CN107526666A - Alarm method, system, device and electronic equipment based on deep learning - Google Patents
Alarm method, system, device and electronic equipment based on deep learning Download PDFInfo
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- CN107526666A CN107526666A CN201710580182.8A CN201710580182A CN107526666A CN 107526666 A CN107526666 A CN 107526666A CN 201710580182 A CN201710580182 A CN 201710580182A CN 107526666 A CN107526666 A CN 107526666A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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Abstract
This application discloses the alarm method based on deep learning, system, device and electronic equipment.Methods described includes：History service data are obtained, the history service data reflection is without the regular traffic scene alarmed and/or the abnormal traffic scene that need to be alarmed；Deep learning is carried out using the history service data, obtains appraising model of alarming；The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；According to the estimation result, decide whether to alarm.
Description
Technical field
The application is related to computer software technical field, more particularly to the alarm method based on deep learning, system, device
And electronic equipment.
Background technology
System alarm is that modern IT enterprises are used to quickly find and solve the effective mechanism of service exception.
In the prior art, the alarm rule that usual human configuration is fixed, and according to these rule analysis daily records, once day
Alarm threshold value in the result triggering rule of will analysis, system will send warning message, to notify person skilled to solve
Certainly problem.
Based on prior art, it would be desirable to more intelligent alarm scheme.
The content of the invention
The embodiment of the present application provides the alarm method based on deep learning, system, device and electronic equipment, to solve
Following technical problem：Based on prior art, it would be desirable to more intelligent alarm scheme.
In order to solve the above technical problems, what the embodiment of the present application was realized in：
A kind of alarm method based on deep learning that the embodiment of the present application provides, including：
History service data are obtained, the regular traffic scene and/or need to report that the history service data reflection need not alarm
Alert abnormal traffic scene；
Deep learning is carried out using the history service data, obtains appraising model of alarming；
The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
According to the estimation result, decide whether to alarm.
A kind of warning system based on deep learning that the embodiment of the present application provides, including：Data Analysis Platform, depth
Practise platform, alarm platform；
The Data Analysis Platform obtains history service data, normal industry of the history service data reflection without alarm
Business scene and/or the abnormal traffic scene that need to be alarmed；
The history service data that the deep learning platform is obtained using the Data Analysis Platform carry out depth
Practise, obtain appraising model of alarming, and the business datum input alarm appraising model to be evaluated is handled, estimated
Calculate result；
The estimation result that the alarm platform obtains according to the deep learning platform, decides whether to alarm.
A kind of warning device based on deep learning that the embodiment of the present application provides, including：
Acquisition module, obtain history service data, regular traffic scene of the history service data reflection without alarm
And/or the abnormal traffic scene that need to be alarmed；
Study module, deep learning is carried out using the history service data, obtains appraising model of alarming；
Estimation block, the business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
Alarm module, according to the estimation result, decide whether to alarm.
The a kind of electronic equipment that the embodiment of the present application provides, including：
At least one processor；And
The memory being connected with least one processor communication；Wherein,
The memory storage has can be by the instruction of at least one computing device, and the instruction is by described at least one
Individual computing device, so that at least one processor can：
History service data are obtained, the regular traffic scene and/or need to report that the history service data reflection need not alarm
Alert abnormal traffic scene；
Deep learning is carried out using the history service data, obtains appraising model of alarming；
The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
According to the estimation result, decide whether to alarm.
Above-mentioned at least one technical scheme that the embodiment of the present application uses can reach following beneficial effect：Based on depth
The alarm appraising model that acquistion is arrived, it can intelligently know regular traffic scene and/or abnormal traffic scene, advantageously reduce wrong report
It is alert.
Brief description of the drawings
, below will be to embodiment or existing in order to illustrate more clearly of the embodiment of the present application or technical scheme of the prior art
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments described in application, for those of ordinary skill in the art, do not paying the premise of creative labor
Under, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of schematic flow sheet for alarm method based on deep learning that the embodiment of the present application provides；
Fig. 2 is that a kind of structure for warning system based on deep learning corresponding to Fig. 1 that the embodiment of the present application provides is shown
It is intended to；
Fig. 3 is that a kind of work for warning system based on deep learning corresponding to Fig. 1 that the embodiment of the present application provides is former
Manage schematic diagram；
Fig. 4 is a kind of working-flow schematic diagram corresponding to Fig. 3 that the embodiment of the present application provides；
Fig. 5 is that a kind of structure for warning device based on deep learning corresponding to Fig. 1 that the embodiment of the present application provides is shown
It is intended to.
Embodiment
The embodiment of the present application provides the alarm method based on deep learning, system, device and electronic equipment.
In order that those skilled in the art more fully understand the technical scheme in the application, it is real below in conjunction with the application
The accompanying drawing in example is applied, the technical scheme in the embodiment of the present application is clearly and completely described, it is clear that described implementation
Example only some embodiments of the present application, rather than whole embodiments.It is common based on the embodiment in the application, this area
The every other embodiment that technical staff is obtained under the premise of creative work is not made, it should all belong to the application protection
Scope.
In order to make it easy to understand, the core concept of the scheme of the application is illustrated：The method for introducing deep learning is carried out
The design of warning system, can be with the ability of imparting system intelligent Understanding business scenario, so as to advantageously reduce false alarm；Not only such as
This, with adding up for data volume, the success rate of deep learning will gradually rise.
Below based on above core concept, the scheme of the application is described in detail.
Fig. 1 is a kind of schematic flow sheet for alarm method based on deep learning that the embodiment of the present application provides.The flow
Possible executive agent includes but is not limited to can be as server or the following equipment of terminal：Personal computer, medium-sized calculating
Machine, computer cluster, mobile phone, tablet personal computer, intelligent wearable device, vehicle device etc..
Flow in Fig. 1 may comprise steps of：
S102：Obtain history service data, history service data reflection without alarm regular traffic scene and/or
The abnormal traffic scene that need to be alarmed.
In the embodiment of the present application, history service data can be daily record, such as, business diary, using daily record etc., daily record
Typically non-structural data；History service data can also be the structured data in Service Database, such as, business datum table,
Index etc..
In the embodiment of the present application, regular traffic scene is without alarm, and abnormal traffic scene needs to alarm, if for just
Normal business has entered alarm, then belongs to false alarm.History service data can be analyzed by artificial or machine mode, with
Determine the corresponding relation between history service data and regular traffic scene or abnormal traffic scene.
S104：Deep learning is carried out using the history service data, obtains appraising model of alarming.
In the embodiment of the present application, the alarm appraising model obtained using deep learning, the business to input can be passed through
Data are estimated, whether belong to regular traffic scene according to the business datum of estimation result intelligently identified input, either
It is no to belong to abnormal traffic scene, and then, can be using the output for appraising model of alarming as the foundation for currently whether needing to alarm.
Estimation result can be alarm appraising model output or alarm appraising model output before centre
Status data, such as, data caused by the hidden layer of neutral net.
The embodiment of the present application is not limited the representation of estimation result, for example can be numerical value, vector, boolean
Value or character string etc..
S106：The business datum input alarm appraising model to be evaluated is handled, obtains estimation result.
In the embodiment of the present application, business datum to be evaluated can be the new caused business for needing to carry out alarm monitoring
Data；Business datum to be evaluated can also be：Alarm decision-making was carried out by other schemes (such as prior art), still
Because whether the uncertain alarm result of decision is correct, it is therefore desirable to the business datum further verified.
S108：According to the estimation result, decide whether to alarm.
In the embodiment of the present application, when decision will alarm, specific actuation of an alarm can be by the execution of the flow in Fig. 1
Main body performs, and can also be performed by another main body.
The executive agent of action involved by Fig. 1 can be same equipment or same program or distinct device or
Distinct program.For example step S102 executive agent is Data Analysis Platform, step S104, S106 executive agent is depth
Learning platform, step S108 executive agent is alarm platform；For another example, the executive agent of each step of the flow in Fig. 1 is equal
For same distributed type assemblies；Etc..
By Fig. 1 method, the alarm appraising model obtained based on deep learning, regular traffic scene can be intelligently known
And/or abnormal traffic scene, advantageously reduce false alarm.
Further, in the case where alarm appraising model is practically applicable into intelligent alarm, can continue while practicality
Current alarm appraising model is trained, further improved so as to advantageously allow the accuracy of alarm appraising model,
It is more preferable compared to relatively-stationary alarm rule growth in the prior art.
Method based on Fig. 1, the embodiment of the present application additionally provide some specific embodiments of this method, and extension side
Case, it is illustrated below.
In the embodiment of the present application, it is described to carry out deep learning using the history service data for step S104, obtain
To alarm appraising model, can specifically include：Data cleansing processing is carried out to the history service data, obtains carrying label
Training data, the label show that its corresponding training data belongs to the regular traffic scene or the abnormal traffic field
Scape；Specified deep neural network is trained using the training data, obtains appraising model of alarming.
Training method in the preceding paragraph belongs to Training, in actual applications, can also be by the way of unsupervised
It is trained.
Further, it is described that specified deep neural network is trained using the training data, it can specifically wrap
Include：By the training data input deep neural network specified carries out feature extraction, regression model calculates, loss function
Contrast, model adjustment.Usually, training process is that iteration is carried out, until training convergence, and then obtain appraising model of alarming.
The scheme of the application can depart from prior art and be independently operated, in this case, described to incite somebody to action for step S102
The business datum input alarm appraising model to be evaluated carries out before processing, can also carry out：Business is monitored；According to
It is described to monitor obtained data, the business datum to be evaluated is obtained, for inputting at the alarm appraising model
Reason.
The scheme of the application can also coordinate prior art to use.Such as the alarm result of decision to prior art
Verified, to filter out the invalid alarm result of decision, so as to be advantageous to prevent false alarm.In this case, for step
Rapid S102, it is described that the business datum input alarm appraising model to be evaluated is subjected to before processing, it can also carry out：Acquisition is treated
The alarm result of decision of verification；Business datum corresponding to the alarm result of decision is obtained, as the business number to be evaluated
According to, for input it is described alarm appraising model handled；Accordingly for step S108, it is described decide whether alarm before,
It can also carry out：According to the estimation result, determine whether the alarm result of decision is null result；If so, it can not enter
Row alarm.
In the embodiment of the present application, the history industry for carrying out deep learning is used in order to obtain preferable results of learning, in Fig. 1
Data of being engaged in are often magnanimity, can be based on distributed type assemblies, deep learning are carried out using history service data, with efficiently complete
Into learning process.
Based on same thinking, the embodiment of the present application additionally provides a kind of alarm based on deep learning corresponding to Fig. 1
System, Fig. 2 are the structural representation of the system.
The system includes：Data Analysis Platform 201, deep learning platform 202, alarm platform 203；
The Data Analysis Platform 201 obtains history service data, and the history service data reflection is without alarming just
Normal business scenario and/or the abnormal traffic scene that need to be alarmed；
The history service data that the deep learning platform 202 is obtained using the Data Analysis Platform 201 are carried out
Deep learning, appraising model of alarming is obtained, and the business datum input alarm appraising model to be evaluated is handled,
Obtain estimation result；
The estimation result that the alarm platform 203 obtains according to the deep learning platform 202, decides whether to report
It is alert.
Alternatively, the history service data include the structured data in daily record and/or Service Database.
Alternatively, after the Data Analysis Platform 201 obtains history service data, also the history service data are carried out
Data cleansing is handled, and obtains carrying the training data of label, and the label shows that its corresponding training data belongs to described normal
Business scenario or the abnormal traffic scene；
The history service data that the deep learning platform 202 is obtained using the Data Analysis Platform 201 are carried out
Deep learning, appraising model of alarming is obtained, is specifically included：
The deep learning platform 202 is trained using the training data to specified deep neural network, is obtained
Alarm appraising model.
Alternatively, the deep learning platform 202 is instructed using the training data to specified deep neural network
Practice, specifically include：
The training data input deep neural network specified is carried out feature and carried by the deep learning platform 202
Take, regression model calculates, loss function contrasts, model adjustment.
Alternatively, the deep learning platform 202 carries out deep learning using the history service data, specifically includes：
The deep learning platform 202 is based on distributed type assemblies, and deep learning is carried out using the history service data,
In this case, deep learning platform 202 is specifically as follows distributed deep learning platform.
Alternatively, the Data Analysis Platform 201 inputs business datum to be evaluated in the deep learning platform 202
The alarm appraising model carries out before processing, and business is monitored, and according to the data for monitoring and obtaining, waits to estimate described in acquisition
The business datum of calculation, handled for inputting the alarm appraising model.
Alternatively, the Data Analysis Platform 201 inputs business datum to be evaluated in the deep learning platform 202
The alarm appraising model carries out before processing, obtains the alarm result of decision to be verified, and it is corresponding to obtain the alarm result of decision
Business datum, as the business datum to be evaluated, handled for inputting the alarm appraising model；
Before the alarm platform 203 decides whether alarm, always according to the estimation result, the alarm result of decision is determined
Whether it is null result.
Based on to the explanation of the system, the embodiment of the present application additionally provides the principle schematic of the system above, such as Fig. 3 institutes
Show.The part of the system, and the critical workflow being related to of each several part has been shown in particular in Fig. 3, is illustrated with reference to Fig. 4.
Fig. 4 is a kind of working-flow schematic diagram corresponding to Fig. 3 that the embodiment of the present application provides.
Workflow in Fig. 4 mainly includes the following steps that：
S402：Data Analysis Platform cleans to business diary, using the structured data in daily record and Service Database
Afterwards, training data is obtained；
S404：Distributed deep learning platform carries out deep learning using training data, obtains appraising model of alarming, its
In, learning process includes：The actions such as feature extraction, regression model calculate, loss function contrasts and model adjusts；
S406：Distributed deep learning platform also using obtained alarm appraising model, enters to business datum to be evaluated
Row processing, obtains estimation result；
S408：Alarm platform decides whether to alarm according to estimation result.
Based on same thinking, the embodiment of the present application additionally provides a kind of alarm based on deep learning corresponding to Fig. 1
Device, Fig. 5 are the structural representation of the device, and the device can be located in Fig. 1 on the executive agent of flow, including：
Acquisition module 501, obtain history service data, regular traffic field of the history service data reflection without alarm
Scape and/or the abnormal traffic scene that need to be alarmed；
Study module 502, deep learning is carried out using the history service data, obtains appraising model of alarming；
Estimation block 503, the business datum input alarm appraising model to be evaluated is handled, obtains estimation knot
Fruit；
Alarm module 504, according to the estimation result, decide whether to alarm.
Based on same thinking, the embodiment of the present application additionally provides corresponding a kind of electronic equipment, including：
At least one processor；And
The memory being connected with least one processor communication；Wherein,
The memory storage has can be by the instruction of at least one computing device, and the instruction is by described at least one
Individual computing device, so that at least one processor can：
History service data are obtained, the regular traffic scene and/or need to report that the history service data reflection need not alarm
Alert abnormal traffic scene；
Deep learning is carried out using the history service data, obtains appraising model of alarming；
The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
According to the estimation result, decide whether to alarm.
Based on same thinking, the embodiment of the present application additionally provides a kind of corresponding nonvolatile computer storage media,
Computer executable instructions are stored with, the computer executable instructions are arranged to：
History service data are obtained, the regular traffic scene and/or need to report that the history service data reflection need not alarm
Alert abnormal traffic scene；
Deep learning is carried out using the history service data, obtains appraising model of alarming；
The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
According to the estimation result, decide whether to alarm.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the action recorded in detail in the claims or step can be come according to different from the order in embodiment
Perform and still can realize desired result.In addition, the process described in the accompanying drawings not necessarily require show it is specific suitable
Sequence or consecutive order could realize desired result.In some embodiments, multitasking and parallel processing be also can
With or be probably favourable.
Each embodiment in this specification is described by the way of progressive, identical similar portion between each embodiment
Divide mutually referring to what each embodiment stressed is the difference with other embodiment.Especially for system,
For device, electronic equipment, nonvolatile computer storage media embodiment, because it is substantially similar to embodiment of the method, institute
With the fairly simple of description, the relevent part can refer to the partial explaination of embodiments of method.
System, device, electronic equipment, nonvolatile computer storage media and the method that the embodiment of the present application provides are pair
Answer, therefore, system, device, electronic equipment, nonvolatile computer storage media also have similar with corresponding method beneficial
Technique effect, due to the advantageous effects of method being described in detail above, therefore, repeat no more here correspondingly
System, device, electronic equipment, the advantageous effects of nonvolatile computer storage media.
In the 1990s, the improvement for a technology can clearly distinguish be on hardware improvement (for example,
Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So
And as the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit.
Designer nearly all obtains corresponding hardware circuit by the way that improved method flow is programmed into hardware circuit.Cause
This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, PLD
(Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate
Array, FPGA)) it is exactly such a integrated circuit, its logic function is determined by user to device programming.By designer
Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, without asking chip maker to design and make
Special IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " patrols
Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development,
And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language
(Hardware Description Language, HDL), and HDL is also not only a kind of, but have many kinds, such as ABEL
(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description
Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL
(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby
Hardware Description Language) etc., VHDL (Very-High-Speed are most generally used at present
Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also should
This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages,
Can is readily available the hardware circuit for realizing the logical method flow.
Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing
Device and storage can by the computer of the computer readable program code (such as software or firmware) of (micro-) computing device
Read medium, gate, switch, application specific integrated circuit (Application Specific Integrated Circuit,
ASIC), the form of programmable logic controller (PLC) and embedded microcontroller, the example of controller include but is not limited to following microcontroller
Device：ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, are deposited
Memory controller is also implemented as a part for the control logic of memory.It is also known in the art that except with
Pure computer readable program code mode realized beyond controller, completely can be by the way that method and step is carried out into programming in logic to make
Controller is obtained in the form of gate, switch, application specific integrated circuit, programmable logic controller (PLC) and embedded microcontroller etc. to come in fact
Existing identical function.Therefore this controller is considered a kind of hardware component, and various for realizing to including in it
The device of function can also be considered as the structure in hardware component.Or even, can be by for realizing that the device of various functions regards
For that not only can be the software module of implementation method but also can be the structure in hardware component.
System, device, module or the unit that above-described embodiment illustrates, it can specifically be realized by computer chip or entity,
Or realized by the product with certain function.One kind typically realizes that equipment is computer.Specifically, computer for example may be used
Think personal computer, laptop computer, cell phone, camera phone, smart phone, personal digital assistant, media play
It is any in device, navigation equipment, electronic mail equipment, game console, tablet PC, wearable device or these equipment
The combination of equipment.
For convenience of description, it is divided into various units during description apparatus above with function to describe respectively.Certainly, this is being implemented
The function of each unit can be realized in same or multiple softwares and/or hardware during application.
It should be understood by those skilled in the art that, embodiments of the invention can be provided as method, system or computer program
Product.Therefore, the embodiment of the present invention can use complete hardware embodiment, complete software embodiment or combine software and hardware side
The form of the embodiment in face.Moreover, the embodiment of the present invention can use wherein includes computer available programs in one or more
Implement in the computer-usable storage medium (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.) of code
The form of computer program product.
Present specification is with reference to method according to embodiments of the present invention, equipment (system) and computer program product
Flow chart and/or block diagram describe.It should be understood that can be by every in computer program instructions implementation process figure and/or block diagram
One flow and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computers can be provided
Processor of the programmed instruction to all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices
To produce a machine so that produce use by the instruction of computer or the computing device of other programmable data processing devices
In the dress for realizing the function of being specified in one flow of flow chart or multiple flows and/or one square frame of block diagram or multiple square frames
Put.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which produces, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that counted
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, so as in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
In a typical configuration, computing device includes one or more processors (CPU), input/output interface, net
Network interface and internal memory.
Internal memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only storage (ROM) or flash memory (flash RAM).Internal memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer-readable instruction, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), moved
State random access memory (DRAM), other kinds of random access memory (RAM), read-only storage (ROM), electric erasable
Programmable read only memory (EEPROM), fast flash memory bank or other memory techniques, read-only optical disc read-only storage (CD-ROM),
Digital versatile disc (DVD) or other optical storages, magnetic cassette tape, the storage of tape magnetic rigid disk or other magnetic storage apparatus
Or any other non-transmission medium, the information that can be accessed by a computing device available for storage.Define, calculate according to herein
Machine computer-readable recording medium does not include temporary computer readable media (transitory media), such as data-signal and carrier wave of modulation.
It should also be noted that, term " comprising ", "comprising" or its any other variant are intended to nonexcludability
Comprising so that process, method, commodity or equipment including a series of elements not only include those key elements, but also wrapping
Include the other element being not expressly set out, or also include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that wanted including described
Other identical element also be present in the process of element, method, commodity or equipment.
The application can be described in the general context of computer executable instructions, such as program
Module.Usually, program module includes performing particular task or realizes routine, program, object, the group of particular abstract data type
Part, data structure etc..The application can also be put into practice in a distributed computing environment, in these DCEs, by
Task is performed and connected remote processing devices by communication network.In a distributed computing environment, program module can be with
In the local and remote computer-readable storage medium including storage device.
Each embodiment in this specification is described by the way of progressive, identical similar portion between each embodiment
Divide mutually referring to what each embodiment stressed is the difference with other embodiment.It is real especially for system
For applying example, because it is substantially similar to embodiment of the method, so description is fairly simple, related part is referring to embodiment of the method
Part explanation.
Embodiments herein is the foregoing is only, is not limited to the application.For those skilled in the art
For, the application can have various modifications and variations.All any modifications made within spirit herein and principle, it is equal
Replace, improve etc., it should be included within the scope of claims hereof.
Claims (16)
1. a kind of alarm method based on deep learning, including：
Obtain history service data, history service data reflection without alarm regular traffic scene and/or need to alarm
Abnormal traffic scene；
Deep learning is carried out using the history service data, obtains appraising model of alarming；
The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
According to the estimation result, decide whether to alarm.
2. the method as described in claim 1, the history service data include the structure number in daily record and/or Service Database
According to.
3. the method as described in claim 1, described to carry out deep learning using the history service data, alarm estimation is obtained
Model, specifically include：
Data cleansing processing is carried out to the history service data, obtains carrying the training data of label, the label shows it
Corresponding training data belongs to the regular traffic scene or the abnormal traffic scene；
Specified deep neural network is trained using the training data, obtains appraising model of alarming.
4. method as claimed in claim 3, described that specified deep neural network is trained using the training data,
Specifically include：
By the training data input deep neural network specified carries out feature extraction, regression model calculates, loss letter
Number contrast, model adjustment.
5. the method as described in claim 1, described to carry out deep learning using the history service data, specifically include：
Based on distributed type assemblies, deep learning is carried out using the history service data.
6. the method as described in claim 1, described by the business datum input alarm appraising model to be evaluated
Before reason, methods described also includes：
Business is monitored；
According to the data for monitoring and obtaining, the business datum to be evaluated is obtained, for inputting the alarm estimation mould
Type is handled.
7. the method as described in claim 1, described by the business datum input alarm appraising model to be evaluated
Before reason, methods described also includes：
Obtain the alarm result of decision to be verified；
Business datum corresponding to the alarm result of decision is obtained, as the business datum to be evaluated, for inputting institute
Alarm appraising model is stated to be handled；
It is described decide whether alarm before, methods described also includes：
According to the estimation result, determine whether the alarm result of decision is null result.
8. a kind of warning system based on deep learning, including：Data Analysis Platform, deep learning platform, alarm platform；
The Data Analysis Platform obtains history service data, regular traffic field of the history service data reflection without alarm
Scape and/or the abnormal traffic scene that need to be alarmed；
The history service data that the deep learning platform is obtained using the Data Analysis Platform carry out deep learning, obtain
Handled to alarm appraising model, and by the business datum input alarm appraising model to be evaluated, obtain estimation knot
Fruit；
The estimation result that the alarm platform obtains according to the deep learning platform, decides whether to alarm.
9. system as claimed in claim 8, the history service data include the structure number in daily record and/or Service Database
According to.
10. system as claimed in claim 8, after the Data Analysis Platform obtains history service data, also to the history
Business datum carries out data cleansing processing, obtains carrying the training data of label, and the label shows its corresponding training data
Belong to the regular traffic scene or the abnormal traffic scene；
The history service data that the deep learning platform is obtained using the Data Analysis Platform carry out deep learning, obtain
To alarm appraising model, specifically include：
The deep learning platform is trained using the training data to specified deep neural network, obtains alarm estimation
Model.
11. system as claimed in claim 10, the deep learning platform is using the training data to specified depth god
It is trained, specifically includes through network：
The training data input deep neural network specified is carried out feature extraction, returned by the deep learning platform
Model calculates, loss function contrasts, model adjustment.
12. system as claimed in claim 8, the deep learning platform carries out depth using the history service data
Practise, specifically include：
The deep learning platform is based on distributed type assemblies, and deep learning is carried out using the history service data.
13. system as claimed in claim 8, the Data Analysis Platform is in the deep learning platform by business to be evaluated
Appraising model of being alarmed described in data input carries out before processing, and business is monitored, and according to the data for monitoring and obtaining, obtains
The business datum to be evaluated, handled for inputting the alarm appraising model.
14. system as claimed in claim 8, the Data Analysis Platform is in the deep learning platform by business to be evaluated
Appraising model of being alarmed described in data input carries out before processing, obtains the alarm result of decision to be verified, obtains the alarm decision-making
As a result corresponding business datum, as the business datum to be evaluated, for inputting at the alarm appraising model
Reason；
Before the alarm platform decides whether alarm, always according to the estimation result, determine the alarm result of decision whether be
Null result.
15. a kind of warning device based on deep learning, including：
Acquisition module, obtains history service data, history service data reflection without alarm regular traffic scene and/or
The abnormal traffic scene that need to be alarmed；
Study module, deep learning is carried out using the history service data, obtains appraising model of alarming；
Estimation block, the business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
Alarm module, according to the estimation result, decide whether to alarm.
16. a kind of electronic equipment, including：
At least one processor；And
The memory being connected with least one processor communication；Wherein,
The memory storage has can be by the instruction of at least one computing device, and the instruction is by least one place
Manage device to perform, so that at least one processor can：
Obtain history service data, history service data reflection without alarm regular traffic scene and/or need to alarm
Abnormal traffic scene；
Deep learning is carried out using the history service data, obtains appraising model of alarming；
The business datum input alarm appraising model to be evaluated is handled, obtains estimation result；
According to the estimation result, decide whether to alarm.
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