CN205510066U - Well short wave transmitting machine fault early -warning device - Google Patents
Well short wave transmitting machine fault early -warning device Download PDFInfo
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Abstract
The utility model provides a well short wave transmitting machine fault early -warning device, the device includes: acquire the module for it reaches to acquire the historical operating data of transmitter before an i cycle of operation real -time operation data in the i cycle of operation, modeling analysis module is used for the basis normal data and fault data confirm the transmitter is at an i cycle of operation's failure diagnosis model, calculation module is used for the basis the transmitter reaches at an i cycle of operation's failure diagnosis model an i cycle of operation's real -time operation data determination the transmitter is in fault probability in the i cycle of operation, with the early warning module that calculation module connects is used for the basis fault probability and predetermined early warning rule are right the transmitter carries out the trouble early warning. Adopt the utility model provides an automatic transmitter trouble of monitoring can be realized to well short wave transmitting machine trouble early warning method and device to carry out the trouble early warning in advance.
Description
Technical field
This utility model embodiment relates to field of broadcast televisions, particularly relates to a kind of intermediate waves transmitter failure prior-warning device.
Background technology
The main flow equipment that the large-scale broadcast signal transmission station uses at present is high-power intermediate waves transmitter.Along with transmitter runs the increase of time, under the influence of complicated inside and outside running environment, unavoidably there will be the situation that equipment performance declines, fault rate increases.And the broadcast transmission station requires the strictest for the stable operation launching equipment, therefore, need transmitter running status is carried out Intelligent real-time monitoring, judge the running status of transmitter in advance, to realize before breaking down rapidly, in generation, broadcasts switching and artificial technology intervenes, it is ensured that normally complete broadcast task.
In prior art, generally use the index value during Automatic monitoring systems collection and record and transmitter real time execution, and corresponding index value is sent to multiple dial plate shows.The running status of transmitter is judged by plant maintenance personnel according to the indices numerical value of display on dial plate.
But, owing to high-power intermediate waves broadcast transmitter Inner Constitution is extremely complex, the reason causing fault is the most intricate, if plant maintenance personnel do not possess extremely strong professional skill and maintenance experience, it is difficult to according to the index value of display on single dial plate, the running status of transmitter apparatus is carried out accurate anticipation.
Utility model content
This utility model embodiment provides a kind of intermediate waves transmitter failure prior-warning device, in order to solve the problem that broadcast transmitter fault pre-alarming mode of the prior art can not accurately judge broadcast transmitter fault.
This utility model embodiment provides a kind of intermediate waves transmitter failure prior-warning device, including:
Acquisition module, for obtaining the real-time running data in transmitter history data before i-th cycle of operation and described i-th cycle of operation；
Modeling analysis module, for determining the described transmitter breakdown judge model at i-th cycle of operation according to described normal data and fault data；I is the integer more than or equal to 1；
Computing module, for determining described transmitter probability of malfunction in described i-th cycle of operation according to described transmitter at the breakdown judge model of i-th cycle of operation and the real-time running data of described i-th cycle of operation；
Warning module, for carrying out fault pre-alarming according to described probability of malfunction and default early warning rule to described transmitter.
In another embodiment, described modeling analysis module, specifically for:
Described normal data and described fault data are normalized；
Described normal data after normalized and described fault data are ranked up, and described normal data and fault data after sequence are carried out equidistant sampling；
It is modeled by the sample of the logistic regression analysis described normal data to obtaining after equidistant sampling and described abnormal data, determines described transmitter failure judgment models.
In another embodiment, described computing module, specifically for:
According to
Determining described transmitter probability of malfunction in described i-th cycle of operation, wherein, β represents the mapping relations of described normal data and described fault data, and z represents the real-time running data of described i-th cycle of operation.
In another embodiment, described modeling analysis module, it is additionally operable to:
Real time data according to the described i-th cycle of operation obtained updates the described transmitter breakdown judge model at i+1 cycle of operation.
In another embodiment, described warning module, specifically for:
When described probability of malfunction is higher than the first predetermined threshold value, described probability of malfunction is more than Second Threshold higher than the number of times of described first predetermined threshold value, and adjacent twice probability of malfunction higher than described first predetermined threshold value when there is the time interval between the moment less than three threshold values, generate alarm logging.
The intermediate waves transmitter failure prior-warning device that this utility model embodiment provides, by obtaining transmitter history data before i-th cycle of operation and the real-time running data in described i-th cycle of operation, determine the described transmitter breakdown judge model at i-th cycle of operation according to described historical data；And determine described transmitter probability of malfunction in described i-th cycle of operation according to described transmitter at the breakdown judge model of i-th cycle of operation and the real-time running data of described i-th cycle of operation；Finally according to described probability of malfunction, described transmitter is carried out fault pre-alarming.Use the intermediate waves transmitter failure method for early warning that this utility model embodiment provides, can determine, according to the real-time running data of transmitter and breakdown judge model, the probability that transmitter breaks down, in advance the failure condition of described transmitter be made early warning according to described probability of malfunction.Avoid according to transmitter service data, prior art artificially judges that transmitter running status causes the true situation of forecasting inaccuracy..
Accompanying drawing explanation
In order to be illustrated more clearly that this utility model embodiment or technical scheme of the prior art, introduce the accompanying drawing used required in embodiment or description of the prior art is done one simply below, apparently, accompanying drawing in describing below is embodiments more of the present utility model, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
The overall framework figure of Fig. 1 this utility model embodiment intermediate waves broadcast transmitter fault early warning system；
Fig. 2 is the schematic flow sheet that this utility model embodiment intermediate waves broadcast transmitter fault pre-alarming device carries out fault pre-alarming；
Fig. 3 is the schematic flow sheet that this utility model embodiment intermediate waves transmitter failure prior-warning device determines transmitter failure judgment models；
Fig. 4 is the schematic diagram of the similarity curve that this utility model embodiment intermediate waves transmitter failure prior-warning device generates during fault pre-alarming；
Fig. 5 is this utility model embodiment intermediate waves transmitter failure prior-warning device accuracy statistical result schematic diagram when carrying out fault pre-alarming；
Fig. 6 is the structural representation of this utility model embodiment intermediate waves transmitter failure prior-warning device.
Detailed description of the invention
For making the purpose of this utility model embodiment, technical scheme and advantage clearer, below in conjunction with the accompanying drawing in this utility model embodiment, technical scheme in this utility model embodiment is clearly and completely described, obviously, described embodiment is a part of embodiment of this utility model rather than whole embodiments.Based on the embodiment in this utility model, the every other embodiment that those of ordinary skill in the art are obtained under not making creative work premise, broadly fall into the scope of this utility model protection.
Fig. 1 is the overall framework figure of this utility model embodiment intermediate waves broadcast transmitter fault early warning system.
Referring to Fig. 1, this utility model embodiment intermediate waves broadcast transmitter fault early warning system includes: calculated off line platform 10, in real time calculate platform 20 and can interactive user interface 30.Described calculated off line platform includes data memory module (Hadoop) 11 and modeling analysis module 12.Described real-time calculating platform includes real-time computing module (storm) 21 and document database (Mongodb) 22.The external data source of native system comes from the transmitter business datum switching centre launching the station, wherein comprises transmitting equipment operating data.The Computational frame of system uses Lambda framework, manages real-time Computational frame and calculated off line framework simultaneously.Basic data passes through HTML (Hypertext Markup Language) application programming interface (Hyper Text Transfer Protocol Application Programming Interface, it is called for short HTTP API) after 40 entrance systems, carry out preliminary identification filtration through data acquisition module 50, recorded in message queue (Kafka).After data enter Kafka, it is simultaneously in described data memory module 11 and described real-time computing module 21 is respectively used to calculated off line and calculate in real time.
Data are written to data memory module 11 described in described data memory module 11 via described data acquisition module 50 and store.In the data of described data memory module 11, the work such as data operation process can be completed by programming model MapReduce Computational frame and Hive inquiry framework etc..Described modeling analysis module 12 is entered through normalized data.Described modeling analysis module 12 uses logistic regression modeling analysis algorithm to be analyzed the transmitter service data of input, determines that the breakdown judge model of transmitter exports model text.Text, after model transformer 60 is changed, is input in described real-time computing module 21.Along with the continuous accumulation of transmitter service data, model regular update modal analysis results.Result after periodically being updated by described model transformer 60 is input in described real-time computing module 21, continues to calculate transmitter service data in real time according to the breakdown judge model after updating for described real-time computing module 21.Additionally, if the user while empirical rule setting can be have changed by interactive user interface 30 by described, a message informing can be produced in system, arrive described modeling analysis module 12 via described document database 22, according to new rule settings model algorithm, regenerate data results.It is then passed through described model transformer 60, in described real-time computing module, updates corresponding content.
After transmitting equipment operating data arrives described real-time computing module 21 via described data acquisition module 50, message can complete the process of streaming, completes response in real time and processes or calculate.Analysis, statistics task on described real-time computing module 21 carry out concrete calculating process according to the result of model analysis, and result of calculation exports in described document database 22.
Described document database 22 is a document object data base, different and traditional Relational DataBase, it is a kind of unstructured search language (Not Only Structured Query Language, it is called for short NoSQL) data base, it does not has the concept of " OK " of traditional database, each data is one " document ", and a document is the data of a json form.In systems, the output database that described document database 22 will calculate in real time as described real-time computing module 21, described real-time computing module 21 reads the data of Real-time Collection from described data acquisition module 50, after completing to calculate, result of calculation exports described document database 22 and stores.User can obtain real-time accounting report by reading described document database 22 result.
Described real-time computing module 21 exports the real-time result of calculation in described document database 22, the real-time report page in Operation and Maintenance Center read, and be shown to described can be on interactive user interface 30.Described can mainly provide three functions by interactive user interface 30: the setting of the visual presentation of equipment running status, empirical rule, history run status poll etc..Can write music line with the holistic health of Real Time Observation transmitter in Web page, when there is unit exception, according to abnormal different brackets, produce Multi-stage alarming respectively, such as: health degree moderate alarm (yellow), health degree severe alarm (orange), transmitter failure early warning (red).
Fig. 2 is the schematic flow sheet that this utility model embodiment intermediate waves broadcast transmitter fault pre-alarming device carries out fault pre-alarming.
Referring to Fig. 2, the intermediate waves transmitter failure prior-warning device that this utility model embodiment provides carries out the process of fault pre-alarming and comprises the following steps:
S101: obtaining transmitter history data before i-th cycle of operation and the real-time running data in described i-th cycle of operation, described history data includes normal data and fault data；I is the integer more than or equal to 1；
Specifically, described history data is to come into the transmitter service data being stored in described data memory module 11 before described i-th cycle of operation.
Before the normal data in history data before described acquisition described i-th cycle of operation, the data to entering described intermediate waves broadcast transmitter fault early warning system are needed to be carried out.Concrete data cleansing mode is as follows:
Read the data after cleaning filing in described data memory module 11, according to data cleansing redundant rule elimination interference data and fault data, be all healthy service data with ensure entrance model analysis.The rule settings of abnormal data is based on the service chart of Broadcast and TV system different transmitters, and broadcasting and TV rules and regulations (" before and after the transmitter pilot tone phase three minutes, warning of breaking down belongs to normal condition ").
Data cleansing rule settings is specific as follows:
1, broadcasting and TV return data is to return with the second by regulation, but occasional exists data back less than, or blocking of causing of a variety of causes.If during data truncation, transmitter breaks down, data model distortion can be caused.So setting rule in advance, within the properly functioning cycle, time to chopping was more than 5 minutes namely 300 seconds, by a period of state data deletion before point of cut-off.
2, only retaining state in broadcasting and TV return data is 11 (operations) or the data of 30 (faults).
3, it is the reliability guaranteeing service data, therefore state is run discontented 2 minute datas and also deletes.
4, it is to guarantee the speed of data platform computing, only retains away from the present nearest 60 day data.
5, for guaranteeing speed and the accuracy that model sets up, the record of identical running status is only retained one, remaining deletion.
If 6 break down, trouble point to this (my god) the earliest run duration data all delete, it is ensured that retain normal data as far as possible.
After data are carried out by above steps, i.e. can get described normal data.The data that state is high frequency broadcast that fault occurs first 3 minutes are left fault data.Two sample sets obtained after cleaning are " normal data set " that include described normal data and " the fault data collection " that include described fault data.
S102: determine the described transmitter breakdown judge model at i-th cycle of operation according to described normal data and fault data；
Specifically, described determine transmitter failure judgment models according to described normal data and fault data, including:
Described normal data and described fault data are normalized；
Described normal data after normalized and described fault data are ranked up, and described normal data and fault data after sequence are carried out equidistant sampling；
It is modeled by the sample of the logistic regression analysis described normal data to obtaining after equidistant sampling and described abnormal data, determines described transmitter failure judgment models.
Fig. 3 is the schematic flow sheet that this utility model embodiment intermediate waves transmitter failure prior-warning device determines transmitter failure judgment models.
Refer to Fig. 3, during implementing, determine that transmitter failure judgment models comprises the following steps:
1, the equidistant sampling of data
First the normal data after screening and fault data are ranked up, the data after sequence are carried out equidistant sampling, with smallest sample collection 20% for extraction radix.The method using equidistant sampling guarantees that sample data is minimum spacing, so that it is guaranteed that sampling is uniform.
Sort algorithm is based on Euclid norm principle, will all fields carry out square, is added and carries out evolution the most again.Equal in a disguised form multidimensional data being integrated into one-dimensional data, then it is ranked up.
Sort algorithm formula is:
Wherein, x represents the field often gone or in every column data, and n represents the quantity of the field often gone or in every column data.
Such as normal sample set sum 100, fault sample set sum 50, that unification is respectively extracted 20 in the way of equidistant sampling according to normal sample set and is modeled.
Sampling algorithm is by circulation operation, and setting one " initial point " is 1, and at " initial point+extraction be spaced " one point of random choose in the range of this, then " initial point " is updated to " initial point+extraction is spaced ".Till being drawn into the extraction quantity of needs always.
2, the normalized of variable
All data are normalized, the dimension relation between uniform variable.Noticing that in data, Partial Variable value may be unchanged, such data normalization will be failed, so needing to delete these part data further.
Transmitter service data mostly is discrete type, therefore uses zero-mean standardization.
Normalization formula is:
Wherein, z represents the normalization result of the service data of each cycle of operation, x represents often row or each column service data value after sort algorithm processes, μ represents the average of all service datas, and σ represents the variance between described often row or each column service data value and the average of described service data after sort algorithm processes.
3, logistic regression judges transmitter failure probability
Logistic regression is a kind of generalized linear regression, is the conventional mathematical modeies of a kind of two classification.This utility model embodiment intermediate waves transmitter failure method for early warning is basic to be predicted as, and purpose is that to judge that the probability that transmitter breaks down has much.
Logic-based returns principle, the set of data samples of two equivalent is modeled, and generates the fault pre-alarming model of transmitter, and the result of described fault pre-alarming model is every time using probability as output.
S103: determine described transmitter probability of malfunction in described i-th cycle of operation at the breakdown judge model of i-th cycle of operation and the real-time running data of described i-th cycle of operation according to described transmitter.
Specifically, the described real-time running data according to described transmitter failure judgment models and described i-th cycle of operation determines described transmitter probability of malfunction in described i-th cycle of operation, including: according to
Determine described transmitter probability of malfunction P (z) in described i-th cycle of operation, wherein, z represents the real-time running data of described i-th cycle of operation, β represents the mapping relations of described normal data and described fault data, is to obtain through training according to the normal data in a large amount of historical datas and fault data.
S104: described transmitter is carried out fault pre-alarming according to described probability of malfunction and default early warning rule.
Specifically, described default early warning rule can be: when described probability of malfunction is higher than the first predetermined threshold value, described probability of malfunction is more than Second Threshold higher than the number of times of described first predetermined threshold value, and adjacent twice probability of malfunction higher than described first predetermined threshold value when there is the time interval between the moment less than three threshold values, generate alarm logging.
Concrete prealarming process is as follows:
1, similarity curve is generated
Fig. 4 is the schematic diagram of the similarity curve that this utility model embodiment intermediate waves transmitter failure prior-warning device generates during fault pre-alarming.
Referring to Fig. 4, each real time data of transmitter apparatus can be calculated, by logistic regression, the possible probability that a fault occurs.Along with the operation of equipment, real-time running data continually enters in model, thus obtains the similarity curve of transmitter apparatus running status.This similarity curve out, features the instant health status of transmitting equipment with visual mode real-time exhibition in Web page.
2, exceptionization normality filters
Often there is the situation of sporadic condition of instant error in transmitter, but abnormal sign the most no longer occurs.This situation tends not to affect normal broadcast, for a kind of normality of transmitter, the exception normality that this phenomenon is referred to as in model.Monitoring transmitter health degree when, need this exceptionization normality is filtered, otherwise can be greatly increased the false alarm rate of system.
The filtering rule of exceptionization normality is: user can input the parameters such as " early warning line ", " warning cumulative frequency ", " alarm interval duration " in Web page according to plant maintenance experience.Only when health degree (similarity that model calculates) is less than " early warning line ", and when " alarm interval duration " is more than n times less than M moment and " warning cumulative frequency ", system just can generate an alarm logging.Alarm logging comprises the variablees such as early warning time started, advanced warning grade, early warning persistent period.
Such as setting " warning cumulative frequency " is 10, and " alarm interval duration " is 60 seconds, and " early warning line " is 0.1.So after health degree value is less than 0.1, alarm times adds 1 and adds up to 1.The situation less than 0.1 whether is also had to occur in checking the most abnormal latter 60 seconds.Were it not for appearance, alarm times adds up to 1, then this time do not generate alarm logging.
If again occurring when the 59th second that the situation less than 0.1 occurs, alarm times adds 1 and adds up to 2, and the situation less than " early warning line " occurs in continuing to check ensuing 60 seconds the most again.If being accumulated to 10 times, then generate alarm logging.
3, classifying alarm
Transmitter holistic health degree can be carried out the warning of three ranks, respectively: health degree moderate alarm (yellow), health degree severe warning (orange), transmitter failure early warning (red).The most corresponding different rule settings of reporting to the police of these three grade, the parameter such as the most different grades of " early warning line ", " warning cumulative frequency ", " alarm interval duration " sets.Adjustable classifying alarm, meets broadcasting and TV side's business demand to fault pre-alarming management work.
On the basis of above-described embodiment, further, more accurate to the judgement of fault pre-alarming in order to ensure in i+1 the cycle, described method also includes:
Real time data according to the described i-th cycle of operation obtained updates the described transmitter breakdown judge model at i+1 cycle of operation.
Specifically, after described i-th cycle of operation terminates, the service data of described i-th cycle of operation becomes historical data, stores in described data memory module 11.Described fault pre-alarming model is updated by described modeling analysis module 12 according to the latest data after adding the service data of i-th cycle of operation, according to the fault pre-alarming model after updating, the data of i+1 cycle of operation are calculated in real time, determine the described transmitter probability of malfunction at described i+1 cycle of operation.
Fig. 5 is this utility model embodiment intermediate waves transmitter failure prior-warning device accuracy statistical result schematic diagram when carrying out fault pre-alarming.
Refer to Fig. 5, added up within one month, after system provides fault pre-alarming, at 24 hours, 2 days, the probability of equipment generation physical fault within 3 days.When the alarm threshold value difference set, the sensitivity of system is different.Alarm threshold value is the lowest, and early warning number of times is the fewest, and early warning accuracy is the highest, but false dismissed rate also can increase accordingly.Therefore, it is not that threshold value sets the lowest more good, it should consider the balance between accuracy and false dismissed rate, in accuracy tolerance interval, reduce false dismissed rate as far as possible.According to using this utility model embodiment intermediate waves fault early warning method to carry out the statistics of the accuracy corresponding to early warning under various threshold conditions, when the threshold value of warning of health degree curve is set as 0.00008, if there are unusual fluctuations in transmitter, model provides fault pre-alarming, in 24 hours, the ratio of device fails is 58% subsequently, the ratio broken down in 2 days is 89%, and the ratio broken down in 3 days is 97%.This result can preferably meet the business demand of the equipment control of Radio, Film and Television Administration.
The intermediate waves transmitter failure method for early warning that this utility model embodiment provides, by obtaining transmitter history data before i-th cycle of operation and the real-time running data in described i-th cycle of operation, determine the described transmitter breakdown judge model at i-th cycle of operation according to described historical data；And determine described transmitter probability of malfunction in described i-th cycle of operation according to described transmitter at the breakdown judge model of i-th cycle of operation and the real-time running data of described i-th cycle of operation；Finally according to described probability of malfunction, described transmitter is carried out fault pre-alarming.Use the intermediate waves transmitter failure method for early warning that this utility model embodiment provides, can determine, according to the real-time running data of transmitter and breakdown judge model, the probability that transmitter breaks down, in advance the failure condition of described transmitter be made early warning according to described probability of malfunction.Avoid according to transmitter service data, prior art artificially judges that transmitter running status causes the true situation of forecasting inaccuracy.
Fig. 6 is the structural representation of this utility model embodiment intermediate waves transmitter failure prior-warning device.Referring to Fig. 3, the intermediate waves transmitter failure prior-warning device device that this utility model embodiment provides at least includes:
Acquisition module 610, for obtaining the real-time running data in transmitter history data before i-th cycle of operation and described i-th cycle of operation；
Modeling analysis module 620, for determining the described transmitter breakdown judge model at i-th cycle of operation according to described normal data and fault data；I is the integer more than or equal to 1；
Computing module 630, for determining described transmitter probability of malfunction in described i-th cycle of operation according to described transmitter at the breakdown judge model of i-th cycle of operation and the real-time running data of described i-th cycle of operation；
Warning module 640, for carrying out fault pre-alarming according to described probability of malfunction and default early warning rule to described transmitter.
Described modeling analysis module 620, specifically for:
Described normal data and described fault data are normalized；
Described normal data after normalized and described fault data are ranked up, and described normal data and fault data after sequence are carried out equidistant sampling；
It is modeled by the sample of the logistic regression analysis described normal data to obtaining after equidistant sampling and described abnormal data, determines described transmitter failure judgment models.
Described computing module 630, specifically for:
According to
Determining described transmitter probability of malfunction in described i-th cycle of operation, wherein, β represents the mapping relations of described normal data and described fault data, and z represents the real-time running data of described i-th cycle of operation.
Described modeling analysis module 620, is additionally operable to:
Real time data according to the described i-th cycle of operation obtained updates the described transmitter breakdown judge model at i+1 cycle of operation.
Described warning module 640, specifically for:
When described probability of malfunction is higher than the first predetermined threshold value, described probability of malfunction is more than Second Threshold higher than the number of times of described first predetermined threshold value, and adjacent twice probability of malfunction higher than described first predetermined threshold value when there is the time interval between the moment less than three threshold values, generate alarm logging.
The intermediate waves transmitter failure prior-warning device that this utility model embodiment provides, obtaining transmitter history data before i-th cycle of operation and the real-time running data in described i-th cycle of operation by acquisition module, modeling analysis module determines the described transmitter breakdown judge model at i-th cycle of operation according to described historical data；Computing module determines described transmitter probability of malfunction in described i-th cycle of operation according to described transmitter at the breakdown judge model of i-th cycle of operation and the real-time running data of described i-th cycle of operation；Finally according to described probability of malfunction, described transmitter is carried out fault pre-alarming by warning module.Use the intermediate waves transmitter failure method for early warning that this utility model embodiment provides, can determine, according to the real-time running data of transmitter and breakdown judge model, the probability that transmitter breaks down, in advance the failure condition of described transmitter be made early warning according to described probability of malfunction.Avoid according to transmitter service data, prior art artificially judges that transmitter running status causes the true situation of forecasting inaccuracy.
Specifically, the shape library extraction element that this utility model embodiment provides is for performing the intermediate waves transmitter failure method for early warning that said method embodiment provides, and it realizes principle and technique effect is similar to, and does not repeats them here.
One of ordinary skill in the art will appreciate that: all or part of step realizing above-mentioned each method embodiment can be completed by the hardware that programmed instruction is relevant.Aforesaid program can be stored in the read/write memory medium of a computer, mobile phone or other portable units.This program upon execution, performs to include the step of above-mentioned each method embodiment；And aforesaid storage medium includes: the various media that can store program code such as ROM, RAM, magnetic disc or CDs.
Last it is noted that various embodiments above is only in order to illustrate the technical solution of the utility model, it is not intended to limit；Although this utility model being described in detail with reference to foregoing embodiments, it will be understood by those within the art that: the technical scheme described in foregoing embodiments still can be modified by it, or the most some or all of technical characteristic is carried out equivalent；And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of this utility model each embodiment technical scheme.
Claims (5)
1. an intermediate waves transmitter failure prior-warning device, it is characterised in that including:
Acquisition module, for obtain transmitter history data before i-th cycle of operation and
Real-time running data in described i-th cycle of operation；
The modeling analysis module being connected with described acquisition module, for according to described normal data and fault
Data determine the described transmitter breakdown judge model at i-th cycle of operation；I is more than or equal to 1
Integer；
The computing module being connected with described acquisition module and described modeling analysis module respectively, for basis
Described transmitter is in the breakdown judge model of i-th cycle of operation and the reality of described i-th cycle of operation
Time service data determine described transmitter probability of malfunction in described i-th cycle of operation；
The warning module being connected with described computing module, for according to described probability of malfunction and default pre-
Police regulations then carry out fault pre-alarming to described transmitter.
Device the most according to claim 1, it is characterised in that described modeling analysis module,
Specifically for:
Described normal data and described fault data are normalized；
Described normal data after normalized and described fault data are ranked up, and right
After sequence, described normal data and fault data carry out equidistant sampling；
By the logistic regression analysis described normal data to obtaining after equidistant sampling and described different
The sample of regular data is modeled, and determines described transmitter failure judgment models.
Device the most according to claim 1, it is characterised in that described computing module, specifically
For:
According to
Determining described transmitter probability of malfunction in described i-th cycle of operation, wherein, β represents
Described normal data and the mapping relations of described fault data, z represents described i-th cycle of operation
Real-time running data.
4. according to the device described in any one of claim 1-3, it is characterised in that described modeling analysis
Module, is additionally operable to:
Real time data according to the described i-th cycle of operation obtained updates described transmitter in i+1
The breakdown judge model of individual cycle of operation.
5. according to the device described in any one of claim 1-3, it is characterised in that described warning module,
Specifically for:
When described probability of malfunction is preset higher than described first higher than the first predetermined threshold value, described probability of malfunction
The number of times of threshold value is more than Second Threshold, and adjacent twice probability of malfunction is higher than described first predetermined threshold value
When there is the time interval between the moment less than three threshold values, generate alarm logging.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109034423A (en) * | 2018-08-29 | 2018-12-18 | 郑州云海信息技术有限公司 | A kind of method, apparatus, equipment and storage medium that fault pre-alarming determines |
| CN111934895A (en) * | 2019-05-13 | 2020-11-13 | 中国移动通信集团湖北有限公司 | Intelligent early warning method and device for network management system and computing equipment |
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2016
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109034423A (en) * | 2018-08-29 | 2018-12-18 | 郑州云海信息技术有限公司 | A kind of method, apparatus, equipment and storage medium that fault pre-alarming determines |
| CN109034423B (en) * | 2018-08-29 | 2023-04-18 | 郑州云海信息技术有限公司 | Fault early warning judgment method, device, equipment and storage medium |
| CN111934895A (en) * | 2019-05-13 | 2020-11-13 | 中国移动通信集团湖北有限公司 | Intelligent early warning method and device for network management system and computing equipment |
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