CN114500680A - Multi-channel intersection data classification type information transmission algorithm - Google Patents

Multi-channel intersection data classification type information transmission algorithm Download PDF

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CN114500680A
CN114500680A CN202210407617.XA CN202210407617A CN114500680A CN 114500680 A CN114500680 A CN 114500680A CN 202210407617 A CN202210407617 A CN 202210407617A CN 114500680 A CN114500680 A CN 114500680A
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channel
convergence
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CN114500680B (en
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马柔珊
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Chuangsi Guangzhou Electronic Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/14—Multichannel or multilink protocols
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2441—Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]

Abstract

The invention relates to the technical field of information transmission, in particular to a data classification type information transmission algorithm with multi-channel intersection. The method comprises the steps of forming a plurality of channels for transmitting data sent by a transmitting end between the transmitting end and a receiving end; all channels are converged before being received by a receiving end, a part before the convergence forms a channel before convergence, and a part after the convergence forms a channel after convergence; and forming a junction area between the pre-convergence channel and the post-convergence channel, classifying data transmitted in the pre-convergence channel through the junction area, and transmitting the classified data of the same type to a receiving end through the post-convergence channel. According to the invention, the interception of data sent by a transmitting terminal is realized through the intersection region, the data transmitted in a channel before convergence is classified, and the classified data of the same type is transmitted to a receiving terminal through a channel after convergence, so that the orderliness of the data after being transmitted to the receiving terminal is improved in a classification mode, and the condition of data accumulation is avoided.

Description

Multi-channel intersection data classification type information transmission algorithm
Technical Field
The invention relates to the technical field of information transmission, in particular to a data classification type information transmission algorithm with multi-channel intersection.
Background
Information transmission is the transmission of commands or status information from one end to the other over a channel and is received by the other. Including transmission and reception. The transmission medium is divided into wired and wireless, and the wired is a telephone line or a special cable; radio uses radio, microwave, satellite technology, and the like. The information cannot be changed during the information transmission process, and the information itself cannot be transmitted or received. There must be a carrier, e.g. data, language, signal, etc., and the transmitting side and the receiving side have a common interpretation of the carrier.
In the prior art, Chinese patent publication No.: CN104935501B discloses a system and method for realizing classified information transmission by user classification, the method determines the user grade of a user terminal according to task completion data and sends the user grade to a storage module for storage, a control module sends corresponding classified information to the user terminal according to the label data of the user terminal and the user grade after receiving a classified information sending instruction of a merchant terminal;
that is to say, in the prior art, classification is assisted by a user to realize hierarchical transmission, however, no matter how to transmit the classification formed by classification has any influence on the receiving end, it is only convenient for the sending of data at the transmitting end, and the receiving end is easy to have a problem of data accumulation.
Disclosure of Invention
The invention aims to provide a data classification type information transmission algorithm for multi-channel intersection so as to solve the problems in the background technology.
In order to achieve the above object, a data classification type information transmission algorithm for multi-channel intersection is provided, which comprises the following steps:
s1, forming multiple channels for transmitting data sent by the transmitting end between the transmitting end and the receiving end;
s2, all channels are converged before being received by a receiving end, the part before convergence forms a channel before convergence, and the part after convergence forms a channel after convergence;
s3, a junction area is formed among the pre-convergence channel and the post-convergence channel, data transmitted in the pre-convergence channel are classified through the junction area, and classified homogeneous data are transmitted to a receiving end through the post-convergence channel.
As a further improvement of the technical solution, in the intersection area in S3, a classification-level input method is adopted for classifying the transmission data in the forward channel, and the method includes the following steps:
s3.1.1, the receiving end inputs classification level to the intersection area;
s3.1.2, the junction receives the classification levels and extracts the classification features in the classification levels
Figure DEST_PATH_IMAGE001
；
S3.1.3, in the process of meeting of the channels before meeting in the meeting area, the data transmitted by different channels before meeting are according to the data characteristics
Figure 676408DEST_PATH_IMAGE002
Comparing the similarity, wherein the similarity reaches a preliminary threshold value
Figure 399820DEST_PATH_IMAGE003
The data in the intersection area are aggregated, and an aggregated feature set is obtained
Figure 200286DEST_PATH_IMAGE004
；
S3.1.4, set feature to be aggregated
Figure 392364DEST_PATH_IMAGE004
And classification features
Figure 645491DEST_PATH_IMAGE001
Comparing one by one to obtain a comparison value
Figure 73061DEST_PATH_IMAGE005
And selecting the maximum comparison value
Figure 711722DEST_PATH_IMAGE006
Corresponding classification feature
Figure 323969DEST_PATH_IMAGE001
And as the classification falling features, the aggregated data fall into the corresponding classification levels through the classification falling features.
As a further improvement of the present technical solution, the post-sink channels in S3 include a classification level post-sink channel and a spare post-sink channel, and transmit data to the receiving end through the classification level post-sink channel and the spare post-sink channel, where:
the classified level post-convergence channel corresponds to the grade number of the classification level, and the data falling into the classification level are transmitted through the classified level post-convergence channel;
the backup post-assembly channel is used for transmitting data which do not fall into the classification level.
As a further improvement of the present technical solution, the generation manner of the backup post-rendezvous channel includes a preset form and an automatic form, wherein:
the preset formula is that the standby post-convergence channel is set while the classified input is carried out in S3.1.1;
the auto-form is to automatically form S3.1.4 a backup post-channel for data that does not fall into the classification level.
As a further improvement of the technical scheme, the automatic forming mode is an aggregation characteristic set according to the aggregated data
Figure 815124DEST_PATH_IMAGE007
And forming a corresponding spare post-sink channel.
As a further improvement of the present technical solution, in S1, a plurality of transmitting ends are provided, and data transmission is performed between the plurality of transmitting ends and the intersection region through a forward channel.
As a further improvement of the present technical solution, in S1, one transmitting end is provided, and one transmitting end includes a plurality of transmitting addresses, and data transmission is performed between the plurality of transmitting addresses and the intersection area through a forward channel.
As a further improvement of this solution, the aggregated feature set
Figure 46385DEST_PATH_IMAGE008
The calculation formula of (a) is as follows:
Figure 555864DEST_PATH_IMAGE009
；
in the formula (I), the compound is shown in the specification,
Figure 591209DEST_PATH_IMAGE004
an aggregated feature set;
Figure 694295DEST_PATH_IMAGE010
for data features
Figure 588301DEST_PATH_IMAGE002
Selecting a feature;
Figure DEST_PATH_IMAGE011
for data features
Figure 296494DEST_PATH_IMAGE002
In (1) removing
Figure 860331DEST_PATH_IMAGE010
Selecting a feature;
Figure 824613DEST_PATH_IMAGE003
is a preliminary threshold.
As a further improvement of the technical scheme, the comparison value
Figure 522311DEST_PATH_IMAGE005
The calculation formula of (a) is as follows:
Figure 491535DEST_PATH_IMAGE012
；
in the formula (I), the compound is shown in the specification,
Figure 491852DEST_PATH_IMAGE005
is a comparison value;
Figure 428584DEST_PATH_IMAGE008
an aggregated feature set;
Figure 182170DEST_PATH_IMAGE001
are classified features.
As a further improvement of the technical solution, the classification stage includes a self-setting classification stage and a pull-in classification stage, and the number of stages of the self-setting classification stage and the pull-in classification stage is at least two.
Compared with the prior art, the invention has the beneficial effects that:
1. in the data classification formula information transmission algorithm of this multichannel intersection, the district that intersects is a node of cutting apart the channel, realize sending data interception to the transfer terminal through the district that intersects (when guaranteeing that data normally receives and dispatches, intercept the data of transmission, avoid transmitting between the data to the receiving terminal and lead to the receiving terminal data to appear piling up), and classify the data of transmitting in the channel before meeting, the data of the same kind that obtains after the classification is through channel transmission to the receiving terminal behind the collection, thereby improve the orderliness behind data transmission to the receiving terminal through categorised mode, avoid appearing the circumstances that the data is piled up.
2. In the multi-channel intersected data classification type information transmission algorithm, comparison is carried out in the intersection process, the environment that the intersection region compresses scattered data can be utilized, the intensity of 'fusion' during data comparison is improved, namely, the distance between characters/bytes between the compared data is shortened by utilizing the compressed environment, so that the sufficiency during comparison is improved, and the path along which the data needs to move during aggregation can be shortened.
Drawings
FIG. 1 is a block diagram of the flow of the data-sorting type information transmission algorithm for multi-channel intersection according to the present invention;
FIG. 2 is a flow chart of the classification level input method steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a multi-channel intersected data classified information transmission algorithm, which is used for data transmission between a transmitting end and a receiving end and is realized by forming a transmission channel between the transmitting end and the receiving end.
Referring to fig. 1, the data classification type information transmission algorithm for multi-channel intersection includes the following steps:
s1, a plurality of channels for transmitting data sent by the transmitting end are formed between the transmitting end and the receiving end, however, the data sent by the transmitting end is directly transmitted to the receiving end through the channels, so that the data of the receiving end is accumulated, the later arrangement or the consultation is not easy, and S2 is carried out after the data enters the channels;
s2, all channels are converged before being received by a receiving end, and the part before convergence forms a pre-convergence channel, so that data sent by a transmitting end is pre-transmitted through the pre-convergence channel but not transmitted to the receiving end, but is transmitted to the receiving end through a post-convergence channel formed by the part after convergence, so that the data can be ensured to normally enter the channels, and the data is prevented from being accumulated at the transmitting end;
s3, a junction area is formed between the pre-convergence channel and the post-convergence channel, namely the junction area is a node for dividing the channel, data interception of a transmitting end is achieved through the junction area (the data are guaranteed to be normally received and transmitted, meanwhile, the data transmitted are intercepted, the phenomenon that the data are transmitted to a receiving end between the data and the receiving end to cause the data of the receiving end to be accumulated is avoided), the data transmitted in the pre-convergence channel are classified, the classified data of the same type are transmitted to the receiving end through the post-convergence channel, the regularity of the data transmitted to the receiving end is improved through a classification mode, and the data accumulation condition is avoided.
In a first embodiment, referring to fig. 2, in S3, the convergence area uses a classification-level input method for classifying the transmission data in the forward channel, and the method includes the following steps:
s3.1.1, the receiving end inputs classification levels to the convergence region, wherein the classification level number is at least two;
s3.1.2, the junction receives the classification levels and extracts the classification features in the classification levels
Figure 255168DEST_PATH_IMAGE001
Since the classification stage number is at least two, it is said that
Figure 301753DEST_PATH_IMAGE013
By passing
Figure 601147DEST_PATH_IMAGE014
To represent different classification features, assuming that the classification level is 3, there are classification features
Figure 640647DEST_PATH_IMAGE015
It should be noted that, in the following description,
Figure 82999DEST_PATH_IMAGE015
features representing only three classification levels, but not limiting the number of features per classification level, i.e.
Figure 549752DEST_PATH_IMAGE016
May contain a plurality of features;
s3.1.3, in the process of meeting of the channels before meeting in the meeting area, the data transmitted by different channels before meeting are according to the data characteristics
Figure 336443DEST_PATH_IMAGE002
Comparing the similarity, wherein the similarity reaches a preliminary threshold value
Figure 399208DEST_PATH_IMAGE003
Are aggregated in the intersection region (i.e., the similarity reaches a preliminary threshold value)
Figure 712377DEST_PATH_IMAGE003
Is aggregated into a whole, and an aggregated feature set of the whole is obtained
Figure 602229DEST_PATH_IMAGE008
) Thus, the comparison is carried out in the intersection process, the environment of compressing the scattered data by the intersection area can be utilized, the intensity of 'fusion' in the data comparison is improved, namely, the distance between characters/bytes between the compared data is shortened by utilizing the compressed environment, thereby improving the sufficiency in the comparison and also shortening the path of the data to be moved in the aggregation process, wherein,
aggregated feature set
Figure 850DEST_PATH_IMAGE008
The calculation formula of (a) is as follows:
Figure 257519DEST_PATH_IMAGE009
；
in the formula (I), the compound is shown in the specification,
Figure 441506DEST_PATH_IMAGE008
to aggregate feature sets, here the feature sets are aggregated
Figure 984483DEST_PATH_IMAGE008
Is a data characteristic
Figure 854088DEST_PATH_IMAGE010
A constructed set;
Figure 914448DEST_PATH_IMAGE010
for data features
Figure 671051DEST_PATH_IMAGE002
One selected characteristic, the same principle
Figure 401241DEST_PATH_IMAGE017
To distinguish between different data features, only
Figure 774453DEST_PATH_IMAGE017
As long as the number is greater than or equal to 1, the selected one feature refers to the feature of the selected one data, so the number of the features of the data is not limited, but is based on the data itself;
Figure 761475DEST_PATH_IMAGE011
for data features
Figure 513530DEST_PATH_IMAGE002
In (1) removing
Figure 929468DEST_PATH_IMAGE010
One feature is selected, provided that here
Figure 275130DEST_PATH_IMAGE002
Is provided with
Figure 801926DEST_PATH_IMAGE018
、
Figure DEST_PATH_IMAGE019
And
Figure 985652DEST_PATH_IMAGE020
then select
Figure 838070DEST_PATH_IMAGE018
As
Figure 795662DEST_PATH_IMAGE010
Then, then
Figure 345723DEST_PATH_IMAGE019
And
Figure 197004DEST_PATH_IMAGE020
then it is taken as
Figure 206942DEST_PATH_IMAGE011
For selection, when the comparison is carried out, the selection is carried out in sequence
Figure 510884DEST_PATH_IMAGE019
And
Figure 254849DEST_PATH_IMAGE020
(if there is more then will not
Figure 976949DEST_PATH_IMAGE010
Until the data characteristics are selected) and
Figure 640011DEST_PATH_IMAGE018
comparing, namely comparing the obtained product with the contrast result of more than 0.80-0.95The data and
Figure 946097DEST_PATH_IMAGE018
corresponding data are gathered, and 0.80-0.95 is the initial threshold value
Figure 228173DEST_PATH_IMAGE003
The efficiency of later-stage classification can be improved by aggregating the collected data into a whole, the efficiency of comparison can be improved by utilizing the compressed environment of the intersection area, the efficiency of data concentration can be improved by aggregating the collected data on the basis, the pressure of later-stage classification can be relieved by utilizing the environment, and the accuracy of classification is ensured by matching with later-stage classification, and the whole of the collected data at the stage contains a plurality of data features, so that the whole contains a plurality of specific features;
s3.1.4, set feature to be aggregated
Figure 788468DEST_PATH_IMAGE008
And classification features
Figure 373164DEST_PATH_IMAGE001
Comparing one by one, thus all the characteristics of the gathered whole can be considered, and the comparison value is obtained after comparing one by one
Figure 182857DEST_PATH_IMAGE005
Same principle of
Figure DEST_PATH_IMAGE022AAA
Only defining the number of comparison values, which number corresponds to the number of classification levels, and selecting the maximum comparison value
Figure 911035DEST_PATH_IMAGE006
Corresponding classification feature
Figure 342148DEST_PATH_IMAGE001
As a class falling feature, the aggregated data is dropped into a corresponding class by the class falling feature, wherein,
comparison ofValue of
Figure 612592DEST_PATH_IMAGE005
The calculation formula of (a) is as follows:
Figure 253789DEST_PATH_IMAGE012
；
in the formula (I), the compound is shown in the specification,
Figure 251570DEST_PATH_IMAGE005
is a comparison value;
Figure 317614DEST_PATH_IMAGE008
an aggregated feature set;
Figure 978534DEST_PATH_IMAGE001
are classification features.
A second embodiment, which is implemented on the basis of the first embodiment, specifically considering that not all data can fall into the classification level in S3 or S3.1.4, for this reason, the post-sink channel in S3 includes a classification level post-sink channel and a spare post-sink channel, and data is transmitted to the receiving end through the classification level post-sink channel and the spare post-sink channel, where:
the classified level post-convergence channel corresponds to the level number of the classification level, and the data falling into the classification level are transmitted through the classified level post-convergence channel;
the backup post-amble is used to transmit data that does not fall into the classification level.
Further, the generation mode of the backup post-convergence channel includes a preset mode and an automatic forming mode, wherein:
the preset formula is that the standby post-sink channel is set while the classified data is input in S3.1.1, so that the data which do not fall into the classified level are transmitted through the standby post-sink channel, the transmission mode is too single, and an automatic forming formula is disclosed, wherein the automatic forming formula is that the standby post-sink channel is automatically formed in S3.1.4 aiming at the data which do not fall into the classified level, and particularly, the standby post-sink channel is formed according to the aggregation characteristic set of the aggregated data
Figure 107027DEST_PATH_IMAGE007
And forming a corresponding spare post-sink channel.
A third embodiment specifically discloses a transmitting end in this embodiment, wherein:
a plurality of transmitting ends are arranged in S1, namely a plurality of users are arranged, and data transmission is carried out between the plurality of transmitting ends and the intersection area through a forward channel;
s1, one transmitting end is provided, and one transmitting end includes a plurality of transmitting addresses, that is, a plurality of data transmitting addresses in one user, and data transmission is performed between the plurality of transmitting addresses and the intersection area through the forward channel.
A fourth embodiment specifically discloses classification stages, including a self-setting classification stage and a pull-in classification stage, where:
the self-setting classification level is suitable for being used on chatting software or some notification software, and a classification level is set in advance, such as: i set two classification levels of social news and sports news first, so that the received notifications on software are not classified according to the users sending the news but classified according to the sent news and then stacked on the classification levels, namely, no matter a plurality of users send the news, the notifications are stacked through the classification levels set first, the number of displayed information notifications is effectively reduced, and the receiving end can conveniently look up the notifications;
the pull-in classification level is suitable for data transmission between two users, and is particularly suitable for being used on a computer, because a receiving end often stores data into a folder on a desktop of the receiving end, at this time, the folder pulled into the intersection area by the receiving end user is the classification level, the classified data are directly stored into the folder, and accumulation of the data on the desktop is avoided.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A data classification type information transmission algorithm for multi-channel intersection is characterized by comprising the following steps:
s1, forming multiple channels for transmitting data sent by the transmitting end between the transmitting end and the receiving end;
s2, all channels are converged before being received by a receiving end, the part before convergence forms a channel before convergence, and the part after convergence forms a channel after convergence;
s3, a junction area is formed between the pre-convergence channel and the post-convergence channel, data transmitted in the pre-convergence channel are classified through the junction area, and the classified homogeneous data are transmitted to a receiving end through the post-convergence channel.
2. The multi-channel converged data-classified information transfer algorithm of claim 1, wherein: in the S3, the convergence region classifies the transmission data in the convergence front channel by using a classification-level input method, which includes the following steps:
s3.1.1, the receiving end inputs classification level to the intersection area;
s3.1.2, the junction receives the classification levels and extracts the classification features in the classification levels
Figure 598349DEST_PATH_IMAGE001
；
S3.1.3, in the process of meeting of the channels before meeting in the meeting area, the data transmitted by different channels before meeting are according to the data characteristics
Figure 128687DEST_PATH_IMAGE002
Comparing the similarity, wherein the similarity reaches a preliminary threshold value
Figure 990202DEST_PATH_IMAGE003
The data in the intersection area are aggregated, and an aggregated feature set is obtained
Figure 730624DEST_PATH_IMAGE005
；
S3.1.4, set feature to be aggregated
Figure 40514DEST_PATH_IMAGE006
And classification features
Figure 549993DEST_PATH_IMAGE001
Comparing one by one to obtain a comparison value
Figure 319759DEST_PATH_IMAGE007
And selecting the maximum comparison value
Figure 547478DEST_PATH_IMAGE008
Corresponding classification feature
Figure 316851DEST_PATH_IMAGE001
And as the classification falling features, the aggregated data fall into the corresponding classification levels through the classification falling features.
3. The multi-channel converged data-classified information transfer algorithm of claim 2, wherein: the post-convergence channel in S3 includes a classification level post-convergence channel and a spare post-convergence channel, and transmits data to the receiving end through the classification level post-convergence channel and the spare post-convergence channel, where:
the classified level post-convergence channel corresponds to the grade number of the classification level, and the data falling into the classification level are transmitted through the classified level post-convergence channel;
the backup post-assembly channel is used for transmitting data which do not fall into the classification level.
4. The multi-channel converged data-classified information transfer algorithm of claim 3, wherein: the generation mode of the standby post-convergence channel comprises a preset mode and an automatic forming mode, wherein:
the preset formula is that the standby post-sink channel is set while the classification level input is carried out in S3.1.1;
the auto-form is to automatically form S3.1.4 a backup post-channel for data that does not fall into the classification level.
5. The multi-channel rendezvous data-based information transfer algorithm of claim 4, wherein: the auto-formation is an aggregated feature set based on the aggregated data
Figure 431569DEST_PATH_IMAGE009
And forming a corresponding spare post-sink channel.
6. The data-categorized information-delivery algorithm for multi-channel intersection as claimed in claim 2, wherein: and a plurality of transmitting ends are arranged in the S1, and data transmission is carried out between the plurality of transmitting ends and the intersection area through a forward channel.
7. The multi-channel converged data-classified information transfer algorithm of claim 2, wherein: one transmitting end is arranged in the S1, one transmitting end comprises a plurality of transmitting addresses, and data transmission is carried out between the plurality of transmitting addresses and the intersection area through the forward channel.
8. The multi-channel converged data-classified information transfer algorithm according to claim 6 or 7, wherein: the aggregated feature set
Figure 120039DEST_PATH_IMAGE010
The calculation formula of (a) is as follows:
Figure 84322DEST_PATH_IMAGE011
；
in the formula (I), the compound is shown in the specification,
Figure 516440DEST_PATH_IMAGE013
an aggregated feature set;
Figure 141457DEST_PATH_IMAGE014
for data features
Figure 751561DEST_PATH_IMAGE002
Selecting a feature;
Figure 688293DEST_PATH_IMAGE015
for data features
Figure 176299DEST_PATH_IMAGE002
In (1) removing
Figure 514877DEST_PATH_IMAGE014
Selecting a feature;
Figure 295882DEST_PATH_IMAGE003
is a preliminary threshold.
9. The multi-channel converged data-classified information transfer algorithm of claim 8, wherein: the comparison value
Figure 595276DEST_PATH_IMAGE007
The calculation formula of (a) is as follows:
Figure 369197DEST_PATH_IMAGE016
；
in the formula (I), the compound is shown in the specification,
Figure 811549DEST_PATH_IMAGE007
is a comparison value;
Figure 278302DEST_PATH_IMAGE017
an aggregated feature set;
Figure 674779DEST_PATH_IMAGE001
are classified features.
10. The multi-channel converged data-classified information transfer algorithm of claim 2, wherein: the classification level comprises a self-setting classification level and a pull-in classification level, and the number of the self-setting classification level and the pull-in classification level is at least two.
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