CN107133669A - Track traffic synthetic monitoring intelligent short message alarm method - Google Patents
Track traffic synthetic monitoring intelligent short message alarm method Download PDFInfo
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- CN107133669A CN107133669A CN201710571446.3A CN201710571446A CN107133669A CN 107133669 A CN107133669 A CN 107133669A CN 201710571446 A CN201710571446 A CN 201710571446A CN 107133669 A CN107133669 A CN 107133669A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/12—Messaging; Mailboxes; Announcements
- H04W4/14—Short messaging services, e.g. short message services [SMS] or unstructured supplementary service data [USSD]
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Abstract
The invention provides a kind of track traffic synthetic monitoring intelligent short message alarm method, comprise the following steps：S1, many hidden layer deep learning mathematical modelings of the foundation based on neutral net；S2, alarm data progress machine learning, processing according to the mathematical modeling of foundation to collecting, and the content after optimization processing is sent to user in the way of short message；S3, the result to user feedback are performed, and the processing of self study storage.Track traffic synthetic monitoring intelligent short message alarm method of the present invention allow users in real time, directly understand and grasp electric substation's field condition, and the situation to generation makes reply in time.Also change short message content in traditional approach and indiscriminately imitate monitoring backstage alarm completely, make prompting more terse, accurate, and interaction can be fed back in time, also save the mass communication expense of user.
Description
Technical field
The invention belongs to Transit Equipment warning technology field, more particularly, to a kind of track traffic synthetic monitoring intelligent
Short message alarm method.
Background technology
With developing rapidly for China railways industry, in order to increase economic efficiency, raise labour productivity, many electric substations
All realize unattended.Existing most electric substation is alerted using short message mode, and the transmission of existing alarm message
There are dilatory content, repetition, frequent, unidirectional no interactions.Can not be effective, timely, accurate by equipment alarm information situation
True transmission is to monitor supervision platform.
The content of the invention
In view of this, the present invention is directed to propose a kind of track traffic synthetic monitoring intelligent short message alarm method, existing to solve
The contents of some short message alarms is dilatory, repeat, frequent, unidirectional no interactions situations such as.
To reach above-mentioned purpose, the technical proposal of the invention is realized in this way：
A kind of track traffic synthetic monitoring intelligent short message alarm method, comprises the following steps：
S1, many hidden layer deep learning mathematical modelings of the foundation based on neutral net；
S2, carry out machine learning processing to the alarm data collected according to the mathematical modeling of foundation, and by optimization processing
Content afterwards is sent to user in the way of short message；
S3, the result to user feedback are performed, and learning knowledge is carried out into learning database storage processing.
Further, in the step S1, many hidden layer deep learning mathematical modelings based on neutral net include from
Performed successively after going to input member, sort out layer, purifying layer, decision-making level, output member, feedback layer.
Further, all real time datas and Trouble Report of the input member of the mathematical modeling including monitor supervision platform is different
Often and alarm, and monitored object subordinate relation, layout.
Further, the layer of sorting out of the mathematical modeling is included to abnormal data and the ownership of alarm, classification；One monitoring
All signs that object extraction is summarized, are described using a short message.
Further, the purifying layer in mathematical modeling includes purifying layer input member, and the purifying layer input member includes classification
The output member of layer, monitored object alarm grade, alarm form, sign cycle, self study basis judge storehouse, and purifying layer is mainly to net
Change layer input member to be purified, prevent the repetition of alarm message from sending, unnecessary transmission, leak the occurrence of send.
Further, the decision-making level of mathematical modeling includes decision-making level's input member, and decision-making level's input member includes purifying layer
Output member, alarm linkage strategy, alert analysis strategy, emergent implementation strategy, learning database, receive object determination strategy.
Further, the decision-making level in mathematical modeling is when warning information carries out short message content generation, the reply to alarm
Strategy is carried out reporting suggestion and analysis, and the sending object to short message content is combed and determined, the short message content includes readding
Read the expectation feedback content of object.
Further, the specific execution method of the step S2 is as follows：
S201, startup event acquisition process, real time data and Trouble Report to monitoring system are acquired；
S202, startup deep learning data model process, the change to real time data and Trouble Report are analyzed and processed；
S203, the alarm data collected and equipment and parameter configuration as input member is subjected to classification processing, to belonging to
The alarm of same equipment or monitored object is sorted out, and combines the input of alarm grade and learning database as next stage；
S204, into after purifying layer, judge etc. to be handled by grade, sequencing and repeating, refine to alerting, it is raw
Into the pure data of letter refining, inputted then in conjunction with linkage strategy, learning database as next stage；
S205, the data source progress short message content for passing through input in decision-making level generation, including warning content, suggestion, place
Reason measure, request feedback content, the determination of sending object, ultimately generate output member；
S206, the output member of alarm pass to short message sending process by message queue；Short message sending process will be received
Warning information sent by mobile communication modem to user mobile phone.
Further, the specific execution method of the step S3 is as follows：
User receive short message after, replied according to short message prompt, reply include validation of information, affirmative, refusal, guidance,
The combination of control command correspondence code, mathematical modeling receives the execution that control command is carried out after feedback, and learning knowledge is deposited
Enter learning database.
Further, in the step S201, the event acquisition process is by KF6600 stipulations from comprehensive monitoring system
The middle change for obtaining real time data and Trouble Report.Relative to prior art, track traffic synthetic of the present invention monitors intelligence
Energy short message alarm method has the advantage that：
(1) track traffic synthetic monitoring intelligent short message alarm method of the present invention be applied to track traffic electric substation without
People's situation on duty, when the object that user monitors in comprehensive monitoring system changes, passes through mobile communication modem
Short message is sent to user mobile phone, allow users in real time, electric substation's field condition is directly understood and grasped, and in time to occurring
Situation make reply.Also change short message content in traditional approach and indiscriminately imitate monitoring backstage alarm completely, make prompting simpler
Practice, precisely, and interaction can be fed back in time, also save the mass communication expense of user.
(2) track traffic synthetic monitoring intelligent short message alarm method of the present invention uses deep learning model, passes through
Constantly study, makes module after alarm is received, and can accurately be sent to corresponding user, warning content more meets user will
Ask, the processing and execution opinion provided is more accurate, the prediction of alarm and reason judge more rationally correct.
Brief description of the drawings
The accompanying drawing for constituting the part of the present invention is used for providing a further understanding of the present invention, schematic reality of the invention
Apply example and its illustrate to be used to explain the present invention, do not constitute inappropriate limitation of the present invention.In the accompanying drawings：
Fig. 1 is that the track traffic synthetic monitoring intelligent short message alarm method mathematical modeling principles described in the embodiment of the present invention show
It is intended to.
Embodiment
It should be noted that in the case where not conflicting, the embodiment in the present invention and the feature in embodiment can phases
Mutually combination.
In the description of the invention, it is to be understood that term " " center ", " longitudinal direction ", " transverse direction ", " on ", " under ",
The orientation or position relationship of the instruction such as "front", "rear", "left", "right", " vertical ", " level ", " top ", " bottom ", " interior ", " outer " are
Based on orientation shown in the drawings or position relationship, it is for only for ease of the description present invention and simplifies description, rather than indicate or dark
Specific orientation must be had, with specific azimuth configuration and operation by showing the device or element of meaning, therefore it is not intended that right
The limitation of the present invention.In addition, term " first ", " second " etc. are only used for describing purpose, and it is not intended that indicating or implying phase
To importance or the implicit quantity for indicating indicated technical characteristic.Thus, the feature for defining " first ", " second " etc. can
To express or implicitly include one or more this feature.In the description of the invention, unless otherwise indicated, " multiple "
It is meant that two or more.
In the description of the invention, it is necessary to illustrate, unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " should be interpreted broadly, for example, it may be being fixedly connected or being detachably connected, or be integrally connected；Can
To be mechanical connection or electrical connection；Can be joined directly together, can also be indirectly connected to by intermediary, Ke Yishi
The connection of two element internals.For the ordinary skill in the art, above-mentioned term can be understood by concrete condition
Concrete meaning in the present invention.
Describe the present invention in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
As shown in figure 1, track traffic synthetic monitoring intelligent short message alarm method, comprises the following steps：
S1, many hidden layer deep learning mathematical modelings of the foundation based on neutral net；
S2, carry out machine learning, processing to the alarm data collected according to the mathematical modeling of foundation, and by optimization processing
Content afterwards is sent to user in the way of short message；
S3, the result to user feedback are performed, and learning knowledge is carried out into learning database storage processing.
The field feedback that deep learning mathematical modeling is received, carries out arrangement study in a model, and study content includes
Warning information is directed to proper alarm level of different user, warning content, and Fault Identification, the handling suggestion of user and mode
Deng by constantly learning, making module after alarm is received, corresponding user can accurately be sent to, warning content more meets
User requires that the processing and execution opinion provided is more accurate, and the prediction of alarm and reason judge more rationally correct.
Wherein, in the step S1, many hidden layer deep learning mathematical modelings based on neutral net are included from going to
Perform successively afterwards input member, sort out layer, purifying layer, decision-making level, output member, feedback layer.
Wherein, the specific execution method of the step S2 is as follows：
S201, startup event acquisition process, real time data and Trouble Report to monitoring system are acquired；
S202, startup deep learning data model process, the change to real time data and Trouble Report are analyzed and processed；
S203, the alarm data collected and equipment and parameter configuration as input member is subjected to classification processing, to belonging to
The alarm of same equipment or monitored object is sorted out, and combines the input of alarm grade and self study storehouse as next stage；
S204, into after purifying layer, judge etc. to be handled by grade and sequencing and repeating, refine to alerting,
The pure data combination linkage strategy of generation letter refining, learning database are inputted as next stage；
S205, the data source progress short message content for passing through input in decision-making level generation, including warning content, suggestion, place
Reason measure, request feedback content, the determination of sending object, ultimately generate output member；
S206, the output member of alarm pass to short message sending process by message queue；Short message sending process will be received
Warning information sent by mobile communication modem to user mobile phone.
Wherein, the specific execution method of the step S3 is as follows：
User receive short message after, replied according to short message prompt, reply include validation of information, affirmative, refusal, guidance,
The combination of control command correspondence code, mathematical modeling receives the execution that control command is carried out after feedback, and generation learns by oneself known
Know deposit knowledge base.
Wherein, in the step S201, the event acquisition process is obtained by KF6600 stipulations from comprehensive monitoring system
Take the change of real time data and Trouble Report.
Wherein, the mathematical modeling input member including monitor supervision platform all real time datas and Trouble Report exception and
Alarm, and monitored object subordinate relation, layout.
Wherein, the layer of sorting out of the mathematical modeling is included to abnormal data and the ownership of alarm, classification；One monitored object
All signs summarized are extracted, are described using a short message.
Wherein, the purifying layer in mathematical modeling includes purifying layer input member, and the purifying layer input member includes classification layer
Output member, monitored object alarm grade, alarm form, sign cycle, self study basis judge storehouse, and purifying layer is mainly to purifying layer
Input member is purified, and is prevented the repetition of alarm message from sending, unnecessary transmission, is leaked the occurrence of send.
Wherein, the decision-making level of mathematical modeling includes decision-making level's input member, and decision-making level's input member is defeated including purifying layer
Go out member, alarm linkage strategy, alert analysis strategy, emergent implementation strategy, learning database, reception object determination strategy.
Wherein, the decision-making level in mathematical modeling is when warning information carries out short message content generation, to the countermeasure of alarm
Carry out reporting suggestion and analysis, the sending object to short message content is combed and determined, the short message content includes reading pair
The expectation feedback content of elephant.
By setting up many hidden layer deep learning mathematical modelings based on neutral net, the alarm number collected to comprehensively monitoring
According to machine learning processing is carried out, the short message content of optimization is exported, the transmission of alarm notification is carried out, and feedback result is performed
And the processing of learning database storage；Detailed process is as follows：Start after event acquisition process, real time data and Trouble Report are adopted
Collection；Start deep learning mathematical modeling process, the change to real time data and Trouble Report is analyzed and processed；First will collection
The alarm data and equipment and parameter configuration arrived carries out classification processing, the announcement to belonging to same equipment or monitored object as input
Police is sorted out, and combines input of the alarm grade and self study storehouse etc. as next stage.Into after purifying layer, alarm is pressed etc.
Level and sequencing and repetition judgement etc. are handled, refined, the pure data combination linkage strategy of generation letter refining, learning database etc.
As next stage input.Carry out the generation of short message content by the data source of input in decision-making level, including warning content, suggestion,
The determination for the treatment of measures, request feedback content, sending object etc., ultimately generates output member.The output member of alarm passes through message team
Biographies pass short message sending process；Short message sending process sends the warning information received by mobile communication modem
To user mobile phone.User receive short message after, can be replied according to short message prompt, including validation of information, affirmative, refusal, guidance,
The correspondence code combination such as control command.Model receives the execution that control command is carried out after feedback or generation self study knowledge deposit
Knowledge base.
The SMS notification deep learning mathematical modeling based on neutral net includes input member, sorts out layer, purifying layer, determines
Plan layer, output member, feedback layer.
Member is inputted in model includes all real time datas of platform monitoring and the exception of Trouble Report and alarm, monitored object
Subordinate relation, layout etc.
Sorting out layer in model includes the ownership and classification to abnormal data and alarm.Extract and return for same monitored object
Receive out all signs, it is abnormal for description object, described in a short message.
Output member, monitored object alarm grade, alarm form, sign of the input member of purifying layer including sorting out layer in model
Cycle, self study basis judge storehouse etc..
Purifying layer mainly combines repeatability to alarm, carried out only by grade, by user feedback, sign cycle etc. in model
Change.Avoid the repetition transmission of interior alarm in short-term, the unnecessary generation for sending, leaking situations such as sending.
Decision-making level inputs output member of the member including purifying layer, alerts linkage strategy, alert analysis strategy, meets an urgent need in model
Implementation strategy, learning database, reception object determination strategy etc..
When decision-making level mainly carries out short message content generation to warning information in model, the countermeasure of alarm is reported
It is recommended that and analysis, the sending object to short message content combed and determined.And the expectation that content includes reading object is anti-
Present content.
User is received after short message, can be replied according to short message prompt, including validation of information, affirmative, refusal, guidance, control
The correspondence code combination such as system order.Model receives the execution that control command is carried out after feedback or generation self study knowledge deposit is known
Know storehouse.
The real time data object includes remote signalling object and teleobjective.
The event acquisition process obtains real time data and Trouble Report by KF6600 stipulations from comprehensive monitoring system
Change.
The short breath transmission process obtains Trouble Report by message transmission mode from deep learning math block process
Warning information and real time data warning information, and counted by mobile communication modem by Trouble Report warning information and in real time
Sent according to warning information to user mobile phone.
Communicated between the short breath transmission process and mobile communication modem by KF6601 stipulations.
Below to receive 213 feeder means communication disruption signals as embodiment, the tool of intelligent SMS alarm is explained further
Body method, deep learning mathematical modeling first carries out equipment identification to this signal, is to belong to when receiving this warning information
The monitoring content of 213 feeder lines, while being also communication disruption class, then goes to search alarm grade, feeder line event in the two classifications
Barrier and communication disruption belong to Level 1Alarming, and highest level alarm can find reception object to this alarm from learning database
Reception attention rate it is higher, extract attention rate, as the optimization level judgment criteria of transmission.Enter after purifying layer, by model
In alarm carry out grade sequence.And just look at that also which device in communication disruption also has same alarm, the quantity of alarm
And recovery situation, judge whether to belong to flash.See which alarm 213 feeder lines also have in addition, carry out classification and collect.And to same class
Alarm carry out arrangement refinement.In decision-making level, the data to purification layer carry out the analysis of failure, now see that communication disruption owns
Device all report one, and do not recover, and time consistency.It can be analyzed from knowledge base, communication failure should be sent out
In the raw communication between exchanger side or interchanger and communication processor.Then decision system judges communication processor and each
The running status of the interface process of device communication, discovery procedure is normally run, but TCP disconnectings, belongs to wait state.Cause
It is exchange fault that this, which may determine that,.Edit exchange fault alarm message content, it is proposed that to check whether interchanger runs
Normal or hardware power down, suggestion tape swap machine carry out on-site maintenance.Search repository and determine whom such failure belongs in work team
Working range, determine short message recipient.Two reception staff are configured with system before this, so this alarm is sent to two
People.By the above-mentioned processing of model, it is to avoid the information that alarm is interrupted in mass communication is sent, and also avoid all dimension pipe personnel
Reception processing is all gone, alarm cause is directly sent to corresponding recipient.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
God is with principle, and any modification, equivalent substitution and improvements made etc. should be included in the scope of the protection.
Claims (10)
1. track traffic synthetic monitoring intelligent short message alarm method, it is characterised in that comprise the following steps：
S1, many hidden layer deep learning mathematical modelings of the foundation based on neutral net；
S2, the alarm data progress machine learning processing according to the mathematical modeling of foundation to collecting, and by after optimization processing
Content is sent to user in the way of short message；
S3, the result to user feedback are performed, and learning knowledge is carried out into learning database storage processing.
2. track traffic synthetic monitoring intelligent short message alarm method according to claim 1, it is characterised in that：The step
In S1, many hidden layer deep learning mathematical modelings based on neutral net include perform successively from front to back input member, return
Class layer, purifying layer, decision-making level, output member, feedback layer.
3. track traffic synthetic monitoring intelligent short message alarm method according to claim 2, it is characterised in that：The mathematics
Model input member including monitor supervision platform all real time datas and Trouble Report exception and alarm, and monitored object from
Category relation, layout.
4. track traffic synthetic monitoring intelligent short message alarm method according to claim 2, it is characterised in that：The mathematics
The layer of sorting out of model is included to abnormal data and the ownership of alarm, classification；One monitored object extracts all signs summarized,
Described using a short message.
5. track traffic synthetic monitoring intelligent short message alarm method according to claim 2, it is characterised in that：Mathematical modeling
In purifying layer include purifying layer and input member, purifying layer input member includes sorting out the output member of layer, monitored object alarm etc.
Level, alarm form, sign cycle, self study basis judge storehouse, and purifying layer mainly purifies to purifying layer input member, prevents from accusing
The occurrence of repetition transmission, unnecessary transmission, the leakage of alert short message are sent.
6. track traffic synthetic monitoring intelligent short message alarm method according to claim 6, it is characterised in that：Mathematical modeling
Decision-making level include decision-making level and input member, output member of the decision-making level input member including purifying layer, alarm linkage strategy, alarm
Analysis strategy, emergent implementation strategy, learning database, reception object determination strategy.
7. the track traffic synthetic monitoring intelligent short message alarm method according to claim 2 or 6, it is characterised in that：Mathematics
Decision-making level in model carries out reporting suggestion and divided when warning information carries out short message content generation to the countermeasure of alarm
Analysis, the sending object to short message content is combed and determined that the short message content includes the expectation feedback content of reading object.
8. track traffic synthetic monitoring intelligent short message alarm method according to claim 2, it is characterised in that the step
S2 specific execution method is as follows：
S201, startup event acquisition process, real time data and Trouble Report to monitoring system are acquired；
S202, startup deep learning data model process, the change to real time data and Trouble Report are analyzed and processed；
S203, the alarm data collected and equipment and parameter configuration as input member is subjected to classification processing, it is same to belonging to
The alarm of equipment or monitored object is sorted out, and combines the input of alarm grade and learning database as next stage；
S204, into after purifying layer, judge etc. to be handled by grade, sequencing and repeating, refine, generation letter to alerting
Pure data are refined, are inputted then in conjunction with linkage strategy, learning database as next stage；
S205, the generation that short message content is carried out in decision-making level by the data source of input, including warning content, suggestion, handle and arrange
The determination of feedback content, sending object is applied, asked, output member is ultimately generated；
S206, the output member of alarm pass to short message sending process by message queue；Short message sending process is by the announcement received
Alert information is sent to user mobile phone by mobile communication modem.
9. track traffic synthetic monitoring intelligent short message alarm method according to claim 8, it is characterised in that：The step
In S201, the event acquisition process obtains from comprehensive monitoring system real time data and Trouble Report by KF6600 stipulations
Change.
10. track traffic synthetic monitoring intelligent short message alarm method according to claim 1, it is characterised in that the step
Rapid S3 specific execution method is as follows：
User is received after short message, is replied according to short message prompt, and reply includes validation of information, affirmative, refusal, guidance, control
The combination of the corresponding code of order, mathematical modeling receives the execution that control command is carried out after feedback, and learning knowledge is stored in into
Practise storehouse.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108563652A (en) * | 2017-12-20 | 2018-09-21 | 海南电网有限责任公司 | Reference information sorting technique based on brute-force searching algorithm |
| CN110474810A (en) * | 2019-08-21 | 2019-11-19 | 具东鹤 | A kind of communication check early warning system based on big data |
| CN115297302A (en) * | 2022-08-08 | 2022-11-04 | 北京中润惠通科技发展有限公司 | Unmanned system of railway substation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101209714A (en) * | 2006-12-28 | 2008-07-02 | 上海轨道交通设备发展有限公司 | Track traffic synthetic monitoring system and method |
| CN101887387A (en) * | 2010-04-07 | 2010-11-17 | 山东高效能服务器和存储研究院 | Method for remotely intelligently monitoring and analyzing RAID faults |
| CN102130783A (en) * | 2011-01-24 | 2011-07-20 | 浪潮通信信息系统有限公司 | Intelligent alarm monitoring method of neural network |
| CN102478844A (en) * | 2010-11-22 | 2012-05-30 | 中国科学院空间科学与应用研究中心 | Multifunctional experiment monitor system |
| CN102681517A (en) * | 2012-05-25 | 2012-09-19 | 许继集团有限公司 | Intelligent alarm system and method of charging station |
| US20130173514A1 (en) * | 2011-12-30 | 2013-07-04 | Rev2 Networks, Inc. | Automated Network Disturbance Prediction System Method & Apparatus |
| CN104359565A (en) * | 2014-10-17 | 2015-02-18 | 中国农业大学 | Cold-chain transport temperature monitoring and early warning method and system |
| CN104978837A (en) * | 2015-05-27 | 2015-10-14 | 苏文电能科技有限公司 | Alarm system orienting user-side transformer station and realization method thereof |
-
2017
- 2017-07-13 CN CN201710571446.3A patent/CN107133669A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101209714A (en) * | 2006-12-28 | 2008-07-02 | 上海轨道交通设备发展有限公司 | Track traffic synthetic monitoring system and method |
| CN101887387A (en) * | 2010-04-07 | 2010-11-17 | 山东高效能服务器和存储研究院 | Method for remotely intelligently monitoring and analyzing RAID faults |
| CN102478844A (en) * | 2010-11-22 | 2012-05-30 | 中国科学院空间科学与应用研究中心 | Multifunctional experiment monitor system |
| CN102130783A (en) * | 2011-01-24 | 2011-07-20 | 浪潮通信信息系统有限公司 | Intelligent alarm monitoring method of neural network |
| US20130173514A1 (en) * | 2011-12-30 | 2013-07-04 | Rev2 Networks, Inc. | Automated Network Disturbance Prediction System Method & Apparatus |
| CN102681517A (en) * | 2012-05-25 | 2012-09-19 | 许继集团有限公司 | Intelligent alarm system and method of charging station |
| CN104359565A (en) * | 2014-10-17 | 2015-02-18 | 中国农业大学 | Cold-chain transport temperature monitoring and early warning method and system |
| CN104978837A (en) * | 2015-05-27 | 2015-10-14 | 苏文电能科技有限公司 | Alarm system orienting user-side transformer station and realization method thereof |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108563652A (en) * | 2017-12-20 | 2018-09-21 | 海南电网有限责任公司 | Reference information sorting technique based on brute-force searching algorithm |
| CN110474810A (en) * | 2019-08-21 | 2019-11-19 | 具东鹤 | A kind of communication check early warning system based on big data |
| CN110474810B (en) * | 2019-08-21 | 2023-09-05 | 内蒙古高投共建科技有限责任公司 | Communication detection early warning system based on big data |
| CN115297302A (en) * | 2022-08-08 | 2022-11-04 | 北京中润惠通科技发展有限公司 | Unmanned system of railway substation |
| CN115297302B (en) * | 2022-08-08 | 2023-07-04 | 北京中润惠通科技发展有限公司 | Unmanned system of railway substation |
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