CN105721561A - Fan fault data center based on cloud platform - Google Patents

Fan fault data center based on cloud platform Download PDF

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Publication number
CN105721561A
CN105721561A CN201610056805.7A CN201610056805A CN105721561A CN 105721561 A CN105721561 A CN 105721561A CN 201610056805 A CN201610056805 A CN 201610056805A CN 105721561 A CN105721561 A CN 105721561A
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fan
task
cloud
progress
time
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罗贤缙
刘长良
甄成刚
武英杰
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North China Electric Power University
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North China Electric Power University
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/10Protocols in which an application is distributed across nodes in the network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/12Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The invention discloses a fan fault data center based on a cloud platform. A fan fault early warning and diagnosis system established above an enterprise-level private cloud platform comprises a cloud real-time historical database system, a relational database, a fan running status data acquisition device, a fan running status data parallel computing module, a fan fault early warning module, a fan fault diagnosis module and a human-computer interaction interface. The traditional fan fault detection system is migrated onto the enterprise-level private cloud platform, and services can be provided for multiple wind power plants by means of strong and flexible computing capability and storage capability of the enterprise-level private cloud platform. The fan fault data center can be used for feature extraction, early warning and diagnosis of fan fault data in time, thus realizing maximum intensification of resources and reducing the operating and maintenance costs.

Description

A kind of fan trouble data center based on cloud platform
Technical field
The invention belongs to areas of information technology, relate to wind energy turbine set information control field, specifically a kind of fan trouble data center based on cloud platform.
Background technology
Along with greatly developing of new forms of energy, wind energy has become a part indispensable in the global power energy.By 2010, wind-power electricity generation accounted for the world and powers the 2% of total amount.Wind-powered electricity generation industry fast development, but Wind turbines on-line monitoring and fault diagonosing system relatively lags behind, and causes Wind turbines Frequent Accidents, and maintenance cost raises.
By 2011, the existing about 4.5 ten thousand typhoon group of motors of China needed the follow-up maintenance of 20-30.For the unit that working life is 20 years, operation expense is estimated to account for the 10%~15% of wind field income;For marine wind field, 20%~25% taken in up to wind field for the cost of wind energy conversion system operation maintenance.In the end of the year 2011, China's wind-powered electricity generation installation total amount is more than 62,000,000 kilowatts, it means that about 4.5 ten thousand typhoon group of motors need the follow-up maintenance of 20-30.
Traditional unit keeps in repair based on planned maintenance and correction maintenance, and scheduled overhaul is with bigger blindness, and the expense of 1/3 belongs to maintenance surplus according to statistics, and overhauls the extension easily causing fault afterwards.About 100,000 typhoon group of motors superior Surveillance center will be had to transmit unit status data to the year two thousand twenty.The data facing magnanimity are processed and the safe storage problem of data by wind energy turbine set remote fault diagnosis center.
Wind power generating set is mainly made up of wind wheel and pitch-variable system, wheel hub, actuating device, gear-box, electromotor, electrical system, control system, brake system, hydraulic system and yaw system etc..As large-sized low-speed slewing, the fault of wind power generating set is concentrated mainly on blade, gear-box and electromotor, and common fault has: leaf destruction, gear destruction, bearing wear, axle system is uneven, misalign and electric fault etc..
Traditional pattern can not play the advantage of cyber-net to greatest extent, is mainly reflected in:
(1) computer at work on the spot station is the isolated island of an information;
(2) safety of data does not ensure;
(3) SCADA system of existing wind field has been achieved with the Centralizing inspection of electric parameter, procedure parameter and minority vibration parameters, and wherein vibration signal is only used for surpassing width warning, and data sampling interval is typically in second level, it is impossible to for fault diagnosis;
(4) vibration measuring point is generally adopted high frequency sampling, and the unit data total amount of a day is about 32G, although equipment can carry out intermittent sampling and data compression process, but still face storage and the transmission problem of mass data.
Cloud platform is incorporated into fault diagnosis of wind turbines, and in the pattern of cloud platform, remote fault diagnosis is changed by " server, client " to " cloud service platform, client ".For wind-powered electricity generation enterprise, its wind field has a very wide distribution, in enterprise, the number of computers of detection system is huge, design a kind of can the method under these computer integrated to cloud platform, effectively utilize intrasystem computer, form powerful fault diagnosis, data mining platform, reach data sharing, resource-sharing.
Summary of the invention
It is an object of the invention to provide a kind of fan trouble data center based on cloud platform, to solve the problem proposed in above-mentioned background technology, for achieving the above object, the present invention provides following technical scheme:
A kind of fan trouble data center based on cloud platform, including cloud real time historical database system, system R, fan operation state data acquisition device, fan operation status data parallel computation module, fan trouble warning module, Fault Diagnosis of Fan module, Man Machine Interface;Described cloud real time historical database system receives the fan operation real-time parameter gathered from fan operation state data acquisition device, and cloud real time historical database system provides fan operation data to blower fan running state data parallel computation module;Described system R receives the Fan Equipment nominal parameter of wind energy turbine set operator's craft typing by Man Machine Interface, and fault pre-alarming has related parameter, provides equipment Foundations data as fan operation status data parallel computation module;Described fan operation state data acquisition device gathers the real-time parameter data of fan operation, and data are carried out pretreatment, is stored in cloud history real-time dataBase system;Real time data in cloud history real-time dataBase system is carried out computing by described fan operation status data parallel computation module, extracts fault signature, and result is stored in cloud history real-time dataBase system;Fault signature data in cloud history real-time dataBase system and the related parameter that has in system R are processed by described fan trouble warning module and Fault Diagnosis of Fan module, send early warning information, and diagnose.
Further scheme as the present invention: software and hardware resources and application system that fan operation status data parallel computation module, fan trouble warning module, the parallel computing trunking of Fault Diagnosis of Fan module and cloud real time historical database system, system R can be distributed by the privately owned cloud platform of described enterprise-level realize unified, automated management.
Further scheme as the present invention: described enterprise-level cloud computing platform is based on the control system that Hadoop (distributed system architecture) platform is set up.
Further scheme as the present invention: it is characterized in that, described enterprise-level cloud computing platform, job scheduling adopts deque's scheduler architecture.
Further scheme as the present invention: described deque scheduler architecture, adopt the software real-time scheduling of Deadline First (EDF) the earliest, it is not required for operation to be previously performed in deadline, then selecting operation according to the priority of operation and startup time, completing time limit operation early can preferentially be performed.
Further scheme as the present invention: described enterprise-level cloud computing platform, supposition execution mechanism based on Hadoop platform, JobTracker (task management server, node) implementation progress of all tasks of each operation can be calculated termly, only consider some task on same node, assuming that this task is T, it performs on computing node A;Owing to JobTracker would know that the implementation progress of all tasks, the task T of certain operation is at the moment t nearest apart from current timeclosedProgress be progress [tclosed](tclosedMoment progress), at tclosedThe progress in-Δ h moment is progress [tclosed-Δ h], utilize the average increasing amount of Task Progress in a nearest Δ h to arrive t with this task 0closedThe average increasing amount of the Task Progress that-Δ h is interior during this period of time compares, and carrys out the implementation status of detection mission by variance, and formula is as follows:
s 2 = 1 2 Σ i = 1 2 ( progress i - progress a v g )
Wherein progressiRepresent the progress increment in certain a period of time (to represent when i=1,2, i=1 0 to tclosedIncrement in the-Δ h time, and i=1 represents at tclosed-Δ h to tclosedTask increment in time period), and progressavgRepresent from 0 moment to tclosedThe task balanced growth progress in moment, namely average advance rate of increase interior during this period of time.
Further scheme as the present invention: based on the supposition execution mechanism of Hadoop platform, if this variance exceedes a certain threshold values, then illustrate that this task run is excessively slow, the deadline of operation belonging to this task is dependent on the deadline of this task, need for one backup tasks of this task start, to accelerate the processing speed of data fragmentation handled by this task.
Further scheme as the present invention: deque's scheduler architecture and SlowNode selection strategy, in a Hadoop cluster, the set S of TaskTracker (tasks carrying node) represents, S={T1, T2..., Tn, wherein TiRepresent TaskTracker, TiPerformance C (Ti) main from the quantity n of Slot, the frequency C (f) of CPU, memory size C (m), disk size C (d) and network bandwidth C (v) be index, C (Ti)=k1*n*C(fi)+k2*C(mi)+k3*C(di)+k4*C(vi), wherein, k is the proportion shared by indices, is used to specify the ratio that indices is shared when carrying out load and calculating, k is more big, illustrate that this index is more big on the impact of load, wherein, k=(0.2,0.3,0,0.5), actual application according to circumstances carries out suitable adjustment;Load L (the T of TaskTrackeri) the utilization rate L (slot) of Main Basis Slot, memory usage L (m), disk utilization rate L (d) and network bandwidth utilization rate L (v):
L(T1)=k1×L(slot1)+k2×L(m1)+k3×L(d1)+k4×L(v1)
Weights W (the T of TaskTracker loadi) it is defined as node load L (Ti) and joint behavior C (Ti) ratio:
W(T1)=L (T1)/C(T1)
The average W (Tavg) of all TaskTracker loads in Hadoop cluster:
W ( T a v g ) = Σ 1 n W ( T i )
W (T will be meti) TaskTracker of > W (Tavg) is labeled as SlowNode, load owing to being marked as the node of SlowNode is bigger, allocating it of task is all local task, to alleviate the load of such TaskTracker substantially, reduce the number of the task of speculating execution in Hadoop cluster.
Compared with prior art, the invention has the beneficial effects as follows:
(1) the invention belongs to one of cloud platform technology case being applied to new energy field, fan trouble early warning and the diagnosis of a wind-power electricity generation company or even an all wind energy turbine set of electricity power group can be realized based on the fan trouble data center of cloud platform.
(2) present invention passes through enterprise-level cloud platform, achieve the privately owned cloud platform unified management of the enterprise-level from hardware resource to application software, utilize the inborn characteristic of cloud computing platform, it is avoided that the problem that the hardware utilization of existing system is not high, it is achieved the rapid deployment ability of the rate that maximally utilizes of hardware resource, flexible quick resource expansion ability and application.
(3) present invention is by being based upon powerful storage and the computing capability of the HPCC on cloud platform, utilizes centralized calculating method of service, avoids double counting, saves valuable calculating resource.
(4) present invention is migrated and from the characteristic serviced by the online of cloud platform, availability and the efficiency of management of system application can be greatly improved, realize breakthrough rapidly and efficiently rate in the new opplication stage of reaching the standard grade, maintenance cost can be greatly reduced again in the later maintenance stage.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of a kind of fan trouble data center based on cloud platform.
Fig. 2 is the schematic diagram of enterprise-level cloud computing platform.
Fig. 3 is the schematic diagram of fan operation state data acquisition device.
Fig. 4 is the schematic diagram of deque's scheduler algorithms block diagram.
Detailed description of the invention
Below in conjunction with detailed description of the invention, the technical scheme of patent of the present invention is described in more detail.
Refer to Fig. 1-4, a kind of fan trouble data center based on cloud platform, the described fan trouble data center based on cloud platform is based upon in the privately owned cloud platform of enterprise-level, including cloud real time historical database system, system R, fan operation state data acquisition device, fan operation status data parallel computation module, fan trouble warning module, Fault Diagnosis of Fan module, Man Machine Interface.The privately owned cloud platform of described enterprise-level, including Hadoop DLL, HDFS access interface, Hbase access interface, it is based upon HBase, Hive, MapReduce, the Zookeeper etc. on HDFS, for each computer cluster in hardware platform layer with server runs cloud real time historical database system provide storage service, simultaneously by speculating that the fan operation status data parallel computation module in application layer, fan trouble warning module, Fault Diagnosis of Fan module are provided the service of calculating by execution mechanism and deque's scheduler architecture;Cloud real time historical database system can accept the blower fan information (SCADA system data) from wind energy turbine set including electric parameter, procedure parameter.Electric parameter includes active power, reactive power, electric moter voltage, electric current, winding current, frequency, power factor etc.;Procedure parameter includes wind speed round, generator speed, winding temperature, electromotor front and back bearings temperature, gear case oil temperature, gear-box front and back bearings temperature, base bearing temperature, torque etc..Cloud real time historical database system can also accept real-time surrounding enviroment information (the real-time anemometer tower data message of wind energy turbine set), such as wind speed, mean wind speed, wind angle, ambient temperature, cabin temperature, humidity, air pressure etc., and it is stored in cloud real time historical database system.Historical data base system when can dispose Semen Caesalpiniae in the fan trouble data center based on the privately owned cloud platform of enterprise-level for each wind energy turbine set, it is disposed in the virtual machine being present in the privately owned cloud platform of enterprise-level and on the privately owned cloud platform network storage equipment of enterprise-level.Wind generating set vibration measuring point generally comprises: main shaft, gear-box low speed, gear-box middling speed, gearbox high-speed, electromotor front end, electromotor rear end and tower, and each measuring point also can be divided into level, vertical and axially.Vibration parameters includes tower, cabin, the displacement at support place, speed or acceleration vibration data, and data is stored in cloud real time historical database system.
System R is for receiving the Fan Equipment nominal parameter of wind energy turbine set operator's craft typing, and fault pre-alarming has related parameter, equipment Foundations data can be provided for fan operation status data parallel computation module, and jointly provide data, services for fan operation status data parallel computation module, fan trouble warning module, Fault Diagnosis of Fan module with cloud real time historical database system.The wind electric field blower information in certain or some moment that its data manually entered are mainly that wind energy turbine set SCADA system is not provided that, it is disposed in the virtual machine being present in the privately owned cloud platform of enterprise-level.
Fault data in fan operation process can be adopted the methods such as filtration, noise reduction, classification to carry out pretreatment by the blower fan information in cloud real time historical database system, system R and fan operation status data by fan operation status data parallel computation module, these data are carried out fault signature extraction again, and fault signature data is stored in cloud real time historical database system and system R.
The statistical analysis module of fan operation status data parallel computation module can carry out data statistics, Correlation Calibration, error statistics, error analysis by cloud real time historical database system and system R;Real-time monitoring module can pass through cloud real time historical database system and system R by the real-time condition display of fan operation on control centre's display screen, facilitates dispatcher to observe in time, assesses the instantaneous operating conditions of blower fan.It is deployed in the virtual machine of the centralized fan trouble data center of the scale based on the privately owned cloud platform of enterprise-level 6, and the whole application on its virtual machine and virtual machine accept the unified management based on the privately owned cloud platform 6 of enterprise-level and monitoring.
Fan trouble characteristic in cloud real time historical database system can be contrasted by fan trouble warning module with the fault characteristic data in system R, the blower fan meeting fault signature is carried out real-time early warning, and early warning information is sent warning by Man Machine Interface to dispatcher, it is stored in cloud real time historical database system and system R simultaneously.
Fan trouble early warning information in cloud real time historical database system and system R can be processed by Fault Diagnosis of Fan module, fan trouble is diagnosed, diagnostic message is informed dispatcher by Man Machine Interface simultaneously, after dispatcher confirms fault, by fault message being stored in cloud real time historical database system, and revise the fault characteristic data in system R, it is beneficial to the accuracy rate improving fault pre-alarming with diagnosis.
The ordinary PC of Man Machine Interface and wind energy turbine set, the wind-powered electricity generation fault data center of the privately owned cloud platform 6 of enterprise-level is logged in by enterprises lan, can enjoy by fan operation status data parallel computation module in the way of webpage and client, fan trouble warning module, the multiple service that Fault Diagnosis of Fan module provides, including the real-time monitoring to all blower fans of wind energy turbine set, the distribution of each wind energy turbine set is shown with the form of map, the map page should show all running state data and the early warning of the blower fan of wind energy turbine set, fault message, the prediction curve of each parameter of every Fans can also be shown simultaneously, the corresponding repair schedule of convenient formulation.Add and management function additionally, Man Machine Interface also provides for system user, support user class and priority assignation, including at least system manager, run operator, browse the user right of the different stages such as user;The inquiry of blower fan essential information, fan operation state-data queries, it is provided that the multiple way of presentation such as form, curve, rectangular histogram.All interaction datas all adopt asymmetric encryption mode to transmit.
The privately owned cloud platform of enterprise-level can be that the centralized fan trouble data center of scale completes unitized security deployment, provides extendible hardware infrastructure service, middleware services and software application service for multiple wind energy turbine set.For newly building the wind energy turbine set of deployment, it is possible to remotely realize the rapid deployment of application.For the HPCC among the privately owned cloud platform of enterprise-level, there is also high-performance, high reliability, automated management instrument flexible flexibly realize the task of automatization and submit to and monitoring.Virtualization monitor in the privately owned cloud platform of enterprise-level has and migrates characteristic online, namely the detection of hardware is realized, the migration carrying out applying was suggested that before hardware is likely to occur fault, can move in other virtual machines or physical machine, can also pass through to have configured the migration of automatization, and application can normally provide the user service in transition process.The privately owned cloud platform of enterprise-level is capable of the function from service, namely carries out unified management when wind energy turbine set user normally uses a series of service at the wind-powered electricity generation fault data center based on cloud platform.
Here centered by embodiments of the invention, expand detailed description, the concrete embodiment of described optimal way or some characteristic, should be understood to this specification and be merely by providing the mode of embodiment to describe the present invention, actually can be varied from some details of composition, structure and use, combination and group including parts are joined, and these deformation and application all should be within the scope of the present invention.
It is obvious to a person skilled in the art that the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when without departing substantially from the spirit of the present invention or basic feature, it is possible to realize the present invention in other specific forms.Therefore, no matter from which point, embodiment all should be regarded as exemplary, and be nonrestrictive, the scope of the invention rather than described above limits, it is intended that all changes in the implication of the equivalency dropping on claim and scope included in the present invention.
In addition, it is to be understood that, although this specification is been described by according to embodiment, but not each embodiment only comprises an independent technical scheme, this narrating mode of description is only for clarity sake, description should be made as a whole by those skilled in the art, and the technical scheme in each embodiment through appropriately combined, can also form other embodiments that it will be appreciated by those skilled in the art that.

Claims (8)

1. the fan trouble data center based on cloud platform, it is based upon on enterprise-level cloud computing platform, including cloud real time historical database system, system R, fan operation state data acquisition device, fan operation status data parallel computation module, fan trouble warning module, Fault Diagnosis of Fan module, Man Machine Interface;It is characterized in that, described cloud real time historical database system receives the fan operation real-time parameter gathered from fan operation state data acquisition device, and cloud real time historical database system provides fan operation data to blower fan running state data parallel computation module;Described system R receives the Fan Equipment nominal parameter of wind energy turbine set operator's craft typing by Man Machine Interface, and fault pre-alarming has related parameter, provides equipment Foundations data as fan operation status data parallel computation module;Described fan operation state data acquisition device gathers the real-time parameter data of fan operation, and data are carried out pretreatment, is stored in cloud history real-time dataBase system;Real time data in cloud history real-time dataBase system is carried out computing by described fan operation status data parallel computation module, extracts fault signature, and result is stored in cloud history real-time dataBase system;Fault signature data in cloud history real-time dataBase system and the related parameter that has in system R are processed by described fan trouble warning module and Fault Diagnosis of Fan module, send early warning information, and diagnose.
2. a kind of fan trouble data center based on cloud platform according to claim 1, it is characterized in that, software and hardware resources and application system that fan operation status data parallel computation module, fan trouble warning module, the parallel computing trunking of Fault Diagnosis of Fan module and cloud real time historical database system, system R can be distributed by the privately owned cloud platform of described enterprise-level realize unified, automated management.
3. a kind of fan trouble data center based on cloud platform according to claim 1 and 2, it is characterised in that described enterprise-level cloud computing platform is based on the control system that Hadoop platform is set up.
4. a kind of fan trouble data center based on cloud platform according to claim 3, it is characterised in that described enterprise-level cloud computing platform, job scheduling adopts deque's scheduler architecture.
5. a kind of fan trouble data center based on cloud platform according to claim 4, it is characterized in that, described deque scheduler architecture, adopt the software real-time scheduling of Deadline First the earliest, it is not required for operation to be previously performed in deadline, then selecting operation according to the priority of operation and startup time, completing time limit operation early can preferentially be performed.
6. a kind of fan trouble data center based on cloud platform according to claim 5, it is characterized in that: described enterprise-level cloud computing platform, supposition execution mechanism based on Hadoop platform, JobTracker can calculate the implementation progress of all tasks of each operation termly, only consider some task on same node, assuming that this task is T, it performs on computing node A;Owing to JobTracker knows the implementation progress of all tasks, the task T of certain operation is at the moment t nearest apart from current timeclosedProgress be progress [tclosed], at tclosedThe progress in-Δ h moment is progress [tclosed-Δ h], utilize the average increasing amount of Task Progress in a nearest Δ h to arrive t with this task 0closedThe average increasing amount of the Task Progress that-Δ h is interior during this period of time compares, and carrys out the implementation status of detection mission by variance, and formula is as follows:
s 2 = 1 2 Σ i = 1 2 ( progress i - progress a v g )
Wherein progressiRepresent the progress increment in certain a period of time (to represent when i=1,2, i=1 0 to tclosedIncrement in the-Δ h time, and i=1 represents at tclosed-Δ h to tclosedTask increment in time period), and progressavgRepresent from 0 moment to tclosedThe task balanced growth progress in moment, namely average advance rate of increase interior during this period of time.
7. a kind of fan trouble data center based on cloud platform according to claim 6, it is characterized in that: based on the supposition execution mechanism of Hadoop platform, if this variance exceedes a certain threshold values, then illustrate that this task run is excessively slow, the deadline of operation belonging to this task is dependent on the deadline of this task, need for one backup tasks of this task start, to accelerate the processing speed of data fragmentation handled by this task.
8. a kind of fan trouble data center based on cloud platform according to claim 7, it is characterised in that deque's scheduler architecture and SlowNode selection strategy, the set S of TaskTracker represents in a Hadoop cluster, S={T1, T2..., Tn, wherein TiRepresent TaskTracker, TiPerformance C (Ti) main from the quantity n of Slot, the frequency C (f) of CPU, memory size C (m), disk size C (d) and network bandwidth C (V) be index, C (Ti)=k1*n*C(fi)+k2*C(mi)+k3*C(di)+k4*C(vi), wherein, k is the proportion shared by indices, is used to specify the ratio that indices is shared when carrying out load and calculating, k is more big, illustrate that this index is more big on the impact of load, wherein, k=(0.2,0.3,0,0.5), actual application according to circumstances carries out suitable adjustment;Load L (the T of TaskTrackeri) according to the utilization rate L (slot) of Slot, memory usage L (m), disk utilization rate L (d) and network bandwidth utilization rate L (v):
L(T1)=k1×L(slot1)+k2×L(m1)+k3×L(d1)+k4×L(vc)
Weight w (the T of TaskTracker loadi) it is defined as node load L (Ti) and joint behavior C (Ti) ratio:
W(T1)=L (T1)/C(T1)
The average W (Tavg) of all TaskTracker loads in Hadoop cluster:
W ( T a v g ) = Σ 1 n W ( T i )
W (T will be meti) TaskTracker of > w (Tavg) is labeled as SlowNode, load owing to being marked as the node of SlowNode is bigger, allocating it of task is all local task, to alleviate the load of such TaskTracker substantially, reduce the number of the task of speculating execution in Hadoop cluster.
CN201610056805.7A 2016-01-28 2016-01-28 Fan fault data center based on cloud platform Pending CN105721561A (en)

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