CN109840690A - A kind of artificial intelligence electric power first-aid system and method based on big data - Google Patents
A kind of artificial intelligence electric power first-aid system and method based on big data Download PDFInfo
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Abstract
The present invention relates to a kind of artificial intelligence electric power first-aid system and method based on big data, it is characterized in that, it includes: data information acquisition module, acquires power equipment, troublshooting, weather information, traffic, recovery vehicle track, repairing processing, service evaluation data in real time；Data platform builds module, builds Hadoop big data platform using MapReduce, Hive, HDFS technology；Learn optimization module, seek rule and knowledge from data, realizes that fault picture identification, speech recognition, fault type is studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, provides accurate basic information for intelligent decision；Decision provides module, is based on electric power first-aid full-service scene Recognition and prediction, by whole process application voice assistant, provides active decision for repair personnel and reminds service, liberate repair personnel's both hands.
Description
Technical field
The invention belongs to big data applied technical fields, and in particular to a kind of artificial intelligence electric power first-aid based on big data
System and method.
Background technique
Power failure guarantee there are mainly two types of mode in the prior art:
The first: being reported for repairment by 95598 service calls:
Client carries out troublshooting by 95598 phones, and 95598 client service centers accept client's demand, formed repairing work order and under
It is sent to power supply company's processing, power supply company's distribution repairing class foreground seat personnel is studied and judged by failure, distributes repairing by region and appoint
Business to breakdown service, repair personnel verifies and confirms the position of fault, by site inspection, judge failure cause after implement repairing, repair
Feedback repairs information step by step after the completion of task.
Second, by being reported for repairment on the APP line such as palm electric power:
Client is automatically positioned customer trouble position, fills in connection by the self-service carry out troublshootings of APP such as palm electric power, APP
Reporting fault after mode and failure-description, system background receive the repairing information of client, it is nearest will to repair work order by staff
Be dispatched to repair personnel, repair personnel by site inspection, judge failure cause after implement repairing, repair process main time section
Point, such as service handling time, node send between working hour, reach situ time, the troubleshooting deadline can actively push away to client
It send.
Mode is reported for repairment compared to 95598 traditional phones, has reported mode for repairment using the APP of " internet+electric service "
It substantially increases power failure and reports service level for repairment, but asked in electric power first-aid efficiency and customer service experience there are still many
Topic:
1. convenience is poor.Client needs to install APP, APP troublshooting function can be used in register account number rear, but power failure report
It repairs and function that non-customer is commonly used, installation APP expends time and mobile phone EMS memory, client, which exists, contradicts psychology, cumbersome behaviour
Constrain service experience.
2. interactivity is poor.Report mode on existing line for repairment, usually simple timing node information push, client cannot be straight
It sees and understands the information such as breakdown repair overall process progress, scene rush to repair situation, telegram in reply scheduled time, client waits the anxiety feelings of telegram in reply
Thread still has.
3. intelligent low.Report mode on existing line for repairment, only provide and simply report function for repairment, lack to it is a large amount of report for repairment data,
Power equipment data carry out depth and excavate application, insufficient to the service supporting capacity of electric power first-aid personnel, it is difficult to fundamentally mention
High first-aid repair efficiency.
This is in place of the deficiencies in the prior art.
Therefore, in view of the above-mentioned drawbacks in the prior art, provide and design a kind of artificial intelligence electric power based on big data and rob
Repair system and method；To solve drawbacks described above in the prior art, it is necessary.
Summary of the invention
It is an object of the present invention in view of the above-mentioned drawbacks of the prior art, provide design it is a kind of based on big data
Artificial intelligence electric power first-aid system and method, to solve the above technical problems.
To achieve the above object, the present invention provides following technical scheme:
A kind of artificial intelligence electric power first-aid system based on big data, which is characterized in that it includes:
Data information acquisition module acquires power equipment, troublshooting, weather information, traffic, recovery vehicle rail in real time
Mark, repairing processing, service evaluation data；
Data platform builds module, builds Hadoop big data platform using MapReduce, Hive, HDFS technology；
Learn optimization module, is based on big data platform, is passed with random forest, decision tree, GBDT, convolutional neural networks, level
Return neural network machine study and deep learning algorithm, seek rule and knowledge from data, realizes fault picture identification, voice
Identification, fault type are studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, are provided accurately for intelligent decision
Basic information；
Decision provides module, electric power first-aid full-service scene Recognition and prediction is based on, by whole process application voice assistant, to rob
It repairs personnel active decision is provided and reminds service, liberates repair personnel's both hands, its is made more efficient to put into breakdown repair work
In work, the speed-raising of repairing business whole process, optimization are realized.
Preferably, the decision offer module includes:
Small routine quickly reports unit for repairment, and Client handset opens wechat scanning small routine two dimensional code or direct search enters failure report
Small routine is repaired, small routine is automatically positioned Customer Location, and client fills in selection failure-description, uploads failure photo, fills in correspondent party
Formula, which can be completed, to be reported for repairment.It is installed without downloading, it is convenient and efficient without registration.
Preferably, the decision offer module includes:
Big data intelligence worksheet processing unit, the abort situation reported according to client, failure-description, in conjunction with recovery vehicle position, meteorology
Information, traffic, repair personnel's professional ability, in way work order quantity, the data of service evaluation, determined by machine learning foundation
Plan model, automatic repairing work order of assigning is to most suitable, i.e., arrival fault in-situ is most fast, handling failure is most fast, repairing service is optimal
Repair personnel, make optimizing decision for each repairing work order, maximize reduction manual allocation and repair work order time and repairing
Personnel reach situ time；
It is identified according to business scenario, actively initiates voice prompting, confirmation repairing work order to repair personnel, and broadcast to repair personnel
Report the essential information, abort situation and failure-description of client for repairment.By using intelligent sound assistant in repairing overall process, liberation is robbed
Personnel's both hands are repaired, repair personnel is helped efficiently to complete repairing task.Information casting+voice command mode is introduced, maximization is simplified
Operating procedure improves interactive efficiency.For time, place, event recognition scene, repair personnel's need are predicted according to scene clue
It asks, actively initiates interactive voice.The monitoring of recovery vehicle hypervelocity and prompting, traveling Dangerous Area are reminded, driving behavior assessment, repairing
Site safety risk prompting etc..
Preferably, the decision offer module includes:
Position of fault safety navigation unit, according to time series forecasting business scenario as a result, actively asking whether that opening safety leads
Boat sends repair personnel's mobile phone for path navigation automatically after obtaining repair personnel's confirmation.
In driving process, safe driving active warning is carried out to recovery vehicle hypervelocity, Dangerous Area, congested link, and right
Driving behavior is assessed.Repair personnel reports client's specifying information for repairment if you need to inquire, can seek advice to big data platform voice.Reach
After fault in-situ, according to the lower business scenario of repair personnel track automatic identification, whether voice prompting repair personnel reaches failure
Scene, repair personnel only need voice to confirm, whole process is not necessarily to operating handset, liberate both hands, it is ensured that whole driving safety.
Preferably, the decision offer module includes:
Failure cause intelligently studies and judges unit, and intelligent recognition repairs business scenario, and voice prompting repair personnel shoots fault in-situ and shines
Piece, equipment photo, together with the failure photo that client uploads, by image recognition technology asset of equipments, customer profile information, and with
Historical failure image comparison, studies and judges fault type and reason, actively informs repair personnel by voice.
Preferably, the decision offer module includes:
Security risk active warning unit, it is by big data analysis, such failure is main after repair personnel confirms failure cause
Security risk voice prompting repair personnel, confirms with repair personnel one by one step by step.
Preferably, the decision offer module includes:
Rush Repair Scheme intelligent decision unit identifies that repairing scene, application decision tree algorithm are known from electric power first-aid according to time series
Know the best Rush Repair Scheme of intelligent Matching in library, to repair personnel's voice broadcast, repair personnel can pass through according to site disposal situation
The novel viewpoint of this repairing of voice feedback realizes the renewal of knowledge according to feedback result to Rush Repair Scheme iteration optimization.
Preferably, it includes: system application natural language processing (NLP) and deep learning that the decision, which provides module,
(Deep Learning) unit, during repairing, repair personnel's information that work order is assigned to, repair personnel track, field failure
The timing node information of picture, the information of failure cause and the confirmation of repair personnel's voice, intelligent customer service all can be with the shape of chat
Formula is pushed to client, and client is very clear to repairing process.
Moreover, system also applies geography fence technology, repairs data according to known scheduled outage and fault outage,
Automatic setting power supply interrupted district and the virtual fence for repairing region, when having, when new client reports for repairment in fence, repairing work order is carried out
Intelligence merges, and is directly that pushes customer repairs progress by intelligent customer service.
Preferably, the decision offer module includes:
Service satisfactory evaluation unit is repaired, after repairing, intelligent customer service initiates evaluation inquiry to client, prompts user from repairing
Personnel reach situ time, attitude, repairing quality etc. and carry out evaluation marking.Service scoring is on the one hand for repairing
Personnel repair business evaluation, and the feature on the other hand assigned as work order provides data for intelligent work order assignment, optimizes the following work
Single distribution.
The artificial intelligence electric power first-aid method based on big data that the present invention also provides a kind of, which is characterized in that including following
Step:
S1: the step of data information acquisition, power equipment, troublshooting, weather information, traffic, breakdown van are acquired in real time
Track, repairing processing, service evaluation data；
S2: the step of data platform is built builds Hadoop big data platform using MapReduce, Hive, HDFS technology；
S3: the step of study optimizes is based on big data platform, with random forest, decision tree, GBDT, convolutional neural networks, layer
Secondary recurrent neural network machine learning and deep learning algorithm, seek from data rule and knowledge, realize fault picture identification,
Speech recognition, fault type are studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, are provided for intelligent decision
Accurate basic information；
S4: the step of decision provides is based on electric power first-aid full-service scene Recognition and prediction, is helped by whole process application voice
Hand provides active decision for repair personnel and reminds service, liberates repair personnel's both hands, its is made more efficient to put into failure
It repairs in work, realizes the speed-raising of repairing business whole process, optimization.
The beneficial effects of the present invention are,
First, breakdown repair procedure links are compressed, intelligence substitution manual operation reduces the same of breakdown repair worksheet time
When, optimal decision is made from the overall situation.
Second, reasonable disposition repairs resource, first is that system applies big data+machine learning framework, passes through work order intelligence
It studies and judges, merges the work order of same fault point, reduce failure and repeat worksheet processing；Second is that the application of geography fence technology, is actively visitor
Family provides outage information push, reduces client's double faults and reports for repairment, improves the repairing level of resources utilization；Third is that system is intelligent
Change adjustment worksheet processing algorithm, reduce recovery vehicle empty driving, makes to repair maximum resource utilization.
Third improves breakdown repair efficiency, first is that assigning by reasonable work order and vehicle scheduling, reduces arrival failure
Situ time；Second is that big data+machine learning provides strong Batch Processing support for repair personnel, based on repairing knowledge
Business " additional " capability improving in library further reduces the fault in-situ processing time, promotes repair personnel's first-aid repair efficiency；Third is that
The whole process application of intelligent sound assistant is provided entirely with scene Recognition+intelligently guiding+speech interaction mode for repair personnel
New service, substantially increases first-aid repair efficiency.
4th, increase the affine viscosity of client, customer service robot chat type visualization repairing information push function enables client
Enough in all directions, overall process understands troublshooting and initiates from work order to the full-service process being disposed, and further client's distance, is promoted
Service experience and satisfaction.
In addition, design principle of the present invention is reliable, structure is simple, has very extensive application prospect.
It can be seen that compared with prior art, the present invention have substantive distinguishing features outstanding and it is significant ground it is progressive, implementation
Beneficial effect be also obvious.
Detailed description of the invention
Fig. 1 is a kind of structural block diagram of artificial intelligence electric power first-aid system based on big data provided by the invention.
Fig. 2 is a kind of flow chart of artificial intelligence electric power first-aid method based on big data provided by the invention.
Specific embodiment
The present invention will be described in detail with reference to the accompanying drawing and by specific embodiment, and following embodiment is to the present invention
Explanation, and the invention is not limited to following implementation.
Embodiment 1:
As shown in Figure 1, a kind of artificial intelligence electric power first-aid system based on big data provided by the invention, which is characterized in that it
Include:
Data information acquisition module acquires power equipment, troublshooting, weather information, traffic, recovery vehicle rail in real time
Mark, repairing processing, service evaluation data；
Data platform builds module, builds Hadoop big data platform using MapReduce, Hive, HDFS technology；
Learn optimization module, is based on big data platform, is passed with random forest, decision tree, GBDT, convolutional neural networks, level
Return neural network machine study and deep learning algorithm, seek rule and knowledge from data, realizes fault picture identification, voice
Identification, fault type are studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, are provided accurately for intelligent decision
Basic information；
Decision provides module, electric power first-aid full-service scene Recognition and prediction is based on, by whole process application voice assistant, to rob
It repairs personnel active decision is provided and reminds service, liberates repair personnel's both hands, its is made more efficient to put into breakdown repair work
In work, the speed-raising of repairing business whole process, optimization are realized.
In the present embodiment, the decision provides module and includes:
Small routine quickly reports unit for repairment, and Client handset opens wechat scanning small routine two dimensional code or direct search enters failure report
Small routine is repaired, small routine is automatically positioned Customer Location, and client fills in selection failure-description, uploads failure photo, fills in correspondent party
Formula, which can be completed, to be reported for repairment.It is installed without downloading, it is convenient and efficient without registration.
In the present embodiment, the decision provides module and includes:
Big data intelligence worksheet processing unit, the abort situation reported according to client, failure-description, in conjunction with recovery vehicle position, meteorology
Information, traffic, repair personnel's professional ability, in way work order quantity, the data of service evaluation, determined by machine learning foundation
Plan model, automatic repairing work order of assigning is to most suitable, i.e., arrival fault in-situ is most fast, handling failure is most fast, repairing service is optimal
Repair personnel, make optimizing decision for each repairing work order, maximize reduction manual allocation and repair work order time and repairing
Personnel reach situ time；
It is identified according to business scenario, actively initiates voice prompting, confirmation repairing work order to repair personnel, and broadcast to repair personnel
Report the essential information, abort situation and failure-description of client for repairment.By using intelligent sound assistant in repairing overall process, liberation is robbed
Personnel's both hands are repaired, repair personnel is helped efficiently to complete repairing task.Information casting+voice command mode is introduced, maximization is simplified
Operating procedure improves interactive efficiency.For time, place, event recognition scene, repair personnel's need are predicted according to scene clue
It asks, actively initiates interactive voice.The monitoring of recovery vehicle hypervelocity and prompting, traveling Dangerous Area are reminded, driving behavior assessment, repairing
Site safety risk prompting etc..
In the present embodiment, the decision provides module and includes:
Position of fault safety navigation unit, according to time series forecasting business scenario as a result, actively asking whether that opening safety leads
Boat sends repair personnel's mobile phone for path navigation automatically after obtaining repair personnel's confirmation.
In driving process, safe driving active warning is carried out to recovery vehicle hypervelocity, Dangerous Area, congested link, and right
Driving behavior is assessed.Repair personnel reports client's specifying information for repairment if you need to inquire, can seek advice to big data platform voice.Reach
After fault in-situ, according to the lower business scenario of repair personnel track automatic identification, whether voice prompting repair personnel reaches failure
Scene, repair personnel only need voice to confirm, whole process is not necessarily to operating handset, liberate both hands, it is ensured that whole driving safety.
In the present embodiment, the decision provides module and includes:
Failure cause intelligently studies and judges unit, and intelligent recognition repairs business scenario, and voice prompting repair personnel shoots fault in-situ and shines
Piece, equipment photo, together with the failure photo that client uploads, by image recognition technology asset of equipments, customer profile information, and with
Historical failure image comparison, studies and judges fault type and reason, actively informs repair personnel by voice.
In the present embodiment, the decision provides module and includes:
Security risk active warning unit, it is by big data analysis, such failure is main after repair personnel confirms failure cause
Security risk voice prompting repair personnel, confirms with repair personnel one by one step by step.
In the present embodiment, the decision provides module and includes:
Rush Repair Scheme intelligent decision unit identifies that repairing scene, application decision tree algorithm are known from electric power first-aid according to time series
Know the best Rush Repair Scheme of intelligent Matching in library, to repair personnel's voice broadcast, repair personnel can pass through according to site disposal situation
The novel viewpoint of this repairing of voice feedback realizes the renewal of knowledge according to feedback result to Rush Repair Scheme iteration optimization.
In the present embodiment, it includes: system application natural language processing (NLP) and deep learning that the decision, which provides module,
(Deep Learning) unit, during repairing, repair personnel's information that work order is assigned to, repair personnel track, field failure
The timing node information of picture, the information of failure cause and the confirmation of repair personnel's voice, intelligent customer service all can be with the shape of chat
Formula is pushed to client, and client is very clear to repairing process.
Moreover, system also applies geography fence technology, repairs data according to known scheduled outage and fault outage,
Automatic setting power supply interrupted district and the virtual fence for repairing region, when having, when new client reports for repairment in fence, repairing work order is carried out
Intelligence merges, and is directly that pushes customer repairs progress by intelligent customer service.
In the present embodiment, the decision provides module and includes:
Service satisfactory evaluation unit is repaired, after repairing, intelligent customer service initiates evaluation inquiry to client, prompts user from repairing
Personnel reach situ time, attitude, repairing quality etc. and carry out evaluation marking.Service scoring is on the one hand for repairing
Personnel repair business evaluation, and the feature on the other hand assigned as work order provides data for intelligent work order assignment, optimizes the following work
Single distribution.
Embodiment 2:
As shown in Fig. 2, a kind of artificial intelligence electric power first-aid method based on big data provided by the invention, which is characterized in that packet
Include following steps:
S1: the step of data information acquisition, power equipment, troublshooting, weather information, traffic, breakdown van are acquired in real time
Track, repairing processing, service evaluation data；
S2: the step of data platform is built builds Hadoop big data platform using MapReduce, Hive, HDFS technology；
S3: the step of study optimizes is based on big data platform, with random forest, decision tree, GBDT, convolutional neural networks, layer
Secondary recurrent neural network machine learning and deep learning algorithm, seek from data rule and knowledge, realize fault picture identification,
Speech recognition, fault type are studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, are provided for intelligent decision
Accurate basic information；
S4: the step of decision provides is based on electric power first-aid full-service scene Recognition and prediction, is helped by whole process application voice
Hand provides active decision for repair personnel and reminds service, liberates repair personnel's both hands, its is made more efficient to put into failure
It repairs in work, realizes the speed-raising of repairing business whole process, optimization.
Disclosed above is only the preferred embodiment of the present invention, but the present invention is not limited to this, any this field
What technical staff can think does not have creative variation, and without departing from the principles of the present invention made by several improvement and
Retouching, should all be within the scope of the present invention.
Claims (10)
1. a kind of artificial intelligence electric power first-aid system based on big data, which is characterized in that it includes:
Data information acquisition module acquires power equipment, troublshooting, weather information, traffic, recovery vehicle rail in real time
Mark, repairing processing, service evaluation data；
Data platform builds module, builds Hadoop big data platform using MapReduce, Hive, HDFS technology；
Learn optimization module, is based on big data platform, is passed with random forest, decision tree, GBDT, convolutional neural networks, level
Return neural network machine study and deep learning algorithm, seek rule and knowledge from data, realizes fault picture identification, voice
Identification, fault type are studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, are provided accurately for intelligent decision
Basic information；
Decision provides module, electric power first-aid full-service scene Recognition and prediction is based on, by whole process application voice assistant, to rob
It repairs personnel active decision is provided and reminds service, liberates repair personnel's both hands.
2. a kind of artificial intelligence electric power first-aid system based on big data according to claim 1, which is characterized in that described
Decision provides module
Small routine quickly reports unit for repairment, and Client handset opens wechat scanning small routine two dimensional code or direct search enters failure report
Small routine is repaired, small routine is automatically positioned Customer Location, and client fills in selection failure-description, uploads failure photo, fills in correspondent party
Formula, which can be completed, to be reported for repairment.
3. a kind of artificial intelligence electric power first-aid system based on big data according to claim 1 or 2, which is characterized in that
The decision provides module
Big data intelligence worksheet processing unit, the abort situation reported according to client, failure-description, in conjunction with recovery vehicle position, meteorology
Information, traffic, repair personnel's professional ability, in way work order quantity, the data of service evaluation, determined by machine learning foundation
Plan model, automatic repairing work order of assigning is to arrival fault in-situ is most fast, handling failure is most fast, repairing services optimal repairing people
Member；And voice prompting actively is initiated to repair personnel, work order is repaired in confirmation, and reports the basic letter of client for repairment to repair personnel
Breath, abort situation and failure-description.
4. a kind of artificial intelligence electric power first-aid system based on big data according to claim 3, which is characterized in that described
Decision provides module
Position of fault safety navigation unit, according to time series forecasting business scenario as a result, actively asking whether that opening safety leads
Boat sends repair personnel's mobile phone for path navigation automatically after obtaining repair personnel's confirmation；To recovery vehicle hypervelocity, dangerous road
Section, congested link carry out safe driving active warning, and assess driving behavior；Repair personnel reports client for repairment if you need to inquire
Specifying information can be seeked advice to big data platform voice；After reaching fault in-situ, according to the lower industry of repair personnel track automatic identification
Whether business scene, voice prompting repair personnel reach fault in-situ, and repair personnel only needs voice to confirm.
5. a kind of artificial intelligence electric power first-aid system based on big data according to claim 4, which is characterized in that described
Decision provides module
Failure cause intelligently studies and judges unit, and intelligent recognition repairs business scenario, and voice prompting repair personnel shoots fault in-situ and shines
Piece, equipment photo, together with the failure photo that client uploads, by image recognition technology asset of equipments, customer profile information, and with
Historical failure image comparison, studies and judges fault type and reason, actively informs repair personnel by voice.
6. a kind of artificial intelligence electric power first-aid system based on big data according to claim 5, which is characterized in that described
Decision provides module
Security risk active warning unit, it is by big data analysis, such failure is main after repair personnel confirms failure cause
Security risk voice prompting repair personnel, confirms with repair personnel one by one step by step.
7. a kind of artificial intelligence electric power first-aid system based on big data according to claim 6, which is characterized in that, institute
Stating decision offer module includes:
Rush Repair Scheme intelligent decision unit identifies that repairing scene, application decision tree algorithm are known from electric power first-aid according to time series
Know the best Rush Repair Scheme of intelligent Matching in library, to repair personnel's voice broadcast, repair personnel can pass through according to site disposal situation
The novel viewpoint of this repairing of voice feedback, according to feedback result to Rush Repair Scheme iteration optimization.
8. a kind of artificial intelligence electric power first-aid system based on big data according to claim 7, which is characterized in that described
It includes: system application natural language processing and deep learning unit that decision, which provides module, and during repairing, what work order was assigned to is robbed
Repair personal information, repair personnel track, field failure picture, the information of failure cause and repair personnel's voice confirmation when
Intermediate node information, intelligent customer service can all be pushed to client in the form of chat.
9. a kind of artificial intelligence electric power first-aid system based on big data according to claim 8, which is characterized in that described
Decision provides module
Service satisfactory evaluation unit is repaired, after repairing, intelligent customer service initiates evaluation inquiry to client, prompts user from repairing
Personnel reach situ time, attitude, repairing quality etc. and carry out evaluation marking.
10. a kind of artificial intelligence electric power first-aid method based on big data, which comprises the following steps:
S1: the step of data information acquisition, power equipment, troublshooting, weather information, traffic, breakdown van are acquired in real time
Track, repairing processing, service evaluation data；
S2: the step of data platform is built builds Hadoop big data platform using MapReduce, Hive, HDFS technology；
S3: the step of study optimizes is based on big data platform, with random forest, decision tree, GBDT, convolutional neural networks, layer
Secondary recurrent neural network machine learning and deep learning algorithm, seek from data rule and knowledge, realize fault picture identification,
Speech recognition, fault type are studied and judged, future malfunction amount is predicted, intelligent worksheet processing, ETA estimate basic function, are provided for intelligent decision
Accurate basic information；
S4: the step of decision provides is based on electric power first-aid full-service scene Recognition and prediction, is helped by whole process application voice
Hand provides active decision for repair personnel and reminds service, liberates repair personnel's both hands.
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Cited By (26)
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