Cloud server data transmission performance optimization management method and system based on big data

Abstract

The invention relates to the technical field of optimization management, and discloses a cloud server data transmission performance optimization management method and system based on big data. The method comprises the following steps: firstly, acquiring initial cloud server transmission data, and identifying and deleting redundant data by using a self-adaptive step length redundancy removal algorithm; secondly, performing data coding compression processing on the processed cloud server transmission data based on a compatibility principle to obtain a processed cloud server transmission data block set; establishing a cloud server data transmission channel, judging whether the network nodes overflow data or not, and adjusting the cloud server data transmission channel to obtain an adjusted cloud server data transmission channel; and finally, constructing a fitness function, and optimizing the adjusted data transmission channel of the cloud server by using a starvation game search algorithm. By processing and analyzing the data of the cloud server, the purpose of optimizing and managing the data transmission performance of the cloud server is achieved, and the method is objective and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization management, and specifically to a cloud server data transmission performance optimization management method and system based on big data. Background Art

[0002] With the widespread application of big data and Internet computing, the demand for cloud server data transmission in urban transportation, science and technology, agriculture, etc. is increasing. Since cloud server data transmission is easily affected by communication distance and network, the characteristics of cloud server data have dynamic volatility. Management and optimization of cloud server data transmission performance can effectively improve data transmission and communication efficiency.

[0003] Traditional cloud server data transmission methods mainly rely on cloud computing technology to cope with complex and changeable transmission environments. They have shortcomings such as easy data loss and unstable transmission during the transmission process. At the same time, traditional data transmission performance management methods do not solve problems such as data security, nor do they optimize cloud server data transmission performance, which brings inconvenience to production and life. Summary of the invention

[0004] In response to the problems in the related technology, the present invention provides a cloud server data transmission performance optimization management method and system based on big data to overcome the above-mentioned technical problems existing in the existing related technology.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a cloud server data transmission performance optimization management method based on big data, comprising the following steps:

[0007] S1, obtaining initial cloud server transmission data, using an adaptive step-length redundancy removal algorithm to identify and delete redundant data on the initial cloud server transmission data, and obtaining processed cloud server transmission data;

[0008] S2. Based on the compatibility principle, data encoding and compression processing is performed on the processed cloud server transmission data to obtain a processed cloud server transmission data block set;

[0009] S3, establishing a cloud server data transmission channel according to the processed cloud server transmission data block set, and determining whether a network node in the cloud server data transmission channel has data overflow, obtaining a data overflow result, and adjusting the cloud server data transmission channel according to the data overflow result to obtain an adjusted cloud server data transmission channel;

[0010] S4. Transmit the cloud server data on the adjusted cloud server data transmission channel, calculate the data transmission time, construct a fitness function, and use the Hunger Game search algorithm to optimize the adjusted cloud server data transmission channel to obtain an optimized cloud server data transmission channel.

[0011] The invention obtains the initial cloud server transmission data, calculates the similarity distance between the initial cloud server transmission data, uses an adaptive step-size redundancy removal algorithm to obtain the maximum time difference, and identifies and deletes redundant data by comparing the relationship between the maximum time difference and the threshold and the similarity distance. This method effectively solves the problem of unreasonable data deletion by the traditional redundancy removal algorithm, reduces the complexity of the algorithm and the energy consumption of data transmission. Secondly, based on the compatibility principle, the processed cloud server transmission data is subjected to data encoding and compression processing, and the continuity and compatibility relationship between adjacent data blocks is used for compression. Compared with traditional encoding compression, the data block mark is omitted, and the compression effect is improved. The Dijkstra algorithm is then used to establish a cloud server data transmission channel, and then Determine whether there is data overflow in the network nodes in the cloud server data transmission channel; in the process of establishing the cloud server data transmission channel, by comparing the size of weights, effectively improve the data transmission efficiency and reliability, determine the data overflow situation according to the receiving and transmission capacity of the network nodes, and adjust the cloud server data transmission channel in time to avoid packet loss and affect the reliability of data transmission; finally, construct a fitness function, and use the hunger game search algorithm to optimize the adjusted cloud server data transmission channel to obtain the optimized cloud server data transmission channel; the algorithm obtains the optimal solution to the optimization problem by simulating the hungry foraging behavior of social animals, and has the characteristics of simple structure, few parameters and strong robustness, and has a good advantage in convergence speed compared with other optimization algorithms.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11, set the time set A1 = {b1, b2, b3, ..., b i} collects the cloud server transmission data at each time point within b i Indicates the i-th time point, obtain the initial cloud server transmission data, denoted as A2 = {a1, a2, a3, ..., a i}, where a i Indicates time point b i The initial cloud server transmission data collected at the time of calculation and time point The corresponding initial cloud servers transmit data Transferring data to the initial cloud server The similarity distance is calculated as follows:

[0014]

[0015] Among them, l(i1,i2) represents the initial cloud server transmission data Transferring data to the initial cloud server Similar distance of

[0016] Calculate the similarity distances of the initial cloud server transmission data corresponding to other time points in sequence to generate a similarity distance matrix;

[0017] S12, set the data similarity threshold to ω1, the maximum time threshold to ω2, and calculate the maximum time difference of the initial cloud server transmitting data by introducing an adaptive step size. The specific steps are as follows:

[0018] S121, set to use a redundant data set to store redundant data in the cloud server data transmission process, when the number of redundant data in the redundant data set is 0 or 1, set the adaptive step length to 1, otherwise, calculate the maximum integer that does not exceed half of the number of redundant data in the redundant data set, record it as the redundancy judgment value, and set the adaptive step length to the redundancy judgment value;

[0019] S122, traversing the similarity distance matrix each time with the adaptive step size, setting the time points corresponding to the initial cloud server transmission data traversed twice in succession to be time points and time point Then the time point and time point The maximum time difference And calculate the maximum time difference of data transmission of other initial cloud servers in turn;

[0020] S13. When the data similarity threshold and the maximum time difference between the initial cloud server transmission data are greater than or equal to the similarity distance of the initial cloud server transmission data and less than or equal to the maximum time threshold, the corresponding initial cloud server transmission data are regarded as redundant data and the redundant data are deleted; otherwise, the corresponding initial cloud server transmission data are not redundant data, and the processed cloud server transmission data are obtained after deleting all redundant data.

[0021] The invention calculates the similarity distance between the initial cloud server transmission data and uses an adaptive step-size redundancy removal algorithm to identify and delete redundant data; this method effectively solves the problem of unreasonable data deletion by the traditional redundancy removal algorithm and reduces the complexity of the algorithm and the energy consumption of data transmission.

[0022] Preferably, S2 comprises the following steps:

[0023] S21, dividing the processed cloud server transmission data into cloud server transmission data blocks of equal length according to the length z, and when the number of cloud server transmission data blocks is less than z, using irrelevant bits for padding to obtain a processed cloud server transmission data block set; transmitting the processed cloud server transmission data blocks in the processed cloud server transmission data block set, and determining the compatibility of the processed cloud server transmission data blocks to obtain an incompatible cloud server transmission data block set and a compatible cloud server transmission data block set, the specific steps are as follows:

[0024] S211, selecting any two processed cloud server transmission data blocks from the processed cloud server transmission data block set, respectively recorded as a first cloud server transmission data block and a second cloud server transmission data block, performing an XOR operation and an intersection operation on the first cloud server transmission data block and the second cloud server transmission data block, respectively, to obtain an XOR operation result and an intersection operation result; when the number of irrelevant bits in the XOR operation result and the intersection operation result is expanded, the first cloud server transmission data block and the second cloud server transmission data block are regarded as compatible, otherwise the first cloud server transmission data block and the second cloud server transmission data block are incompatible, and a compatibility judgment result is obtained;

[0025] S212, according to the compatibility judgment result, when the compatibility judgment result is that the processed cloud server transmission data blocks are incompatible, performing XOR operations on the processed cloud server transmission data block sets in sequence until the encoding is completed to obtain an incompatible cloud server transmission data block set; when the compatibility judgment result is that the processed cloud server transmission data blocks are compatible, performing intersection operations on the processed cloud server transmission data block sets in sequence until the encoding is completed to obtain a compatible cloud server transmission data block set;

[0026] S22. Combining the incompatible cloud server transmission data block set and the compatible cloud server transmission data block set to form a processed cloud server transmission data block set, and completing data encoding and compression processing.

[0027] The invention performs data encoding and compression processing on the processed cloud server transmission data based on the compatibility principle. Compared with traditional encoding compression, it omits data block marking and uses the continuity and compatibility relationship between adjacent data blocks for compression, thereby improving the compression effect.

[0028] Preferably, S3 comprises the following steps:

[0029] S31, constructing an initial cloud server data transmission channel, wherein there is no cloud server data transmission path in the initial cloud server data transmission channel, randomly selecting a network node in the cloud server network as a source node, traversing with the source node as the center, finding a network node closest to the source node, recording it as the first network node, adding the source node and the first network node to a path set, and constructing a cloud server data transmission graph according to the path set;

[0030] The links in the cloud server data transmission graph are undirected edges, and the weights of the network nodes that have undirected edges with the first network node and the source node are recorded to obtain a weight set and a first path set; the undirected edges centered on the first network node in the first path set are decomposed to obtain a first directed edge and a second directed edge, and the weight of the first directed edge and the weight of the second directed edge are equal to the weight of the undirected edge centered on the first network node, forming a directed edge set, completing the update of the cloud server data transmission graph, and obtaining an updated cloud server data transmission graph;

[0031] S32. On the updated cloud server data transmission graph, with the source node and the first network node as the center, find the network node closest to the source node and the first network node, record it as the second network node, add the second network node to the path set, decompose the second network node, and add the decomposition result to the directed edge set; at this time, delete the directed edges in the updated cloud server data transmission graph, and then determine whether the path set and the directed edge set have repeated common edges. When there are repeated common edges, delete the repeated common edges, and re-find a new path to add to the path set; repeat S32 until all network nodes are traversed, and connect the paths in the path set to obtain a cloud server data transmission channel;

[0032] S33, when the processed cloud server transmission data block set is transmitting data in the cloud server data transmission channel, it is determined whether the network node in the cloud server data transmission channel has data overflow, and a data overflow result is obtained, all network nodes in the cloud server data transmission channel are traversed in sequence, and the cloud server data transmission channel is adjusted according to the data overflow result to obtain an adjusted cloud server data transmission channel, and the specific steps are as follows:

[0033] S331. Setting the network node of the cloud server data transmission channel To network node The time is Network nodes in the cloud server data transmission channel To network node The probability of failure of the cloud server to transmit a processed data block is β. Within the unit time t, the network nodes in the cloud server data transmission channel The total received data block capacity is calculated as follows:

[0034]

[0035] Where B′ represents the network node in the cloud server data transmission channel The total data block capacity received, c represents the size of the processed cloud server transmission data block, and d represents the size of the network node The number of network nodes that transmit the processed cloud server data blocks;

[0036] Set the network nodes in the cloud server data transmission channel The average time it takes to propagate a data block is t, so the network nodes in the cloud server data transmission channel The total data block capacity for transmission is calculated as follows:

[0037]

[0038] Where B″ represents the network node in the cloud server data transmission channel The total data block capacity transmitted;

[0039] S332, combined with the network nodes in the cloud server data transmission channel Received total block capacity and network nodes Total data block capacity for transmission, setting network nodes The data block size that can be stored is B″′, then the network nodes in the cloud server data transmission channel The formula for calculating data overflow probability is as follows:

[0040]

[0041] Where χ represents the network node in the cloud server data transmission channel Data overflow probability;

[0042] Set the overflow threshold to ξ. When the network node in the cloud server data transmission channel When the data overflow probability is greater than the overflow threshold, the network node is discarded Will be with the network node The network node corresponding to the minimum weight of the connection is used as the replacement node. The data block is transmitted to the replacement node to obtain a data overflow result, all network nodes in the cloud server data transmission channel are traversed in sequence, and the cloud server data transmission channel is adjusted according to the data overflow result to obtain an adjusted cloud server data transmission channel.

[0043] The invention effectively improves data transmission efficiency and reliability by establishing a cloud server data transmission channel, and then determines whether there is data overflow in the network nodes in the cloud server data transmission channel. The data overflow situation is determined according to the receiving and transmission capacity of the network nodes, and the cloud server data transmission channel is adjusted in time to avoid packet loss and affect the reliability of data transmission.

[0044] Preferably, S4 comprises the following steps:

[0045] S41, transmitting cloud server data on the adjusted cloud server data transmission channel, generating

[0046] Network bandwidth, network delay and network congestion, calculate the data transmission time according to the network bandwidth, network delay and network congestion, establish a fitness function, use the Hunger Game search algorithm to optimize the adjusted cloud server data transmission channel, and obtain the optimized cloud server data transmission channel. The specific steps are as follows:

[0047] S411. Assume that there is a population of gregarious animals in the search space. Individuals in the population of gregarious animals start searching for food when they are hungry. Set the starting position, the activity range to ε, the current number of iterations to e, the maximum number of iterations to E, the first hunger weight to δ1, the second hunger weight to δ2, the position of the individual in the population of gregarious animals at the e-th iteration to C(e), and the best position of the individual in the population of gregarious animals at the e-th iteration to The fitness function value of the mth individual in the gregarious animal population at the e-th iteration is f(e,m), and the optimal fitness function value of the individual in the gregarious animal population at the e-th iteration is g1 represents a random number and g1∈[0,1], then the calculation formulas for the starvation parameter and the variational control parameter are as follows:

[0048]

[0049] Among them, φ represents the hunger parameter, represents the variational control parameter, sech represents the hyperbolic function;

[0050] Let h represent the improved parameter, g2 represent the random number and g2∈[0,1], g3 represent the random number and g3∈[0,1], Indicates a random number that satisfies the normal distribution. Combined with the hunger parameter and the variational control parameter, the e+1th iteration position of the individual in the gregarious animal population is obtained. The calculation formula is as follows:

[0051]

[0052] In each iteration, the individuals in the social animal population, i.e., the network nodes in the adjusted cloud server data transmission channel, calculate the fitness function values ​​of the individuals in the social animal population in each iteration, and select the current best fitness function value, which corresponds to the current shortest data transmission time.

[0053] S412, set the total number of gregarious animal populations to D, record the k-th hungry individual in the gregarious animal population as hungry(k), calculate the fitness function value of the k-th hungry individual in the gregarious animal population at the e-th iteration, record it as f(e,k); obtain the hunger degree γ according to the best fitness function value of the individuals in the gregarious animal population at the e-th iteration and the worst fitness function value of the individuals in the gregarious animal population at the e-th iteration; when , hungry(k)=0, otherwise hungry(k)=hungry(k)+γ; as the hunger degree of each iteration changes continuously, the first hunger weight and the second hunger weight are updated to update the e+1th iteration position of the individual in the gregarious animal population; when the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the final position, the starting position and the final position are connected to obtain the optimized cloud server data transmission channel;

[0054] S42. The cloud server data is transmitted on the optimized cloud server data transmission channel, thereby completing the cloud server data transmission performance optimization.

[0055] This invention optimizes the adjusted cloud server data transmission channel by using the Hunger Game search algorithm to obtain an optimized cloud server data transmission channel; this algorithm obtains the optimal solution to the optimization problem by simulating the hungry foraging behavior of social animals, and has the characteristics of simple structure, few parameters and strong robustness, and has a good advantage in convergence speed compared to other optimization algorithms.

[0056] This embodiment also discloses a system for optimizing the management method of cloud server data transmission performance based on big data, which specifically includes: a redundant data identification and deletion module, a data encoding and compression processing module, a cloud server data transmission channel preliminary establishment module, and a vehicle and cloud server data transmission channel optimization module;

[0057] The redundant data identification and deletion module is used to identify and delete redundant data of the initial cloud server transmission data using an adaptive step-length redundancy removal algorithm;

[0058] The data encoding and compression processing module is used to perform data encoding and compression processing on the processed cloud server transmission data based on the compatibility principle;

[0059] The cloud server data transmission channel preliminary establishment module is used to establish the cloud server data transmission channel and adjust the cloud server data transmission channel according to the data overflow result;

[0060] The cloud server data transmission channel optimization module is used to optimize the adjusted cloud server data transmission channel using the Hunger Games search algorithm.

[0061] The present invention has the following beneficial effects:

[0062] 1. The invention calculates the similarity distance between the initial cloud server transmission data and uses an adaptive step-size redundancy removal algorithm to identify and delete redundant data; this method effectively solves the problem of unreasonable data deletion by the traditional redundancy removal algorithm, reduces the complexity of the algorithm and the energy consumption of data transmission.

[0063] 2. The present invention performs data encoding and compression processing on the processed cloud server transmission data based on the compatibility principle. Compared with traditional encoding compression, it omits data block marking and utilizes the continuity and compatibility relationship between adjacent data blocks for compression, thereby improving the compression effect.

[0064] 3. The invention effectively improves the data transmission efficiency and reliability by establishing a cloud server data transmission channel, and then determines whether the network nodes in the cloud server data transmission channel have data overflow, determines the data overflow situation according to the receiving and transmission capacity of the network nodes, and timely adjusts the cloud server data transmission channel to avoid packet loss and affect the data transmission reliability.

[0065] 4. This invention optimizes the adjusted cloud server data transmission channel by using the Hunger Game search algorithm, and obtains the optimal solution to the optimization problem by simulating the hungry foraging behavior of social animals. It has the characteristics of simple structure, few parameters and strong robustness, and has a good advantage in convergence speed compared with other optimization algorithms.

[0066] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0068] Figure 1 A schematic diagram of the process of optimizing the data transmission performance of a cloud server in the cloud server data transmission performance optimization management system based on big data provided by the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0071] Example 1

[0072] See also Figure 1 This implementation discloses a cloud server data transmission performance optimization management method based on big data, which specifically includes the following contents:

[0073] S1, obtaining initial cloud server transmission data, using an adaptive step-length redundancy removal algorithm to identify and delete redundant data on the initial cloud server transmission data, and obtaining processed cloud server transmission data;

[0074] The S1 comprises the following steps:

[0075] S11, set the time set A1 = {b1, b2, b3, ..., b i} collects the cloud server transmission data at each time point within b i Indicates the i-th time point, obtain the initial cloud server transmission data, denoted as A2 = {a1, a2, a3, ..., a i}, where a i Indicates time point b i The initial cloud server transmission data collected at the time of calculation and time point The corresponding initial cloud servers transmit data Transferring data to the initial cloud server The similarity distance is calculated as follows:

[0076]

[0077] Among them, l(i1,i2) represents the initial cloud server transmission data Transferring data to the initial cloud server Similar distance of

[0078] Calculate the similarity distances of the initial cloud server transmission data corresponding to other time points in sequence to generate a similarity distance matrix;

[0079] S12, set the data similarity threshold to ω1, the maximum time threshold to ω2, and calculate the maximum time difference of the initial cloud server transmitting data by introducing an adaptive step size. The specific steps are as follows:

[0080] S121, set to use a redundant data set to store redundant data in the cloud server data transmission process, when the number of redundant data in the redundant data set is 0 or 1, set the adaptive step length to 1, otherwise, calculate the maximum integer that does not exceed half of the number of redundant data in the redundant data set, record it as the redundancy judgment value, and set the adaptive step length to the redundancy judgment value;

[0081] S122, traversing the similarity distance matrix each time with the adaptive step size, setting the time points corresponding to the initial cloud server transmission data traversed twice in succession to be time points and time point Then the time point and time point The maximum time difference And calculate the maximum time difference of data transmission of other initial cloud servers in turn;

[0082] S13, when the data similarity threshold and the maximum time difference between the initial cloud server transmission data are greater than or equal to the similarity distance of the initial cloud server transmission data and less than or equal to the maximum time threshold, the corresponding initial cloud server transmission data are regarded as redundant data and the redundant data are deleted; otherwise, the corresponding initial cloud server transmission data are not redundant data, and the processed cloud server transmission data are obtained after deleting all redundant data;

[0083] S2. Based on the compatibility principle, data encoding and compression processing is performed on the processed cloud server transmission data to obtain a processed cloud server transmission data block set;

[0084] The S2 comprises the following steps:

[0085] S21, dividing the processed cloud server transmission data into cloud server transmission data blocks of equal length according to the length z, and when the number of cloud server transmission data blocks is less than z, using irrelevant bits for padding to obtain a processed cloud server transmission data block set; transmitting the processed cloud server transmission data blocks in the processed cloud server transmission data block set, and determining the compatibility of the processed cloud server transmission data blocks to obtain an incompatible cloud server transmission data block set and a compatible cloud server transmission data block set, the specific steps are as follows:

[0086] S211, selecting any two processed cloud server transmission data blocks from the processed cloud server transmission data block set, respectively recorded as a first cloud server transmission data block and a second cloud server transmission data block, performing an XOR operation and an intersection operation on the first cloud server transmission data block and the second cloud server transmission data block, respectively, to obtain an XOR operation result and an intersection operation result; when the number of irrelevant bits in the XOR operation result and the intersection operation result is expanded, the first cloud server transmission data block and the second cloud server transmission data block are regarded as compatible, otherwise the first cloud server transmission data block and the second cloud server transmission data block are incompatible, and a compatibility judgment result is obtained;

[0087] S212, according to the compatibility judgment result, when the compatibility judgment result is that the processed cloud server transmission data blocks are incompatible, performing XOR operations on the processed cloud server transmission data block sets in sequence until the encoding is completed to obtain an incompatible cloud server transmission data block set; when the compatibility judgment result is that the processed cloud server transmission data blocks are compatible, performing intersection operations on the processed cloud server transmission data block sets in sequence until the encoding is completed to obtain a compatible cloud server transmission data block set;

[0088] S22, combining the incompatible cloud server transmission data block set and the compatible cloud server transmission data block set to form a processed cloud server transmission data block set, and completing data encoding and compression processing;

[0089] S3, establishing a cloud server data transmission channel according to the processed cloud server transmission data block set, and determining whether a network node in the cloud server data transmission channel has data overflow, obtaining a data overflow result, and adjusting the cloud server data transmission channel according to the data overflow result to obtain an adjusted cloud server data transmission channel;

[0090] The S3 comprises the following steps:

[0091] S31, constructing an initial cloud server data transmission channel, wherein there is no cloud server data transmission path in the initial cloud server data transmission channel, randomly selecting a network node in the cloud server network as a source node, traversing with the source node as the center, finding a network node closest to the source node, recording it as the first network node, adding the source node and the first network node to a path set, and constructing a cloud server data transmission graph according to the path set;

[0092] The links in the cloud server data transmission graph are undirected edges, and the weights of the network nodes that have undirected edges with the first network node and the source node are recorded to obtain a weight set and a first path set; the undirected edges centered on the first network node in the first path set are decomposed to obtain a first directed edge and a second directed edge, and the weight of the first directed edge and the weight of the second directed edge are equal to the weight of the undirected edge centered on the first network node, forming a directed edge set, completing the update of the cloud server data transmission graph, and obtaining an updated cloud server data transmission graph;

[0093] S32. On the updated cloud server data transmission graph, with the source node and the first network node as the center, find the network node closest to the source node and the first network node, record it as the second network node, add the second network node to the path set, decompose the second network node, and add the decomposition result to the directed edge set; at this time, delete the directed edges in the updated cloud server data transmission graph, and then determine whether the path set and the directed edge set have repeated common edges. When there are repeated common edges, delete the repeated common edges, and re-find a new path to add to the path set; repeat S32 until all network nodes are traversed, and connect the paths in the path set to obtain a cloud server data transmission channel;

[0094] S33, when the processed cloud server transmission data block set is transmitting data in the cloud server data transmission channel, it is determined whether the network node in the cloud server data transmission channel has data overflow, and a data overflow result is obtained, all network nodes in the cloud server data transmission channel are traversed in sequence, and the cloud server data transmission channel is adjusted according to the data overflow result to obtain an adjusted cloud server data transmission channel, and the specific steps are as follows:

[0095] S331. Setting the network node of the cloud server data transmission channel To network node The time is Network nodes in the cloud server data transmission channel To network node The probability of failure of the cloud server to transmit a processed data block is β. Within the unit time t, the network nodes in the cloud server data transmission channel The total received data block capacity is calculated as follows:

[0096]

[0097] Where B′ represents the network node in the cloud server data transmission channel The total data block capacity received, c represents the size of the processed cloud server transmission data block, and d represents the size of the network node The number of network nodes that transmit the processed cloud server data blocks;

[0098] Set the network nodes in the cloud server data transmission channel The average time it takes to propagate a data block is t, so the network nodes in the cloud server data transmission channel The total data block capacity for transmission is calculated as follows:

[0099]

[0100] Where B″ represents the network node in the cloud server data transmission channel The total data block capacity transmitted;

[0101] S332, combined with the network nodes in the cloud server data transmission channel Received total block capacity and network nodes Total data block capacity for transmission, setting network nodes The data block size that can be stored is B″′, then the network nodes in the cloud server data transmission channel The formula for calculating data overflow probability is as follows:

[0102]

[0103] Where χ represents the network node in the cloud server data transmission channel Data overflow probability;

[0104] Set the overflow threshold to ξ. When the network node in the cloud server data transmission channel When the data overflow probability is greater than the overflow threshold, the network node is discarded Will be with the network node The network node corresponding to the minimum weight of the connection is used as the replacement node. Transmit the data block to the replacement node, obtain the data overflow result, traverse all network nodes in the cloud server data transmission channel in turn, adjust the cloud server data transmission channel according to the data overflow result, and obtain the adjusted cloud server data transmission channel;

[0105] S4, transmitting cloud server data on the adjusted cloud server data transmission channel, calculating the data transmission time, constructing a fitness function, and using the Hunger Game search algorithm to optimize the adjusted cloud server data transmission channel to obtain an optimized cloud server data transmission channel;

[0106] The S4 comprises the following steps:

[0107] S41, transmitting cloud server data on the adjusted cloud server data transmission channel, generating network bandwidth, network delay and network congestion, calculating data transmission time according to the network bandwidth, network delay and network congestion, establishing a fitness function, and using the Hunger Game search algorithm to optimize the adjusted cloud server data transmission channel to obtain an optimized cloud server data transmission channel, the specific steps are as follows:

[0108] S411. Assume that there is a population of gregarious animals in the search space. Individuals in the population of gregarious animals start searching for food when they are hungry. Set the starting position, the activity range to ε, the current number of iterations to e, the maximum number of iterations to E, the first hunger weight to δ1, the second hunger weight to δ2, the position of the individual in the population of gregarious animals at the e-th iteration to C(e), and the best position of the individual in the population of gregarious animals at the e-th iteration to The fitness function value of the mth individual in the gregarious animal population at the e-th iteration is f(e,m), and the optimal fitness function value of the individual in the gregarious animal population at the e-th iteration is g1 represents a random number and g1∈[0,1], then the calculation formulas for the starvation parameter and the variational control parameter are as follows:

[0109]

[0110] Among them, φ represents the hunger parameter, represents the variational control parameter, sech represents the hyperbolic function;

[0111] Set h to represent the improved parameter, g2 to represent a random number and g2∈[0,1], g3 to represent a random number and g3∈[0,1], g~ to represent a random number that satisfies the normal distribution, and combine the hunger parameter and the variational control parameter to obtain the e+1th iteration position of the individual in the gregarious animal population. The calculation formula is as follows:

[0112]

[0113] In each iteration, the individuals in the social animal population, i.e., the network nodes in the adjusted cloud server data transmission channel, calculate the fitness function values ​​of the individuals in the social animal population in each iteration, and select the current best fitness function value, which corresponds to the current shortest data transmission time.

[0114] S412, set the total number of gregarious animal populations to D, record the k-th hungry individual in the gregarious animal population as hungry(k), calculate the fitness function value of the k-th hungry individual in the gregarious animal population at the e-th iteration, record it as f(e,k); obtain the hunger degree γ according to the best fitness function value of the individuals in the gregarious animal population at the e-th iteration and the worst fitness function value of the individuals in the gregarious animal population at the e-th iteration; when , hungry(k)=0, otherwise hungry(k)=hungry(k)+γ; as the hunger degree of each iteration changes continuously, the first hunger weight and the second hunger weight are updated to update the e+1th iteration position of the individual in the gregarious animal population; when the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the final position, the starting position and the final position are connected to obtain the optimized cloud server data transmission channel;

[0115] S42. The cloud server data is transmitted on the optimized cloud server data transmission channel, thereby completing the cloud server data transmission performance optimization.

[0116] Example 2

[0117] This embodiment also discloses a system for optimizing the management method of cloud server data transmission performance based on big data, which specifically includes: a redundant data identification and deletion module, a data encoding and compression processing module, a cloud server data transmission channel preliminary establishment module, and a vehicle and cloud server data transmission channel optimization module;

[0118] The redundant data identification and deletion module is used to identify and delete redundant data of the initial cloud server transmission data using an adaptive step-length redundancy removal algorithm;

[0119] The data encoding and compression processing module is used to perform data encoding and compression processing on the processed cloud server transmission data based on the compatibility principle;

[0120] The cloud server data transmission channel preliminary establishment module is used to establish the cloud server data transmission channel and adjust the cloud server data transmission channel according to the data overflow result;

[0121] The cloud server data transmission channel optimization module is used to optimize the adjusted cloud server data transmission channel using the Hunger Games search algorithm.

[0122] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0123] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A cloud server data transmission performance optimization management method based on big data, characterized in that: The steps include: S1, obtaining initial cloud server transmission data, using an adaptive step-length redundancy removal algorithm to identify and delete redundant data on the initial cloud server transmission data, and obtaining processed cloud server transmission data; S2. Based on the compatibility principle, data encoding and compression processing is performed on the processed cloud server transmission data to obtain a processed cloud server transmission data block set; S3, establishing a cloud server data transmission channel according to the processed cloud server transmission data block set, and determining whether a network node in the cloud server data transmission channel has data overflow, obtaining a data overflow result, and adjusting the cloud server data transmission channel according to the data overflow result to obtain an adjusted cloud server data transmission channel; S4. Transmitting cloud server data on the adjusted cloud server data transmission channel, calculating the data transmission time, constructing a fitness function, optimizing the adjusted cloud server data transmission channel, and obtaining an optimized cloud server data transmission channel.

2. The cloud server data transmission performance optimization management method based on big data according to claim 1 is characterized in that: The S1 comprises the following steps: S11, collecting cloud server transmission data, obtaining initial cloud server transmission data, calculating the similarity distance of the initial cloud server transmission data, and generating a similarity distance matrix; S12, setting a data similarity threshold and a maximum time threshold, and calculating the maximum time difference of the initial cloud server transmitting data by introducing an adaptive step size; S13, comparing the maximum time difference, data similarity threshold and maximum time threshold of the initial cloud server transmission data, identifying and deleting redundant data, and obtaining processed cloud server transmission data.

3. The cloud server data transmission performance optimization management method based on big data according to claim 2 is characterized in that: The S12 comprises the following steps: S121, set a redundant data set, when the number of redundant data in the redundant data set is 0 or 1, set the adaptive step length to 1, otherwise, calculate the maximum integer that does not exceed half of the number of redundant data in the redundant data set, record it as the redundancy judgment value, and set the adaptive step length to the redundancy judgment value; S122, traversing the similarity distance matrix with the adaptive step size to calculate the maximum time difference of the initial cloud server transmitting data.

4. The cloud server data transmission performance optimization management method based on big data according to claim 3 is characterized in that: The S2 comprises the following steps: S21, dividing the processed cloud server transmission data into cloud server transmission data blocks of equal length, and filling them with irrelevant bits to obtain a processed cloud server transmission data block set; judging the compatibility of the processed cloud server transmission data block set to obtain an incompatible cloud server transmission data block set and a compatible cloud server transmission data block set; S22. Combining the incompatible cloud server transmission data block set and the compatible cloud server transmission data block set to form a processed cloud server transmission data block set, and completing data encoding and compression processing.

5. The cloud server data transmission performance optimization management method based on big data according to claim 4 is characterized in that: The S3 comprises the following steps: S31, constructing an initial cloud server data transmission channel, randomly selecting a network node as a source node in the cloud server network, traversing with the source node as the center, obtaining a path set, and constructing a cloud server data transmission graph; decomposing the undirected edges in the cloud server data transmission graph, and adding the decomposition results to the directed edge set to obtain an updated cloud server data transmission graph; S32, on the updated cloud server data transmission graph, comparing the path set and the directed edge set to see if there are duplicate common edges, and if there are duplicate common edges, deleting the duplicate common edges, and re-finding a new path to add to the path set; After all network nodes are traversed, the paths in the path set are connected to obtain a cloud server data transmission channel; S33, determine whether there is data overflow in the network nodes in the cloud server data transmission channel, obtain the data overflow result, traverse all network nodes in the cloud server data transmission channel in turn, adjust the cloud server data transmission channel according to the data overflow result, and obtain the adjusted cloud server data transmission channel.

6. The cloud server data transmission performance optimization management method based on big data according to claim 5 is characterized in that: The S33 comprises the following steps: S331, setting the network node cj2 in the cloud server data transmission channel, calculating the total data block capacity received by the network node cj2 in the server data transmission channel and the total data block capacity transmitted by the network node cj2; S332, calculating the data overflow probability based on the total data block capacity received by the network node cj2 in the cloud server data transmission channel and the total data block capacity transmitted by the network node cj2; Set an overflow threshold, and obtain the data overflow result by comparing the data overflow probability and the overflow threshold; All network nodes in the cloud server data transmission channel are traversed in sequence, and the cloud server data transmission channel is adjusted according to the data overflow result to obtain the adjusted cloud server data transmission channel.

7. The method for optimizing the data transmission performance of a cloud server based on big data according to claim 6 is characterized in that: The S4 comprises the following steps: S41, transmitting cloud server data on the adjusted cloud server data transmission channel, generating network bandwidth, network delay and network congestion, calculating data transmission time according to the network bandwidth, network delay and network congestion, establishing a fitness function, and using the Hunger Game search algorithm to optimize the adjusted cloud server data transmission channel to obtain an optimized cloud server data transmission channel; S42. The cloud server data is transmitted on the optimized cloud server data transmission channel, thereby completing the cloud server data transmission performance optimization.

8. The cloud server data transmission performance optimization management method based on big data according to claim 7 is characterized in that: The step of optimizing the adjusted cloud server data transmission channel using the Hunger Game search algorithm to obtain the optimized cloud server data transmission channel comprises the following steps: Assume that there is a population of gregarious animals in the search space. Individuals in the population of gregarious animals start searching for food when they are hungry. Set the starting position, the activity range to ε, the current number of iterations to e, the maximum number of iterations to E, the first hunger weight to δ1, the second hunger weight to δ2, the position of the individual in the population of gregarious animals at the e-th iteration to C(e), and the best position of the individual in the population of gregarious animals at the e-th iteration to The fitness function value of the mth individual in the gregarious animal population at the e-th iteration is f(e,m), and the optimal fitness function value of the individual in the gregarious animal population at the e-th iteration is g1 represents a random number and g1∈[0,1], then the calculation formulas for the starvation parameter and the variational control parameter are as follows: Among them, φ represents the hunger parameter, represents the variational control parameter, sech represents the hyperbolic function; Set h to represent the improved parameter, g2 to represent a random number and g2∈[0,1], g3 to represent a random number and g3∈[0,1], g~ to represent a random number that satisfies the normal distribution, and combine the hunger parameter and the variational control parameter to obtain the e+1th iteration position of the individual in the gregarious animal population. The calculation formula is as follows: In each iteration, the individuals in the social animal population, i.e., the network nodes in the adjusted cloud server data transmission channel, calculate the fitness function values ​​of the individuals in the social animal population in each iteration, and select the current best fitness function value, which corresponds to the current shortest data transmission time. Assume that the total number of gregarious animal populations is D, the kth hungry individual in the gregarious animal population is denoted as hungry(k), calculate the fitness function value of the kth hungry individual in the gregarious animal population at the eth iteration, denoted as f(e,k); according to the best fitness function value of the individuals in the gregarious animal population at the eth iteration and the worst fitness function value of the individuals in the gregarious animal population at the eth iteration, the hunger degree γ is obtained; when , hungry(k)=0, otherwise hungry(k)=hungry(k)+γ; as the hunger degree of each iteration changes continuously, the first hunger weight and the second hunger weight are updated to update the e+1th iteration position of the individual in the gregarious animal population; when the current number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the final position, the starting position and the final position are connected to obtain the optimized cloud server data transmission channel.

9. A system for implementing the cloud server data transmission performance optimization management method based on big data as described in any one of claims 1 to 8, characterized in that: Specifically include: Redundant data identification and deletion module, data encoding and compression processing module, cloud server data transmission channel preliminary establishment module and vehicle and cloud server data transmission channel optimization module; The redundant data identification and deletion module is used to identify and delete redundant data of the initial cloud server transmission data using an adaptive step-length redundancy removal algorithm; The data encoding and compression processing module is used to perform data encoding and compression processing on the processed cloud server transmission data based on the compatibility principle; The cloud server data transmission channel preliminary establishment module is used to establish the cloud server data transmission channel and adjust the cloud server data transmission channel according to the data overflow result; The cloud server data transmission channel optimization module is used to optimize the adjusted cloud server data transmission channel using the Hunger Games search algorithm.

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