CN117118807B - Data analysis method and system based on artificial intelligence - Google Patents
Data analysis method and system based on artificial intelligence Download PDFInfo
- Publication number
- CN117118807B CN117118807B CN202311299570.0A CN202311299570A CN117118807B CN 117118807 B CN117118807 B CN 117118807B CN 202311299570 A CN202311299570 A CN 202311299570A CN 117118807 B CN117118807 B CN 117118807B
- Authority
- CN
- China
- Prior art keywords
- communication equipment
- equipment cabinet
- monitoring
- data
- server
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000007405 data analysis Methods 0.000 title claims abstract description 23
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 18
- 238000004891 communication Methods 0.000 claims abstract description 198
- 238000012544 monitoring process Methods 0.000 claims abstract description 168
- 230000002159 abnormal effect Effects 0.000 claims abstract description 66
- 238000004458 analytical method Methods 0.000 claims abstract description 52
- 230000005856 abnormality Effects 0.000 claims abstract description 37
- 238000012423 maintenance Methods 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 25
- 230000005540 biological transmission Effects 0.000 claims description 77
- 230000008569 process Effects 0.000 claims description 15
- 238000012546 transfer Methods 0.000 claims description 2
- 230000008439 repair process Effects 0.000 abstract description 5
- 230000015556 catabolic process Effects 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000006731 degradation reaction Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 239000013307 optical fiber Substances 0.000 description 5
- 238000013461 design Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000017525 heat dissipation Effects 0.000 description 2
- 238000011056 performance test Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
Classifications
-
- 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
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
Abstract
The invention discloses a data analysis method and a system based on artificial intelligence, comprising a data acquisition module, a central processing unit, an abnormality sensing module and an abnormality analysis module; the data acquisition module acquires a plurality of pieces of information when the communication equipment cabinet operates, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and the electric power environment monitoring information and the software performance monitoring information are transmitted to the central processing unit after being processed after being acquired. According to the invention, through sensing the situation that the running state of the communication equipment cabinet is poor, when the running state of the communication equipment cabinet is abnormal, the situation is found in time, and the communication equipment cabinet is maintained and managed in advance, so that the communication equipment is prevented from being broken down due to the fact that the influence caused by the abnormality is increased, the difficulty of maintenance of the communication equipment cabinet is effectively reduced, and the fault that the communication equipment cabinet is difficult to repair is effectively prevented.
Description
Technical Field
The invention relates to the technical field of monitoring of communication equipment cabinets, in particular to a data analysis method and system based on artificial intelligence.
Background
A communication equipment cabinet is a physical structure for installing, managing, and protecting communication equipment. The communication equipment cabinet generally comprises switching equipment, a server, storage equipment, a firewall, power equipment, network management equipment, fans, heat dissipation equipment, optical fiber terminal equipment and the like;
switching device: such as a switch, a router and the like, which are used for realizing the functions of forwarding and routing data and are responsible for connection and control of network communication;
and (3) a server: for providing various network services, such as Web servers, mail servers, database servers, etc., for storing and processing data;
a storage device: including disk arrays (RAID) and network storage devices (NAS) for storage and backup of data, providing a high capacity and high reliability storage solution;
a firewall: the method is used for protecting network security, monitoring and filtering network traffic, and preventing potential malicious attacks and unauthorized access;
a power supply device: the system comprises a Power Supply Unit (PSU) and an Uninterruptible Power Supply (UPS), wherein the PSU and the UPS are used for providing stable power supply, ensuring the normal operation of communication equipment and preventing data loss;
network management device: such as a network switch, a management control unit, etc., for managing and monitoring the communication equipment, and implementing remote management and fault diagnosis;
fan and heat sink device: the device is used for maintaining the proper temperature in the communication equipment cabinet, keeping the normal heat dissipation of equipment and preventing equipment failure caused by overheating;
optical fiber terminal equipment: such as optical fiber transceiver, optical fiber exchanger, etc. for connection and transmission of optical fiber network, realizing high-speed and long-distance data transmission;
the prior art has the following defects:
the management of the communication equipment cabinet is carried out, namely the communication equipment in the communication equipment cabinet is usually managed, the communication equipment cabinet is usually managed in a regular maintenance mode in the prior art, the situation that the operation state of the communication equipment cabinet is poor cannot be perceived, when the operation state of the communication equipment cabinet is abnormal, the abnormal situation cannot be found timely, the influence caused by the abnormal situation is larger and larger along with the time, the communication equipment cabinet is finally caused to be faulty, when the situation occurs, the difficulty of the maintenance of the communication equipment cabinet is improved, and the communication equipment cabinet is possibly damaged difficultly.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a data analysis method and a system based on artificial intelligence, which are used for timely finding out and carrying out early maintenance management on a communication equipment cabinet when an abnormal hidden danger occurs in the operation state of the communication equipment cabinet by sensing the situation that the operation state of the communication equipment cabinet is poor, so that the communication equipment cabinet is effectively prevented from being broken down due to the fact that the influence caused by the abnormality is increased, the maintenance difficulty of the communication equipment cabinet is effectively reduced, and the fault of the communication equipment cabinet which is difficult to repair is effectively prevented, and the problems in the background technology are solved.
In order to achieve the above object, the present invention provides the following technical solutions: the data analysis system based on artificial intelligence comprises a data acquisition module, a central processing unit, an abnormality sensing module and an abnormality analysis module;
the data acquisition module is used for acquiring a plurality of pieces of information when the communication equipment cabinet runs, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and the electric power environment monitoring information and the software performance monitoring information are transmitted to the central processing unit after being processed after being acquired;
the central processing unit is used for comprehensively analyzing the processed power environment monitoring information and the software performance monitoring information in the running process of the communication equipment cabinet to generate a monitoring index, and transmitting the monitoring index to the abnormality sensing module;
the abnormality sensing module is used for comparing and analyzing a monitoring index generated when the communication equipment cabinet operates with a preset monitoring index reference threshold value, generating a high-operation state signal or a low-operation state signal, and transmitting the signals to the abnormality analysis module;
and the abnormality analysis module is used for acquiring a plurality of monitoring indexes to establish an analysis set through the central processing unit after receiving a low-operation-state signal generated when the communication equipment cabinet is operated, comprehensively analyzing the monitoring indexes in the analysis set and judging the abnormal state of the communication equipment cabinet.
Preferably, the power environment monitoring information during operation of the communication equipment cabinet comprises a server overtemperature duration coefficient and a power supply voltage fluctuation coefficient, and after acquisition, the data acquisition module respectively marks the server overtemperature duration coefficient and the power supply voltage fluctuation coefficient asAnd->；
The software performance monitoring information during the operation of the communication equipment cabinet comprises data abnormal transmission frequency, and the data acquisition module calibrates the data abnormal transmission frequency into the data abnormal transmission frequency after acquisition。
Preferably, the method for acquiring the server overtemperature duration coefficient comprises the following steps:
s101, acquiring actual operation temperatures of the server at different moments in t time during operation, and calibrating the actual operation temperatures as，vA number representing the actual operating temperature at different times during the operation of the server in time t,v=1、2、3、4、……、n，nis a positive integer;
s102, setting a temperature reference threshold for the temperature of the server in normal operation, and calibrating the temperature reference threshold asWhen->Less than->When the server is in normal operation, the server is not in over-temperature operation, when +.>Greater than or equal to->When the server is running at an excessive temperature, the server is indicated to be running at an excessive temperature, and the acquired +.>And->Alignment is performed and ∈ ->Is greater than->Is calibrated to +.>，xRepresentation->Is greater than->Is a number of times of the number of times,x=1、2、3、4、……、N，Nis a positive integer;
s103, calculating an overtemperature duration coefficient of the server, wherein the calculated expression is as follows:wherein->And the total duration of the over-temperature operation of the server in the t time is represented.
Preferably, the method for obtaining the power supply voltage fluctuation coefficient is as follows:
s201, acquiring actual power supply voltages at different moments in t time when the communication equipment cabinet operates, and calibrating the actual power supply voltages as，kThe number representing the actual supply voltage at different times during operation of the telecommunication equipment cabinet,k=1、2、3、4、……、M，Mis a positive integer;
s202, calculating an actual power supply voltage standard deviation and an actual power supply voltage average value through an actual power supply voltage acquired in t time when the communication equipment cabinet operates, and calibrating the actual power supply voltage standard deviation and the actual power supply voltage average value as respectivelyAnd->Then:
wherein->；
S203, through the actual power supply voltage standard deviation in t time when the communication equipment cabinet operatesAnd the actual supply voltage average>Calculating a power supply voltage fluctuation coefficient, wherein the calculated expression is as follows:。
preferably, the method for acquiring the abnormal transmission frequency of the data is as follows;
s301, acquiring average data transmission rates of different time periods in t time when the communication equipment cabinet operates, and calibrating the average data transmission rates as，pA number representing the average data transmission rate for different periods of time during which the communications equipment cabinet is operating,p=1、2、3、4、……、/>，/>is a positive integer;
s302, setting a gradient range for the optimal data transmission rate range when the communication equipment cabinet operatesWhen->In gradient range->When in between, the data transmission rate is normal, when +.>Not in gradient range->When the data transmission speed is abnormal, the data transmission speed is indicated;
s303, acquiring that the communication equipment cabinet is not in gradient range in t time when in operationThe total times are calibrated asCThe expression for data abnormal transmission frequency acquisition is: />In which, in the process,representing the total number of average data transfer rates acquired during operation of the communications equipment cabinet at time t.
Preferably, the central processing unit acquires the over-temperature duration coefficient of the serverCoefficient of supply voltage fluctuationData anomaly transmission frequency->Then, a data analysis model is built, and a monitoring index is generated>The data analysis model according to the method is as follows:
in which, in the process,respectively is the server overtemperature duration coefficient +.>Power supply voltage fluctuation coefficient->Abnormal data transmission frequency->Is a preset proportionality coefficient of>Are all greater than 0.
Preferably, after the abnormality sensing module obtains the generated monitoring index, the monitoring index is compared with a preset monitoring index reference threshold, if the monitoring index is larger than the monitoring index reference threshold, a low running state signal is generated through the abnormality sensing module and is transmitted to the abnormality analysis module, and if the monitoring index is smaller than or equal to the monitoring index reference threshold, a high running state signal is generated through the abnormality sensing module and is transmitted to the abnormality analysis module.
Preferably, after the anomaly analysis module receives a low operation state signal generated during operation of the communication equipment cabinet, a central processing unit acquires a plurality of subsequent monitoring indexes to establish an analysis set, and the analysis set is calibrated asWThen，uA number representing the monitoring index within the analysis set,u=1、2、3、4、……、R，Ris a positive integer;
calculating a monitoring index average value and a monitoring index standard deviation by analyzing the monitoring indexes in the collection, and respectively comparing the monitoring index average value and the monitoring index standard deviation with a preset monitoring index reference threshold value and a preset standard deviation reference threshold value, wherein the comparison is as follows:
if the average value of the monitoring indexes is larger than or equal to the reference threshold value of the monitoring indexes, a first-level maintenance management signal is generated through an abnormality analysis module and is sent to a manager mobile terminal, and when the first-level maintenance management signal is generated when the communication equipment cabinet operates, the operation state of the communication equipment cabinet is indicated to be poor;
if the average value of the monitoring indexes is smaller than the reference threshold value of the monitoring indexes and the standard deviation of the monitoring indexes is larger than or equal to the reference threshold value of the standard deviation, generating a secondary maintenance management signal through an abnormality analysis module, and sending the signal to a manager mobile terminal, wherein when the secondary maintenance management signal is generated when the communication equipment cabinet operates, the poor stability of the operation state of the communication equipment cabinet is indicated;
if the average value of the monitoring indexes is smaller than the reference threshold value of the monitoring indexes and the standard deviation of the monitoring indexes is smaller than the reference threshold value of the standard deviation, generating three-level maintenance management signals through the abnormality analysis module, and when the three-level maintenance management signals are generated when the communication equipment cabinet operates, indicating that the operation state of the communication equipment cabinet is normal.
An artificial intelligence based data analysis method, comprising the steps of:
s1, collecting a plurality of pieces of information when a communication equipment cabinet runs, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and processing the electric power environment monitoring information and the software performance monitoring information after the collection;
s2, comprehensively analyzing the processed power environment monitoring information and the software performance monitoring information in the operation of the communication equipment cabinet to generate a monitoring index;
s3, comparing and analyzing a monitoring index generated when the communication equipment cabinet operates with a preset monitoring index reference threshold value to generate a high-operation state signal or a low-operation state signal;
s4, after receiving a low-operation state signal generated when the communication equipment cabinet operates, acquiring a plurality of subsequent monitoring indexes to establish an analysis set, comprehensively analyzing the monitoring indexes in the analysis set, and judging the abnormal state of the communication equipment cabinet.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, through sensing the situation that the running state of the communication equipment cabinet is poor, when the running state of the communication equipment cabinet has abnormal hidden trouble, the situation is discovered in time, and the communication equipment cabinet is maintained and managed in advance, so that the communication equipment is effectively prevented from being broken down due to the fact that the influence caused by the abnormality is increased, the maintenance difficulty of the communication equipment cabinet is effectively reduced, and the fault that the communication equipment cabinet is difficult to repair is effectively prevented;
according to the invention, when the stability of the running state of the communication equipment is monitored to be poor, a manager is prompted to increase the frequency of monitoring the communication equipment so as to discover the abnormal running condition of the communication equipment in time, so that the communication equipment is maintained in time, and secondly, when the running state of the communication equipment is accidentally abnormal, no prompting signal is generated, the prompting interference of the accidental situation is eliminated, the trust degree of the manager on the monitoring signal is improved, and the stable and efficient running of the cabinet of the communication equipment is ensured.
Drawings
For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a schematic diagram of a system module of an artificial intelligence based data analysis method and system according to the present invention.
FIG. 2 is a flow chart of a method and system for data analysis based on artificial intelligence in accordance with the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a data analysis system based on artificial intelligence as shown in figure 1, which comprises a data acquisition module, a central processing unit, an abnormality sensing module and an abnormality analysis module;
the data acquisition module is used for acquiring a plurality of pieces of information when the communication equipment cabinet runs, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and the electric power environment monitoring information and the software performance monitoring information are transmitted to the central processing unit after being processed after being acquired;
the power environment monitoring information during operation of the communication equipment cabinet comprises a server overtemperature duration coefficient and a power supply voltage fluctuation coefficient, and after acquisition, the data acquisition module respectively marks the server overtemperature duration coefficient and the power supply voltage fluctuation coefficient asAnd->；
The high temperatures of servers in a communications equipment cabinet can negatively impact the performance and reliability of the communications equipment, as are some of the following possible effects:
performance degradation: the high temperature environment can cause the temperature of the internal components (such as a processor, a memory and the like) of the server to rise, thereby influencing the working efficiency and the performance of the internal components of the server, and the server can have the problems of frequent faults, slow operation or lost data packets and the like;
reliability is reduced: the high temperature environment increases the aging speed of the server components, accelerates the loss and wear of hardware, which may lead to an increase in hardware failure rate, shortens the service life of the server, and increases the risk of system crashes or downtime;
data loss and corruption: the high temperature environment can increase the fault risk of storage equipment (such as a hard disk), so that data is lost or damaged, which can seriously affect key data and application programs in communication equipment, and cause problems of data recovery, service interruption and the like;
the energy consumption is increased: the server requires more cooling in a high temperature environment to maintain a normal operating temperature, which may result in additional energy consumption, increase energy costs, and adversely affect the environment;
power supply problem: in a high temperature environment, the power supply of the server may be unstable, and voltage fluctuation or power failure may occur, which may cause a sudden shutdown, restart or other power-related problems of the server;
therefore, the operation temperature of the server in the communication equipment cabinet is monitored, and the abnormal hidden danger of the operation temperature of the server can be found in time;
the method for acquiring the server overtemperature duration coefficient comprises the following steps:
s101, acquiring actual operation temperatures of the server at different moments in t time during operation, and calibrating the actual operation temperatures as，vA number representing the actual operating temperature at different times during the operation of the server in time t,v=1、2、3、4、……、n，nis a positive integer;
it should be noted that, temperature sensors are installed in the cabinet of the communication equipment, the sensors can measure the temperature in the cabinet in real time, the digital temperature sensors generally provide instant data, and can monitor in real time through a network or a monitoring system, and a plurality of sensors can be placed at different positions of the cabinet to ensure that comprehensive temperature information is obtained, so that the actual running temperature of the server at different moments in running is obtained;
s102, setting a temperature reference threshold for the temperature of the server in normal operation, and calibrating the temperature reference threshold asWhen->Less than->Time, watchThe operation temperature of the bright server is normal and no over-temperature operation exists, when +.>Greater than or equal to->When the server is running at an excessive temperature, the server is indicated to be running at an excessive temperature, and the acquired +.>And->Alignment is performed and ∈ ->Is greater than->Is calibrated to +.>，xRepresentation->Is greater than->Is a number of times of the number of times,x=1、2、3、4、……、N，Nis a positive integer;
it should be noted that manufacturers of servers and communication devices provide temperature operating ranges and recommended temperature thresholds in product specifications and documents, which are typically based on device design and performance characteristics, and can serve as a reference to help determine the temperature range during normal operation;
s103, calculating an overtemperature duration coefficient of the server, wherein the calculated expression is as follows:wherein->The total duration of the server running at the overtemperature in the t time is represented;
the calculation expression of the server overtemperature duration coefficient shows that the larger the expression value of the server overtemperature duration coefficient generated in the time t when the server operates, the worse the overall operation state of the communication equipment cabinet is indicated, and otherwise, the better the overall operation state of the communication equipment cabinet is indicated;
power supply voltage fluctuations anomalies in the operation of a communications equipment cabinet can have a number of negative effects on communications equipment, including the following:
equipment failure and damage: the abnormal fluctuation of the power supply voltage can cause damage or failure of electronic elements in the communication equipment, and the excessive high or low voltage can cause the circuits and elements of the equipment to not work normally, so that the equipment is failed, damaged and even burnt;
data loss and corruption: the abnormal power supply voltage fluctuation can cause that a storage device (such as a hard disk) in the communication device cannot read and write data normally, so that the data is lost or damaged, and the key data and application programs in the communication device can be seriously influenced;
performance degradation: the abnormal fluctuation of the power supply voltage can cause the performance of the communication equipment to be reduced, and the unstable power supply voltage can cause the equipment to operate unstably, so that the problems of delay, packet loss, transmission error and the like are caused, and the normal operation and performance of the communication equipment are affected;
system crash and downtime: the abnormal power supply voltage fluctuation can cause system breakdown and downtime of the communication equipment, and the excessively high or low voltage fluctuation can cause the equipment to be unable to start or suddenly shut down, so as to cause communication interruption and service interruption;
incomplete data and erroneous transmissions: the abnormal fluctuation of the power supply voltage can cause data transmission errors, loss, damage or incomplete transmission of the data packets, which can cause the degradation of communication quality and influence the normal operation of the communication equipment and the reliability of data transmission;
therefore, the power supply voltage in the communication equipment cabinet is monitored, and the potential hazards of abnormal fluctuation of the power supply voltage can be found in time;
the method for acquiring the power supply voltage fluctuation coefficient comprises the following steps:
s201, acquiring actual power supply voltages at different moments in t time when the communication equipment cabinet operates, and calibrating the actual power supply voltages as，kThe number representing the actual supply voltage at different times during operation of the telecommunication equipment cabinet,k=1、2、3、4、……、M，Mis a positive integer;
it should be noted that voltage sensors mounted on the power line can measure the power supply voltage and provide real-time data, and these sensors are typically integrated with a monitoring system or data center management system to remotely monitor and record the voltage data;
s202, calculating an actual power supply voltage standard deviation and an actual power supply voltage average value through an actual power supply voltage acquired in t time when the communication equipment cabinet operates, and calibrating the actual power supply voltage standard deviation and the actual power supply voltage average value as respectivelyAnd->Then:
wherein->；
S203, performing actual power supply voltage standard deviation in t time when the communication equipment cabinet operates,And the actual supply voltage average>Calculating a power supply voltage fluctuation coefficient, wherein the calculated expression is as follows:wherein->Representing the change of the actual supply voltage during t time when the communication equipment cabinet is in operation, +.>The larger the expression value of the communication equipment cabinet is, the larger the fluctuation of the actual power supply voltage in the t time is, otherwise, the smaller the fluctuation of the actual power supply voltage in the t time is;
the calculation expression of the power supply voltage fluctuation coefficient shows that the larger the expression value of the server overtemperature duration coefficient generated in the t time when the server operates, the worse the overall operation state of the communication equipment cabinet is indicated, and otherwise, the better the overall operation state of the communication equipment cabinet is indicated;
the software performance monitoring information during the operation of the communication equipment cabinet comprises data abnormal transmission frequency, and the data acquisition module calibrates the data abnormal transmission frequency into the data abnormal transmission frequency after acquisition；
The abnormal data transmission frequency, that is, the frequency when the abnormal data transmission rate of the communication device occurs, the abnormal data transmission rate of the communication device may have different effects on the communication device, and the following are possible effects:
performance degradation: the abnormal data transmission rate may cause performance degradation of the communication device, and if the transmission rate is abnormally high or low, the device may not effectively process and transmit data, resulting in increased delay, increased packet loss rate, or reduced service quality;
data loss: when the data transmission rate is abnormal, the situation of data loss may occur, when the transmission rate is abnormally high, the device may not be capable of timely processing a large amount of data, so that part of data packets are discarded or lost, and the integrity of the data may be damaged or the application program is abnormally operated;
network congestion: the abnormal data transmission rate may cause network congestion, and when the transmission rate is abnormally high, the device may not timely process and transmit a large amount of data, resulting in network traffic accumulation and congestion, which may cause increased data transmission delay and affect network experience of other devices and users;
communication failure: abnormal transmission rate may cause communication failure, resulting in communication interruption or unstable communication, and when abnormal transmission rate, the device may not be able to properly establish, maintain or disconnect communication connection, resulting in communication interruption, unstable connection or frequent reconnection;
waste of resources: when the transmission rate is abnormal, the resource waste may be caused, the excessive network bandwidth and the equipment resource may be consumed by the excessive transmission rate, and the insufficient utilization of the network resource may be caused by the excessively low transmission rate;
therefore, the data transmission rate in the communication equipment is monitored, and the abnormal hidden danger exists in the data transmission of the communication equipment and can be found out in time;
the method for acquiring the abnormal data transmission frequency is as follows;
s301, acquiring average data transmission rates of different time periods (the time periods in the time period can be equal or in a crossed form, and the time period in the time period is not specifically limited here) in t time when the communication equipment cabinet operates, and calibrating the average data transmission rates as follows，pA number representing the average data transmission rate for different periods of time during which the communications equipment cabinet is operating,p=1、2、3、4、……、/>，/>is a positive integer;
it should be noted that there are various options for monitoring the data transmission rate, and the following are some common tools and devices:
network performance monitoring tool: these tools can monitor network performance parameters in real time, including data transmission rate, delay, packet loss rate, etc., e.g., wireshark, pingPlotter, solarWinds, etc.;
network traffic analyzer: these devices can monitor and analyze network traffic, providing information about data transmission rates, bandwidth utilization, traffic distribution, etc., such as network traffic analyzers (Network Traffic Analyzer) and gigabit ethernet testers (Gigabit Ethernet Tester), etc.;
network test equipment: these devices are used to perform Network performance testing and measurements, including data transmission rates, delays, packet loss rates, etc., such as Network performance testing devices (Network Performance Tester), network analyzers, etc.;
s302, setting a gradient range for the optimal data transmission rate range when the communication equipment cabinet operatesWhen->In gradient range->When in between, the data transmission rate is normal, when +.>Not in gradient range->When the data transmission speed is abnormal, the data transmission speed is indicated;
it should be noted that, performance tests and benchmark tests are performed to evaluate performance of the communication device under different data transmission rates, where the tests can help to determine an optimal working rate range of the device, and the optimal data transmission rate range of the communication device when the cabinet of the communication device is operated is not specifically limited herein, and can be adjusted according to an actual performance test, benchmark test and requirements;
s303, acquiring that the communication equipment cabinet is not in gradient range in t time when in operationThe total times are calibrated asCThe expression for data abnormal transmission frequency acquisition is: />In which, in the process,representing a total number of average data transmission rates acquired during operation of the communications equipment cabinet over a time t;
the calculation expression of the abnormal data transmission frequency shows that the larger the expression value of the abnormal data transmission frequency generated in the t time when the server operates, the worse the overall operation state of the communication equipment cabinet is, and otherwise, the better the overall operation state of the communication equipment cabinet is;
the central processing unit is used for comprehensively analyzing the processed power environment monitoring information and the software performance monitoring information in the running process of the communication equipment cabinet to generate a monitoring index, and transmitting the monitoring index to the abnormality sensing module;
the central processing unit obtains the over-temperature time length coefficient of the serverPower supply voltage fluctuation coefficient->Data anomaly transmission frequency->Then, a data analysis model is built, and a monitoring index is generated>The data analysis model according to the method is as follows:
in which, in the process,respectively is the server overtemperature duration coefficient +.>Power supply voltage fluctuation coefficient->Abnormal data transmission frequency->Is a preset proportionality coefficient of>Are all greater than 0;
as can be seen from the formula, the larger the server overtemperature duration coefficient generated in the t time when the communication equipment cabinet operates, the larger the power supply voltage fluctuation coefficient and the larger the data abnormal transmission frequency are, namely the monitoring index generated in the t time when the communication equipment cabinet operatesThe larger the expression value of the system is, the worse the overall operation state of the communication equipment cabinet is, the smaller the server overtemperature duration coefficient generated in the t time is, the smaller the power supply voltage fluctuation coefficient is, the smaller the abnormal data transmission frequency is, namely the monitoring index (x) generated in the t time is when the communication equipment cabinet is in operation>The smaller the expression value of the communication equipment cabinet is, the better the overall operation state of the communication equipment cabinet is;
it should be noted that, the selection of the time t is a time period with a short time, the time in the time period is not limited in detail herein, and can be set according to practical situations, so as to monitor the situation of the communication equipment cabinet in the time t during operation, thereby monitoring the operation state of the communication equipment cabinet in different time periods (in the time t) in real time;
the abnormality sensing module is used for comparing and analyzing a monitoring index generated when the communication equipment cabinet operates with a preset monitoring index reference threshold value, generating a high-operation state signal or a low-operation state signal, and transmitting the signals to the abnormality analysis module;
after the abnormal sensing module obtains the generated monitoring index, comparing the monitoring index with a preset monitoring index reference threshold, if the monitoring index is larger than the monitoring index reference threshold, indicating that the running state of the communication equipment cabinet is poor, generating a low running state signal through the abnormal sensing module, transmitting the signal to the abnormal analysis module, and if the monitoring index is smaller than or equal to the monitoring index reference threshold, indicating that the running state of the communication equipment cabinet is good, generating a high running state signal through the abnormal sensing module, and transmitting the signal to the abnormal analysis module;
the abnormality analysis module is used for acquiring a plurality of subsequent monitoring indexes through the central processing unit to establish an analysis set after receiving a low-operation state signal generated when the communication equipment cabinet is operated, comprehensively analyzing the monitoring indexes in the analysis set and judging the abnormal state of the communication equipment cabinet;
after the anomaly analysis module receives a low-running state signal generated when the communication equipment cabinet runs, a central processing unit acquires a plurality of subsequent monitoring indexes to establish an analysis set, and the analysis set is calibrated asWThen，uA number representing the monitoring index within the analysis set,u=1、2、3、4、……、R，Ris a positive integer;
calculating a monitoring index average value and a monitoring index standard deviation by analyzing the monitoring indexes in the collection, and respectively comparing the monitoring index average value and the monitoring index standard deviation with a preset monitoring index reference threshold value and a preset standard deviation reference threshold value, wherein the comparison is as follows:
if the average value of the monitoring indexes is larger than or equal to the reference threshold value of the monitoring indexes, a first-level maintenance management signal is generated through an anomaly analysis module and is sent to a mobile terminal of a manager, the manager is prompted to maintain and manage the communication equipment cabinet which sends the first-level maintenance management signal in advance, when the first-level maintenance management signal is generated when the communication equipment cabinet operates, the operation state of the communication equipment cabinet is indicated to be poor, so that the situation that the operation state of the communication equipment cabinet is poor is perceived in the mode, when the operation state of the communication equipment cabinet has abnormal hidden trouble, the situation that the communication equipment cabinet is timely discovered and is maintained and managed in advance, the communication equipment cabinet is effectively prevented from being broken down due to the fact that the influence caused by the anomaly is enlarged, and therefore the difficulty in maintenance of the communication equipment cabinet is effectively reduced, and the fault which is difficult to repair is effectively prevented from occurring in the communication equipment cabinet;
if the average value of the monitoring indexes is smaller than the reference threshold value of the monitoring indexes and the standard deviation of the monitoring indexes is larger than or equal to the reference threshold value of the standard deviation, generating a secondary maintenance management signal through an abnormality analysis module, sending the signal to a mobile terminal of a manager, and when the secondary maintenance management signal is generated when the communication equipment cabinet operates, indicating that the stability of the operation state of the communication equipment cabinet is poor, prompting the manager in the mode to improve the monitoring frequency of the communication equipment cabinet so as to discover the abnormal operation condition of the communication equipment cabinet in time and further carrying out maintenance management on the communication equipment cabinet in advance;
if the average value of the monitoring indexes is smaller than the reference threshold value of the monitoring indexes and the standard deviation of the monitoring indexes is smaller than the reference threshold value of the standard deviation, generating a three-level maintenance management signal through an anomaly analysis module, and sending the signal to a mobile terminal of a manager;
it should be noted that, a specific calculation formula of the standard deviation of the monitoring index refers to a calculation formula of the standard deviation of the actual power supply voltage, and is not specifically described herein, and then, after analyzing the monitoring index in the analysis set, the abnormality analysis module transmits the signal after the analysis to the data acquisition module, and repeats the above process again;
according to the invention, through sensing the situation that the running state of the communication equipment cabinet is poor, when the running state of the communication equipment cabinet has abnormal hidden trouble, the situation is discovered in time, and the communication equipment cabinet is maintained and managed in advance, so that the communication equipment is effectively prevented from being broken down due to the fact that the influence caused by the abnormality is increased, the maintenance difficulty of the communication equipment cabinet is effectively reduced, and the fault that the communication equipment cabinet is difficult to repair is effectively prevented;
according to the invention, when the stability of the running state of the communication equipment is monitored to be poor, a manager is prompted to increase the frequency of monitoring the communication equipment so as to discover the abnormal running condition of the communication equipment in time, so that the communication equipment is maintained in time, and secondly, when the running state of the communication equipment is accidentally abnormal, no prompting signal is generated, the prompting interference of the accidental situation is eliminated, the trust degree of the manager on the monitoring signal is improved, and the stable and efficient running of the cabinet of the communication equipment is ensured.
The invention provides a data analysis method based on artificial intelligence as shown in fig. 2, which comprises the following steps:
s1, collecting a plurality of pieces of information when a communication equipment cabinet runs, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and processing the electric power environment monitoring information and the software performance monitoring information after the collection;
s2, comprehensively analyzing the processed power environment monitoring information and the software performance monitoring information in the operation of the communication equipment cabinet to generate a monitoring index;
s3, comparing and analyzing a monitoring index generated when the communication equipment cabinet operates with a preset monitoring index reference threshold value to generate a high-operation state signal or a low-operation state signal;
s4, after receiving a low-operation state signal generated when the communication equipment cabinet operates, acquiring a plurality of subsequent monitoring indexes to establish an analysis set, comprehensively analyzing the monitoring indexes in the analysis set, and judging the abnormal state of the communication equipment cabinet;
the method for managing the communication equipment cabinet provided by the embodiment of the invention is realized through the management system of the communication equipment cabinet, and the specific method and the flow of the method for managing the communication equipment cabinet are detailed in the embodiment of the management system of the communication equipment cabinet, and are not repeated here.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. The data analysis system based on the artificial intelligence is characterized by comprising a data acquisition module, a central processing unit, an abnormality sensing module and an abnormality analysis module;
the data acquisition module is used for acquiring a plurality of pieces of information when the communication equipment cabinet runs, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and the electric power environment monitoring information and the software performance monitoring information are transmitted to the central processing unit after being processed after being acquired;
the power environment monitoring information during operation of the communication equipment cabinet comprises a server overtemperature duration coefficient and a power supply voltage fluctuation coefficient, and after acquisition, the data acquisition module respectively marks the server overtemperature duration coefficient and the power supply voltage fluctuation coefficient asAnd->；
The method for acquiring the server overtemperature duration coefficient comprises the following steps:
s101, acquiring actual operation temperatures of the server at different moments in t time during operation, and calibrating the actual operation temperatures as，vA number representing the actual operating temperature at different times during the operation of the server in time t,v=1、2、3、4、……、n，nis a positive integer;
s102, setting a temperature reference threshold for the temperature of the server in normal operation, and calibrating the temperature reference threshold asWhen->Less than->When the server is in normal operation, the server is not in over-temperature operation, when +.>Greater than or equal to->When the server is running at an excessive temperature, the server is indicated to be running at an excessive temperature, and the acquired +.>And->Alignment is performed and ∈ ->Is greater than->Is calibrated to +.>，xRepresentation->Is greater than->Is a number of times of the number of times,x=1、2、3、4、……、N，Nis a positive integer;
s103, calculating an overtemperature duration coefficient of the server, wherein the calculated expression is as follows:in which, in the process,the total duration of the server running at the overtemperature in the t time is represented;
the method for acquiring the power supply voltage fluctuation coefficient comprises the following steps:
s201, acquiring actual power supply voltages at different moments in t time when the communication equipment cabinet operates, and calibrating the actual power supply voltages as，kThe number representing the actual supply voltage at different times during operation of the telecommunication equipment cabinet,k=1、2、3、4、……、M，Mis a positive integer;
s202, calculating an actual power supply voltage standard deviation and an actual power supply voltage average value through an actual power supply voltage acquired in t time when the communication equipment cabinet operates, and calibrating the actual power supply voltage standard deviation and the actual power supply voltage average value as respectivelyAnd->Then:
wherein->；
S203, through the actual power supply voltage standard deviation in t time when the communication equipment cabinet operatesAnd the actual supply voltage average>Calculating a power supply voltage fluctuation coefficient, wherein the calculated expression is as follows: />；
The software performance monitoring information during the operation of the communication equipment cabinet comprises data abnormal transmission frequency, and the data acquisition module calibrates the data abnormal transmission frequency into the data abnormal transmission frequency after acquisition；
The central processing unit is used for comprehensively analyzing the processed power environment monitoring information and the software performance monitoring information in the running process of the communication equipment cabinet to generate a monitoring index, and transmitting the monitoring index to the abnormality sensing module;
the abnormality sensing module is used for comparing and analyzing a monitoring index generated when the communication equipment cabinet operates with a preset monitoring index reference threshold value, generating a high-operation state signal or a low-operation state signal, and transmitting the signals to the abnormality analysis module;
and the abnormality analysis module is used for acquiring a plurality of monitoring indexes to establish an analysis set through the central processing unit after receiving a low-operation-state signal generated when the communication equipment cabinet is operated, comprehensively analyzing the monitoring indexes in the analysis set and judging the abnormal state of the communication equipment cabinet.
2. The artificial intelligence based data analysis system of claim 1, wherein the method of data anomaly transmission frequency acquisition is as follows;
s301, acquiring average data transmission rates of different time periods in t time when the communication equipment cabinet operates, and calibrating the average data transmission rates as，pA number representing the average data transmission rate for different periods of time during which the communications equipment cabinet is operating,p=1、2、3、4、……、/>，/>is a positive integer;
s302, setting a gradient range for the optimal data transmission rate range when the communication equipment cabinet operatesWhen->In gradient range->When in between, the data transmission rate is normal, when +.>Not in gradient range->When the data transmission speed is abnormal, the data transmission speed is indicated;
s303, acquiring that the communication equipment cabinet is not in gradient range in t time when in operationThe total times are calibrated asCThe expression for data abnormal transmission frequency acquisition is: />Wherein->Representing the total number of average data transfer rates acquired during operation of the communications equipment cabinet at time t.
3. The artificial intelligence based data analysis system according to claim 2, wherein the central processing unit obtains the server overtemperature duration coefficientPower supply voltage fluctuation coefficient->Data abnormal transmission frequency =After that, data division is establishedAnalysis model, generating monitoring index->The data analysis model according to the method is as follows:
in which, in the process,respectively is the server overtemperature duration coefficient +.>Power supply voltage fluctuation coefficient->Abnormal data transmission frequency->Is a preset proportionality coefficient of>Are all greater than 0.
4. The artificial intelligence based data analysis system of claim 3, wherein after the anomaly sensing module obtains the generated monitoring index, the monitoring index is compared with a preset monitoring index reference threshold, if the monitoring index is greater than the monitoring index reference threshold, a low running state signal is generated by the anomaly sensing module and transmitted to the anomaly analysis module, and if the monitoring index is less than or equal to the monitoring index reference threshold, a high running state signal is generated by the anomaly sensing module and transmitted to the anomaly analysis module.
5. The artificial intelligence based data analysis system of claim 4, wherein the anomaly analysis module receives a low run state generated when the communication equipment cabinet is runningAfter the state signal, a central processing unit acquires a plurality of subsequent monitoring indexes to establish an analysis set, and the analysis set is calibrated asWThen，uA number representing the monitoring index within the analysis set,u=1、2、3、4、……、R，Ris a positive integer;
calculating a monitoring index average value and a monitoring index standard deviation by analyzing the monitoring indexes in the collection, and respectively comparing the monitoring index average value and the monitoring index standard deviation with a preset monitoring index reference threshold value and a preset standard deviation reference threshold value, wherein the comparison is as follows:
if the average value of the monitoring indexes is larger than or equal to the reference threshold value of the monitoring indexes, a first-level maintenance management signal is generated through an abnormality analysis module and is sent to a manager mobile terminal, and when the first-level maintenance management signal is generated when the communication equipment cabinet operates, the operation state of the communication equipment cabinet is indicated to be poor;
if the average value of the monitoring indexes is smaller than the reference threshold value of the monitoring indexes and the standard deviation of the monitoring indexes is larger than or equal to the reference threshold value of the standard deviation, generating a secondary maintenance management signal through an abnormality analysis module, and sending the signal to a manager mobile terminal, wherein when the secondary maintenance management signal is generated when the communication equipment cabinet operates, the poor stability of the operation state of the communication equipment cabinet is indicated;
if the average value of the monitoring indexes is smaller than the reference threshold value of the monitoring indexes and the standard deviation of the monitoring indexes is smaller than the reference threshold value of the standard deviation, generating three-level maintenance management signals through the abnormality analysis module, and when the three-level maintenance management signals are generated when the communication equipment cabinet operates, indicating that the operation state of the communication equipment cabinet is normal.
6. An artificial intelligence based data analysis method implemented by an artificial intelligence based data analysis system according to any one of claims 1 to 5, comprising the steps of:
s1, collecting a plurality of pieces of information when a communication equipment cabinet runs, wherein the plurality of pieces of information comprise electric power environment monitoring information and software performance monitoring information, and processing the electric power environment monitoring information and the software performance monitoring information after the collection;
s2, comprehensively analyzing the processed power environment monitoring information and the software performance monitoring information in the operation of the communication equipment cabinet to generate a monitoring index;
s3, comparing and analyzing a monitoring index generated when the communication equipment cabinet operates with a preset monitoring index reference threshold value to generate a high-operation state signal or a low-operation state signal;
s4, after receiving a low-operation state signal generated when the communication equipment cabinet operates, acquiring a plurality of subsequent monitoring indexes to establish an analysis set, comprehensively analyzing the monitoring indexes in the analysis set, and judging the abnormal state of the communication equipment cabinet.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311299570.0A CN117118807B (en) | 2023-10-09 | 2023-10-09 | Data analysis method and system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311299570.0A CN117118807B (en) | 2023-10-09 | 2023-10-09 | Data analysis method and system based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117118807A CN117118807A (en) | 2023-11-24 |
| CN117118807B true CN117118807B (en) | 2024-01-02 |
Family
ID=88804123
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311299570.0A Active CN117118807B (en) | 2023-10-09 | 2023-10-09 | Data analysis method and system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117118807B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007081519A2 (en) * | 2005-12-30 | 2007-07-19 | Steven Kays | Genius adaptive design |
| CN114116225A (en) * | 2021-11-30 | 2022-03-01 | 北京百度网讯科技有限公司 | Communication equipment cabinet, management system and method of communication equipment cabinet |
| CN115792423A (en) * | 2022-09-02 | 2023-03-14 | 杭州集联科技有限公司 | Modularized cabinet based on Internet of things and running state monitoring system thereof |
| CN116828412A (en) * | 2023-07-31 | 2023-09-29 | 沧州荣盛达电器有限公司 | New energy automobile fills and trades electric box and becomes rack wireless communication system |
-
2023
- 2023-10-09 CN CN202311299570.0A patent/CN117118807B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007081519A2 (en) * | 2005-12-30 | 2007-07-19 | Steven Kays | Genius adaptive design |
| CN114116225A (en) * | 2021-11-30 | 2022-03-01 | 北京百度网讯科技有限公司 | Communication equipment cabinet, management system and method of communication equipment cabinet |
| CN115792423A (en) * | 2022-09-02 | 2023-03-14 | 杭州集联科技有限公司 | Modularized cabinet based on Internet of things and running state monitoring system thereof |
| CN116828412A (en) * | 2023-07-31 | 2023-09-29 | 沧州荣盛达电器有限公司 | New energy automobile fills and trades electric box and becomes rack wireless communication system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117118807A (en) | 2023-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5922051A (en) | System and method for traffic management in a network management system | |
| WO2018126645A1 (en) | Communication network management method and apparatus therefor | |
| US8774023B2 (en) | Method and system for detecting changes in network performance | |
| US7962806B2 (en) | Method and system for providing bit error rate characterization | |
| CN101227329B (en) | System, apparatus and method for managing network device | |
| CN113054706A (en) | Balance monitoring control system and method for power battery | |
| CN110650060A (en) | Processing method, equipment and storage medium for flow alarm | |
| US20240106737A1 (en) | Application-aware links | |
| US20160191359A1 (en) | Reactive diagnostics in storage area networks | |
| CN112468592B (en) | Terminal online state detection method and system based on electric power information acquisition | |
| CN108282355B (en) | Equipment inspection device in cloud desktop system | |
| CN117118807B (en) | Data analysis method and system based on artificial intelligence | |
| US7719992B1 (en) | System for proactive time domain reflectometry | |
| CN116389304A (en) | SG-TMS-based network operation state trend analysis system | |
| US20210409849A1 (en) | Link fault management for optical adapters | |
| CN117155757A (en) | Power information communication fault early warning analysis method based on big data technology | |
| CN113810238A (en) | Network monitoring method, electronic device and storage medium | |
| KR20190047809A (en) | Ict equipment management system and method there of | |
| CN115766526A (en) | Test method and device for switch physical layer chip and electronic equipment | |
| KR101556781B1 (en) | fault and lifetime prediction information service supply system for network eauipment | |
| Wu et al. | An empirical study on change-induced incidents of online service systems | |
| Ma et al. | Troubleshooting and Maintenance of SDH Optical Transmission System | |
| KR101857289B1 (en) | System and method for analyzing vulnerability of control system protocol | |
| CN117292515A (en) | Power communication equipment management method and system based on power Internet of things | |
| KR20040042702A (en) | A Network Management Method using Availability Prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |