CN108599995B - Network line fault determination method and server - Google Patents

Network line fault determination method and server Download PDF

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CN108599995B
CN108599995B CN201810263719.2A CN201810263719A CN108599995B CN 108599995 B CN108599995 B CN 108599995B CN 201810263719 A CN201810263719 A CN 201810263719A CN 108599995 B CN108599995 B CN 108599995B
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quantity ratio
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CN108599995A (en
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段苏敏
李正平
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Beijing Dami Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/069—Management of faults, events, alarms or notifications using logs of notifications; Post-processing of notifications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0677—Localisation of faults
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0604—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0604—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
    • H04L41/0622—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time based on time
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

Abstract

The invention discloses a network line fault judgment method and a server, wherein the specific implementation mode of the method comprises the following steps: acquiring historical data of error reporting information, and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days; establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point; and acquiring error reporting information of the line at the current time point in real time, calculating the error reporting quantity ratio, and judging that the line has a fault if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value. The implementation mode has the advantages of strong real-time performance, low cost and high accuracy.

Description

Network line fault determination method and server
Technical Field
The invention relates to the technical field of networks. And more particularly, to a network line fault determination method and a server.
Background
The existing peer-to-peer network takes an online education network as an example, and is characterized in that the physical distances of terminals of both teachers and students are possibly far, the network usually comprises edge nodes and center nodes, in the online education network, the terminals of both teachers and students are respectively connected with the center nodes through the corresponding edge nodes, so that bidirectional communication between the terminals of both teachers and students is realized, and bidirectional communication links between the terminals of both teachers and students are called as lines.
In the online classroom, if the terminal of the teacher or the student can not see the video or hear the audio of the other party, the teacher or the student can upload the error information through the terminal to report the error. The reason why the terminal of the teacher or the student cannot see the video or hear the audio of the other party may be that the line between the terminals of the teacher and the student has a network fault, or may not be a network problem, but the student does not enter the network classroom. Therefore, there is a part of the error information that reflects not the network failure.
At present, the network fault of the online education network is usually determined by monitoring the working state (e.g. delay of transmitting and receiving data packets, packet loss rate, etc.) of each node including the edge node and the central node in the network in real time. This approach requires high cost and cannot be monitored for a line.
Therefore, it is desirable to provide an optimized network line fault determination method and server.
Disclosure of Invention
The invention aims to provide a network line fault determination method and a server.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the present invention provides a method for determining a network line fault, including:
acquiring historical data of error reporting information, and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days;
establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and acquiring error reporting information of the line at the current time point in real time, calculating the error reporting quantity ratio, and judging that the line has a fault if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value.
Preferably, the continuous time points in the network classroom time period are selected as fixed time points in units of seconds.
Preferably, the method further comprises: and carrying out invalid error reporting information filtering on the error reporting information of the line at the current time point acquired in real time.
Preferably, the method further comprises: when the line is judged to have a fault, whether the fault occurs or not is verified and/or fault analysis is carried out according to the working logs of all the nodes in the line at the current time point.
Preferably, the method further comprises: when it is determined that a line has a fault, the line is switched to another line.
A second aspect of the present invention provides a server, comprising:
the statistical unit is used for acquiring historical data of the error reporting information and counting the ratio of the error reporting quantity of the line in a plurality of fixed time periods in a plurality of days;
the prediction unit is used for acquiring historical data of error reporting information and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days; establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and the judging unit is used for acquiring error reporting information of the line at the current time point in real time and calculating the error reporting quantity ratio, and if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value, judging that the line has a fault.
Preferably, the statistical unit selects continuous time points in the network classroom time period as fixed time points in units of seconds.
Preferably, the server further comprises:
and the filtering unit is used for filtering invalid error reporting information of the line at the current time point, which is acquired by the judging unit in real time.
Preferably, the server further comprises:
and the fault analysis unit is used for verifying whether the fault occurs and/or performing fault analysis according to the working log of each node in the line at the current time point when the judgment unit judges that the line has the fault.
Preferably, the server further comprises:
and a switching unit that switches the line to another line when the determining unit determines that the line has a failure.
A third aspect of the present invention provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the network line fault determination method provided in the first aspect of the present invention is implemented.
The invention has the following beneficial effects:
the technical scheme of the invention judges the line fault based on the error reporting information uploaded by the user, and has strong real-time performance, low cost and high accuracy.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings;
fig. 1 is a flowchart illustrating a line fault determination method of an online education network according to an embodiment of the present invention.
Fig. 2 shows a distribution graph of the ratio of the number of errors reported by the line at a plurality of fixed time points in a plurality of days.
Fig. 3 shows a prediction graph.
Fig. 4 is a schematic diagram of a server provided in an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the invention, the invention is further described below with reference to preferred embodiments and the accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides a line fault determination method for an online education network, including:
acquiring historical data of error reporting information, and counting the ratio of error reporting quantity of a line at a plurality of fixed time points in a plurality of days, wherein the ratio of error reporting quantity is the ratio of the error reporting quantity of the line at a certain time point to the total number of network classes at the time point, for example, the total number of network classes of the line at 18:15 is 25, and if 7 error reporting occurs, the ratio of error reporting quantity is 7/25;
establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and acquiring error reporting information of the line at the current time point in real time, calculating the error reporting quantity ratio, and judging that the line has a fault if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value.
The line fault determination method for the online education network provided by the embodiment can determine whether the line has a fault according to the ratio of the number of errors reported by the line at the current time point, and has the advantages of strong real-time performance, low cost and high accuracy.
In specific implementation, the embodiment selects continuous time points in the network classroom time interval as fixed time points in units of seconds. For example, the network classroom time interval of each day is 18:00-18:30, the ratio of the error reporting quantity of the line at the continuous time points in 18:00-18:30 in a plurality of days is counted.
In specific implementation, this embodiment further includes: and carrying out invalid error reporting information filtering on the error reporting information of the line at the current time point acquired in real time. This may make the determination more accurate, and it should be noted that the history data of the error information is also the history data after the invalid error information is filtered.
In specific implementation, this embodiment further includes: when the line is judged to have a fault, whether the fault occurs or not is verified and/or fault analysis is carried out according to the working logs of all the nodes in the line at the current time point. Therefore, the judgment is more accurate, the misjudgment condition is eliminated, and the position of the fault in the line can be analyzed.
In specific implementation, this embodiment further includes: when it is determined that a line has a fault, the line is switched to another line. Therefore, the circuit can be automatically switched quickly when the circuit is judged to have faults, and the normal operation of a network classroom is guaranteed.
The method for determining a line fault in an online education network according to the present embodiment will be further described below by substituting specific data.
According to the service characteristics of the network classroom, the data distribution trend of each time interval of each line is similar, and the time intervals of a certain line 18:00-18:30 are taken as an example in the example.
Each piece of data in the history data as error reporting information of the data sample comprises: the ratio of the number of error reports at a certain time point on a certain date to the number of error reports at that time point.
As shown in fig. 2, fig. 2 is a distribution curve of the ratio of the number of errors reported at a time point of a certain line in a period of 18:00 to 18:30 among a plurality of dates, the x-axis is the number of seconds relative to the starting time of the period, and the y-axis is the ratio of the number of errors reported at the time point (for each time point, the ratio of the number of errors reported at the time point of a plurality of dates exists).
In this example, a fault reporting number ratio prediction model of the line is established based on polynomial curve fitting, and a 9 th-order polynomial model is adopted:
hθ(x)＝θ0+θ1x+θ2x2+θ3x3+θ4x4+θ5x5+θ6x8+θ7x7+θ8x8+θ9x9
x is the characteristic value of the data sample, θ0-θ9Is a parameter that takes a real number.
Then, through feature scaling, the high-order equation model is changed into a multivariate linear regression model, and the specific method is that:
x1＝x,x2＝x2,x3＝x3,x4＝x4,x5＝x,
x6＝x6,x7＝x7,x8＝x8,x9＝x9
each data is marked with a characteristic of (x)1，x2，…，x9) Obtaining a multivariate linear regression model:
hθ(x)＝θ0+θ1x1+θ2x2+θ3x3+θ4x4+θ5x5+θ6x6+θ7x7+θ8x8+θ9x9
it can be seen that there are 10 parameters and 9 variables in the expression, and the parameter theta of the variable is1，θ2，…，θ9Vectorization is a 9-dimensional vector θ, and the model function can be simplified as:
hθ(X)＝θTX+b
where b is θ, θTIs a transpose of theta. For the ith data sample at a certain point in time, xiIs a 9-dimensional vector; for m data samplesThe dimension of the feature matrix X is m 9, m represents the days of the date, and the value is a positive integer.
After the model function is obtained, the next step is to construct a cost function, which in this example uses MSE (mean square error):
the cost function is:
Figure BDA0001610833620000051
m is the number of data samples at a certain time point, the value is a positive integer, x(i)Is the eigenvalue vector (9-dimensional vector) of the ith data sample, h(i)Is proportional to the number of errors reported in the ith data sample.
By the gradient descent method:
Figure BDA0001610833620000052
Figure BDA0001610833620000053
where α is the learning rate of the model (hyper-parametric, without training), and a set of parameters θ and b that minimize the value of the cost function J (θ, b) are found by iterative iterations.
To this end, a model function is obtained: h isθ(X)＝θTX + b, and m sample sets (X, h). According to the model function, the predicted value h of the m data samples can be calculatedθAccording to h and hθAn average error value between them is calculated.
Initially, a set of parameters is randomly selected, after all the predicted results are calculated, the parameter values are updated, the process is repeated until convergence, and finally, a curve is fitted as shown in fig. 3.
And then, predicting the threshold value of each time point in real time according to the prediction model, and analyzing error information of the user in real time. The real-time prediction of the threshold value of each time point according to the model specifically comprises the following steps: and predicting the error-reporting feedback ratio predicted value as a threshold value according to the fitted prediction model. The real-time analysis of the error information of the user specifically comprises the following steps: analyzing the error between the current error reporting feedback ratio and the threshold value; and if the error of the current error reporting feedback ratio and the threshold exceeds the average error value, judging the current line fault.
As shown in fig. 4, another embodiment of the present invention provides a server including:
the statistical unit is used for acquiring historical data of the error reporting information and counting the ratio of the error reporting quantity of the line in a plurality of fixed time periods in a plurality of days;
the prediction unit is used for acquiring historical data of error reporting information and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days; establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and the judging unit is used for acquiring error reporting information of the line at the current time point in real time and calculating the error reporting quantity ratio, and if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value, judging that the line has a fault.
In specific implementation, the statistical unit selects continuous time points in the network classroom time interval as fixed time points in units of seconds.
In specific implementation, the server further includes:
and the filtering unit is used for filtering invalid error reporting information of the line at the current time point, which is acquired by the judging unit in real time.
In specific implementation, the server further includes:
and the fault analysis unit is used for verifying whether the fault occurs and/or performing fault analysis according to the working log of each node in the line at the current time point when the judgment unit judges that the line has the fault.
In specific implementation, the server further includes:
and a switching unit that switches the line to another line when the determining unit determines that the line has a failure.
It should be noted that the principle and the workflow of the server provided in this embodiment are similar to the line fault determination method of the online education network, and reference may be made to the above description for relevant points, which are not described herein again.
As shown in fig. 5, a computer system suitable for implementing the server provided in the present embodiment includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage section into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the computer system are also stored. The CPU, ROM, and RAM are connected thereto via a bus. An input/output (I/O) interface is also connected to the bus.
An input section including a keyboard, a mouse, and the like; an output section including a speaker and the like such as a Liquid Crystal Display (LCD); a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN card, a modem, or the like. The communication section performs communication processing via a network such as the internet. The drive is also connected to the I/O interface as needed. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive as necessary, so that a computer program read out therefrom is mounted into the storage section as necessary.
In particular, it is mentioned that the processes described in the above flowcharts can be implemented as computer software programs according to the present embodiment. For example, the present embodiments include a computer program product comprising a computer program tangibly embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium.
The flowchart and schematic diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to the present embodiments. In this regard, each block in the flowchart or schematic diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the schematic and/or flowchart illustration, and combinations of blocks in the schematic and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the present embodiment may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a statistics unit, a prediction unit, and a decision unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves. For example, the determination unit may also be described as a "unit for determining a line fault".
On the other hand, the present embodiment also provides a nonvolatile computer storage medium, which may be the nonvolatile computer storage medium included in the terminal in the foregoing embodiment, or may be a nonvolatile computer storage medium that exists separately and is not assembled into the terminal. The non-volatile computer storage medium stores one or more programs that, when executed by a server, cause the server to:
acquiring historical data of error reporting information, and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days;
establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and acquiring error reporting information of the line at the current time point in real time, calculating the error reporting quantity ratio, and judging that the line has a fault if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value.
In the description of the present invention, it should be noted that the terms "upper", "lower", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It is further noted that, in the description of the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations and modifications can be made on the basis of the above description, and all embodiments cannot be exhaustive, and all obvious variations and modifications belonging to the technical scheme of the present invention are within the protection scope of the present invention.

Claims (11)

1. A method for determining a network line fault, comprising:
acquiring historical data of error reporting information, and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days;
establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and acquiring error reporting information of the line at the current time point in real time, calculating the error reporting quantity ratio, and judging that the line has a fault if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value.
2. The network line fault determination method according to claim 1, wherein the continuous time points in the network class period are selected as fixed time points in units of seconds.
3. The network line fault determination method according to claim 1, characterized by further comprising: and carrying out invalid error reporting information filtering on the error reporting information of the line at the current time point acquired in real time.
4. The network line fault determination method according to claim 1, characterized by further comprising: when the line is judged to have a fault, whether the fault occurs or not is verified and/or fault analysis is carried out according to the working logs of all the nodes in the line at the current time point.
5. The network line fault determination method according to claim 1, characterized by further comprising: when it is determined that a line has a fault, the line is switched to another line.
6. A server, comprising:
the statistical unit is used for acquiring historical data of the error reporting information and counting the ratio of the error reporting quantity of the line in a plurality of fixed time periods in a plurality of days;
the prediction unit is used for acquiring historical data of error reporting information and counting the error reporting quantity ratio of a line at a plurality of fixed time points in a plurality of days; establishing a fault reporting quantity ratio prediction model of the line based on polynomial curve fitting, fitting a prediction curve according to the prediction model, and calculating a fault reporting quantity ratio prediction value and an average error value of the line at each fixed time point;
and the judging unit is used for acquiring error reporting information of the line at the current time point in real time and calculating the error reporting quantity ratio, and if the error reporting quantity ratio of the line at the current time point exceeds the numerical value of the corresponding predicted value and is greater than the average error value of the predicted value, judging that the line has a fault.
7. The server according to claim 6, wherein the statistical unit selects successive time points in the network classroom period as fixed time points in seconds.
8. The server of claim 6, further comprising:
and the filtering unit is used for filtering invalid error reporting information of the line at the current time point, which is acquired by the judging unit in real time.
9. The server of claim 6, further comprising:
and the fault analysis unit is used for verifying whether the fault occurs and/or performing fault analysis according to the working log of each node in the line at the current time point when the judgment unit judges that the line has the fault.
10. The server of claim 6, further comprising:
and a switching unit that switches the line to another line when the determining unit determines that the line has a failure.
11. A server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-5 when executing the program.
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* Cited by examiner, † Cited by third party
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CN112363442A (en) * 2020-10-19 2021-02-12 云南电网有限责任公司 Method for predicting, detecting and disposing machine room equipment fault alarm
CN112629709B (en) * 2020-12-21 2023-10-20 广东高标电子科技有限公司 Temperature sensor fault detection method, detection device and electric vehicle controller
CN115101187B (en) * 2022-07-14 2022-11-15 西南医科大学附属医院 Anesthesia machine operation fault prediction system based on big data

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105162994A (en) * 2015-09-24 2015-12-16 携程计算机技术（上海）有限公司 Method and system for detecting traffic fault of call center and server
CN106874280A (en) * 2015-12-10 2017-06-20 博雅网络游戏开发（深圳）有限公司 The alarm method and device of abnormal data
CN107819631A (en) * 2017-11-23 2018-03-20 东软集团股份有限公司 A kind of unit exception detection method, device and equipment

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101551884B (en) * 2009-05-08 2011-07-27 华北电力大学 A fast CVR electric load forecast method for large samples
KR101748122B1 (en) * 2015-09-09 2017-06-16 삼성에스디에스 주식회사 Method for calculating an error rate of alarm
US10268561B2 (en) * 2016-02-22 2019-04-23 International Business Machines Corporation User interface error prediction

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105162994A (en) * 2015-09-24 2015-12-16 携程计算机技术（上海）有限公司 Method and system for detecting traffic fault of call center and server
CN106874280A (en) * 2015-12-10 2017-06-20 博雅网络游戏开发（深圳）有限公司 The alarm method and device of abnormal data
CN107819631A (en) * 2017-11-23 2018-03-20 东软集团股份有限公司 A kind of unit exception detection method, device and equipment

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