CN116112985A - 5G network slicing system based on intelligent power grid - Google Patents
5G network slicing system based on intelligent power grid Download PDFInfo
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Abstract
The invention discloses a 5G network slicing system based on a smart grid, which relates to the technical field of network slicing, and is used for acquiring all target objects through a target acquisition unit, acquiring target data of the target objects through the target acquisition unit, and acquiring all the target objects and corresponding analysis sections Di and resource nuclear values Hi thereof; then carrying out habit analysis on the target objects and the corresponding analysis segments Di and the resource core values Hi by means of a habit analysis unit, and determining the distribution value of each target object in different analysis segments according to the distribution condition of the resource core values Hi of the analysis segments Di of each target object; the network resources can be flexibly allocated according to different conditions of each analysis section, and simultaneously, the real-time network resources can be monitored, and when the fluctuation is excessive, the rest network resources are immediately scheduled to be supported; the invention is simple and effective, and is easy and practical.
Description
Technical Field
The invention belongs to the technical field of network slicing, and particularly relates to a 5G network slicing system based on a smart grid.
Background
The patent with publication number CN112888069A discloses a 5G network slicing system serving urban center environment, which is characterized in that: the system comprises a 5G communication module, a data storage module, a weight calculation module, a network slicing module and a resource scheduling module, wherein the system is used for calculating weights of various communication services and wireless network resources according to a set processing mode in an urban central environment for a 5G mobile phone user, performing slicing scheduling processing conforming to service priority, and slicing physical network resources by acquiring 5G mobile communication base station layout data and network link data in real time to obtain communication service priority ordering as a resource scheduling result.
However, for the slicing of network resources, temporary adjustment is not performed according to the real-time situation; based on this, a technical solution is now presented.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art;
to achieve the above object, an embodiment according to a first aspect of the present invention proposes a smart grid-based 5G network slicing system, including:
the system comprises a target acquisition unit, a habit analysis unit, a habit record library, a self-slicing unit and an execution unit;
the target acquisition unit is used for acquiring all target objects, acquiring target data of the target objects by using the target acquisition unit, and acquiring all target objects and corresponding analysis segments Di and resource core values Hi;
the target acquisition unit is used for transmitting all target objects and corresponding analysis segments Di and resource core values Hi to the habit analysis unit; the habit analysis unit is used for performing habit analysis on the target objects and the corresponding analysis segments Di and the resource core values Hi, and determining the distribution value of each target object in different analysis segments according to the distribution condition of the resource core values Hi of the analysis segments Di of each target object;
the habit analysis unit is used for transmitting the distribution values of the target objects in different analysis sections to the habit record library, and the habit record library is used for storing the distribution values of all the target objects in different analysis sections in real time;
the self-slicing unit is used for carrying out network slicing by combining the habit record library and the execution unit, and the specific mode is as follows:
according to the real-time, the analysis section where the current analysis section is located is obtained, network resources are distributed according to the distribution value of each target object in the analysis section, and the network resources are executed by an execution unit.
Compared with the prior art, the invention has the beneficial effects that:
the invention is used for acquiring all target objects through the target acquisition unit, acquiring target data of the target objects by utilizing the target acquisition unit, and obtaining all target objects and corresponding analysis segments Di and resource core values Hi thereof; then carrying out habit analysis on the target objects and the corresponding analysis segments Di and the resource core values Hi by means of a habit analysis unit, and determining the distribution value of each target object in different analysis segments according to the distribution condition of the resource core values Hi of the analysis segments Di of each target object;
the network resources can be flexibly allocated according to different conditions of each analysis section, and simultaneously, the real-time network resources can be monitored, and when the fluctuation is excessive, the rest network resources are immediately scheduled to be supported; the invention is simple and effective, and is easy and practical.
Drawings
Fig. 1 is a block diagram of the structure of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present application provides a smart grid-based 5G network slicing system,
as an embodiment of the present invention, it specifically includes:
the system comprises a target acquisition unit, a habit analysis unit, a habit record library, a self-slicing unit and an execution unit;
the target acquisition unit is used for acquiring all target objects, wherein the target objects are all corresponding 5G network services, including real-time conversation services, streaming services, interactive services, background services and best effort services, and are generally represented as voice transmission, live video streaming, network pages, file transmission protocols, short message service, emails and the like when the target acquisition unit is applied specifically;
the target data of the target object is acquired by a target acquisition unit, and the specific acquisition mode is as follows:
step one: firstly, dividing a day into 24 time periods, wherein the specific time period is determined according to the specific situation, the time period is divided from zero, and each hour is divided into an analysis section which is marked as Di, i=1, & gt, 24;
step two: then selecting a target object, acquiring a resource occupation value of each analysis section, wherein the resource occupation value is the occupation ratio of the target object to the resources of the whole network, and marking the target object as the resource occupation value;
the resource occupation value of a single period is acquired once at each interval T1 time, the average value is automatically calculated after the acquisition is finished to obtain a representation value, and T1 is a value preset by an administrator;
step three: let i=1, select the corresponding analysis segment D1, obtain the resource occupation value of this analysis segment continuously for X1 days, X1 is the preset value;
then, all the resource occupation values of the analysis section are obtained and marked as Yj, j=1, & gt, and X1, wherein the resource occupation values are expressed as resource occupation values in X1 days; the average value of Yj is obtained and marked as P;
calculating the eccentricity value W of Yj by using a formula, wherein the specific calculation formula is as follows:
when W exceeds X2, marking the average value P at the moment as a resource core value, otherwise, acquiring the number of the numerical values exceeding P in Yj, marking the number as an upper number, and marking the number of the numerical values smaller than P in Yj as a lower number; x2 is a preset value;
when the upper number exceeds the lower number, marking the average value of the maximum value and P in Yj as a resource core value;
otherwise, marking the minimum value in Yj and the average value of P as a resource core value;
obtaining a resource core value of a corresponding analysis section;
step four: adding one to the i value, continuously selecting all Di, obtaining resource core values of all analysis sections according to the principle of the third step, and marking the resource core values as Hi, i=1, & gt, 24; and Hi and Di are in one-to-one correspondence;
step five: obtaining all analysis segments Di and corresponding resource core values Hi thereof;
step six: carrying out the same treatment on all other target objects to obtain all target objects, and corresponding analysis segments Di and resource nuclear values Hi;
the target acquisition unit is used for transmitting all target objects and corresponding analysis segments Di and resource core values Hi to the habit analysis unit; the habit analysis unit is used for performing habit analysis on the target object and the corresponding analysis section Di and the resource core value Hi, and the specific mode of the habit analysis is as follows:
s1: optionally selecting a target object to obtain resource core values Hi of all analysis sections Di;
s2: then automatically calculating the average value of the resource core values Hi, marking the average value as a core average value, calculating an eccentric value according to the core average value, and marking the numerical value corresponding to each resource core value Hi as an assigned value of a corresponding analysis section when the eccentric value is smaller than or equal to X3 to obtain the assigned values of the target object in different analysis sections;
s3: when the eccentric value exceeds X3, automatically sequencing analysis segments Di according to the sequence from the large value to the small value of the resource core value Hi, marking the analysis segments of Hi corresponding to the value which exceeds the core average value by 1.3 times with high frequency marks, and marking the average value of the Hi values of the analysis segments corresponding to the high frequency marks as a corresponding distribution value;
marking an analysis section of Hi corresponding to a value which is 0.7 times lower than the kernel mean value as a low-frequency mark, and marking the mean value of Hi values of the analysis section corresponding to the low-frequency mark as a corresponding distribution value;
marking the remaining analysis sections with intermediate frequency marks, and marking the average value of Hi values corresponding to the analysis sections marked with the intermediate frequency marks as a corresponding distribution value;
obtaining distribution values of all analysis sections;
s4: S1-S3 processing is carried out on all other target objects to obtain assigned values of each target object in different analysis sections;
the habit analysis unit is used for transmitting the distribution values of the target objects in different analysis sections to the habit record library, and the habit record library is used for storing the distribution values of all the target objects in different analysis sections in real time;
the self-slicing unit is used for carrying out network slicing by combining the habit record library and the execution unit, and the specific mode is as follows:
according to the real-time, acquiring the analysis section where the current analysis section is located, distributing network resources according to the distribution value of each target object in the analysis section, and executing by using an execution unit;
the second embodiment of the present invention further includes a resource synchronization unit, where the resource synchronization unit is configured to monitor network resources used by all objects in real time, mark the network resources as practical values, and transmit the practical values to the self-slicing unit, where the self-slicing unit is configured to perform real-time analysis on the practical values, where the real-time analysis is specifically implemented in the following manner:
obtaining a target object corresponding to the practical value, marking the target object as an over-frequency object, and then obtaining a current analysis section and an allocation value corresponding to the over-frequency object of the analysis section;
when the practical value exceeds twice of the assigned value, automatically acquiring the assigned values of all the target objects in the current analysis section at the moment, and sorting the target objects according to the manner that the assigned values are from small to large;
the allocation value of the target object is adjusted to be twice, the lowest allocation value is reserved for the selected target object according to the sorting mode of the target object in real time from the beginning to the end, the redundant allocation values are all given to the over-frequency object, and the lowest allocation value is preset by an administrator;
transmitting the modified condition to an execution unit, and executing by the execution unit according to the corresponding modified assigned value;
as a third embodiment of the present invention, the present invention further includes a management unit, which is communicatively connected with the self-slicing unit, and is configured to record all preset values.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. A smart grid-based 5G network slicing system, comprising:
the system comprises a target acquisition unit, a habit analysis unit, a habit record library, a self-slicing unit and an execution unit;
the target acquisition unit is used for acquiring all target objects, acquiring target data of the target objects by using the target acquisition unit, and acquiring all target objects and corresponding analysis segments Di and resource core values Hi;
the target acquisition unit is used for transmitting all target objects and corresponding analysis segments Di and resource core values Hi to the habit analysis unit; the habit analysis unit is used for performing habit analysis on the target objects and the corresponding analysis segments Di and the resource core values Hi, and determining the distribution value of each target object in different analysis segments according to the distribution condition of the resource core values Hi of the analysis segments Di of each target object;
the habit analysis unit is used for transmitting the distribution values of the target objects in different analysis sections to the habit record library, and the habit record library is used for storing the distribution values of all the target objects in different analysis sections in real time;
the self-slicing unit is used for carrying out network slicing by combining the habit record library and the execution unit, and the specific mode is as follows:
according to the real-time, the analysis section where the current analysis section is located is obtained, network resources are distributed according to the distribution value of each target object in the analysis section, and the network resources are executed by an execution unit.
2. The smart grid-based 5G network slicing system of claim 1, wherein the target object is all corresponding 5G network services including real-time session services, streaming services, interactive services, background services, best effort services.
3. The smart grid-based 5G network slicing system of claim 1, wherein the manner of obtaining all target objects and their corresponding analysis segments Di and resource core values Hi is:
step one: firstly, dividing a day into 24 time periods, dividing the time periods from zero, dividing each hour into an analysis section, and marking the analysis section as Di, i=1, & gt, 24;
step two: then selecting a target object, acquiring a resource occupation value of each analysis section, wherein the resource occupation value is the occupation ratio of the target object to the resources of the whole network, and marking the target object as the resource occupation value;
the resource occupation value of a single period is acquired once at each interval T1 time, the average value is automatically calculated after the acquisition is finished to obtain a representation value, and T1 is a value preset by an administrator;
step three: let i=1, select the corresponding analysis segment D1, obtain the resource occupation value of this analysis segment continuously for X1 days, X1 is the preset value;
then, all the resource occupation values of the analysis section are obtained and marked as Yj, j=1, & gt, and X1, wherein the resource occupation values are expressed as resource occupation values in X1 days; the average value of Yj is obtained and marked as P;
calculating the eccentricity value W of Yj by using a formula, wherein the specific calculation formula is as follows:
when W exceeds X2, marking the average value P at the moment as a resource core value, otherwise, acquiring the number of the numerical values exceeding P in Yj, marking the number as an upper number, and marking the number of the numerical values smaller than P in Yj as a lower number; x2 is a preset value;
when the upper number exceeds the lower number, marking the average value of the maximum value and P in Yj as a resource core value;
otherwise, marking the minimum value in Yj and the average value of P as a resource core value;
obtaining a resource core value of a corresponding analysis section;
step four: adding one to the i value, continuously selecting all Di, obtaining resource core values of all analysis sections according to the principle of the third step, and marking the resource core values as Hi, i=1, & gt, 24; and Hi and Di are in one-to-one correspondence;
step five: obtaining all analysis segments Di and corresponding resource core values Hi thereof;
step six: and carrying out the same processing on all other target objects to obtain all target objects, and corresponding analysis segments Di and resource core values Hi.
4. The smart grid-based 5G network slicing system of claim 1, wherein the habit analysis specifically comprises:
s1: optionally selecting a target object to obtain resource core values Hi of all analysis sections Di;
s2: then automatically calculating the average value of the resource core values Hi, marking the average value as a core average value, calculating an eccentric value according to the core average value, and marking the numerical value corresponding to each resource core value Hi as an assigned value of a corresponding analysis section when the eccentric value is smaller than or equal to X3 to obtain the assigned values of the target object in different analysis sections;
s3: when the eccentric value exceeds X3, automatically sequencing analysis segments Di according to the sequence from the large value to the small value of the resource core value Hi, marking the analysis segments of Hi corresponding to the value which exceeds the core average value by 1.3 times with high frequency marks, and marking the average value of the Hi values of the analysis segments corresponding to the high frequency marks as a corresponding distribution value;
marking an analysis section of Hi corresponding to a value which is 0.7 times lower than the kernel mean value as a low-frequency mark, and marking the mean value of Hi values of the analysis section corresponding to the low-frequency mark as a corresponding distribution value;
marking the remaining analysis sections with intermediate frequency marks, and marking the average value of Hi values corresponding to the analysis sections marked with the intermediate frequency marks as a corresponding distribution value;
obtaining distribution values of all analysis sections;
s4: and (3) processing the rest all the target objects in the steps S1-S3 to obtain the assigned value of each target object in different analysis sections.
5. The smart grid-based 5G network slicing system of claim 1, further comprising a resource synchronization unit, wherein the resource synchronization unit is configured to monitor network resources used by all target objects in real time, mark the network resources as utility values, and transmit the utility values to the self-slicing unit, and the self-slicing unit is configured to perform real-time analysis on the utility values, where the real-time analysis is specifically:
obtaining a target object corresponding to the practical value, marking the target object as an over-frequency object, and then obtaining a current analysis section and an allocation value corresponding to the over-frequency object of the analysis section;
when the practical value exceeds twice of the assigned value, automatically acquiring the assigned values of all the target objects in the current analysis section at the moment, and sorting the target objects according to the manner that the assigned values are from small to large;
the allocation value of the target object is adjusted to be twice, the lowest allocation value is reserved for the selected target object according to the sorting mode of the target object in real time from the beginning to the end, the redundant allocation values are all given to the over-frequency object, and the lowest allocation value is preset by an administrator;
and transmitting the modified condition to an execution unit, and executing by the execution unit according to the corresponding modified assigned value.
6. The smart grid-based 5G network slicing system of claim 1, further comprising a management unit communicatively coupled to the self-slicing unit for entering all preset values.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112888069A (en) * | 2021-01-12 | 2021-06-01 | 温州科技职业学院 | 5G network slicing system serving city center environment |
| CN115955698A (en) * | 2022-12-27 | 2023-04-11 | 中国南方电网有限责任公司 | 5G network slicing system based on smart power grid |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112888069A (en) * | 2021-01-12 | 2021-06-01 | 温州科技职业学院 | 5G network slicing system serving city center environment |
| CN115955698A (en) * | 2022-12-27 | 2023-04-11 | 中国南方电网有限责任公司 | 5G network slicing system based on smart power grid |
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