CN115348615B - Low-delay 5G communication method based on big data - Google Patents
Low-delay 5G communication method based on big data Download PDFInfo
- Publication number
- CN115348615B CN115348615B CN202211285175.2A CN202211285175A CN115348615B CN 115348615 B CN115348615 B CN 115348615B CN 202211285175 A CN202211285175 A CN 202211285175A CN 115348615 B CN115348615 B CN 115348615B
- Authority
- CN
- China
- Prior art keywords
- communicated
- data
- dimension
- target
- data set
- 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
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/10—Flow control between communication endpoints
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Maintenance And Management Of Digital Transmission (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention relates to the technical field of wireless communication networks, in particular to a low-delay 5G communication method based on big data, which comprises the following steps: acquiring an original data set and a data set to be communicated; according to the original data set and the data set to be communicated, the abnormal deviation coefficient and the distribution centralization corresponding to each dimensionality in the high-dimensional space are determined, the abnormal degree of the data to be communicated on the centralization side and the abnormal degree of the data to be communicated on the dispersion side corresponding to each dimensionality are further determined, the overall abnormal degree corresponding to the data to be communicated is further determined, and the data set to be communicated is further communicated and transmitted. The invention can realize the low-delay communication of 5G communication, and effectively improves the accuracy of the priority setting of the communication transmission of the data to be communicated.
Description
Technical Field
The invention relates to the technical field of wireless communication networks, in particular to a low-delay 5G communication method based on big data.
Background
The wireless communication technology has the advantages of being free from the limitation of wires, having certain mobility, being capable of communicating through wireless connection in a mobile state, low in construction difficulty and low in cost, and the wireless communication technology realizes the leap-over progress from the 2G voice technology to the 5G large-bandwidth mass access. In the 5G communication technology, the network delay generated by the resource scheduling request and assignment part is generally improved by using a pre-emption scheduling manner, which is to empty the resources that have been originally allocated to the communication data with low priority for the communication data with high priority, so that the communication data with higher requirement on time delay can be transmitted faster, thereby reducing the network delay, wherein the problem of data transmission error generated by the communication data with low priority and preempted resources is solved in a retransmission manner. The method can solve the network delay generated by the resource scheduling request and the assignment link according to the requirements of different communication data on the network delay to a certain extent. The communication data with larger abnormal degree is required to be uploaded to the control center more quickly for abnormal elimination analysis, that is, the communication data with larger abnormal degree is required to have higher priority. Since the device generating the communication data often cannot judge the degree of abnormality of the communication data, priority cannot be set for the communication data. For example, when the communication data is a speed, a speed sensor acquiring the speed often cannot judge the degree of abnormality of the acquired speed. Therefore, when the pre-emptive scheduling is used to improve the network delay caused by the resource scheduling request and the assignment part, it is important to set the priority for the communication data. At present, when setting the priority corresponding to communication data, the method generally adopted is as follows: and judging the abnormal degree of the communication data in a manual mode, and determining the abnormal degree as the priority corresponding to the communication data.
However, when the above-described manner is adopted, there are often technical problems as follows:
when the priority setting is performed manually, the degree of abnormality of the communication data is often determined based on artificial subjective feeling, and the determination is often inaccurate.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The present invention is directed to a low latency 5G communication method based on big data to solve one or more of the problems set forth in the background section above.
The invention provides a low-delay 5G communication method based on big data, which comprises the following steps:
acquiring an original data set and a data set to be communicated;
respectively corresponding each original data in the original data set and each data to be communicated in the data set to be communicated to a high-dimensional space, and normalizing to obtain a target high-dimensional space, a target data set and a target data set to be communicated;
determining an abnormal deviation coefficient and a central distribution center corresponding to each dimension in the target high-dimensional space according to the target data set;
for each data to be communicated in the data set to be communicated, determining the abnormal degree of the data to be communicated on the concentrated side and the abnormal degree of the data to be communicated on the dispersed side corresponding to the dimension according to the data to be communicated in the data set to be communicated corresponding to the data to be communicated and the distribution concentration center corresponding to each dimension in the target high-dimensional space;
for each data to be communicated in the data set to be communicated, determining the overall abnormal degree corresponding to the data to be communicated according to the abnormal bias coefficient corresponding to each dimensionality in the target high-dimensional space, the concentrated side abnormal degree and the dispersed side abnormal degree corresponding to each dimensionality of the data to be communicated in the target high-dimensional space;
and carrying out communication transmission on the data sets to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data sets to be communicated.
Further, the determining an abnormal bias coefficient and a distribution center corresponding to each dimension in the target high-dimensional space according to the target data set includes:
uniformly dividing the dimensionality to obtain a preset number of distribution line sections corresponding to the dimensionality;
determining an abnormal deviation coefficient corresponding to the dimension according to the preset number of distribution line segments corresponding to the dimension and the target data set;
for each distribution line segment in a preset number of distribution line segments corresponding to the dimension, determining a distribution concentration corresponding to the distribution line segment according to the number of projections in each distribution line segment in the preset number of distribution line segments corresponding to the dimension;
and screening out the maximum distribution concentration from the distribution concentrations corresponding to the preset number of distribution line sections corresponding to the dimensionality, and determining the numerical value corresponding to the central point of the distribution line section corresponding to the maximum distribution concentration as the distribution concentration center corresponding to the dimensionality.
Further, the above formula for determining the abnormal deviation coefficient corresponding to the dimension is:
wherein, the first and the second end of the pipe are connected with each other,
is the abnormal deviation coefficient corresponding to the dimension,
is the above-mentioned predetermined number of bits,
when each target data in the target data set is projected on the dimension, the first target data in a preset number of distribution line segments corresponding to the dimension
The number of projections within a respective wire segment,
is the total number of projections in the dimension when each target data in the target data set is projected onto the dimension.
Further, the formula for determining the distribution concentration corresponding to the distribution line segment is as follows:
wherein the content of the first and second substances,
is a preset number corresponding to the above dimensionThe first in the mesh distribution line segment
The distribution concentration corresponding to each distribution line section,
is the above-mentioned predetermined number of bits,
when each target data in the target data set is projected onto the dimension, the first target data in a preset number of distribution line segments corresponding to the dimension
The number of projections within a respective wire segment.
Further, values corresponding to projections of the target data in the target data set and the target data to be communicated in the target data set on each dimension in the target high-dimensional space are both within a preset normalization range.
Further, the determining, according to the target data to be communicated in the target data set to be communicated corresponding to the data to be communicated and the distribution center corresponding to each dimension in the target high-dimensional space, the degree of abnormality of the data to be communicated on the concentrated side and the degree of abnormality on the dispersed side corresponding to the dimension includes:
determining the abnormal degree of the data to be communicated on the concentrated side corresponding to the dimension according to the distribution concentration center corresponding to the dimension and the target data to be communicated corresponding to the data to be communicated;
according to the number of projections of target data in each distribution line segment in the preset number of distribution line segments corresponding to the dimensionality, ascending the preset number of distribution line segments corresponding to the dimensionality to obtain a distribution line segment sequence corresponding to the dimensionality;
and determining the abnormal degree of the data to be communicated on the dispersion side corresponding to the dimension according to the distribution line segment sequence corresponding to the dimension and the target data to be communicated corresponding to the data to be communicated.
Further, the formula for determining the degree of abnormality of the data to be communicated on the centralized side corresponding to the dimension is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the abnormal degree of the data to be communicated at the concentration side corresponding to the dimension,
is a numerical value corresponding to the projection of the target data to be communicated corresponding to the data to be communicated on the dimension,
is the distribution concentration center corresponding to the above dimensions.
Further, the formula for determining the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the abnormal degree of the data to be communicated on the dispersion side corresponding to the dimension,
is a serial number of a distribution line segment on which the projection of the target data to be communicated corresponding to the data to be communicated on the dimension falls, and the position of the distribution line segment in the sequence corresponding to the dimension is located,
is the above-mentioned predetermined number.
Further, the above formula for determining the overall abnormal degree corresponding to the data to be communicated is:
wherein the content of the first and second substances,
is the above-mentioned overall degree of abnormality corresponding to the data to be communicated,
is the number of dimensions in the target high-dimensional space,
is the first in the target high-dimensional space
The abnormal deviation coefficients corresponding to the dimensions are obtained,
is the first in the target high-dimensional space of the data to be communicated
The degree of abnormality of the dispersion side corresponding to each dimension,
is the first of the data to be communicated in the target high-dimensional space
And the abnormal degree of the concentration side corresponding to each dimension.
Further, the performing communication transmission on the data set to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data set to be communicated includes:
determining the data to be communicated, which corresponds to the data to be communicated in the data set to be communicated and has a total abnormal degree greater than a preset abnormal threshold value, as priority communication data, and performing priority communication transmission on the priority communication data;
and determining the data to be communicated, of which the total abnormal degree corresponding to the data to be communicated in the data set to be communicated is less than or equal to the preset abnormal threshold value, as the common communication data, and performing delayed communication transmission on the common communication data.
The above embodiments of the present invention have the following beneficial effects:
according to the low-delay 5G communication method based on the big data, the priority of the communication transmission of the data to be communicated can be determined by determining the total abnormal degree corresponding to the data to be communicated, the low-delay communication of the 5G communication can be realized, and the accuracy of the priority setting of the communication transmission of the data to be communicated is effectively improved. Whether the data to be communicated is abnormal data or not is judged, and the data to be communicated is often compared with the original data (namely, data under normal conditions) in the original data set, so that the original data set and the data set to be communicated are obtained, and a data basis can be provided for subsequently determining the abnormal degree of the data to be communicated in the data set to be communicated. Secondly, the more the amount of the original data in the original data set is, the more the distribution trend of the original data is often represented, so the more the amount of the original data in the original data set is, the more the distribution trend that should be satisfied when the data to be communicated is normal is often represented. The larger the abnormal bias coefficient corresponding to the dimension is, the more the projection distribution of the data to be communicated on the dimension tends to be distributed discretely, and the smaller the abnormal bias coefficient corresponding to the dimension is, the more the projection distribution of the data to be communicated on the dimension tends to be distributed concentratedly. The distribution set center corresponding to the dimension can represent the numerical value corresponding to the aggregation center point of the dimension. The aggregate center point of the dimension may be a coordinate point corresponding to a smallest target distance among the target distances corresponding to the respective coordinate points on the dimension. The target distance may be the sum of the distances of the coordinate point to the respective projections in the dimension in which the coordinate point is located. The closer the projection position of the data to be communicated on one dimension is to the position corresponding to the distribution concentration center corresponding to the dimension, the more the data to be communicated conforms to the concentration distribution rule on the dimension, and the smaller the abnormal degree of the data to be communicated on the concentration side corresponding to the dimension is, the more the data to be communicated conforms to the concentration distribution rule on the dimension. The smaller the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension is, the more the discreteness of the distribution of the projection on the dimension is enhanced by the data to be communicated. The larger the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension is, the less the discreteness of the distribution of the projection on the dimension is enhanced by the data to be communicated is. The larger the abnormal deviation coefficient corresponding to the dimension is, the more dispersed the distribution of the projection of the data on the dimension is, and the more attention should be paid to the abnormal degree of the dispersion side for the evaluation of the abnormal degree of the data to be communicated on the dimension. The smaller the abnormal deviation coefficient corresponding to the dimension is, the more concentrated the distribution of the projection points of the data on the dimension is, and the more concentrated the abnormal degree of the data to be communicated should be focused on for evaluating the abnormal degree of the data on the dimension. Therefore, the abnormality bias coefficient, the concentrated-side abnormality degree and the dispersed-side abnormality degree are comprehensively considered, and the accuracy of determining the overall abnormality degree corresponding to the data to be communicated is improved. Therefore, the invention can determine the priority of the communication transmission of the data to be communicated by determining the total abnormal degree corresponding to the data to be communicated, can realize the low-delay communication of the 5G communication, and effectively improves the accuracy of the priority setting of the communication transmission of the data to be communicated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow chart of a big data based low latency 5G communication method according to the present invention;
FIG. 2 is a schematic illustration of a discrete distribution according to the present invention;
fig. 3 is a schematic diagram of a concentration profile according to the present invention.
Wherein the reference numerals include: a first dimension 201, a second dimension 301, and a focus center point 302.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the technical solutions according to the present invention will be given with reference to the accompanying drawings and preferred embodiments. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment provides a low-delay 5G communication method based on big data, which comprises the following steps:
acquiring an original data set and a data set to be communicated;
respectively corresponding each original data in the original data set and each data to be communicated in the data set to be communicated to a high-dimensional space, and normalizing to obtain a target high-dimensional space, a target data set and a target data set to be communicated;
determining an abnormal deviation coefficient and a central distribution center corresponding to each dimension in the target high-dimensional space according to the target data set;
for each data to be communicated in the data set to be communicated, determining the abnormal degree of the data to be communicated on the concentrated side and the abnormal degree of the data to be communicated on the dispersed side corresponding to the dimensionality according to the data to be communicated in the data set to be communicated corresponding to the data to be communicated and the distribution concentration center corresponding to each dimensionality in the target high-dimensional space;
for each data to be communicated in the data set to be communicated, determining the overall abnormal degree corresponding to the data to be communicated according to the abnormal bias coefficient corresponding to each dimensionality in the target high-dimensional space, and the abnormal degree of the concentrated side and the abnormal degree of the dispersed side corresponding to each dimensionality of the data to be communicated in the target high-dimensional space;
and carrying out communication transmission on the data sets to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data sets to be communicated.
The above modules are developed in detail as follows:
referring to fig. 1, a flow of a large data based low latency 5G communication method according to the present invention is shown. The low-delay 5G communication method based on the big data comprises the following steps:
step S1, an original data set and a data set to be communicated are obtained.
In some embodiments, a raw data set and a data set to be communicated may be obtained.
The data to be communicated in the data set to be communicated may be data that needs to be transmitted in a target communication system. The target Communication system may be a 5G (5 th Generation Mobile Communication Technology, fifth Generation Mobile Communication Technology) Communication system. The data type of the data to be communicated in the data set to be communicated may be the same as the data type of the original data in the original data set. The raw data in the raw data set may be normal.
For example, the data to be communicated in the data set to be communicated may include a plurality of adjustable parameters during operation of the industrial equipment. For example, when the industrial plant is a desulfurization plant, the plurality of adjustable parameters for operation of the industrial plant may be raw flue gas
Content, current of a slurry circulating pump, pH value, slurry density, frequency of a frequency converter of the slurry circulating pump and actual load of a boiler. Wherein, the original data in the original data set can be de-processedData collected during normal desulfurization of sulfur plants. The normal desulfurization by the desulfurization apparatus may be desulfurization performed when the desulfurization apparatus is not malfunctioning. The data to be communicated in the data set to be communicated may be data collected in an unknown state of the desulfurization device. The unknown state may be faulty or not faulty. For example, the data set to be communicated may be { [ raw flue gas { [
The content is as follows: 2612.012, slurry circulation pump current: 100.106, pH: 4.6, slurry density: 1100.191, frequency of a frequency converter of a slurry circulating pump 46.102, actual load of a boiler: 350.361][ raw flue gas ]
The contents are as follows: 2812.015, slurry circulation pump current: 108.745,pH: 3.6, slurry density: 1172.081, slurry circulation pump frequency converter frequency 45.126, boiler actual load: 346.476]}. The raw data set may be { [ raw flue gas { [
The content is as follows: 2611.035, slurry circulation pump current: 105.745, pH: 5.6, slurry density: 1182.891, frequency of frequency converter of slurry circulating pump 48.123, actual load of boiler: 350.377][ raw flue gas ]
The content is as follows: 2851.913, slurry circulating pump current: 106.343, pH: 4.489, slurry density: 1190.594, frequency of frequency converter of slurry circulating pump 38.035, actual load of boiler: 347.65][ raw flue gas ]
The content is as follows: 2604.961, slurry circulation pump current: 105.85, pH: 4.284, slurry density: 1193.051, frequency of frequency converter of slurry circulating pump 38.043, actual load of boiler: 348.588]}。
And S2, respectively corresponding each original data in the original data set and each data to be communicated in the data set to be communicated to a high-dimensional space, and normalizing to obtain a target high-dimensional space, a target data set and a target data set to be communicated.
In some embodiments, each original data in the original data set and each data to be communicated in the data set to be communicated may be respectively corresponding to a high-dimensional space and normalized to obtain a target high-dimensional space, a target data set, and a target data set to be communicated.
The projection values of the target data in the target data set and the projection values of the target data to be communicated in the target data set on each dimension in the target high-dimensional space may be both within a preset normalization range. The preset normalization range may be [0,1]. The value range of each dimension in the target high-dimensional space may be [0,1]. There may be at least one dimension in the high dimensional space. In practice, the raw data in the raw data set may often include a plurality of adjustable parameters during operation of the industrial equipment. There can often be multiple dimensions in the high dimensional space.
As an example, each original data in the original data set and each to-be-communicated data in the to-be-communicated data set may be respectively corresponding to a high-dimensional space, and each original data in the original data set and each to-be-communicated data in the to-be-communicated data set are normalized, so as to obtain a target data set corresponding to the original data set and a target to-be-communicated data set corresponding to the to-be-communicated data set. When the target data to be communicated and the target data correspond to the high-dimensional space, the projection of the target data to be communicated and the target data on each dimension in the high-dimensional space may correspond to a numerical value within [0,1]. The high-dimensional space can be normalized to make the numerical value of each dimension in the normalized high-dimensional space within [0,1], and the target high-dimensional space is obtained. For example, the data to be communicated in the data set to be communicated and the raw data in the raw data set may include a plurality of adjustable parameters for the same industrial equipment to operate. Wherein an adjustable parameter may correspond to a dimension in a high dimensional space.
And S3, determining an abnormal deviation coefficient and a distribution concentration center corresponding to each dimension in the target high-dimensional space according to the target data set.
In some embodiments, the anomaly bias coefficient and the center of the distribution set corresponding to each dimension in the target high-dimensional space may be determined according to the target data set.
The distribution central center corresponding to the dimension can represent a numerical value corresponding to the aggregation central point of the dimension. The aggregation center point of the dimension may be a coordinate point corresponding to a minimum target distance among the target distances corresponding to the respective coordinate points on the dimension. The target distance may be the sum of the distances of the coordinate point to the respective projections in the dimension in which the coordinate point is located. The abnormal deviation coefficient corresponding to the dimension can represent the distribution trend of the target data in the target data set on the dimension. The distribution in the distribution tendency may be a discrete distribution or a concentrated distribution. For example, as shown in FIG. 2, the distribution over the first dimension 201 may be a discrete distribution. Wherein the reference numerals of fig. 2 include: a first dimension 201. As shown in fig. 3, the distribution in the second dimension 301 may be a concentrated distribution. Wherein a solid dot in the first dimension 201 may be a projection of the target data in the target data set onto the first dimension 201. The solid dots in the second dimension 301 may be projections of the target data in the target data set onto the second dimension 301. The aggregate center point in the second dimension 301 may be the aggregate center point 302. Wherein the reference numerals of fig. 3 include: a second dimension 301 and an aggregate center point 302.
The larger the number of the target data in the target data set, the more the distribution trend of the target data in the dimension is likely to be represented, so the larger the number of the target data in the target data set, the more the distribution trend that should be satisfied when the data to be communicated is normal is likely to be represented.
As an example, this step may include the steps of:
the first step is to uniformly divide the dimensionality to obtain a preset number of distribution line segments corresponding to the dimensionality.
The preset number may be a preset number. The preset number is preferably 10.
For example, when the preset number is 3, the 3 distribution line segments corresponding to the dimension may be a value range in the dimension of
、
And
the line segment of (2).
And secondly, determining an abnormal deviation coefficient corresponding to the dimensionality according to the preset number of distribution line segments corresponding to the dimensionality and the target data set.
For example, the above formula for determining the abnormal bias coefficient corresponding to the dimension may be:
wherein the content of the first and second substances,
is the abnormal deviation coefficient corresponding to the dimension.
Is the above-mentioned predetermined number.
When each target data in the target data set is projected on the dimension, the first target data in a preset number of distribution line segments corresponding to the dimension
The number of projections within a respective wire segment.
Is the above objectThe total number of projections in the dimension when each target data in the data set is projected onto the dimension. When each target data in the target data set is projected onto the dimension, the total number of projections on the dimension may be the same as the number of target data in the target data set.
The more the abnormal bias coefficient corresponding to the dimension approaches to 1, the more the projection distribution of the data to be communicated on the dimension approaches to discrete distribution, the more the abnormal bias coefficient corresponding to the dimension approaches to 0, the more the projection distribution of the data to be communicated on the dimension approaches to concentrated distribution.
And thirdly, determining the distribution concentration corresponding to the distribution line section according to the number of projections in each distribution line section in the preset number of distribution line sections corresponding to the dimensionality for each distribution line section in the preset number of distribution line sections corresponding to the dimensionality.
Wherein the projection in the profile wire segment may be a projection of the target data as projected onto the profile wire segment.
For example, the formula for determining the distribution concentration corresponding to the distribution line segment may be:
wherein the content of the first and second substances,
is the first of a predetermined number of distribution line segments corresponding to the above dimension
The distribution concentration corresponding to each distribution line section.
Is the above-mentioned predetermined number.
When each of the above target data setsWhen the target data is projected on the dimension, the target data is projected on the second segment in the preset number of distribution line segments corresponding to the dimension
The number of projections within a respective wire segment.
And fourthly, screening out the maximum distribution concentration from the distribution concentrations corresponding to the preset number of distribution line sections corresponding to the dimensionality, and determining the numerical value corresponding to the central point of the distribution line section corresponding to the maximum distribution concentration as the distribution concentration center corresponding to the dimensionality.
And S4, determining the abnormal degree of the data to be communicated on the concentrated side and the abnormal degree of the data to be communicated on the dispersed side corresponding to the dimensionality according to the data to be communicated in the target data set to be communicated corresponding to the data to be communicated and the distribution concentration center corresponding to each dimensionality in the target high-dimensional space.
In some embodiments, for each piece of data to be communicated in the set of data to be communicated, the degree of abnormality of the data to be communicated on the concentrated side and the degree of abnormality on the dispersed side corresponding to the dimension may be determined according to the target data to be communicated in the set of data to be communicated corresponding to the piece of data to be communicated and the distribution concentration center corresponding to each dimension in the target high-dimensional space.
As an example, this step may include:
the method comprises the steps of firstly, determining the abnormal degree of the data to be communicated at the concentration side corresponding to the dimensionality according to the distribution concentration center corresponding to the dimensionality and the target data to be communicated corresponding to the data to be communicated.
For example, the formula for determining the degree of abnormality of the data to be communicated on the centralized side corresponding to the dimension may be:
wherein, the first and the second end of the pipe are connected with each other,
the degree of abnormality of the data to be communicated on the concentration side corresponding to the dimension is determined.
The data to be communicated is a numerical value corresponding to the projection of the target data to be communicated corresponding to the data to be communicated on the dimension.
Is the distribution concentration center corresponding to the above dimensions.
And secondly, according to the number of projections of the target data in each distribution line segment in the preset number of distribution line segments corresponding to the dimensionality, ascending the sequence of the preset number of distribution line segments corresponding to the dimensionality to obtain a distribution line segment sequence corresponding to the dimensionality.
For example, when the preset number is 3, the number of projections of the target data in each of the preset number of segment lines corresponding to the dimension may be 2, 6, and 1, respectively. In this case, the distribution line segment sequence corresponding to the dimension may be { line segments with a projection number of 1, line segments with a projection number of 2, and line segments with a projection number of 6 }.
And thirdly, determining the abnormal degree of the data to be communicated on the dispersion side corresponding to the dimension according to the distribution line segment sequence corresponding to the dimension and the target data to be communicated corresponding to the data to be communicated.
For example, the formula for determining the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension may be:
wherein the content of the first and second substances,
the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension is determined.
The serial number is a distribution line segment on which the projection of the target data to be communicated corresponding to the data to be communicated on the dimension falls, and the serial number is the position of the target data to be communicated in the distribution line segment sequence corresponding to the dimension.
Is the above-mentioned predetermined number. For example, the sequence number at which the position of the first segment in a sequence of segment lines is located can be 1.
And S5, determining the total abnormal degree corresponding to the data to be communicated according to the abnormal deviation coefficient corresponding to each dimensionality in the target high-dimensional space, the concentrated side abnormal degree and the dispersed side abnormal degree corresponding to each dimensionality of the data to be communicated in the target high-dimensional space for each data to be communicated in the data set to be communicated.
In some embodiments, for each piece of data to be communicated in the set of data to be communicated, the overall abnormal degree corresponding to the piece of data to be communicated may be determined according to the abnormal bias coefficient corresponding to each dimension in the target high-dimensional space, the concentrated-side abnormal degree and the dispersed-side abnormal degree corresponding to each dimension in the target high-dimensional space of the piece of data to be communicated.
As an example, the above formula for determining the overall abnormal degree corresponding to the data to be communicated may be:
wherein the content of the first and second substances,
is the overall abnormal degree corresponding to the data to be communicated.
Is the number of dimensions in the target high-dimensional space described above.
Is the first in the target high-dimensional space
And abnormal deviation coefficients corresponding to the dimensions.
Is the first of the data to be communicated in the target high-dimensional space
The degree of abnormality of the dispersion side corresponding to each dimension.
Is the first of the data to be communicated in the target high-dimensional space
And the abnormal degree of the concentration side corresponding to each dimension.
And S6, carrying out communication transmission on the data set to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data set to be communicated.
In some embodiments, the data sets to be communicated may be communicated and transmitted according to a total abnormal degree corresponding to each data to be communicated in the data sets to be communicated.
As an example, this step may include the steps of:
the method comprises the steps of firstly, determining data to be communicated, which corresponds to the data to be communicated in the data set to be communicated and has the overall abnormal degree larger than a preset abnormal threshold value, as priority communication data, and carrying out priority communication transmission on the priority communication data.
And secondly, determining the data to be communicated, which corresponds to the data to be communicated in the data set to be communicated and has the overall abnormal degree smaller than or equal to the preset abnormal threshold value, as the common communication data, and performing delayed communication transmission on the common communication data.
For example, when the amount of idle communication resources of the target communication system is less than the amount of communication resources required by the to-be-communicated data in the to-be-communicated data set, the transmission of the normal communication data may be stopped first, and the communication resources may be preferentially allocated to the priority communication data, so as to achieve low-delay transmission of the to-be-communicated data in the target communication system.
Optionally, when the amount of idle communication resources of the target communication system is less than the amount of communication resources required by the data to be communicated in the data set to be communicated, the data to be communicated may be sorted from large to small according to the total abnormal degree corresponding to the data to be communicated, so as to obtain a data sequence to be communicated. The communication resources may be allocated to the data to be communicated in the sequence of data to be communicated in turn.
Optionally, when the amount of idle communication resources of the target communication system is less than the amount of communication resources required by the to-be-communicated data in the to-be-communicated data set, the total abnormal degree corresponding to the to-be-communicated data may be determined as the priority corresponding to the to-be-communicated data, and the network delay caused by insufficient communication resources is improved through pre-clearing scheduling.
According to the low-delay 5G communication method based on the big data, the priority of communication transmission of the data to be communicated can be determined by determining the total abnormal degree corresponding to the data to be communicated, the low-delay communication of the 5G communication can be realized, and the accuracy of setting the priority of the communication transmission of the data to be communicated is effectively improved. First, an original data set and a data set to be communicated are acquired. Whether the data to be communicated is abnormal data or not is judged, and the data to be communicated is often required to be compared with the original data (namely, data under normal conditions) in the original data set, so that the original data set and the data set to be communicated are obtained, and a data basis can be provided for subsequently determining the abnormal degree of the data to be communicated in the data set to be communicated. Secondly, the more the amount of the original data in the original data set, the more the distribution trend of the original data can be represented, so the more the amount of the original data in the original data set, the more the distribution trend that the data to be communicated should satisfy when being normal can be represented. And continuing to respectively correspond each original data in the original data set and each data to be communicated in the data set to be communicated to a high-dimensional space, and normalizing to obtain a target high-dimensional space, a target data set and a target data set to be communicated. And then, according to the target data set, determining an abnormal deviation coefficient and a distribution concentration center corresponding to each dimension in the target high-dimensional space. The larger the abnormal bias coefficient corresponding to the dimension is, the more the projection distribution of the data to be communicated on the dimension tends to approach to discrete distribution, and the smaller the abnormal bias coefficient corresponding to the dimension is, the more the projection distribution of the data to be communicated on the dimension tends to approach to concentrated distribution. The distribution center corresponding to the dimension may represent a value corresponding to the aggregation center point of the dimension. The aggregation center point of the dimension may be a coordinate point corresponding to a minimum target distance among the target distances corresponding to the respective coordinate points on the dimension. The target distance may be the sum of the distances of the coordinate point to the respective projections in the dimension in which the coordinate point is located. Then, for each data to be communicated in the data set to be communicated, determining the abnormal degree of the data to be communicated on the centralized side and the abnormal degree of the data to be communicated on the distributed side corresponding to each dimension in the high-dimensional space according to the data to be communicated corresponding to the data to be communicated and the distribution centralized center corresponding to each dimension in the target data set to be communicated. The closer the projection position of the data to be communicated on one dimension is to the position corresponding to the distribution center corresponding to the dimension, the more the data to be communicated conforms to the centralized distribution rule on the dimension, and the smaller the abnormal degree of the data to be communicated on the centralized side corresponding to the dimension is, the more the data to be communicated conforms to the centralized distribution rule on the dimension. The smaller the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension is, the more the discreteness of the distribution of the projection on the dimension is enhanced by the data to be communicated. The larger the degree of abnormality of the data to be communicated on the dispersion side corresponding to the dimension is, the less the discreteness of the distribution of the projection on the dimension is enhanced by the data to be communicated is. And then, for each data to be communicated in the data set to be communicated, determining the overall abnormal degree corresponding to the data to be communicated according to the abnormal deviation coefficient corresponding to each dimension in the target high-dimensional space, and the concentrated side abnormal degree and the dispersed side abnormal degree corresponding to each dimension of the data to be communicated in the target high-dimensional space. The larger the abnormal deviation coefficient corresponding to the dimension is, the more dispersed the distribution of the projection of the data on the dimension is, and the more attention should be paid to the abnormal degree of the dispersion side for the evaluation of the abnormal degree of the data to be communicated on the dimension. The smaller the abnormal deviation coefficient corresponding to the dimension is, the more concentrated the distribution of the projection points of the data on the dimension is, and the more concentrated the abnormal degree of the data to be communicated should be focused on for evaluating the abnormal degree of the data on the dimension. Therefore, the abnormality bias coefficient, the centralized-side abnormality degree and the distributed-side abnormality degree are comprehensively considered, and the accuracy of determining the overall abnormality degree corresponding to the data to be communicated is improved. And finally, carrying out communication transmission on the data sets to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data sets to be communicated. The invention can determine the priority of the communication transmission of the data to be communicated by determining the total abnormal degree corresponding to the data to be communicated, can realize the low-delay communication of 5G communication, and effectively improves the accuracy of the priority setting of the communication transmission of the data to be communicated.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.
Claims (2)
1. A low-delay 5G communication method based on big data is characterized by comprising the following steps:
acquiring an original data set and a data set to be communicated;
respectively corresponding each original data in the original data set and each data to be communicated in the data set to be communicated to a high-dimensional space, and normalizing to obtain a target high-dimensional space, a target data set and a target data set to be communicated;
according to the target data set, determining an abnormal deviation coefficient and a central distribution center corresponding to each dimension in the target high-dimensional space;
for each data to be communicated in the data set to be communicated, determining the abnormal degree of the data to be communicated on the concentrated side and the abnormal degree of the data to be communicated on the dispersed side corresponding to the dimensionality according to the data to be communicated in the data set to be communicated corresponding to the data to be communicated and the distribution concentration center corresponding to each dimensionality in the target high-dimensional space;
for each data to be communicated in the data set to be communicated, determining the overall abnormal degree corresponding to the data to be communicated according to the abnormal deviation coefficient corresponding to each dimensionality in the target high-dimensional space, and the abnormal degree of the data to be communicated on the concentrated side and the abnormal degree of the scattered side corresponding to each dimensionality in the target high-dimensional space;
performing communication transmission on the data sets to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data sets to be communicated;
determining an abnormal bias coefficient and a central distribution center corresponding to each dimension in the target high-dimensional space according to the target data set, including:
uniformly dividing the dimensionality to obtain a preset number of distribution line segments corresponding to the dimensionality;
determining an abnormal deviation coefficient corresponding to the dimensionality according to the preset number of distribution line segments corresponding to the dimensionality and the target data set;
for each distribution line segment in the preset number of distribution line segments corresponding to the dimension, determining the distribution concentration corresponding to the distribution line segment according to the number of projections in each distribution line segment in the preset number of distribution line segments corresponding to the dimension;
screening out the maximum distribution concentration from the distribution concentrations corresponding to the preset number of distribution line sections corresponding to the dimensionality, and determining a numerical value corresponding to the central point of the distribution line section corresponding to the maximum distribution concentration as a distribution concentration center corresponding to the dimensionality;
the formula for determining the abnormal deviation coefficient corresponding to the dimension is as follows:
wherein the content of the first and second substances,
is the abnormal deviation coefficient corresponding to the dimension,
is the predetermined number of the first,
is the first in a preset number of distribution line segments corresponding to the dimension when each target data in the target data set is projected onto the dimension
The number of projections within a respective wire segment,
is the total number of projections on the dimension when each target data in the target data set is projected on the dimension;
the formula for determining the distribution concentration corresponding to the distribution line section is as follows:
wherein the content of the first and second substances,
is a preset number of distribution line segments corresponding to the dimensionTo (1)
The distribution concentration corresponding to each distribution line section,
is the predetermined number of the first,
is the first in a preset number of distribution line segments corresponding to the dimension when each target data in the target data set is projected onto the dimension
The number of projections within a respective distribution line segment;
the determining, according to the target data to be communicated in the target data set to be communicated corresponding to the data to be communicated and the distribution concentration center corresponding to each dimension in the target high-dimensional space, the degree of abnormality of the data to be communicated at the concentration side and the degree of abnormality at the dispersion side corresponding to the dimension includes:
determining the abnormal degree of the data to be communicated at the concentration side corresponding to the dimension according to the distribution concentration center corresponding to the dimension and the target data to be communicated corresponding to the data to be communicated;
according to the number of projections of target data in each distribution line segment in the preset number of distribution line segments corresponding to the dimensionality, ascending the preset number of distribution line segments corresponding to the dimensionality to obtain a distribution line segment sequence corresponding to the dimensionality;
determining the abnormal degree of the data to be communicated on the dispersion side corresponding to the dimension according to the distribution line segment sequence corresponding to the dimension and the target data to be communicated corresponding to the data to be communicated;
the formula for determining the abnormal degree of the data to be communicated on the concentrated side corresponding to the dimension is as follows:
wherein the content of the first and second substances,
is the abnormal degree of the data to be communicated on the concentration side corresponding to the dimension,
is a numerical value corresponding to the projection of the target data to be communicated corresponding to the data to be communicated on the dimension,
is the distribution concentration center corresponding to the dimension;
the formula for determining the abnormal degree of the data to be communicated on the dispersion side corresponding to the dimension is as follows:
wherein the content of the first and second substances,
is the abnormal degree of the data to be communicated on the dispersion side corresponding to the dimension,
the serial number of the position of the distribution line segment sequence corresponding to the dimension where the projection of the target data to be communicated corresponding to the data to be communicated falls on the dimension,
is the preset number;
the formula corresponding to the total abnormal degree corresponding to the data to be communicated is determined as follows:
wherein the content of the first and second substances,
is the overall abnormal degree corresponding to the data to be communicated,
is the number of dimensions in the target high-dimensional space,
is the first in the target high-dimensional space
The abnormal deviation coefficients corresponding to the dimensions are obtained,
is the first of the data to be communicated in the target high-dimensional space
The degree of abnormality of the dispersion side corresponding to each dimension,
is the first of the data to be communicated in the target high-dimensional space
The abnormal degree of the concentration side corresponding to each dimension;
the performing communication transmission on the data set to be communicated according to the overall abnormal degree corresponding to each data to be communicated in the data set to be communicated comprises:
determining the data to be communicated, which corresponds to the data to be communicated in the data set to be communicated and has a total abnormal degree greater than a preset abnormal threshold value, as priority communication data, and performing priority communication transmission on the priority communication data;
and determining the data to be communicated, of which the total abnormal degree corresponding to the data to be communicated in the data set to be communicated is less than or equal to the preset abnormal threshold value, as the common communication data, and performing delayed communication transmission on the common communication data.
2. The method according to claim 1, wherein the projection of the target data in the target data set and the projection of the target data to be communicated in the target data set on each dimension in the target high-dimensional space correspond to a numerical value within a preset normalization range.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211285175.2A CN115348615B (en) | 2022-10-20 | 2022-10-20 | Low-delay 5G communication method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211285175.2A CN115348615B (en) | 2022-10-20 | 2022-10-20 | Low-delay 5G communication method based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115348615A CN115348615A (en) | 2022-11-15 |
| CN115348615B true CN115348615B (en) | 2022-12-20 |
Family
ID=83957442
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211285175.2A Active CN115348615B (en) | 2022-10-20 | 2022-10-20 | Low-delay 5G communication method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115348615B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111026570A (en) * | 2019-11-01 | 2020-04-17 | 支付宝(杭州)信息技术有限公司 | Method and device for determining abnormal reason of business system |
| CN112202638A (en) * | 2020-09-29 | 2021-01-08 | 北京百度网讯科技有限公司 | Data processing method, device, equipment and computer storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7081695B2 (en) * | 2019-02-05 | 2022-06-07 | 日本電気株式会社 | Priority determination device, priority determination method, and control program |
| CN117376088A (en) * | 2019-07-18 | 2024-01-09 | 华为技术有限公司 | Network abnormal root cause positioning method, device and system and computer storage medium |
-
2022
- 2022-10-20 CN CN202211285175.2A patent/CN115348615B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111026570A (en) * | 2019-11-01 | 2020-04-17 | 支付宝(杭州)信息技术有限公司 | Method and device for determining abnormal reason of business system |
| CN112202638A (en) * | 2020-09-29 | 2021-01-08 | 北京百度网讯科技有限公司 | Data processing method, device, equipment and computer storage medium |
Non-Patent Citations (2)
| Title |
|---|
| An Optimal Over-frequency Generator Tripping Strategy for Regional Power Grid with High Penetration Level of Renewable Energy;Zhihang Zhou.et al;《JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY》;20210930;全文 * |
| 基于深度自编码器的移动通信基站异常度检测;马敏等;《移动通信》;20210930;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115348615A (en) | 2022-11-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110248417B (en) | Resource allocation method and system for communication service in power Internet of things | |
| CN110647115B (en) | Data acquisition method based on industrial internet operating system | |
| CN1839645A (en) | System and method for reporting measurements in a communication system | |
| US20180136974A1 (en) | Task scheduling system and method suitable for mobile health | |
| CN104679590A (en) | Map optimization method and device in distributive calculating system | |
| CN112543366A (en) | Video playing method and device | |
| CN116418603A (en) | Safety comprehensive management method and system for industrial Internet | |
| CN110535705B (en) | Service function chain construction method capable of adapting to user time delay requirement | |
| CN115348615B (en) | Low-delay 5G communication method based on big data | |
| CN112596892A (en) | Data interaction method and system of multi-node edge computing equipment | |
| CN115278908A (en) | Wireless resource allocation optimization method and device | |
| CN111124439B (en) | Intelligent dynamic unloading algorithm with cloud edge cooperation | |
| CN112084034B (en) | MCT scheduling method based on edge platform layer adjustment coefficient | |
| KR20230041031A (en) | Power control method, device, communication node and storage medium | |
| CN110366253B (en) | Channel allocation method, device and storage medium based on cognitive radio network | |
| CN116647507A (en) | Internet of things data processing method and system based on load self-adaption | |
| CN108900804B (en) | Self-adaptive video stream processing method based on video entropy | |
| CN110245019A (en) | A kind of the thread concurrent method and device of Adaptable System resource | |
| CN115801896A (en) | Calculation network node distribution method and device, electronic equipment and storage medium | |
| CN101765144B (en) | Method for processing service access and device thereof | |
| CN114281544A (en) | Electric power task execution method and device based on edge calculation | |
| CN112257948A (en) | Method, device and equipment for predicting moisture content of moistening leaf feeding outlet | |
| CN117971629B (en) | Method and device for testing performance parameters of server and storage medium | |
| CN111355557A (en) | Method and device for adjusting Modulation and Coding Scheme (MCS) | |
| CN114070748B (en) | Multi-objective optimization-based operation and maintenance service scheduling method and related 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 |