CN107730108A - A kind of highway fast charge network intelligence cloud service system and device - Google Patents
A kind of highway fast charge network intelligence cloud service system and device Download PDFInfo
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Abstract
A kind of highway fast charge network intelligence cloud service system and device provided by the invention, including set charging equipment on a highway and cloud server, charging equipment as follows in working method by network connection to cloud server, the cloud server：For each target charging equipment, its repair schedule is adjusted accordingly according to its current state；And for each target charging equipment that current state is failure, according to its current operational information, corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.To the charging equipment in abnormal condition, timely inspection or maintenance are carried out, and then reduces the probability of device fails, improves the utilization rate of equipment.
Description
Technical field
The present invention relates to charging equipment of electric automobile field, more specifically to a kind of highway fast charge network intelligence
Can cloud service system and device.
Background technology
New energy car, powerful is also environmentally friendly, and still " mileage anxiety " is a big problem.Need further to implement new energy
Auto Taxes preferential policy, automobile charging pile and highway fast charge net construction are promoted, promote energy-saving and environment-friendly automobile consumption.
At present, the development that country has put into effect multinomial preferential policy to stimulate and help ev industry, but charge
Problem is still an important factor for restricting Development of Electric Vehicles, by electric automobile itself charge capacity, charging interval, scope of activities
Etc. the influence of reason, building for charging equipment starts from the large-scale charging station of concentration to scattered discrete charging pile, small-sized charging
The form conversion stood, and start to explore 5 kilometers of charging circles, 10 kilometers of types of service such as circle, highway charge point that charge, radiation
Scope is increasing, greatly extends the zone of action of electric automobile, eliminates the electricity anxiety of user, promotes electric automobile
Popularization.
But traditional charging equipment of electric automobile regular visit mode, because by geographical position, operation maintenance personnel, time etc.
Multiple resources limit, and cause equipment fault, defect not to solve in time, influence charging electric vehicle, waste social resources.
The quick charge for carrying out electric automobile on a highway how is realized, and highway fast charge station is carried out effective
Management and monitoring also without related solution.
The content of the invention
In view of this, the present invention proposes a kind of highway fast charge network intelligence cloud service system and device, is intended to realize and subtracts
The probability of few device fails, improve the utilization rate purpose of equipment.
To achieve these goals, it is proposed that scheme it is as follows：
A kind of highway fast charge network intelligence cloud service system, including：
Set charging equipment on a highway and cloud server, charging equipment to pass through network connection to high in the clouds to take
Business device, the cloud server are as follows in working method：
Obtain the current operational information of all target charging equipments；
For each target charging equipment, according to its current operational information, the state evaluation mould of charging equipment is utilized
Type, obtains its current state, and the state includes normal, early warning, warning and failure；
For each target charging equipment, its repair schedule is adjusted accordingly according to its current state；
For each target charging equipment that current state is failure, according to its current operational information, maintenance is utilized
Knowledge base obtains corresponding maintenance program.
Preferably, according to its current operational information, charging equipment is utilized for each target charging equipment described
State evaluation model, after obtaining its current state, in addition to：
Each target charging equipment for current state for normal, early warning or warning, according to its current operation letter
Breath, using the deterioration analysis model of charging equipment, at the time of predicting its failure；
According to each corresponding fault message of target charging equipment, Parts Inventory Managed Solution is generated.
Preferably, before the current operational information for obtaining all target charging equipments, in addition to：
Sample data is analyzed using decision Tree algorithms, obtains the deterioration analysis model of the charging equipment.
Preferably, before the current operational information for obtaining all target charging equipments, in addition to：
Sample data is analyzed using decision Tree algorithms, obtains the state evaluation model of the charging equipment.
A kind of charging equipment of electric automobile Automotive maintenance and diagnosis management device, including：Charging equipment on a highway is set
And cloud server, charging equipment are included by network connection to cloud server, the cloud server：
Information acquisition unit, for obtaining the current operational information of all target charging equipments；
State evaluation unit, for for each target charging equipment, according to its current operational information, utilizing charging
The state evaluation model of equipment, obtains its current state, and the state includes normal, early warning, warning and failure；
Plan rescheduling unit, for for each target charging equipment, according to its current state to its repair schedule
Adjust accordingly；
Maintenance program unit, it is current according to its for each target charging equipment for current state for failure
Operation information, corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.
Preferably, described device, in addition to：
Failure predication unit, for each target charging equipment for current state for normal, early warning or warning,
According to its current operational information, using the deterioration analysis model of charging equipment, at the time of predicting its failure；
Stock-keeping unit, for according to each corresponding fault message of target charging equipment, generating Parts Inventory
Managed Solution.
Preferably, described device, in addition to：
Forecast model unit, for being analyzed using decision Tree algorithms sample data, obtain the charging and set
Standby deterioration analysis model.
Preferably, described device, in addition to：
State model unit, for being analyzed using decision Tree algorithms sample data, obtain the charging and set
Standby state evaluation model.
Compared with prior art, technical scheme has advantages below：
The highway fast charge network intelligence cloud service system and device that above-mentioned technical proposal provides, the application is for each
The target charging equipment, its repair schedule is adjusted accordingly according to its current state；And for current state it is failure
Each target charging equipment, according to its current operational information, corresponding maintenance side is obtained using Knowledge of Maintenance storehouse
Case.To the charging equipment in abnormal condition, timely inspection or maintenance are carried out, and then reduces the probability of device fails,
Improve the utilization rate of equipment.
In addition, communicated all fast charge charging equipments with cloud server in the application, it is real using Cloud Server
Now to the efficient management of the fast charge charging equipment on highway, the behaviour in service and work of fast charge charging equipment can be monitored in time
Go out corresponding emergency measure.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
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 of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Flow when Fig. 1 is a kind of highway fast charge network intelligence cloud service system work provided in an embodiment of the present invention
Figure；
Stream when Fig. 2 is another highway fast charge network intelligence cloud service system work provided in an embodiment of the present invention
Cheng Tu；
Fig. 3 is a kind of schematic diagram of highway fast charge network intelligence cloud service device provided in an embodiment of the present invention；
Fig. 4 is the schematic diagram of another highway fast charge network intelligence cloud service device provided in an embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
The embodiments of the invention provide a kind of highway fast charge network intelligence cloud service system and device, referring to Fig. 1 institutes
State, including set charging equipment on a highway and cloud server, charging equipment to pass through network connection to high in the clouds and take
Business device, the cloud server are as follows in working method：
Step S11：Obtain the current operational information of all target charging equipments；
Target charging equipment is the charging equipment set in advance for needing to monitor.Operation information includes but is not limited to following
It is one or more of：Current data, voltage data, alarm data, charged state data, charging equipment interact number with electric automobile
According to, charging equipment and interaction data of mobile terminal or server etc..
Step S12：For each target charging equipment, according to its current operational information, the shape of charging equipment is utilized
State evaluation model, obtains its current state, and the state includes normal, early warning, warning and failure.
Step S13：For each target charging equipment, its repair schedule is accordingly adjusted according to its current state
It is whole；
If the current state of target charging equipment is normal, to the repair schedule of the target charging equipment without adjusting
It is whole, i.e., normal polling period and content are performed to the target charging equipment, do not adjusted；If the current shape of target charging equipment
State is early warning, then adds the inspection mark emphatically of corresponding component in inspection content according to early warning content；If target charging is set
Standby current state is warning, then formulates ad hoc inspection and repair plan to the target charging equipment, overhauled in advance, and according to alarm
Content added in inspection content to corresponding component re-detection mark, utilize Knowledge of Maintenance storehouse generation breakdown maintenance scheme；
If the current state of target charging equipment is failure, ad hoc inspection and repair plan is formulated to the target charging equipment, immediately to it
Overhauled, and added in inspection content according to defect content corresponding component re-detection mark, and formulate corresponding spare part
Outbound list, remind maintainer to carry corresponding spare part and set out scene.
Normal inspection is triggered by timing patrol task, is periodically performed after once formulating, have the fixed execution time and
Executor, detection device scope, detection project etc..Repair schedule formulate after be assigned to corresponding executor, have specified time,
The equipment and content specified.Inspection registers inspection result after terminating, and the detection of state evaluation model is compareed by the inspection result
As a result, state evaluation model is modified.
Step S14：For each target charging equipment that current state is failure, according to its current operational information,
Corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.
With reference to the rule in Knowledge of Maintenance storehouse, using FST (Finite State Transducer) algorithm to Knowledge of Maintenance storehouse
Retrieved to obtain corresponding maintenance program.Knowledge of Maintenance storehouse, a series of rule mainly formed according to expertise and experience
Then.Knowledge of Maintenance stock puts various rules and conclusion, realizes the functions such as storage, the management of maintenance program.Knowledge of Maintenance storehouse is more
New mechanism is that the new problem that will be continuously emerged in practice is added to after summary in Knowledge of Maintenance storehouse, passes through the side of data mining
Method and expert instruct the Dynamic Maintenance for the mode implementation rule being combined.If for example, scene find maintenance program it is wrong,
Maintenance program is modified and recorded after returning, Knowledge of Maintenance storehouse is modified.
The charging equipment of electric automobile Automotive maintenance and diagnosis management method that the present embodiment provides, set for each target charging
It is standby, its repair schedule is adjusted accordingly according to its current state；And each target for current state for failure
Charging equipment, according to its current operational information, corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.To in improper
The charging equipment of state, timely inspection or maintenance are carried out, and then reduce the probability of device fails, improve the utilization of equipment
Rate.
Before the current operational information of all target charging equipments is obtained, in addition to：Using decision Tree algorithms to sample number
According to being analyzed, the state evaluation model of charging equipment is obtained.The learning process of specific state evaluation model is as follows：
Prepare the sample data under various states, described sample data item includes operational factor and the outside of charging equipment
Ambient parameter, wherein operational factor include but is not limited to following one or more：Output voltage, output current, auxiliary output
Electric current, auxiliary output voltage, active power, reactive power, temperature, battery input battery, battery input voltage, battery temperature
Deng external environment condition parameter includes following one or more：Temperature, humidity, wind-force, wind direction, rainfall, immersion, air quality
(travel fatigue quantity of PM10 and the above) etc..
The running status of charging equipment has starting state, precharging state, charged state, its deflated state, halted state, standby
State, off-mode；The malfunction of charging equipment has over-current state, overvoltage condition, over-temperature condition, overcharging state, Wu Fating
Only charged state etc..The operational factor of collection and every kind of running status and fault status is as sample data.The number of every kind of state
According to not less than 100 groups.Prepare data as two parts, portion is used as training data, and portion is as checking data.
One group of data=[output voltage, output current, aid in output current, auxiliary output voltage, active power is idle
Power, temperature, battery input battery, battery input voltage, battery temperature, etc., ' charge normal ']
One group of data=[output voltage, output current, aid in output current, auxiliary output voltage, active power is idle
Power, temperature, battery input battery, battery input voltage, battery temperature, etc., ' over-current state ']
One group of data=[output voltage, output current, aid in output current, auxiliary output voltage, active power is idle
Power, temperature, battery input battery, battery input voltage, battery temperature, etc., ' overvoltage condition ']
One group of data=[output voltage, output current, aid in output current, auxiliary output voltage, active power is idle
Power, temperature, battery input battery, battery input voltage, battery temperature, etc., ' overcharging state ']
One group of data=[output voltage, output current, aid in output current, auxiliary output voltage, active power is idle
Power, temperature, battery input battery, battery input voltage, battery temperature, etc., ' over-temperature condition ']
…………
Data normalization processing is carried out to sample data.Such as：
Calculate the average X of the output voltage in all sample datas：(output voltage values in output voltage values 1+ groups 2 in group 1
Output voltage values n)/n in output voltage values 3+ ...+group n in 2+ groups 3
Calculate the standard variance of the output voltage in all sample datas：(| magnitude of voltage 1- averages X |+| magnitude of voltage 2- averages
|+| magnitude of voltage 3- averages X |+...+| magnitude of voltage n- averages X |) evolution after/n.
Calculate characterizing magnitudes corresponding to sample data：(magnitude of voltage-average X)/standard variance+0.1
Each numerical value in sample is calculated successively (not including running status amount), obtains sample characteristics quantity set D.
Decision-tree model is produced as state evaluation model using sample characteristics quantity set D as training tuple.Detailed process
It is as follows：
(1) a node N is created.
(2) if the tuple in D is all in same class C, then and return N as leaf node, and with C flag it
(3) if candidate attribute collection is sky, N is returned to as leaf node, labeled as the more several classes of of D, the establishment of the attribute
Terminate.
(4) concentrated from D candidate attribute and find out best split criterion, be divided into suitable split point and Split Attribute.
(5) using split criterion mark node N.
(6) if all centrifugal pumps of the value for being marked as the subset of the Split Attribute, established for each value of the attribute
One branch, and marked with the value.This Split Attribute is deleted simultaneously.
(7) optimal Split Attribute is picked out, tuple is divided according to the split point of the attribute and each subregion is produced
Subtree.Using all data tuple set for meeting split point requirement in D as a subregion, if this subregion is sky, plus
One leaf is to node N, labeled as more several classes of in D；If subregion is not sky, one is added (to be made by the subtree of the partition creating
Second step is repeated to the process of the 8th step for node) arrive node N.
Using checking data as input evaluation of running status model, statistical model output result and state in checking data
Inconsistent quantity is measured, variance rate is obtained with quantity divided by total quantity, if variance rate is in the accurate of 1% model described above
Spend relatively low.If the degree of accuracy is too low, it is necessary to which reformulating split criterion carries out modeling rendering.Repetition training and verification process, and
Calculate and check variance rate.
If multipass modification come the obtained variance rate of re -training it is still very high if, it is necessary to collect training again
Sample set and checking sample, increase the scope spread all over of training sample set, it is ensured that parameter is extensive enough, increases enough unusual
Value.Repeat training and verification process, change collecting sample, the process of modification spreading coefficient, until variance rate is less than 1%.Most
What is obtained eventually meets the state evaluation model that the model of variance rate standard is the model device.
During use state evaluation model carries out corresponding charging equipment state evaluation, if output result and reality
The situation that running status mismatched or can not evaluated state occurs, then shows that the state evaluation model needs to update.Obtained with new
Service data corrigendum training is carried out to state evaluation model, obtain new state evaluation model.
The present embodiment provides another highway fast charge network intelligence cloud service system, further solves Parts Inventory and matches somebody with somebody
Put unreasonable, the problem of not reaching resource optimal allocation.Referring to described in Fig. 2, wherein step S21, S22, S23, S24 respectively with step
Rapid S11, S12, S13, S14 are identical, and this method includes：
Step S21：Obtain the current operational information of all target charging equipments；
Step S22：For each target charging equipment, according to its current operational information, the shape of charging equipment is utilized
State evaluation model, obtains its current state, and the state includes normal, early warning, warning and failure.
Step S23：For each target charging equipment, its repair schedule is accordingly adjusted according to its current state
It is whole；
Step S24：For each target charging equipment that current state is failure, according to its current operational information,
Corresponding maintenance program is obtained using Knowledge of Maintenance storehouse；
Step S25：Each target charging equipment for current state for normal, early warning or warning, works as according to it
Preceding operation information, using the deterioration analysis model of charging equipment, at the time of predicting its failure；
The operation information history of forming database of target charging equipment is gathered, using decision Tree algorithms from being subordinate in database
Relevant information is excavated, the degradation trend change of charging equipment is analyzed, derives equipment degradation trend model (i.e. inference machine).Using certainly
The inference strategy of plan tree algorithm is Modus ponens logic rules, goes out the fact that new by regular and known facts inference.
Step S26：According to each corresponding fault message of target charging equipment, Parts Inventory Managed Solution is generated.
Adjustment charging equipment is needed to correspond to the stockpile number of spare part after the completion of failure predication, for example, being for current state
Normal and early warning target charging equipment, then the procurement cycle to the target charging equipment and buying content do not adjust；For
Current state is the target charging equipment of warning, then increases the portion for purchasing respective numbers according to 70% probability according to warning content
Part, such as the target charging equipment alerted for a certain part be present is 10, then the 10*70% part is purchased in increase；It is right
In the target charging equipment that current state is failure, then according to defect content according to 90% probability increase buying respective numbers
Part.If the part needed in the repair schedule formulated number in warehouse is 0 or less than safety value, interim buying meter is formulated
Draw, and the procurement cycle of the corresponding component stored in reduction system.
Before the current operational information for obtaining all target charging equipments, in addition to：Using decision Tree algorithms to sample
Notebook data is analyzed, and obtains the deterioration analysis model of the charging equipment.The learning process of specific deterioration analysis model is same
The learning process of state evaluation model is similar, and difference is that training data is different, and the training data of deterioration analysis model is necessary
It is at least continuous two-by-two in time, and second group of equipment state and first group differ in continuous data two-by-two.Make
By the use of the service data of first group of data and the equipment state of second group of data as the input of training data, mould is established and improved
Type.
For foregoing each method embodiment, in order to be briefly described, therefore it is all expressed as to a series of combination of actions, but
It is that those skilled in the art should know, the present invention is not limited by described sequence of movement, because according to the present invention, certain
A little steps can use other orders or carry out simultaneously.
Following is apparatus of the present invention embodiment, can be used for performing present inventive concept embodiment.It is real for apparatus of the present invention
The details not disclosed in example is applied, refer to the inventive method embodiment.
The embodiment of the present invention provides a kind of highway fast charge network intelligence cloud service device, shown in Figure 3, the device
Including：Charging equipment on a highway and cloud server, charging equipment is set to pass through network connection to cloud service
Device, the cloud server include：
Information acquisition unit 11, for obtaining the current operational information of all target charging equipments；
State evaluation unit 12, for for each target charging equipment, according to its current operational information, using filling
The state evaluation model of electric equipment, obtains its current state, and the state includes normal, early warning, warning and failure；
Plan rescheduling unit 13, for for each target charging equipment, meter to be overhauled to it according to its current state
Draw and adjust accordingly；
Maintenance program unit 14, for for each target charging equipment that current state is failure, being worked as according to it
Preceding operation information, corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.
The charging equipment of electric automobile Automotive maintenance and diagnosis management device that the present embodiment provides, Plan rescheduling unit 13 is for each
The target charging equipment, its repair schedule is adjusted accordingly according to its current state；Maintenance program unit 14 is for working as
Preceding state is each target charging equipment of failure, according to its current operational information, obtained using Knowledge of Maintenance storehouse and its
Corresponding maintenance program.To the charging equipment in abnormal condition, timely inspection or maintenance are carried out, and then reduce equipment
The probability of failure, improve the utilization rate of equipment.
Preferably, the device can also include state model unit, for using decision Tree algorithms to sample data
Analyzed, obtain the state evaluation model of the charging equipment.
The embodiment of the present invention provides a kind of charging equipment of electric automobile Automotive maintenance and diagnosis management device, shown in Figure 4, the dress
Put including：Set charging equipment on a highway and cloud server, charging equipment to pass through network connection to high in the clouds to take
Business device, the cloud server include：
Information acquisition unit 11, for obtaining the current operational information of all target charging equipments；
State evaluation unit 12, for for each target charging equipment, according to its current operational information, using filling
The state evaluation model of electric equipment, obtains its current state, and the state includes normal, early warning, warning and failure；
Plan rescheduling unit 13, for for each target charging equipment, meter to be overhauled to it according to its current state
Draw and adjust accordingly；
Maintenance program unit 14, for for each target charging equipment that current state is failure, being worked as according to it
Preceding operation information, corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.
Failure predication unit 15, for being set for each target charging of the current state for normal, early warning or warning
It is standby, according to its current operational information, using the deterioration analysis model of charging equipment, at the time of predicting its failure；
Stock-keeping unit 16, for according to each corresponding fault message of target charging equipment, generating part warehouse
Deposit Managed Solution.
The device, it can also include：Forecast model unit, for being divided using decision Tree algorithms sample data
Analysis, obtains the deterioration analysis model of the charging equipment.
For device embodiment, because it essentially corresponds to embodiment of the method, so related part is real referring to method
Apply the part explanation of example.Device embodiment described above is only schematical, wherein described be used as separating component
The unit of explanation can be or may not be physically separate, can be as the part that unit is shown or can also
It is not physical location, you can with positioned at a place, or can also be distributed on multiple NEs.Can be according to reality
Need to select some or all of module therein to realize the purpose of this embodiment scheme.Those of ordinary skill in the art are not
In the case of paying creative work, you can to understand and implement.
Herein, such as first and second or the like relational terms be used merely to by an entity or operation with it is another
One entity or operation make a distinction, and not necessarily require or imply between these entities or operation any this reality be present
Relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to the bag of nonexcludability
Contain, so that process, method, article or equipment including a series of elements not only include those key elements, but also including
The other element being not expressly set out, or also include for this process, method, article or the intrinsic key element of equipment.
In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that including the key element
Process, method, other identical element also be present in article or equipment.
Each embodiment is described by the way of progressive in this specification, what each embodiment stressed be and other
The difference of embodiment, between each embodiment identical similar portion mutually referring to.
To the described above of disclosed embodiment of this invention, professional and technical personnel in the field is realized or use this
Invention.A variety of modifications to these embodiments will be apparent for those skilled in the art, institute herein
The General Principle of definition can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore,
The present invention is not intended to be limited to the embodiments shown herein, and is to fit to special with principles disclosed herein and novelty
The consistent most wide scope of point.
Claims (8)
- A kind of 1. highway fast charge network intelligence cloud service system, it is characterised in that including：Charging equipment on a highway and cloud server, charging equipment is set to pass through network connection to cloud service Device, the cloud server are as follows in working method：Obtain the current operational information of all target charging equipments；For each target charging equipment, according to its current operational information, using the state evaluation model of charging equipment, obtain To its current state, the state includes normal, early warning, warning and failure；For each target charging equipment, its repair schedule is adjusted accordingly according to its current state；For each target charging equipment that current state is failure, according to its current operational information, Knowledge of Maintenance is utilized Storehouse obtains corresponding maintenance program.
- 2. a kind of highway fast charge network intelligence cloud service system according to claim 1, it is characterised in that described For each target charging equipment, according to its current operational information, using the state evaluation model of charging equipment, it is obtained After current state, in addition to：Each target charging equipment for current state for normal, early warning or warning, according to its current operational information, profit With the deterioration analysis model of charging equipment, at the time of predicting its failure；According to each corresponding fault message of target charging equipment, Parts Inventory Managed Solution is generated.
- 3. a kind of highway fast charge network intelligence cloud service system according to claim 2, it is characterised in that described Before the current operational information for obtaining all target charging equipments, in addition to：Sample data is analyzed using decision Tree algorithms, obtains the deterioration analysis model of the charging equipment.
- 4. a kind of highway fast charge network intelligence cloud service system according to claim 1, it is characterised in that described Before the current operational information for obtaining all target charging equipments, in addition to：Sample data is analyzed using decision Tree algorithms, obtains the state evaluation model of the charging equipment.
- A kind of 5. highway fast charge network intelligence cloud service device, it is characterised in that including：Filling on a highway is set Electric equipment and cloud server, charging equipment are included by network connection to cloud server, the cloud server：Information acquisition unit, for obtaining the current operational information of all target charging equipments；State evaluation unit, for for each target charging equipment, according to its current operational information, utilizing charging equipment State evaluation model, obtain its current state, the state includes normal, early warning, warning and failure；Plan rescheduling unit, for for each target charging equipment, being carried out according to its current state to its repair schedule Corresponding adjustment；Maintenance program unit, for for each target charging equipment that current state is failure, currently being run according to it Information, corresponding maintenance program is obtained using Knowledge of Maintenance storehouse.
- 6. device according to claim 5, it is characterised in that described device, in addition to：Failure predication unit, for each target charging equipment for current state for normal, early warning or warning, according to Its current operational information, using the deterioration analysis model of charging equipment, at the time of predicting its failure；Stock-keeping unit, for according to each corresponding fault message of target charging equipment, generating Parts Inventory management Scheme.
- 7. device according to claim 6, it is characterised in that described device, in addition to：Forecast model unit, for being analyzed using decision Tree algorithms sample data, obtain the charging equipment Deterioration analysis model.
- 8. device according to claim 5, it is characterised in that described device, in addition to：State model unit, for being analyzed using decision Tree algorithms sample data, obtain the charging equipment State evaluation model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110928765A (en) * | 2019-10-11 | 2020-03-27 | 京东数字科技控股有限公司 | Link testing method and device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106650963A (en) * | 2016-12-30 | 2017-05-10 | 山东鲁能智能技术有限公司 | Electric car charging equipment detection and maintenance managing method and device |
-
2017
- 2017-10-11 CN CN201710942321.7A patent/CN107730108A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106650963A (en) * | 2016-12-30 | 2017-05-10 | 山东鲁能智能技术有限公司 | Electric car charging equipment detection and maintenance managing method and device |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110928765A (en) * | 2019-10-11 | 2020-03-27 | 京东数字科技控股有限公司 | Link testing method and device |
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Application publication date: 20180223 |