and the task identification is opened with the front end
Hair-like state is corresponding；
The task identification, developer mark are associated with guarantor with the front end development status according to task creation instruction
It deposits.
3. the method according to claim 1, wherein obtaining abnormal letter when appearance exception is developed in the front end
Breath obtains output vector in the exception information input exception information processing model trained, comprising:
When determine the front end development task deposit when abnormal, obtain exception information, judge the exception information type；
According to the exception information type obtain it is corresponding trained exception information handle model, by the exception information input institute
State it is corresponding trained exception information processing model in, obtain output vector.
4. according to the method described in claim 3, it is characterized in that, the exception information type includes that Web page picture type is abnormal；
It has trained exception information to handle model according to exception information type acquisition is corresponding, the exception information is inputted into institute
State it is corresponding trained exception information processing model in, obtain output vector, comprising:
When determining the exception information type is Web page picture type exception, obtained extremely according to the Web page picture type corresponding
Training convolutional neural networks model, the convolutional neural networks are according to history web pages picture type exception and corresponding solution
It is trained to obtain using convolutional neural networks algorithm；
By in the exception information input training convolutional neural networks model, output vector is obtained.
5. according to the method described in claim 3, it is characterized in that, the exception information type includes that code type is abnormal；
The corresponding exception information trained is obtained according to the exception information type and handles model, and the exception information is inputted
In the exception information processing model trained, output vector is obtained, comprising:
When determine the exception information type be code type exception when, according to the code type obtain extremely it is corresponding trained it is more
Layer BP network model, the multilayer feedforward neural network model is according to history codes type exception and corresponding solution
It is trained to obtain using multilayer feedforward neural network algorithm；
By in the exception information input Training Multilayer Feedforward Neural Networks model, output vector is obtained.
6. the method according to claim 1, wherein the method also includes:
Exception information database is established, the exception information database includes identification code field, exception information field and solution party
Case field；
Then after the acquisition exception information, further includes:
Judge the corresponding exception information type of the exception information, corresponding target identifying code is obtained according to exception information type；
The target identifying code described in the exception information database lookup obtains institute when finding consistent target identifying code
The corresponding solution of target identifying code is stated, the solution is returned into exploitation terminal.
7., will according to the method described in claim 6, it is characterized in that, obtain the corresponding solution of the target identifying code
The solution returns to after exploitation terminal, further includes:
The targeted solution selection instruction that exploitation terminal is sent is received, the target solution party is recorded according to the selection instruction
The corresponding selection number of case；
According to the priority of the selection number setting solution；
The solution is then returned into exploitation terminal, comprising:
The solution is returned to exploitation terminal according to the priority by the priority for obtaining the solution.
8. exception handling device is developed in a kind of front end, which is characterized in that described device includes:
Monitoring module, the goal task for obtaining front end development task identifies and target developing person mark, the front end exploitation
Task is in just in development status, according to goal task mark and target developing person mark to goal task mark pair
The front end development task answered is monitored；
Vector obtains module, for depositing when abnormal when the determining front end development task, exception information is obtained, by the exception
In the exception information processing model trained described in information input, obtain output vector, the exception information processing model according to
History exception information and corresponding solution are trained to obtain using neural network algorithm；
It is abnormal to solve module, for obtaining abnormal solution according to the output vector, the abnormal solution is returned
Corresponding exploitation terminal is identified to the target developing person.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the step of processor realizes any one of claims 1 to 7 the method when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 7 is realized when being executed by processor.
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