CN116887338B - Big data-based 5G mobile network real-time adjustment method - Google Patents
Big data-based 5G mobile network real-time adjustment method Download PDFInfo
- Publication number
- CN116887338B CN116887338B CN202311142565.9A CN202311142565A CN116887338B CN 116887338 B CN116887338 B CN 116887338B CN 202311142565 A CN202311142565 A CN 202311142565A CN 116887338 B CN116887338 B CN 116887338B
- Authority
- CN
- China
- Prior art keywords
- mobile network
- time
- signal
- data transmission
- network line
- 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 22
- 230000005540 biological transmission Effects 0.000 claims abstract description 94
- 238000011156 evaluation Methods 0.000 claims abstract description 68
- 230000002159 abnormal effect Effects 0.000 claims abstract description 41
- 238000007405 data analysis Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 5
- 230000006641 stabilisation Effects 0.000 claims description 11
- 238000011105 stabilization Methods 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000008447 perception Effects 0.000 abstract description 4
- 238000004891 communication Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 238000004590 computer program Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 230000003190 augmentative effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The application discloses a 5G mobile network real-time adjustment method based on big data, which comprises the following steps: s101, acquiring information of a 5G mobile network line in operation, including data transmission information and signal quality information, and performing data processing on the acquired data transmission information and signal quality information; s102, data analysis is carried out on the data transmission information and the signal quality information when the 5G mobile network line runs, and a state evaluation index is generated. The application monitors the running state of the 5G mobile network line, can realize the intelligent perception of the abnormal hidden trouble of the 5G mobile network line, and when the abnormal hidden trouble exists in the 5G mobile network line, the 5G mobile network line is planned and adjusted in advance, so that the timeliness of the 5G mobile network line planning and adjustment is improved, the condition of service interruption is effectively prevented, and the 5G mobile network is convenient for a user to experience efficiently.
Description
Technical Field
The application relates to the technical field of network adjustment, in particular to a 5G mobile network real-time adjustment method based on big data.
Background
The 5G mobile network is a fifth generation mobile communication network, is the most advanced wireless communication technology at present, and the 5G network provides a higher data transmission rate, which can theoretically reach a speed of tens of Gb per second. The method enables a user to download and upload large-capacity data faster, supports applications such as high-definition video, virtual reality and augmented reality, and enables the 5G network to achieve lower transmission delay and millisecond delay. This is very important for delay-sensitive applications, such as real-time control of internet of things devices, interaction of autonomous vehicles, telemedicine, etc., 5G networks support network slicing techniques, where network resources can be divided into multiple independent virtual network slices to meet the needs of different applications. Each network slice can be optimally configured according to specific requirements, personalized network services are provided, and in general, a 5G mobile network has higher speed, low delay, large capacity, high reliability and more application potential, and wide possibility is provided for innovation of various industries and fields.
In the 5G network line, faults and abnormal conditions are difficult to avoid, rapid fault recovery and fault transfer can be realized by real-time adjustment, and a standby path or resource can be switched in time by real-time adjustment, so that service interruption is reduced and user experience is influenced.
The prior art has the following defects: however, in the prior art, the real-time adjustment mode of the 5G mobile network is mostly to plan and adjust when the network line fails or is abnormal, and because the network cannot be intelligently perceived as to the hidden trouble of the network, the planning and adjustment cannot be performed in advance, so that the network line planning and adjustment have serious hysteresis, and the situation of service interruption is likely to exist, thereby being unfavorable for the user to experience the 5G mobile network efficiently.
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 application aims to provide a real-time 5G mobile network adjustment method based on big data, which can realize intelligent perception of potential abnormality of a 5G mobile network line by monitoring the running state of the 5G mobile network line, and when the potential abnormality exists in the 5G mobile network line, the 5G mobile network line is planned and adjusted in advance, so that timeliness of the planning and adjustment of the 5G mobile network line is improved, the situation of service interruption is effectively prevented, and a user can efficiently experience the 5G mobile network, thereby solving the problems in the background art.
In order to achieve the above object, the present application provides the following technical solutions: the real-time adjustment method of the 5G mobile network based on big data comprises the following steps:
s101, acquiring information of a 5G mobile network line in operation, including data transmission information and signal quality information, and performing data processing on the acquired data transmission information and signal quality information;
s102, carrying out data analysis on data transmission information and signal quality information when a 5G mobile network line runs, and generating a state evaluation index;
s103, establishing a data set of a plurality of state evaluation indexes generated during the running of the 5G mobile network line, and comprehensively analyzing the state evaluation indexes in the data set to generate a high-risk state signal and a low-risk state signal;
and S104, generating an adjustment prompt for a high-risk state signal generated during the running of the 5G mobile network line, performing planning adjustment in advance for the 5G mobile network line, and not generating an adjustment prompt for a low-risk state signal generated during the running of the 5G mobile network line, and performing planning adjustment for the 5G mobile network.
Preferably, the data transmission information includes a data transmission rate anomaly coefficient and a delay jitter stabilization coefficient, and after acquisition, the data transmission rate anomaly coefficient and the delay jitter stabilization coefficient are respectively calibrated asAnd->The signal quality information comprises signal-to-noise ratio abnormal coefficients, and after acquisition, the signal-to-noise ratio abnormal coefficients are calibrated to be +.>。
Preferably, the logic for obtaining the abnormal coefficient of the data transmission rate is as follows:
s1, setting a data transmission rate reference threshold value for a 5G mobile network line, and calibrating the data transmission rate reference threshold value as；
S2, acquiring real-time data transmission rates of the 5G mobile network circuit at different moments in the T time, and calibrating the real-time data transmission rates as，yNumbers representing real-time data transmission rates of the 5G mobile network line at different times during the T time,y=1、2、3、4、……、N，Nis a positive integer;
s3, the reference threshold value of the data transmission rate is smaller thanIs calibrated to +.>，/>Representing less than the data transmission rate reference threshold +.>Number of real-time data transmission rate of +.>=1、2、3、4、……、n，nIs a positive integer;
s4, calculating abnormal coefficients of the data transmission rate, wherein the calculated expression is as follows:in which, in the process,and the frequency of abnormal data transmission rate of the 5G mobile network line in the T time is represented.
Preferably, the logic for obtaining the delay jitter stabilization factor is as follows:
s1, acquiring a plurality of data packets received by a 5G mobile network circuit in T time, recording the time stamp of each received data packet reaching a receiving end, and marking the time stamp as，hRepresenting the number of packets received by the 5G mobile network line during the T time,h=1、2、3、4、……、H，His a positive integer;
s2, calculating the time delay difference between adjacent data packets in the T time of the 5G mobile network line, namely calculating the difference between the arrival time of each data packet and the arrival time of the previous data packet, and calibrating the time delay difference as，jA number representing the delay difference between adjacent packets,j=1、2、3、4、……、m，mis a positive integer;
s3, calculating the standard deviation of the time delay difference value between adjacent data packets, and calibrating the standard deviation of the time delay difference value asEStandard deviation of time delay differenceEIs calculated as follows:wherein->For the average value of the time delay difference values between adjacent data packets, the obtained calculation formula is as follows: />；
S4, calculating a delay jitter stability coefficient, wherein the calculated expression is as follows:。
preferably, the logic for obtaining the signal-to-noise ratio anomaly coefficient is as follows:
s1, setting a signal-to-noise ratio reference threshold value for a 5G mobile network line, and calibrating the signal-to-noise ratio reference threshold value as；
S2, acquiring actual signal-to-noise ratios of the 5G mobile network circuit at different moments in the T time, and calibrating the actual signal-to-noise ratios as，kThe number representing the actual signal to noise ratio of the 5G mobile network line at different times during the T time,k=1、2、3、4、……、p，pis a positive integer;
s3, the signal to noise ratio reference threshold value is smallerIs calibrated to +.>，fRepresenting less than the signal-to-noise reference threshold +.>Is used to determine the number of actual signal to noise ratios,f=1、2、3、4、……、M，Mis a positive integer;
s4, passing the minimum value of the optimal signal-to-noise ratio rangeAnd less than the optimal signal to noise ratio rangeIs>Calculating the signal-to-noise ratio abnormal coefficient, wherein the calculated expression is as follows:。
preferably, the data transmission rate anomaly coefficient is obtainedDelay jitter stabilization factor->S/N anomaly coefficient->Then, a data analysis model is built, and a state evaluation index is generated>The formula according to is:wherein->、/>、/>Respectively data transmission rate anomaly coefficient ++>Delay jitter stabilization factor->S/N anomaly coefficient->Is a preset proportionality coefficient of>、/>、/>Are all greater than 0.
Preferably, several state evaluation indexes generated during the running of the 5G mobile network line are generatedEstablishing a data set and calibrating the analysis set as +.>Then->，/>Number representing state evaluation index within data set, +.>，uIs a positive integer; calculating a state evaluation index average value and a state evaluation index standard deviation in the data set, and calibrating the state evaluation index average value and the state evaluation index standard deviation asXSAndYSthen: />，/>。
Preferably, a state evaluation index average is obtainedXSAnd state evaluation index standard deviationYSAnd then, comparing the state evaluation index average value with a first reference threshold value, and comparing the state evaluation index standard deviation with a second reference threshold value, wherein the comparison result is as follows:
if the state evaluation index average value is smaller than the first reference threshold value and the state evaluation index standard deviation is smaller than the second reference threshold value, generating a low-risk state signal;
and if the state evaluation index average value is smaller than the first reference threshold value and the state evaluation index standard deviation is larger than or equal to the second reference threshold value or the state evaluation index average value is larger than or equal to the first reference threshold value, generating a high risk state signal.
Preferably, when the 5G mobile network line runs, an adjustment prompt is generated, and the 5G mobile network line is planned and adjusted in advance, and when the 5G mobile network line runs, a low-risk state signal is generated, no adjustment prompt is generated, and the 5G mobile network is not planned and adjusted.
In the technical scheme, the application has the technical effects and advantages that:
the application monitors the running state of the 5G mobile network line, can realize the intelligent perception of the abnormal hidden trouble of the 5G mobile network line, when the abnormal hidden trouble exists in the 5G mobile network line, the 5G mobile network line is planned and adjusted in advance, the timeliness of the 5G mobile network line planning and adjustment is improved, the condition of service interruption is effectively prevented, and the 5G mobile network is convenient for users to experience efficiently;
according to the application, by comprehensively analyzing the state evaluation index generated during the running of the 5G mobile network line instead of single analysis, the accidental situation of single analysis can be effectively prevented, the accuracy of data analysis is ensured, the accuracy of monitoring the running state of the 5G mobile network line is further improved, and the efficient running of the 5G mobile network line is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
Fig. 1 is a flow chart of a method for real-time adjustment of a 5G mobile network based on big data according to the present application.
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 application provides a big data-based 5G mobile network real-time adjustment method shown in fig. 1, which comprises the following steps:
s101, acquiring information of a 5G mobile network line in operation, including data transmission information and signal quality information, and performing data processing on the acquired data transmission information and signal quality information;
the data transmission information comprises a data transmission rate abnormal coefficient and a delay jitter stable coefficient, and after acquisition, the data transmission rate abnormal coefficient and the delay jitter stable coefficient are respectively calibrated asAnd->；
As the data transmission rate of the 5G mobile network line becomes slower, the following serious effects may be caused to the operation state of the 5G mobile network line:
and the user experience is reduced: the slower data transmission rate can cause users to not enjoy high-speed network connection, so that the experience of activities such as real-time application, high-definition video streaming media, online games and the like on the mobile equipment is affected, and the users may feel problems such as delay increase, video jamming, slow downloading speed and the like;
network capacity reduction: when the data transmission rate is slow, the capacity of the network line is limited, and a large number of users cannot be supported to perform high-bandwidth activities at the same time, which may cause network congestion and bottlenecks to affect the connection quality and speed of other users;
impact real-time application: the 5G mobile network is widely applied to a plurality of real-time application fields, such as telemedicine, intelligent transportation systems, industrial automation and the like, and when the data transmission rate is slow, the performance of the real-time application is affected, and the problems of delay increase, data loss or transmission errors and the like can be caused, so that the reliability and the safety of related systems are affected;
influence the connection of the internet of things: the 5G technology is widely applied to connection of internet of things (IoT) devices, and when the data transmission rate becomes slow, communication between the internet of things devices is affected, which may cause problems of delay increase, unstable connection, data loss and the like, which may negatively affect applications in various fields of smart cities, smart home, industrial automation and the like;
affecting the development of emerging technologies: the 5G mobile network is the basis for supporting many emerging technologies, such as Augmented Reality (AR), virtual Reality (VR), autopilot, etc., whose performance and application are limited when the data transmission rate is slow, may not realize its full potential, affecting the development of related industries;
therefore, the data transmission rate of the 5G mobile network line is monitored, the data transmission rate of the 5G mobile network line can be timely perceived when the data transmission rate of the 5G mobile network line is slow, and corresponding network line planning and adjusting measures are adopted in advance;
the logic for acquiring the abnormal coefficient of the data transmission rate is as follows:
s1, setting a data transmission rate reference threshold value for a 5G mobile network line, and calibrating the data transmission rate reference threshold value as；
It should be noted that suppliers and operators related to 5G mobile networks typically provide information about network performance and rate, and technical specifications related to 5G networks, including information about data transmission rates, which can be typically found in official websites, technical documents, or advisory services, from which data transmission rate reference thresholds for 5G mobile network lines, i.e. minimum data transmission rates allowed in the network, can be determined;
s2, acquiring real-time data transmission rates of the 5G mobile network circuit at different moments in the T time, and calibrating the real-time data transmission rates as，yNumbers representing real-time data transmission rates of the 5G mobile network line at different times during the T time,y=1、2、3、4、……、N，Nis a positive integer;
it should be noted that, the speed of the current network connection can be measured by using the mobile network speed measuring application and tool, these applications can test the indexes such as the actual download speed, upload speed and delay of the device at specific time and place, the common mobile network speed measuring application includes Speedtest, fast.com, nPerf, etc., and the actual data transmission rate of the 5G mobile network line can be obtained by running these applications;
s3, the reference threshold value of the data transmission rate is smaller thanReal-time data transmission rate calibration of (a)Is->，/>Representing less than the data transmission rate reference threshold +.>Number of real-time data transmission rate of +.>=1、2、3、4、……、n，nIs a positive integer;
s4, calculating abnormal coefficients of the data transmission rate, wherein the calculated expression is as follows:in which, in the process,the frequency of abnormal data transmission rate of the 5G mobile network line in the time T is represented;
the expression calculated by the abnormal data transmission rate coefficient shows that the larger the expression value of the abnormal data transmission rate coefficient of the 5G mobile network circuit in the T time is, the worse the running state of the 5G mobile network circuit in the T time is, and otherwise, the better the running state of the 5G mobile network circuit in the T time is;
delay jitter refers to delay instability of data transmission in a network, namely, inconsistent time variation of arrival of a data packet at a destination, and when delay jitter stability of a 5G mobile network line is poor, the following serious influence may be caused on an operation state of the network:
impact real-time application: delay jitter can cause unstable arrival time of data packets in real-time applications (such as video call, online game, teleconference and the like), which can cause poor communication experience, generate problems of intermittent, stuck, unclear sound and the like, and influence communication and communication efficiency of users;
and (5) increasing the packet loss rate: the delay jitter may cause the data packet to cross and delay in the transmission process, so as to increase the probability of data packet loss, which may cause unstable network and require retransmission, thereby reducing the overall data transmission efficiency;
affecting multimedia fluency: the 5G network is widely applied to multimedia applications such as high-definition video streaming media and large file downloading, and the existence of delay jitter can lead to slow video loading, playing and blocking, unstable downloading speed and influence enjoyment of users on multimedia contents;
the stability of the equipment of the Internet of things is reduced: delay jitter has a negative effect on the connection stability of the internet of things equipment, and the internet of things equipment generally needs real-time response and stable connection to ensure normal operation of the internet of things equipment, and the delay jitter can cause unstable communication between the internet of things equipment, so that the reliability and the efficiency of the internet of things system are affected;
reducing network throughput: the delay jitter may cause congestion and reduced data transmission efficiency in the network, the throughput of the network is limited, and a large number of users cannot be supported to perform high-bandwidth activities at the same time, so that the overall performance of the network is affected;
therefore, the time delay jitter condition of the 5G mobile network line is monitored, the time delay jitter condition of the 5G mobile network line can be timely perceived when the time delay jitter stability of the 5G mobile network line is poor, and corresponding network line planning and adjusting measures are adopted in advance;
the logic for obtaining the delay jitter stabilization coefficient is as follows:
s1, acquiring a plurality of data packets received by a 5G mobile network circuit in T time, recording the time stamp of each received data packet reaching a receiving end, and marking the time stamp as，hRepresenting the number of packets received by the 5G mobile network line during the T time,h=1、2、3、4、……、H，His a positive integer;
it should be noted that network devices (such as network cards, switches, routers, etc.) typically have hardware-level time stamping functions, which can be captured and recorded as data packets enter or leave the device to provide accurate time stamp information, and applications can access and extract the hardware time stamps by using specific APIs or drivers;
s2, calculating the time delay difference between adjacent data packets in the T time of the 5G mobile network line, namely calculating the difference between the arrival time of each data packet and the arrival time of the previous data packet, and calibrating the time delay difference as，jA number representing the delay difference between adjacent packets,j=1、2、3、4、……、m，mis a positive integer;
s3, calculating the standard deviation of the time delay difference value between adjacent data packets, and calibrating the standard deviation of the time delay difference value asEStandard deviation of time delay differenceEIs calculated as follows:wherein->For the average value of the time delay difference values between adjacent data packets, the obtained calculation formula is as follows: />；
Standard deviation of time delay difference of 5G mobile network circuit in T timeEIt can be known that the standard deviation of the delay difference of the 5G mobile network circuit in the T timeEThe larger the representation value of (2) is, the delay difference value is indicatedThe larger the fluctuation of the 5G mobile network line is, the standard deviation of the time delay difference of the 5G mobile network line in the T time isEThe smaller the expression value of (2) is, the delay difference is indicated>The smaller the fluctuations of (2);
s4, calculating a delay jitter stability coefficient, wherein the calculated expression is as follows:；
the expression calculated by the time delay jitter stability coefficient shows that the larger the expression value of the time delay jitter stability coefficient of the 5G mobile network circuit in the T time is, the worse the running state of the 5G mobile network circuit in the T time is, and otherwise, the better the running state of the 5G mobile network circuit in the T time is;
the signal quality information comprises signal-to-noise ratio abnormal coefficients, and after acquisition, the signal-to-noise ratio abnormal coefficients are calibrated as；
When the signal-to-noise ratio of the 5G mobile network line decreases, the operation state of the network may be seriously affected, including:
data transmission error rate increases: the lower signal-to-noise ratio can cause the error rate in data transmission to increase, noise interference in the signal can cause a receiving end to fail to accurately decode and recover the transmitted data, so that the data transmission is wrong, and the high error rate can reduce the reliability and the data integrity of the network;
the packet loss rate is increased: the loss rate of the data packets may be increased due to the decrease of the signal-to-noise ratio, the lower signal-to-noise ratio can cause the receiving end to not correctly receive the transmitted data packets, the data packets are lost, the incomplete data transmission can be caused by the lost data packets, and the reliability and the performance of the network are affected;
the data transmission rate decreases: a lower signal-to-noise ratio can limit the data transmission rate, noise interference in the signal can cause errors and retransmissions of the data transmission, resulting in reduced data transmission rates, which can affect user experience and network performance, particularly for high bandwidth applications and large-scale data transmission;
coverage area reduction: the signal-to-noise ratio is reduced, so that the signal strength is reduced, the coverage of a 5G network is further limited, the lower signal-to-noise ratio can lead the signal not to be transmitted to a base station or an edge area, the coverage is reduced, and the network connection quality of users in the areas is affected;
real-time applications are affected: the performance of real-time application, such as video call, online game, teleconference and the like, can be affected by the reduced signal-to-noise ratio, and the lower signal-to-noise ratio can cause the increase of delay and delay jitter of data packets, so that the smoothness and user experience of the real-time application are affected;
therefore, the signal-to-noise ratio of the 5G mobile network line is monitored, the signal-to-noise ratio of the 5G mobile network line can be perceived in time when the signal-to-noise ratio of the 5G mobile network line becomes low, and corresponding network line planning and adjusting measures are adopted in advance;
the logic for obtaining the signal-to-noise ratio abnormal coefficient is as follows:
s1, setting a signal-to-noise ratio reference threshold value for a 5G mobile network line, and calibrating the signal-to-noise ratio reference threshold value as；
It should be noted that, the operators and network managers perform performance test and optimization on the 5G mobile network, and in these processes, the performance information of the network under different signal-to-noise ratio conditions is monitored and evaluated to realize the best network performance and user experience, where the signal-to-noise ratio reference threshold is the lowest signal-to-noise ratio allowed in the network;
s2, acquiring actual signal-to-noise ratios of the 5G mobile network circuit at different moments in the T time, and calibrating the actual signal-to-noise ratios as，kThe number representing the actual signal to noise ratio of the 5G mobile network line at different times during the T time,k=1、2、3、4、……、p，pis a positive integer;
it should be noted that, the spectrum analyzer is a professional testing device for analyzing the spectrum characteristics of the wireless signal, and some modern spectrum analyzers have functions for 5G networks, which can provide real-time signal quality data, including signal-to-noise ratio, and these instruments are generally used for professional network testing and optimization;
s3, the signal to noise ratio reference threshold value is smallerIs calibrated to +.>，fRepresenting less than the signal-to-noise reference threshold +.>Is used to determine the number of actual signal to noise ratios,f=1、2、3、4、……、M，Mis a positive integer;
s4, passing the minimum value of the optimal signal-to-noise ratio rangeAnd less than the optimal signal to noise ratio rangeIs>Calculating the signal-to-noise ratio abnormal coefficient, wherein the calculated expression is as follows:；
the expression calculated by the delay jitter stability coefficient shows that the larger the expression value of the signal-to-noise ratio abnormal coefficient of the 5G mobile network circuit in the T time is, the worse the running state of the 5G mobile network circuit in the T time is, and otherwise, the better the running state of the 5G mobile network circuit in the T time is;
s102, carrying out data analysis on data transmission information and signal quality information when a 5G mobile network line runs, and generating a state evaluation index;
acquiring abnormal coefficients of data transmission rateDelay jitter stabilization factor->S/N anomaly coefficient->Then, a data analysis model is built, and a state evaluation index is generated>The formula according to is:wherein->、/>、/>Respectively data transmission rate anomaly coefficient ++>Delay jitter stabilization factor->S/N anomaly coefficient->Is a preset proportionality coefficient of>、/>、/>Are all greater than 0;
the calculation formula shows that the larger the abnormal coefficient of the data transmission rate of the 5G mobile network circuit in the T time is, the larger the stable coefficient of the time delay jitter is, and the larger the abnormal coefficient of the signal to noise ratio is, namely the state evaluation indexThe larger the expression value of the (5G) mobile network circuit is, the worse the running state of the 5G mobile network circuit in the T time is, the smaller the abnormal coefficient of the data transmission rate of the 5G mobile network circuit in the T time is, the smaller the stable coefficient of the delay jitter is, the smaller the abnormal coefficient of the signal to noise ratio is, namely the state evaluation index is->The smaller the expression value of the 5G mobile network line is, the better the running state of the 5G mobile network line in the T time is;
s103, establishing a data set of a plurality of state evaluation indexes generated during the running of the 5G mobile network line, and comprehensively analyzing the state evaluation indexes in the data set to generate a high-risk state signal and a low-risk state signal;
running 5G mobile network line generated state evaluation indexesEstablishing a data set and calibrating the analysis set as +.>Then->，/>Number representing state evaluation index within data set, +.>，uIs a positive integer; calculating a state evaluation index average value and a state evaluation index standard deviation in the data set, and calibrating the state evaluation index average value and the state evaluation index standard deviation asXSAndYSthen: />，/>；
Setting a first reference threshold value for a state evaluation index generated when the 5G mobile network line runs, comparing the state evaluation index with the first reference threshold value, and when the state evaluation index is greater than or equal to the first reference threshold value, indicating that the running state of the 5G mobile network line is poor, and when the state evaluation index is less than the first reference threshold value, indicating that the running state of the 5G mobile network line is good;
after the state evaluation index average value XS and the state evaluation index standard deviation YS are obtained, the state evaluation index average value is compared with a first reference threshold value, and the state evaluation index standard deviation is compared with a second reference threshold value, wherein the comparison result is as follows:
if the state evaluation index average value is smaller than the first reference threshold value and the state evaluation index standard deviation is smaller than the second reference threshold value, indicating that the condition that the state evaluation index is larger than or equal to the first reference threshold value in the analysis set is an emergency, generating a low-risk state signal;
if the state evaluation index average value is smaller than the first reference threshold value and the state evaluation index standard deviation is larger than or equal to the second reference threshold value, or the state evaluation index average value is larger than or equal to the first reference threshold value, indicating that the situation that the state evaluation index is larger than or equal to the first reference threshold value in the analysis set is not an emergency situation, generating a high risk state signal;
s104, generating an adjustment prompt for a high-risk state signal generated during the running of the 5G mobile network line, performing planning adjustment in advance for the 5G mobile network line, and not generating an adjustment prompt for a low-risk state signal generated during the running of the 5G mobile network line, and performing planning adjustment for the 5G mobile network;
when a high-risk state signal is generated during the running of the 5G mobile network line, an adjustment prompt is generated, the 5G mobile network line is planned and adjusted in advance, the timeliness of the 5G mobile network line planning and adjustment is improved, the condition of service interruption is effectively prevented, the user can efficiently experience the 5G mobile network, when the low-risk state signal is generated during the running of the 5G mobile network line, the adjustment prompt is not generated, and the 5G mobile network is not planned and adjusted;
the application monitors the running state of the 5G mobile network line, can realize the intelligent perception of the abnormal hidden trouble of the 5G mobile network line, when the abnormal hidden trouble exists in the 5G mobile network line, the 5G mobile network line is planned and adjusted in advance, the timeliness of the 5G mobile network line planning and adjustment is improved, the condition of service interruption is effectively prevented, and the 5G mobile network is convenient for users to experience efficiently;
according to the application, by comprehensively analyzing the state evaluation index generated during the running of the 5G mobile network line instead of single analysis, the accidental situation of single analysis can be effectively prevented, the accuracy of data analysis is ensured, the accuracy of monitoring the running state of the 5G mobile network line is further improved, and the efficient running of the 5G mobile network line is ensured.
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.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
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.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within 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 (4)
1. The real-time adjustment method of the 5G mobile network based on the big data is characterized by comprising the following steps of:
s101, acquiring information of a 5G mobile network line in operation, including data transmission information and signal quality information, and performing data processing on the acquired data transmission information and signal quality information;
the data transmission information comprises a data transmission rate abnormal coefficient and a delay jitter stable coefficient, and after acquisition, the data transmission rate abnormal coefficient and the delay jitter stable coefficient are respectively calibrated asAnd->The signal quality information comprises signal-to-noise ratio abnormal coefficients, and after acquisition, the signal-to-noise ratio abnormal coefficients are calibrated to be +.>；
The logic for acquiring the abnormal coefficient of the data transmission rate is as follows:
s1, setting a data transmission rate reference threshold value for a 5G mobile network line, and calibrating the data transmission rate reference threshold value as；
S2, acquiring real-time data transmission rates of the 5G mobile network circuit at different moments in the T time, and calibrating the real-time data transmission ratesIs that，yThe table shows the numbers of real-time data transmission rates of the 5G mobile network lines at different moments in time T,y=1、2、3、4、……、N，Nis a positive integer;
s3, the reference threshold value of the data transmission rate is smaller thanIs calibrated to +.>，/>Representing less than the data transmission rate reference threshold +.>Number of real-time data transmission rate of +.>=1、2、3、4、……、n，nIs a positive integer;
s4, calculating abnormal coefficients of the data transmission rate, wherein the calculated expression is as follows:wherein->The frequency of abnormal data transmission rate of the 5G mobile network line in the time T is represented;
the logic for obtaining the delay jitter stabilization coefficient is as follows:
s1, acquiring a plurality of data packets received by a 5G mobile network circuit in T time, recording the time stamp of each received data packet reaching a receiving end, and marking the time stamp as，hRepresenting the number of packets received by the 5G mobile network line during the T time,h=1、2、3、4、……、H，His a positive integer;
s2, calculating the time delay difference between adjacent data packets in the T time of the 5G mobile network line, namely calculating the difference between the arrival time of each data packet and the arrival time of the previous data packet, and calibrating the time delay difference as，j A number representing the delay difference between adjacent packets,j=1、2、3、4、……、m，mis a positive integer;
s3, calculating the standard deviation of the time delay difference value between adjacent data packets, and calibrating the standard deviation of the time delay difference value asEStandard deviation of time delay differenceEIs calculated as follows:wherein->For the average value of the time delay difference values between adjacent data packets, the obtained calculation formula is as follows: />；
S4, calculating a delay jitter stability coefficient, wherein the calculated expression is as follows:；
the logic for obtaining the signal-to-noise ratio abnormal coefficient is as follows:
s1, setting a signal-to-noise ratio reference threshold value for a 5G mobile network line, and calibrating the signal-to-noise ratio reference threshold value as；
S2, acquiring that the 5G mobile network line does not exist in the T timeThe actual signal-to-noise ratio at the same time is calibrated as，kThe number representing the actual signal to noise ratio of the 5G mobile network line at different times during the T time,k=1、2、3、4、……、p，pis a positive integer;
s3, the signal to noise ratio reference threshold value is smallerIs calibrated to +.>，fRepresenting less than the signal-to-noise reference threshold +.>Is used to determine the number of actual signal to noise ratios,f=1、2、3、4、……、M，Mis a positive integer;
s4, consulting threshold value through signal to noise ratioAnd less than the signal-to-noise reference threshold +.>Is>Calculating the signal-to-noise ratio abnormal coefficient, wherein the calculated expression is as follows: />；
S102, carrying out data analysis on data transmission information and signal quality information when a 5G mobile network line runs, and generating a state evaluation index;
acquiring abnormal coefficients of data transmission rateDelay jitter stabilization factor->Signal to noise ratio anomaly coefficientThen, a data analysis model is built, and a state evaluation index is generated>The formula according to is:wherein->、/>、/>Respectively data transmission rate anomaly coefficient ++>Delay jitter stabilization factor->S/N anomaly coefficient->Is a preset proportionality coefficient of>、/>、/>Are all larger than0；
S103, establishing a data set of a plurality of state evaluation indexes generated during the running of the 5G mobile network line, and comprehensively analyzing the state evaluation indexes in the data set to generate a high-risk state signal and a low-risk state signal;
and S104, generating an adjustment prompt for a high-risk state signal generated during the running of the 5G mobile network line, performing planning adjustment in advance for the 5G mobile network line, and not generating an adjustment prompt for a low-risk state signal generated during the running of the 5G mobile network line, and performing planning adjustment for the 5G mobile network.
2. The big data based 5G mobile network real time adjustment method of claim 1, wherein the 5G mobile network line is run-time generated with a plurality of state evaluation indexesEstablishing a data set and calibrating the analysis set as +.>Then->，/>Number representing state evaluation index within data set, +.>，uIs a positive integer;
calculating a state evaluation index average value and a state evaluation index standard deviation in the data set, and calibrating the state evaluation index average value and the state evaluation index standard deviation asXSAndYSthen:，。
3. the big data based 5G mobile network real time adjustment method of claim 2, wherein a state evaluation index average value is obtainedXSAnd state evaluation index standard deviationYSAnd then, comparing the state evaluation index average value with a first reference threshold value, and comparing the state evaluation index standard deviation with a second reference threshold value, wherein the comparison result is as follows:
if the state evaluation index average value is smaller than the first reference threshold value and the state evaluation index standard deviation is smaller than the second reference threshold value, generating a low-risk state signal;
and if the state evaluation index average value is smaller than the first reference threshold value and the state evaluation index standard deviation is larger than or equal to the second reference threshold value or the state evaluation index average value is larger than or equal to the first reference threshold value, generating a high risk state signal.
4. The big data based real-time adjustment method for 5G mobile network according to claim 3, wherein when the 5G mobile network line is running, an adjustment prompt is generated, and the 5G mobile network line is planned and adjusted in advance, and when the 5G mobile network line is running, a low risk status signal is generated, no adjustment prompt is generated, and no planning and adjustment are performed on the 5G mobile network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311142565.9A CN116887338B (en) | 2023-09-06 | 2023-09-06 | Big data-based 5G mobile network real-time adjustment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311142565.9A CN116887338B (en) | 2023-09-06 | 2023-09-06 | Big data-based 5G mobile network real-time adjustment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116887338A CN116887338A (en) | 2023-10-13 |
| CN116887338B true CN116887338B (en) | 2023-12-08 |
Family
ID=88262517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311142565.9A Active CN116887338B (en) | 2023-09-06 | 2023-09-06 | Big data-based 5G mobile network real-time adjustment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116887338B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117098166B (en) * | 2023-10-20 | 2024-01-30 | 深圳曼瑞德科技有限公司 | Operation monitoring system of wrist type monitoring equipment |
| CN117240658B (en) * | 2023-11-13 | 2024-01-12 | 湖南承希科技有限公司 | E1 transmission method for realizing cross-ring network organization |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102625360A (en) * | 2012-03-05 | 2012-08-01 | 黄东 | Method for controlling service rate in wireless network |
| CN105191402A (en) * | 2013-01-18 | 2015-12-23 | 瑞典爱立信有限公司 | Adapting a mobile network |
| CN110046794A (en) * | 2019-03-01 | 2019-07-23 | 深圳和而泰数据资源与云技术有限公司 | Appraisal procedure, device, computer equipment and the storage medium of network quality |
| CN114430582A (en) * | 2022-01-24 | 2022-05-03 | 库卡机器人(广东)有限公司 | Network selection method, network selection device, robot, and storage medium |
| CN115208504A (en) * | 2022-07-22 | 2022-10-18 | 福州大学 | Network clock synchronization method based on ARIMA-LSTM estimation |
| CN115278811A (en) * | 2022-07-28 | 2022-11-01 | 中国科学院计算技术研究所 | MPTCP connection path selection method based on decision tree model |
| CN115798244A (en) * | 2022-10-26 | 2023-03-14 | 东风悦享科技有限公司 | Intelligent bus network diagnosis arrangement system |
| CN116383023A (en) * | 2023-04-06 | 2023-07-04 | 山东第一医科大学(山东省医学科学院) | Computer running performance intelligent supervision system based on data analysis |
| CN116611712A (en) * | 2023-07-17 | 2023-08-18 | 国网信息通信产业集团有限公司 | Semantic inference-based power grid work ticket evaluation system |
| CN116708208A (en) * | 2023-08-07 | 2023-09-05 | 山东慧贝行信息技术有限公司 | Network data transmission situation prediction method based on machine learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10721751B2 (en) * | 2017-06-05 | 2020-07-21 | At&T Intellectual Property I, L.P. | Facilitation of efficient spectrum utilization for 5G or other next generation networks |
-
2023
- 2023-09-06 CN CN202311142565.9A patent/CN116887338B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102625360A (en) * | 2012-03-05 | 2012-08-01 | 黄东 | Method for controlling service rate in wireless network |
| CN105191402A (en) * | 2013-01-18 | 2015-12-23 | 瑞典爱立信有限公司 | Adapting a mobile network |
| CN110046794A (en) * | 2019-03-01 | 2019-07-23 | 深圳和而泰数据资源与云技术有限公司 | Appraisal procedure, device, computer equipment and the storage medium of network quality |
| CN114430582A (en) * | 2022-01-24 | 2022-05-03 | 库卡机器人(广东)有限公司 | Network selection method, network selection device, robot, and storage medium |
| CN115208504A (en) * | 2022-07-22 | 2022-10-18 | 福州大学 | Network clock synchronization method based on ARIMA-LSTM estimation |
| CN115278811A (en) * | 2022-07-28 | 2022-11-01 | 中国科学院计算技术研究所 | MPTCP connection path selection method based on decision tree model |
| CN115798244A (en) * | 2022-10-26 | 2023-03-14 | 东风悦享科技有限公司 | Intelligent bus network diagnosis arrangement system |
| CN116383023A (en) * | 2023-04-06 | 2023-07-04 | 山东第一医科大学(山东省医学科学院) | Computer running performance intelligent supervision system based on data analysis |
| CN116611712A (en) * | 2023-07-17 | 2023-08-18 | 国网信息通信产业集团有限公司 | Semantic inference-based power grid work ticket evaluation system |
| CN116708208A (en) * | 2023-08-07 | 2023-09-05 | 山东慧贝行信息技术有限公司 | Network data transmission situation prediction method based on machine learning |
Non-Patent Citations (2)
| Title |
|---|
| Huawei.S5-204665 "Rel-16 CR TS 28.541 add TCE mapping info in GNBCUCPFunction".3GPP tsg_sa\wg5_tm.2020,(第tsgs5_132e期),全文. * |
| 多路径并行传输的路径动态决策方法;陶晓玲;于萌;王勇;;广西师范大学学报(自然科学版)(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116887338A (en) | 2023-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116887338B (en) | Big data-based 5G mobile network real-time adjustment method | |
| EP3379419B1 (en) | Situation analysis | |
| EP2679044B1 (en) | Service problem diagnosis for mobile wireless networks | |
| EP3694149B1 (en) | Network quality determining method and apparatus | |
| AU2016204716A1 (en) | Method and system for using a downloadable agent for a communication system, device, or link | |
| US11671341B2 (en) | Network monitoring method and network monitoring apparatus | |
| JP4748226B2 (en) | Quality degradation detection device, wired wireless judgment device | |
| US10841193B2 (en) | Monitoring quality of service | |
| CN111327539A (en) | Service scheduling method, device and equipment | |
| CN111464396A (en) | CAN bus load rate abnormity early warning method and electronic equipment | |
| US10176068B2 (en) | Methods, systems, and computer readable media for token based message capture | |
| US20120151043A1 (en) | System for Internet Scale Visualization and Detection of Performance Events | |
| US20220103442A1 (en) | Internet of things operations monitoring system | |
| CN113207146B (en) | Wireless communication network quality monitoring system and method | |
| CN110677327A (en) | Chip-based real-time detection method for RTP flow fault | |
| US20210084095A1 (en) | Method and Apparatus for Cross Layer Network Diagnostics and Self-Healing Platform for Point-to-Multipoint Networks | |
| CN115996259B (en) | Smart phone communication test system based on communication monitoring technology | |
| WO2023098360A1 (en) | Method, device and medium for wireless local area network equipment testing, and electronic device and program product | |
| CN113765727B (en) | Data center network time delay detection method, device, equipment and medium | |
| Algar et al. | A quality of experience management framework for mobile users | |
| CN110324608B (en) | Method and device for detecting video stream quality and computer equipment | |
| WO2021117230A1 (en) | Wireless frame estimation device, system, method, program, and recording medium | |
| CN109121073B (en) | Method, device and equipment for monitoring quality of mobile communication service | |
| CN112449369B (en) | Method, device and equipment for identifying problem cell and computer storage medium | |
| US11146466B2 (en) | Method for identifying an event impacting the QoE of an end-user of a network equipment |
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 |