Strong coupling optical fiber communication network link transmission layer data adaptive grouping method

Abstract

The invention discloses a strong coupling optical fiber communication network link transmission layer data adaptive grouping method, and relates to the technical field of data transmission, the method comprises the following steps: extracting data characteristics in an optical fiber communication network link transmission layer, and carrying out data dimension reduction processing on the extracted data characteristics to obtain a corresponding dimension reduction data set; carrying out quality evaluation on a transmission channel in an optical fiber communication network link transmission layer according to the obtained dimension reduction data set, and carrying out channel allocation on the dimension reduction data set according to an evaluation result; according to the method, feature data in a dimensionality reduction data set is subjected to clustering processing, corresponding data grouping is performed on the feature data according to a clustering processing result, and data self-adaptive grouping can perform corresponding adjustment on the grouping size and the transmission path fully according to the load capacity borne by a current network and specific requirements in the aspect of transmission, so that the data self-adaptive grouping efficiency is improved. Therefore, optimization of resource utilization is realized, the time consumed by transmission is shortened, and the overall transmission efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to an adaptive packetization method for data transmission layer in strongly coupled optical fiber communication network links. Background Technology

[0002] Strongly coupled fiber optic communication networks refer to network architectures where multiple users and services share the same physical medium and spectrum resources. This sharing leads to mutual interference and coupling of signals during transmission. With the continuous growth in the number of users and data demands, fiber optic communication networks face increasing pressure. As transmission distance increases, signal attenuation gradually intensifies, especially under heavy network load. Signal strength weakens due to resource contention, not only reducing data transmission rates but also causing data packet corruption, impacting overall network performance and user experience.

[0003] How to solve the problems of low data transmission efficiency and packet disorder caused by factors such as signal attenuation is a problem we need to solve. To this end, we now provide an adaptive packetization method for data transmission layer of strongly coupled optical fiber communication network links. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive packetization method for data transmission layer in strongly coupled optical fiber communication network links.

[0005] The objective of this invention can be achieved through the following technical solution: an adaptive data packetization method for the transport layer of a strongly coupled optical fiber communication network link, comprising:

[0006] Extract data features from the transmission layer of the optical fiber communication network link, and perform dimensionality reduction processing on the extracted data features to obtain the corresponding dimensionality-reduced dataset;

[0007] The quality of the transmission channels in the transmission layer of the fiber optic communication network link is evaluated based on the obtained dimensionality reduction dataset, and channels are allocated based on the evaluation results.

[0008] Clustering is performed on the feature data within the dimensionality-reduced dataset, and the feature data is grouped accordingly based on the clustering results.

[0009] Furthermore, the process of extracting data features from the transmission layer of the optical fiber communication network link and performing dimensionality reduction processing on the extracted data features to obtain the corresponding dimensionality-reduced dataset includes:

[0010] Data is collected from each channel within the transport layer of the optical fiber communication network link, and the collected data sets are denoted as high-dimensional data D. i ;

[0011] The obtained high-dimensional data are summarized to obtain the corresponding high-dimensional dataset;

[0012] The obtained high-dimensional dataset is subjected to dimensionality reduction processing using an autoassociative neural network to obtain a dimensionality-reduced dataset T consisting of several data features. i .

[0013] Furthermore, the process of evaluating the quality of the transmission channel in the transport layer of the fiber optic communication network link based on the obtained dimensionality reduction dataset includes:

[0014] Obtain the various optical fiber transmission links contained in the transmission layer of the optical fiber communication network, as well as the gateway nodes contained in each optical fiber transmission link.

[0015] Obtain the gateway node used for uploading data and the gateway node used for receiving data, respectively. Also obtain all fiber optic transmission links between the two gateway nodes and the link load 'a' of each fiber optic transmission link. q,p ;

[0016] Based on the obtained link load, the expected carrying capacity of each optical fiber link, the channel allocation priority coefficient, and the interference coefficient of each channel in the optical fiber transmission link are obtained.

[0017] Based on the expected capacity of the fiber optic transmission link, the channel allocation priority coefficient, and the interference coefficient of each channel, the channel quality assessment coefficient within each fiber optic transmission link is obtained, denoted as δ. r .

[0018] Furthermore, the expected capacity of the fiber optic transmission link is denoted as φ. r ,in:

[0019]

[0020] Among them, A(b) q ,b p A represents the total number of fiber optic transmission links between gateway node q and gateway node p. r (b q ,b p N(b) represents the number of other fiber optic transmission links passing through this fiber optic transmission link. q ,b p ) represents the amount of data transmitted between gateway node q and gateway node p.

[0021] Furthermore, a priority coefficient is assigned to the channel, denoted as a. r ,in:

[0022]

[0023] Here, random(1) represents a random number between [0,1].

[0024] Furthermore, the interference coefficient of each channel in the optical fiber transmission link is denoted as... in:

[0025]

[0026] Where l represents the channel within the optical fiber transmission link r, and I(l,r) represents the set of other optical fiber transmission links that both occupy channel l and interfere with optical fiber transmission link r.

[0027] Furthermore, the process of channel allocation for the dimensionality-reduced dataset based on the evaluation results includes:

[0028] Set several quality coefficient ranges and associate the standard data transmission volume with each quality coefficient range;

[0029] After sorting the quality evaluation coefficients of the channels in each optical fiber transmission link from largest to smallest, each quality evaluation coefficient is matched with the set quality coefficient range to obtain the corresponding matching result.

[0030] When the quality evaluation coefficient of the first-ranked data falls within a certain range, it indicates a dimensionality reduction dataset that can be allocated the corresponding standard data transmission volume of the channel.

[0031] If the amount of data in the dimensionality reduction dataset is greater than the standard data transmission volume of the corresponding channel, the dimensionality reduction dataset is decomposed into a first data block and a second data block, where the size of the first data block is the size of the standard data transmission volume, and the size of the second data block is the remaining size of the dimensionality reduction dataset.

[0032] The second data block is assigned to the channel corresponding to the next quality evaluation coefficient, and so on, until the channel assignment is completed.

[0033] Furthermore, the process of clustering the feature data within the dimensionality-reduced dataset and grouping the feature data accordingly based on the clustering results includes:

[0034] The number of clusters is set according to the channel allocation results, denoted as K, and each category is labeled as k, k = 1, 2, ... K;

[0035] Then construct the clustering quantity function, denoted as φ(K), where:

[0036]

[0037] Wherein, minF(H q H pF(H) represents the shortest distance between data classes in optical fiber communication, max_f(k) is the maximum difference measure within a class, sb_d(k) is the average distance between classes, sb_f(k) is the average distance within a class, ω and β are weight values, and F(H) represents the shortest distance between classes. q H p () represents the distance between data classes in fiber optic communication;

[0038] Based on the obtained clustering results, adaptive grouping results of the fiber optic communication link transport layer clustering data are obtained, and the grouping results are denoted as v. k ,in:

[0039] v k =φ(K)F(H) q H p )T i ;

[0040] Set the minimum threshold φ;

[0041] When satisfied When the time comes, the grouping ends.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Adaptive data grouping is achieved by using data clustering methods, and finally, transmission channels are allocated to the links to reduce redundancy and waiting time during transmission. Adaptive data grouping can fully adjust the size of the packets and the transmission path according to the current network load and specific transmission requirements, thereby optimizing resource utilization and improving overall transmission efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0045] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0046] like Figure 1 As shown, the adaptive packetization method for data transmission layer in a strongly coupled optical fiber communication network link includes:

[0047] Extract data features from the transmission layer of the optical fiber communication network link, and perform dimensionality reduction processing on the extracted data features to obtain the corresponding dimensionality-reduced dataset;

[0048] The quality of the transmission channels in the transmission layer of the fiber optic communication network link is evaluated based on the obtained dimensionality reduction dataset, and channels are allocated based on the evaluation results.

[0049] Clustering is performed on the feature data within the dimensionality-reduced dataset, and the feature data is grouped accordingly based on the clustering results.

[0050] It should be further explained that, in the specific implementation process, the process of extracting data features from the transmission layer of the optical fiber communication network link and performing dimensionality reduction processing on the extracted data features to obtain the corresponding dimensionality-reduced dataset includes:

[0051] Data is collected from each channel within the transmission layer of the optical fiber communication network link. Each set of collected data is denoted as high-dimensional data, and each high-dimensional data is labeled as i, where i = 1, 2, ..., n, where n is the total number of high-dimensional data. The high-dimensional data labeled i is denoted as D. i ;

[0052] The obtained high-dimensional data are summarized to obtain the corresponding high-dimensional dataset;

[0053] The obtained high-dimensional dataset is dimensionality reduced by an autoassociative neural network to obtain the corresponding dimensionality-reduced dataset, which is denoted as D.

[0054] Where, D = {T i |i = 1, 2, ..., n};

[0055]

[0056] Where M is the number of neurons in the bottleneck layer of the autoassociative neural network, and T... i v represents the data features obtained after processing the high-dimensional data labeled i through the bottleneck layer. m Let g(·) represent the offset of the m-th neuron in the bottleneck layer, and g(·) be the nonlinear activation function of the bottleneck layer.

[0057] It should be further explained that, in the specific implementation process, the process of assessing the quality of the transmission channel in the transmission layer of the optical fiber communication network link based on the obtained dimensionality reduction dataset includes:

[0058] Obtain the various optical fiber transmission links contained in the transmission layer of the optical fiber communication network, as well as the gateway nodes contained in each optical fiber transmission link.

[0059] Obtain the gateway node used for uploading data and the gateway node used for receiving data respectively, and obtain all fiber optic transmission links between the two gateway nodes. Denote the two gateway nodes as q and p respectively, and denote any gateway node on the fiber optic transmission link as G.

[0060] Obtain the link load of each fiber optic transmission link, and denote the link load of each fiber optic transmission link as a. q,p ;

[0061] in

[0062] Among them, h1(b q b G h1(b) represents the shortest path hop count from gateway node q to gateway node G on the fiber optic transmission link. p b G ) represents the shortest path hop count from gateway node p to gateway node G on the fiber optic transmission link;

[0063] Then the expected capacity of each fiber optic transmission link is obtained, denoted as . in:

[0064]

[0065] Among them, A(b) q ,b p A represents the total number of fiber optic transmission links between gateway node q and gateway node p. r (b q ,b p N(b) represents the number of other fiber optic transmission links passing through this fiber optic transmission link. q ,b p ) represents the amount of data transmitted between gateway node q and gateway node p; it should be noted that if any gateway node of other fiber optic transmission links is the same as any gateway node of this fiber optic transmission link, it means that the data has passed through this fiber optic transmission link.

[0066] Based on the expected capacity of the fiber optic transmission link, the channel allocation priority coefficient is obtained, denoted as a. r ,in:

[0067]

[0068] Where random(1) represents a random number between [0,1];

[0069] Based on the expected capacity and channel allocation priority coefficients of the obtained optical fiber transmission link, the interference coefficient of each channel in the optical fiber transmission link is obtained, denoted as . in:

[0070]

[0071] Where l represents the channel within the optical fiber transmission link r, and I(l,r) represents the set of other optical fiber transmission links that both occupy channel l and interfere with optical fiber transmission link r.

[0072] Based on the expected capacity of the fiber optic transmission link, the channel allocation priority coefficient, and the interference coefficient of each channel, the channel quality assessment coefficient within each fiber optic transmission link is obtained, denoted as δ. r ,in:

[0073]

[0074] Where k1, k2, and k3 are weighting coefficients, and k1 + k2 + k3 = 1.

[0075] It should be further explained that, in the specific implementation process, the process of channel allocation for the dimensionality-reduced dataset based on the evaluation results includes:

[0076] Set several quality coefficient ranges and associate the standard data transmission volume with each quality coefficient range;

[0077] After sorting the quality evaluation coefficients of the channels in each optical fiber transmission link from largest to smallest, each quality evaluation coefficient is matched with the set quality coefficient range to obtain the corresponding matching result.

[0078] When the quality evaluation coefficient of the first-ranked data falls within a certain range, it indicates a dimensionality reduction dataset that can be allocated the corresponding standard data transmission volume of the channel.

[0079] If the amount of data in the dimensionality reduction dataset is greater than the standard data transmission volume of the corresponding channel, the dimensionality reduction dataset is decomposed into a first data block and a second data block, where the size of the first data block is the size of the standard data transmission volume, and the size of the second data block is the remaining size of the dimensionality reduction dataset.

[0080] The second data block is assigned to the channel corresponding to the next quality evaluation coefficient, and so on, until the channel assignment is completed.

[0081] It should be further explained that, in the specific implementation process, the process of clustering the feature data within the dimensionality reduction dataset and grouping the feature data accordingly based on the clustering results includes:

[0082] The number of clusters is set according to the channel allocation results, denoted as K, and each category is labeled as k, k = 1, 2, ... K;

[0083] Then construct the clustering count function, denoted as in:

[0084]

[0085] Wherein, minF(H q H p F(H) represents the shortest distance between data classes in optical fiber communication, max_f(k) is the maximum difference measure within a class, sb_d(k) is the average distance between classes, sb_f(k) is the average distance within a class, ω and β are weight values, and F(H) represents the shortest distance between classes. q H p () represents the distance between data classes in fiber optic communication;

[0086] Based on the obtained clustering results, adaptive grouping results of the fiber optic communication link transport layer clustering data are obtained, and the grouping results are denoted as v. k ,in:

[0087] v k =φ(K)F(H) q H p )T i ;

[0088] Set the minimum threshold φ;

[0089] When satisfied When the time comes, the grouping ends.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A data adaptive packetization method for the transport layer of a strongly coupled optical fiber communication network link, characterized in that: include: Extract data features from the transmission layer of the optical fiber communication network link, and perform dimensionality reduction processing on the extracted data features to obtain the corresponding dimensionality-reduced dataset; The quality of the transmission channels in the transmission layer of the fiber optic communication network link is evaluated based on the obtained dimensionality reduction dataset, and channels are allocated based on the evaluation results. Clustering is performed on the feature data within the dimensionality-reduced dataset, and the feature data is grouped accordingly based on the clustering results.

2. The adaptive packetization method for data transmission layer of a strongly coupled optical fiber communication network link according to claim 1, characterized in that, The process of extracting data features from the transport layer of an optical fiber communication network link and performing dimensionality reduction on the extracted data features to obtain the corresponding dimensionality-reduced dataset includes: Data is collected from each channel within the transport layer of the optical fiber communication network link, and the collected data sets are denoted as high-dimensional data D. i ; The obtained high-dimensional data are summarized to obtain the corresponding high-dimensional dataset; The obtained high-dimensional dataset is subjected to dimensionality reduction processing using an autoassociative neural network to obtain a dimensionality-reduced dataset T consisting of several data features. i .

3. The adaptive packetization method for data transmission layer of a strongly coupled optical fiber communication network link according to claim 2, characterized in that, The process of evaluating the quality of transmission channels in the transport layer of an optical fiber communication network link based on the obtained dimensionality reduction dataset includes: Obtain the various optical fiber transmission links contained in the transmission layer of the optical fiber communication network, as well as the gateway nodes contained in each optical fiber transmission link. Obtain the gateway node used for uploading data and the gateway node used for receiving data, respectively. Also obtain all fiber optic transmission links between the two gateway nodes and the link load 'a' of each fiber optic transmission link. q,p ; Based on the obtained link load, the expected carrying capacity of each optical fiber link, the channel allocation priority coefficient, and the interference coefficient of each channel in the optical fiber transmission link are obtained. Based on the expected capacity of the fiber optic transmission link, the channel allocation priority coefficient, and the interference coefficient of each channel, the channel quality assessment coefficient within each fiber optic transmission link is obtained, denoted as δ. r .

4. The adaptive data packetization method for the transport layer of a strongly coupled optical fiber communication network link according to claim 3, characterized in that, The expected capacity of the fiber optic transmission link is denoted as φ. r ,in: Among them, A(b) q ,b p A represents the total number of fiber optic transmission links between gateway node q and gateway node p. r (b q ,b p N(b) represents the number of other fiber optic transmission links passing through this fiber optic transmission link. q ,b p ) represents the amount of data transmitted between gateway node q and gateway node p.

5. The adaptive data packetization method for the transport layer of a strongly coupled optical fiber communication network link according to claim 4, characterized in that, The channel allocation priority coefficient is denoted as a. r ,in: Here, random(1) represents a random number between [0,1].

6. The adaptive data packetization method for the transport layer of a strongly coupled optical fiber communication network link according to claim 5, characterized in that, The interference coefficient of each channel in the optical fiber transmission link is denoted as... in: Where l represents the channel within the optical fiber transmission link r, and I(l,r) represents the set of other optical fiber transmission links that both occupy channel l and interfere with optical fiber transmission link r.

7. The adaptive packetization method for data transmission layer of a strongly coupled optical fiber communication network link according to claim 6, characterized in that, The process of channel assignment for the dimensionality-reduced dataset based on the evaluation results includes: Set several quality coefficient ranges and associate the standard data transmission volume with each quality coefficient range; After sorting the quality evaluation coefficients of the channels in each optical fiber transmission link from largest to smallest, each quality evaluation coefficient is matched with the set quality coefficient range to obtain the corresponding matching result. When the quality evaluation coefficient of the first-ranked data falls within a certain range, it indicates a dimensionality reduction dataset that can be allocated the corresponding standard data transmission volume of the channel. If the amount of data in the dimensionality reduction dataset is greater than the standard data transmission volume of the corresponding channel, the dimensionality reduction dataset is decomposed into a first data block and a second data block, where the size of the first data block is the size of the standard data transmission volume, and the size of the second data block is the remaining size of the dimensionality reduction dataset. The second data block is assigned to the channel corresponding to the next quality evaluation coefficient, and so on, until the channel assignment is completed.

8. The adaptive packetization method for data transmission layer of a strongly coupled optical fiber communication network link according to claim 7, characterized in that, The process of clustering the feature data within a dimensionality-reduced dataset and grouping the feature data accordingly based on the clustering results includes: The number of clusters is set according to the channel allocation results, denoted as K, and each category is labeled as k, k = 1, 2, ... K; Then construct the clustering quantity function, denoted as φ(K), where: Wherein, minF(H q H p F(H) represents the shortest distance between data classes in optical fiber communication, max_f(k) is the maximum difference measure within a class, sb_d(k) is the average distance between classes, sb_f(k) is the average distance within a class, ω and β are weight values, and F(H) represents the shortest distance between classes. q H p () represents the distance between data classes in fiber optic communication; Based on the obtained clustering results, adaptive grouping results of the fiber optic communication link transport layer clustering data are obtained, and the grouping results are denoted as v. k ,in: v k =φ(K)F(H q ,H p )T i ; Set the minimum threshold φ; When satisfied When the time comes, the grouping ends.

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